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Another Fine Mesh top

► This Week in CFD
  23 Jul, 2021

For reasons I can’t explain, I’m really excited and energetic today about CFD and work in general. Is it because of summer? Start of the Olympics? Being three months in to our new relationship with Cadence and Numeca and really … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► This Week in CFD
  16 Jul, 2021

Welcome to this week’s roundup and recap of all the CFD news that’s fit to print that could be documented in the time available. Of particular note is a Digital Engineering article on V&V that’s worth your time to read. … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► Cadence & CFD: How Often Do You See the Word “Delight” Used to Describe the CAE Business?
  13 Jul, 2021

Since Cadence acquired Pointwise back in mid-April, many of you, our customers and partners, have been curious about what the future holds for our eponymous meshing software. This natural curiosity is amplified because of Cadence’s acquisition of Numeca earlier in … Continue reading

The post Cadence & CFD: How Often Do You See the Word “Delight” Used to Describe the CAE Business? first appeared on Another Fine Mesh.

► This Week in CFD
    9 Jul, 2021

So much CFD news, so little time. Lots of conference news which is great, especially as we move toward live events. At several of these events you’ll have the chance to hear from the Cadence CFD team. Speaking of Cadence … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► This Week in CFD
    2 Jul, 2021

I wish I could say there was something specific that stands out in this week’s compilation of CFD news. But it’s a fairly uniform compendium of CFD applications, software releases, company and tech introductions, computing insights, business and event updates, … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► This Week in CFD
  25 Jun, 2021

This week’s CFD news represents only a fraction of what we have bookmarked but is still chock full of goodness. It covers geometry modeling for CFD, design, a must-read article on programmer productivity, new software releases including one from our … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

F*** Yeah Fluid Dynamics top

► “The Goblet of Fire”
  23 Jul, 2021

Sometimes the mundane events of life hide extraordinary phenomena. This award-winning photograph by Sarang Naik shows yellow-brown spores streaming off a mushroom during monsoon season. The plume is abstract and beautiful; you could easily mistake it for the flames of an Olympic torch. But common as they are, the lowly mushroom hides interesting depths. To get their spores to travel further, mushrooms actually generate their own breezes! (Image credit: S. Naik; via Big Picture Competition)

With the Olympics kicking off today, FYFD will follow our usual tradition of Olympic-themed posts for the next couple weeks, so be sure to come back each day for the latest featured sport!

► Pump Problems
  22 Jul, 2021

Pumps are a critical piece of infrastructure, but to keep them operating, engineers have to account for several potential pitfalls. In this Practical Engineering video, Grady discusses some of the common fluid dynamical effects that can destroy a pump and its performance. As you’ll see in the video, a lot of the challenges boil down to keeping air out of the pump. Since air and water are vastly different in their density and compressibility, most pumps cannot handle both of them at the same time. Pumps need to be primed to displace any air inside them and allow them to develop the suction needed to pump water. On the other hand, too much suction can create cavitation, which damages pump parts. And, finally, the intake systems for pumps have to be designed to keep air from getting sucked in. If nothing else, having too much air in the lines reduces the pump’s efficiency. (Image and video credit: Practical Engineering)

► Contact-Line Dissipation
  21 Jul, 2021

In the confines of a narrow tube, a flow’s energy gets dissipated in two places: inside the bulk fluid and along the contact line. The former is standard for all flows; viscosity acts like internal friction in the fluid and dissipates a flow’s kinetic energy into heat. Contact line dissipation is trickier. While it isn’t hard to imagine that a moving contact line would dissipate energy, it’s been unclear just how much energy the contact line eats up.

To answer that question, researchers performed a novel experiment using an extremely narrow capillary tube, initially filled with air. By dipping one end of a horizontal tube in an oil reservoir, they sucked some oil into the tube. Then they set the oil-filled end of the tube against a water reservoir, causing it to suck up water. The oil slug then moves along the tube at a constant speed, which enables the team to separate out the two sources of dissipation. They found that contact-line dissipation accounted for a surprisingly large amount of the overall dissipation — between 20 and 50 percent, depending on the length of the oil slug! (Image credit: N. Sharp; research credit and submission: B. Primkulov et al.)

► Mud Pots
  20 Jul, 2021

Mud pots, or mud volcanoes, form when volcanic gases escape underlying magma and rise through water and earth to form bubbling mud pits. I had the chance to watch some at Yellowstone National Park a few years ago and they are bizarrely fascinating. In this Physics Girl video, Dianna recounts her adventures in trying to locate some mud pots in southern California and explains the geology that enables them there. And if you haven’t seen it yet, check out her related video on the only known moving mud puddle! (Image and video credit: Physics Girl)

► Devising Greener Chemistry
  19 Jul, 2021

Not all microfluidic devices use tiny channels to pump and mix fluids. Some, like the Vortex Fluidic Device (VFD), conduct their microfluidic mixing in thin films of fluid. The VFD is essentially a tube spinning at several thousand RPM that can be tilted to various angles. Coriolis forces, shear, and Faraday instabilities in the thin fluid film create a complex microfluidic flow field that’s excellent for mixing, crystallization, and processing of injected chemicals. One rather notorious application of this device was unboiling an egg, a feat for which the researchers won an Ig Nobel Prize. But other, more practical applications abound, including a waste-free method for coating particles. (Image and research credit: T. Alharbi et al.; video credit: Flinders University; via Cosmos; submitted by Marc A.)

► Inside Old-Fashioned Butter
  16 Jul, 2021

Today’s video is a little different: it’s an inside look at a butter-making shop in France that uses traditional nineteenth-century methods to process the butter. Watching workers fold and shape 50 kilos of butter is mesmerizing, and it highlights the amorphous, pseudo-fluid nature of the butter. Yes, the butter holds its shape like a solid, but it’s a soft solid at best and certainly shows fluid-like qualities when force is applied. A word of warning: you might not want to watch this on an empty stomach! (Image and video credit: Art Insider; via Colossal)

CFD Online top

► A note for CFD developers and the Spalart-Allmaras model
  12 Jun, 2021
This is a pretty specific issue which relates to my experience working on wall functions and the Spalart-Allmaras model, but might be useful to others or, more importantly, the users of their code.

If you followed my previous posts here, you know I developed a wall function formulation based on the Musker wall function which, thanks to a math trick, is integrable also for arbitrary Pr/Pr_t (Sc/Sc_t) numbers and also for some forms of non equilibrium terms.

One of the nice things about this formulation is that it uses the same form of turbulent viscosity approximation that holds for the Spalart-Allmaras model near a wall, as they both use:


where k is the Von Karman constant and a is the y^+ value where \frac{\mu_t}{\mu}=1. The formulation implies \frac{\mu_t}{\mu}=ky^+ for large y^+ and \frac{\mu_t}{\mu}=C{y^+}^n near the wall, with:

C = \frac{ka}{a^n\left(ka-1\right)}

The classical Musker formulation uses n=3, which is the correct near wall behavior, while the SA model uses instead n=4, which was claimed to be ininfluent in the original paper, yet certainly uncorrect. Also, the constant implied by the SA model is given by:

C_{v1}^3 = \left(ka\right)^4-\left(ka\right)^3

which for C_{v1} = 7.1 gives, approximately, a=4.6228/k (but could be also expressed in closed form for arbitrary C_{v1}).

Besides the near wall behavior, a striking difference between the SA near wall behavior and the Musker one is that latter is easily integrable to a nice closed form wall function and, as I have shown in previous posts, the same holds for the temperature and other scalars. While the same is formally true also for the SA model, as shown here:

it is debatable that the "nice" and "closed" form description applies to that as well; also, it is unclear if the approach can be extended to temperature and scalars. Nonetheless, the above SA wall function has already found widespread use, at least in academic applications.

This note comes from the desire and attempt to modify the SA model in a well known CFD code that, indeed, allows the user to implement their own turbulent viscosity formulation. The idea was to force the SA model to behave at wall as the Musker wall function.

Indeed, the SA model is built in such a way that, in cases where wall functions conditions apply (i.e., S=du/dy=\rho u_{\tau}^2/(\mu+\mu_t)), the prescribed f_{v2} formula, independently from how f_{v1} is defined, allows the linear solution \widetilde{\nu}=u_{\tau} k y to hold in the whole viscous+buffer+log zone. That is, under the classical equilibrium assumptions, a proper f_{v2} should ensure that the SA solution is always of the following form, independently from f_{v1}:

\chi = k y^+
\frac{du^+}{dy^+} = \frac{1}{1+\chi f_{v1}}

This preliminary examination then suggests that the Musker behavior could be reached by simply using:

\mu_t = \mu \frac{\chi^n}{\chi^{n-1}+\left(ka\right)^n-\left(ka\right)^{n-1}}

with n=3, where:

\chi = \frac{\rho \widetilde{\nu}}{\mu}

However, this was not possible as the code was hardcoded to use:

f_{v2} = 1-\frac{\chi}{1+\chi f_{v1}} = 1-\frac{\chi\left(\chi^3+ C_{v1}^3\right)}{\chi^4+\chi^3+C_{v1}^3}

in the production and destruction terms, instead of:

f_{v2} = 1-\frac{\chi}{1+\frac{\mu_t}{\mu}}

So, in order to apply the correct modification, an additional source term has to be supplied, that deletes the old production and destruction and uses the new ones. This, of course, is just as cumbersome as it sounds, and requires some insight into the SA model that a typical user wouldn't probably have (not even myself, considering that the first implementations of this approach didn't recognize the need to alter also the destruction term).

In conclusion, the present note is to suggest that implementations of the SA model should use the following definition of f_{v2}:

f_{v2} = 1-\frac{\chi}{1+\frac{\mu_t}{\mu}} = 1-\frac{\rho \widetilde{\nu}}{\mu+\mu_t}

which makes it valid for whatever definition of the turbulent viscosity. The immediate gain is that now one can easily implement a SA version with the correct near wall behavior and simple, all-y+, analytical wall function.

In the end, the implementation used in the attached source code here worked much better than the one only affecting the turbulent viscosity.

Still, the match is not as perfect as when one compares the original SA with its underlying wall function. In particular, for some reason, the turbulent viscosity ratio fails to reach exactly 1 at the exact prescribed location (a=11.0409) and there is a slight bump in the otherwise linear \widetilde{\nu} behavior.

Note that for a closed source tool like the one used in this test, the exercise above is basically cherry-picking until you get all the terms right (as it is impossible to know which terms were implemented using the turbulent viscosity and which ones using f_{v1}). So, this must be considered just as an exercise or, at best, a work in progress. The very point here is, a correct implementation is independent from f_{v1} .

OpenFOAM, for example, seems to be correctly implemented, so that one should really just modify the definition of f_{v1} in SpalartAllmaras.C and is ready to go.
Attached Files
File Type: c sa_mu_t.c (2.1 KB, 45 views)
► OpenFOAM tips and tricks
  16 Mar, 2021
I am sharing a few tips and tricks for better OpenFOAM simulations. These are not any new tips I discovered. These are just some which I felt most useful based on my experience.

1. Mesh is the most important aspect - I have spent countless hours fixing issues in other places while the real issue was with my mesh. As a thumb rule, make sure the mesh has: no non-orthogonality issues, low aspect ratio cells, no refinement zones in the region with high gradients

2. Always start with a coarse mesh - fix your problems fast!

3. OpenFOAM tutorial cases are not fine tuned, so don't use them blindly for your case. Check the boundary conditions used by tutorial in Doxygen (or the header file in src) and ensure that the right parameters are chosen for your model physics.
Eg: totalPressure BC uses rho=none for incompressible and rho=rho for compressible subsonic.

4. For internal flow, ensure that the mass flux is stable at the inlet and outlet. For statistically stationary flows, the average mass flux should be constant/ periodic.

<more to be added as I keep learning :)>
► Boundary conditions for multiphase flow
  16 Mar, 2021
I have struggled a lot with boundary conditions for multiphase flow, particularly where the multiphase spray is impinging on the outlet patch (typically the case of an internal flow simulation of a fuel injector spray). I have finally settled on the following Boundary conditions which work!

Simulation details: I am doing a 2D axisymmetric flow simulation using a wedge BC at front/ back planes. I am using a custom solver based on homogeneous relaxation model. This is similar to cavitatingFoam solver available in OpenFOAM which uses homogeneous equilibrium model.

Mesh: For multiphase flow, it is very very important to ensure a good quality mesh, which includes the following:
1. Avoid refinement zones - At the refinement zones boundaries, I saw spurious pressure fluctuations. Therefore, avoid them at all costs.
2. Low aspect ratio cells - Ensure that the aspect ratio of cells remains low.
3. Smooth grading in cells

Boundary conditions:

I tried several combinations for pressure boundary condition but ended up getting pressure oscillations within the domain due to the two phase flow at the outlet patch. The solution I found is to use totalPressure BC for pressure at Inlet, outlet patch.

BCs combination I finally settled on:

Pressure: totalPressure at Inlet, Outlet
Velocity: pressureInletVelocity at Inlet, pressureInletOutletVelocity at Outlet
density: zeroGradient works (I use a custom BC which is similar to ZG)

Refer to the following link for useful BC combinations for internal flow:
► Turbomachinery Solver OpenFOAM
  13 Jan, 2021
Turbomachinery Solver OpenFOAM

This forum has been a big source of information for me since I started doing CFD. Now, I want to share my work to thank your altruistic knowledge sharing. Regarding the main topic of this blog, I am going to talk about my first turbomachinery simulations with rhoSimpleFoam, the problems I found with this solver and my solution, which I called turboSimpleFoam. Feel free to ask me whatever you want or tell me if something is wrong.

Turbomachinery is a very interesting topic in my opinion, so I started the design of a turbocharger a year ago just for fun. After modelling some of the geometry, I thought that doing some CFD simulations would be of interest to understand the behaviour of the air when it passes through the device. Therefore, a computational domain was defined by the following properties:
• Compressible
• K-Omega SST
• Subsonic
• Inlet T = 300 K
• Inlet p = 1 atm
• Mass flow = 0.1 Kg/s
• Rotation Speed = 50 000 rpm

Until here the problem seemed challenging, but nothing I hadn’t done before. Taking into account the conditions of the problem to be solved, I chose rhoSimpleFoam as my solver, snappyHexMesh as my mesher and then I performed some simulations. Surprisingly for me, the temperature decreased to 280 K at the exit of the rotor so, obviously, something was terribly wrong.

Firstly, I tried several things like changing its thermodynamic properties or its boundary conditions, without significant changes. The problem was driving me crazy for a few days, but then I introduced an energy source in the rotating zone, and it worked. However, I hadn't solved it yet, because I picked an energy source of a random number of watts, but it was a good starting point.

Once I noticed that the energy along the rotating zone wasn’t being solved properly, I studied turbomachinery theory to calculate the energy source, and this was my conclusion:

𝑊𝑢 = 𝑚̇ · ( 𝑢1 · 𝑐𝑢1 − 𝑢2 · 𝑐𝑢2 )
𝑢 = 𝑟 · 𝜔
𝑑𝑊𝑢 = 𝑑𝑖𝑣𝑒𝑟𝑔𝑒𝑛𝑐𝑒( 𝑚̇ · 𝑢 · 𝑐𝑢 )

Where 𝑊𝑢 is the energy source, 𝑚̇ is the mass flow, 𝑢 is the rotation velocity, 𝑐𝑢 is the tangential velocity of the flow, 𝑟 is the radius and 𝜔 is the rotation speed.

After that, I introduced the energy source in the code and some extra variables like the rotation velocity, the tangential velocity, the radial velocity and something I called the zone term, Z. The last term is necessary due to the energy source value being zero at the static zone and one at the rotating zone. Taking all of this into account, the energy equation in the code is:

fvScalarMatrix EEqn
fvm::div(phi, he)
+ ( == "e"
? fvc::div(phi, volScalarField("Ekp", 0.5*magSqr(U) + p/rho))
: fvc::div(phi, volScalarField("K", 0.5*magSqr(U)))
+ thermophysicalTransport->divq(he)
fvOptions(rho, he)+Z*fvc::div(phi,u*Ut)

Finally, the results obtained are logical and the temperature rises to 350 K. Also, I have solved the problem in EXCEL using velocity triangles and thermodynamics, so the results of the CFD simulation can be compared with the theory of turbomachinery. As it can be seen at the end of this blog, the results obtained by both ways are quite similar.

In conclusion, the new solver turboSimpleFoam gives excellent results in comparison with the theory of turbomachinery. Also, the temperature, the pressure and the density at the outlet are in line with the reality using both ways.

Solver (Rotation axis must be in the Z direction):

PostProcess Paraview:


Attached Thumbnails
Click image for larger version

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Size:	41.5 KB
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Attached Images
File Type: jpg mesh_Turbocharger.jpg (72.0 KB, 213 views)
File Type: jpg rhoSimpleFoam_Turbocharger.jpg (25.7 KB, 209 views)
File Type: jpg turboSimpleFoam_Turbocharger.jpg (26.4 KB, 220 views)
Attached Files
File Type: zip (256.2 KB, 112 views)
► Energies: special issue on microscale and mesoscale modelling
  22 Dec, 2020
I will be editing this special issue of Energies

Contributions from the CFD community are welcome!
► Panel Method
  16 Dec, 2020
Panel method for 3D.
Attached Files
File Type: pptx Panel_method.pptx (62.9 KB, 103 views)
File Type: pdf Integral Equation Methods, Lecture 1.pdf (587.5 KB, 192 views)

GridPro Blog top

► Automated Structured Meshing of Wire-Wrapped Fuel Rods
  26 Jun, 2021

Figure 1: Structured multi-block meshing of a wire-wrapped nuclear fuel rod assembly.

2200 words / 11 minutes read

This article is a part of the series on Nuclear fuel rods CFD

Part 1: Thermal Hydraulics of Wire Wrapped Nuclear Fuel Rods
Part 2: Role of Structured and Unstructured Meshes in Nuclear Fuel Rods CFD
Part 3: Meshing Wire-Wrapped Fuel Rod Bundle with GridPro

In this article, we cover aspects of meshing wire-wrapped nuclear fuel rod bundle using GridPro, more precisely on the automation of the meshing process which is scalable, versatility and robust with high level of mesh quality control.


The geometry of the fuel rod bundles with helically wrapped wires is complex. The helical nature of the wire along the rod length makes meshing challenging for both structured and unstructured meshing algorithms. Adding to the complexity are the presence of high acute angle wire-rod junction and the small gaps between the wire and neighbouring rod.

The geometric complexities mentioned above are intimidating enough for a user to look for solutions other than structured mesh. But as we discussed in our previous article, Structured meshes seem to be the Holy grail for Thermal Hydraulics of the Nuclear Fuel Rods.

Empathising with the needs of the community, we at GridPro have developed an automation script that focuses on providing a solution that will provide the user the highest quality mesh with minimal input. 

Our goal was  to build a solution that would be 

  1. Automatic 
  2. Flexible
  3. Scalable
  4. User Controllable
  5. Versatile

Though the above objectives are conflicting in nature, we wanted to provide a solution that would require minimal input and have the turnaround time as that of an unstructured mesher and provide all the benefits of a structured mesh-like high quality, Multi-Grid Scalability, and user control in terms of mesh size and refinement. 

Over the years, with GridPro our goal is to provide a platform for automating Structured Multi-Block Meshing. For self-replicable configurations like nuclear fuel rod bundles, it is possible to nearly reach these ideal requirements using the tools in GridPro. The following sections elaborate on this aspect in greater detail.


Normally, the block generation step in the multi-block approach is time and labor-intensive. However, nuclear fuel rod assemblies being self-replicable is favourable for automation. In GridPro, automation of fuel rod assembly was possible by adopting a template-based approach. Standard templates for a single rod are built for different rod wire configurations. Picking the right template and providing more details is the first input the user provides for the automation.

Figure 2: Image source Ref [4].

The automation script requires the following inputs,

  1. Rod diameter,
  2. Wire diameter,
  3. Pitch of the wire
  4. Distance between two rods, as shown in Figure 2.
  5. Number of rings, as shown in Figure 3.

When the script is executed, the blocking for the single rod configuration is replicated and translated, and merged to complete the topology building for the entire bundle.


The scalability with respect to nuclear fuel bundle can be reasoned as, 

  1. Scalability based on the number of rods.
  2. Scalability based on different sizes of mesh.

Figure 3: Rings:- a. Ring 1 in a 7-rod bundle. b. Ring 1 and Ring 2 in a 19-rod bundle. c. Rings 1 to 8 in a 217-rod bundle.

Scalability in terms of the number of rods

The scripting strategy ensures that the user is able to extract the full benefit of structured multi-block strategy, The regularity of the patterns makes the number of rings as a parameter that can be used to scaled the model from a mere single rod configuration to a 7-rod bundle when it is a single ring, 19 rods for 2 rings to the farthest extent of 8 rings for a 217-rod full-scale configuration.

Figure 4: Structured multi-block mesh for 19-rod bundle and 217-rod bundle using GridPro.

Scalability based on different sizes of mesh

One of the main tasks of a CFD analyst is to find the right mesh size for their configuration with all the local limitations in mind. GridPro’s multi-block approach is very conducive for scalability w.r.t grid refinement as well. The blocking or topology can create a series of sequential grids as required for a grid-convergence study by changing a single parameter or by providing a sequential ratio. The edge density of each block is modified based on the user input to get a family of grids. The algorithm reads the parameters and ensures that there is no deterioration in grid quality when subjected to grid refinement.

Figure 5: Sequential grid-convergence-grids. Mesh resolution around a wire. a. Coarse. b. Medium. c. Fine.
Figure 6: Progressive mesh refinement. Grid densification in the near vicinity of a wire-rod region. a. Coarse. b. Medium. c. Fine. 

Local grid-refinement

As discussed in our previous article, because of the need to accurately model sensitivity to heat transfer, the meshes in the contact regions between wires and rods, may need more refinement. For configurations like wire-wrapped fuel rod bundles, where geometric scales differ by a large magnitude, the ability to do local mesh refinement could be a lifesaver since the cell count could be kept under desirable limits and make the simulation computationally feasible.

Figure 7: Local refinement in the wire-rod intersection region for the blended contact geometric variant.

When the flow trips over the wire placed across its flow path vortices are generated. Fine mesh points are needed to discretise the region in the near vicinity of the wire, especially in the wake region where the vortices are present. Also, the location where the wire comes in contact with the rod is susceptible to shoot-up in temperature. Such hot spot regions need high-resolution meshes to accurately predict the local temperature. Figure 7 shows the local grid refinement at the wire-rod junction. Local refinement by Enriching in GridPro ensures that the refinement is contained locally and is not allowed to propagate to the larger domain.

Figure 8: Accurate geometric capturing of the thin wire with an optimal number of cells.

Figure 8, shows the local refinement by Enrichment along the entire length of the rod, in the near vicinity of the wire. The ratio of rod diameter to wire diameter is nearly 20:1. The mesh element size needed to discretise also varies by the same ratio. Enrichment in GridPro ensures appropriate resolution of the thin wire and smooth transition to the bigger cells used to discretise the rods without abnormal shoot-up in cell count.

Optimising cell count

From a numerical point of view, hexahedral elements are the most efficient elements. They consume the least memory and computing time per element. The grid built using hex elements are well aligned to the flow and hence well adapted for long and thin shear layers on the wall and in the wake. Compared to the unstructured grids, to fill a volume of space with a fixed edge length, the hex meshing approach needs the least number of cells.

One of the major advantages of hex meshing is its ability to generate high aspect ratio grids without any deterioration in cell quality. This ability, unlike in the unstructured approach helps in generating grids with directional refinement. In the case of fuel rod meshing, this is a powerful asset, as it helps to reduce the cell count in the rod axial direction and more optimally refine the grid in the other two directions.

Figure 9: Axial coarsening:- a. Edge density = 16, b. Edge density = 8, c. Edge density = 4.

A study by TerraPower shows that they were able to reduce the cell count by 27 million i.e a reduction of 32% in total cell count by employing stretched structured grids in the axial direction without compromise in solution accuracy. This simple ability helps in a drastic reduction of computational time also. Further, the additional cells in the non-axial direction help in accurate capturing of the flow physics in the sub-channel assemblies.


Generating meshes with high quality is always the goal of a CFD Analyst who is looking beyond pretty images. Hence, ensuring cell quality parameters like skew, aspect ratios, face warpage, negative volumes, right-handedness is very essential before performing large and complex CFD analyses like nuclear-fuel rod subassemblies.

In GridPro, internally the algorithm ensures that essential quality criteria are met. For example, the algorithm strives to place cells adjacent to the wall as orthogonally as possible and also maintain the cell angles larger than 20 degrees and lesser than 160 degrees. Management of the cell aspect ratios is easy and efficient. By varying the edge density, the cell aspect ratio can be modified. Block smoothing algorithm ensures that there is a smooth variation of cells in the domain and the cell aspect-ratio are kept in the range of 10-50 – a range well acceptable to most CFD solvers to obtain good solution convergence.

Figure 10: Cell skewness distribution:- Good quality cells all along the length of the rod. With a value of 1.0 representing maximum skew, the red cell in the image has a quality of 0.6.  

The generated blocks are not rigid. Automatic block smoothing ensures the gradual transition of the cell size from the region of high density to coarser regions and avoids jumps in cell size. The algorithm tries to ensure that the growth rate in adjacent cell volume is always lesser than 2. Further, block smoothening ensures maintenance of grid quality even in narrow gaps and high acute regions. In GridPro, gaps as small as 4 microns have been meshed with cell quality well within acceptable limits.

Solutions obtained on grids generated with these tools are also of superior quality. Blocks are placed aligned to the fluid flow. The presence of hexahedral cells aligned to the predominant flow direction ensures a drastic reduction in discretization errors. Further, the blocks generated in GridPro have a one-to-one connected interface. Since there are no non-matching grid interfaces, there is no degradation in the flow prediction.

Figure 11: Cell aspect ratio distribution:- The red cells in the above image show cells with aspect ratios above 400.


In GridPro it is easy to accommodate different variants of wire-rod junctions. Researchers regularly test with different variants of the wire-rod configuration, such as point contact between wire-rod, sharp angle wire-rod intersection, filleted wire-rod intersection, square cross-section wires, wires represented by thin sheets, etc. Out of these, the first three are challenging to mesh, while the other variants are easily manageable.

Figure 12: Image source Ref [5].

The point contact case is geometrically not meshable as the wire tangentially comes in contact with the rod. Instead, they are slightly approximated by providing as small a gap. Figures 13a and 14a show a grid with a gap of 4 microns. Even in this micro-gap, the cell quality is well maintained and the fineness is locally contained.

Figure 13: Meshing for different geometric variants of the wire-rod junction. a. Near point contact with a 4-micron gap. b. Wire intersecting rod with acute angle formation. c. Blending of wire-rod intersection by a fillet. 

Figures 13b and 14b shows the second case of displaced wire, where the wire is offset by a larger margin resulting in the creation of an acute-angle intersection between wire and rod. The geometric acuteness is accurately captured with fine grid points. 

Sometimes, studies are done with the wire physically not touching the rod. Figure 14a shows a mesh for such a geometric variant with a small gap. Accurate capturing of these narrow gaps with high-resolution grids having high cell quality is key to obtaining accurate reliable solutions. Tools in GridPro help to meet the meshing requirement of all the possible variants regularly used in the nuclear fuel rod assembly simulations.

Figure 14: Zoomed view of the mesh in the contact zone. a. Contact with a small gap. b. Sharp intersecting contact c. Blending contact.


With this, we come to the end of this Part 3 in the series on Nuclear fuel rods.

For adequate resolution of the geometry and flow field, meshes with a sufficiently large number of cells are essential. Since, the number of elements is proportional to storage requirements and computing time, for many large-scale 3D problems like nuclear fuel rod subassemblies, Engineers usually end up compromising between desired accuracy level and the number of cells.

Using GridPro, the need to make such a compromise can be eliminated. Optimised grids can be automatically generated for wire-wrapped nuclear fuel-rod bundles in no time and with ease. Whether it is a 7-rod bundle or a 217-rod bundle the time and effort are just the same.  

Case Studies

CFD computational studies using GridPro’s structured multi-block meshes for wire-wrapped nuclear fuel rod bundles have been made by TerraPower and CEA. Here are the links to the case studies.


Nuclear Fuel Rods Series

Part 1: Thermal Hydraulics of Wire Wrapped Nuclear Fuel Rods
Part 2: Role of Structured and Unstructured Meshes in Nuclear Fuel Rods CFD
Part 3: Meshing Wire-Wrapped Fuel Rod Bundle with GridPro


1. “Best Practice Guidelines for the use of CFD in Nuclear Reactor Safety Applications“, NEA/CSNI/R(2007)5, JT03227125, 15-May-2007.
2. “Best Practice Guidelines for the Use of CFD in Nuclear Reactor Safety Applications – Revision“, Nuclear Safety, NEA/CSNI/R(2014)11, February 2015.
3. “Computational Fluid Dynamics for Nuclear Reactor Safety-5“, Workshop Proceedings, 9-11, September 2014, Zurich, Switzerland.
4. . “CFD Investigation of Wire-Wrapped Fuel Rod Bundles and Flow Sensitivity to Bundle Size”, L.M. Brockmeyer et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
5. ” CFD calculations of wire wrapped fuel bundles : modelling and validation strategies“, Ulrich Bieder et al, NEA-CSNI-R–2011-14, INIS Volume 44, Issue 33, 2012.

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► Role of Structured and Unstructured Meshes in Nuclear Fuel Rods CFD
    2 Jun, 2021

Figure 1: Structured multi-block meshing of a wire-wrapped nuclear fuel rod assembly.

1910 words / 9 minutes read

This is Part 2 of the series on Nuclear Fuel Rods. Part 1 / Part 2 / Part 3.

In Part 1 of the series, we covered aspects of the next generation of fuel-efficient nuclear reactors and the flow physics in wire-wrapped fuel rod assemblies. 

In this article Part 2, we try to cover some of the aspects of CFD modeling of the wire-wrapped fuel pins – the challenges, the geometric approximations, gridding requirements, etc.


An accurate understanding of the intricate flow fields of fuel rod sub-assemblies is extremely essential for the proper design of these critical nuclear core components considering the high levels of safety requirements needed while operating nuclear power plants. CFD has emerged lately as a reliable computational technique used extensively for design and safety evaluation purposes. Particularly for wire-wrapped fuel bundles, CFD has been pivotal in understanding and appreciating the complex flow physics and thermal-hydraulics. 

In fuel-assemblies analysis, CFD is routinely used to compute pressure drop across the fuel bundle, velocity and temperature distribution, quantifying hot-spot temperatures, and heat flux distribution.

Geometrical modeling and discretizing the flow field around the helical wire wound fuel rods is very intricate and poses a significant challenge. For the accurate resolution of the flow features, the configuration demands a mesh with a massively large number of grid points. This means, a need for larger computer memory and time. The more the number of fuel rods considered for CFD simulation, the larger is the requirement for computational resources.

Video 1: Pressure distribution in a 217-pin fuel bundle with wire-wrapped spacers. The Nek5000 simulation calculations were run with 3 million spectral elements of order N=7 (one billion points total) with sustained 80% parallel efficiency.

Typically, nuclear reactors utilizing wire-wrapped pins, pack them in bundles of up to 217. Modeling with 217 rods is very expensive. So, researchers often make use of a smaller bundle size ranging from 7 to 61 rods. However, one has to ensure that using a smaller bundle does not significantly alter the flow physics. Studies addressing this issue have shown that using at least 19 pins weakens the influence of the number of pins and effectively captures all the predominant flow physics.

Geometric Approximations

Accurate capturing of the helical wires is very essential. The wires which help in suppressing vibrations and prevention of horizontal displacement of the rods, also aid in effective mixing of the coolant, by reducing the temperature gradients and critical heat flux. However, along with these beneficial effects they also induce increased pressure loss. Further, in the wake of the wire, in regions of low velocities, a shoot-up in surface temperature can also occur. It is therefore very essential to model the wire accurately for accurate prediction of hot spots in the vicinity of the wire and drop in axial pressure. What this means is that we need to have good resolution grids with high quality in the vicinity of the wire especially at the contact region between the rod and the wire.

Dr. Michael Böttcher from Karlsruhe Institute of Technology elaborates on the meshing needs for obtaining accurate CFD predictions for wire-wrapped nuclear fuel rod bundles in this talk presented at the Pointwise User Group Meeting.

Figure 2: a. Point contact. b. Displaced wire. c. Open wires with blended contact zone. d. Square wire. Image source Ref [9].

