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Another Fine Meshtop

► This Week in CFD
7 May, 2021

Welcome to another adventure in curated CFD news, curated being a fancy word for “whatever I find interesting.” Lots of hypersonics this week, lots of built environment applications this week, and a couple of cool animations. And toward the end … Continue reading

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

► This Week in CFD
23 Apr, 2021

This week’s compilation of CFD news begins with a must-read article on how to choose colors properly when visualizing data. AI comes up twice this week as does Fortran which makes one wonder whether anyone’s programming AI in Fortran. There’s … Continue reading

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

► This Week in CFD
16 Apr, 2021

This Week in CFD reached convergence long before I had exhausted the two-week backlog of news. With baseball season underway here in the US, fans will enjoy the case study describing how high-fidelity CFD can predict the trajectory of various … Continue reading

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

► This Week in CFD
2 Apr, 2021

It’s a good Friday for the latest roundup of CFD flotsam and jetsam from the ocean that is the internet. [Making this the Great CFD Garbage Gyre?] We use the marine theme because, for unexplained reasons, there are a lot … Continue reading

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

► Resolving Boundary Layers with Unstructured Quad and Hex Meshing: On-Demand Webinar
31 Mar, 2021

All things being equal, CFD practitioners prefer to use hexahedral mesh cells in the boundary layer for the improved robustness and accuracy they bring to the flow solver. Traditionally, a hex grid would be created using a structured grid technique … Continue reading

The post Resolving Boundary Layers with Unstructured Quad and Hex Meshing: On-Demand Webinar first appeared on Another Fine Mesh.

► This Week in CFD
19 Mar, 2021

Completely fungible and non-tokenized, today’s CFD news begins with a hopeful glimpse at a potential in-person CFD event. You should check-out the list of tips for simulation projects and let us know what was missed. And the coolest news this … Continue reading

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

F*** Yeah Fluid Dynamicstop

► Metallic Magma
7 May, 2021

Metallic paint flows like silver lava in this macro video from Chemical Bouillon. The paint has been mixed with an unknown fluid (my guess is alcohol) to produce the flows we see here. My suspicion is that we’re seeing solutal convection where variations in surface tension create convective flow within the liquid. What do you think? (Video and image credit: Chemical Bouillon)

► Oil-Coated Bubbles
6 May, 2021

Bubbles in industrial applications are often more complicated than a simple pocket of air surrounded by water. Here researchers investigate the formation of an air bubble coated in oil before it rises through water. The photo above shows a series of snapshots as the bubble forms. Initially, a droplet of oil sits pinned on the surface. As air gets injected, the oil stretches around the growing bubble. Eventually, buoyancy pulls the bubble off the injector, creating a rising air bubble coated in oil. The team found that oil-coated bubbles could grow much larger than those in water alone. (Image and research credit: B. Ji et al.)

► Meeting Without Mixing
5 May, 2021

When bodies of water meet, they don’t always mix right away. Here we see the confluence of the Back and Hayes Rivers in the Canadian Arctic. The Back River appears as a darker blue-green color compared to the light turquoise Hayes River. The different colors reflect the levels of algae and sediment carried in their waters. As seen in both the aerial and satellite photos here, there’s a distinct line where the two waters meet without mixing, and that line persists for kilometers beyond their initial confluence. Typically, this lack of mixing between bodies of water is caused by differences in temperature, salinity, and turbidity (amount of sediment) that make the density of each river’s water different. (Image credit: top – R. Macdonald/Univ. of Manitoba, bottom – J. Stevens/USGS; via NASA Earth Observatory)

► How the Hummingbird Got Its Hum
4 May, 2021

Summer hikes in the Rocky Mountains are frequently pierced by a hum that can deepen to a bomber-like buzz as hummingbirds flit by. They’re so small and fast that they’re hard to see, but they’re never hard to hear. A new study pins down just where that telltale hum comes from.

To determine the specific origin of the hummingbird’s sound, researchers observed hovering hummingbirds with an array of over 2,000 microphones and multiple high-speed cameras. With this set-up, they could create a 3D acoustic map of the bird’s sounds, correlated with its motions. They found that the bird’s sounds come primarily from aerodynamic forces generated during their distinctive wingstroke – not from vortices or the fluttering of their feathers.

They also found that the hummingbird’s fast wingstroke — about 40 times per second — fed into sounds at 40 and 80 Hz, as well as higher frequency overtones. Since these sounds are well within human hearing range, they make up most of what we hear from the birds. (Image credit: P. Bonnar; research credit: B. Hightower; via The Guardian; submitted by Kam-Yung Soh)

► Underwater Explosions and Submarines
3 May, 2021

In the early days of submarines, it did not take physicists and engineers long to discover how destructive underwater explosions can be. In this Slow Mo Guys video, Gav gives us a glimpse of that destruction using a model submarine in a fish tank and several small explosives. You’ll have to be quick to notice the initial shock waves that ripple through the tank, but the footage captures spectacular detail on some of the slower-moving phenomena. You can see the uneven ripples of the explosion bubble’s surface as it expands. There are some great shots from the front and side showing the bubbly vortex ring that forms when the explosion hits the side of the tank wall (something that wouldn’t happen out in the ocean, of course). You can even catch a glimpse of some unexploded powder streaking out of the explosion. (Image and video credit: The Slow Mo Guys)

► “Columbia”
30 Apr, 2021

“Columbia” is a music video illustrated with fluid dynamics, chemistry, and biology by the Beauty of Science team. It’s got everything from precipitation to crystallization, from infrared imagery of wakes to timelapses of growing molds. How many phenomena can you identify? (Video and image credit: Beauty of Science)

Symscapetop

► 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

► 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 Radiator
Simulation shows separate air and water streamline paths colored by temperature

► 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 Plesiosaur: Streamline VectorsIllustration only, not part of the study

► 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 Parvancorina: Forward directionIllustration only, not part of the study

► 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.

Dragonfly: Yellow-winged DarterLicense: CC BY-SA 2.5, André Karwath

► 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

CFD Onlinetop

► 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

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.

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:

https://www.openfoam.com/documentati...binations.html
► 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:
• RANS
• MRF
• Compressible
• K-Omega SST
• Subsonic
• Inlet T = 300 K
• Inlet p = 1 atm
• Mass flow = 0.1 Kg/s
• Rotation Speed = 50 000 rpm
https://www.cfd-online.com/Forums/bl...1&d=1610557096

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.

https://www.cfd-online.com/Forums/bl...1&d=1610557130

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)
+ (
he.name() == "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.

https://www.cfd-online.com/Forums/bl...1&d=1610557130

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.

https://www.cfd-online.com/Forums/bl...1&d=1610557130

Solver (Rotation axis must be in the Z direction):https://mega.nz/folder/NxZ2QRzJ#u3DE_1RVBJT8DbhAUsCkHQ

PostProcess Paraview:https://mega.nz/file/p4ZnwC6B#KmIzSS...fLq0aYTiJZZtyI

Simulation:https://mega.nz/file/gsYWmJIB#izgg1_...K2XYll5K55lMq0

PDF:https://mega.nz/file/90gwEIoC#JMq9Uf...ssbfEMcSm910z0
Attached Thumbnails

Attached Images
 mesh_Turbocharger.jpg (72.0 KB, 145 views) rhoSimpleFoam_Turbocharger.jpg (25.7 KB, 149 views) turboSimpleFoam_Turbocharger.jpg (26.4 KB, 159 views)
Attached Files
 turboSimpleFoam.zip (256.2 KB, 81 views)
► Energies: special issue on microscale and mesoscale modelling
22 Dec, 2020
I will be editing this special issue of Energies

https://www.mdpi.com/journal/energie...ale_microscale

Contributions from the CFD community are welcome!
► Panel Method
16 Dec, 2020
Panel method for 3D.
Attached Files
 Panel_method.pptx (62.9 KB, 75 views) Integral Equation Methods, Lecture 1.pdf (587.5 KB, 110 views)
► Title: rhoSimpleFoam for stationary compressible turbulent internals flows
9 Dec, 2020
1/ Introduction

The scope of these notes is to discuss the simulation of stationary compressible turbulent flow in OpenFoam using the rhoSimpleFoam solver. We assume the reader to be familiar with the concept of stationary incompressible turbulent flows and the simulation of these flows in OpenFoam. This allows us to focus here on the transition from incompressible to compressible. We target internal flow with Mach number between 0.4 and 2. The application to external flows and/or higher mach Numbers is out of scope and described elsewhere. We thus consider the case of e.g. the mixing of various gasses injected into a vessel. In target scenarios the gas enters the vessel through a nozzle with small diameter at high mass flow rate. The assumption that the Mach number is bounded by 0.4 is violated in proximity of the nozzle outlet. An incompressible formulation no longer applies. We limit ourselves to case in which the various gasses mix without chemically reacting. We do have the modeling of the reacting case as final scope however.

what is rhoSimpleFoam: Stationary compressible turbulent flow in OpenFoam can be modeled using the rhoSimpleFoam solver.

why this notes: A lot of information on rhoSimpleFoam can be found elsewhere (cite wiki, cite Moukalled). Our aim here is to provide timely and updated description of the solver that includes
* a description of the thermodynamics (rhoThermo vs. psiThermo); rhoSimpleFoam is a pressure based solver. We thus need to recover density (rho) from pressure (p) using either rhoThermo or psiThermo;
* a description of transonic option (when to use it, change in energy equation, what are its advantages in terms of computational cost, what its limitations);
* numerical stability, sign of source terms, the presence of pressure waves (or the absence thereof and techniques to avoid them, see e.g. \cite{pressure-waves-sprayFoam});
* the comparison of convergence criteria (residuals vs. outlet quantities);
* a description of possible limitations of the solver (what happens e.g. at sufficiently high Mach numbers, break-down of multigrid for pressure solve due to transport term, advice on when to switch to alternative density base and/or coupled solvers), comparison between segregated and coupled solvers (HISA project);

what concept are assumed to be known to the reader (and thus not discussed here):
rhoSimpleFoam is a pressure-based segregated solver that iterates between the pressure, velocity and energy fields using the SIMPLE (or SIMPLE-C) algorithm (and possibly turbulent quantities) (cite wikipedia, Malaseekara, Moukalled). rhoSimpleFoam thus builds on components developed elsewhere in OpenFoam. In particular
* from basic solvers in OpenFoam: cell-centered finite volume discretization for scalar fields, non-linear iteration for scalar fields, linear solvers, parallel set-up and solver run;
* from incompressible solvers in OpenFoam: Reynolds averaging, cell-centered finite volume discretization for pressure-velocity coupling, Rhie-Chow interpolation, SIMPLE iteration for pressure-velocity coupling, consistent SIMPLE (SIMPLEC);
* from incompressible solvers in OpenFoam: solving for turbulent quantities;

what is outside scope of this page: transient formulation (using local time stepping), turbulence modeling beyond RANS with two-equation turbulence models (thus no Reynolds stress model, no LES), no thermodynamics beyond the ideal gas law; no adaptive mesh refinement;

References:
* wikipedia, RANS: https://en.wikipedia.org/wiki/Reynol...okes_equations
* CFD-Online, pressure-waves-sprayFoam: https://www.cfd-online.com/Forums/op...ombustion.html
* CFD-Online, e-vs-h-in-energy-equation-1: https://www.cfd-online.com/Forums/op...implefoam.html
* CFD-Online, e-vs-h-in-energy-equation-2: https://www.cfd-online.com/Forums/op...roperties.html
* CFD-Online, e-vs-h-in-energy-equation-3: https://github.com/OpenFOAM/OpenFOAM...25ab9817b3ec62 (It is generally more convergent and stable to solve for internal energy if the fluid is incompressible or weakly compressible.)