Figure 2 shows four of the most common approaches to model the wire-rod junction. In actuality, the contact between wire and rod is point contact (Figure 2a). Though modeling this is ideal for predicting temperature in the contact zone, it is very difficult to mesh due to tangencies. However, in the second modeling variant (Figure 2), a small radial inward displacement for the wire, say, 5% of the wire diameter is provided. With this small geometric modification, meshing the configuration becomes more feasible. The geometric approximation helps to achieve a homogenous mesh with only a few layers of skewed cells in the acute regions. Which makes it possible for a reasonably accurate prediction of the hotspots region.   

Another type of approximation used by researchers to ease the meshing process is the blending between the wire and the rod by filleting (Figure 2c) of the acute region. This geometric variant allows for placing fine meshes of smaller size at the contact zone along with an approximate prediction of the hotspot with small uncertainty. However, depending on the rod-wire contact form, CFD computations have shown to predict a 15% more pressure drop compared to wires with a point contact.

Another make-do approximation is, using a square profile (Figure 2d) with a cross-sectional area equivalent to that of the circular wire. Though this model is easy to mesh, accurate prediction of hotspots is not possible. Also, it is observed that square wires tend to predict 5% increased pressure loss when compared to the model with displaced wire. Interestingly, studies with hexagonal and rhombi forms have also shown to overestimate the pressure drop by a large value of 16% and 19% respectively, relative to the displaced wire approach. 

Considering all the merits and demerits of the 4 modeling approaches, researchers feel that the displaced wire approach with a displacement of the wire by 5% of the wire diameter into the rod is an acceptable compromise to model the wire-rod junction. 

Figure 3: Body forces are applied at the black cells: cells that are within the bounds of the wire-wrapping. Image source Ref [7].

At times, the entire fuel assembly is considered for CFD analysis, including the inlet and outlet headers. For such large domains, accurate prediction of the flow field with high-resolution grids is not possible. So to reduce the computational effort but still maintain reasonable accuracy, low resolution or under-resolved meshes are used. On occasions, researchers sought out approaches like geometric simplification of the rod bundle by modeling the wire as a spiral fin or as a momentum source. These are approximate methods that need experimental validation.

In the momentum source (MS) approach, the simulations employ meshes of bare pins without the wire-wrap geometry explicitly modeled. Instead, the effect of the wire-wrap on the flow is accounted for by introducing a momentum source into the governing fluid equations. This MS is only applied to cells corresponding to the location of the wire wrap, and its vector components in each cell are based on the local flow field.

This wire simplification method by momentum source is ideal for initial scoping studies of wire-wrapped fuel assemblies, as they reduce computational cost and also avoid complications due to body-fitted meshing of wires. The benefits of this modification can be seen in the quick turnaround time for design modifications.

Figure 4: Helically wrapped wire around a single fuel rod.

Gridding Requirements

Accurate prediction of flow fields inside the subchannels demands high-resolution grids. The thin wires wrapped around the rods need to be finely discretized. The small junction where the wire meets the rod needs to have highly refined cells as they are potential locations for hot spots.

The wire-wrapped rods are compactly packed and the space between the rods is fairly narrow. If we want to resolve the flow features developing in these narrow passages, they need to be filled with finely refined cells. The vortices generated in the inner channels, swirling flow in the outer channels, all need good resolution cells.

Figure 5: Fine mesh in the sub-channels between the fuel rods.

Along with appropriate discretization of the inviscid flow field, it is equally important to resolve the viscous boundary layer. The flow in the subchannels is viscous-dominated flows. So, fully resolving the boundary layer of the rod as well as the wire is critically essential. Viscous padding with a Y+ of about 1 with a small stretching factor is highly recommended. Good resolution of the boundary layer helps in the accurate prediction of heat transfer quantities and velocity profiles.

Care should be taken while meshing the small gap between the wire of the parent rod and the neighboring rod. The gap tends to be extremely small and getting good quality meshes with low skewness is very essential.

Figure 6: Local enrichment to resolve the wire and wire-rod junction all along the rod length.

Because of the helical nature of the wrapped wire and the all-length association with the rod, many CFD practitioners go with unstructured gridding techniques like hybrid, polyhedral or cartesian. Though these approaches are quicker to generate grids, they result in abnormal grid size demanding huge computational resources. Along with that, the unstructured approaches are more dissipative in nature, which smear off the subtle flow features.

Figure 7: High-resolution boundary layer clustering around the wrapped wire and fuel rods.

It is for this reason, researchers who look for high-quality solutions prefer structured multi-block grids. The flow-aligned nature of grid cell placement, helps in better, crisper capturing of the flow features with less dissipation. Structured approaches, unlike their unstructured counterpart, have the added advantage of employing stretched cells in the axial direction. This not only helps in reducing the cell count drastically but aids in using the cells more optimally by increasing the resolution in the other two directions. The challenge of creating the structured mesh is a tough one, primarily the thought process required to design the blocking structure and the time taken to build such a structure. With recent advances in structured meshing, this challenge has become the problem of the yesteryears.

Figure 8 shows the comparative results obtained by TerraPower corporation, by cell reduction in the axial direction. The fine mesh was axially coarsened to the number of axial cells in the coarsest mesh. Cell reduction of 27 million equivalent to 32% of total cell count was achieved with the same level of solution accuracy as the fine grid.

Figure 8: Axial coarsening of the fine mesh. Image source Ref [7].

Often, it is very difficult to know what is the right mesh size to get accurate CFD prediction. Under such circumstances, a grid convergence study is conducted. A set of grids sequentially refined are generated and CFD solvers are run to check the level of variation in the flow parameters. Analyzing the results, a grid with an acceptable level of error tolerance is picked for repeated runs. Figure 9 shows a grid convergence study by TerraPower for a fuel rod assembly.

Figure 9: Heated mesh sensitivity. Grid convergence study with Richardson Extrapolation. Image source Ref [7]. 

Challenges in modeling a full-scale 217 fuel pin bundle

It is estimated that, for a one-pitch helical wire, the cell count requirements for a 217 pin bundle is 36 times that needed for a 7-pin bundle. If the complete length of the rod is considered the cell count will be 15 times that needed for one pitch. This means, the total cell count will turn out to be around 500 times that needed for a 7-pin bundle, which will come out to be about 200 million. The memory requirements will turn out to be about 200 Gb, while the expected CPU time will be around 2500 hours or ~ 100 days. If we want to make the simulation computationally more feasible, we need to discretize the domain with structured multiblock and use a highly scalable CFD solver.

Figure 10: Structured multiblock grid for a complete 217 wire-wrapped nuclear fuel rod bundle.

Parting thoughts

The top priority of nuclear power plants is safety. All safety concerns can be addressed if there is reliable data for all possible scenarios. CFD as a simulation tool helps in giving the Engineers a clear perception of all the possible scenarios in intricate detail. Structured grids with their less dissipative nature, flow-aligned cell placement, reduced cell count help in obtaining high-quality reliable solutions. They not only make computations for smaller fuel rod bundles less time-consuming and computationally cheaper, but they also make the simulation for full-scale 217-rod bundles more affordable.

This brings us to the end of Part 2 in the series on Nuclear Fuel Rods. In the next article Part 3 – Meshing Wire-Wrapped Fuel Rods in GridPro, we cover aspects of generating high-quality structured multi-block grids for various geometric variants of wire-wrapped fuel rod assemblies, automation, etc, using GridPro.


1. “Numerical investigation on vortex behavior in wire-wrapped fuel assembly for a sodium fast reactor”, Min Seop Song et al, Nuclear Engineering and Technology 51 (2019) 665-675.
2. “Status and Future Challenges of CFD for Liquid Metal Cooled Reactors”, F. Roelofs et al, International Atomic Energy Agency, March 2013.
3. “CFD investigation of helical wire-wrapped 7-pin fuel bundle and the challenges in modeling full scale 217 pin bundle”, R. Gajapathy et al, Nuclear Engineering and Design, December 2007.
4. “Thermal-Hydraulic study of the LBE-Cooled Fuel Assembly in the MYRRHA Reactor: Experiments and Simulations”, J. Pacio et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
5. “CFD Investigation of Wire-Wrapped Fuel Rod Bundles and Flow Sensitivity to Bundle Size”, L.M. Brockmeyer et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
6. “High-Fidelity Numerical Simulation of the Flow Through an Infinite Wire-Wrapped Fuel Assembly”, A. Shams et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
7. “Verification and Model Sensitivity Analyses for Computational Fluid Dynamics Simulations of Wire-Wrapped Nuclear Fuel Assemblies”, Daniel Leonard, Ph.D. et al, ASME Verification and Validation Symposium, May 18-20, Las Vegas, NV.
8. “The role of High Fidelity Numerical Simulations for Nuclear Reactor Safety Analyses”, Ed Komen, SNETP FORUM, 2 – 4 Februari.
9. ” CFD calculations of wire wrapped fuel bundles : modelling and validation strategies“, Ulrich Bieder et al, NEA-CSNI-R–2011-14, INIS Volume 44, Issue 33, 2012.

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► Thermal Hydraulics of Wire Wrapped Nuclear Fuel Rods
  25 May, 2021

Figure 1: Structured multi-block grid for a wire-wrapped 19 nuclear fuel rod bundle.

1700 words / 8 minutes read


Nuclear power plants generate about 16% of the world’s electricity. Nuclear fuels are an incredibly compact source of energy that liberates a tremendous amount of energy, which is used to generate electricity. One nuclear fission reaction generates 200 million units of electricity when compared to a chemical reaction which liberates only one unit of electricity. It is for this reason, nuclear energy is very attractive from an engineering perspective.Though, the nuclear energy industry has been marred with accidents and environmental concerns it has managed to stand the test of times and continues to contribute energy for human consumption.

Need for 4th generation Nuclear Reactors
Nuclear energy  does outweigh the other forms of energy generation, however , the current reactors ( 3rd generation) worldwide are far behind in terms of fuel efficiency. The conventional pressurized water reactors (PWR) use only about 2-3 percent of the uranium atoms in the nuclear fuel. This is an astoundingly low utilization ratio. On top of that, they use the open once through type of fuel cycle, i.e, fuel enters a reactor, 2-3 percent of the fuel is used, it is taken out, and disposed of. The fuel is grossly under-utilized and hence, there is a strong need to come up with ways to use it more efficiently if we want to make the process sustainable.

Figure 2: a. Fuel is used once in a reactor and discarder. b. Actinides are separated from the used fuel and burnt in appropriate reactor types, while waste contains only fission products.

For this reason, nuclear scientists and engineers have been investigating on building a new type of reactor called Generation 4 nuclear reactors. Firstly, these fast breeder reactors that come in this category apply a fully closed cycle and utilize 70-80 percent of the uranium fuel before they are disposed of. This leads to a 20-30 times increase in the efficiency of the use of uranium.

Secondly, the nuclear wastes produced by the current once-through cycle need 300,000 years to reach the mine radiotoxicity level. However, in the fully-closed cycle, if we can segregate the elements called actinides which are generated around plutonium and uranium, and then store the nuclear waste geologically for 300 years, the radiotoxicity levels will return to that in the mine from where it was taken out in the first place.

Figure 3: a. Relative Radiotoxicity. b. The number of years of energy at our disposal.

Lastly, as shown in Figure 3b, if we were to use uranium in a fast breeder reactor, we increase the number of years they can be used from 200 to 800 years and if we were to add minerals like thorium in the fuel cycle, we have around another 2000 years of energy at our disposal.

The above reasons make the 4th generation fast breeder reactors the hot research topic today and many companies both government and privately funded, are aggressively pursuing it to make it a reality.

 Need for Thermal-Hydraulic analysis in FBR

The central core of a nuclear plant consists of a few hundred fuel assemblies consisting of a large number of fuel rods. Fast breeder reactors utilize ducted fuel assemblies with helically wire-wrapped fuel pins. Coolants flow around these rods/pins and absorb the heat liberated during the nuclear fission reactions. In these reactors, liquid metals like sodium or lead are envisaged as coolants instead of water. This is because, they have 100 times more thermal conductivity than water, higher boiling temperature, and lesser neutron interactive property. The coolant moving out of the core rotates a set of turbine blades and generates electricity.

Safety studies are mandated by the safety authorities in order to license a nuclear power plant, thus ensuring the prevention of nuclear catastrophe like core melt-down, etc. This fundamental requirement necessitates designing all the components to meet safety requirements. Detailed thermal-hydraulic investigations of the core, fuel assemblies, and sub-channel are one such requirement.

The technical challenges in the core include the pressure drop and heat transport efficiency under nominal, transient, and incidental conditions. As for the safety issue, the limitation is on the clad temperature.There are other issues like fuel rod vibration due to coolant flow which leads to gradual fretting wear and fatigue at contact surfaces.

Figure 4: a. Bill Gates explaining the wire-wrapped fuel pins at TerraPower. b. A closer look at the wire-spacer pin bundle. Image source Ref [9,7].

At the fuel assembly level, thermal-hydraulic accident analysis concentrates on blockage scenarios and thermal fatigue evaluation. Lastly, at the sub-channel level, the focus is on the detection of hot spots.

These thermal investigations have larger significance especially in fast breeder reactors due to the large heat flux of about 1.5MW per square meter. Interestingly, it is only very recently that CFD tools have become advanced enough to model core coolant flow with high details and resolution. Traditionally, the core design was performed entirely using what are called sub-channel codes. Lately, CFD has become increasingly relevant to the core design. The increased resolution and fidelity CFD provides are very beneficial especially for complex geometries like wire-wrapped pins.

Figure 5: Wire-wrapped fuel pin. Image source Ref [7].

Wire-Wrapped Fuel Rods

Hexagonal array of wire-wrapped fuel pins are the trademark fuel arrangement system in sodium-cooled fast reactors. The wires serve as a support grid between fuel rods and they also help to maintain the gap between rods.

The helically wound wires enhance the mixing of coolants by redirecting the coolant to neighbouring sub-channels. This increased mixing is beneficial as it aids in better heat transfer and also prevents temperature peaking in hot channels.

Furthermore, the wire wrappers acting as spacers, separate the rods and minimize flow-induced vibrations which may induce reactivity fluctuations possibly leading to mechanical failure of the fuel cladding. However, wire wrappers cause a drop in pressure through the core compared to bare rods. The pressure drop is observed to be marginal at low Reynolds number but becomes quite significant at high Reynolds number.

Figure 6: Variation of flow pattern with wire angle. The red box shows the swirl flow in the outer channels. Image source Ref [1].

Flow Field characteristics

The flow inside a fuel bundle can be divided into two regions, a peripheral region where large swirl flow exists and the inner region, where the complex transverse flow exists. Figure 6 shows the variation in contours of axial velocity and streamlines of transverse flow with change in wire angle. What can be observed is that the axial velocity is higher in the edge sub-channel compared to that in the interior sub-channel. Also, the interior sub-channel’s axial velocity and streamline pattern tend to be similar irrespective of the position of the interior channel. However, the flow near the outermost region in the edge channel has large swirl flows which tend to rotate with the wire.

In the interior sub-channels, the wrapped wires make the flow inside the fuel bundles complicated by generating sweeping flow and vortex flow. What happens is that a portion of the axial flow sweeps along the wire and transforms itself into a transverse flow. In addition, another segment of the axial flow creates a vortex structure by tripping over the wire.

Figure 7: Generation and destruction of vortices around the wire in the interior sub-channel.  Image source Ref [1].

Figure 7 shows the generation and destruction of the vortices in the interior sub-channel. Vortices are periodically created in the interior sub-channel at a frequency of 3 times for every wire rotation. The vortices affect the flow field and the heat transfer inside the sub-channels and hence understanding their flow characteristics is essential. The main flow in the interior sub-channel is axial and when the flow gets blocked by the wire, the pressure on the windward side of the flow increases relative to the leeward side. As the wire passes the interior sub-channel ( station P1 to P5), part of the main flow which has traveled over the wire gets converted to a large center vortex (V1). It rotates in a direction opposite to that of the wire rotation and its length scale depends on the width of the sub-channel and transverse flow. Another vortex created in the sub-channel is the back vortex (V2), which is formed due to the transverse flow behind the wire. This vortex is fairly small in nature and is mostly confined between the surface of the pin and the wire.

It is observed that the largest of vortices occur in the edge sub-channel, which tends to block the swirling flow in the peripheral region. The vortices formed in the corner sub-channel are relatively small.

One thing to note is that the occurrence of these vortices is directly related to the position of the wire and does not depend on any geometric variables like the number of pins or pin pitch to diameter ratio, etc.

The transversal flow developed due to helical wire bring in many benefits. One, the coolant outlet temperature is now more uniform leading to lower levels of fluctuation in the readings of the core monitoring thermocouples which is essential for safer reactor control operations. The second advantage is that the clad temperature becomes more uniform in the circumferential direction due to the gyratory flow created by the helical wire. The coolant is made to impinge and sweep the corners formed by the junction of the pin and the spacer wire, thereby preventing a possible hot spot beneath the wire wrap. Lastly, it allows the FSA to be designed to generate a larger power without exceeding the temperature limits of the clad and sodium.

Video 1: Coolant Flow in Sodium Reactor Subassemblies. 

Parting Thoughts

A better understanding of these intricate flow fields is extremely essential for the proper design of these critical nuclear core components considering the high levels of safety requirements. CFD has emerged lately as a reliable computational technique used extensively for design and safety evaluation purposes. Particularly for wire-wrapped fuel bundles, CFD has been pivotal in understanding and appreciating the complex flow physics and thermal-hydraulics. 

With this, we have come to the end of Part 1 of the series on Nuclear Fuel Rods. This is a 3 Part series, starting with this article on flow physics.

Part 1 – Flow Field Inside a Wire Wrapped Nuclear Fuel Rod Bundle
Part 2 – Role of Structured and Unstructured Meshes in Nuclear Fuel Rods CFD
Part 3 – Meshing Wire Wrapped Fuel Rods in GridPro

In the next article, Part 2 – Role of Structured and Unstructured Meshes in Nuclear Fuel Rods CFD, we try to cover aspects of CFD simulation of these wire-wrapped fuel pins – the challenges, the geometric approximations, gridding requirements, etc. In the last part, Part 3 – Meshing Wire-Wrapped Fuel Rods in GridPro, we cover, how to generate high-quality structured multi-block grids for various geometric variants of wire-wrapped fuel rod assemblies, automation, etc, using GridPro. 


1. “Numerical investigation on vortex behavior in wire-wrapped fuel assembly for a sodium fast reactor”, Min Seop Song et al, Nuclear Engineering and Technology 51 (2019) 665-675.
2. “Status and Future Challenges of CFD for Liquid Metal Cooled Reactors”, F. Roelofs et al, International Atomic Energy Agency, March 2013.
3. “CFD investigation of helical wire-wrapped 7-pin fuel bundle and the challenges in modeling full scale 217 pin bundle”, R. Gajapathy et al, Nuclear Engineering and Design, December 2007.
4. “Thermal-Hydraulic study of the LBE-Cooled Fuel Assembly in the MYRRHA Reactor: Experiments and Simulations”, J. Pacio et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
5. “CFD Investigation of Wire-Wrapped Fuel Rod Bundles and Flow Sensitivity to Bundle Size”, L.M. Brockmeyer et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
6. “High-Fidelity Numerical Simulation of the Flow Through an Infinite Wire-Wrapped Fuel Assembly”, A. Shams et al, NURETH-16, Chicago, IL, August 30-September 4, 2015.
7. “Verification and Model Sensitivity Analyses for Computational Fluid Dynamics Simulations of Wire-Wrapped Nuclear Fuel Assemblies”, Daniel Leonard, Ph.D. et al, ASME Verification and Validation Symposium, May 18-20, Las Vegas, NV.
8. “The role of High Fidelity Numerical Simulations for Nuclear Reactor Safety Analyses”, Ed Komen, SNETP FORUM, 2 – 4 Februari.
9. “How Bill Gates’ company TerraPower is building next-generation nuclear power“, CNBC article.

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The post Thermal Hydraulics of Wire Wrapped Nuclear Fuel Rods appeared first on GridPro Blog.

► Engine Nacelle Aerodynamics
  16 Apr, 2021

Figure 1: Vortex system from a nacelle-wing-pylon junction. Image source Ref [12].

2231 words / 11 minutes read


The aviation industry accounts for 2% of global greenhouse gas emissions. With an annual increase of around 4.8% in air passenger transport, greenhouse gas emissions are very likely to go up unless some drastic measures are taken to curb them. In order to reduce the environmental footprint of the aviation industry, various governing bodies around the world are coming up with ambitious goals to cut carbon dioxide and Nox emissions by as high as 75 to 95% by 2050.

The solution to this challenge lies in lower fuel consumption and increasing aircraft efficiency. One promising approach the aircraft industry is currently pursuing is the development of Ultra-high bypass ratio (UHBR) engines. UHBR engines, as the name suggests, maximises the air mass flowing through the bypass duct, thereby reduce thrust-specific fuel consumption. The ratio of the amount of air that is allowed to bypass compared to that entering the engine core is called bypass ratio. Since 1975, the bypass ratio has increased from 6 to 12 and the next generation of engines are expected to have a bypass ratio even higher, ranging from 15 to 21.

Figure 2: Bypassing flow. a. Schematic diagram. b. CFD computations. Image source Ref [8, 5].

A larger bypass ratio means larger diameter engines. Fitting a larger diameter nacelle below the wing becomes a major challenge as compliance with ground clearance regulations requires a close coupling of nacelle and wing. This necessitates either to cut off a large chunk of the slat in order to avoid collision with nacelle during landing and take-off or fit the engine nacelle to the upper surface of the wing or embedded into the fuselage. Figure 3 shows, some of the different ways to mount engines in aircraft.

Figure 3: a. UHBR – Ultra-High Bypass Ratio under wing engines. b. OWN – Over Wing Nacelle configuration c. Fuselage embedded engines. Image source – arstechnica,, Ref [10].

Each approach has their own set of problems and challenges. For example, removing a segment of the slat leads to the creation of premature separation on the main wing leading to early stall and reduced maximum achievable lift.

In this article, we limit ourselves to understanding the issues in the under-wing installation of large bypass ratio engines and their complex flow physics.

Video 1: Why bypassing of air necessary?

Before we get into the details of under-wing installed engine nacelles, let’s start our appreciation of the nacelles from ground zero.

Aircraft engine nacelles

Nacelles are nothing but the housing for the aircraft engines as they protect the gas turbine from foreign object ingestion(FOI). They are designed with the objective of delivering air efficiently and with minimum distortion to the fan and also expand the gases in the exhaust system with maximum efficiency. Figure 4a shows the different parts of a nacelle.

Though they are designed to ensure good engine performance, their presence leads to a drop in lift and an increase in drag by a large percentage. Optimisation of nacelle design is very essential as high drag-generating flow phenomena like flow separation, shock waves and wake may develop during flight. A thorough analysis is needed to find the right engine location on the wing that provides the best integration of engine and airframe. This integration not only depends on the design of the nacelle and wing individually but also on the resulting interference effects.

Propulsion system integration is considered quite complex as it dramatically affects both the aircraft and the propulsive system performance. With bigger and larger engines, the propulsive system is becoming highly coupled with the airframe. Therefore, for correct evaluation of the performance of both systems, it is essential to take into account the installation effects. In other words, the airframe design and propulsion system design cannot be considered as two separate tasks but their designs need to evolve considering the interference effects they have on each other as well.

Figure 4: a. Parts of a typical nacelle. b. Engine Position. Image source Ref [9].

Parameters affecting interference phenomena

When a nacelle is being installed onto the airframe, a large number of influencing factors need to be taken into consideration such as engine positioning, the shape of the nacelle, pylon and wing, etc. Studies have shown that about half of the overall lift loss can be attributed to pylon shapes as it alters the lower wing pressure distribution. Another reason for the loss in lift is due to the intersection of the pylon with the fan cowl, as the flow tends to stagnate on it and then later accelerate over the top of the structure reaching supersonic velocities.

Along with the airframe shapes, engine positioning has a large influence on the interference phenomena. The position of an engine can be varied in a multitude of ways by moving it up/down, fore/aft, spanwise, and also by changing the pith and toe angle.

If the vertical distance is reduced, the shock on the upper surface gets shifted more upstream, resulting in loss of lift. Simultaneously, on the lower wing surface, the flow is less accelerated resulting in pressure gain. The loss in lift on the upper surface and gain in pressure on the lower, almost negate each other and thereby reduce the vertical positioning influence on lift and drag.

However, horizontal positioning strongly influences the wing performance, as moving the engine downstream results in a downstream shift of the shock leading to a loss in lift. Spanwise shifting mainly influences the lower surface by modifying the virtual flow channel between the inboard side of the pylon and the wing causing a more accelerated flow in the case of inboard engine placement.

Interestingly, the pitch angle influences both the upper and the lower wing surfaces and modifies the total drag and lift. Toe angle, on the other hand, has an effect similar to spanwise position, as it modifies the shape of the virtual flow channel around the pylon, influencing mainly the wing lower surface.

The presence of these many influencing parameters make the nacelle installation on the wing a daunting task. A large amount of critical analysis is needed, as a bad installation can increase the total drag by about 4.2 percent, which, in a transport aircraft is equivalent to 1000 kg of payload.

Figure 5: Installation effects: a. On the upper surface of the wing. b. On the lower surface of the wing. Image source Ref [9].

Now, that we have understood the factors to be considered in nacelle-airframe integration, let’s try to have a birds-eye-view of the flow physics developing because of this integration.

Below wing nacelle integration – cruise condition

The flow field development during the cruise is largely controlled by the adverse interference in junction regions such as the wing-pylon and nacelle-pylon junctions. The presence of an engine modifies the location of the stagnation point on the wing and reduces the angle of attack at the wing-pylon junction. This results in an upstream displacement of the shock front on the upper surface. Further, the reduction in incidence also increases the pressure on the suction side.

On the lower wing surface, a virtual flow channel between the inboard side of the pylon and the wing develops, causing the flow to accelerate. This later leads to flow separation. Additionally, the flow acceleration causes buffeting – a shock boundary layer interaction that causes the shock wave to oscillate which in turn causes, oscillation of lift and pitching moment. This is a major concern, as buffeting at transonic conditions, limits the speed at which an aircraft can cruise.

Lastly, a form of drag called blowing drag or jet effect is generated because of the reduction in wing circulation as the exhaust jet induces a higher velocity which is against the direction of natural circulation. Further, additional losses can occur, if the jet-induced velocity exceeds sonic speed, resulting in shock formations and possible flow separation.

Figure 6: High lift propulsion system integration aerodynamic effects: up-wash flow. Image source Ref [9].

High lift propulsion system integration

Just as in cruise conditions, nacelle in high-lift conditions during landing and takeoff has the same effect of increased drag and reduced lift. However, the effects are more damaging during high-lift conditions where there are severe interactions between the engine nacelle and the wing flow field, especially at high angles of attack. The maximum lift (Cl_max), the key design parameter in high-lift configurations could get severely compromised during their integration with engines.

What happens is that, in order to accomplish the engine installation under the wing, a segment of the slat needs to be cut out to accommodate the pylon and as a consequence, a part of the precious lifting surface is lost. Apart from the reduced lift, the exposed adjacent part of the wing profile now faces a higher alpha flow and increases the probability of early flow separation.

The sheer physical presence of nacelle generates an up-wash flow as shown in Figure 6. This up-wash flow interacts with the low-pressure flow field on the upper surfaces of the wing, pylon, and the slat cut-outs resulting in multiple vortices.

Figure 7: Flow topology around wing-pylon-nacelle. 1 – outboard slat vortex, 2- outboard leading-edge vortex, 3- nacelle vortex, 4- pylon shoulder vortex, 5-strake vortex, 6- inboard leading-edge vortex, 7- inboard slat vortex. Image source Ref [11].

The vortex system

The up-wash deflecting more flow to the upper surfaces at high incidences is responsible for generating 6 vortices, namely, the pylon vortex, two slat vortices, two leading edge-pylon vortices and the nacelle vortex. If vortex generators called nacelle strakes are mounted, then another pair of vortices are generated. These vortices which actively interact with each other play a major role in controlling the boundary layer separation and strongly dictate the maximum achievable lift.

The nacelle vortex is generated when the flow on the slat interacts with the nacelle up-wash flow. When compared to a simple wing-body configuration, the flow angle as seen by the slat in high-lift propulsive configuration is higher due to the presence of nacelle. The upper slat flow’s direction, especially from the inboard slat side, due to its close proximity to nacelle may be pitched in the opposite direction to that of nacelle flow, resulting in reduced local velocity. This may lead to flow separation, paving the way for the formation of the nacelle vortex.

Next, the slat vortex generation is something similar to that of the wing-tip vortex. The slat cut-out creates two vortices one on either side of the slat gap, due to the pressure difference between the slat’s suction side and pressure side.

Further, as a cumulative effect of the presence of the nacelle vortex, the slat vortex, and the upstream positioning of the inboard slat, a pressure difference can set in between the two sides of the pylon, triggering a flow displacement from one side to the other. As this happens, a flow recirculation on the pylon upper surface gets established which subsequently develops as a pylon vortex.

Furthermore, the slat-cut out portion exposes the adjacent part of the main wing profile to a higher angle of attack flows, thereby subjecting them to early flow separation. These flow separation ultimately culminates as vortices at the main wing-pylon junction.

Figure 8: Iso-vorticity surfaces with underlined high lift installation vortices at an AOA of 17°. Image source Ref [9].

What one should realize is that the strength and position of the vortices are directly dependent on the nacelle, pylon, slat and main-element wing geometries and their installation. Geometric optimisation of these components will tremendously aid in reducing the installation penalties.

Now that we came to know how these vortices are generated, let us now try to understand what happens due to the presence of these vortices. If the engine is close to the wing, the nacelle vortices attach themselves to the wing’s upper surface under the influence of the low-pressure zone at the leading edge. This interaction is beneficial as these vortices supply additional energy to the particles in the boundary layer to resist the adverse pressure gradients and prevent flow separation. In a way, this flow phenomenon mitigates the side effect of nacelle installation by decreasing the loss in lift.

Although the installation vortices are generally favourable since they originate in a zone of low kinetic energy (wing-pylon junction), they tend to have a low axial velocity and as a consequence, they are eventually bound to breakdown and cause flow separation when faced against a high-pressure gradient, especially at higher alphas. In severe cases, flow separation can happen both on the inboard and as well as on the outboard sides of the main wing as shown in Figure 9.

Figure 9: Cfx distribution with skin friction streamlines: 14 to 18.5 degrees. Image source Ref [2].

Further, since the inboard side of the slat compared to the outboard, is forward positioned relative to the nacelle, the inboard vortex is more exposed to higher pressure fields. This means they are more susceptible to breakdown, leading to easier flow separation.

All these flow interactions have a detrimental effect on the total lift and drag of the aircraft. Figure 10 shows the comparative plots of lift and drag polars for a wing-body configuration with and without nacelle-pylon. It can be observed that nacelle introduction reduces the Clmax and stall angle. While the stall angle reduces from 32 degrees to 21 degrees, the lift at alpha 21 degrees reduces by nearly 12%. This degrading effect can be seen even at low angles of attack. For example, at alpha 6 degrees, the lift is reduced by about 2%.

Figure 10: Lift and drag polar for a Wing-Body (WB) and Wing-Body-Nacelle-Pylon configuration. Image source Ref [9].

Nacelle strakes

To reduce the negative impact of the vortex system on the aerodynamic performance of the wings, Engineers came up with the idea of mounting a pair of strakes, popularly called chimes, to generate two additional strong vortices to regulate the flow separation on the wings.

Figure 11: a. Double chime strakes vortices. b. Lift and drag polar for WB (configuration 1), WBNP (configuration 2) and WBNP with strakes (configuration 3). Image source Ref [9].

As can be seen in the lift and drag polars in Figure 11, strake vortices have a positive impact, as they aid tremendously in energizing the boundary layer and prevent flow separation. Appreciable recovery of lift happens with the introduction of nacelle strakes.

Figure 12: Active flow control technique: Pulsed jet blowing. Image source Ref [4].

Parting Remarks

However, nacelle strakes are not good enough to overcome the adverse pressure gradients in the flow field around Ultra-high bypass ratio engines. The energy supplied by them is not sufficient enough to compensate for the losses in lift due to the missing slat section. Currently, researchers are looking towards active flow control techniques like pulsed blowing and synthetic jet actuation to control flow separation. Studies so far have shown them to be quite successful in counteracting the setbacks caused by extended stat cut-outs.

Ultra-High Bypass Ratio engines are pitched as the power plant for future commercial transport aircraft. Increasing environmental and economic requirements are pushing the aviation industry to embrace such newer technologies. This is a positive step going forward. Though they pose immense engineering challenges, the industry is able to develop technologies that help to push the envelope and make air transport more affordable, cleaner, and less noisier.