2/ Representation of the Thermodynamics

The representation of the thermodynamics or the equation of state in a finite volume computation requires a separate data structure. In OpenFoam, the thermodynamics of the fluid in represented by the fluidThermo class (collection of data and operations of this data). The fluidThermo class is a parent class for rhoThermo and psiThermo. Both classes store the density (rho), compressibility (psi) and dynamic viscosity (mu). Both of the latter classes allow to update the density once a new pressure field has been computed. This update is performed through the correct() member function.

Questions
* how exactly is thermophysicalTransport->correct() in rhoSimpleFoam.C and thermo.correct in EEqn.H complementary to each other?
* what is rhoDelta as argument of correct() in rhoThermo?
* what is the body of correct() in psiThermo left body?

3/ Pressure in Compressible Flow Computations

In an incompressible flow simulation, density is constant, the flow equations (conservation of mass and momentum) can be divided by density and the kinematic pressure (p/rho [m^2/s^2]) is solved for. In a compressible flow simulation instead, the static pressure (p [Pa]) is solved for. This will have an impact on the boundary conditions being imposed, and thus the case set-up in OpenFoam. More … .

References:
* OpenFoam v2006 Users manual: various forms of the pressure: https://www.openfoam.com/documentati...-pressure.html

4/ Internal Energy vs. Enthalpy in Compressible Flow Computations

1.4/ Analytical Considerations

Heat capacity at constant volume ([units]): cv
Heat capacity at constant volume ([units]): cp
Thermal conductivity ([units]): k (heat flux = k (temperature flux) )
Thermal diffusivity when solving for enthalpy (h [units]): \alpha_{h} = \kappa / (\rho cv)
Thermal diffusivity when solving for internal energy (e [units]): \alpha_{e} = \kappa / (\rho cp)
Prandtl number is ratio of momentum diffusivity (kinematic viscosity nu) and thermal diffusivity \alpha
Prandtl number when solving for enthalpy = \nu/\alpha = (cv \mu)/ k
Prandtl number when solving for internal energy = \nu/\alpha = (cp \mu)/ k

This difference are taken care of in the implementation. Details are here: https://www.cfd-online.com/Forums/op...implefoam.html

2.4/ Numerical Considerations

It is generally more convergent and stable to solve for internal energy (instead of enthalpy) if the fluid is incompressible or weakly compressible. See https://github.com/OpenFOAM/OpenFOAM...25ab9817b3ec62 and https://www.cfd-online.com/Forums/op...roperties.html . Keep energy positive by keeping source term positive. See book Patankar.

References:

5/ Problem formulation

1.5/ Conservation Equations (conversation of mass, momentum and energy closed by an equation of state to which turbulent quantities are added)

Density no longer constant. Pressure is dynamic pressure. Various expressions for the energy (internal energy, enthalpy and temperate exists);

1.1.5/ Conversation of mass

2.1.5/ Conservation of momentum

3.1.5/ Conservation of energy

4.1.5/ Solving for turbulent quantities

5.1.5/ Update of density through thermo-physical quantities (psi = R T)

2.5/ Boundary conditions (inlet, outlet and walls)

6/ Segregated Solution via SIMPLE Algorithm

After finite volume discretization, flow equations need to solve. Given SIMPLE for incompressible flow (and implemented in e.g. simpleFoam), add two steps. First step is update of the density (using the equation of state). Second step is the solve for the energy (enthalpy or temperature);

7/ Implementation in rhoSimpleFoam in OpenFoam

Show here UEqn.H, pEqn.H and EEqn.H and turbulent quantities;

8/ Guidelines on using the Solver

1.8/ Starting from Initial Guess Provided by simpleFoam (see notes)

2.8/ Handle on converge
limitT and limitU (first print values, then limit)

9/ Tutorials

1.9/ sBend

2.9/ elbow

3.9/ Sandia Flame D

4.9/ reverseBurner

References
* https://github.com/OpenFOAM/OpenFOAM...b71e7cf0172c34

User guide/Wiki-1/Wiki-2/Code guide/Code Wiki
Nilsson/Guerrero/Holzinger/Holzmann/Nagy/Santos/Nozaki/Jasak-FSB
OpenFOAM Governance and Technical Committees
Report bugs/Request features: FOAM-Extend (Wikki-FSB)
Report bugs: OpenFOAM (Foundation)
How to create a MWE.

10/ References
* Greenshield
* Moukalled
* Malalaseekara and Versteeg
* wiki on compressible Navier-Stokes Equations: https://en.wikipedia.org/wiki/Navier...wtonian_fluids
* SIMPLE algorithms: https://en.wikipedia.org/wiki/SIMPLE_algorithm

11/ Open End
* is similar description above available elsewhere?
* when does rhoSimpleFoam break?
* how do implementation in various flavor of OpenFoam differ

curiosityFluidstop

► 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:  https://openfoam.org
\\  /    A nd           | Version:  6
\\/     M anipulation  |
\*---------------------------------------------------------------------------*/
FoamFile
{
version     2.0;
format      ascii;
class       dictionary;
object      blockMeshDict;
}

// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //

convertToMeters 1;

vertices
(
(-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
);

blocks
(
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)
);

edges
(
);

boundary
(
inlet
{
type patch;
faces
(
(0 8 12 4)
);
}
outlet
{
type patch;
faces
(
(3 7 15 11)
);
}
lowerWall
{
type wall;
faces
(
(0 1 9 8)
(1 2 10 9)
(2 3 11 10)
);
}
upperWall
{
type patch;
faces
(
(4 12 13 5)
(5 13 14 6)
(6 14 15 7)
);
}
frontAndBack
{
type empty;
faces
(
(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:

edges
(
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!

Cheers.

This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via http://www.openfoam.com, 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 (https://curiosityfluids.com/2016/03/28/mach-1-5-flow-over-23-degree-wedge-rhocentralfoam/) 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:

Once you’ve selected this, we then need to set the properties so that we are going to operate on the density field:

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:

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:

Vertical Knife Edge

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:

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: https://curiosityfluids.com/2019/02/15/sutherlands-law/

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 (
https://webbook.nist.gov/), 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) 200 0.000012924 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:

T=[200,
400,
600,
800,
1000,
1200,
1400,
1600,
1800,
2000]

mu=[0.000012924,
0.000022217,
0.000029602,
0.000035932,
0.000041597,
0.000046812,
0.000051704,
0.000056357,
0.000060829,
0.000065162]

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)
As=popt[0]
Ts=popt[1]

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')

xplot=np.linspace(200,2000,100)
yplot=sutherland(xplot,As,Ts)

plt.plot(T,mu,'ok',xplot,yplot,'-r')
plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])
plt.show()

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,
800,
1000,
1200,
1400,
1600,
1800,
2000]

mu=[0.000012924,
0.000022217,
0.000029602,
0.000035932,
0.000041597,
0.000046812,
0.000051704,
0.000056357,
0.000060829,
0.000065162]

popt, pcov = curve_fit(sutherland, T, mu)
As=popt[0]
Ts=popt[1]
print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')

xplot=np.linspace(200,2000,100)
yplot=sutherland(xplot,As,Ts)

plt.plot(T,mu,'ok',xplot,yplot,'-r')
plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])
plt.show()



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!

Summary

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:

http://www.wolfdynamics.com/wiki/OFtipsandtricks.pdf

(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:

https://holzmann-cfd.de/publications/mathematics-numerics-derivations-and-openfoam

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:

https://wiki.openfoam.com/%223_weeks%22_series

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 (https://curiosityfluids.com/2019/02/14/high-level-overview-of-meshing-for-openfoam/) most things can be accomplished in OpenFOAM, and there are enough third party meshing programs out there that you should have no problem.

Summary

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 http://www.openfoam.com, and owner of theOPENFOAM®  andOpenCFD®  trade marks.

► Automatic Airfoil C-Grid Generation for OpenFOAM – Rev 1
22 Apr, 2019

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!

https://github.com/curiosityFluids/curiosityFluidsAirfoilMesher

Here you will also find a template based on the airfoil2D OpenFOAM tutorial.

Instructions

(1) Copy curiosityFluidsAirfoilMesher.py to the root directory of your simulation case.
(2) Copy your airfoil coordinates in Selig .dat format into the same folder location.
(3) Modify curiosityFluidsAirfoilMesher.py 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 curiosityFluidsAirfoilMesher.py
(5) If no errors – run blockMesh

PS
You need to run this with python 3, and you need to have numpy installed

Inputs

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

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.

Examples

12% Joukowski Airfoil

Inputs:

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).

Inputs:

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

Summary

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!

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 http://www.openfoam.com, 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 www.stfsol.com. 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 www.stfsol.com 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.

Hanley Innovationstop

► 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.

http://www.hanleyinnovations.com/stallion3d.html

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 > http://www.hanleyinnovations.com

► 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.

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 > http://www.hanleyinnovations.com

► 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).

► 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.

► 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.

http:/www.hanleyinnovations.com/airfoildigitizerhelp.html

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.

http://www.hanleyinnovations.com/stallion3d.html

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

► 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.

► 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
 CL CD p = 1 2.020 0.293 p = 2 2.411 0.282 p = 3 2.413 0.283 Experiment 2.479 0.252

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 hocfd.com to download the solver. A GUI has been developed to simplify problem setup. Your thoughts and comments are highly welcome.

Happy 2018!

► Sub-grid Scale (SGS) Stress Models in Large Eddy Simulation
17 Nov, 2017
The simulation of turbulent flow has been a considerable challenge for many decades. There are three main approaches to compute turbulence: 1) the Reynolds averaged Navier-Stokes (RANS) approach, in which all turbulence scales are modeled; 2) the Direct Numerical Simulations (DNS) approach, in which all scales are resolved; 3) the Large Eddy Simulation (LES) approach, in which large scales are computed, while the small scales are modeled. I really like the following picture comparing DNS, LES and RANS.

DNS (left), LES (middle) and RANS (right) predictions of a turbulent jet. - A. Maries, University of Pittsburgh

Although the RANS approach has achieved wide-spread success in engineering design, some applications call for LES, e.g., flow at high-angles of attack. The spatial filtering of a non-linear PDE results in a SGS term, which needs to be modeled based on the resolved field. The earliest SGS model was the Smagorinsky model, which relates the SGS stress with the rate-of-strain tensor. The purpose of the SGS model is to dissipate energy at a rate that is physically correct. Later an improved version called the dynamic Smagorinsky model was developed by Germano et al, and demonstrated much better results.

In CFD, physics and numerics are often intertwined very tightly, and one may draw erroneous conclusions if not careful. Personally, I believe the debate regarding SGS models can offer some valuable lessons regarding physics vs numerics.