1. “Reynolds number and wind tunnel wall effects on the flow field around a generic UHBR engine high‑lift configuration”, Junaid Ullah et al, CEAS Aeronautical Journal (2020) 11:1009–1023.
2. “Simulations of an Aircraft with Constant and Pulsed Blowing Flow Control at the Engine/Wing Junction”, David Hue et al, HAL Id: hal-01721678, 2 Mar 2018.
3. “Optimal Design and Installation of Ultra High Bypass Ratio Turbofan Nacelle”, Andrey Savelyev et al, ICMAR, Oct 2016.
4. “Active Flow Control Applied at the Engine-Wing Junction”, Sebastian Fricke et al, CEAS 2015 paper no. 249.
5. “DLR TAU-Code uRANS Turbofan Modeling for Aircraft Aerodynamics Investigations”, Arne Stuermer et al, Aerospace 2019, 6, 121.
6. “Overview on nacelle design”, Jesuíno Takachi Tomita et al, 18th International Congress of Mechanical Engineering, November 6-11, 2005, Ouro Preto, MG.
7. “CFD Study of an Over-Wing Nacelle Configuration”, Steven H. Berguin et al, Georgia Institute of Technology, Atlanta, October 5, 2018.
8. “Aerodynamic Evaluation of Nacelles for Engines with Ultra High Bypass Ratio”, Andreas Petrusson, Master’s thesis 2017:02, Chalmers University of Technology.
9. “Modelling the aerodynamics of propulsive system integration at cruise and high-lift conditions”, Thierry Sibilli, PhD Academic Year: 2011-2012, Cranfield University.
10. “Fan noise due to boundary layer ingestion in novel aircraft architectures“, CEAS-ASC Workshop ‘Future Aircraft Design and Noise Impact’, 6-7 Sep 2018, Amsterdam.
11. “Application of active flow control on aircraft – state of the art“, Ahmad Batikh1 et al, AST 2017, February 21–22, Hamburg, Germany.
12. “CFD Prediction for High Lift Aerodynamics”, Jeffrey Slotnick, Technical Fellow, Boeing Commercial Airplanes, RAeS Conference on Aerodynamics Tools and Methods in Aircraft Design, 15 October 2019.

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► Spiked Blunt Bodies for Hypersonic Flights
  19 Mar, 2021

Figure 1: Structured multiblock grid for a blunt body with a spike.

1950 words / 10 minutes read


In the design of aerospace vehicles such as missiles, rockets and reentry vehicles which typically travel at hypersonic velocities, reducing pressure drag and aerodynamic heating are the two most critical design requirements. On one hand, high drag is needed to decelerate a reentry capsule, on the other, for vehicles escaping the earth’s atmosphere, drag reduction is a primary requirement.

Drag reduction in hypersonic vehicles is essential because it aids in economizing the fuel usage, maximizing the range, simplification of the propulsion system and maximizing the ratio of payload to take-off gross weight. Also, an increase in drag means an increase in surface temperature. Excessive heating can cause ablation of the vehicle surface material resulting in fluctuations in vehicle performance and in some fatal cases complete material failure. Further, the generation of ionized gases at elevated temperatures will result in communication black-outs also.

One common and effective way to reduce the heat load is by providing a blunt body shape for the hypersonic vehicle. Blunt shapes are preferred because they are better at heat distribution and also during the descent phase they are very effective in generating the desired vehicle deceleration. However, there will be a dramatic increase in drag due to the formation of a detached bow shock in front of the blunt nose during the ascent phase also, which is something designers want to avoid at all cost.

If on the other hand, pointed slender bodies are used, they generate lower drag but they are less effective in dissipating the heat away from the body. Hence it is a tough design challenge to minimize the pressure drag and aerodynamic heating at the same time. As a consequence, there will be a trade-off between these two critical requirements.

Figure 2: Trident II (D5) on the left, and the Trident I (C4) on the right with aerodisk spikes. Photo courtesy of Lockheed-Martin Missile and Space.

Since the introduction of hypersonic vehicles in the late 1940s, there is ongoing research to conquer these problems. The understanding is that the two requirements of lower aerodynamic heating for re-entry and lower drag for atmospheric flights can be achieved by altering the flowfield pattern around the blunt-body which results in eliminating the strong detached bow shock. A large array of techniques have been explored and implemented. Some of them are, usage of spikes, forward-facing cavity and aero discs, energy deposition using plasma torch, focussed gas jet, projection of laser or microwave beams upstream of the nose stagnation point, arc discharge, DC corona discharge, non-ablative thermal protection, etc.

Out of these wide arrays of approaches, the spiked blunt body is considered the most promising because of its simplicity, efficiency and effectiveness. Occupying only a small space, spikes transform the strong bow shock in front of the blunt nose to weak oblique shock waves. The flow phenomena which develop due to spikes are quite complex and have many fascinating characteristics. Before we delve more into the flow around spiked bodies, let’s understand the unspiked flow physics first.

Figure 3: Features of flowfield around a blunt body with and without a spike. Image source Ref [10].

Blunt body in hypersonic flight

A blunt body moving at high speeds generates a detached bow shock wave in front of it. Fluid particles experience a sudden deceleration as it crosses the bow shock. Moving further downstream, the flow loses most of its kinetic energy but simultaneously gains an increase in local pressure and temperature. This results in an increase in pressure drag and aerodynamic heat transfer to the body.

When we consider aerodynamic heating and distribution, a larger blunt nose radius is preferred since wall heat flux is inversely proportional to the square root of nose radius. The larger the nose radius, the lesser is the heat transfer to the wall and vice-versa. So in a way, a larger radius scales back the heat from the body and dissipates the warmth to the outside air stream. In this way, a blunt nose protects the vehicle from the severe mechanics of heating.

Figure 4: Typical features of the flowfield associated with (a). Unspiked blunt bodies (b). Disk-spiked blunt bodies with separation shock. (c) Disk-spiked blunt bodies without separation shocks. Image source Ref [1].

Spike flow physics

Now if we attach a slender rod at the stagnation point of a blunt body, it brings in two major modifications to the flow field. Firstly, it replaces the strong detached bow shock with a system of weaker oblique shock waves. Secondly, it acts as a flow-separator, i.e., it enforces boundary layer flow separation and creation of a shear layer. Figure 4 b-c, illustrates the main features of a spiked blunt body flow field.

Because of the spike, the strong detached bow shock is replaced by an oblique shock wave called foreshock which significantly reduces the rise in levels of pressure and temperature. The presence of adverse pressure gradient ahead of the body and friction on the spike surface triggers boundary layer detachment downstream of the oblique shock, leading to the creation of a shear layer. Simultaneously, at the point of flow separation, a shock called flow separation shock is formed which turns the flow parallel to the shear layer. Sometimes due to geometric effects, the separation shock may coincide with the foreshock formed ahead of the spike.

The detached shear layer propagates downstream and reattaches onto the blunt nose surface, creating an enclosed zone of recirculating air at low pressure and temperature. This dead zone screens a large part of the blunt forebody, resulting in a significant drop in drag and aerodynamic heating.

However, at the location where the shear layer reattaches to the blunt-body, there is a shoot-up in surface pressure and heating rates. Further, to align the flow outside the shear layer parallel to the body surface, a shock is formed at the point of reattachment called the reattachment shock. The strength of this shock depends on the flow turning angle which in turn is governed by the location of the reattachment point on the nose surface. Furthermore, whether the reattachment point can be moved backward or forward or completely removed depends on the nose radius and spike length.

To summarise, the weak oblique shock wave creation and establishment of the recirculation zone, both contribute to the dramatic reduction in wave drag and aerodynamic heating relative to the case without a spike.

Figure 5: Structured grid for a disk spike-tipped hemispherical nose body. Image source Ref [1].

Unsteady flow

For certain flow conditions with certain nose shape and spike dimensions, flow instability may set in characterized by a continuously changing flow pattern in a self-exited, self-sustained manner. Depending on the level of instability, the flow can be categorized into a pulsatory or oscillatory mode.

If the flow is pulsatory, there will be a drastic change in the foreshock shape, varying from conical to hemispherical shape, along with variations in the recirculation zone, high oscillation frequency and high local pressure amplitudes on the body surface.

On the other hand, if the flow turns out to be oscillatory in nature, there will be small lateral changes to the foreshock and shear layer shape, regularly shifting from concave to a convex shape, with lower oscillation frequency and lower local pressure amplitudes on the nose face.

Flow instabilities will result in fluctuations in surface pressure and heat flux, increased acoustic and structural dynamic loads, spike bending, flight perturbations and difficulties in flight control. These issues can become worse due to hysteresis phenomena setting in when there are extension and retraction of the telescopic spikes.

Studies have shown that the flow stability is mainly dependent on the nose contour and spike geometry parameters. Therefore, for each specific flight condition there exists an optimal nose profile and spike length which ensures good aerothermodynamic performances and structural stability.

Figure 6: Possible geometric variants for forebody. Image source Ref [8].

Geometric effects

The diminution of drag and aero heating is governed by design parameters like nose shape, spike length and tip geometric shapes. Multiple research studies have consistently shown that spike length controls the drag response while forebody shape dictates the aerodynamic heating levels.

Figure 7: Flow field variation with change in spike length. Image source Ref [11].

Effect of spike length
It is observed that irrespective of the aerodisk shape, an increase in spike length up to a certain length reduces both pressure and heat flux. What happens is that, when we increase the spike length, the reattachment point moves towards the shoulder of the blunt nose and the recirculation region expands covering a larger area of the nose. As this happens, the strength of the reattachment shock reduces due to a reduction in angle required to deflect the flow outside the shear layer to make the flow parallel to the body surface. The net effect is reduced levels of pressure increase on the nose surface. However, if we increase the spike length beyond a critical value, the recirculation zone is split into two bubbles, with a shock sitting in between them, resulting in an increase in overall drag.

Heat flux reduction
Researchers have observed that the aero heating levels at the reattachment region are mainly determined by the reattachment angle. The smaller the reattachment angle, the larger is the peak value of heat flux. So when we increase the spike length, the point of reattachment shifts towards the nose shoulders, resulting in increased reattachment angle, which in turn decreases the peak value of heat flux.

Also, the total heat transfer depends on the type of flow. Under fully turbulent shear layer conditions, the total heat transfer rate doubles while it reduces under laminar separated flow conditions. Further, the spike’s drag reduction performance is also determined by the surface temperature. An increase in wall temperature increases recirculation area temperature which results in the outward movement of the reattachment point.

In this way longer spikes improve the vehicle performance both w.r.t reduced pressure drag and aerodynamic heating levels.

Figure 8: Possible geometric variants for aerodisk. Image source Ref [8].

Effect of spike tip geometry
Over the years, various spike tip geometric shapes have been experimented ranging from sharp-pointed, to flat head aerodisk to hemispherical tip to biconical shapes. Further, other unique profiles like a double hemisphere, double disks, etc have also been tested.

As per general consensus, it is observed that aerodisks yield better performance for thermal protection and drag reduction compared with the pointed spike. Drag reductions from 30 percent to as high as 75 percent have been claimed by various researchers. Also, double-headed profiles are observed to show better drag reduction compared to single profile spikes.

Furthermore, it is observed that, for a fixed aerospike length, flat aerodisks generate the least drag compared to biconical, hemispherical, or pointed spikes.

Figure 9: Mach flowfield around: (a) Single flat-faced disc aerospike. (b). Double flat-faced disc aerospike. Image source Ref [11].

Disk diameter effect
If on the other hand, we keep the length fixed and keep increasing the tip disk diameter, the reattachment point starts to shift towards the nose shoulder. Expansion of the recirculation zone reduces the surface pressure. An increase in disk diameter enhances the bowed foreshock, but its effect on the drag is local. However, once the disc diameter exceeds a critical value, the stronger bowed foreshock neutralizes the benefit gained from the expansion of the recirculation zone to the drag reduction.

Studies have shown that an optimum disk diameter is related to the spike length and is found to be inversely proportional to the spike length. This implies spike length should be greater than disk diameter for stable flow, reduced drag, and aeroheating.

Figure 10: Effect of the spike on shock wave structure at Mach 2, alpha = 20 degs. (a). Body without spike. (b). Body with a conventional fixed spike. (c). Body with an aligned spike of the same length. Image source DLR.

Additionally, irrespective of the spike head shape, drag increases with the angle of attack for a large range of Mach number. As a solution, some researchers proposed the concept of ‘pivoting spike’ in which the spike is kept aligned to the freestream direction while the rest of the body is aligned to the flight path. This arrangement helps in maintaining the drag at a similar value as that at level flight.

Figure 11: Streamlines on symmetry plane at 8 degs alpha. (a) 0 deg pivoting. (b) 8 deg pivoting. Image source Ref [11].

Parting thoughts

Blunt profile approach for hypersonic vehicle design has been in existence since the late 1950s. Though it has its inherent limitations, it has been in wide usage in all most all reentry vehicles for decades as it was considered to be the most practical workable solution. Aerospike with its simplicity and efficiency has presented itself as an effective solution to the limitations in blunt bodies aerothermal performances. It will come as no surprise if we see more often of these aerospikes in hypersonic vehicles in the coming years.


1. “Drag Reduction and Aerodynamic Shape Optimization for Spike-Tipped Supersonic Blunt Nose”, Yuan Xue et al, Journal of Spacecraft and Rockets · April 2018.
2. “Heat Transfer Analysis without and with Forward Facing Spike Attached to a Blunt Body at High Speed Flow”, Rakhab Chandra Mehta, Chapter 9, IntechOpen -2018.
3.“ Experimental investigation on spiked body in hypersonic flow”, R. Kalimuthu et al, The Aeronautical Journal, Paper No. 3227, October 2008.
4.“Drag Reduction Optimization for Hypersonic Blunt Body with Aerospikes”, Want T et al, Journal of Aeronautics & Aerospace Engineering, 2017.
5. “Numerical analysis Of Drag-On Blunt Bodies With The Use Of Different Conical Spikes At Supersonic Speed”, Parag P. Mangave et al, Proceedings of 4th RIT Post Graduates Conference, April 13th 2018.
6. “Recent advancements in shape optimization of aero spiked high-speed re-entry vehicle using CFD”, Harish Panjagala et al, MATEC Web of Conferences 172, 01007 (2018), ICDAMS 2018.
7. “Drag Reduction Using Aerodisks for Hypersonic Hemispherical Bodies”, M. Y. M. Ahmed et al, Journal of Spacecraft and Rockets, Vol. 47, No. 1, January–February 2010.
8. “Surrogate-Based Multi-Objective Aerothermodynamic Design Optimization of Hypersonic Spiked Bodies”, M. Y. M. Ahmed et al, 14th International Conference on Aerospace Sciences & Aviation Technology, ASAT – 14 – May 24 – 26, 2011.
9. “Fluid–Thermal Analysis of Aerodynamic Heating over Spiked Blunt Body Configurations”, Qihao Qin et al, Acta Astronautica 132 (2017) 230–242.
10. “Fluid–Thermal Interaction Investigation of Spiked Blunt Bodies at Hypersonic Flight Condition”, Shuai Guo et al, Journal of Spacecraft and Rockets, Vol. 53, No. 4, July-August 2016.
11. “Spike Effects on Drag Reduction for Hypersonic Lifting-body”, Fan Deng et al, Journal of Spacecraft and Rockets, 54 (6). pp. 1185-1195.

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► Influence of Mesh in Open Water Propeller CFD
  10 Mar, 2021

Figure 1: Structured multiblock grid for a Potsdam propeller.

1700 words / 8 minutes read


In the previous article on open water propeller, we covered aspects of domain, zones, and interfaces, boundaries conditions, grid refinement, in particular the wake, blade tip refinement to capture the cavitation vortices. In this final article in the series, we try to address aspects of grid influences on the solution flow field, including, grid uncertainty analysis for wetted vs cavitating flows conditions, mesh refinement studies using both sequential grids and adaptively refined grids and lastly model vs full-scale propeller performance.

Video 2: Cavitation CFD simulation using MARIN ReFRESCO solver.

Uncertainty analysis of wetted vs cavitation flows 

Overall, the grid uncertainty levels of both wetted and cavitating results are low and is less than 1 percent. But the main difference between them is in the order of convergence of performance coefficients, which is low for the cavitating case than wetted flow. In wetted flow conditions, since grid sensitivities mainly occur around the blade tip, the thrust converges more slowly than torque. Figure 2 shows the radial distribution of thrust coefficient for the two cases.

Radial thrust loads in wetted and cavitating conditions
Figure 2: a. Comparision of radial thrust loads in wetted condition on various grids. b. Comparison between radial thrust loading for wetted and cavitating conditions. Image source ref [4].
Radial thrust loading prediction improves with grid refinement for wetted flow case, with the main differences observed closer to the blade tip. With refinement, the leading edge vortex resolution improves thereby modifying the pressure distribution and radial loading distribution. But after the level 3 grid, the improvement is marginal. For the cavitation case, the influence of grid refinement is even less, resulting in slightly lower uncertainty numbers.

Figures 2a and 2b show the comparison for the two cases. The reduction in tip loading for the cavitating case is quite evident. This is mainly because of the presence of sheet cavitation which lowers the suction peak strength and leading-edge vortex. Interestingly, even though the suction peak close to the tip is absent compared to the wetted flow condition, a larger lower pressure area exists due to the cavity.

Grid refinement study ( GCS and AGR)

Cavitation observed in the experimental setup
Figure 3: Cavitation observed in the experimental setup. Image source ref [4].
Grid sensitivity studies have shown a steady convergence in prediction accuracy with grid refinement. The results predicted by coarse grids emphasize the importance of grid density and refinement. Refinement can be done globally by generating a sequence of grids, while it can be done locally by applying adaptive grid refinement.

One region which shows remarkable improvement with grid refinement is the tip vortex region, where the cavity extensions are visibly significant with successive grids. One noteworthy aspect is that, even though the cavity extent is limited by the increasing eddy viscosity when using RANS, adaptive grid refinement aids in better capturing of tip vortex cavitation with lesser total cell count.

In fact, one researcher concludes in a paper that, the grid resolution is more important than cavitation modeling parameters when predicting the inception of tip vortex cavitation.

Cavity extents for three different grids visualised using isosurfaces of the vapour volume fraction
Figure 4: Cavity extents for three different grids visualized using isosurfaces of the vapour volume fraction. Image source ref [4].

Vapour volume uncertainty analysis

This analysis helps to examine the effect of grid refinement on cavitation flow prediction. Figure 4 shows the cavity extents for 3 different grids refined at a rate of 1.5, visualized using iso-surfaces of vapour volume fraction.

From Figure 4, it can be observed that the form of the sheet cavity is less sensitive to grid refinement. This is somewhat expected, as given the low grid uncertainty of the thrust, which is directly related to the pressure distribution on the blade.

However, the tip vortex cavity shows a large grid dependency. For the first grid, there is no noticeable tip vortex cavitation. For the second refined grid, the tip vortex begins to cavitate. For the third refined grid, the cavity extension increases. On the finest grid, one can observe that the vortex rolls-up and form the characteristic ‘necked’ appearance.

Propeller - contour of vapour volume fraction near blade tip
Figure 5: Contour of vapour volume fraction. Image source ref [4].

Analysis of the cavitating flow field

Even though the predicted sheet cavity shape did not appear to vary significantly with grid refinement, the variation on a 2D slice shows more subtle differences as shown in Figure 5. Here, the liquid-vapour interface and re-entrant jet resolution become sharper with refinement.

Propeller vapour volume fraction distribution
Figure 6: Vapour volume fraction. Image source ref [4].
Propeller pressure coefficient distribution
Figure 7: Pressure coefficient. Image source ref [4].
propeller eddy viscosity ratio distribution
Figure 8: Eddy viscosity ratio. Image source ref [4].
Further analysis of the effect of grid refinement on the cavitating tip vortex is made in Figure 6-8, where slices of the vapour volume fraction, pressure coefficient, and normalized eddy viscosity are shown at an axial station in the propeller wake, 0.05D downstream of the propeller plane. As anticipated, grid refinement brings remarkable improvement in predicting tip vortex cavitation. As seen in Figure 7, the minimum pressure inside the vortex gets reduced significantly with refinement. Just a grid refinement ratio of 2.4 was good enough to develop a much-extended tip vortex.

Adaptively refined grids for propeller
Figure 9: Views of the adapted grid. The helix shown represents cells refined during the third refinement stage. Image source ref [4].

Adaptive grid refinement

Improvement in predicting tip vortex cavitation can be done using adaptive grid refinement with fewer grid cells.

By comparing Figure 10 and Figure 4 with Figure 3, one can appreciate the differences between computations with and without adaptive grid refinement. The tip vortex cavity extension in the L1 adaptive grid is of similar length to that obtained on the finest grid in the sequential family (Figure 10a). With further increase in local refinement, not only does the tip vortex cavitation becomes longer, but the roll-up of the tip vortex also gets better resolved (Figure 10b,10c).

Cavity extents for different combinations of adapted grid
Figure 10: Cavity extents for different combinations of adapted grid. Cavity visualized using isosurfaces of the vapour volume fraction. Image source ref [4].
Interestingly, with more and more refined grid cells inside the tip vortex, the eddy viscosity increases significantly with RANS solvers. This has a negative impact on the predicted cavity extent and hence many researchers conclude that RANS is not suitable for tip vortex prediction especially when the focus is on the dynamics.

Model vs Full-scale performance

Contrary to intuition, solutions obtained on a model scale cannot be extrapolated to the real-life full-scale propeller model. This is rightly so because with increase in geometry dimensions the Reynolds number at which computations are done increases and as a consequence, the flow patterns also change significantly. The numerical predictions performed at the model scale are more challenging than those at full scale due to the simultaneous cohabitation of laminar and turbulent flow regimes and the subsequent difficulty in the accurate prediction of the transition from laminar to turbulence in the computations.

Also, the propeller performance characteristics vary between the model and a full-scale geometry. Analysis of the results shows that the full-scale simulations yield a bit higher thrust coefficient, a slightly lower torque coefficient and consequently a higher open-water efficiency. It is interesting to note that, just like the model-scale, full-scale propeller efficiency does not show a great dependency on the grid density and the performance characteristics converge monotonically towards the experimental values.

Comparison of near-blade cavitation results with different grid densities
Figure 11: Comparison of near-blade cavitation results with different grid densities. Image source [10].
Just like the solutions from the model scale cannot be extrapolated to the full-scale models, the model scale grid also cannot be scaled to fit full-scale geometry. Though the surface mesh and outer volume mesh can be scaled, the boundary layer needs to be regenerated as the Reynolds number perceived by the full model is much higher. At higher Reynolds number, the total thickness of the boundary layer reduces and so also the first spacing.

Figures 11, 12 show the near-blade and overall cavitation predicted on different grid densities for both model and full-scaled propeller. As can be observed from Figure 11, near-blade cavitation is quite similar to each other on various grids. On all grids, sheet cavitation develops at the leading edge which then transforms to tip vortex cavitation. The root cavitation also grows in extent with increase in grid resolution but only marginally. Looking at Figure 11, it will be safe to say that the near-blade cavitation is mostly independent of the utilized grid densities.

Comparison of cavitation patterns for the PPTC propeller with different grid densities
Figure 12: Comparison of cavitation patterns for the PPTC propeller with different grid densities. On the left: model-scale simulations. On the right: full-scale simulations. Image source [10].
However, visible big differences in the prediction between different grids can be observed in the propeller wake as shown in Figure 12. On a coarse grid, very limited vortex cavitation is seen, with the tip vortex cavitation seizing just after the blade trailing edge. In the medium grid, the tip vortex cavitation extends more than in the coarse grid but is less than one diameter in the axial direction and the modal shapes are barely distinguishable. Fine grids predict a well-established vortex cavity extension with proper modal shapes.

However, we do not see such a clear grid density influence in hub vortex cavitation. The hub vortex predicted by the medium grid matches very well with fine grid results and experiments.

Pressure coefficient in the track of the tip vortex with different grid densities
Figure 13: Pressure coefficient in the track of the tip vortex with different grid densities. The figure on the left shows the fine grid simulations and the track as a dashed line along which the Cp is evaluated. Image source ref [10].
Figure 13 shows the pressure coefficient (Cp) comparison on various grids in the wake at the track of the tip vortex. The pressure minima is greater for the coarse and medium grids as the axial distance from the propeller increases. The Cp in the first vortex core for the coarse grid is significantly less than the vapor pressure and therefore the tip vortex cavitation terminates just after the blades. For the medium grid, the first tip vortex core on the curve maintains a low Cp level and a cavitation vortex as seen in Figure 12b develops. Beyond this point, the Cp decreases and the tip vortex cavitation disappears.

Parting thoughts

As these results emphasize, local refined grids bring in a significant improvement in flow feature prediction and which in turn makes the flow parameter prediction more accurate and compares well with the experimental results. Whether the refinement is through sequential grids or by adaptive grid refinement, the benefits of such an exercise are quite evident as underscored in this article.


1. “Comparison of Hexa-Structured and Hybrid-Unstructured Meshing Approaches for Numerical Prediction of the Flow Around Marine Propellers”, Mitja Morgut et al, First International Symposium on Marine Propulsors smp’09, Trondheim, Norway, June 2009.

2. “Grid Type and Turbulence Model Influence on Propeller Characteristics Prediction”, Ante Sikirica et al, Journal of Marine Science and Engineering, J. Mar. Sci. Eng. 2019, 7, 374.

3. “Numerical simulation of propeller open water characteristics using RANSE method”, Tran Ngoc Tu, Alexandria Engineering Journal, (2019) 58, 531–537.

4. “Computational fluid dynamics prediction of marine propeller cavitation including solution verification”, Thomas Lloyd et al, Fifth International Symposium on Marine Propulsors smp’17, Espoo, June 2017.

5. “Improving accuracy and efficiency of CFD predictions of propeller open water performance”, M. F. Islam et al, Journal of Naval Architecture and Marine Engineering, June, 2019.

6. “An Investigation into Computational Modelling of Cavitation in a Propeller’s Slipstream”, Naz Yilmaz et al, Fifth International Symposium on Marine Propulsion smp’17, Espoo, Finland, June 2017.

7. “A Numerical Study on the Characteristics of the System Propeller and Rudder at Low Speed Operation”, Vladimir Krasilnikov et al, Second International Symposium on Marine Propulsors smp’11, Hamburg, Germany, June 2011.

8. “Numerical characterization of a ship propeller”, Borna Seb et al, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, 2017.

9. “Scale Effects on a Tip Rake Propeller Working in Open Water”, Adrian Lungu et al, Journal of Marine Science and Engineering, 2019, 7, 404.

10. “Cavitation on Model- and Full-Scale Marine Propellers: Steady and Transient Viscous Flow Simulations at Different Reynolds Numbers”, Ville Viitanen et al, Journal of Marine Science and Engineering, 2020, 8, 141.

11. “Energy balance analysis of a propeller in open water“, Jennie Andersson et al, Ocean Engineering, 158 (2018) 162–170.


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Hanley Innovations top

► Accurate Aircraft Performance Predictions using Stallion 3D
  26 Feb, 2020

Stallion 3D uses your CAD design to simulate the performance of your aircraft.  This enables you to verify your design and compute quantities such as cruise speed, power required and range at a given cruise altitude. Stallion 3D is used to optimize the design before moving forward with building and testing prototypes.

The table below shows the results of Stallion 3D around the cruise angles of attack of the Cessna 402c aircraft.  The CAD design can be obtained from the OpenVSP hangar.

The results were obtained by simulating 5 angles of attack in Stallion 3D on an ordinary laptop computer running MS Windows 10 .  Given the aircraft geometry and flight conditions, Stallion 3D computed the CL, CD, L/D and other aerodynamic quantities.  With this accurate aerodynamics results, the preliminary performance data such as cruise speed, power, range and endurance can be obtained.

Lift Coefficient versus Angle of Attack computed with Stallion 3D

Lift to Drag Ratio versus True Airspeed at 10,000 feet

Power Required versus True Airspeed at 10,000 feet

The Stallion 3D results shows good agreement with the published data for the Cessna 402.  For example, the cruse speed of the aircraft at 10,000 feet is around 140 knots. This coincides with the speed at the maximum L/D (best range) shown in the graph and table above.

 More information about Stallion 3D can be found at the following link.

About Hanley Innovations
Hanley Innovations is a pioneer in developing user friendly and accurate software that is accessible to engineers, designers and students.  For more information, please visit >

► 5 Tips For Excellent Aerodynamic Analysis and Design
    8 Feb, 2020
Stallion 3D analysis of Uber Elevate eCRM-100 model

Being the best aerodynamics engineer requires meticulous planning and execution.  Here are 5 steps you can following to start your journey to being one of the best aerodynamicist.

1.  Airfoils analysis (VisualFoil) - the wing will not be better than the airfoil. Start with the best airfoil for the design.

2.  Wing analysis (3Dfoil) - know the benefits/limits of taper, geometric & aerodynamic twist, dihedral angles, sweep, induced drag and aspect ratio.

3. Stability analysis (3Dfoil) - longitudinal & lateral static & dynamic stability analysis.  If the airplane is not stable, it might not fly (well).

4. High Lift (MultiElement Airfoils) - airfoil arrangements can do wonders for takeoff, climb, cruise and landing.

5. Analyze the whole arrangement (Stallion 3D) - this is the best information you will get until you flight test the design.

About Hanley Innovations
Hanley Innovations is a pioneer in developing user friendly and accurate software the is accessible to engineers, designs and students.  For more information, please visit >

► Accurate Aerodynamics with Stallion 3D
  17 Aug, 2019

Stallion 3D is an extremely versatile tool for 3D aerodynamics simulations.  The software solves the 3D compressible Navier-Stokes equations using novel algorithms for grid generation, flow solutions and turbulence modeling. 

The proprietary grid generation and immersed boundary methods find objects arbitrarily placed in the flow field and then automatically place an accurate grid around them without user intervention. 

Stallion 3D algorithms are fine tuned to analyze invisid flow with minimal losses. The above figure shows the surface pressure of the BD-5 aircraft (obtained OpenVSP hangar) using the compressible Euler algorithm.

Stallion 3D solves the Reynolds Averaged Navier-Stokes (RANS) equations using a proprietary implementation of the k-epsilon turbulence model in conjunction with an accurate wall function approach.

Stallion 3D can be used to solve problems in aerodynamics about complex geometries in subsonic, transonic and supersonic flows.  The software computes and displays the lift, drag and moments for complex geometries in the STL file format.  Actuator disc (up to 100) can be added to simulate prop wash for propeller and VTOL/eVTOL aircraft analysis.

Stallion 3D is a versatile and easy-to-use software package for aerodynamic analysis.  It can be used for computing performance and stability (both static and dynamic) of aerial vehicles including drones, eVTOLs aircraft, light airplane and dragons (above graphics via Thingiverse).

More information about Stallion 3D can be found at:

► Hanley Innovations Upgrades Stallion 3D to Version 5.0
  18 Jul, 2017
The CAD for the King Air was obtained from Thingiverse

Stallion 3D is a 3D aerodynamics analysis software package developed by Dr. Patrick Hanley of Hanley Innovations in Ocala, FL. Starting with only the STL file, Stallion 3D is an all-in-one digital tool that rapidly validate conceptual and preliminary aerodynamic designs of aircraft, UAVs, hydrofoil and road vehicles.

  Version 5.0 has the following features:
  • Built-in automatic grid generation
  • Built-in 3D compressible Euler Solver for fast aerodynamics analysis.
  • Built-in 3D laminar Navier-Stokes solver
  • Built-in 3D Reynolds Averaged Navier-Stokes (RANS) solver
  • Multi-core flow solver processing on your Windows laptop or desktop using OpenMP
  • Inputs STL files for processing
  • Built-in wing/hydrofoil geometry creation tool
  • Enables stability derivative computation using quasi-steady rigid body rotation
  • Up to 100 actuator disc (RANS solver only) for simulating jets and prop wash
  • Reports the lift, drag and moment coefficients
  • Reports the lift, drag and moment magnitudes
  • Plots surface pressure, velocity, Mach number and temperatures
  • Produces 2-d plots of Cp and other quantities along constant coordinates line along the structure
The introductory price of Stallion 3D 5.0 is $3,495 for the yearly subscription or $8,000.  The software is also available in Lab and Class Packages.

 For more information, please visit or call us at (352) 261-3376.
► Airfoil Digitizer
  18 Jun, 2017

Airfoil Digitizer is a software package for extracting airfoil data files from images. The software accepts images in the jpg, gif, bmp, png and tiff formats. Airfoil data can be exported as AutoCAD DXF files (line entities), UIUC airfoil database format and Hanley Innovations VisualFoil Format.

The following tutorial show how to use Airfoil Digitizer to obtain hard to find airfoil ordinates from pictures.

More information about the software can be found at the following url:

Thanks for reading.

► Your In-House CFD Capability
  15 Feb, 2017

Have you ever wish for the power to solve your 3D aerodynamics analysis problems within your company just at the push of a button?  Stallion 3D gives you this very power using your MS Windows laptop or desktop computers. The software provides accurate CL, CD, & CM numbers directly from CAD geometries without the need for user-grid-generation and costly cloud computing.

Stallion 3D v 4 is the only MS windows software that enables you to solve turbulent compressible flows on your PC.  It utilizes the power that is hidden in your personal computer (64 bit & multi-cores technologies). The software simultaneously solves seven unsteady non-linear partial differential equations on your PC. Five of these equations (the Reynolds averaged Navier-Stokes, RANs) ensure conservation of mass, momentum and energy for a compressible fluid. Two additional equations captures the dynamics of a turbulent flow field.

Unlike other CFD software that require you to purchase a grid generation software (and spend days generating a grid), grid generation is automatic and is included within Stallion 3D.  Results are often obtained within a few hours after opening the software.

 Do you need to analyze upwind and down wind sails?  Do you need data for wings and ship stabilizers at 10,  40, 80, 120 degrees angles and beyond? Do you need accurate lift, drag & temperature predictions at subsonic, transonic and supersonic flows? Stallion 3D can handle all flow speeds for any geometry all on your ordinary PC.

Tutorials, videos and more information about Stallion 3D version 4.0 can be found at:

If your have any questions about this article, please call me at (352) 261-3376 or visit

About Patrick Hanley, Ph.D.
Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

CFD and others... top

► A Benchmark for Scale Resolving Simulation with Curved Walls
  28 Jun, 2021

Multiple international workshops on high-order CFD methods (e.g., 1, 2, 3, 4, 5) have demonstrated the advantage of high-order methods for scale-resolving simulation such as large eddy simulation (LES) and direct numerical simulation (DNS). The most popular benchmark from the workshops has been the Taylor-Green (TG) vortex case. I believe the following reasons contributed to its popularity:

  • Simple geometry and boundary conditions;
  • Simple and smooth initial condition;
  • Effective indicator for resolution of disparate space/time scales in a turbulent flow.

Using this case, we are able to assess the relative efficiency of high-order schemes over a 2nd order one with the 3-stage SSP Runge-Kutta algorithm for time integration. The 3rd order FR/CPR scheme turns out to be 55 times faster than the 2nd order scheme to achieve a similar resolution. The results will be presented in the upcoming 2021 AIAA Aviation Forum.

Unfortunately the TG vortex case cannot assess turbulence-wall interactions. To overcome this deficiency, we recommend the well-known Taylor-Couette (TC) flow, as shown in Figure 1.


Figure 1. Schematic of the Taylor-Couette flow (r_i/r_o = 1/2)

The problem has a simple geometry and boundary conditions. The Reynolds number (Re) is based on the gap width and the inner wall velocity. When Re is low (~10), the problem has a steady laminar solution, which can be used to verify the order of accuracy for high-order mesh implementations. We choose Re = 4000, at which the flow is turbulent. In addition, we mimic the TG vortex by designing a smooth initial condition, and also employing enstrophy as the resolution indicator. Enstrophy is the integrated vorticity magnitude squared, which has been an excellent resolution indicator for the TG vortex. Through a p-refinement study, we are able to establish the DNS resolution. The DNS data can be used to evaluate the performance of LES methods and tools. 


Figure 2. Enstrophy histories in a p-refinement study

A movie showing the transition from a regular laminar flow to a turbulent one is posted here. One can clearly see vortex generation, stretching, tilting, breakdown in the transition process. Details of the benchmark problem has been published in Advances in Aerodynamics.
► The Darkest Hour Before Dawn
    2 Jan, 2021

Happy 2021!

The year of 2020 will be remembered in history more than the year of 1918, when the last great pandemic hit the globe. As we speak, daily new cases in the US are on the order of 200,000, while the daily death toll oscillates around 3,000. According to many infectious disease experts, the darkest days may still be to come. In the next three months, we all need to do our very best by wearing a mask, practicing social distancing and washing our hands. We are also seeing a glimmer of hope with several recently approved COVID vaccines.

2020 will be remembered more for what Trump tried and is still trying to do, to overturn the results of a fair election. His accusations of wide-spread election fraud were proven wrong in Georgia and Wisconsin through multiple hand recounts. If there was any truth to the accusations, the paper recounts would have uncovered the fraud because computer hackers or software cannot change paper votes.

Trump's dictatorial habits were there for the world to see in the last four years. Given another 4-year term, he might just turn a democracy into a Trump dictatorship. That's precisely why so many voted in the middle of a pandemic. Biden won the popular vote by over 7 million, and won the electoral college in a landslide. Many churchgoers support Trump because they dislike Democrats' stances on abortion, LGBT rights, et al. However, if a Trump dictatorship becomes reality, religious freedom may not exist any more in the US. 

Is the darkest day going to be January 6th, 2021, when Trump will make a last-ditch effort to overturn the election results in the Electoral College certification process? Everybody knows it is futile, but it will give Trump another opportunity to extort money from his supporters.   

But, the dawn will always come. Biden will be the president on January 20, 2021, and the pandemic will be over, perhaps as soon as 2021.

The future of CFD is, however, as bright as ever. On the front of large eddy simulation (LES), high-order methods and GPU computing are making LES more efficient and affordable. See a recent story from GE.

the darkest hour is just before dawn...

► Facts, Myths and Alternative Facts at an Important Juncture
  21 Jun, 2020
We live in an extraordinary time in modern human history. A global pandemic did the unthinkable to billions of people: a nearly total lock-down for months.  Like many universities in the world, KU closed its doors to students since early March of 2020, and all courses were offered online.

Millions watched in horror when George Floyd was murdered, and when a 75 year old man was shoved to the ground and started bleeding from the back of his skull...

Meanwhile, Trump and his allies routinely ignore facts, fabricate alternative facts, and advocate often-debunked conspiracy theories to push his agenda. The political system designed by the founding fathers is assaulted from all directions. The rule of law and the free press are attacked on a daily basis. One often wonders how we managed to get to this point, and if the political system can survive the constant sabotage...It appears the struggle between facts, myths and alternative facts hangs in the balance.

In any scientific discipline, conclusions are drawn, and decisions are made based on verifiable facts. Of course, we are humans, and honest mistakes can be made. There are others, who push alternative facts or misinformation with ulterior motives. Unfortunately, mistaken conclusions and wrong beliefs are sometimes followed widely and become accepted myths. Fortunately, we can always use verifiable scientific facts to debunk them.

There have been many myths in CFD, and quite a few have been rebutted. Some have continued to persist. I'd like to refute several in this blog. I understand some of the topics can be very controversial, but I welcome fact-based debate.

Myth No. 1 - My LES/DNS solution has no numerical dissipation because a central-difference scheme is used.

A central finite difference scheme is indeed free of numerical dissipation in space. However, the time integration scheme inevitably introduces both numerical dissipation and dispersion. Since DNS/LES is unsteady in nature, the solution is not free of numerical dissipation.  

Myth No. 2 - You should use non-dissipative schemes in LES/DNS because upwind schemes have too much numerical dissipation.

It sounds reasonable, but far from being true. We all agree that fully upwind schemes (the stencil shown in Figure 1) are bad. Upwind-biased schemes, on the other hand, are not necessarily bad at all. In fact, in a numerical test with the Burgers equation [1], the upwind biased scheme performed better than the central difference scheme because of its smaller dispersion error. In addition, the numerical dissipation in the upwind-biased scheme makes the simulation more robust since under-resolved high-frequency waves are naturally damped.   

Figure 1. Various discretization stencils for the red point
The Riemann solver used in the DG/FR/CPR scheme also introduces a small amount of dissipation. However, because of its small dispersion error, it out-performs the central difference and upwind-biased schemes. This study shows that both dissipation and dispersion characteristics are equally important. Higher order schemes clearly perform better than a low order non-dissipative central difference scheme.  

Myth No. 3 - Smagorisky model is a physics based sub-grid-scale (SGS) model.

There have been numerous studies based on experimental or DNS data, which show that the SGS stress produced with the Smagorisky model does not correlate with the true SGS stress. The role of the model is then to add numerical dissipation to stablize the simulations. The model coefficient is usually determined by matching a certain turbulent energy spectrum. The fact suggests that the model is purely numerical in nature, but calibrated for certain numerical schemes using a particular turbulent energy spectrum. This calibration is not universal because many simulations produced worse results with the model.

► What Happens When You Run a LES on a RANS Mesh?
  27 Dec, 2019

Surely, you will get garbage because there is no way your LES will have any chance of resolving the turbulent boundary layer. As a result, your skin friction will be way off. Therefore, your drag and lift will be a total disaster.

To actually demonstrate this point of view, we recently embarked upon a numerical experiment to run an implicit large eddy simulation (ILES) of the NASA CRM high-lift configuration from the 3rd AIAA High-Lift Prediction Workshop. The flow conditions are: Mach = 0.2, Reynolds number = 3.26 million based on the mean aerodynamic chord, and the angle of attack = 16 degrees.

A quadratic (Q2) mesh was generated by Dr. Steve Karman of Pointwise, and is shown in Figure 1.

 Figure 1. Quadratic mesh for the NASA CRM high-lift configuration (generated by Pointwise)

The mesh has roughly 2.2 million mixed elements, and is highly clustered near the wall with an average equivalent y+ value smaller than one. A p-refinement study was conducted to assess the mesh sensitivity using our high-order LES tool based on the FR/CPR method, hpMusic. Simulations were performed with solution polynomial degrees of p = 1, 2 and 3, corresponding to 2nd, 3rd and 4th orders in accuracy respectively. No wall-model was used. Needless to say, the higher order simulations captured finer turbulence scales, as shown in Figure 2, which displays the iso-surfaces of the Q-criteria colored by the Mach number.    

p = 1

p = 2

p = 3
Figure 2. Iso-surfaces of the Q-criteria colored by the Mach number

Clearly the flow is mostly laminar on the pressure side, and transitional/turbulent on the suction side of the main wing and the flap. Although the p = 1 simulation captured the least scales, it still correctly identified the laminar and turbulent regions. 

The drag and lift coefficients from the present p-refinement study are compared with experimental data from NASA in Table I. Although the 2nd order results (p = 1) are quite different than those of higher orders, the 3rd and 4th order results are very close, demonstrating very good p-convergence in both the lift and drag coefficients. The lift agrees better with experimental data than the drag, bearing in mind that the experiment has wind tunnel wall effects, and other small instruments which are not present in the computational model. 

Table I. Comparison of lift and drag coefficients with experimental data

p = 1
p = 2
p = 3

This exercise seems to contradict the common sense logic stated in the beginning of this blog. So what happened? The answer is that in this high-lift configuration, the dominant force is due to pressure, rather than friction. In fact, 98.65% of the drag and 99.98% of the lift are due to the pressure force. For such flow problems, running a LES on a RANS mesh (with sufficient accuracy) may produce reasonable predictions in drag and lift. More studies are needed to draw any definite conclusion. We would like to hear from you if you have done something similar.

This study will be presented in the forthcoming AIAA SciTech conference, to be held on January 6th to 10th, 2020 in Orlando, Florida. 

► Not All Numerical Methods are Born Equal for LES
  15 Dec, 2018
Large eddy simulations (LES) are notoriously expensive for high Reynolds number problems because of the disparate length and time scales in the turbulent flow. Recent high-order CFD workshops have demonstrated the accuracy/efficiency advantage of high-order methods for LES.

The ideal numerical method for implicit LES (with no sub-grid scale models) should have very low dissipation AND dispersion errors over the resolvable range of wave numbers, but dissipative for non-resolvable high wave numbers. In this way, the simulation will resolve a wide turbulent spectrum, while damping out the non-resolvable small eddies to prevent energy pile-up, which can drive the simulation divergent.

We want to emphasize the equal importance of both numerical dissipation and dispersion, which can be generated from both the space and time discretizations. It is well-known that standard central finite difference (FD) schemes and energy-preserving schemes have no numerical dissipation in space. However, numerical dissipation can still be introduced by time integration, e.g., explicit Runge-Kutta schemes.     

We recently analysed and compared several 6th-order spatial schemes for LES: the standard central FD, the upwind-biased FD, the filtered compact difference (FCD), and the discontinuous Galerkin (DG) schemes, with the same time integration approach (an Runge-Kutta scheme) and the same time step.  The FCD schemes have an 8th order filter with two different filtering coefficients, 0.49 (weak) and 0.40 (strong). We first show the results for the linear wave equation with 36 degrees-of-freedom (DOFs) in Figure 1.  The initial condition is a Gaussian-profile and a periodic boundary condition was used. The profile traversed the domain 200 times to highlight the difference.

Figure 1. Comparison of the Gaussian profiles for the DG, FD, and CD schemes

Note that the DG scheme gave the best performance, followed closely by the two FCD schemes, then the upwind-biased FD scheme, and finally the central FD scheme. The large dispersion error from the central FD scheme caused it to miss the peak, and also generate large errors elsewhere.

Finally simulation results with the viscous Burgers' equation are shown in Figure 2, which compares the energy spectrum computed with various schemes against that of the direct numerical simulation (DNS). 

Figure 2. Comparison of the energy spectrum

Note again that the worst performance is delivered by the central FD scheme with a significant high-wave number energy pile-up. Although the FCD scheme with the weak filter resolved the widest spectrum, the pile-up at high-wave numbers may cause robustness issues. Therefore, the best performers are the DG scheme and the FCD scheme with the strong filter. It is obvious that the upwind-biased FD scheme out-performed the central FD scheme since it resolved the same range of wave numbers without the energy pile-up. 

► Are High-Order CFD Solvers Ready for Industrial LES?
    1 Jan, 2018
The potential of high-order methods (order > 2nd) is higher accuracy at lower cost than low order methods (1st or 2nd order). This potential has been conclusively demonstrated for benchmark scale-resolving simulations (such as large eddy simulation, or LES) by multiple international workshops on high-order CFD methods.

For industrial LES, in addition to accuracy and efficiency, there are several other important factors to consider:

  • Ability to handle complex geometries, and ease of mesh generation
  • Robustness for a wide variety of flow problems
  • Scalability on supercomputers
For general-purpose industry applications, methods capable of handling unstructured meshes are preferred because of the ease in mesh generation, and load balancing on parallel architectures. DG and related methods such as SD and FR/CPR have received much attention because of their geometric flexibility and scalability. They have matured to become quite robust for a wide range of applications. 

Our own research effort has led to the development of a high-order solver based on the FR/CPR method called hpMusic. We recently performed a benchmark LES comparison between hpMusic and a leading commercial solver, on the same family of hybrid meshes at a transonic condition with a Reynolds number more than 1M. The 3rd order hpMusic simulation has 9.6M degrees of freedom (DOFs), and costs about 1/3 the CPU time of the 2nd order simulation, which has 28.7M DOFs, using the commercial solver. Furthermore, the 3rd order simulation is much more accurate as shown in Figure 1. It is estimated that hpMusic would be an order magnitude faster to achieve a similar accuracy. This study will be presented at AIAA's SciTech 2018 conference next week.

(a) hpMusic 3rd Order, 9.6M DOFs
(b) Commercial Solver, 2nd Order, 28.7M DOFs
Figure 1. Comparison of Q-criterion and Schlieren  

I certainly believe high-order solvers are ready for industrial LES. In fact, the commercial version of our high-order solver, hoMusic (pronounced hi-o-music), is announced by hoCFD LLC (disclaimer: I am the company founder). Give it a try for your problems, and you may be surprised. Academic and trial uses are completely free. Just visit to download the solver. A GUI has been developed to simplify problem setup. Your thoughts and comments are highly welcome.

Happy 2018!     

AirShaper top

► Cycling Aerodynamics - Interview with Team DSM
  29 Jun, 2021
Aerodynamics are crucial to cycling and teams are continuously searching improvements. Learn how Team DSM embeds science in their quest for lower drag.
► AVL RACING - Putting AirShaper to the test
  15 Jun, 2021
AVL RACING has over 25 years of experience helping race teams. In this blog, they compare the automated AirShaper approach to their own manual simulation methods
► Wind Engineering
    8 Jun, 2021
Wind strongly influences the design of tall buildings - not just the forces on the building itself but also the windflow patterns around it require proper analysis to ensure safety & comfort.
► Flapper Drones - Bio-inspired Flight
  18 May, 2021
Bio-inspired flight draws inspiration from nature to make drones fly using bird-like wings
► Aerodynamic Shape Optimization
  22 Apr, 2021
Adjoint optimization techniques have made it possible to automatically optimize the shape of a vehicle, athlete, plane or drone to improve its aerodynamic performance.
► The Oceanbird Wind Powered Vessel
  13 Apr, 2021
The Oceanbird vessel claims a 90% reduction in emissions through the use of large sails to propel the ship. Is this the future of shipping?

Convergent Science Blog top

► The Collaboration Effect: Developing a New Generation of Gas Turbine &#038; Rotating Detonation Engines
  22 Mar, 2021

From the Argonne National Laboratory + Convergent Science Blog Series

Imagine this: You’re flying on a plane. Maybe you’re sitting in the window seat, eating airline pretzels, happily watching an in-flight movie. But then—the flame in one of the plane’s gas turbine engines blows out. Should you panic? Well, ideally you wouldn’t even notice as the engine automatically relights and you continue cruising safely to your destination. But why did the engine blow out? Can we prevent that from happening? And if it does blow out, how can we ensure the plane stays airborne?

These are among the critical questions that Argonne National Laboratory and Convergent Science investigate together. If you’ve been following this series, you’ll know their collaboration started off focused on piston engines for automotive applications. But combustion engines across the board, including airplane engines, feature similar physical processes, and the research goals are frequently the same: increase efficiency and reduce emissions. In addition, CONVERGE’s unique combination of autonomous meshing, fully coupled detailed chemistry, and high-fidelity physical models for spray, turbulence, and combustion make it a great tool to help engine designers reach those goals. 

Before industry can implement 3D simulation into their design process, however, they need appropriate, well-validated models. This is where Argonne and Convergent Science come in—the core objective of their collaboration is performing fundamental research and developing models that industry can use to advance technology. In pursuit of this objective, Argonne and Convergent Science expanded their research efforts to aviation engines and beyond.

Gas Turbines

Gas turbine engines today are the most commonly used propulsion system for airplanes, and they are also widely used for power generation. Two key areas of current gas turbine research are increasing efficiency and reducing pollutant emissions. There are several approaches to achieving these goals, including the use of alternative fuels, altering the combustion environment (e.g., increasing operating pressures and temperatures), or reducing the fuel flow rate and moving toward a leaner combustion regime.

This lean burn approach, while effective at reducing emissions, poses significant design challenges. If you run the gas turbine engine too lean, the primary zone of the combustor can get too cold and the flame can blow out. This phenomenon, called lean blow-off or lean blow-out (LBO), is the reason the plane engine went out during our imaginary flight. Clearly, LBO is undesirable, and predicting the conditions at which it occurs is a primary focus for Argonne and Convergent Science, as well as for the broader gas turbine community.

LBO limits vary from fuel to fuel, and understanding these differences is critical, especially as alternative fuels become increasingly widespread. “The flame stabilization characteristics depend on the physical as well as the chemical properties of a given fuel, so our aim is to develop computational models that can predictively capture this behavior and the difference in performance between conventional and alternative fuels,” said Dr. Prithwish Kundu, Research Scientist at Argonne National Laboratory.

Using CONVERGE, Argonne and Convergent Science engineers investigated the LBO limits for two fuels: A-2 (a conventional Jet-A fuel) and C-1 (an alternative fuel)1. They conducted large eddy simulations (LES) of a realistic aviation gas turbine combustor from the U.S. National Jet Fuels Combustion Program (NJFCP). The combustor geometry preserved all flow passages and included the dome, liners, dilution jets, and effusion cooling holes. A Lagrangian approach was used to model the spray and atomization of the liquid fuels, and detailed chemistry was used to simulate combustion.

Gas turbine simulations tend to be computationally intensive because of the large computational domain, complicated geometry featuring a wide range of length scales (e.g., from millimeter-sized holes to a meter-long combustor), and complex physical processes. Argonne and Convergent Science engineers leveraged CONVERGE’s autonomous meshing to speed up the simulation setup and runtime. Automatic mesh generation saved weeks of time on the simulation setup, and Adaptive Mesh Refinement (AMR) helped shape the optimal mesh for desired spatial resolution to capture the complicated physical phenomena while keeping the overall cell count relatively low. 

With this method, Argonne and Convergent Science were able to accurately predict the difference in LBO limits for A-2 and C-1 fuels (Figure 1). Original equipment manufacturers (OEMs) have long desired a tool capable of predicting LBO, and demonstrating that CONVERGE is able to identify these limits in a reasonable amount of time is a significant achievement.

Figure 1: LBO results for A-2 and C-1 fuels in a gas turbine combustor.
CONVERGE simulation of LBO in a gas turbine combustor for A-2 (top) and C-1 (bottom) fuels.

Having validated CONVERGE’s ability to predict LBO for conventional and alternative fuels, Argonne and Convergent Science engineers are turning to high altitude relight, which is the key to keeping our planes in the air should LBO occur. High altitude relight happens under challenging conditions, i.e., very low temperature and pressure. The NJFCP is currently developing an experimental database for high altitude relight, which Argonne and Convergent Science plan to use to validate their CONVERGE simulations. Overall, these studies pave the way for creating cleaner gas turbine engines, while also ensuring the safety of air travel.

Rotating Detonation Engines

Improving traditional gas turbines is only one way to achieve high-efficiency, low-emissions engines for the aerospace and power generation industries.

“Emissions standards are regularly becoming more stringent, so gas turbines have to evolve accordingly,” said Dr. Gaurav Kumar, Principal Engineer at Convergent Science. “With stricter regulations, the technologies may need to be not just evolutionary, but revolutionary.”

Rotating detonation engines (RDEs) are one potentially revolutionary technology. RDE is an advanced engine concept that is both robust and scalable—you can run an RDE at a fairly wide range of fuel-air equivalence ratios, and you can produce both small and large engines from essentially the same design (Figure 2).

Figure 2: The basic RDE design, consisting of two cylinders, one inside the other, with a thin gap in between, called the annulus. Fuel and air are fed in at the bottom of the cylinder, and ignited with a spark to generate a combustion wave. The combustion wave becomes supersonic (i.e., detonative) and rotates around the RDE in the annulus.

Compared to deflagrative combustion (which is typical in most gas turbine engines), detonative combustion offers a number of benefits, including a substantial increase in efficiency and decrease in emissions. Detonative combustion also provides greater thrust for the same amount of fuel, which is a significant advantage for propulsion applications, such as powering aircrafts and rockets. 

However, RDEs are still in the development phase, and there are certain challenges that have kept them from becoming widely adopted.

“First, maintaining a stable detonation wave is tricky, given that the mixing is highly complex and chaotic,” said Dr. Pinaki Pal, Research Scientist at Argonne. “Thermal management is another challenge, because RDEs have a high thermal load that is unequally distributed throughout the device due to the cyclic combustion wave. This behavior can fatigue the device and shorten its lifespan.”

In addition, an RDE is a difficult environment in which to take experimental measurements. Any instrument you use must be able to capture the high frequencies and large amplitude range of the RDE, while also surviving the harsh conditions inside the device. Moreover, many experimental tools provide averaged results, such as the average temperature or pressure at the device exit. These tools fail to capture the transient nature of an RDE as the detonation wave travels around the engine. Ultimately, new methods to analyze RDEs are needed.

CFD allows you to probe any point in time and space within your computational domain, so researchers can leverage simulations to better understand the chaotic, supersonic combustion in an RDE. To that end, Argonne and Convergent Science engineers simulated both hydrogen- and ethylene-fueled RDEs in CONVERGE using detailed chemistry, LES, and autonomous meshing2,3.

CONVERGE simulation of a hydrogen-fueled RDE, with a geometry that corresponds to a design from the U.S. Air Force Research Laboratory. A spark-ignited flame travels up the pre-detonation tube and initiates a rotating detonation wave in the annulus. Hydrogen and air are injected through the inlet ports and the circumferential slot, respectively, at the bottom of the annular chamber. Before entering the annulus, the fuel and air undergo jet-in-crossflow type mixing within the mixing channel. When limit-cycle is reached, a self-sustaining rotating detonation wave continues to propagate within the RDE channel (shown in the second view).

Argonne and Convergent Science engineers quantified several key characteristics of the detonation wave, including wave height and frequency, for the hydrogen- and ethylene-fueled RDEs. The results are shown in Tables 1 and 2, respectively. For both cases, CONVERGE accurately captures the key RDE parameters compared with experimental data from the U.S. Air Force Research Laboratory.

Case Wave frequency (kHz) Wave height (mm) Fill height (mm) Oblique shock angle (mm) Air plenum pressure (kPa) Fuel plenum pressure (kPa) Channel pressure at 2.54 cm (kPa)
Expt. 3.69 34 ± 7 46 ± 4 53 ± 5 239 276 139
Sim. 3.60 35.6 47.5 51 256 292 142
Table 1: Comparison of experimental and simulation detonation wave characteristics for a hydrogen-fueled RDE2.
Case (method) Wave speed (m/s) Lift-off height (normalized) Wave height (normalized)
1 (expt.) 1035.9 ± 50 1 1
1 (sim.) 975.2 ± 40 1 1
2 (expt.) 1036 ± 50 1.1 0.78
2 (sim.) 978.8 ± 20 1.05 0.63
3 (expt.) 1014.5 ± 50 0.85 1.4
3 (sim.) 958.4 ± 30 0.83 1.39
Table 2: Comparison of experimental and simulation detonation wave characteristics for an ethylene-fueled RDE3.

“With CONVERGE, we’re able to get good quality combustion results with about 10–15 million cells, when other codes were using 90 million cells or more,” said Scott Drennan, Director of Gas Turbine and Aftertreatment Applications at Convergent Science. “And one of the key ways we’re able to do that is through Adaptive Mesh Refinement, which allows us to track the detonation wave by refining the mesh when and where it’s needed at every time step.”

Argonne and Convergent Science also employed a computational diagnostic tool called chemical explosive mode analysis (CEMA) to better understand the local combustion regime. This technique had previously been applied to diesel and scramjet engines, but this was the first time it was implemented for an RDE. Based on an eigenanalysis of the local chemical Jacobian, CEMA is able to identify local combustion modes, such as auto-ignition, deflagrative fronts, and local extinction.

“We demonstrated that CEMA is able to accurately capture the local combustion behavior within an RDE,” said Dr. Pal. “What we would like to do next is develop an on-the-fly dynamic adaptive modeling technique to prescribe regime-dependent combustion models based on the local combustion regime identified by CEMA, which would drastically reduce the computational cost and enhance the accuracy of a CFD simulation.”

In addition to further CEMA studies, there are several other areas of research that Argonne and Convergent Science plan to pursue. One project currently underway is extending the modeling approach used for the studies described above to rocket RDEs. Up to this point, Argonne and Convergent Science have simulated air-breathing RDEs. Now, they are investigating a methane-fueled rocket RDE that uses oxygen instead of air as the oxidizer. Another upcoming project is to simulate the combustor coupled with the turbine in order to evaluate the overall performance of the system. These predictive CFD models will enable engineers to gain more insight into the combustion phenomena in an RDE and to develop design strategies that can help propel the technology into the mainstream.


As Argonne and Convergent Science work to achieve more predictive engine simulations, one area that holds significant potential for improvement is spray modeling. One of the simplest questions we can ask is, “Where does the fuel go?” The trajectory of the spray impacts all of the downstream processes in a combustor: fuel-air mixing, ignition, combustion, emissions, and thrust. But actually determining where the fuel goes is anything but simple.

“It’s a beautifully complex problem,” said Dr. Gina Magnotti, Research Scientist at Argonne National Laboratory. “The spray is sensitive to the local operating conditions, the injector geometry, the fuel properties—and we don’t necessarily have a full grasp on all of the salient physics that control the fuel spray atomization. What happens in the first few millimeters from the injector or atomizer exit has great consequences for the fuel-air mixing and the dispersion of the spray.”

Both gas turbines and RDEs feature jet-in-crossflow type mixing, so Dr. Magnotti and her colleagues conducted a CFD study to better understand this process4. They synthetically imposed realistic surface roughness inside the injector geometry. For their CONVERGE simulations, they coupled LES with a volume of fluid (VOF) approach to understand how the initial flow development impacts the spray formation process. The results were compared to experimental measurements taken at Argonne’s Advanced Photon Source (APS). Ultimately, they found that imposing realistic surface roughness affects crosswise stretching of the jet and distribution of liquid mass, as shown in Figure 3.

Figure 3: A local increase in equivalent path length (EPL) or mass distribution relative to the nominal geometry for jets issued from injectors with imposed surface roughness level of 1.25 μm (left) and 2.50 μm (right).

This study demonstrated that there is still much to learn about the fuel injection process, and Argonne and Convergent Science plan to continue research in this area. A better understanding of the link between internal injector flow and spray formation will provide more accurate boundary conditions for gas turbine and RDE simulations, which will improve their predictive capability. 

Fearless Engineering

The overarching goal of all these projects is to develop predictive computational models that industry can use to design revolutionary technology. The collaboration between Argonne and Convergent Science enables the fundamental research necessary to develop these models and provides a path for the models to get into the hands of industry. Working with Argonne also helps Convergent Science extend CONVERGE’s capabilities to new application areas and enables cutting-edge research in new, exciting fields. As Dr. Dan Lee, Co-Owner and Vice President of Convergent Science, puts it:

It’s a privilege to work with organizations like Argonne. One of the greatest ways to learn about new applications or expand your value proposition in new applications is to partner with people who already have experience in that area. When we partner with Argonne, we’re dealing with experts in a wide variety of applications. And what’s more is that any new area we want to go into, even if Argonne doesn’t currently have expertise in that particular area, they’re used to going into new research areas—they’re fearless. And that’s a great combination: talented, experienced, fearless.

Pushing fearlessly into these new research areas—aerospace, power generation, and more—allows for a greater impact on society, helping to bring about a cleaner and safer world.

In case you missed the other posts in this series, you can find them here:


[1] Hasti, V.R., Kundu, P., Kumar, G., Drennan, S.A., Sibendu, S., Won, S.H., Dryer, F.L., and Gore, J.P., “Lean Blow-Out (LBO) Computations in a Gas Turbine Combustor,” 2018 AIAA/SAE/ASEE Joint Propulsion Conference, AIAA 2018-4958, Cincinnati, OH, United States, Jul 9–11, 2018. DOI: 10.2514/6.2018-4958

[2] Pal, P., Xu, C., Kumar, G., Drennan, S.A., Rankin, B.A., and Som, S., “Large-Eddy Simulation and Chemical Explosive Mode Analysis of Non-Ideal Combustion in a Non-Premixed Rotating Detonation Engine,” AIAA SciTech 2020 Forum, AIAA 2020-2161, Orlando, FL, United States, Jan 6–10, 2020. DOI: 10.2514/6.2020-2161

[3] Pal, P., Xu, C., Kumar, G., Drennan, S.A., Rankin, B.A., and Som, S., “Large-Eddy Simulations and Mode Analysis of Ethylene/Air Combustion in a Non-Premixed Rotating Detonation Engine,” AIAA Propulsion and Energy 2020 Forum, AIAA 2020-3876, Online, Aug 24–28, 2020. DOI: 10.2514/6.2020-3876

[4] Magnotti, G.M., Lin, K.-C., Carter, C.D., Kastengren, A., and Som, S., “A Computational Investigation of the Effect of Surface Roughness on the Development of a Liquid Jet in Subsonic Crossflow,” AIAA Propulsion and Energy 2020 Forum, AIAA 2020-3880, Online, Aug 24–28, 2020. DOI: 10.2514/6.2020-3880

► 2020: THE YEAR of CFD (Computing From a Distance)
  23 Dec, 2020

We’ve reached the end of 2020, and I think it’s fair to say this year did not go as planned. The coronavirus pandemic disrupted our lives and brought on unexpected challenges and hardships. However, this difficult time has also highlighted the resiliency of people all around the globe—we have adapted and innovated to meet these challenges head on. At Convergent Science, that meant finding new ways to communicate and collaborate to ensure we could continue to deliver the best possible software and support to our users, all while keeping our employees safe.

Despite the pandemic, we experienced exciting opportunities, advancements, and milestones at Convergent Science this past year. We hosted two virtual conferences, continued to expand into new markets and new application areas, began new collaborations, increased our employee count, and, of course, continued to improve and develop CONVERGE. 

CONVERGE 3.1: A Preview

We have spent much of 2020 developing the next major release of our CONVERGE CFD software: version 3.1. There’s a lot to look forward to in CONVERGE 3.1, which will be released next year. In CONVERGE 3.0, we added the ability to incorporate stationary inlaid meshes into a simulation. In 3.1, these inlaid meshes will be able to move within the underlying Cartesian grid. For example, you will be able to create an inlaid mesh around each of the intake valves in an IC engine simulation, and the mesh will move with the valve as it opens and closes. With this method, you can achieve high grid resolution normal to the valve surface using significantly fewer cells than with traditional fixed embedding. 