It is well known that a central finite difference scheme does not contain numerical dissipation.  However, time integration can introduce dissipation. For example, a 2nd order central difference scheme is linearly stable with the SSP RK3 scheme (subject to a CFL condition), and does contain numerical dissipation. When this scheme is used to perform a LES, the simulation will blow up without a SGS model because of a lack of dissipation for eddies at high wave numbers. It is easy to conclude that the successful LES is because the SGS stress is properly modeled. A recent study with the Burger's equation strongly disputes this conclusion. It was shown that the SGS stress from the Smargorinsky model does not correlate well with the physical SGS stress. Therefore, the role of the SGS model, in the above scenario, was to stabilize the simulation by adding numerical dissipation.

For numerical methods which have natural dissipation at high-wave numbers, such as the DG, SD or FR/CPR methods, or methods with spatial filtering, the SGS model can damage the solution quality because this extra dissipation is not needed for stability. For such methods, there have been overwhelming evidence in the literature to support the use of implicit LES (ILES), where the SGS stress simply vanishes. In effect, the numerical dissipation in these methods serves as the SGS model. Personally, I would prefer to call such simulations coarse DNS, i.e., DNS on coarse meshes which do not resolve all scales.

I understand this topic may be controversial. Please do leave a comment if you agree or disagree. I want to emphasize that I support physics-based SGS models.

AirShapertop

► 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?
► Vehicle Water Management
30 Mar, 2021
Vehicle water and dirt management is a key area where manufacturers are investing a lot of resource. Using hydrodynamic and CFD simulations, companies can now model and optimise the behaviour of water flows and dirt particles.
► The Case of the Mercedes EQC Drag Coefficient
16 Feb, 2021
The Mercedes EQC drag coefficient showed some interesting results when AirShaper CFD simulations were conducted with the car in reverse. What does this tell us about the aerodynamic behaviour of SUV’s?
► Camera Pod Aerodynamics
5 Jan, 2021
Trains may move in a straight line, but the aerodynamics are very fascinating: tunnel boom, cross-winds, efficiency and more determine their shapes!
► Rally Cars in Wind Tunnels
22 Dec, 2020
Aerodynamics aren't just for Formula 1 - More & more, they are becoming the key factor for a good Rally Car. This blog details how they have been tested in the wind tunnel over the past years.

Convergent Science Blogtop

► 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.

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).

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.

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.

“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.

Jet-in-Crossflow

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.

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:

References

[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 fuelmech.org. 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.

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.

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.

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

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.

► 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.

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.

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.

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.

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:

References

[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.

A CFD Case Study

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.

Results

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.

Significance

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.

References

[1] Marcy, C., “U.S. renewable electricity generation has doubled since 2008,” https://www.eia.gov/todayinenergy/detail.php?id=38752, accessed on Nov 11, 2016.

[2] Center for Sustainable Systems, University of Michigan, “U.S. Renewable Energy Factsheet”, http://css.umich.edu/factsheets/us-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.

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.

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.

Conclusion

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!

References

[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. https://api.convergecfd.com/wp-content/uploads/David-Rowinski_Multiphase-Modeling-Gearbox-Power-Losses-Oil-Pump-Cavitation-and-Fuel-Tank-Sloshing.pdf

[9] Willie, J., “Simulation and Optimization of Flow Inside Claw Vacuum Pumps,” 2018 CONVERGE User Conference–Europe, Bologna, Italy, Mar 19–23, 2018. https://api.convergecfd.com/wp-content/uploads/james-willie-simulation-and-optimization-of-flow-inside-claw-vacuum-pumps.pdf

[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-3Dtop

► Communicate Your CFD Results with Confidence
14 Apr, 2021

The ability to visualize and present clear, meaningful analysis of simulation data is a crucial part of the CFD simulation process. FLOW-3D POST provides all the necessary tools to effectively communicate your simulation findings. In this blog, we’ll explore some powerful features of FLOW-3D POST so that you can communicate your CFD results with confidence.

Here is an example of an aluminum wheel casting, simulated using the low pressure die casting workspace in FLOW-3D CAST. The objective of this simulation is to find the steady state thermal condition within the mold. Thermal die cycling is used to visualize mold performance and ensure that there is a proper thermal gradient in the mold before beginning the filling simulation. This image shows a few easily accessible features in FLOW-3D POST.

Using multiple viewports allows the user to get a complete visualization to examine areas and features of interest. This is also useful when wanting to compare multiple designs. Notice that the top left viewport is using a 3-dimensional slice; 2D and 3D slicing options allow the user to visualize the internal features of geometries and flow systems.

The bottom viewports show a graphical output of history data based on the thermal energy transferred to the mold components.

The legend and annotation properties are fully customizable, allowing you to label and display your data clearly and effectively. Additional features shown here include a customized color scale and opacity setting for the mold visualization.

As we go deeper into the functionality of FLOW-3D POST we see some powerful ways to extrapolate information. In this tailings breach example, we can see the use of a spreadsheet data view on the right. Here, data from individual cells can be highlighted and isolated, helping the user illustrate flow features at a mesh cell-level of precision. In the panel on the left, we can see the calculator function, which can be used to manipulate history data on the fly. If quick decision-making is important to the project, built-in capabilities such as exporting data for statistical analysis can provide macro- and micro-level viewpoints of the results efficiently.

Stay tuned! We will be diving into some of the new features including ray tracing, cell-level data extrapolation, and key framing. In the meantime, go to our FLOW-3D POST product page to see some of examples of how powerful of a tool FLOW-3D POST is.

At Flow Science we develop innovative solutions that help our customers conceptualize, create, and analyze their simulations with confidence. If you would like more information or a personal demonstration of any of our FLOW-3D products and FLOW-3D POST, please contact us at webdemo@flow3d.com.

Ajit D'Brass

Metal Casting Engineer at Flow Science

► Good Hardware Means Improved FLOW-3D POST Experience
18 Mar, 2021

Good Hardware Means Improved FLOW-3D POST Experience

To take full advantage of FLOW-3D POST, our advanced state-of-the-art postprocessor, it is essential that you have good hardware. In this blog, Stephen Sanchez, GUI Developer/Manager, Software Engineering, gives his two cents on how you can obtain the optimal FLOW-3D POST experience by following these hardware recommendations.

High quality graphics hardware

We highly recommend that you start with a graphics card with at least 3GB of VRAM. This is especially important if you will be doing a lot of volume rendering. Volume rendering is an advanced capability of FLOW-3D POST that visualizes the details of a variable throughout the fluid domain, instead of just the iso-surface. This feature is quite insightful but requires good hardware to be used effectively during postprocessing.

Next, you should not use Intel integrated graphics as your primary graphics hardware. Intel integrated graphics are common in most laptops, even in laptops with dedicated graphics hardware (more on this below). Most of FLOW-3D POST’s functionality does not work with this configuration, and as a result, we do not support Intel integrated graphics. FLOW-3D POST performs best when used with NVIDIA graphics cards. We highly recommend NVIDIA graphics hardware from the Maxwell architecture family and higher, as we have found it works well with FLOW-3D POST. NVIDIA Quadro cards have proven to be the most reliable. While high-end AMD cards should also work, we have found that they are not as reliable as NVIDIA hardware and drivers, so we always recommend NVIDIA over AMD.

Dual graphics cards on laptops – A simple but hidden solution

Many laptops now come with the ability to switch between an NVIDIA graphics card and Intel Integrated graphics. It is important that you make sure FLOW-3D POST is being launched with the NVIDIA card. Forcing your laptop to launch with the NVIDIA card can be done through the NVIDIA control panel.

We recommend that you check to make sure that your video driver is updated. We have had reports of artifacts and display issues in FLOW-3D POST which were easily resolved by updating the video driver. Keeping your video driver current is a good way to avoid such issues.

RAM, RAM, RAM!

It is important to be aware of memory requirements, as insufficient memory can not only lead to the inability to postprocess your simulation; it can lead to as high as a 10x performance decrease! The amount of RAM needed for FLOW-3D POST depends on several factors, especially the size of your simulation. In order to provide users with the most flexibility, we have the following RAM recommendations based on the number of cells in your meshes:

• Extra-large (200 million+ cells): At least 128GB
• Large (between 60-150 million cells): 64-128GB
• Medium (between 30-60 million cells): 32-64GB
• Small (30 million cells and below): At least 32GB

FLOW-3D POST can be memory intensive. If you have a rough idea of the simulation sizes you will be running, then we recommend that you follow these guidelines as closely as possible.  That said, we always recommend getting the most RAM possible, regardless of the problem size, to maximize flexibility and ensure the smoothest FLOW-3D POST experience.

► Senior Applications CFD Engineer – Water
16 Mar, 2021

Flow Science, Inc. is the developer of FLOW-3D HYDRO, a computational fluid dynamics (CFD) software specializing in transient, free-surface flows. FLOW-3D HYDRO is recognized as the premier tool for 3D free surface modeling in applications related to the civil and environmental engineering industry, including dams & spillways, conveyance infrastructure, rivers & environmental, ports & coastal, and water treatment. We have a large global user base that includes governmental agencies, private consultants, and academic research institutions.

Senior Applications Engineer – Water

Flow Science has an immediate opening for a Senior Applications CFD Engineer – Water. The candidate will work in collaboration with our sales, marketing, support, and development teams to deliver market growth leadership for the water treatment and conveyance application areas.

Principal responsibilities

The ideal candidate will have a strong background in fluid mechanics, physical, chemical and biological processes in WWTP, and applying industry standard modeling and design approaches. The candidate will also have experience and a strong passion for CFD and developing its use as a design and analysis tool to advance the state of practice within the civil and environmental engineering industry. Candidates should have exceptional oral and written communication, presentation, and interpersonal skills. The candidate should have the ability to work both independently and as part of a team.

Responsibilities include:

• Provide technical leadership as a subject matter expert on all areas related to CFD for water treatment and conveyance infrastructure applications areas.
• Provide market growth leadership by developing close working relationships with industry partners to help guide account growth and developments needed to meet customer needs.
• Run sample simulations and present detailed results in areas of interest for potential users.
• Actively participate in technical marketing efforts such as presentations, webinars, user conferences, trade shows, and customer visits.
• Perform internal research, testing and validations to guide future developments and identify growth areas for CFD in the application area.
• Provide advanced technical support services to existing users.
• Develop and deliver user training workshops, technical webinars, and presentations.
• Perform technical sales consultations with prospective users.
• Regularly present at industry conferences and events.
• Participate and represent Flow Science in professional associations, member of technical committees, and/or officer of local or national organization.

Required skills and experience

• 5-10+ years of industry related worked experience that includes water and/or wastewater treatment facilities, collection systems, combined/sanitary sewer overflow, water transmission/distribution projects, and/or pump stations.
• MS or PhD in Civil/Environmental Engineering or related engineering degree.
• A strong background in fluid mechanics, open channel hydraulics and physical, chemical and biological processes in WWTP.
• Experience and expertise in conventional or industry standard modeling techniques and software (Biowin, Sumo, SWMM, CAD, GIS, HEC-RAS, MIKE).
• Experience in applying and developing 3D CFD models.
• Strong written and verbal skills for communicating critical concepts.
• Desire to learn and understand new and challenging concepts related to all water and environmental application areas.

Preferred skills and experience

• Strong 3D CAD skills and GIS skills are highly desired.
• Registration as a Professional Engineer is a plus.
• Experience working in dams, spillways, rivers, and open channel systems is a plus.

Benefits

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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► FLOW-3D World Users Conference 2022
11 Mar, 2021

We invite our customers from around the world to join us at the FLOW-3D World Users Conference 2022 to celebrate 40+ years of FLOW-3D.

The conference will be held on May 16-18, 2022 at the Maritim Hotel in Munich, Germany. Join engineers, researchers and scientists from some of the world’s most renowned companies and institutions to hone your simulation skills, explore new modeling approaches and learn about the latest software developments. The conference will feature metal casting and water & environmental application tracks, advanced training sessions, in-depth technical presentations by our customers, and the latest product developments presented by Flow Science’s senior technical staff. The conference will be co-hosted by Flow Science Deutschland.

We are extremely pleased to confirm that Hubert Lang of BMW will be the conference keynote speaker.

Keynote Speaker Announced!

15 years of FLOW-3D at BMW

Hubert Lang studied Mechanical Engineering with a focus on automotive engineering at Landshut University of Applied Sciences. In 1998, he started in BMW’s Light Metal Foundry in Landshut, working in their tool design department, where he oversaw the development of casting tools for six-cylinder engines. In 2005, Hubert moved to the foundry’s simulation department, where he was introduced to FLOW-3D’s metal casting capabilities. Since then, he has led considerable expansion in the use of FLOW-3D, both in the volume of simulations as well as the number of application areas.

Today, BMW uses FLOW-3D for sand casting, permanent mold gravity casting, low pressure die casting, high pressure die casting, and lost foam casting. FLOW-3D has also been applied to several special projects at BMW, such as supporting the development of an inorganic binder system for sand cores through the development of a core drying model; calculation of the heat input during coating of cylinder liners; the development of the casting geometry for the injector casting procedure; and the layout and dimensioning of cooling systems for casting tools.

BMW Museum Tour

We are pleased to offer a tour of the BMW Museum as part of the conference offerings. The tour will take place at 17:30 after the technical proceedings on Tuesday, May 17. You can sign up for the tour when you register for the conference.

Conference Information

Important Dates

• March 18, 2022: Abstracts Due
• April 1, 2022: Abstracts Accepted
• May 6, 2022: Presentations Due
• May 16, 2022: Advanced Training Sessions
• May 16, 2022: Opening Reception
• May 17, 2022: Tour of the BMW Museum
• May 17, 2022: Conference Dinner

Registration Fees

• Day 1 and 2 of the conference: 300 €
• Day 1 of the conference: 200 €
• Day 2 of the conference: 200 €
• Guest Fee (social events only): 50 €
• Opening Reception: included with registration
• BMW Tour: included with registration
• Conference Dinner: included with registration

Taught by senior technical staff and experts in their fields, advanced training topics include Version Up seminars for FLOW-3D CAST and FLOW-3D AM users, a session on municipal applications using FLOW-3D HYDRO as well as a general session focused on Troubleshooting techniques and Municipal applications. The courses are scheduled so that everyone, regardless of their application, can participate in the Troubleshooting Session. You can sign up for these training sessions when you register online.

Training Times and Fees

• May 16, 2022 – 13:00 – 14:00 – Version Up: FLOW-3D CAST – 100 €
• May 16, 2022 – 14:00 – 15:00 – Version Up: FLOW-3D AM – 100 €
• May 16, 2022 – 13:00 – 15:00 – FLOW-3D HYDRO Municipal Applications  – 200 €
• May 16, 2022 – 15:00 – 17:00 – Troubleshooting – 200 €

Call for Abstracts

Share your experiences, present your success stories and obtain valuable feedback from the FLOW-3D user community and our senior technical staff. We welcome abstracts on all topics including those focused on the following applications:

• Metal Casting
• Civil & Environmental Engineering
• Consumer Products
• Micro/Nano/Bio Fluidics
• Energy
• Aerospace
• Automotive
• Coating
• Coastal Engineering
• Maritime
• General Applications

Abstracts should include a title, author(s) and a 200 word description. Please email your abstract to info@flow3d.com.

Registration and training fees will be waived for presenters.

Presenter Information

Each presenter will have a 30 minute speaking slot, including Q & A. All presentations will be distributed to the conference attendees and on our website after the conference. A full paper is not required for this conference. Please contact us if you have any questions about presenting at the conference. Flow Science Deutschland will sponsor Best Presentation Awards for each track.

Conference Dinner

This conference dinner will be held in the ever-popular Augustiner-Keller. All conference attendees and their guests are invited to join us on Tuesday, June 8 for a traditional German feast in a beautiful and famous beer garden. The conference dinner will take place following the BMW Tour.

Travel

Conference Hotel

Maritim Hotel Munich
+49 (0) 89 55235-0
info.mun@maritim.de

► Training Sessions at the FLOW-3D World Users Conference 2022
9 Mar, 2021

In conjunction with the FLOW-3D World Users Conference 2022, advanced training sessions will be held the afternoon of May 16, 2022 at the conference hotel. Taught by senior technical staff and experts in their fields, advanced training topics include version up seminars for FLOW-3D CAST and FLOW-3D AM users, as well as sessions focused on troubleshooting techniques and municipal applications using FLOW-3D HYDRO. The courses are scheduled so that everyone, regardless of their application, can participate in the troubleshooting session. You can sign up for multiple training sessions when you register online.

Version Up: FLOW-3D CAST

Instructor: Dr.-Ing. Dipl.-Phys. Matthias Todte, Flow Science Germany

This one-hour FLOW-3D CAST training course will begin with an introductory overview and a review of the new features and GUI design changes in FLOW-3D CAST v5.1. Through examples, new workspaces will be covered in detail, including Investment Casting, Continuous, Sand Core Making, Centrifugal as well as the new Exothermic Sleeve capabilities and database. We will also discuss the new chemistry-based solidification model available in FLOW-3D CAST v5.1.

Training Details

Date: Monday, May 16, 2022
Time: 13:00 – 14:00
Cost: 100 €

Version Up: FLOW-3D AM

Instructor: Raed Marwan, President, Flow Science Japan

This one-hour course is open to FLOW-3D AM users as well as those interested in exploring the powerful capabilities of FLOW-3D AM for simulating additive manufacturing and laser welding processes. An overview of the additive manufacturing processes that can be simulated using FLOW-3D AM will be briefly introduced at the beginning of the training. The training will then focus on how to set up simulations for Selective Laser Melting (SLM) processes. The training will cover the powder laying process for single and multi-layer beds, powder spreading, and powder melting.

Training Details

Date: Monday, May 16, 2022
Time: 14:00 – 15:00
Cost: 100 €

FLOW-3D HYDRO Municipal Applications

Instructor: Brian Fox, MSc, Senior Water & Environmental Applications Engineer, Flow Science

CFD is rapidly gaining use as an advanced tool for the design and analysis of municipal systems for stormwater conveyance and water/wastewater treatment. FLOW-3D HYDRO’s well-known strengths in free surface simulation provide excellent capabilities for simulating the complex flows encountered in stormwater conveyance structures. Our multiphysics capabilities offer a powerful tool for linking the physical, chemical and biological processes that are critical for the design and analysis of water/wastewater treatment systems.

In this two-hour training session we will explore FLOW-3D HYDRO’s current capabilities along with recent and proposed developments for municipal applications. This class will be divided into four segments:

• Review of air entrainment and two-fluid options for spiral, baffle and tangential dropshafts
• Simulation of contact tanks with the reaction kinetics model
• How to use the settling sludge model for clarifier technology applications
• Activated sludge modeling: advanced chemistry in FLOW-3D HYDRO

Attendees will leave the training with in-depth knowledge of FLOW-3D HYDRO’s modeling capabilities for municipal applications. For users interested in expanding their service offerings, this is an excellent opportunity to learn about capabilities for this exciting and fast growing market.

Training Details

Date: Monday, May 16, 2022
Time: 13:00 – 15:00
Cost: 200 €

Troubleshooting Techniques

This two hour training is intended for all users of FLOW-3D products, regardless of application.

Instructor: Brian Fox, Senior Water & Environmental Applications Engineer, Flow Science

Understanding how to identify and resolve simulation and setup issues is a critical skill for every serious CFD modeler. In this workshop we will discuss how to efficiently diagnose and address issues with FLOW-3D simulations to help keep projects moving forward on schedule. Beginning from troubleshooting techniques and the overall process, we will review the methods and practical tools available in FLOW-3D for identifying, investigating and diagnosing problems simulation errors. We will then proceed to discuss model setup options that can be used to address these issues. Throughout the class, we will apply the approach to interactively troubleshoot several real simulations to demonstrate the troubleshooting process and techniques that will help you work more efficiently on your own simulations. Finally, we will describe how to use the ideas from this training in a preventive manner in your workflow.

Training Details

Date: Monday, May 16, 2022
Time: 15:00 – 17:00
Cost: 200 €

► Flow Science Earns Family Friendly Business Award® at the Platinum Level
4 Mar, 2021

Flow Science is recognized as a distinguished leader for its family friendly policies.

Santa Fe, NM, March 4, 2021 – Flow Science, Inc. has earned Platinum level distinction for its workplace policies by Family Friendly New Mexico, a statewide initiative developed to recognize employers that have adopted policies that give New Mexico businesses an advantage in recruiting and retaining the best employees. This is Flow Science’s fifth year in a row winning a Family Friendly New Mexico award.

“We are proud to receive Platinum level recognition as a family friendly employer in New Mexico. Flow Science continues to be a great place to work, offering best-in-class benefits and generous policies. In 2021, Flow Science added even more policies and benefits that support our employees in community volunteerism and charitable giving, which has taken us from Gold to Platinum level recognition,” said Aimee Abby, Flow Science’s HR Manager.

The Family Friendly New Mexico initiative offers training, support and resources to businesses on how to implement family friendly policies, provides recognition to businesses and organizations that offer their employees family friendly benefits, and acts as a resource for businesses and community leaders as they develop policies on issues such as paid family leave and childcare assistance.

“As we grow the state’s economy, we have the opportunity to be a national leader in offering New Mexicans workplaces that help companies attract and keep the best workers,” said Giovanna Rossi, founder and Director of Family Friendly New Mexico. “Implementing family friendly policies can be a simple, concrete investment a company can make to ensure it can compete for highly qualified employees. Studies have shown that costs associated with creating family friendly benefits are more than made up for in improved productivity, employee morale and employee retention. We are happy to recognize Flow Science as a distinguished leader in implementing family friendly policies.”

Flow Science offers its employees medical, dental and vision insurance plans employer-paid at 90%, flexible spending accounts, life and disability insurance, retirement and financial planning assistance, a 401(k) plan with generous employer matching contributions, vacation and sick pay, maternity and paternity leave, commuter benefit, training and education, and a wellness benefit. A new program implemented in 2021 encourages employee volunteerism and offers a charitable matching program.

We are proud to receive Platinum level recognition as a family friendly employer in New Mexico. Flow Science continues to be a great place to work, offering best-in-class benefits and generous policies, said Aimee Abby, Flow Science’s HR Manager.

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 products in nations throughout the Americas, Europe, Australasia 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
info@flow3d.com
+1 505-982-0088

Mentor Blogtop

► 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 Blogtop

► Back to Basics: Tecplot Chorus
22 Apr, 2021

Tecplot Chorus Basics

This video shows you the unique set of features in Tecplot Chorus. You’ll explore datasets from multiple simulations, compare results, and evaluate overall system performance.