Another enhancement will allow you to use different solvers, meshes, physical models, and chemical mechanisms for different streams (i.e., portions of the domain). This means you will be able to tailor your simulation settings to each stream, which will improve solver speed and numerical performance. CONVERGE 3.1 will also feature new sealing capabilities that enable you to have any objects come into contact with one another in your simulation or have objects enter or leave your simulation. 

Furthermore, CONVERGE 3.1 will support solid- and gas-phase parcels in addition to the traditional liquid-phase parcels. This can be useful when modeling, for example, soot or injectors operating at flash-boiling conditions. CONVERGE 3.1 will also feature an improved steady-state solver that will provide significant improvements in speed, and we have enhanced our fluid-structure interaction, volume of fluid, combustion, and emissions modeling capabilities. There are many more exciting features and enhancements coming in 3.1, so stay tuned for more information!

Pursuing High-Performance Computing with Oracle

Improving the scalability of CONVERGE continues to be a strong focus of our development efforts. We work with several companies and institutions, testing CONVERGE on different high-performance computing (HPC) architectures and optimizing our software to ensure good scaling. To that end, we were thrilled to begin a new collaboration this year with Oracle, a leader in cloud computing and enterprise software. In our benchmark testing, we have seen near perfect scaling of CONVERGE on Oracle Cloud Infrastructure on thousands of cores. This collaboration presents a great opportunity for CONVERGE users to take advantage of Oracle’s advanced HPC resources to efficiently run large-scale simulations in the cloud. 

Best Use of HPC in Industry

For the second year in a row, we were honored to win an HPCwire award for research performed with our colleagues at Aramco Research Center–Detroit and Argonne National Laboratory. This year, we received the HPCwire Readers’ Choice Award for Best Use of HPC in Industry for our work using HPC and machine learning to accelerate injector design optimization for next-generation high-efficiency, low-emissions engines. Our collaborative work is forging the way to leverage HPC, novel experimental measurements, and CFD to perform rapid optimization studies and reduce our carbon footprint from transportation.

Computational Chemistry Consortium

In another collaborative effort, the Computational Chemistry Consortium (C3) made significant progress in 2020. Co-founded by Convergent Science, C3 is working to create the most accurate and comprehensive chemical reaction mechanism for automotive fuels that includes NOx and PAH chemistry to model emissions. The first version of the mechanism was completed last year and is currently available to C3’s industry sponsors. Once the mechanism is published, it will be released to the public on This past year, C3 has continued to refine the mechanism, which has now reached version 2.1. The results of these efforts have been rewarding—we’ve seen a significant decrease in error in selected validation cases. The next year of the consortium will focus on increasing the accuracy of the NOx and PAH chemistry. To that end, C3 welcomed a new member this year, Dr. Stephen Klippenstein from Argonne National Laboratory. Dr. Klippenstein will perform high-level ab initio calculations of rate constants in NOx chemistry. Ultimately, the C3 mechanism is expected to be the first publicly available mechanism that includes everything from hydrogen chemistry all the way up to PAH chemistry in a single high-fidelity mechanism.

Driving Mobility Forward

In 2020, we celebrated our 10-year anniversary of collaboration with Argonne National Laboratory. Over the past decade, this collaboration has helped us extend CONVERGE’s capabilities and broach new application areas. We have performed cutting-edge research in the transportation field, developing new methods and models that are proving to be instrumental in designing the next generation of engines. In the aerospace field, we’ve broken ground in applying CFD to gas turbines, rotating detonation engines, drones, and more. We’ve made great strides in the last ten years, and we’re looking forward to the next decade of collaboration!

Bringing CONVERGE Online

Every year, we look forward to getting together with our users, discussing the latest exciting CONVERGE research and having some fun at our user conferences. When the pandemic struck and countries began locking down earlier this year, we were determined to still hold our 2020 CONVERGE User Conference–Europe, even if it looked a bit different. Our conference was scheduled for the end of March, so we didn’t have much time to transition from an in-person to an online event, but our team was up for the challenge. In less than three weeks, we planned a whole new event and successfully held one of the first pandemic-era virtual conferences. We were so pleased with the result! More than 400 attendees from around the world tuned in for an excellent lineup of technical presentations, which spanned topics from IC engines to compressors to electric motors and battery packs. 

While we hoped to hold our North American user conference in Detroit later in the year, the continued pandemic made that impossible. Once again, we took to the internet. We incorporated some more networking opportunities, including various social groups and discussion topics, and created some fun polls to help attendees get to know one another. We were also able to offer our usual slate of conference-week CONVERGE training and virtual exhibit booths for our sponsors. The presentations at this conference showcased the breadth and diversity of applications for which CONVERGE is suited, with speakers discussing rockets, gas turbines, exhaust aftertreatment, biomedical applications, renewable energy, and electromobility in addition to a host of IC engine-related topics.

It’s hard to know what 2021 will look like, but rest assured we will be hosting more conferences, virtual or otherwise. We’re looking forward to the day we can get together in person once again!

CONVERGE Around the World

Even with the pandemic, 2020 was an exciting and productive year for Convergent Science around the globe. We gained nearly a dozen new employees, including bringing on team members in newly created roles to help expand our relationships with universities and to increase our in-house CAD design capabilities. We also continued to find new markets for CONVERGE as we entered the emobility, rocket, and burner industries. 

Our Indian office flourished in 2020. Since its creation three years ago, Convergent Science India has grown to more than 20 employees, adding nine new team members this year alone. To accommodate our growing team, we moved to a spacious new building in Pune. Our team in India expanded our global reach, bringing new academic and industry clients on board. In addition, we continued to work on growing our presence in new applications such as gas turbines, aftertreatment, motor cooling, battery failure, oil churning, and spray painting.

In Europe, despite the challenging circumstances, we increased our client base and our license sales considerably, and we were able to successfully and seamlessly support our customers to help them achieve their CFD goals. In addition to moving our European CONVERGE user conference online in record time, we attended and exhibited at many virtual tradeshows and events and are looking forward to attending in-person conferences as soon as it is safe to do so.

Our partners at IDAJ continued to do excellent work supporting our customers in Japan, China, and Korea. Due to the pandemic, they held their first-ever IDAJ Conference Online 2020, where they had both live lectures and Q&A sessions as well as on-demand streaming content. While they support many IC engine clients, they are also supporting clients working on other applications such as motor cooling, battery failure, oil churning, and spray painting.

Looking Ahead

2020 was a difficult year for many of us, but I am impressed and inspired by the way the CFD community and beyond has come together to make the most of a challenging situation. And the future looks bright! We’re looking forward to releasing CONVERGE 3.1 and helping our users take advantage of the increased functionality and new features that will be available. We’re excited to expand our presence in electromobility, renewable energy, aerospace, and other new fields. In the upcoming year, we look forward to forming new collaborations and strengthening existing partnerships to promote innovation and keep CONVERGE on the cutting-edge of CFD software.

Can we help you meet your 2021 CFD goals? Contact us today!

► Cool Your Pistons Like You Cool Your Cocktail
    8 Dec, 2020

In my first year of graduate school, a friend always filled up her water bottle, dropped some ice cubes into it, and then shook it up in order to cool the water faster. If she had added the ice cubes and let the water bottle sit, eventually all the water would equilibrate to the same temperature, but that would take a while without any movement—the water next to the ice cubes would cool down quickly, but the water farther away would cool down at a much slower rate. By shaking it up, she agitated the water and ice so that the ice came into contact with more of the warm water that needed to be cooled. This “cocktail shaker effect,” I would later find out, also applies to cooling engines. 

Combustion in an internal combustion (IC) engine occurs on top of the piston, which means that there is an extraordinary amount of heat generated on the piston crown. If left unmediated, this heat can cause the piston to break. The threat of piston damage is particularly high in diesel engines because more heat is generated in the cylinder than in a traditional gasoline engine. Unlike a bottle of warm water, though, we can’t just drop a few ice cubes into the cylinder to act as a heat sink. 

Here we see how engineers can use CONVERGE to efficiently solve the problem of cooling the piston so that it isn’t damaged by heat. The idea is simple—use engine oil as a heat sink—but the implementation is complex since the piston is constantly moving and nothing can be in contact with the piston crown inside the cylinder. 

Figure 1: Image of an oil jet-cooled piston with relevant features labeled.

Since the heat sink can’t be inside the cylinder on the piston crown, there is an oil gallery in contact with the undercrown of the piston, as shown in Figure 1. Engine oil is taken through a pump, pressurized, and constantly sprayed at the oil gallery inlet hole. In the video below, you will see how the oil enters the gallery, and, as the piston motion continues, the oil sloshes inside the oil gallery, absorbing heat from the piston before exiting the outlet hole on the other side of the gallery. 

There are several factors that are important to consider when designing this type of cooling system, all of which CONVERGE is well-equipped to handle. What size and shape should the inlet and outlet holes be to capture the stream of oil? How much oil will enter the gallery compared to how much was sprayed (i.e., capture ratio)? What is the best design of the gallery so that the oil effectively absorbs heat from the piston? What ratio of the gallery volume should be occupied (i.e., fill ratio) to ensure that the oil can move and absorb heat efficiently? CONVERGE provides answers to these questions and others through a volume of fluid (VOF) simulation

Figure 2: CONVERGE’s Adaptive Mesh Refinement refines the mesh around the oil gallery, where more heat transfer occurs.

Because a simple boundary condition is not predictive of the heat transfer throughout the entire piston, we use conjugate heat transfer (CHT) to more accurately predict the piston cooling by solving the heat distribution inside the piston. Understanding how heat transfer affects the whole piston is an essential step toward designing a geometry that will effectively cool more than just the piston surface. While CHT can be computationally expensive due to the difference in time-scales of heat transfer in the solid and fluid regions, CONVERGE provides the option to use super-cycling, which can significantly reduce the computational cost of this type of simulation.

In the video below, you will see how the above factors have been optimized to dissipate heat from the piston crown and throughout the piston as a whole. In the video on the left, you can watch the temperature contours change during the simulation as heat dissipates. The second view shows how CONVERGE’s Adaptive Mesh Refinement (AMR) is in action throughout the simulation, providing increased grid resolution near the inlet and around the oil gallery, where it is needed most. 

Ready to run your own simulations to optimize oil jet piston cooling? Contact us today!

Video of an oil jet-cooled piston. The first view shows the temperature contours. The second view contains the same piston with mesh visualized, showing that the mesh is more refined around the oil gallery where more heat transfer occurs. As the simulation proceeds, AMR provides increased grid resolution near the features of interest.
► The Collaboration Effect: Advancing Engines Through Simulation &#038; Experimentation
    9 Nov, 2020

From the Argonne National Laboratory + Convergent Science Blog Series

Through the collaboration between Argonne National Laboratory and Convergent Science, we provide fundamental research that enables manufacturers to design cleaner and more efficient engines by optimizing combustion. 

–Doug Longman, Manager of Engine Research at Argonne National Laboratory

The internal combustion engine has come a long way since its inception—the engine in your car today is significantly quieter, cleaner, and more efficient than its 1800s-era counterpart. For many years, the primary means of achieving these advances was experimentation. Indeed, we have experiments to thank for a myriad of innovations, from fuel injection systems to turbocharging to Wankel engines.

More recently, a new tool was added to the engine designer’s toolbox: simulation. Beginning in the 1970s and ‘80s, computational fluid dynamics (CFD) opened the door to a new level of refinement and optimization.

“One of the really cool things about simulation is that you can look at physics that cannot be easily captured in an experiment—details of the flow that might be blocked from view, for example,” says Eric Pomraning, Co-Owner of Convergent Science.

Of course, experiments remain vitally important to engine research, since CFD simulations model physical processes, and experiments are necessary to validate your results and ground your simulations in reality.

Argonne National Laboratory and Convergent Science combine these two approaches—experiments and simulation—to further improve the internal combustion engine. Two of the main levers we have to control the efficiency and emissions of an engine are the fuel injection system and the ignition system, both of which have been significant areas of focus during the collaboration.

Fuel Injection

The combustion process in an internal combustion engine really begins with fuel injection. The physics of injection determine how the fuel and air in the cylinder will mix, ignite, and ultimately combust. 

Argonne National Laboratory is home to the Advanced Photon Source (APS), a DOE Office of Science User Facility. The APS provides a unique opportunity to characterize the internal passages of injector nozzles with incredibly high spatial resolution through the use of high-energy x-rays. This data is invaluable for developing accurate CFD models that manufacturers can use in their design processes.

Early on in the collaboration, Christopher Powell, Principal Engine Research Scientist at Argonne, and his team leveraged the APS to investigate needle motion in an injector.

“Injector manufacturers had long suspected that off-axis motion of the injector valve could be present. But they never had a way to measure it before, so they weren’t sure how it impacted fuel injection,” says Chris.

The x-ray studies performed at the APS were the first in the world to confirm that some injector needles do exhibit radial motion in addition to the intended axial motion, a phenomenon dubbed “needle wobble.” Argonne and Convergent Science engineers simulated this experimental data in CONVERGE, prescribing radial motion to the injector needle. They found that needle wobble can substantially impact the fuel distribution as it exits the injector. Manufacturers were able to apply the results of this research to design injectors with a more predictable spray pattern, which, in turn, leads to a more predictable combustion event.

More recently, researchers at Argonne have used the APS to investigate the shape of fuel injector flow passages and characterize surface roughness. Imperfections in the geometry can influence the spray and the subsequent downstream engine processes. 

“If we use a CAD geometry, which is smooth, we will miss out on some of the physics, like cavitation, that can be triggered by surface imperfections,” says Sameera Wijeyakulasuriya, Senior Principal Engineer at Convergent Science. “But if we use the x-ray scanned geometry, we can incorporate those surface imperfections into our numerical models, so we can see how the flow field behaves and responds.”

Argonne and Convergent Science engineers performed internal nozzle flow simulations that used the real injector geometries and that incorporated real needle motion.1 Using the one-way coupling approach in CONVERGE, they mapped the results of the internal flow simulations to the exit of each injector orifice to initialize a multi-plume Lagrangian spray simulation. As you can see in Figure 1, the surface roughness and needle motion significantly impact the spray plume—the one-way coupling approach captures features that the standard rate of injection (ROI) method could not. In addition, the real injector parameters introduce orifice-to-orifice variability, which affects the combustion behavior down the line.

Figure 1: Comparison of the spray plume (top) and the effect of orifice-to-orifice variability on combustion behavior (bottom) simulated using the standard ROI method (left) and the one-way coupling method (right), which accounts for the real injector geometry and needle motion.

The real injector geometries not only allow for more accurate computational simulations, but they also can serve as a diagnostic tool for manufacturers to assess how well their manufacturing processes are producing the desired nozzle shape and size.

Spark Ignition

Accurately characterizing fuel injection sets the stage for the next lever we can optimize in our engine: ignition. In spark-ignition engines, the ignition event initiates the formation of the flame kernel, the growth of the flame kernel, and the flame propagation mechanism.

“In the past, ignition was just modeled as a hot source—dumping an amount of energy in a small region and hoping it transitions to a flame. The amount of physics in the process was very limited,” says Sibendu Som, Manager of the Computational Multi-Physics Section at Argonne.

These simplified models are adequate for most stable engine conditions, but you can run into trouble when you start simulating more advanced combustion concepts. In these scenarios, the simplified ignition models fall short in replicating experimental data. Over the course of their collaboration, Argonne and Convergent Science have incorporated more physics into ignition models to make them robust for a variety of engine conditions. 

For example, high-performance spark-ignition engines often feature high levels of dilution and increased levels of turbulence. These conditions can have a significant impact on the ignition process, which consequently affects combustion stability and cycle-to-cycle variation (CCV). To capture the elongation and stretch experienced by the spark channel under highly turbulent conditions, Argonne and Convergent Science engineers developed a new ignition model, the hybrid Lagrangian-Eulerian spark-ignition (LESI) model.

In Figure 2, you can see that the LESI model more accurately captures the behavior of the spark under turbulent conditions compared to a commonly used energy deposition model.2 The LESI model will be available in future versions of CONVERGE, accessible to manufacturers to help them better understand ignition and mitigate CCV.

Figure 2: Comparison of experimental results (A) with a commonly used energy deposition model (B) and the LESI model (C) at turbulent engine-like conditions.

Cycle-to-Cycle Variation

Ideally, every cycle of an internal combustion engine would be exactly identical to ensure smooth operation. In real engines, variability in the injection, ignition, and combustion means that not every cycle will be the same. Cyclic variability is especially prevalent in high-efficiency engines that push the limits of combustion stability. Extreme cycles can cause engine knock and misfires—and they can influence emissions.

“Not every engine cycle generates significant emissions. Often they’re primarily formed only during rare cycles—maybe one or two out of a hundred,” says Keith Richards, Co-Owner of Convergent Science. “Being able to capture cyclic variability will ultimately allow us to improve our predictive capabilities for emissions.”

Modeling CCV requires simulating numerous engine cycles, which is a highly (and at times prohibitively) time-consuming process. Several years ago, Keith suggested a potential solution—starting several engine cycles concurrently, each with a small perturbation to the flow field, which allows each simulation to develop into a unique solution. 

Argonne and Convergent Science compared this approach—called the concurrent perturbation method (CPM)—to the traditional approach of simulating engine cycles consecutively. Figure 3 shows CCV results obtained using CPM compared to concurrently run cycles, which you can see match very well.3 This means that with sufficient computational resources, you can predict CCV in the amount of time it takes to run a single engine cycle.

Figure 3: CCV results from consecutively run simulations (left) versus concurrently run simulations (right) for the same gasoline direct injection engine case.

The study described above, and the vast majority of all CCV simulation studies, use large eddy simulations (LES), because LES allows you to resolve some of the turbulence scales that lead to cyclic variability. Reynolds-Averaged Navier-Stokes (RANS), on the other hand, provides an ensemble average that theoretically damps out variations between cycles. At least this was the consensus among the engine modeling community until Riccardo Scarcelli, a Research Scientist at Argonne, noticed something strange.

“I was running consecutive engine cycle simulations to move away from the initial boundary conditions, and I realized that the cycles were never converged to an average solution—the cycles were never like the cycle before or the cycle after,” Riccardo says. “And that was strange because I was using RANS, not LES.”

Argonne and Convergent Science worked together to untangle this mystery, and they discovered that RANS is able to capture the deterministic component of CCV. RANS has long been the predominant turbulence model used in engine simulations, so how had this phenomenon gone unnoticed? In the past, most engine simulations modeled conventional combustion, which shows little cyclic variability in practice in either diesel or gasoline engines. The more complex combustion regimes simulated today—along with the use of finer grids and more accurate numerics—allows RANS to pick up on some of the cycle-to-cycle variations that these engines exhibit in the real world. While RANS will not provide as accurate a picture as LES, it can be a useful tool to capture CCV trends. Additionally, RANS can be run on a much coarser mesh than LES, so you can get a faster turnaround on an inherently expensive problem, making CCV studies more practical for industry timelines.

Advancing Engine Technology

The gains in understanding and improved models developed during the Argonne and Convergent Science collaboration provide great benefit to the engine community. One of the primary missions of Argonne National Laboratory is to transfer knowledge and technology to industry. To that end, the models developed during the collaboration will continue to be implemented in CONVERGE, putting the technology in the hands of manufacturers, so they can create better engines. 

What can we look forward to in the future? There will continue to be a strong focus on developing high fidelity numerics, expanding and improving chemistry tools and mechanisms, integrating machine learning into the simulation process, and speeding up CFD simulations—establishing more efficient models and further increasing the scalability of CONVERGE to take advantage of the latest computational resources. Moreover, we can look forward to seeing the innovations of the last decade of collaboration incorporated into the engines of the next decade, bringing us closer to a clean transportation future.

In case you missed the other posts in the series, you can find them here:


[1] Torelli, R., Matusik, K.E., Nelli, K.C., Kastengren, A.L., Fezzaa, K., Powell, C.F., Som, S., Pei, Y., Tzanetakis, T., Zhang, Y., Traver, M., and Cleary, D.J., “Evaluation of Shot-to-Shot In-Nozzle Flow Variations in a Heavy-Duty Diesel Injector Using Real Nozzle Geometry,” SAE Paper 2018-01-0303, 2018. DOI: 10.4271/2018-01-0303

[2] Scarcelli, R., Zhang, A., Wallner, T., Som, S., Huang, J., Wijeyakulasuriya, S., Mao, Y., Zhu, X., and Lee, S.-Y., “Development of a Hybrid Lagrangian–Eulerian Model to Describe Spark-Ignition Processes at Engine-Like Turbulent Flow Conditions,” Journal of Engineering for Gas Turbines and Power, 141(9), 2019. DOI: 10.1115/1.4043397
[3] Probst, D., Wijeyakulasuriya, S., Pomraning, E., Kodavasal, J., Scarcelli, R., and Som, S., “Predicting Cycle-to-Cycle Variation With Concurrent Cycles In A Gasoline Direct Injected Engine With Large Eddy Simulations”, Journal of Energy Resources Technology, 142(4), 2020. DOI: 10.1115/1.4044766

► Exploring Offshore Wind Energy: Creating a Cleaner Future With CFD
  19 Oct, 2020

Renewable energy is being generated at unprecedented levels in the United States, and those levels will only continue to rise. The growth in renewable energy has been driven largely by wind power—over the last decade, wind energy generation in the U.S. has increased by 400% 1. It’s easy to see why wind power is appealing. It’s sustainable, cost-effective, and offers the opportunity for domestic energy production. But, like all energy sources, wind power doesn’t come without drawbacks. Concerns have been raised about land use, noise, consequences to wildlife habitats, and the aesthetic impact of wind turbines on the landscape 2.

However, there is a potential solution to many of these issues: what if you move wind turbines offshore? In addition to mitigating concerns over land use, noise, and visual impact, offshore wind turbines offer several other advantages. Compared to onshore, wind speeds offshore tend to be higher and steadier, leading to large gains in energy production. Also, in the U.S., a large portion of the population lives near the coasts or in the Great Lakes region, which minimizes problems associated with transporting wind-generated electricity. But despite these advantages, only 0.03% of the U.S. wind-generating capacity in 2018 came from offshore wind plants 1. So why hasn’t offshore wind energy become more prevalent? Well, one of the major challenges with offshore wind energy is a problem of engineering—wind turbine support structures must be designed to withstand the significant wind and wave loads offshore.

Today, there are computational tools that engineers can use to help design optimized support structures for offshore wind turbines. Namely, computational fluid dynamics (CFD) simulations can offer valuable insight into the interaction between waves and the wind turbine support structures. 

Two-phase CONVERGE simulation of a solitary wave breaking on a monopile. The water phase is shown, colored by horizontal velocity.

A CFD Case Study

Hannah Johlas, NSF Graduate Research Fellow

Hannah Johlas is an NSF Graduate Research Fellow in Dr. David Schmidt’s lab at the University of Massachusetts Amherst. Hannah uses CFD to study fixed-bottom offshore wind turbines at shallow-to-intermediate water depths (up to approximately 50 meters deep). Turbines located at these depths are of particular interest because of a phenomenon called breaking waves. As waves move from deeper to shallower water, the wavelength decreases and the wave height increases in a process called shoaling. If a wave becomes steep enough, the crest can overturn and topple forward, creating a breaking wave. Breaking waves can impart substantial forces onto turbine support structures, so if you’re planning to build a wind turbine in shallower water, it’s important to know if that turbine might experience breaking waves.

Hannah uses CONVERGE CFD software to predict if waves are likely to break for ocean characteristics common to potential offshore wind turbine sites along the east coast of the U.S. She also predicts the forces from breaking waves slamming into the wind turbine support structures. The results of the CONVERGE simulations are then used to evaluate the accuracy of simplified engineering models to determine which models best capture wave behavior and wave forces and, thus, which ones should be used when designing wind turbines.

CONVERGE Simulations

In this study, Hannah simulated 39 different wave trains in CONVERGE using a two-phase finite volume CFD model 3. She leveraged the volume of fluid (VOF) method with the Piecewise Linear Interface Calculation scheme to capture the air-water interface. Additionally, automated meshing and Adaptive Mesh Refinement ensured accurate results while minimizing the time to set up and run the simulations.

“CONVERGE’s adaptive meshing helps simulate fluid interfaces at reduced computational cost,” Hannah says. “This feature is particularly useful for resolving the complex air-water interface in breaking wave simulations.”

Some of the breaking waves were then simulated slamming into monopiles, the large cylinders used as support structures for offshore wind turbines in shallow water. The results of these CONVERGE simulations were validated against experimental data before being used to evaluate the simplified engineering models.

Experimental setup at Oregon State University (left) and the corresponding CONVERGE simulation (right) of a wave breaking on a monopile.


Four common models for predicting whether a wave will break (McCowan, Miche, Battjes, and Goda) were assessed. The models were evaluated by how frequently they produced false positives (i.e., the model predicts a wave should break, but the simulated wave does not break) and false negatives (i.e., the model predicts a wave should not break, but the simulated wave does break) and how well they predicted the steepness of the breaking waves. False positives are preferable to false negatives when designing a conservative support structure, since breaking wave loads are usually higher than non-breaking waves.

The study results indicate that none of the models perform well under all conditions, and instead which model you should use depends on the characteristics of the ocean at the site you’re considering.

“For sites with low seafloor slopes, the Goda model is the best at conservatively predicting whether a given wave will break,” Hannah says. “For higher seafloor slopes, the Battjes model is preferred.”

Four slam force models were also evaluated: Goda, Campbell-Weynberg, Cointe-Armand, and Wienke-Oumerachi. The slam models and the simulated CFD wave forces were compared for their peak total force, their force time history, and breaking wave shape. 

The results show that all four slam models are conservative (i.e., predict higher peak forces than the simulated waves) and assume the worst-case shape for the breaking wave during impact. The Goda slam model is the least conservative, while the Cointe-Armand and Wienke-Oumerachi slam models are the most conservative. All four models neglect the effects of runup on the monopiles, which was present in the CFD simulations. This could explain some of the discrepancies between the forces predicted by the engineering models and the CFD simulations.


Offshore wind energy is a promising technology for clean energy production, but to gain traction in the industry, there needs to be sound engineering models to use when designing the turbines. Hannah’s research provides guidelines on which engineering models should be used for a given set of ocean characteristics. Her results also highlight the areas that could be improved upon. 

“The slam force models don’t account for variety in wave shape at impact or for wave runup on the monopiles,” Hannah says. “Future studies should focus on incorporating these factors into the engineering models to improve their predictive capabilities.”

CONVERGE for Renewable Energy

CFD has a fundamental role to play in the development of renewable energy. CONVERGE’s combination of autonomous meshing, high-fidelity physical models, and ability to easily handle complex, moving geometries make it particularly well suited to the task. Whether it’s studying the interaction of waves with offshore turbines, optimizing the design of onshore wind farms, or predicting wind loads on solar panels, CONVERGE has the tools you need to help bring about the next generation of energy production.

Interested in learning more about Hannah’s research? Check out her paper here.


[1] Marcy, C., “U.S. renewable electricity generation has doubled since 2008,”, accessed on Nov 11, 2016.

[2] Center for Sustainable Systems, University of Michigan, “U.S. Renewable Energy Factsheet”,, accessed on Nov 11, 2016.

[3] Johlas, H.M., Hallowell, S., Xie, S., Lomonaco, P., Lackner, M.A., Arwade, S.A., Myers, A.T., and Schmidt, D.P., “Modeling Breaking Waves for Fixed-Bottom Support Structures for Offshore Wind Turbines,” ASME 2018 1st International Offshore Wind Technical Conference, IOWTC2018-1095, San Francisco, CA, United States, Nov 4–7, 2018. DOI: 10.1115/IOWTC2018-1095

► CONVERGE for Pumps &#038; Compressors: The Engineering Solution for Design Optimization
  12 Oct, 2020

Across industries, manufacturers share many of the same goals: create quality products, boost productivity, and reduce expenses. In the pumps and compressors business, manufacturers must contend with the complexity of the machines themselves in order to reach these goals. Given the intricate geometries, moving components, and tight clearances between parts, designing pumps and compressors to be efficient and reliable is no trivial matter. 

First, assessing the device’s performance by building and testing a prototype can be time-consuming and costly. And when you’re performing a design study, machining and switching out various components further compounds your expenses. There are also limitations in how many instruments you can place inside the device and where you can place them, which can make fully characterizing the machine difficult. New methods for testing and manufacturing can help streamline this process, but there remains room for alternative approaches.

Centrifugal pump

Computational fluid dynamics (CFD) offers significant advantages for designing pumps and compressors. Through CFD simulations, you can obtain valuable insight into the behavior of the fluid inside your machine and the interactions between the fluid and solid components—and CONVERGE CFD software is well suited for the task.

Designed to model three-dimensional fluid flows in systems with complex geometries and moving boundaries, CONVERGE is equipped to simulate any positive displacement or dynamic pump or compressor. And with a suite of advanced models, CONVERGE allows you to computationally study the physical phenomena that affect efficiency and reliability—such as surge, pressure pulsations, cavitation, and vibration—to design an optimal machine.

The Value of CONVERGE

CFD provides a unique opportunity to visualize the inner workings of your machine during operation, generating data on pressures, temperatures, velocities, and fluid properties without the limitations of physical measurements. The entire flow field can be analyzed with CFD, including areas that are difficult or impossible to measure experimentally. This additional data allows you to comprehensively characterize your pump or compressor and pinpoint areas for improvement.

Since CONVERGE leads the way in predictive CFD technology, you can analyze pump and compressor designs that have not yet been built and still be confident in your results. Compared to building and testing prototypes, simulations are fast and inexpensive, and altering a computer-modeled geometry is trivial. Iterating through designs virtually and building only the most promising candidates reduces the expenses associated with the design process. 

While three-dimensional CFD is fast compared to experimental methods, it is typically slower than one- or two-dimensional analysis tools, which are often incorporated into the design process. However, 1D and 2D methods are inherently limited in their ability to capture the 3D nature of physical flows, and thus can miss important flow phenomena that may negatively affect performance. 

CONVERGE drastically reduces the time required to set up a 3D pump or compressor simulation with its autonomous meshing capabilities. Creating a mesh by hand—which is standard practice in many CFD programs—can be a weeks-long process, particularly for cases with complex moving geometries such as pumps and compressors. With autonomous meshing, CONVERGE automatically generates an optimized Cartesian mesh based on a few simple user-defined parameters, effectively eliminating all user meshing time. 

In addition, the increased computational resources available today can greatly reduce the time requirements to run CFD simulations. CONVERGE is specifically designed to enable highly parallel simulations to run on many processors and demonstrates excellent scaling on thousands of cores. Additionally, Convergent Science partners with cloud service providers, who offer affordable on-demand access to the latest computing resources, making it simple to speed up your simulations.

Validation Cases

Accurately capturing real-world physical phenomena is critical to obtaining useful simulation results. CONVERGE features robust fluid-structure interaction (FSI) modeling capabilities. For example, you can simulate the interaction between the bulk flow and the valves to predict impact velocity, fatigue, and failure points. CONVERGE also features a conjugate heat transfer (CHT) model to resolve spatially varying surface temperature distributions, and a multi-phase model to study cavitation, oil splashing, and other free surface flows of interest. 

CONVERGE has been validated on numerous types of compressors and pumps1-10, and we will discuss two common applications below. 

Scroll Compressor

Scroll compressors are often used in air conditioning systems, and the major design goals for these machines today are reducing noise and improving efficiency. Scroll compressors consist of a stationary scroll and an orbiting scroll, which create a complex system that can be challenging to model. Some codes use a moving mesh to simulate moving boundaries, but this can introduce diffusive error that lowers the accuracy of your results. CONVERGE automatically generates a stationary mesh at each time-step to accommodate moving boundaries, which provides high numerical accuracy. In addition, CONVERGE employs a unique Cartesian cut-cell approach to perfectly represent your compressor geometry, no matter how complex. 

In this study1, CONVERGE was used to simulate a scroll compressor with a deforming reed valve. An FSI model was used to capture the motion of the discharge reed valve. Figure 1 shows the CFD-predicted mass flow rate through the scroll compressor compared to experimental values. As you can see, there is good agreement between the simulation and experiment. 

This method is particularly useful for the optimization phase of design, as parametric changes to the geometry can be easily incorporated. In addition, Adaptive Mesh Refinement (AMR) allows you to accurately capture the physical phenomena of interest while maintaining a reasonable computational expense.

Figure 1: Top: Representative cut-plane of a scroll compressor simulation with the mesh overlaid, colored by velocity. Bottom: Experimental (black square and triangles) and CONVERGE simulation (pink circles) results1 for mass flow rate.