• Exploring a Cart3D dataset using Tecplot Chorus [4:54] Cart3D Delta Wing Model
• Viewing Images [10:49]
• Identifying Cases of Interest [20:20]
• Diving Deep into the Simulation Data [24:12]
• Comparing Grids [28:28]
• Creating New Images & Variables [30:40]
• Q&A [42:47]

Read more about the capabilities of Tecplot Chorus. See tutorials in Tecplot Chorus videos. Tecplot Chorus is included with Tecplot 360 for customers with a TecPLUS maintenance agreement.

Q&A from the Webinar

What is the purpose of Tecplot Chorus? What is it primarily used for?

This tool helps engineers who run and generate many simulations or test data sets. The most common applications are:

• Developing aero databases.
• Predicting performance over the operating envelope.
• Investigation an engineering problem.

In all these scenarios engineers need to manage their solution data, discover the trends and anomalies in output variables, and understand the underlying physics that cause these variations.

How do you easily visualize cell sizes of the grid as a density plot? [43:00]

In Tecplot 360, go to the menu Analyze > Calculate Variables. Click Select and choose the option to calculate the Cell Volume. This will compute the cell area for your 2D cells. The dataset we’re using is a finite-element triangle, therefore we’re going to be computing the cell areas. And because the areas are a cell-based value, we want to choose Cell Center and then Calculate. It takes just a minute to calculate. Let’s go into contour and multi coloring (just double click on the legend to bring up the dialog) and select Cell Volume.

Now the cell area has been calculated, and you can see that we have just a few values. Click add contour level to add a little bit more detail. Now you can see a contour plot of cell sizes or cell areas. If you need a cell volume, you might be able to use an isosurface to find areas of high cell volume, or use value blanking, because then you’re going through a three-dimensional object. You could also use a slice and pass it through there, because that will adopt the cell volume of the source cell.

Is there a way to change the default fonts and sizes or at least is there a way to change for the entire frame and not for each access separately? [45:08]

Let’s go back to a 2D plot because axes are available by default. First, select Frame > Frame Linking. Second, go to the tab Within Frame, and then check the Axis Style checkbox. Now, when you double click on the X axis and change the color to red, you can see that the X and Y change simultaneously. Similarly with labels, change the labels to green and you can see both the X and Y labels are changed.

The defaults can be changed in the Tecplot configuration file, tecplot.cfg located in your installation folder. This file will execute Tecplot macro commands to define the font defaults. More information in the Tecplot 360 User’s Manual Chapter 30-2 Configuration Files.

Note: If you are on a Linux machine and it’s a shared installation, then changing tecplot.cfg file in the Tecplot directory will change it for all users. If your company has a set of defaults that they want everyone to use, that is a great place to make the change. But if you want to make the change only for yourself, you can have a tecplot.cfg file in your home directory on Linux. And in that case, you’re going to want to add a “dot” to the filename: “.tecplot.cfg” – Tecplot 360 will use that as your personal default.

Tecplot Chorus is included in the download of Tecplot 360 for Windows and Linux. There is a toggle in the installation of Tecplot 360 to also install Tecplot Chorus, which is on by default. Tecplot Chorus is not supported on Mac OS.

Are there plans to support Tecplot Chorus on Mac OS? [48:25]

Not yet. Tecplot products use a GUI toolkit called a Qt or “cute”, and we’ve encountered bugs with it on the Mac. We feel the user experience is not yet good enough to release a version on Mac OS. However, with each Qt release, we check to see whether the issues get resolved. We’re still waiting.

Is it possible to load point Cp data, in addition to the line plots forces and moments shown earlier? [50:40]

Let’s look at the plots I produced. I’ll go to Create Images. In this case I ran a macro to create a slice, extract that slice, and plot X versus Cp. You should be able to a create data command to select this and write a macro that creates the Cp curve. And then writes out only the data that you care about.

You could also use a custom action to create the data that you need. In that case, you would then have a new data file object that you could reference in the list.

Can you create images that required the loading of two files or multiple files? For example, if I wanted an image of the surface contours, a PLT file with a slice overlaid using an additional PLT file. [52:50]

This is kind of a tough one with the Tecplot Chorus. If we go back to the CSV file, you see that each tag references a single data file. There is a way, and we have tested this with fluent case and data files. In that case, you need two columns with the same tag name.

Is there a limit to the number of aux files for each solution? [54:40]

I’m assuming this means “Is there a limit for the number of columns for each row that I could have with aux files?”

There is no hard limit, but you may run into performance issues with having very long list.

Note that if I select a bunch of cases and select View Data and hit OK, Tecplot Chorus warns you that you’re about to load more than five data files. Tecplot 360 could get overwhelmed with large data, because of the way that frames are tiled.

Do deltas need to be on the same grid, just like normal Tecplot 360? [56:00]

Yes. All the cases used in the webinar have the same grid. But the XYZ locations don’t have to be in the same locations. Tecplot Chorus will look at how many points I have in each zone. In this example, I have 739,000. Tecplot Chorus will do a straight up subtraction of the Cp variable because that is what is assigned as my contour variable. If instead I happened to have 739,168 points, Tecplot Chorus wouldn’t give you the option. This toggle would be gray because we recognize that the grids are of different dimensions.

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

► The Purpose is Communication, the Goal is a Decision
12 Apr, 2021

Design, analysis, and testing are the fun parts of the job, but the best scientists and engineers go the extra mile to communicate their results clearly. When it comes time to present your work to others, it’s important to remember that the purpose of the presentation is clear communication, and the goal is (usually) to make or defend a decision. Today we’re going to highlight a few simple things that might improve the clarity and function of your plots and presentations.

This 4th blog in the series is about making more effective plots and presentations. Our first three posts covered the importance of consistency, the value of tailoring your presentations for your audience, and what to plot and what not to plot.

Match Expectations & Conventions

Figure 1. Thermal maps for each different region of interest (volume, spray, and wall).

The first thing to remember is that for a given industry, discipline, or company, there are always going to be certain conventions and expectations. Adhering to these expected conventions will help your audience quickly understand what they are seeing. This might have to do with the orientation of the images, the layout of your plots, the definition of your axes, the colormap chosen for your contours, the units of your variables, or even the sequence of your slides. One classic example in the field of aerodynamics is to present the coefficient of pressure distributions with the Y-axis ‘flipped’. If you must deviate from your audience’s expectations make sure you have a good reason for it and take care to explain the discrepancies.

Figure 1 was created for an audience of combustion engineers who are expecting to see multiple thermal maps, one for each of the different regions of interest (volume, spray, and wall). And, of course, they want to see the temperatures in Kelvin!

Stick to The Point

Most people don’t need to be told not to include slides that are unrelated to the topic – but oftentimes that guideline is too permissive. During the design, analysis, or test you may have generated or collected a lot of data. You may have also completed validation studies, performed mesh sensitivity experiments, reviewed historical datasets, or any number of ancillary activities. Just because those activities contributed to the overall success of your project does not mean that they need to take up space in your slide deck. Try to pare down your presentation to show only the minimum amount of information that is necessary. If you’re concerned that your audience may ask questions that extend beyond the core of your presentation you can always include that information in your backup slides.

Line Plots & Scalar Data Are Your Friends

Figure 2. Drag polar at 3 separate Mach numbers.

Your post-processor shouldn’t function solely as a visualization tool, it’s also an analysis tool. What’s the difference?

Visualization turns raw data into pretty pictures (which certainly have their uses), but analysis refines the same raw data to make engineering decisions possible. All the isometric 3D contour maps in the world won’t give you a clear answer for how much drag your wing design produces at its cruise flight condition. And no, adding stream traces doesn’t change that. You plotted an isosurface of Mach number? That’s cool, but we still don’t know if the design meets the range requirements.

The point is simulation & test data are useful only if they help answer questions that provide guidance in engineering decision making. Line plots (for example, drag polars, resonance curves, distribution plots, etc.) can help make a complex dataset much easier to understand and act upon. Integrated quantities/scalar values are even easier to digest – and oftentimes these values play the biggest role in engineering decisions.

Figure 2 shows a drag polar at 3 separate Mach numbers. This simple line plot may not be the fanciest image ever produced – but drag polars are a staple of the aerodynamic design process and can inform a lot about the drag performance of your vehicle.

As always, these tips are intended to be general purpose. Conventions will vary widely depending on your discipline, and supporting plots will change based on the specific goals of your presentation. What should never change, no matter what, is your desire to make your presentations effective.

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Tecplot 360 layouts, stylesheets, and scripts can help ensure consistency in your plots.

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The post The Purpose is Communication, the Goal is a Decision appeared first on Tecplot.

► Back to Basics: Tecplot 360
24 Mar, 2021

It’s Back to Basics here at Tecplot. This first in a 3-part series is about Getting Started with Tecplot 360. In this webinar, you will learn tips, tricks, and best practices to help you work faster and more efficiently.

Here’s the webinar agenda [02:41], with timestamps!

• Touring the Tecplot 360 user interface [04:00]
• Calculating new quantities [26:35]
• Extracting data over time [34:08]
• Exporting images and videos [38:11]
• Q&A [42:18]

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Q&A From the Webinar

What is the difference between Tecplot 360 and Tecplot Focus?

Tecplot Focus is a scaled down version of Tecplot 360, which is primarily used for engineering plotting. The main differences are that Tecplot Focus has:

• No CFD Analyze tools.
• No Python automation.
• 5 million data point limit.

Read the detailed differences in our Comparison PDF Tecplot 360 vs. Tecplot Focus.

Can a style file be saved for multiple frames?

A style file is specific to a frame and doesn’t reference the dataset. A layout allows you to save properties for multiple frames But also includes reference to a specific dataset.

How do I copy and paste the graphics into Word and PowerPoint documents?

To make a quick copy and paste into your presentation, click in a Tecplot 360 frame to select it, and use ctrl-c and ctrl-v to copy and paste your plot into Microsoft Word or PowerPoint.

The post Back to Basics: Tecplot 360 appeared first on Tecplot.

► NASA SLS Flight Condition Analyses Time Reduced from Days to Hours
23 Mar, 2021
The NASA Space Launch System (SLS) is a key element of the Artemis program that will provide the capabilities required for manned deep-space exploration. As currently planned, this family of heavy-lift vehicles will enable exploration of the Moon and Mars. Three classes of the SLS configuration, each of which includes both a crew and cargo version, are shown in Fig. 1. The large propulsive force needed to carry the targeted payloads is provided by two solid rocket boosters side-mounted onto a centerbody that embodies four RS-25 engines. The payload, whether the Orion crew capsule or a cargo compartment, is mounted atop the centerbody.

A new parallel processing toolbox for PyTecplot has been developed … reducing the postprocessing time by a factor of 12 …

Figure 1. SLS Configurations

Analyzing Wide Range of SLS Flight Conditions

Aerodynamic support for the SLS requires the use of both wind-tunnel tests and computational simulations to develop aerodynamic databases across the flight mission profile, seen in Fig. 2. These data are generated for a range of flight regimes including launch, liftoff, ascent, and booster separation. Flight conditions for the SLS vary from low-speed conditions on or near the launchpad to supersonic speeds during ascent. Because of this wide range of flight conditions, numerous tools are required to accurately capture the properties of the complex flowfields that evolve over time. While experimental results are useful and necessary, computational simulations yield results at flight conditions not easily tested in a wind tunnel facility, and these results include some fine-scale details that are not measurable in a wind tunnel.

Figure 2: SLS mission profile.

In order to accurately capture the massively-separated flowfields that arise during launch of the vehicles, an unsteady CFD solver was utilized. These unsteady IDDES (improved delayed detached eddy simulation) CFD calculations yield a wealth of information that can be interrogated to provide visualizations of the evolving flowfield. As an example, a solution animation in which isosurfaces of constant Q criterion are colored by the magnitude of vorticity is shown in Fig. 3.
Figure 3: Animation showing isosurfaces of constant Q criterion flooded by the magnitude of vorticity.

Unfortunately, the data files from which the animation was extracted are extremely large, often being in excess of 10 terabytes of data, even when saving just a subset of the simulation data. Consequently, reduction of these data is time intensive from both human and computational perspectives, to the point where it is prohibitive to employ these techniques as an everyday tool for database-level analyses.

12x Reduction in Postprocessing Time with PyTecplot

In collaboration with Tecplot developers, a new parallel processing toolbox for PyTecplot has been developed and implemented to reduce the aforementioned datasets. Use of these new methods has reduced the postprocessing time by a factor of 12 relative to the baseline reduction methods. These newly-developed parallel data reduction routines reduced the time to make a movie, such as this one, from days to hours, thus enabling analyses across a much wider range of flight conditions.

More information on Artemis and SLS can be found on the NASA website.

The post NASA SLS Flight Condition Analyses Time Reduced from Days to Hours appeared first on Tecplot.

► Plot This, Not That – Visual Communication
17 Mar, 2021

Welcome back for our third post in a blog series about making more effective plots and presentations. Our first two posts covered the importance of consistency and the value of tailoring your presentations for your audience. This time we’re keeping it very simple by discussing a few common pitfalls with plot styling and formatting, and how avoiding them can improve your plot game.

XY-Line Plots

Let’s start off with the simplest of them all – line plots. If you have an engineering or science degree you have made dozens, perhaps hundreds, of these simple plots. When you’re making them for your own reference, you might not pay much heed to styling and formatting. But when it comes time to communicate your results to outside parties they become almost as essential as the analysis itself.

How many lines is too many? Well, that’s a tough one to answer because it depends on so many variables. Rather than make up an arbitrary rule of thumb for not overcrowding your XY-line plots – we thought it best to simply discuss how to make the lines stand out in a way that makes sense. First, let’s look at the sample dataset in Figure 1.

Figure 1. XY-Line Plot – hard to read.

Rainfall data for three cities is plotted over time: Seattle, Dallas, and Miami. Each city has three samples. This results in a total of nine lines on the plot. As you can see in Figure 1, it is not easy to quickly differentiate between the various lines. Although each line is a different color, there is insufficient contrast between them. In addition, although we know there are subsets of data for each city, it is not easy to pick out the related lines quickly.

Line and Marker Styling

Now, look at Figure 2 and see how much easier it is to digest.

Figure 2. XY-Line Plot – with differentiated lines.

No matter how it’s styled it’s still a lot of data to digest, but the improvement between Figures 1 and 2 is obvious. In Figure 2 we have not reduced the dimensionality of our plot, but we have added line and marker styling to make distinguishing each dataset easier. We have also matched the line color per the city-type. This makes it easy to see which series are related to one another. The moral of the lesson is – be sure to use all the plot formatting tools at your disposal to provide meaningful visual distinction between your datasets. This trick can be applied to much more than just XY line plots – try it on bar charts, scatter plots, and more.

Contour Plots

Whether you think Contours are just pretty colors meant to satisfy managers or a valuable tool for aiding engineering decision making is up to you. But if you’re going to use them, at least use them correctly. We’ll look at two separate issues related to contour plots. The first up: contour levels. Look at Figure 3.

Figure 3. Contour plot – pretty worthless, right?

Pretty worthless, right? Well forgive us if we are using extreme examples to prove our point, but Figure 3 obviously doesn’t tell us much. Contour plots are designed to show gradients of a field variable along a surface or plane. And to that end, you must set the contour levels at an interval that reveals the gradient. Depending on your dataset, you might wish to use a linear or an exponential distribution. To improve Figure 3 on our sample dataset, we’ll choose the latter.

Figure 4. Contour plot showing exponential distribution.

Figure 4 shows the same dataset but uses an exponential distribution to reveal the change in turbulent frequency. Depending on your dataset, you may wish to continue with a linear distribution and ensure your max, min, and interval values are adjusted to highlight the gradient.

Colorblind-friendly Colormaps

Figure 4 is already infinitely more useful than Figure 3, but it can be improved even further by applying another plot trick. Look at Figure 5.

Figure 5. Contour plot with colorblind friendly colormap

In Figure 5 we have the same dataset and the same contour levels as in Figure 4, but now we’ve applied a new colormap. Not only does using a colormap like this one provide a more intuitive sense of the field variable gradient, but it is also colorblind friendly. Colorblindness is not something you hear much about – but it’s a real issue that affects millions of people. Do your audience a favor and make your plots as colorblind friendly as you are able! There are web apps available that can help you evaluate your images.

Plot Zoom Level

Last, but not least, is ensuring that you are plotting your data with a field of view that is appropriate for your results. In this example, we’ll adjust the zoom level on plots showing flow over a cylinder. If the zoom is too far out, pertinent results are washed out by uninteresting far-field values. If the zoom is too close, it’s difficult to discern how the region of interest fits into the broader dataset. Figures 6 and 7 illustrate how NOT to set your zoom level.

Figure 6. Zoom level is too far out.

Figure 7. Zoom level is too far in.

Clearly, Figure 6 is zoomed out too far, and therefore gives us a view of the entirety of the far field. The result leaves it unclear what the analysis was of, let alone what the results were. Figure 7 has the opposite problem. The perspective is too close, which shows a clear view of the cylinder but very little information about how the flow develops.

Figure 8. Shows us a zoom level that is just right.

Ahh, that’s better! With the zoom set just right Figure 8 shows us a clear view of the cylinder and a detailed view of the upstream and downstream flows. Of course “just right” may look very different depending on your dataset and what you want to communicate. but just make sure to pay attention to how well your audience can see the pertinent information!

Building visuals to effectively communicate complex data is a very broad discipline, so forgive us if our examples are a tad elementary. The truth is that compelling plot styling can be accomplished in a variety of ways – and only you are in the position to determine what’s right for your data and audience. You’ll be alright if you just make sure it’s easy for your audience to understand your visuals.

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► Webinar: Visualizing Isosurfaces of Higher Order Elements
25 Feb, 2021

This webinar welcomes Dr. Scott Imlay, Tecplot CTO. He will discuss his research on visualization of higher-order CFD results. CFD code developers are adopting higher-order finite-element CFD methods due to their potential to reduce computation cost while maintaining accuracy. These techniques have been an area of research for many years and are becoming more widely available in popular CFD codes. The webinar content is based on Scott’s technical presentation at AIAA SciTech 2021. Note that this is preliminary research on adding higher-order element visualization to Tecplot 360.​

Agenda (01:19)​

• What are higher-order elements​ (1:42)
• Why use higher-order elements (3:30)​
• Higher-Order Visualization Challenges (6:37)
• Basis Functions (8:32)
• Brick (Hexahedral) Sub-Division (12:06)
• Tetrahedral Sub-Division (12:51)
• Prism Sub-Division (13:32)
• Pyramid Sub-Division (14:38)
• Implementation (15:26)
• Results and Future work (17:33)​
• Q&A (23:02)

We are looking for partners to try our prototype add-on or to provide data for testing. If you are interested, please contact us! The best way is through our website contact form, or email scottimlay@tecplot.com.

Online Resources

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Q&A From the Webinar

Is support planned for Tecplot file formats?

This is early research and we’ve not made definite plans for HOE support in the Tecplot file formats. We’re initially targeting reading CGNS format because it has an HOE specification. We don’t have definite plans for supporting HOE in Tecplot file formats yet. But we are looking at using the CGNS standard.

We are implementing this now as a Tecplot 360 add-on. The add-on is functional for showing the isosurfaces and the surface data. We are looking for research partners to collaborate with because we do believe that the industry will need HOE. You can contact me at scottimlay@tecplot.com.

Is the assumption that we have iso-parametric nodal data?

To rephrase your question, is our assumption that we allow curved elements of the same order as the basis function for this position (which would be iso-parametric)? The answer is yes, but that isn’t the solution for the long run. Our goal is to not require iso-parametric. We know that some people are doing linear geometry and higher order basis functions for the solution. Sometimes they have higher order basis functions for the geometry than they have for the solution. So it can go either way and we would like to support that. In the add-on we do use iso-parametric. If you want to create a linear element, in this case, put your edge-center nodes and location to give you a linear element.

Would you comment on how your algorithm relates to one in GMSH (algorithm for plotting high order solutions)?

I’m not super familiar with how GMSH does it. But my understanding is that GMSH also uses a subdivision technique. And it does not do it selectively for the isosurfaces. I think I should leave it there because I don’t want to state that I know more about GMSH than I do. Here is a paper about GMSH.

Did you consider using a Bézier representation rather than a heuristic buffer approach to make sure not to exclude extrema that cannot be seen via the nodal data itself?

It is a very good idea. We’ve thought about it, but we have not gone any farther than thinking about it. It is true that if you were to use Bézier representation, then they wouldn’t be nodal values anymore, but they would be the points in space that are used to adjust the shape of a B spline, for instance. And the minimum and the maximum are at those points. And so that way we could guarantee it.

The customers we have talked to are not using that as their basis functions. And so we would have to find a way to convert from more common basis functions to that before. If any of you are using those Bezier (or Bernstein polynomial) basis functions for not just the geometry, but for your solution data, I would really like to hear from you and learn more about it. Please contact me at scottimlay@tecplot.com

How does the presence of higher-level (higher-order) elements affect interpolations onto a different grid in terms of fidelity and cost?

The add-on does not currently change the interpolations within Tecplot 360. If you were to interpolate to another grid, it wouldn’t take advantage of that at this time, but in the long run we certainly intend to do that.

In the future, the underlying basis functions would be utilized exactly. And your interpolation to any new nodes would be based on the higher-order basis functions. In terms of fidelity, that would mean it has the same fidelity as the higher-order solution.

The second part of your question is about cost. The cost is going to be higher per cell because it must solve of a nonlinear system of equations. If you have nonlinear geometry, it will have to solve a system of nonlinear equations to do the interpolation. But you have far fewer elements generally in a higher-order mesh than you do in a linear mesh. And so that means it’s quite possible that it would be cheaper overall than if you’re just going from a linear mesh to your new mesh.

How does Tecplot know the basis functions used for the higher-order solution? Does Tecplot expect solutions to be converted to a predefined format?

We support only the CGNS format, the quadratic or Lagrangian elements. If you are, for instance, converted into CGNS then it will have element type for each of the zones. And when we read that CGNS file, that’s how we know. The basis functions are effectively defined by the file format and element type.

The post Webinar: Visualizing Isosurfaces of Higher Order Elements appeared first on Tecplot.

Schnitger Corporation, CAE Markettop

► Altair has a great Q1 on the convergence of CAE, HPC & AI
7 May, 2021

Altair has a great Q1 on the convergence of CAE, HPC & AI

Altair CEO Jim Scapa started off the company’s Q1 earnings call by saying that their vision of the “convergence of simulation, HPC [high-performance computing] and AI [artificial intelligence] driving enterprise decisions is emerging as a clear imperative embraced by customers” and that customers are investing in these technologies as a strategic move.