Screw Compressor

Next, we will look at a twin screw compressor. These compressors have two helical screws that rotate in opposite directions, and are frequently used in industrial, manufacturing, and refrigeration applications. A common challenge for designing screw compressors—and many other pumps and compressors—is the tight clearances between parts. Inevitably, there will be some leakage flow between chambers, which will affect the device’s performance.

CONVERGE offers several methods for capturing the fluid behavior in these small gaps. Using local mesh embedding and AMR, you can directly resolve the gaps. This method is highly accurate, but it can come with a high computational price tag. An alternative approach is to use one of CONVERGE’s gap models to account for the leakage flows without fully resolving the gaps. This method balances accuracy and time costs, so you can get the results you need when you need them.

Another factor that must be taken into account when designing screw compressors is thermal expansion. Heat transfer between the fluid and the solid walls means the clearances will vary down the length of the rotors. CONVERGE’s CHT model can capture the heat transfer between the solid and the fluid to account for this phenomenon.

This study2 of a dry twin screw compressor employs a gap model to account for leakage flows, CHT modeling to capture heat transfer, and AMR to resolve large-scale flow structures. Mass flow rate, power, and discharge temperature were predicted with CONVERGE and compared to experimentally measured values. This study also investigated the effects of the base grid size on the accuracy of the results. In Figure 2, you can see there is good agreement between the experimental and simulated data, particularly for the most refined grid. The method used in this study provides accurate results in a turn-around time that is practical for engineering applications.

Figure 2: Top: Representative cut-plane of a dry twin screw compressor simulation with the mesh overlaid (colored by velocity). Bottom: Mass flow rate, power, and discharge temperature results2 of the experiment (black squares) and the CONVERGE simulations (colored circles).


The benefits CONVERGE offers for designing pumps and compressors directly translate to a tangible competitive advantage. CFD benefits your business by reducing costs and enabling you to bring your product to market faster, and CONVERGE features tools to help you optimize your designs and produce high-quality products for your customers. To find out how CONVERGE can benefit you, contact us today!


[1] Rowinski, D., Pham, H.-D., and Brandt, T., “Modeling a Scroll Compressor Using a Cartesian Cut-Cell Based CFD Methodology with Automatic Adaptive Meshing,” 24th International Compressor Engineering Conference at Purdue, 1252, West Lafayette, IN, United States, Jul 9–12, 2018.

[2] Rowinski, D., Li, Y., and Bansal, K., “Investigations of Automatic Meshing in Modeling a Dry Twin Screw Compressor,” 24th International Compressor Engineering Conference at Purdue, 1528, West Lafayette, IN, United States, Jul 9–12, 2018.

[3] Rowinski, D., Sadique, J., Oliveira, S., and Real, M., “Modeling a Reciprocating Compressor Using a Two-Way Coupled Fluid and Solid Solver with Automatic Grid Generation and Adaptive Mesh Refinement,” 24th International Compressor Engineering Conference at Purdue, 1587, West Lafayette, IN, United States, Jul 9–12, 2018.

[4] Rowinski, D.H., Nikolov, A., and Brümmer, A., “Modeling a Dry Running Twin-Screw Expander using a Coupled Thermal-Fluid Solver with Automatic Mesh Generation,” 10th International Conference on Screw Machines, Dortmund, Germany, Sep 18–19, 2018.

[5] da Silva, L.R., Dutra, T., Deschamps, C.J., and Rodrigues, T.T., “A New Modeling Strategy to Simulation the Compression Cycle of Reciprocating Compressors,” IIR Conference on Compressors, 0226, Bratislava, Slovakia, Sep 6–8, 2017. DOI: 10.18462/iir.compr.2017.0226

[6] Willie, J., “Analytical and Numerical Prediction of the Flow and Performance in a Claw Vacuum Pump,” 10th International Conference on Screw Machines, Dortmund, Germany, Sep 18–19, 2018. DOI: 10.1088/1757-899X/425/1/012026

[7] Jhun, C., Siedlecki, C., Xu, L., Lukic, B., Newswanger, R., Yeager, E., Reibson, J., Cysyk, J., Weiss, W., and Rosenberg, G., “Stress and Exposure Time on Von Willebrand Factor Degradation,” Artificial Organs, 2018. DOI: 10.1111/aor.13323

[8] Rowinski, D.H., “New Applications in Multi-Phase Flow Modeling With CONVERGE: Gerotor Pumps, Fuel Tank Sloshing, and Gear Churning,” 2018 CONVERGE User Conference–Europe, Bologna, Italy, Mar 19–23, 2018.

[9] Willie, J., “Simulation and Optimization of Flow Inside Claw Vacuum Pumps,” 2018 CONVERGE User Conference–Europe, Bologna, Italy, Mar 19–23, 2018.

[10] Scheib, C.M., Newswanger, R.K., Cysyk, J.P., Reibson, J.D., Lukic, B., Doxtater, B., Yeager, E., Leibich, P., Bletcher, K., Siedlecki, C.A., Weiss, W.J., Rosenberg, G., and Jhun, C., “LVAD Redesign: Pump Variation for Minimizing Thrombus Susceptibility Potential,” ASAIO 65th Annual Conference, San Francisco, CA, United States, Jun 26–29, 2019.

Numerical Simulations using FLOW-3D top

► DevOps Engineer
    9 Jul, 2021

Come work in one of the best small cities in the US1 for one of the best companies in New Mexico2! Flow Science is a growing tech company with a long, reputable history of more than 40 years and a trusted brand in the engineering software domain. We are looking for a talented DevOps Engineer to help build and deploy our software to engineers, designers, and scientists at top manufacturers and institutions throughout the world.

Principal responsibilities and key requirements

We’re looking for a creative and motivated individual to collaborate with our development, testing, and IT teams to establish, streamline, and maintain the CI/CD infrastructure for our growing suite of FLOW-3D products. Strong candidates for this challenging and dynamic role are passionate about identifying opportunities for automation, following clearly documented APIs, ensuring consistent code style, unit testing, and correcting compile-time errors and warnings. The following skills are expected:

  • BS or higher degree in Computer Science, Computer Engineering, or related fields
  • 5+ years of relevant experience
  • Expertise with CI/CD systems
  • Expertise in build environments and tools utilizing CMake for Windows and Linux systems
  • Expertise in version control systems (Git/SVN)
  • Expertise in scripting and Python
  • Expertise in programming and debugging in C/C++ and FORTRAN
  • Experience with integrating third-party libraries or open source projects into large existing codebases
  • Experience with using Github or Gitlab for code reviews, documentation, and release management
  • Familiarity with Agile practices and DevOps
  • Excellent communication and interpersonal skills to work seamlessly within and across our diverse, multicultural teams
  • Excellent organizational skills, common sense, and an unending desire to learn

Preferred skills and experience

Exceptional DevOps Engineers will have:

  • Knowledge of C/Fortran and C/Python programming interoperability
  • Experience with writing and maintaining software and build documentation using Doxygen and Sphinx
  • Experience with Squish GUI testing framework


Flow Science offers an exceptional benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit-sharing plans with generous employer matching, and an incentive compensation plan that offers a year-end bonus opportunity up to 30% of base salary. Learn more about careers at Flow Science >

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► Reconstruction and extension of the Méricourt locks: Study of hydraulic operations
    2 Jul, 2021
FLOW-3D HYDRO Case Studies

Reconstruction and extension of the Méricourt locks: Study of hydraulic operations

This article was contributed by Gwenaël CHEVALLET, Chloé CHENE, Antoine HALBARDIER, and Franck RANGOGNIO, BRL ingenierie.

With more than 60 years of experience in large scale hydraulic infrastructures, BRL Ingénierie is a leading company in the navigation sector both in France and abroad.

Modeling Premise

The design of a lock and the associated lock management operations are complex problems that are typically addressed using:

  • Scaled physical models that can be laborious to implement.
  • Issue-specific empirical methods often coupled with calculation approaches.
  • 1D transient hydraulic studies to verify compliance with average velocity, water line slopes and locking times criteria.
  • 3D steady-state hydraulic models for the filling and emptying of valve elements.
  • Charts or simplified calculation approaches for mooring problems.
  • Feedback from operators.

The BRL ingénierie teams has implemented a methodology to address these modeling needs in a combined way using transient 3D CFD hydraulic analysis with the CFD software FLOW-3D.


The renovation and extension project of the Méricourt locks on the Seine aims to rebuild the existing locks, as they present visible structural disorder, particularly through the deformation of the lock walls. The site currently holds two parallel functional locks, one with a 160m capacity lock chamber, and the other with a 185m capacity lock chamber. As part of the scope of the project, the owner (Voies Navigables de France, VNF), aimed to, among other objectives:

Mericourt locks aerial view
Figure 1. Aerial view of Méricourt locks. On far left is a decommissioned lock; next to it are Locks no. 1 (middle) and 2 (right).
  • Extend the 160m lock to standardize the capacity of the locks, thus securing the navigation axis. This extension will lead to an increase in the filling and emptying volumes.
  • Install floating bollards to replace the existing fixed bollards.
  • Replace the downstream valve parts (2 aqueducts replaced by 18 valves).

These changes come with a strong requirement from the owner to maintain locking times close to the current 15min locking time, and at the same time comply with maximum force limits on the bollards, 250 to 300kN per bollard (25 to 30 tons).

The model presented here is for lock no. 1 (L=185m, W=17m) and includes:

  • A 3D CAD lock geometry
  • A FLOW-3D transient 3D hydraulic model capable of simulating all the complexities of the flow (stationary flows, eddies, air entrainment, cavitation, water hammer, etc.)
  • Prescribed and coupled moving objects modeling in FLOW-3D
    • Coupled to the fluid:
      • A Grand Rhénan type boat (ECMT class Va, L=110 m, w =11,4 m, capacity 1500 to 3000 tons)
Grand Rhenan type boat geometry
      • Floating bollards
    • Prescribed motions of upstream aqueduct gates or downstream valves in accordance management instructions.
  • A mooring module linking the vessel to the bollards
  • A collision module between the boat and the lock walls
FLOW-3D model of lock no.1
Figure 2. FLOW-3D model of lock no.1 in project situation - Grand Rhénan
Lock no. 1 project simulation


Once the boundary conditions were set (forebay and tailbay water levels) and the characteristics of the vessel and the mooring plan were chosen, the implemented model allowed for detailed evaluation of the following conditions:

  • Duration of a filling or emptying cycle for given management instructions.
  • 3D hydraulic conditions of the flows in the airlock (mainly velocity distribution).
  • Forces transmitted in the bollards during a filling or emptying cycle.
Lock no. 1 project simulation
Figure 5. Simulation of filling of lock n°1 – project situation (2 mooring lines) - Grand Rhénan

Based on simulation results, it was then possible to optimize the filling or emptying management instructions in order to:

  • Ensure compliance with the maximum forces in the bollards
  • Minimize the duration of the locking times (about 10 to 11 minutes) while respecting the material constraints of the valve components (range of operating speeds of the oil circuit pump in particular).
Optimized law of filling by aqueducts
Figure 7: Optimized law of filling by aqueducts
Optimized law of filling by aqueducts
Optimized law of filling by aqueducts


FLOW-3D made it possible to evaluate the design and optimization strategies related to locking (emptying/filling time, hydraulic loads, forces on the boat and forces on floating bollards, etc.) with a single tool. It is in fact a real step forward for the practice. Indeed, this methodology is applicable to all types of locks and all types of vessels.

The results of the modeling carried out so far are particularly satisfactory and are aligned with all order of magnitude calculations using charts, simplified methods or based on the operator’s feedback (emptying/filling laws, flow coefficients of the valves, maximum forces on the bollards, etc.).

Concerning the forces on the bollards (essential dimensioning parameters), the results are obviously tied to the filling schedules, the free length of the mooring lines and their rigidity, as well as to the general mooring plan (number and position of mooring lines), detailed parameters which are all included in the FLOW-3D model.

► EREDOS PROJECT: Numerical modeling of flows in covered streams
    2 Jul, 2021
FLOW-3D HYDRO Case Studies

EREDOS PROJECT: Numerical Modeling of Flows in Covered Streams

European fund of regional development

This article was contributed by Gwenaël CHEVALLET, Marie-Christine GERMAIN, and Sarah LASNE, BRL ingenierie

With more than 60 years of experience in large-scale hydraulic infrastructure, BRL ingenierie is a key player in the field of water engineering both in France and abroad.

The mining industry has led to the construction of many underground structures to manage the exploited territories and to accompany their economic and industrial development. This activity has created voids and has been accompanied by the creation of slag heaps and the filling of valley bottoms with different materials, mainly waste rock. These fillings were preceded by masonry work above the watercourses to maintain the flow through the valley. They were later accompanied by other deposits of materials resulting from the creation of dwellings or infrastructure.

Since the decline of mining activity, these constructions have not received additional maintenance. The November 2012 collapse of a covered brook in Robiac-Rochessadoule (France, Gard) showed that it is important to pay renewed attention to these constructions that have been forgotten over time.

Collapse of covered stream
Figure 1. Collapse of the covered stream in Robiac-Rochessadoule in November 2012

The EREDOS research project, in which BRL ingenierie participates, has the following objectives:

  • To develop tools and methods for carrying out diagnostic studies (monitoring system, mechanical and hydraulic behavior, etc.) of these covered streams and the structures that cross them.
  • To define risk indicators and intervention protocols.

Within this research framework, BRLi tested the use of 3D CFD to address concerns related to the issues of covered streams. The CFD model was built using FLOW-3D software with the input of a detailed 3D scan of the covered stream (RICHER firm – Geometer-Expert).

3D Scan of the Tunnel

The Valette stream is located in the commune of Robiac-Rochessadoule, 20 km north of Alès in France. The masonry structure has a total length of approximately 250 m. The photos below are presented looking downstream and are taken from the film made with the help of the 3D scanner. The collection of high-resolution geometry data allowed for creating a highly accurate 3D CAD model to be used as input for the FLOW-3D simulations. 

Hydraulic Model

The main task was a parametric study based on a hydraulic 3D CFD model built with FLOW-3D software of the entire underground stream. The main parameters that were tested were:

  • Upstream and downstream boundary conditions
    • Upstream: imposition of flows or water levels
    • Downstream: free outflow or imposed water levels
  • Absolute roughness of the tunnels
  • Mesh size
  • Turbulence models (K-epsilon, K-omega, RNG)
  • Consideration of flow aeration phenomena (single fluid [water] + specific air model or two-fluid [water+air] model)
  • Numerical options (1st order, 2nd order…)
  • Law of walls

In total, more than 40 3D CFD simulations were carried out.

Hydraulic Results

Despite tests varying many parameters (sometimes in very wide ranges), the maximum calculated flows that can pass through the tunnel remain robustly confined to a range of 100-125 m3/s. The simulation results, for this specific premise and these spatial scales, appear not to be particularly sensitive to the parameter space variations explored by the modeler.

The maximum physical flow that can pass through the tunnel is estimated to be about 100 m³/s. By maximum physical flow, we mean a flow that generates an upstream level of about 8 to 9 m (model reference), compatible with the natural topography in the vicinity of the upstream entrances.

The upstream rating curve of the tunnel resulting from this approach was then inserted. In the flow range of 60-120 m³/s, a culvert law applied to the first tunnel with a flow coefficient of 0.6 aligns well with the rating curve obtained using FLOW-3D.

Hydraulic Stresses of Structures

This type of 3D CFD model offers the possibility of extracting from the results of the simulations many parameters related to the evaluation of hydraulic stresses on the structure: dynamic pressure, shear stresses, dissipated energy, etc.

These outputs make it possible to diagnose the current state of the structure stability and to design for a possible reinforcement. They constitute input data for the structural analysis of the structures.

In the flow regimes of concern, an alternation of pressurized and free surface flow conditions, it should be noted that it is possible to observe beating phenomena at the origin of depressions on the walls that can prove to be prejudicial.

The figures below illustrate the type of rendering that can reveal pressure solicitations on the hydraulic structures.

Energy dissipation hydraulic structure
Figure 12. Postprocessing of the results on the structures (dissipated energy)
Postprocessing results hydraulic structures
Figure 11. Postprocessing of the results on the structures (pressure)


High fidelity 3D scan data can be used as the foundation for sophisticated 3D CFD modeling of complex flow conditions using advanced modeling tools such as FLOW-3D. Discharge curves and detailed representations of the flow, with the resulting transient pressure conditions on the surrounding infrastructure are all part of the deliverables that naturally result from this kind of study.

► FLOW-3D (x): Connect, Automate, Optimize
  14 Jun, 2021

FLOW-3D (x): Connect, Automate, Optimize

In this blog, we’ll look at FLOW-3D (x) – a completely new product from Flow Science that will change the way you work with FLOW-3D products, make you more productive, improve your designs beyond what you thought was possible, and give you a deeper insight into your simulations than ever before. First, we’ll talk about how users typically incorporate simulation into their workflow and where bottlenecks often occur. Then we’ll talk about how FLOW-3D (x) removes these bottlenecks by automating the entire user workflow. And then we’ll look at some actual projects that were completed with FLOW-3D (x). FLOW-3D simulations provide users with the ability to predict how their designs will behave without building expensive prototypes. Many combinations of parameters and geometry can be simulated to find the optimal design. However, simulating many designs to achieve the optimal behavior can be time and cost prohibitive when done manually. And there is no guarantee that the solution achieved is the best since there is usually no simple way to know the relationship between parameter changes and design performance since we’re choosing the parameters’ values blindly.

Running Parametric Geometry Designs

A common scenario is to have a parametric geometry designed in CAD. To understand the effect of geometry changes on the performance of the design, the user has to modify the geometry in CAD, export the geometry to STL, run the simulation, then postprocess the results. The number of design alternatives that can be investigated in this way is very limited due to the time required. Additionally, it is often useful to examine the behavior of a design through a range of fluid properties. If we wanted to investigate the results of viscosity varying over a range of values, we’d have to modify the input files for each value we’d like to simulate, execute each, and then postprocess. This way of working can quickly become prohibitively time consuming. The solution is to use FLOW-3D (x) to automate this iterative testing process.

Optimization Workflow

The first step in creating an optimization workflow in FLOW-3D (x) is to define the goal of the optimization. The goal may be to minimize or maximize a simulation output (e.g., air entrainment) or some statistical value such as the average flow rate that is computed using the Statistics plugin. Next, the parameter space to be examined is specified along with the possible range these parameters can assume in the optimization. There is no limit to the number of parameters that can be studied. Finally, a Budget is defined which tells FLOW-3D (x) how many simulations it can execute in its search for an optimal solution. The larger the simulation budget, the closer the solution will be to the actual optimal solution.

Connecting & Automating

A wide range of plugins are available which allow almost any workflow to be replicated and automated:

  • SolidWorks
  • Catia
  • NX
  • PTC Creo
  • Rhino Grasshopper
  • SpaceClaim
  • Autodesk Inventor
  • Abaqus
  • Matlab
  • Math/statistics
  • Excel

The STL Morpher CAD plugin allows us to automate the typically tedious task of opening our CAD geometry, modifying it, and then exporting it to the simulation directory in a FLOW-3D (x) workflow. For example, let’s say we had a parameterized design of a pipe network in SolidWorks. We’d like to study the effect of a change in a particular pipe diameter on the flow rate through the pipe. To automate this, we would drag a SolidWorks plugin into our workflow in FLOW-3D (x), open the SolidWorks part file in the SolidWorks plugin, and then select the diameter as a variable we’d like to control in our optimization. Then we’d specify the allowable range of this diameter. FLOW-3D (x) will run a series of simulations with geometries of various diameters generated though the SolidWorks plugin. No interaction between the user and the software is necessary. We could have FLOW-3D (x) identify the optimal diameter which minimizes or maximizes some flow quantity such as turbulent kinetic energy, for example.

Below is an example of a workflow created in FLOW-3D (x) that uses the STL Morpher plugin to modify the STL geometry of a manifold to achieve a balanced flow through each distribution pipe of the manifold. The manifold is shown here.

With the drag-and-drop feature in the FLOW-3D (x) interface, this type of workflow can be set up and running in minutes.

Each time a workflow cycle is completed, the new data is added to the response surface, further refining the relationship between the inputs and the outputs. Based on the computed response surface, a new set of inputs is created by FLOW-3D (x) and another cycle of the workflow is executed. This cycle repeats until the optimization goal is achieved or the user-specified budget is reached.

A natural output from this process is a sensitivity plot which indicates how strongly the simulation results depend on the inputs. For example, we’d typically be interested in knowing whether a particular simulation parameter is worth optimizing. If its effect on the results is minimal, we know that we need to look at some other parameter in the simulation to improve our design. The sensitivity graphs below show the standard deviation of the flow rates through the manifold outlets on the vertical axis and the variations in the outlet diameter. The interaction is strong for all three, indicating they all contribute significantly to the results and are indeed what we should be considering.

The sensitivity graphs shown here show the standard deviation of the flow rates through the manifold outlets on the vertical axis and the variations in the outlet diameter. The interaction is strong for all three, indicating they all contribute significantly to the results and are indeed what we should be considering.

Workflow Automation

Aside from optimization and parameter sensitivity studies, FLOW-3D (x) can also be used for workflow automation without performing any optimization. For example, if we simply wanted to run a series of simulations with a specified set of inputs and then create a set of post-processed results, we could do that. In that case, we would define a CSV file with the inputs we’d like to simulate (e.g., viscosity, turbulence model selection, mesh size, inlet velocity) and execute these simulations automatically.

As you can see, using FLOW-3D (x) alongside any FLOW-3D product makes you more productive, provides more in-depth clarity about your design, and allows you to get the most value possible from your CFD workflow.

John Ditter

John Ditter

Principal CFD Engineer at Flow Science

► Flow Science Launches FLOW-3D (x)
  18 May, 2021

Flow Science Launches FLOW-3D (x)

Flow Science launches a new optimization and workflow automation product that will change the way its customers use CFD software.

Santa Fe, NM, May 18, 2021 – Flow Science, Inc. has launched FLOW-3D (x), an optimization and workflow automation software, the latest addition to the FLOW-3D product family. The FLOW-3D (x) interface allows customers to build automation and optimization workflows graphically and intuitively to dynamically connect parametric CAD features to the CFD solution and provides the framework for effective batch simulations, postprocessing and data extraction. From optimizing geometric parts for improved weight-to-strength ratios to evaluating sensitivities in the design parameter space, FLOW-3D (x) offers a powerful platform for users to arrive at the best design solution, achieving greater certainty while reducing modeling and analysis time.

FLOW-3D (x) is a game changer for our customers. It’s not just about saving time and money; it’s about discovery through optimization and workflow automation. It offers a really elegant way to solve the toughest CFD problems. Once you start using FLOW-3D (x), you won’t ever want to go back to the old way of doing things, said Dr. Amir Isfahani, CEO of Flow Science.

FLOW-3D (x) puts power and efficiency into the hands of its users through its core functionality and connectivity to explore solutions using optimization, workflow automation, distributed solving, parameter sensitivity studies, simulation calibration, CAD and Microsoft Excel plugins, and Python interoperability.

A live product webinar with expert technical panelists will be held on Wednesday, June 16 at 1:00 pm ET. 

About Flow Science

Flow Science, Inc. is a privately-held software company specializing in transient, free-surface CFD flow modeling software for industrial and scientific applications worldwide. Flow Science has distributors for FLOW-3D sales and support in nations throughout the Americas, Europe, and Asia. Flow Science is located in Santa Fe, New Mexico.

Media Contact

Flow Science, Inc.
683 Harkle Rd.
Santa Fe, NM 87505
Attn: Amanda Ruggles
+1 505-982-0088

► CFD Engineer
  15 May, 2021

CFD Engineer

Flow Science is not offering H1B sponsorship for this position.

Come work in one of the best small cities in the US1 for one of the best companies in New Mexico2! Flow Science is growing tech company with deep roots looking for outstanding engineers with an interest or expertise in the aerospace, automotive, additive manufacturing, and consumer products industries.

Principal responsibilities and key requirements

CFD engineers work at the intersection of classical physics, numerical methods, and computer science. We apply our expertise in these fields to help clients solve complex real-world engineering problems, teach applied CFD, and guide our development teams to create new models that grow our capabilities and application areas. This challenging and dynamic role requires the following skills to be successful:

  • An engineering degree from an ABET or equivalently accredited university and some work experience
    • MS degree (mechanical, aerospace, or chemical engineering preferred) and engineering internship experience OR
    • BS degree (mechanical, aerospace, or chemical engineering preferred) and 2+ years of engineering work experience
  • Strong understanding of engineering fundamentals, particularly fluid mechanics, heat transfer, and solid mechanics
  • Excellent oral communication, technical writing, and interpersonal skills
  • Ability to comfortably navigate a diverse, multicultural environment
  • Excellent organizational skills
  • Common sense and an unending desire to learn

Preferred skills and experience

Exceptional CFD engineers usually draw heavily on the following skills and experience:

  • 2+ years of relevant industry or academic experience (e.g., additive manufacturing, propellant management design, slosh analysis, consumer product processing, coating, analysis of complex fluids, etc.)
  • Experience with CFD, FEA, or other numerical analysis
  • Experience with experimental setups and data analysis
  • Experience with 3D CAD
  • Programming experience (FORTRAN and Python)
  • Demonstrated initiative in work projects
  • EIT certification


Flow Science offers an exceptional benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit-sharing plans with generous employer matching, and an incentive compensation plan that offers a year-end bonus opportunity up to 30% of base salary. Learn more about careers at Flow Science >

1 HuffPost listed Santa Fe, NM as one of the top 5 small cities in the US

2 Flow Science has been named one of the Best Places to Work in New Mexico by Albuquerque Business First.

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► News Article: Graphcore leverages multiple Mentor technologies for its massive, second-generation AI platform
  10 Nov, 2020

Graphcore has used a range of technologies from Mentor, a Siemens business, to successfully design and verify its latest M2000 platform based on the Graphcore Colossus™ GC200 Intelligence Processing Unit (IPU) processor.

► Technology Overview: Simcenter FLOEFD 2020.1 Package Creator Overview
  20 Jul, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, Simcenter FLOEFD helps users create thermal models of electronics packages easily and quickly. Watch this short video to learn how.

► Technology Overview: Simcenter FLOEFD 2020.1 Electrical Element Overview
  20 Jul, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, Simcenter FLOEFD allows users to add a component into a direct current (DC) electro-thermal calculation by the given component’s electrical resistance. The corresponding Joule heat is calculated and applied to the body as a heat source. Watch this short video to learn how.

► Technology Overview: Simcenter FLOEFD 2020.1 Battery Model Extraction Overview
  17 Jun, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, the software features a new battery model extraction capability that can be used to extract the Equivalent Circuit Model (ECM) input parameters from experimental data. This enables you to get to the required input parameters faster and easier. Watch this short video to learn how.

► Technology Overview: Simcenter FLOEFD 2020.1 BCI-ROM and Thermal Netlist Overview
  17 Jun, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, Simcenter FLOEFD allows users to create a compact Reduced Order Model (ROM) that solves at a faster rate, while still maintaining a high level of accuracy. Watch this short video to learn how.

► On-demand Web Seminar: Avoiding Aerospace Electronics Failures, thermal testing and simulation of high-power semiconductor components
  27 May, 2020

High semiconductor temperatures may lead to component degradation and ultimately failure. Proper semiconductor thermal management is key for design safety, reliability and mission critical applications.

Tecplot Blog top

► Telling a Story
  30 Jun, 2021

Every scientist or engineer is, at some point, going to have to present their findings based on their simulations or experiments. In earlier posts, we discussed several tips and tricks for making your plots and presentations as effective as possible by tailoring for your audience, using consistent conventions, and avoiding common pitfalls, but what about the flow or narrative of your presentation? How can you use the work that you performed to tell a story about the problem, your analysis, and your conclusions? Storytelling makes us think of creativity, and the skills to become a good storyteller seem to us to be mostly innate, abstract things that are difficult to learn or define, but that is not always the case. Let us talk about some basic steps you can take to tell a story in your technical presentations – keeping in mind that the exact parts or sequence may vary depending on your situation.

1 – Introduce the Feature, Part, Subsystem, or System in Question

First, especially if you are presenting simulation results, is to make sure the audience understands the context of your model. What feature, part, or subsystem did you analyze and how does it fit into the bigger picture? Valuable context might help the audience understand things like the location, geometry, or function. An easy example of this might be if you are performing a structural analysis of a turbine vane – to provide context you could show a cutaway rendering of the relevant turbine stage with the analyzed component highlighted. Similarly, if you were performing trade studies on the filet radius of the same turbine vane – the contextualizing image may simply be of the entire vane with a close-up or highlight of the subject feature. A good rule of thumb is to show or describe how the relevant zone fits into the next level up – but as we have discussed in earlier posts you may wish to add context depending on your audience.

Figure 1 gives context for an internal airflow analysis of a sedan by showing a cutaway of the exterior and highlighted internals. Image courtesy of FieldView CFD.

2 – Explain the Problem, Goal, or Question

Next – it is critical to explain the purpose of your experiment or analysis. What were you seeking to discover, prove, or quantify and why? Your problem statement may benefit from an additional visual aide, or perhaps the context you provided earlier will suffice. If your analysis is of a component that has had reliability issues in the field, you might first explain the nature of the reliability issues and show a photo of the failed component, and then state that the goal of your analysis is to identify the root cause of the problem and design a remedy. If you are presenting a set of wind tunnel data on a subscale aircraft model your goal may be to validate CFD results used in the design process. If you’re a climate scientist – you might be trying to answer the question – what happens if we don’t reduce our GHG emissions by 20XX? In any case, explaining the ‘why’ (also commonly referred to as the ‘motivation’) is an essential element in telling a story.

3 – Summarize the Approach to Generate, Measure, and Reduce your Data

The stage has been set, the characters have been introduced, and now it’s time to let the plot unfold. This next section may be a couple, or a couple dozen, pages long depending on how complex the work was. You’ll want to explain the pre-processing steps taken, the assumptions you’ve made, and the calculations that were used. A CFD engineer, for example, might explain the source of the geometry, show the computational mesh that was created, and outline the boundary-conditions, flux-functions, limiters, and turbulence models chosen. Depending on the purpose of the presentation, they may also wish to explain the computational environment that was used. You still haven’t presented any results or answered the opening question(s) – but you’re building confidence that the results you are about to present are meaningful.

4 – Present the Results, Highlighting Key Trends & Conclusions

The climactic ending is what you would expect – presenting the results. All too often one is tempted to skip right to the goods, but as we’ve discussed above, the work you’ve done to get to this point is critical to making your story clear & believable. The way you present your results matters, and the results you choose to present (or to save as ‘back-up’ slides) matter as well. We won’t reiterate the specifics here – but this section is your chance to showcase all the hard work you’ve done, so make it look good and make it relevant.

So there you have it – a beginning, middle, and end. And here you thought that the elements of a story were something you could forget about after your senior literature courses in high school… True, a traditional story has 5 elements (setting, characters, plot, conflict, & resolution), but we didn’t think it was necessary to stretch the analogy that far 😉. Regardless of how you count, telling a story about your project is a worthwhile habit to cultivate that will make the impact of your work more effective.

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The post Telling a Story appeared first on Tecplot.

► Back to Basics: TecIO
  23 Jun, 2021

TecIO: What It Is and When to Use It


Download TecIO from MyTecplot

TecIO source code is available in our customer portal, MyTecplot.

What is TecIO and why should I use it?

What is TecIO?

  • TecIO Library reads *.szplt file format and writes Tecplot binary *.plt and *.szplt file formats. 
  • Provides a consistent API to protect you from internal format changes.
  • Available in C & Fortran.
  • Can be used via Python.
  • Can be used via MATLAB.

Who should use TecIO?

  • Anyone who wants a fast reliable way to store data in Tecplot file format.
  • CFD code developers.
  • PIV users & hardware suppliers.
  • Users of SU2 (source code included in the SU2 repository).
  • Users of FUN3D (requires downloading TecIO from MyTecplot).
    See the blog post, Tecplot SZL File Output from FUN3D.

How do I get TecIO? Is it free?

Precompiled libraries are included in the Tecplot 360 installation. Source code is available in our customer portal MyTecplot.

Do I need to compile TecIO?

  • If you are using MPI on Linux/Mac you’ll almost certainly need compile TecIO.
  • If you need a static library (shared libraries are included in the Tecplot 360 install), you’ll need to compile TecIO.
  • For build instructions see ‘readme.txt’ in the source distribution.

Which format should I use – PLT or SZL?

SZL File Format

  • TecIO-MPI can only write SZL files.
  • You can use SZL server.
  • SZL loads into Tecplot 360 faster.
  • Smaller files when using unstructured grids.
  • Can use TecIO to read.
  • Can use TecIO-Insitu (research).


  • Polyhedral zone type.
  • Published format (can write your own reader/writer).