First, the details and then some thoughts:

• Total revenue in Q1 was $150 million, up 14%. Altair reports that “a few million dollars” in deals that had been expected to close in Q2 jumped ahead to Q1 • Software revenue was$130 million, up 20%
• Within that Software total, License revenue was $96 million, up 24% • Maintenance and other services revenue was$33 million, up 7%
• “These results were balanced across all regions and product categories” but were not quantified
• Client engineering services revenue was down 23% to $11 million. Altair said this decrease again reflects customers furloughing and reducing CES staff working hours as a result of COVID-19 • Other revenue was down 16% to just under$2 million, “primarily due to reduced sales from toggled, our LED lighting business, driven by COVID-19”. Interesting, no? A software company that makes lightbulbs

Just about all of this was ahead of the company’s forecasts and investment analysts’ consensus. Part of this is due to Altair’s incredibly conservative guidance (it’s easier to beat a modest target than an ambitious one) but it seems that a lot of it is just natural caution given all of the unknowns in Altair’s end-markets.

Looking ahead, Altair guides to total Q2 revenue of $111 million to$114 million (up 14%ish), and total revenue between $504 million and$512 million, up 8%ish, for 2021.

A lot of this growth (though not quantified by the company) is due to SimSolid, which is the “fastest-growing product in Altair’s software portfolio”. In Q1 SimSolid was adopted by a strategically important aerospace account that Mr. Scapa believes holds the potential for internal expansion as well as leadership across aero that could see SimSolid’s broader adoption. He also highlighted a study done by the structural analysis department of General Motors that he believes showed the ability of SimSolid to decrease analysis timelines while still achieving 97% correlation to traditional simulation methods in modal analysis at the component level. Mr. Scapa believes that SimSolid is emerging as the standard for structural simulation among designers, a potentially huge market.

Mr. Scapa told investors that AI and ML (machine learning) are in their early stages and that Altair is working to embed more elements of neural networks across the portfolio, and on generalized tools that enable customers to build their own tools to take AI into their work processes.

As far as customer sentiment, Mr. Scapa said that “it’s very clear this is a much more robust year [than last year], as good as we’ve ever seen. It’s very broad-based: all verticals are very healthy, all regions are growing.” He also spoke about conditions in India but said that Altair isn’t seeing anything unusual there at this point.

Altair will hold an investor event on May 27th, when we should learn more about performance in the various business segments and end industries.

And if you see Mr. Scapa in a hallway somewhere, please congratulate him. He participated in the earnings call after welcoming his fifth grandchild. More little Scapas!

► Ansys has a solid Q1 but ACV concerns drag down the stock price
6 May, 2021

Ansys has a solid Q1 but ACV concerns drag down the stock price

Ansys reported Q1 results yesterday that were both good and not-so good –are you seeing a pattern here? PTC and ESI also reported mixed news, with positive Q1 results tamped down by uncertainty around the rest of 2021. First, the details:

• Ansys reported Q1 GAAP total revenue of $363 million, up 19%. That’s huge, and should be positive but • Ansys also reports a metric it calls ACV, which combines the annualized value of maintenance and leases plus the value of perpetual license contracts with start dates during the quarter, plus the annualized value of fixed-term services contracts with start dates or anniversary dates during the quarter, plus the value of work performed during the quarter on fixed-deliverable services contracts. Basically, it’s an attempt to turn things that cover many periods into a figure that can be compared across time periods. And that’s the problem. ACV in Q1 was up 6% (up 3% cc) from a year ago to$319 million but that’s up basically flat when compared to Q1 2019. Investors are seeing this as showing zero organic growth and a letdown from strong performance in the second half of 2020. That’s what sent the share price down 8% or so since the announcement hit the news wires yesterday afternoon
• But back to revenue. In Q1 2021, Ansys reported software license revenue of $132.6 million, up 51% • Within the software total, lease revenue of$66 million, up 45% as reported and up 42% in constant currencies (cc)
• Perpetual revenue was $68 million, up 57% (up 53%). This was an unusually high perpetual total; since most things are trending towards subscriptions these days. CFO Nicole Anasenes told investors several times during the earnings call that this was simply due to customer choice and not the result of any long-term trends or efforts on Ansys’ part • Maintenance revenue was$214 million, up 7% (up 3% cc) but down from $224 million in Q4. I’m not sure why that is and will try to find out • Service revenue was$17 million, up 2% (down 1% cc)
• By geo, revenue from the Americas was $160 million, up 21% (up 21% cc) in part due to a 5-year, multi-million dollar deal with a North American automotive OEM that is looking to “dramatically shorten their electric vehicle development time” and to million-dollar deals with aerospace and defense companies • From EMEA, total revenue was$103 million, up 16% (up 8% cc), as Ansys reports also signing aerospace deals there
• From Asia, revenue was $100 million, up 20% (up 16% cc) also on strength in automotive, as Ansys signed deals with several OEMs in Japan • Lots was made of strength in SMB. What Ansys said in its prepared remarks “we saw wide-ranging growth across industry and geography in our small- and medium-sized customer set. These customers have been slowest to return to normal patterns of spend, and we are increasingly optimistic about the patterns we saw in Q1.” What (I think) investors heard:” SMB SMB SMB is our growth driver”. Ansys didn’t quantify what proportion of revenue came from SMB but it’s likely that some of this is catchup from 2020, when a lot of SMB companies, with less cash overall, spent very cautiously while waiting to see what the pandemic would do to their businesses. It’s awesome that SMBs are investing, but it’s at most a small part of Ansys’ overall business • We don’t have a concrete number but we do know about Ansys direct (not likely to be SMBs) and indirect (more likely to be SMB) sales – and it hasn’t changed much. Direct sales were 71.8% of total (down from 73.6% of total revenue in Q1 2020), which means indirect/channel sales were 28.2% (versus 26,4%) of total revenue. Doing the math, the channel did just over$100 million in Q1 2021, up from $81 million a year ago. Not likely to move Ansys’ needle So, a very good Q1 and some investor alarm about the rest of 2021. Ansys itself does not sound at all alarmed about the year. tweaking its guidance for currency movements. The company expects Q2 revenue of$410 million to $440 million; and 2021 revenue of$1791 million to $1856 million. Two interesting notes from the earnings call. Ansys CEO Ajei Gopal was asked if he’s seeing any changes in the competitive landscape (without naming names, it was clear the investor was asking because Cadence is snapping up CAE companies). Paraphrasing, Mr. Gopal replied that it’s an attractive space, so he’s not surprised at the activity. But it doesn’t bother him, since Ansys has a broad, connected offering that others can’t match. When a large company acquires a point solution, it just adds to the buzz around the solution type (CFD, meshing, etc.) while, in effect, steering companies to Ansys, which can solve huge complex CFD problems and lots of others as well. And finally, about large versus SMB accounts. One investor said that Ansys’s largest accounts are making up an ever-increasing proportion of revenue; how does this drive Ansys’ investment priorities? Ms. Anasenes said that it’s not surprising that the biggest companies are investing more in simulation to be competitive, but that this “all-in” approach to CAE spreads down the customer pyramid to … yes, SMB. She said something like “Our investment strategy is consistent — technologies like multiphysics benefit all customers. This quarter’s momentum in SMB is a proof point of that broader appeal. We don’t invest for one set of customers — we personalization the selling motion and how we interact with customers” but they all benefit from the same technology. Are investors right to send the share price down 8% as I finish this? I don’t know; WAY above my pay grade. I do know that Ansys had a great Q1, forecasts growth for the rest of the year, and seems to be chugging along, delivering what its (large andSMB) customers need. ► ESI turns the corner, back to growth in Q1 5 May, 2021 ESI turns the corner, back to growth in Q1 Catching up on more earnings announcements. Last week, ESI Group reported Q1 revenue of €55.5 million, up 1% as reported and up 4% in constant currencies. ESI has been reinventing itself and first issued guidance to investors during the last earnings call. Q1 2021 beat the guidance CFO Olfa Zorgati had laid out –revenue between €52 million and €55 million– and COO Mike Salari explained what ESI is doing to more closely manage the sales pipeline. First, the details: • Total revenue was €56 million, up 1% as reported and up 4% in constant currencies (cc) • License revenue was up 2% (up 4% cc) to just under €50 million. ESI said that license revenue grew in all geos but highlighted that revenue from the Americas up 3% (up 12% cc) — important because ESI has long struggled in North America • ESI divides license revenue into New and Repeat Business: New Business revenue was basically flat at just over €3 million, while Repeat Business was up 1% (up 4% cc) to €54 million • Why does that add up to more than what’s reported as License revenue? Because ESI adds New Business (€3 million) and Repeat Business (€54 million) then subtracts out deferred revenue (€7 million) to get to the License revenue total of €50 million. We’re more likely to see companies reporting similar categories with the deferred revenue already netted out. (Deferred revenue, for those not up on Accounting 101, is payments that have been received that can’t be recognized in the current period. Think subscriptions. Companies can recognize this quarter’s payments but not prepayments.) • Services revenue was €6 million, down 3% (flat cc), with revenue up in EMEA and the Americas by down sharply in Asia • By geo, revenue from EMEA was €32 million, up 1% (up 2% cc); from Asia, revenue was flat as reported and up 5% cc to €16 million; and from the Americas, €7 million, up 2% (up 11% cc) • By vertical, ESI said that automotive continues to lead, with license revenue up 3% in the quarter Ms. Zorgati pointed out that Q1 was ESI’s first quarter of growth in the last four, finally return to pre-pandemic revenue. Even so, the guidance for Q2 is very conservative; Ms. Zorgati forecast H1 2021 revenue of €80.5 million to €82.5 million, up from €80.8 million for H1 2020, which implies just 1% growth at the midpoint for the half-year and essentially no growth for Q2. Q2 2020 was when the bottom fell out for most companies as COVID took hold; ESI saw an 8% decline in license revenue in Q2 2020. So it should be easy to grow from that low base — but the company feels that conservative guidance in what is still an uncertain economy is better than over-optimism. Co-COO Mike Salari added that he is looking at how ESI can improve its sales pipeline management, giving more confidence to forecasts. Another area of change for ESI is that it has created a go-to-market strategy around its Focus Accounts –the largest and/or most innovative companies in ESI’s end-industries– that it plans to pursue. Revenue from those specific accounts was up 5% in Q1, which is a good indicator of underlying demand. CEO Cristel de Rouvray summed things up by saying that Q1 2021 results confirmed that “our top 20 customers have clearly signaled their need for more virtual prototyping to navigate the global industry crisis and enable their digital transformation. On this foundation of growing repeat business, we can drive ambitious efforts to increase new business for sustainable and profitable revenue growth.” ESI will release Q2 earnings in June and hold an investor event this Fall, at which point we’ll know a lot more about ESI’s reinvention. But, so far, so good. ► Hexagon continues to improve; Q1 revenue up 10% 4 May, 2021 Hexagon continues to improve; Q1 revenue up 10% Hexagon reported results last week that were surprisingly good and that –remember that Hexagon doesn’t itself issue guidance– beat analyst consensus. The details: • Total revenue in Q1 was €978 million, up 10% as reported and up 11% on an organic, constant currency (occ) basis. That’s 4% above consensus, especially significant considering that CEO Ola Rollén said the company saw a 5% currency headwind, as the Euro contracted against the US Dollar and Chinese renminbi • Overall, the company reports seeing an acceleration in Europe and China. By geo, revenue was up 27% occ in Asia, up 10% occ in EMEA and flat in the Americas. In Asia, China recorded 73% occ growth, in part due to easy comparables (a 40% contraction a year ago) — but also driven by a strong recovery in manufacturing, infrastructure, and construction. South Korea, South-Eastern Asia, and Australia recorded solid growth, while Japan and India declined on weak demand in manufacturing in Japan and weakness in the power and energy sector in India. • In EMEA, revenue from Western Europe was up 7% occ, on strong demand in the surveying, infrastructure, and construction segments. The manufacturing and power and energy segments sequentially improved and recorded a low single-digit decline. Russia and Eastern Europe recorded high double-digit organic growth. • In the Americas, North America reported a 1% occ decline partly due to a strong quarter a year ago but also on weakness in the aerospace and power and energy segments. The surveying and infrastructure and construction segments recorded solid growth in the region. South America recorded high single-digit growth supported by a solid development in the agriculture, power and energy, and public safety segments. Regarding North America, Mr. Rollén said that he sees an “automotive sector that is improving month-by-month. Aerospace has been weak up to now. But I think, we do believe that aerospace has hit bottom and is slowly recovering”. • Below is the graphics Mr. Rollén shared about how each industry sector performed in each region — I don’t think he expects us to draw anything meaningful from it, other than that the sales environment right now is not consistent in any way (click on the graphic to go to the entire earnings presentation): • By industry bucket, Geospatial Enterprise Solutions (GES) reported revenue of €503 million, up 11% (up 13% occ). On an occ basis, revenue was up 30% in organic growth in Asia (boosted by a 79% occ growth in China), up 17% in EMEA, and up 3% in the Americas. • Revenue from Geosystems was up a “fantastic” 22% occ, on “robust global activity in construction and infrastructure markets plus increased demand for new solutions” such as the Leica BLK series • Revenue from Safety and Infrastructure was down 2% occ, as growth in public safety couldn’t offset weakness in defense. • The Autonomy & Positioning division recorded a revenue decline of 2% occ, impacted by order delays from defense customers and a weak automotive market. Even so, the agriculture business continued to record strong growth • The Industrial Enterprise Solutions bucket reported revenue of €475 million, up 9% (up 8% occ). By geo and on an occ basis, revenue was up 25% in Asia (with a 70% increase in China), up 1% in EMEA, and down 4% in the Americas. • Manufacturing Intelligence “recovered strongly in the quarter,” with revenue up 12% occ, driven by demand in China, a recovery in the automotive sector • PPM revenue was down 4% occ, as it continues to face a challenging oil and gas market. Mr. Rollén did say that PPM saw solid growth in asset information management, the newly-acquired cybersecurity offering, and the growing AEC portfolio. Mr. Rollén believes that PPM will return to growth, “but it’s probably a story for the second half of this year and not in Q2.” • Mr. Rollén said that the software portfolios continued to record favorable growth, without offering much detail. In response to investor questions, he said that “software grew at roughly 5% organic growth on a base that didn’t see as much contraction a year ago” but it’s clear that if overall revenue grew 11% and software grew at 5%, then hardware outgrew software in Q1. Mr. Rollén highlighted a few new products that will be launched in 2021, including a partnership with Boston Dynamics to put an autonomous reality capture solution into the market that combines Spot (the dog-like robot) with Hexagon’s Leica solutions. Right now, humans walk or drive reality capture stations around an asset; with Spot (and other, coming robotics solutions), Hexagon hopes that infrastructure, defense, industrial facilities, and safety & security benefit from the automation of repetitive scanning tasks. More here. Since I know many of you are interested in MSC Software, it really only came up twice. Mr. Rollén spoke about how the University of Perugia in Italy, is using Hexagon’s multibody dynamics software to study accidental falls in the construction industry. Perugia is using biomechanical analyses to model the initial position and force of a fall based on the final location of the body. On a less downbeat note, he also gave Hyundai as an example, where simulations of gearbox models and full-system analyses earlier in design drive time and cost savings. And M&A. Always M&A. Mr. Rollén said that “M&A might happen … Prices are still high for quality assets. If you’re looking for a SaaS business with recurring revenue and a strong double-digit margin, then you have to pay a lot for those assets. That seems to continue into 2021.” Mr. Rollén doesn’t issue forecasts but did say that Hexagon has big product launches planned for Q3 which will result in “relevant numbers” in Q4. It seems that financial analysts are modeling total revenue for 2021 of around €4.1 billion, which would be overall growth of 9% or so. For Q2, the consensus seems to be for revenue of €1 billion, so a slight sequential increase and a year/year increase of around 12%. ► PTC reports Core up 21%cc in FQ2 but leaves F2021 targets (mostly) alone 3 May, 2021 PTC reports Core up 21%cc in FQ2 but leaves F2021 targets (mostly) alone PTC reported results last week that were both good and worrisome. The details, then what I think it might mean: • Total revenue in PTC’s fiscal Q2 2021 was$462 million, up 28% as reported and up 22% in constant currencies (cc) — well ahead of guidance and everyone’s (meaning Wall Street’s) expectations. CFO Kristian Talvitie said that solid execution, longer contract durations, and a modest contribution from Arena led to the beat
• Recurring revenue was $415 million, up a cool$100 million (or 32%) from a year ago
• Perpetual license revenue was $7 million, continuing the planned decline, down 16% • Services revenue was$40 million, up 13%
• PTC also supplies non-GAAP metrics to help investors understand the future impact of subscription bookings. ARR, or Annual Run Rate, “represents the annualized value of [the] portfolio of active subscription software, cloud, SaaS, and support contracts as of the end of the reporting period.” Using PTC’s definitions, ARR was up 18% in FQ2, (up 15% cc) over a year ago. On an organic basis (meaning, excluding Arena), ARR was up 14% (up 11% cc)
• Looking at that software revenue in a different way, license revenue was $198 million, up 55%, while support (aka maintenance) and cloud services revenue was$224 million, up 14%
• Looking at this by product groupings, PTC says the Core Products Group reported software revenue of $299 million, up 27% (up 21% cc). The Growth Product Group had software revenue of$70 million, up 63% (up 58% cc), and the Focused Solutions Group had software revenue of $53 million, up 13% (up 9% cc) • The problem for investors came with ARR, which in the case of Core was far lower than the reported revenue. Core’s ARR in FQ2 was up 13% (up 10% cc) year over year so far slower than revenue growth of 21% cc. Even so, the company said that the “demand we see for our core products and SaaS offerings, combined with a strong pipeline heading into the second half of 2021, supports our outlook for the year.” • Within Core, PTC said CAD performance was “solid, with ARR growth in the high single digits” and that demand was strong across all major geographies, while PLM ARR growth was in the mid-teens and especially in medical devices, industrials, and aerospace. • PTC also gave insight into Onshape, saying it had a strong quarter, with “ARR growth of more than 40% [and] with bookings on track to grow more than 100% for FY’21.” PTC reported that the opportunity to cross-sell Onshape and the rest of the PTC portfolio is increasing, which I take to mean more enterprise engagement and expansions. • And about Arena: integration is proceeding, and Q2 performance seems as expected. PTC reports that it saw “ARR growth in mid-teens” with medical devices and high-tech manufacturing continuing to be the top verticals. PTC says it has a roadmap for cross-selling programs, and to expand geographically. Renewal rates remained high. • I usually fill in the revenue-by-geo section using data from the company’s SEC filing, but that hasn’t been made public as of this writing (nothing nefarious — it’s usually a bit after the earnings release goes public). Looking at ARR (so NOT revenue), PTC says that in the Americas, ARR was up 22% (up 21% cc), driven by Arena, Augmented Reality (AR), and solid Core performance. ARR in Europe was up 14% (up 8% cc) on strength in AR and high-teens growth in IoT. Finally, ARR in Asia was up 19% (up 16% cc) on “strong performance across all segments”. The news was mostly good – the concern the investors I spoke with had was the lack of sequential growth in ARR. In a perfect world, revenue-related metrics go up both year/year and from quarter to quarter. It doesn’t always work that way, especially when foreign exchange is factored in. Too, the lack of growth in Focused Solutions (the cash cow of legacy products at PTC, kept around to be slow but steady earners for the company) was concerning, as most see continued challenges for that part of the business’s main customers in commercial aviation and retail. PTC’s performance in FQ! an FQ2 as well as the closing of the Arena acquisition led the company to tweak but not raise its guidance for the year, tuning the revenue growth target range from 16% to 19% to 17% to 19% — meaning that revenue guidance is now$1,710 million to \$1,740 million.

During the earnings call, CEO Jim Heppelmann also talk about PTC’s SaaS vision, now that Arena has closed (and, subtext, Autodesk is acquiring UpChain). Mr.Heppelmann essentially reiterated PTC’s commitment to using PTC Atlas as the platform on which legacy CAD (read, Creo) and PLM (read Windchill) offerings will reside by 2024. atlas architecture by 2024. They’ll join Onshape, Arena, Vuforia, and Thingworx on Atlas, which will, the company believes, make it far easier to cross-sell the entire portfolio. (But not to worry: Mr. Heppelmann said that PTC would “continue to offer on-premise versions of core products indefinitely, [since] a growing number of our customers want to enjoy the great benefits of SaaS but at the same time, prefer not to switch off their current enterprise systems.”

So, TL;DR. What does it all mean? Investors seem to focus more on the 30% of the business that is volatile than on the 70% –Core CAD, PLM, etc.– that is the engine that drives this bus. Core revenue growth of over 20% is great. As Mr. Heppelmann pointed out, FQ2 was “14 quarters in a row of double-digit growth in the Core business”. A vision that refreshes the legacy products and gives customers and prospects a way forward is also very attractive. Yes, there are ARR problems and foreign exchange challenges — but if 70% of your business is growing at 27%, (and BTW grew at 25% for all of last fiscal year), why not focus on that instead? I don’t think CAD is dead, and PTC’s customers seem to agree.

► Quickie: Bentley acquires for iTwin offering
30 Apr, 2021

Quickie: Bentley acquires for iTwin offering

Bentley just announced that it is acquiring sensemetrics and Vista Data Vision, both of which make software for Internet of Things (IoT) applications targeted at infrastructure use cases. Bentley says the companies will “expand the scope of the Bentley iTwin platform to add intrinsic IoT capabilities for infrastructure digital twins to incorporate real-time sensor data.”

According to Bentley, the sensemetrics platform is “used extensively for real-time safety and risk monitoring in infrastructure, mining, and construction activities including to measure and visualize civil structural movement, for condition assessment, and to help detect and prevent damage.”

This stuff is never boring. Bentley says that Vista Data Vision was spun out of Iceland’s Vista Engineering, which pioneered remote, real-time monitoring of power, traffic, and sanitation systems. Vista Data Vision has been developing software to analyze and manage project data since the 1990s, “to be used in solving real-world engineering problems that required real-time or near real-time observations, and that could be configured by nontechnical users, with no software engineering required and no vendor lock-in.”

Terms were not announced but Bentley said the combined revenues of sensemetrics and Vista Data Vision are not material to Bentley’s revenue for 2021. We may learn more when Bentley discusses Q1 and subsequent events next week. In the meantime, you can read much more here.

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