Where can I find documentation and example code?

Data Format Guide (PDF)

  • Chapter 3: Binary Data
  • Chapter 5: Reading SZL Data Files
  • Chapter 6: Writing SZL Data Files

Example Code

  • In the Tecplot 360 installation
    <install dir>/utils/tecio/examples
  • In the TecIO source distribution in the ‘examples’ directory

Example Code Walkthrough – Old and New APIs


  • Supports PLT & SZL file formats
  • Data types defined for the entire file (limited to single and double precision)
  • Long parameter lists
  • Difficult to support writing to multiple files simultaneously

New API (tecFileWriterOpen, tecZoneCreateFE, etc.)

  • Supports SZL files only (no-polyhedral cell type support)
  • Data types may be specified per variable (and allows integer types)
  • Shorter parameter lists
  • Uses file handles to more easily support writing to multiple files

IJ-Ordered Zone – Old API

res = TECINI142("IJ Ordered Zone", "X Y P", "outfile.plt", ".", &FileFormat, &FileType, &Debug, &IsDouble);
res = TECZNE142("Ordered Zone", &ZoneType, &IMax, &JMax, &KMax, 0, 0, 0, &SolutionTime, &StrandID, 0, &IsBlock, 0, 0, 0, 0, 0, NULL, NULL, NULL, 0);
// Create X, Y, P array data...
III = IMax*JMax
res = TECDAT142(III,X,0);
res = TECDAT142(III,Y,0);
res = TECDAT142(III,P,0);
res = TECEND142();

IJ-Ordered Zone – New API

void* fileHandle = NULL;
res = tecFileWriterOpen("outfile.szplt", "IJ Ordered Dataset", "X,Y,P", fileFormat, fileType, defaultDataType, NULL, fileHandle);
// Create a IJ-Ordered Zone
int32_t zoneHandle;
int varTypes[3] = {1,1,2}; // single, single, double
res = tecZoneCreateIJK(fileHandle, "IJ Ordered Zone", IMax, JMax, KMax, &varTypes[0], NULL, NULL, NULL, 0, 0, 0, &zoneHandle);
// Create X, Y, P array data...
int64_t numPoints = IMax*JMax;
res = tecZoneVarWriteFloatValues(fileHandle, zoneHandle, xVarNum, 0, numPoints , X);
res = tecZoneVarWriteFloatValues(fileHandle, zoneHandle, yVarNum, 0, numPoints , Y);
res = tecZoneVarWriteDoubleValues(fileHandle, zoneHandle, pVarNum, 0, numPoints , P);
res = tecFileWriterClose(&fileHandle);

TecIO In-Situ Research

Research TecIO code has the ability to export SZL files with only the volume cells that meet a specific criteria (e.g. Temperature < 2100 and Temperature > 1900).  This can result in 93% reduction in file size with no “post-processing” on the solver side.

See the attached white papers for details:

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The post Back to Basics: TecIO appeared first on Tecplot.

► What is TecIO?
  10 Jun, 2021

TecIO Webinar

Upcoming Webinar:
TecIO: What it is and When to Use it

Wednesday, June 23, 2021, 10 AM Pacific Time

Register Now

TecIO is Tecplot’s free software library that enables reading & writing of Tecplot file formats (*.plt, *.szplt). We support two distinct APIs that users of the library can choose from depending on their specific needs. The *.plt format has been around for ages and is a reliable way to read data into Tecplot 360 (and a host of other tools as well).  The *.szplt file format (SZL) is relatively new and boasts some impressive file size improvements as well as massive load time speed ups. If you want to get into the weeds regarding the differences between Tecplot’s file types (including the ASCII option, *.dat) check out this post from Dr. Scott Imlay from 2018.

Who cares about TecIO?

That’s a fair question – after all, software libraries for the reading and writing of data don’t typically have large fan followings (even if they are awesome). For the majority of Tecplot 360 users the TecIO library isn’t likely to be of much interest – so who should be paying attention?

First and foremost, the audience for all things TecIO includes code developers who want to create a smooth path into Tecplot 360 for their users. Perhaps your user base has requested this, or perhaps you’ve realized (very astutely) that proactively adding Tecplot format export options is a great way to improve your user experience. Using TecIO gives you the power to export results quickly and efficiently in a widely used format. Just how widely used? Well, – just check out the list of codes exporting Tecplot formats.

But even if you aren’t a code developer you might be interested in TecIO if you’re using an opensource code that requires TecIO to support Tecplot output. Both Stanford’s SU2 and NASA’s FUN3D can write out Tecplot file formats if properly compiled with the TecIO library. For SU2 the library is baked into the source repo, so just follow the standard steps to compile the solver and you’ll have it. If you’re using FUN3D check out the steps in our blog Tecplot SZL File Output from FUN3D.
MyTecplot Customer Portal

How do I get access to TecIO?

Well, if you have Tecplot 360 installed on your machine – you already have it! Every Tecplot 360 installation comes with a precompiled TecIO library. But if you don’t have access to Tecplot 360 or if you’d simply rather download the source code and compile it yourself you can download the source code through the MyTecplot Portal (request a free login if you don’t have one already).

In summary, TecIO can be a powerful asset to a few specific classes of users. After all, the engineering tool and the post-processing software aren’t much use if you can’t quickly and effectively transfer data from one to the other. TecIO provides a path for your data that is the easiest, fastest, and most capable – ensuring your engineering analysis isn’t limited by your engineering toolchain.

For more general information about TecIO you can visit the TecIO product page on our website.

Register for the upcoming TecIO Webinar

The post What is TecIO? appeared first on Tecplot.

► Back to Basics: PyTecplot
  26 May, 2021
This is the third and final webinar in a 3-part Tecplot 360 Basics series. We’ll show you how to take advantage of the power of the Python scripting language, and gain direct access to your data with PyTecplot. PyTecplot integrates fully with other Python-compatible tools, making Tecplot 360 part of your engineering ecosystem.
  • Automate workflows that use multiple engineering tools, with a single scripting language.
  • Perform detailed calculations on all your CFD data sets.
  • Access CFD data directly for custom analysis.
  • Read any data type.
  • Extract values, figures and animations directly from CFD data sets.
  • Connect Tecplot 360 to other tools and processes.

Download the data and scripts used in this webinar

The post Back to Basics: PyTecplot appeared first on Tecplot.

► 360²: The Flow360 and Tecplot 360 Dynamic Duo
  19 May, 2021

We teamed up with our friends at FlexCompute to showcase their ultra-fast cloud-based Flow360 CFD solver in conjunction with Tecplot 360 post-processing; the results are impressive.

Flow360 and Tecplot 360 have more in common than just a number – they are both excellent CFD tools. Flow360 uses a proprietary CFD solver architecture to leverage cloud-hosted, accelerated hardware resources, enabling blisteringly fast convergence and results. Tecplot 360 brings industry-leading ease of use, performance, and reliability alongside an impressive suite of features for effectively post-processing your CFD results.

Traditionally, large unsteady simulations are highly time-consuming. Engineers are more forgiving for the computing and loading time spent on post-processing because they are negligible compared to flow solver. Flow360 changes this situation by producing solutions in a fraction of the time (by >2 orders of magnitude). With faster flow solutions, those same engineers are now more sensitive to the time spent on post-processing. Fortunately, Tecplot 360 can reliably and efficiently visualize the massive data produced by Flow360 running large unsteady simulations.

Bell XV-15 Rotor Analysis

Flow360 makes short work of complex CFD problems with its unsteady rotating frame capability. The Bell XV-15 is an excellent example, given its similarities to the tilt-rotor vehicles in active development today.

XV-15 in its helicopter mode (left) and airplane mode (right)

Figure 1. XV-15 in its helicopter mode (left). Figure 2. XV-15 in its and airplane mode (right)

The team at FlexCompute performed an analysis of the full-scale XV-15 tiltrotor in both rotorcraft and fixed-wing modes of flight using detached eddy simulation. The movement of the rotor requires a sliding mesh interface. We’ll show some of the highlights of the analysis below, but if you’re hungry for a full briefing, you can view the validation paper here.

When choosing which post-processor to work with for this validation case FlexCompute used two key criteria:

1.   Efficient Handling of Big Data

When performing large unsteady simulations, Flow360 can generate a large volume of data very rapidly. Thus, it is critical to load gigabytes of data efficiently and reliably, then quickly finish processing them before the next batch of data arrives. Tecplot 360 is perfect here. Compared to other visualization tools, there are two significant advantages:

  1. Efficiency: Data loading and processing is fast.
  2. Stability: Software does not crash.

Failure to handle big data fast enough or crashing during processing will break the entire workflow, resulting in a frustrating user experience.

2.   Visualization of Q-Criterion and Tip Vortex

Designing the tip portion of the rotor and propellers is a fundamental reason people resort to high-fidelity CFD simulation. Designers need a fast and intuitive way to visualize the data. They need to see the path of the tip vortices and understand whether flow separation occurs on any part of the blade. Tecplot 360 can compute the isosurface of Q-criterion very efficiently. It accurately illustrates the flow physics around the critical part of the propellers. By contrast, the computing in other software is often much slower, and the resulting isosurface may be less clean.

Problem Set-Up

For CFD nerds in the audience (so, most of you), you’ll be interested in the details of the problem set-up. In the images below, you can see a bit about how the computational domain was structured. The mesh includes 40 million points. The simulation of one revolution of the rotor takes less than one hour to finish. For more information about Flow360 speed, you can find a white paper here.

Dimension of far field background domain

Figure 3. Dimension of far field background domain

Dimensions of near field background domain

Figure 4. Dimensions of near field background domain


Results Visualization

Now for the fun stuff. Tecplot 360 makes rendering beautiful iso-surfaces a breeze. The following three images depict the vortices of the 3-bladed propeller in hover mode, propeller mode, and forward flight mode, respectively.

Hovering XV-15 rotor in its helicopter mode.

Figure 5. Hovering XV-15 rotor in its helicopter mode.

The XV-15 rotor in its airplane mode.

Figure 6. The XV-15 rotor in its airplane mode.

The XV-15 rotor when starting to transition from helicopter mode to airplane mode.

Figure 7. The XV-15 rotor when starting to transition from helicopter mode to airplane mode.


Results Validation

The team at FlexCompute is not simply interested in beautiful visuals, they have also validated that Flow360 is as accurate as it is fast. The following charts show that Flow360 (shown as red squares) produces accurate solutions compared with established experimental and CFD results.

Hovering thrust versus torque of the XV-15 rotor.

Figure 8. Hovering thrust versus torque of the XV-15 rotor. Flow360 achieves impressive agreement with experimental results.

Hovering thrust versus the Figure of Merit of the XV-15 rotor.

Figure 9. Hovering thrust versus the Figure of Merit of the XV-15 rotor. Flow360 achieves impressive agreement with experimental results.

Pressure coefficient on the XV-15 rotor blade at hovering condition.

Figure 10. Pressure coefficient on the XV-15 rotor blade at hovering condition. Flow360 achieves good agreement with established CFD solvers.


So What?

What is the takeaway of this simple case study? There are several. Flow360 is already known for its raw speed; their validation studies prove that it is more than just hype – this solver is ready to compute accurate results on complex and meaningful cases. In addition, we saw some beautiful examples of visualizations generated with Tecplot 360. Flow360 uses Tecplot’s TecIO Library to export Tecplot-formatted files for the smoothest post-processing workflow possible. We asked the team at FlexCompute some questions about their decision to work with Tecplot 360, and here’s what they said:

TP: Why did you choose to write Tecplot format from Flow360, particularly why the SZL format?

FC: Tecplot SZL format (.szplt) seems to handle big data way better. We are a big computing company that generates big data. Managing big data is the key.

TP: How has your experience with TecIO been?

FC: From a developer’s point of view, TecIO is much easier to work with than some other open-source software. We don’t need to do too much low-level data conversion. We can call TecIO API to direct output. It improves our efficiency in our development.

TP: What are the attributes of Tecplot 360 that drove you to choose it over other post-processors?

FC: The code works well; it has all the features we need: good support team, scriptable, and lots of input formats. Tecplot is versatile, efficient, and reliable.


One crucial feature is a fast and accurate interpolation. During the design process, engineers need to compare the flow field of different geometries. The interpolation function is beneficial because the mesh for each design is different. To quantitatively compare the flow field of different designs, engineers must be able to quickly interpolate solutions from different meshes to the same set of grid points.

When we need to examine the field at a particular location, the interface of Tecplot 360 makes the job fast and straightforward. It is accurate and easy to use. Overall, the productivity is much higher and less frustrating.

Learn more about Flow360 here.

Try Tecplot 360 for Free

The post 360²: The Flow360 and Tecplot 360 Dynamic Duo appeared first on Tecplot.

► Postprocessing on AWS – Part 2: Saving Cost and Time
  13 May, 2021

Welcome back for the second post in a series about running Tecplot 360 on Amazon Web Services (AWS) cloud compute resources. In our first post we discussed how to set up your license server, spin up a compute node, and install Tecplot 360 on AWS. The goal of this second article is to highlight different ways to work in the cloud, each satisfying a different objective. At times you may be interested in cost efficiency, and at others in speed. We’ll also talk about how to access AWS in a way that imitates a typical on-premise HPC environment. Lastly, we’ll share a few best practices for working in the cloud to save you time, effort, and anxiety.

Astrid Walle

Astrid Walle, the author of this article, is a mechanical engineer with a PhD in CFD and more than a decade of experience in applied fluid mechanics. She has held several positions in gas turbine aeromechanics, R&D and AI development at Siemens Energy, Vattenfall SE and Rolls Royce. As a recognized industry expert she has recently taken on the challenge of starting her own business, CFD Solutions. As a freelancer, Astrid is following her professional determination to combine numerical simulation and data analytics. 


There are some prerequisites, which are assumed to be in place and won’t be discussed in detail in this article but are covered in the previous blog. These are:

  • You have an AWS Account.
  • You set up a ssh key pair to connect to virtual machines from your local one.
  • You have installed the AWS CLI on your local machine.
  • Your files for postprocessing are located in a S3 bucket.
  • You have a Tecplot 360 network license, and the license server is set-up and running.

This article focuses on the setup with Linux OS, the relevant links for Windows users are provided where possible.

Creating a Visualization Node on AWS from the Command Line

If you have the need for more resources in terms of memory or CPU power for your postprocessing from time to time, then this section is for you. This text guides you through creating an AWS EC2 instance and accessing this remote machine via remote desktop virtualization without the need to log onto the AWS console. In combination with the following section, which describes how to speed the set-up procedure, this enables you to get the resources you need to get your postprocessing done fast and with a great user experience. So, all we need is a terminal with AWS CLI installed.

Considerations When Configuring a Visualization Node

Before creating your visualization node you’ll need to make some decisions, such as:

  • What Amazon Machine Image (AMI, aka OS) to use? Linux systems generally have better file system performance than Windows systems.
  • What Amazon Instance Type to use? This defines hardware resources such as CPU, GPU, and available RAM.
  • How much storage is needed to host the data for post-processing?

Amazon Machine Image (AMI)

In the last blog we used the AWS parallel cluster service, as this comes along with Nice DCV remote desktop virtualization preinstalled. But now there is also the possibility to start a single EC2 instance, which fits your resource needs exactly and uses an Amazon Machine Image (AMI) with Nice DCV instead. These AMIs can be found in the AWS marketplace. Depending on the operating system you prefer or need for your applications, there are various images from different vendors. You can search for your requirements like OS and software and screen the resulting images.

For Tecplot 360, Amazon Linux works well. So, for this tutorial we can search for “Nice DCV” and select the first result with Amazon Linux and no additional costs. In order to get the ID of this AMI, we have to navigate to the subscription (Figure 1) and the configuration (Figure 2). For region eu-west-1 we get this AMI ID ami-0547e6987ff6a09e6. Please note, that the image ID is region specific, so you might need to adapt it.

Figure 1. AMI Subscription

Figure 1. AMI Subscription


Figure 2. AMI Configuration


Instance Type

Besides the image, you also need to decide which instance type you want to use. For that you must consider the requirements in terms of CPU/GPU/memory defined by the application and the amount of data. Here you get an overview of all provided instance types and the recommended applications. For Nice DCV it is recommended to use the g4dn.xlarge instance which has a NVIDIA T4 GPU onboard.

Tecplot 360 also benefits from this graphics acceleration. Now you just need to decide whether you are fine with the smallest instance of the g4dn family, or if need more cores or memory. Here we will select the g4dn.xlarge instance with 4 virtual CPUs and 16 GB of memory.

And just in case you find out that you should have selected another instance type, you can always stop the instance, change the instance type and restart. It might happen that you need to request an increase of your AWS quota to enable the g4dn instances for your account. This usually takes one day. Nevertheless, you can continue with this tutorial for now and use a c4 instance instead. The remote desktop experience won’t be as good as with the g4dn instance, but for testing the procedure it is absolutely sufficient.


The default g4dn.xlarge instance comes with 8 GB root base storage and additional 125 GB NVMe SSD. As we want to use the instance as a remote desktop, with the possibility of turning it on and off whenever needed, we will ignore the NVMe SSD and adapt the root base storage to our needs. Hence you have to estimate how much storage you will need. But don’t worry, in case you miscalculated, you always can increase the root base storage and extend the filesystem afterwards or even easier add an additional block storage to your instance.

Security and Management

When creating an instance on AWS you’ll also need to understand how to ensure proper access and security to the machine, since it will contain potentially proprietary information. This is done through IAM (Identity Access Management) and security groups.

The tagging system is also important to help organize your AWS resources.

IAM Role

Our EC2 instance needs access to S3 storage. For speeding up the setup procedure, we will store some installation files on S3. We also need to fetch the files, which we want to process with Tecplot 360 from S3. Furthermore, we need to grab a Nice DCV license from S3 for our remote desktop visualization. So, you need to create a so-called Identity Access Management role for enabling instances to access S3. You can find instructions on how to do that here. For the ec2 run-instances command we will provide the name of this IAM role with the option –iam-instance-profile.

Security Groups

With security groups you control inbound and outbound traffic for your EC2 instance. Here we will create two security groups. The security group #1 is dedicated for communication with the Tecplot 360 license server, if this server is also running on an AWS EC2 instance. This security group allows all inbound and outbound traffic within this security group. For details about the set-up procedure, please check out the previous blog. The second security group has the following rules:

  • Inbound traffic on Port 22 from your local IP to enable ssh access.
  • Inbound traffic on Port 8443 from your local IP to access the Nice DCV remote desktop session.
  • If your license server is running on an on-premise server, you need to allow traffic from that IP.
  • All outbound traffic to enable web browsing.

When launching the instance later, we need to add these two security groups to our instance. This will allow all traffic listed above and the communication between our instance and the Tecplot license server.

Key Pair

For establishing a ssh connection to your EC2 instance you need to have a key pair for the desired region set up in your account.


To make our instance searchable and for easier accounting of the used resources, we will also add tags.

Using Remote Desktop Visualization Without the AWS Console

In this section we will follow the procedure for launching an EC2 instance with AWS CLI from our local terminal as described here, enriched with some additional options. All the following terminal commands can be replaced by interactions with the AWS console web interface. But the goal of this guide is to minimize the effort by accelerating and automating the setup procedure, so we will just use our local terminal. Please notice that all of the commands below were run in a macOS Linux shell with AWS CLI version 2.1.11. For other OS and other AWS CLI versions please refer to the documentation to check the correct syntax.

Start the Instance

The command we have to type into our local terminal for launching the instance is as follows (you CAN replace and alter all options, but you HAVE to replace the red strings with the values applicable to your account. And please make sure that image_id and region are compatible as described above). Using the tag-specifications and providing a “Name” key has the advantage that this key value will also be displayed in the AWS console, so we can easily identify our machine in the web interface.

aws ec2 run-instances \
        --image-id ami-0547e6987ff6a09e6 \
        --count 1 \
        --instance-type g4dn.xlarge \ 
        --key-name MyKeyPair \
        --security-group-ids SG1 SG2\
        --iam-instance-profile  Name=S3_access_for_EC2 
        --block-device-mappings  'DeviceName=ROOT_DEVICE, Ebs={ VolumeSize=50,VolumeType=gp2,DeleteOnTermination=false}' \ 
        --tag-specifications  'ResourceType=instance,Tags=[{Key=Name,Value=tecplot-dcv}]' \ 
        --region eu-west-1

Now your AWS instance is running. Next, we will log onto that machine via ssh to launch Nice DCV. Afterwards we can access our remote desktop session and do all necessary installations.

Connect to Your New Instance Using ssh

At first you need the public IP address of your instance, with which you can establish a ssh connection. Therefore, we can use the AWS CLI describe-instances command and write the output into a variable.

aws ec2 describe-instances \
        --filters Name=tag:Name, Values=tecplot-dcv \ 
        --region eu-west-1 \ 
        --query 'Reservations[*].Instances[*].[PublicDnsName]' \ 
        --output text

Now you can connect to the machine via ssh with username ec2-user:

ssh ec2-user@"${instance}"

Start NiceDCV and Access Your Instance Via a Web Browser

Now you are logged on your remote machine and for starting the remote desktop session, you just need to execute the following two commands. The first one is for setting a password for our user ec2-user and the second command is for starting the remote desktop session. Afterwards you can check if the session creation was successful and logout.

sudo passwd ec2-user 
dcv create-session session1
dcv list-sessions

You can either use the browser or the native dcv client to connect to your remote session. Type the following command into your terminal, then you can copy and paste the URL into the browser.

echo https://$instance:8443

Use ec2-user as username and the password you just created for login (Figure 3) and now you can access the AWS instance via your remote desktop in the browser (Figure 4). For more information about the DCV functionalities, please check out this guide.

Figure 3: Nice DCV Login Screen

Figure 4: Remote Desktop of our AWS instance

Speeding Up the Setup Procedure for Your Virtual Machine

Now that you can access your instance via ssh and remote desktop, you can run all installations you need. In this tutorial we will do the first-time installations for

  • Firefox browser
  • Gedit editor (or your favorite text editor)
  • Tecplot 360

and while we are doing that, we prepare a script, with which we can automate the installation procedure for our next instance launches.

Installing Firefox

At first, we start with the Firefox browser and using this description, we enter the following commands into the terminal in our remote desktop session:

cd /usr/local 
sudo wget
tar xvjf firefox-83.0.tar.bz2
sudo ln -s /usr/local/firefox/firefox /usr/bin/firefox

Installing Tecplot 360

Now we will install Tecplot 360 and we can do this via the terminal as described in the last blog or we can download the software from MyTecplot and install it manually. After the successful installation we will store Tecplot 360 in a S3 bucket to retrieve it superfast for our next instance launches. Thanks to the IAM role, which we assigned to our instance, we don’t have to do anything else to enable the S3 access. So, for packing the Tecplot 360 files and copying it to your S3 bucket, type:

tar czvf tecplot360ex2020r2.tgz tecplot360ex2020r2 
aws s3 cp tecplot360ex2020r2.tgz s3://yourbucket/tecplot360ex2020r2.tgz

Automating the Installation of Our Tools

To our script for automating the procedure we have to add the lines for copying the zipped archive from S3 to our instance and unpacking it. Also, we add one line for adding the Tecplot 360 executable to the $PATH system variable. By combining these commands for Firefox, Gedit, Tecplot 360 and additional tools you need into one script, saving it on our local machine and calling it when we launch a new instance with the following option, we have automated and accelerated our set up procedure.

aws ec2 run-instances \ 
        --user-data file://post_install_script_tecplot.txt

The complete post install script with all commands described in this article is available on GitHub and the combined command for starting the instance is as follows:

aws ec2 run-instances \ 
        --image-id ami-0547e6987ff6a09e6 \ 
        --count  1 \ 
        --instance-type  g4dn.xlarge \ 
        --key-name  MyKeyPair \ 
        --security-group-ids SG1 SG2 SG3\ 
        --iam-instance-profile Name=S3_access_for_EC2 \ 
        --block-device-mappings  'DeviceName=/dev/xvda,Ebs={VolumeSize=50,VolumeType=gp2,DeleteOnTermination=false}' \ 
        --tag-specifications  'ResourceType=instance,Tags=[{Key=Name,Value=Tecplot-dcv}]' \ 
        --user-data file:///PATH_TO_FILE/post_install_script_tecplot.txt \
        --region eu-west-1

Saving Costs: Stop and Terminate Instances When Not Needed

To avoid unnecessary expenses, you should terminate or at least stop your instances when you don’t need them. Stopping the instance has the advantage that you can restart it anytime and it is in the same state as before, meaning all your installations, data and settings are still there. But you will be charged for the storage, which is needed to keep the snapshot of your instance. So, stopping and starting your instance is recommended when you have to leave your desk, but haven’t finished your project, so you haven’t moved your postprocessed data from the instance to S3 yet. But when you have to pause your work for a longer period of time, then you should consider terminating it and launching a new one at a later point in time. Of course, only after you saved all your data to S3. And of course, these considerations also depend on your budget and the chosen instance type.

Sometimes you have several instances launched and have lost track of which ones are still running. With the following command you can check for running and stopped instances:

aws ec2 describe-instances \
        --filters  Name=instance-state-name,Values=running \ 
        --query  'Reservations[*].Instances[*].[InstanceID, Tags[?Key==`Name`]|[0].Value]' \ 
        --region  eu-west-1 \ 
        --output  text

Now you can call stop and start or terminate commands with the resulting instance ID’s.

aws ec2 stop-instances \ 
        --instance-ids Your-Instance-ID
aws ec2 start-instances \
        --instance-ids Your-Instance-ID
aws ec2 terminate-instances \
        --instance-ids Your-Instance-ID

The start-instances command can be used for a previously stopped instance. When you terminated your instance and you want to have a new one, then you need to launch a new instance with the previously described run-instances command.

Tagging your instances is also a good idea, if you want to make sure that you don’t terminate an instance by mistake. So, assuming that you have started a postprocessing script, which will run for a long period of time, you can give your instance an extra tag. You can do this also after you’ve launched the instance and started your work. First get the instance ID with the command described above and then run the following command.

aws ec2 create-tags \ 
        --resources Your-Instance-ID \
        --tags Key=longrun,Value=yes \
        --region  eu-west-1

Now you can modify the command for listing all running instances and get all instances, which are supposed to stay running by adding this option:

 aws ec2 describe-instances \
         --filters  Name=tag:longrun,Values=yes \

In the next article we will take a closer look at these topics, which can further improve your cloud experience:

  • Data Management
  • Batch Processing
  • Parallel Postprocessing

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The post Postprocessing on AWS – Part 2: Saving Cost and Time appeared first on Tecplot.

Schnitger Corporation, CAE Market top

► Siemens to acquire FORAN marine design solutions
  20 Jul, 2021

Siemens to acquire FORAN marine design solutions

Siemens just announced that it is acquiring the FORAN software business from SENER, a Spanish engineering, procurement, and construction firm. FORAN’s solutions are used in the design and construction of ships of all sorts, and marine structures like offshore oil rigs. Siemens Digital Industries Software plans to add FORAN to its Xcelerator portfolio. (SENER will retain its naval architecture consultancy; the software, its developers and the support team join Siemens.)

This is a big deal–a really significant and exciting announcement. Foran has many, many devoted users in the world of ship design and production, but SENER hasn’t invested enough in the technology to keep it truly competitive in a world that increasingly relies on data management, optimization, and other technologies outside the realm of naval architectural calculations. Those calculations are critical, but no longer sufficient, so the dovetailing of FORAN’s strengths with the broader Siemens Xcelerator portfolio will be a great benefit to FORAN’s base.

In addition to naval architecture tools, Siemens gains technology that may be useful in other industries too, such as world-class plate nesting, bending and cutting technology, as well as jig design for forming curved panels, and dimensional control for pipe bending and cutting. I’ve also spoken with happy customers of FORAN’s pipeline, cable tray, and equipment modeling modules, so perhaps there’s an offer to be made to broader AEC users as well.

Here’s what we know about the transaction:

FORAN will be added to Siemens’ Xcelerator portfolio, offering a comprehensive and integrated portfolio of software and services that covers all aspects of the ship lifecycle, from concept design through production to operations and optimized service lifecycle management”. Much more detail on this will be forthcoming after the transaction closes later this year — but SIemens has a solid track record of integrating acquisitions and creating a way forward for users.

This is important for Siemens. According to the press release, FORAN brings with it “over 150 shipyard and design office customers across 40 countries” — including some of the biggest shipyards and defense contractors in the world. Acquiring FORAN sends the message to the marine economy that Siemens intends to invest in this industry.

Finally, this is as much about people as it is about technology. When the SIemens team briefed me on this acquisition, they stressed that they are very excited to have these industry and technology experts joining Siemens.

(No, terms of the deal were not disclosed. It’s Siemens, after all.)

As you can tell, I think this is great news. Shipyards around the world are struggling with low demand for new vessels, even as we need to make those operating now, significantly more sustainable. As a result, many yards are searching for new relevance to ship owners and operators, perhaps by becoming more digital. Integrating FORAN’s tools with Siemens’ design, manufacturing, and operations technology will open new possibilities for marine industry players at many levels.

I’m especially excited about that “service lifecycle management” tag in the Siemens quote above. Digitalization takes on an urgent connotation when we think of the Ever Given, and its crew and the billions of dollars worth of cargo, stuck in the Suez Canal (and legal limbo) for weeks — could an onboard system have prevented the ship from getting stuck in the first place? Should ships this large be allowed in the Suez Canal — what design choices could have/should have been made differently? Could we have avoided the incident if we had had access to more timely information? Closer ties between design and operations are critical to answering some of these questions — and, perhaps, preventing such an incident from ever happening again.

FORAN is expected to become part of the Siemens family by year-end, at which point we should learn a lot more about the integration plans and paths forward. I’m really excited to hear what those are!

► 3 on a Tuesday: Autodesk walks away from Altium deal; MarkForged is a public company; DS 3DX moves beyond CAD in AEC
  20 Jul, 2021

3 on a Tuesday: Autodesk walks away from Altium deal; MarkForged is a public company; DS 3DX moves beyond CAD in AEC

I know – it’s not Friday, no matter how much we might wish it to be. But there’s a lot to catch up on, so let’s get right to it:

Remember last month, when news broke that Autodesk was interested in acquiring Altium, the maker of EDA solutions? Seems like a long time ago, doesn’t it?! Well, that deal is now off the table. Autodesk didn’t explain much but an Autodesk person was quoted in an Australian newspaper as saying, “We are not commenting on matters with Altium but can confirm that acquisition discussions have ceased at this time”. According to people quoted in that article, Altium is expecting a BIG offer, an expectation unlikely to be met.

Remember, too, when MarkForged announced that it was going to merge with an SPAC as a way of listing on a stock exchange? That’s now a done deal, with MarkForged trading on the New York Stock Exchange as MKFG. In the transaction with the SPAC, Markforged received about $360 million of gross proceeds (before transaction expenses), which the company expects to use to “build new relationships as a critical partner to even more leading global manufacturers, leveraging its expanded platform and proceeds from the transaction to accelerate its impact and growth”.

Finally, for now, an announcement came today that ReNew Power, a renewable energy utlity in India, will use Dassault Systèmes’ 3DExperience platform to manage its solar, wind and hybrid engineering, procurement and construction projects. I don’t often write about this type of anouncement because it’s typically very self-serving, but this one is notable because ReNew will use DS’ “Capital Facilities Information Excellence” solution without doing any design work on the platform — it’s intent is to use 3DX to track the progress of its complex projects to ensure on-time,on-budget delivery, mange risk, etc. The age when DS was (just) the CATIA company is truly gone. According to DS, ReNew expects its implementation to “provide centralized project execution and monitoring data, personalized dashboards, progress graphs, timely insights and intuitive reports to improve productivity”. I need to write more about this, but for many in AEC, 3DX is a “CDE” or common data environment, that’s incrediby useful outside the typical CAD/CAM arena.

TTFN – more to come.

► Hexagon adds location to AR, snaps up Immersal
  16 Jul, 2021

Hexagon adds location to AR, snaps up Immersal

What’s new: Hexagon acquires Immersal, which makes an SDK that adds spatial location to AR solutions.

Why it matters: Sometimes, the location of an AR experience is irrelevant (doing maintenance on a unique item, for example), but in other cases, knowing that this specific item is at a defined grid reference is critical. That’s great and important, but I believe this deal is also about the artificial intelligence Immersal uses to build its geo data and its potential across the Hexagon portfolio.

The details: Hexagon just announced that it has acquired Immersal Oy, maker of an eponymous software development kit (SDK) that enables developers to add location into augmented reality (AR) applications. The Immersal SDK lets developers anchor digital content to real-world objects by location rather than a tag or other reference identifier. According to Immersal, a user’s mobile device can define its location and orientation in the surrounding physical world via machine-readable maps. These maps are constructed from image data and hosted in the Immersal Cloud Service.

Hexagon CEO Ola Rollén described it this way: “This acquisition puts the power of [location-based intelligence] into the hands of those on-site, enhancing their field of view with superimposed digital information, meaning they can literally do more with what they see. For example, direct access to information about an asset – while working with that asset – including step-by-step instructions on how to repair it, can streamline maintenance tasks while reducing material waste and re-work.”

Important, yes, but there’s also this: Immersal developed artificial intelligence (AI) and machine learning tools to help it comb through all of its geo-located images to create the “anchors” that define an object’s location. It’s not unreasonable to imagine that this will have direct impact on Hexagon’s other image-processing businesses.

The Immersal SDK lets AR developers embed these location-based services into lots of end-use cases. Immersal lists business use cases, such as mapping factory floors, warehouses, engine rooms, aiports, and shopping malls; but also gamification of “virtually anything, deepening engagement, adding value, and sparking joy along the way. Use Immersal to map spaces and then add fun and entertaining virtual layers, multiplayer interactive elements, contests, and more”.

Paticulars of the deal weren’t disclosed, other than Hexagon saying that “[t]he acquisition has no significant impact on Hexagon’s earnings.” Clearly, not as big as last week’s Infor EAM acquisition. Immersal will operate as part of Hexagon’s Geosystems division.

► Quickie: Bentley expands underground modeling with acquisition of Aarhus
    7 Jul, 2021

Quickie: Bentley expands underground modeling with acquisition of Aarhus

I’m watching the UEFA Cup, so this will be brief:

Bentley just announced that the Seequent business unit Bentley just acquired is, in turn, acquiring the Danish company, Aarhus GeoSoftware. Aarhus GeoSoftware makes AGS Workbench, SPIA, Res2DInv, and Res3DInv for the processing and visualization of “geophysical data from ground-based and airborne electromagnetic (EM), electrical resistivity tomography (ERT) remote sensing, and other sources”. Users create 2D and 3D images of the subsurface based on electrical resistivity. These outputs are used to identify and differentiate subsurface materials — and can then be modeled using Seequent’s Leapfrog to understand what’s there and design underground and surface structures accordingly.

Bentley says Aarhus will extend “Seequent’s solutions for operational groundwater management, and for sustainability projects involving exploration, contaminants, and infrastructure resilience.”

Financial details were not disclosed.

You can learn more here,

► Hexagon to acquire Infor’s EAM business for $2.75 billion
    6 Jul, 2021

Hexagon to acquire Infor’s EAM business for $2.75 billion

Much more to come on this, but news broke earlier today that, pending regulatory approvals, Hexagon will acquire Infor’s EAM (Enterprise Asset Management) business, for approximately $2.75 billion in cash and newly issued securities. That’s not this year’s largest PLMish deal, but it is certainly up there — and I think it’s Hexagon’s largest acquisition to date (after paying just over $2 billion for Intergraph a decade ago).

You may recall that a subsidiary of Koch Industries acquired Infor last year; this starts to divide up that asset and establishes what the companies are calling a Strategic Alliance to resell, cross-sell and otherwise work together to “accelerate cross-selling of complementary solutions across common customers in markets such as automotive, discrete manufacturing, and public safety.”

But back to Hexagon and EAM. Infor’s EAM is a SaaS-based asset management offering that is used to track assets, and plan and execute maintenance tasks to optimize operations. According to the press release, Infor EAM is “highly scalable and easily configurable to meet the needs of specific verticals, such as mass transit, food and beverage, facilities management and much more”. According to Hexagon CEO Ola Rollén, “Hexagon’s decision to acquire this business is a strong endorsement of our mission to put data to work to enable autonomous, connected ecosystems that boost efficiency, productivity, quality and safety for our customers … By integrating Infor EAM’s built-in, industry-specific asset management capabilities with our digital reality solutions and platforms, we can improve capital asset performance in ways beyond what EAM can achieve standalone – from enhancing predictive maintenance and reducing energy usage to supporting other sustainability initiatives. Infor EAM customers and partners can expect a smooth transition with significant synergies that will produce faster growth and greater opportunities, including expansion into new verticals as well as underserved markets such as Asia Pacific.”

Hexagon says that the Infor EAM business is forecasted to have revenue of $184 million in 2021, as it continues to drive customers to a SaaS business model. That makes this transaction have a hefty 15x revenue multiple. But it does appear to be a profitable and growing business: Hexagon said SaaS is expected to be over 40% of revenue in 2021 (and over half of software revenues) and has been growing at a 3-year compound annual growth rate of 35%.

A few of the details: Hexagon will pay $800 million in cash and issue series B shares to Koch to cover the rest of the purchase price. After the transaction, Koch will own 4.9% of the equity in Hexagon and will become a “long-term, active shareholder in addition to the business partnership.” The deal is expected to close in the fourth quarter of 2021.

► Gamma Technologies acquires to add power converter tech to GT-SUITE
    1 Jul, 2021

Gamma Technologies acquires to add power converter tech to GT-SUITE

CAE consolidation continues — and it isn’t just at the big-company level. Gamma Technologies (GT) just announced that it has acquired Power Design Technologies SAS (PDT), maker of the PowerForge SaaS solution for power converter design. You might not recognize the GT name but are likely to know its GT-SUITE, for multi-physics CAE simulation. A lot of auto industry players use GT-SUITE for powertrain and controls simulation.

GT’s press release says that “PowerForge complements and extends GT’s product and technology portfolio … [with] a built-in database of semiconductor devices, a fast solver and a multi-parameter benchmarking utility, PowerForge empowers engineers with the ability to predictively select and compare various power converter topologies, material technologies (e.g. SiC, GaN) and modulation strategies, to deliver the optimum solution in terms of power loss, electrical waveforms, size, weight and cost … With PowerForge, GT-SUITE’s capabilities now span the predictive simulation of battery, motors and power electronics within its unified multi-physics and multi-scale environment”.

Dimple Shah, CEO of Gamma Technologies, added that “[w]e continue to invest and expand GT-SUITE to meet the emerging needs of a rapidly transforming industry that has set aggressive goals towards a more sustainable future. The talented PDT team has built a visionary product that, together with GT-SUITE, offers state of the art technologies to address the critical systems of electrified mobility applications. On behalf of the GT user community, we welcome PDT to join us in our exciting journey ahead.”

Terms of the deal were not disclosed.

Symscape top

► CFD Simulates Distant Past
  25 Jun, 2019

There is an interesting new trend in using Computational Fluid Dynamics (CFD). Until recently CFD simulation was focused on existing and future things, think flying cars. Now we see CFD being applied to simulate fluid flow in the distant past, think fossils.

CFD shows Ediacaran dinner party featured plenty to eat and adequate sanitation

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► Background on the Caedium v6.0 Release
  31 May, 2019

Let's first address the elephant in the room - it's been a while since the last Caedium release. The multi-substance infrastructure for the Conjugate Heat Transfer (CHT) capability was a much larger effort than I anticipated and consumed a lot of resources. This lead to the relative quiet you may have noticed on our website. However, with the new foundation laid and solid we can look forward to a bright future.

Conjugate Heat Transfer Through a Water-Air RadiatorConjugate Heat Transfer Through a Water-Air Radiator
Simulation shows separate air and water streamline paths colored by temperature

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► Long-Necked Dinosaurs Succumb To CFD
  14 Jul, 2017

It turns out that Computational Fluid Dynamics (CFD) has a key role to play in determining the behavior of long extinct creatures. In a previous, post we described a CFD study of parvancorina, and now Pernille Troelsen at Liverpool John Moore University is using CFD for insights into how long-necked plesiosaurs might have swum and hunted.

CFD Water Flow Simulation over an Idealized PlesiosaurCFD Water Flow Simulation over an Idealized Plesiosaur: Streamline VectorsIllustration only, not part of the study

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► CFD Provides Insight Into Mystery Fossils
  23 Jun, 2017

Fossilized imprints of Parvancorina from over 500 million years ago have puzzled paleontologists for decades. What makes it difficult to infer their behavior is that Parvancorina have none of the familiar features we might expect of animals, e.g., limbs, mouth. In an attempt to shed some light on how Parvancorina might have interacted with their environment researchers have enlisted the help of Computational Fluid Dynamics (CFD).

CFD Water Flow Simulation over a ParvancorinaCFD Water Flow Simulation over a Parvancorina: Forward directionIllustration only, not part of the study

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► Wind Turbine Design According to Insects
  14 Jun, 2017

One of nature's smallest aerodynamic specialists - insects - have provided a clue to more efficient and robust wind turbine design.

DragonflyDragonfly: Yellow-winged DarterLicense: CC BY-SA 2.5, André Karwath

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► Runners Discover Drafting
    1 Jun, 2017

The recent attempt to break the 2 hour marathon came very close at 2:00:24, with various aids that would be deemed illegal under current IAAF rules. The bold and obvious aerodynamic aid appeared to be a Tesla fitted with an oversized digital clock leading the runners by a few meters.

2 Hour Marathon Attempt

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curiosityFluids top

► Creating curves in blockMesh (An Example)
  29 Apr, 2019

In this post, I’ll give a simple example of how to create curves in blockMesh. For this example, we’ll look at the following basic setup:

As you can see, we’ll be simulating the flow over a bump defined by the curve:

y=H*\sin\left(\pi x \right)

First, let’s look at the basic blockMeshDict for this blocking layout WITHOUT any curves defined:

/*--------------------------------*- C++ -*----------------------------------*\
  =========                 |
  \\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox
   \\    /   O peration     | Website:
    \\  /    A nd           | Version:  6
     \\/     M anipulation  |
    version     2.0;
    format      ascii;
    class       dictionary;
    object      blockMeshDict;

// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //

convertToMeters 1;

    (-1 0 0)    // 0
    (0 0 0)     // 1
    (1 0 0)     // 2
    (2 0 0)     // 3
    (-1 2 0)    // 4
    (0 2 0)     // 5
    (1 2 0)     // 6
    (2 2 0)     // 7

    (-1 0 1)    // 8    
    (0 0 1)     // 9
    (1 0 1)     // 10
    (2 0 1)     // 11
    (-1 2 1)    // 12
    (0 2 1)     // 13
    (1 2 1)     // 14
    (2 2 1)     // 15

    hex (0 1 5 4 8 9 13 12) (20 100 1) simpleGrading (0.1 10 1)
    hex (1 2 6 5 9 10 14 13) (80 100 1) simpleGrading (1 10 1)
    hex (2 3 7 6 10 11 15 14) (20 100 1) simpleGrading (10 10 1)


        type patch;
            (0 8 12 4)
        type patch;
            (3 7 15 11)
        type wall;
            (0 1 9 8)
            (1 2 10 9)
            (2 3 11 10)
        type patch;
            (4 12 13 5)
            (5 13 14 6)
            (6 14 15 7)
        type empty;
            (8 9 13 12)
            (9 10 14 13)
            (10 11 15 14)
            (1 0 4 5)
            (2 1 5 6)
            (3 2 6 7)

// ************************************************************************* //

This blockMeshDict produces the following grid:

It is best practice in my opinion to first make your blockMesh without any edges. This lets you see if there are any major errors resulting from the block topology itself. From the results above, we can see we’re ready to move on!

So now we need to define the curve. In blockMesh, curves are added using the edges sub-dictionary. This is a simple sub dictionary that is just a list of interpolation points:

        polyLine 1 2
                (0	0       0)
                (0.1	0.0309016994    0)
                (0.2	0.0587785252    0)
                (0.3	0.0809016994    0)
                (0.4	0.0951056516    0)
                (0.5	0.1     0)
                (0.6	0.0951056516    0)
                (0.7	0.0809016994    0)
                (0.8	0.0587785252    0)
                (0.9	0.0309016994    0)
                (1	0       0)

        polyLine 9 10
                (0	0       1)
                (0.1	0.0309016994    1)
                (0.2	0.0587785252    1)
                (0.3	0.0809016994    1)
                (0.4	0.0951056516    1)
                (0.5	0.1     1)
                (0.6	0.0951056516    1)
                (0.7	0.0809016994    1)
                (0.8	0.0587785252    1)
                (0.9	0.0309016994    1)
                (1	0       1)

The sub-dictionary above is just a list of points on the curve y=H\sin(\pi x). The interpolation method is polyLine (straight lines between interpolation points). An alternative interpolation method could be spline.

The following mesh is produced:

Hopefully this simple example will help some people looking to incorporate curved edges into their blockMeshing!


This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via, and owner of theOPENFOAM®  andOpenCFD®  trademarks.

► Creating synthetic Schlieren and Shadowgraph images in Paraview
  28 Apr, 2019

Experimentally visualizing high-speed flow was a serious challenge for decades. Before the advent of modern laser diagnostics and velocimetry, the only real techniques for visualizing high speed flow fields were the optical techniques of Schlieren and Shadowgraph.

Today, Schlieren and Shadowgraph remain an extremely popular means to visualize high-speed flows. In particular, Schlieren and Shadowgraph allow us to visualize complex flow phenomena such as shockwaves, expansion waves, slip lines, and shear layers very effectively.

In CFD there are many reasons to recreate these types of images. First, they look awesome. Second, if you are doing a study comparing to experiments, occasionally the only full-field data you have could be experimental images in the form of Schlieren and Shadowgraph.

Without going into detail about Schlieren and Shadowgraph themselves, primarily you just need to understand that Schlieren and Shadowgraph represent visualizations of the first and second derivatives of the flow field refractive index (which is directly related to density).

In Schlieren, a knife-edge is used to selectively cut off light that has been refracted. As a result you get a visualization of the first derivative of the refractive index in the direction normal to the knife edge. So for example, if an experiment used a horizontal knife edge, you would see the vertical derivative of the refractive index, and hence the density.

For Shadowgraph, no knife edge is used, and the images are a visualization of the second derivative of the refractive index. Unlike the Schlieren images, shadowgraph has no direction and shows you the laplacian of the refractive index field (or density field).

In this post, I’ll use a simple case I did previously ( as an example and produce some synthetic Schlieren and Shadowgraph images using the data.

So how do we create these images in paraview?

Well as you might expect, from the introduction, we simply do this by visualizing the gradients of the density field.

In ParaView the necessary tool for this is:

Gradient of Unstructured DataSet:

Finding “Gradient of Unstructured DataSet” using the Filters-> Search

Once you’ve selected this, we then need to set the properties so that we are going to operate on the density field:

Change the “Scalar Array” Drop down to the density field (rho), and change the name to Synthetic Schlieren

To do this, simply set the “Scalar Array” to the density field (rho), and change the name of the result Array name to SyntheticSchlieren. Now you should see something like this:

This is NOT a synthetic Schlieren Image – but it sure looks nice

There are a few problems with the above image (1) Schlieren images are directional and this is a magnitude (2) Schlieren and Shadowgraph images are black and white. So if you really want your Schlieren images to look like the real thing, you should change to black and white. ALTHOUGH, Cold and Hot, Black-Body radiation, and Rainbow Desatured all look pretty amazing.

To fix these, you should only visualize one component of the Synthetic Schlieren array at a time, and you should visualize using the X-ray color preset:

The results look pretty realistic:

Horizontal Knife Edge

Vertical Knife Edge

Now how about ShadowGraph?

The process of computing the shadowgraph field is very similar. However, recall that shadowgraph visualizes the Laplacian of the density field. BUT THERE IS NO LAPLACIAN CALCULATOR IN PARAVIEW!?! Haha no big deal. Just remember the basic vector calculus identity:

\nabla^2\left[\right]  = \nabla \cdot \nabla \left[\right]

Therefore, in order for us to get the Shadowgraph image, we just need to take the Divergence of the Synthetic Schlieren vector field!

To do this, we just have to use the Gradient of Unstructured DataSet tool again:

This time, Deselect “Compute Gradient” and the select “Compute Divergence” and change the Divergence array name to Shadowgraph.

Visualized in black and white, we get a very realistic looking synthetic Shadowgraph image:

Shadowgraph Image

So what do the values mean?

Now this is an important question, but a simple one to answer. And the answer is…. not much. Physically, we know exactly what these mean, these are: Schlieren is the gradient of the density field in one direction and Shadowgraph is the laplacian of the density field. But what you need to remember is that both Schlieren and Shadowgraph are qualitative images. The position of the knife edge, brightness of the light etc. all affect how a real experimental Schlieren or Shadowgraph image will look.

This means, very often, in order to get the synthetic Schlieren to closely match an experiment, you will likely have to change the scale of your synthetic images. In the end though, you can end up with extremely realistic and accurate synthetic Schlieren images.

Hopefully this post will be helpful to some of you out there. Cheers!

► Solving for your own Sutherland Coefficients using Python
  24 Apr, 2019

Sutherland’s equation is a useful model for the temperature dependence of the viscosity of gases. I give a few details about it in this post:

The law given by:

\mu=\mu_o\frac{T_o + C}{T+C}\left(\frac{T}{T_o}\right)^{3/2}

It is also often simplified (as it is in OpenFOAM) to:

\mu=\frac{C_1 T^{3/2}}{T+C}=\frac{A_s T^{3/2}}{T+T_s}

In order to use these equations, obviously, you need to know the coefficients. Here, I’m going to show you how you can simply create your own Sutherland coefficients using least-squares fitting Python 3.

So why would you do this? Basically, there are two main reasons for this. First, if you are not using air, the Sutherland coefficients can be hard to find. If you happen to find them, they can be hard to reference, and you may not know how accurate they are. So creating your own Sutherland coefficients makes a ton of sense from an academic point of view. In your thesis or paper, you can say that you created them yourself, and not only that you can give an exact number for the error in the temperature range you are investigating.

So let’s say we are looking for a viscosity model of Nitrogen N2 – and we can’t find the coefficients anywhere – or for the second reason above, you’ve decided its best to create your own.

By far the simplest way to achieve this is using Python and the Scipy.optimize package.

Step 1: Get Data

The first step is to find some well known, and easily cited, source for viscosity data. I usually use the NIST webbook (, but occasionally the temperatures there aren’t high enough. So you could also pull the data out of a publication somewhere. Here I’ll use the following data from NIST:

Temparature (K) Viscosity (Pa.s)
400 0.000022217
600 0.000029602
800 0.000035932
1000 0.000041597
1200 0.000046812
1400 0.000051704
1600 0.000056357
1800 0.000060829
2000 0.000065162

This data is the dynamics viscosity of nitrogen N2 pulled from the NIST database for 0.101 MPa. (Note that in these ranges viscosity should be only temperature dependent).

Step 2: Use python to fit the data

If you are unfamiliar with Python, this may seem a little foreign to you, but python is extremely simple.

First, we need to load the necessary packages (here, we’ll load numpy, scipy.optimize, and matplotlib):

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

Now we define the sutherland function:

def sutherland(T, As, Ts):
    return As*T**(3/2)/(Ts+T)

Next we input the data:



Then we fit the data using the curve_fit function from scipy.optimize. This function uses a least squares minimization to solve for the unknown coefficients. The output variable popt is an array that contains our desired variables As and Ts.

popt = curve_fit(sutherland, T, mu)

Now we can just output our data to the screen and plot the results if we so wish:

print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')


plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])

Overall the entire code looks like this:

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def sutherland(T, As, Ts):
    return As*T**(3/2)/(Ts+T)

T=[200, 400, 600,


popt, pcov = curve_fit(sutherland, T, mu)
print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')


plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])

And the results for nitrogen gas in this range are As=1.55902E-6, and Ts=168.766 K. Now we have our own coefficients that we can quantify the error on and use in our academic research! Wahoo!


In this post, we looked at how we can simply use a database of viscosity-temperature data and use the python package scipy to solve for our unknown Sutherland viscosity coefficients. This NIST database was used to grab some data, and the data was then loaded into Python and curve-fit using scipy.optimize curve_fit function.

This task could also easily be accomplished using the Matlab curve-fitting toolbox, or perhaps in excel. However, I have not had good success using the excel solver to solve for unknown coefficients.

► Tips for tackling the OpenFOAM learning curve
  23 Apr, 2019

The most common complaint I hear, and the most common problem I observe with OpenFOAM is its supposed “steep learning curve”. I would argue however, that for those who want to practice CFD effectively, the learning curve is equally as steep as any other software.

There is a distinction that should be made between “user friendliness” and the learning curve required to do good CFD.

While I concede that other commercial programs have better basic user friendliness (a nice graphical interface, drop down menus, point and click options etc), it is equally as likely (if not more likely) that you will get bad results in those programs as with OpenFOAM. In fact, to some extent, the high user friendliness of commercial software can encourage a level of ignorance that can be dangerous. Additionally, once you are comfortable operating in the OpenFOAM world, the possibilities become endless and things like code modification, and bash and python scripting can make OpenFOAM worklows EXTREMELY efficient and powerful.

Anyway, here are a few tips to more easily tackle the OpenFOAM learning curve:

(1) Understand CFD

This may seem obvious… but its not to some. Troubleshooting bad simulation results or unstable simulations that crash is impossible if you don’t have at least a basic understanding of what is happening under the hood. My favorite books on CFD are:

(a) The Finite Volume Method in Computational Fluid Dynamics: An Advanced Introduction with OpenFOAM® and Matlab by
F. Moukalled, L. Mangani, and M. Darwish

(b) An introduction to computational fluid dynamics – the finite volume method – by H K Versteeg and W Malalasekera

(c) Computational fluid dynamics – the basics with applications – By John D. Anderson

(2) Understand fluid dynamics

Again, this may seem obvious and not very insightful. But if you are going to assess the quality of your results, and understand and appreciate the limitations of the various assumptions you are making – you need to understand fluid dynamics. In particular, you should familiarize yourself with the fundamentals of turbulence, and turbulence modeling.

(3) Avoid building cases from scratch

Whenever I start a new case, I find the tutorial case that most closely matches what I am trying to accomplish. This greatly speeds things up. It will take you a super long time to set up any case from scratch – and you’ll probably make a bunch of mistakes, forget key variable entries etc. The OpenFOAM developers have done a lot of work setting up the tutorial cases for you, so use them!

As you continue to work in OpenFOAM on different projects, you should be compiling a library of your own templates based on previous work.

(4) Using Ubuntu makes things much easier

This is strictly my opinion. But I have found this to be true. Yes its true that Ubuntu has its own learning curve, but I have found that OpenFOAM works seamlessly in the Ubuntu or any Ubuntu-like linux environment. OpenFOAM now has Windows flavors using docker and the like- but I can’t really speak to how well they work – mostly because I’ve never bothered. Once you unlock the power of Linux – the only reason to use Windows is for Microsoft Office (I guess unless you’re a gamer – and even then more and more games are now on Linux). Not only that- but the VAST majority of forums and troubleshooting associated with OpenFOAM you’ll find on the internet are from Ubuntu users.

I much prefer to use Ubuntu with a virtual Windows environment inside it. My current office setup is my primary desktop running Ubuntu – plus a windows VirtualBox, plus a laptop running windows that I use for traditional windows type stuff. Dual booting is another option, but seamlessly moving between the environments is easier.

(5) If you’re struggling, simplify

Unless you know exactly what you are doing, you probably shouldn’t dive into the most complicated version of whatever you are trying to solve/study. It is best to start simple, and layer the complexity on top. This way, when something goes wrong, it is much easier to figure out where the problem is coming from.

(6) Familiarize yourself with the cfd-online forum

If you are having trouble, the cfd-online forum is super helpful. Most likely, someone else is has had the same problem you have. If not, the people there are extremely helpful and overall the forum is an extremely positive environment for working out the kinks with your simulations.

(7) The results from checkMesh matter

If you run checkMesh and your mesh fails – fix your mesh. This is important. Especially if you are not planning on familiarizing yourself with the available numerical schemes in OpenFOAM, you should at least have a beautiful mesh. In particular, if your mesh is highly non-orthogonal, you will have serious problems. If you insist on using a bad mesh, you will probably need to manipulate the numerical schemes. A great source for how schemes should be manipulated based on mesh non-orthogonality is:

(8) CFL Number Matters

If you are running a transient case, the Courant-Freidrechs-Lewis (CFL) number matters… a lot. Not just for accuracy (if you are trying to capture a transient event) but for stability. If your time-step is too large you are going to have problems. There is a solid mathematical basis for this stability criteria for advection-diffusion problems. Additionally the Navier-Stokes equations are very non-linear and the complexity of the problem and the quality of your grid etc can make the simulation even less stable. When I have a transient simulation crash, if I know my mesh is OK, I decrease the timestep by a factor of 2. More often than not, this solves the problem.

For large time stepping, you can add outer loops to solvers based on the pimple algorithm, but you may end up losing important transient information. Excellent explanation of how to do this is given in the book by T. Holzmann:

For the record, this points falls into point (1) of Understanding CFD.

(9) Work through the OpenFOAM Wiki “3 Week” Series

If you are starting OpenFOAM for the first time, it is worth it to work through an organized program of learning. One such example (and there are others) is the “3 Weeks Series” on the OpenFOAM wiki:

If you are a graduate student, and have no job to do other than learn OpenFOAM, it will not take 3 weeks. This touches on all the necessary points you need to get started.

(10) OpenFOAM is not a second-tier software – it is top tier

I know some people who have started out with the attitude from the get-go that they should be using a different software. They think somehow Open-Source means that it is not good. This is a pretty silly attitude. Many top researchers around the world are now using OpenFOAM or some other open source package. The number of OpenFOAM citations has grown every year consistently (

In my opinion, the only place where mainstream commercial CFD packages will persist is in industry labs where cost is no concern, and changing software is more trouble than its worth. OpenFOAM has been widely benchmarked, and widely validated from fundamental flows to hypersonics (see any of my 17 publications using it for this). If your results aren’t good, you are probably doing something wrong. If you have the attitude that you would rather be using something else, and are bitter that your supervisor wants you to use OpenFOAM, when something goes wrong you will immediately think there is something wrong with the program… which is silly – and you may quit.

(11) Meshing… Ugh Meshing

For the record, meshing is an art in any software. But meshing is the only area where I will concede any limitation in OpenFOAM. HOWEVER, as I have outlined in my previous post ( most things can be accomplished in OpenFOAM, and there are enough third party meshing programs out there that you should have no problem.


Basically, if you are starting out in CFD or OpenFOAM, you need to put in time. If you are expecting to be able to just sit down and produce magnificent results, you will be disappointed. You might quit. And frankly, thats a pretty stupid attitude. However, if you accept that CFD and fluid dynamics in general are massive fields under constant development, and are willing to get up to speed, there are few limits to what you can accomplish.

Please take the time! If you want to do CFD, learning OpenFOAM is worth it. Seriously worth it.

This offering is notapproved or endorsed by OpenCFD Limited, producer and distributorof the OpenFOAM software via, and owner of theOPENFOAM®  andOpenCFD®  trade marks.

► Automatic Airfoil C-Grid Generation for OpenFOAM – Rev 1
  22 Apr, 2019
Airfoil Mesh Generated with

Here I will present something I’ve been experimenting with regarding a simplified workflow for meshing airfoils in OpenFOAM. If you’re like me, (who knows if you are) I simulate a lot of airfoils. Partly because of my involvement in various UAV projects, partly through consulting projects, and also for testing and benchmarking OpenFOAM.

Because there is so much data out there on airfoils, they are a good way to test your setups and benchmark solver accuracy. But going from an airfoil .dat coordinate file to a mesh can be a bit of pain. Especially if you are starting from scratch.

The two main ways that I have meshed airfoils to date has been:

(a) Mesh it in a C or O grid in blockMesh (I have a few templates kicking around for this
(b) Generate a “ribbon” geometry and mesh it with cfMesh
(c) Or back in the day when I was a PhD student I could use Pointwise – oh how I miss it.

But getting the mesh to look good was always sort of tedious. So I attempted to come up with a python script that takes the airfoil data file, minimal inputs and outputs a blockMeshDict file that you just have to run.

The goals were as follows:
(a) Create a C-Grid domain
(b) be able to specify boundary layer growth rate
(c) be able to set the first layer wall thickness
(e) be mostly automatic (few user inputs)
(f) have good mesh quality – pass all checkMesh tests
(g) Quality is consistent – meaning when I make the mesh finer, the quality stays the same or gets better
(h) be able to do both closed and open trailing edges
(i) be able to handle most airfoils (up to high cambers)
(j) automatically handle hinge and flap deflections

In Rev 1 of this script, I believe I have accomplished (a) thru (g). Presently, it can only hand airfoils with closed trailing edge. Hinge and flap deflections are not possible, and highly cambered airfoils do not give very satisfactory results.

There are existing tools and scripts for automatically meshing airfoils, but I found personally that I wasn’t happy with the results. I also thought this would be a good opportunity to illustrate one of the ways python can be used to interface with OpenFOAM. So please view this as both a potentially useful script, but also something you can dissect to learn how to use python with OpenFOAM. This first version of the script leaves a lot open for improvement, so some may take it and be able to tailor it to their needs!

Hopefully, this is useful to some of you out there!


You can download the script here:

Here you will also find a template based on the airfoil2D OpenFOAM tutorial.


(1) Copy to the root directory of your simulation case.
(2) Copy your airfoil coordinates in Selig .dat format into the same folder location.
(3) Modify to your desired values. Specifically, make sure that the string variable airfoilFile is referring to the right .dat file
(4) In the terminal run: python3
(5) If no errors – run blockMesh

You need to run this with python 3, and you need to have numpy installed


The inputs for the script are very simple:

ChordLength: This is simply the airfoil chord length if not equal to 1. The airfoil dat file should have a chordlength of 1. This variable allows you to scale the domain to a different size.

airfoilfile: This is a string with the name of the airfoil dat file. It should be in the same folder as the python script, and both should be in the root folder of your simulation directory. The script writes a blockMeshDict to the system folder.

DomainHeight: This is the height of the domain in multiples of chords.

WakeLength: Length of the wake domain in multiples of chords

firstLayerHeight: This is the height of the first layer. To estimate the requirement for this size, you can use the curiosityFluids y+ calculator

growthRate: Boundary layer growth rate

MaxCellSize: This is the max cell size along the centerline from the leading edge of the airfoil. Some cells will be larger than this depending on the gradings used.

The following inputs are used to improve the quality of the mesh. I have had pretty good results messing around with these to get checkMesh compliant grids.

BLHeight: This is the height of the boundary layer block off of the surfaces of the airfoil

LeadingEdgeGrading: Grading from the 1/4 chord position to the leading edge

TrailingEdgeGrading: Grading from the 1/4 chord position to the trailing edge

inletGradingFactor: This is a grading factor that modifies the the grading along the inlet as a multiple of the leading edge grading and can help improve mesh uniformity

trailingBlockAngle: This is an angle in degrees that expresses the angles of the trailing edge blocks. This can reduce the aspect ratio of the boundary cells at the top and bottom of the domain, but can make other mesh parameters worse.


12% Joukowski Airfoil


With the above inputs, the grid looks like this:

Mesh Quality:

These are some pretty good mesh statistics. We can also view them in paraView:

Clark-y Airfoil

The clark-y has some camber, so I thought it would be a logical next test to the previous symmetric one. The inputs I used are basically the same as the previous airfoil:

With these inputs, the result looks like this:

Mesh Quality:

Visualizing the mesh quality:

MH60 – Flying Wing Airfoil

Here is an example of a flying with airfoil (tested since the trailing edge is tilted upwards).


Again, these are basically the same as the others. I have found that with these settings, I get pretty consistently good results. When you change the MaxCellSize, firstLayerHeight, and Grading some modification may be required. However, if you just half the maxCell, and half the firstLayerHeight, you “should” get a similar grid quality just much finer.

Grid Quality:

Visualizing the grid quality


Hopefully some of you find this tool useful! I plan to release a Rev 2 soon that will have the ability to handle highly cambered airfoils, and open trailing edges, as well as control surface hinges etc.

The long term goal will be an automatic mesher with an H-grid in the spanwise direction so that the readers of my blog can easily create semi-span wing models extremely quickly!

Comments and bug reporting encouraged!

DISCLAIMER: This script is intended as an educational and productivity tool and starting point. You may use and modify how you wish. But I make no guarantee of its accuracy, reliability, or suitability for any use. This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via, and owner of the OPENFOAM®  and OpenCFD®  trademarks.

► Normal Shock Calculator
  20 Feb, 2019

Here is a useful little tool for calculating the properties across a normal shock.

If you found this useful, and have the need for more, visit One of STF Solutions specialties is providing our clients with custom software developed for their needs. Ranging from custom CFD codes to simpler targeted codes, scripts, macros and GUIs for a wide range of specific engineering purposes such as pipe sizing, pressure loss calculations, heat transfer calculations, 1D flow transients, optimization and more. Visit STF Solutions at for more information!

Disclaimer: This calculator is for educational purposes and is free to use. STF Solutions and curiosityFluids makes no guarantee of the accuracy of the results, or suitability, or outcome for any given purpose.


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