This week’s CFD news begins with a report on the state of the aerospace industry that’s worth your time. Another high level report discusses the impact of fluid dynamics on the UK’s economy. For whatever reason, the automotive applications of … Continue reading →

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

This week’s compilation of CFD news begins with quantum mechanics and interpretive dance. Not a typo. Your CFD skills could win you some cash in a challenge being run by our friends in Washington. There are many good things going … Continue reading →

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

We begin 2021 Q4 with a lot of reading. No internet sound bites this week: V&V, quantum computing, physics and math, machine learning, and lots more. For students, do not leave this blog post until you’ve looked into the scholarships … Continue reading →

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

This week’s CFD news is “just the facts” and no games. I’m hoping to get some comments about whether simulation is a “risk-free space” and your wrong beliefs about meshing. Emerging technologies is an inadvertent subtheme in this edition which … Continue reading →

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

This week of CFD news slowly slides into the weekend with this edition which begins with a fantastic article about wind tunnel testing (not CFD!). Lots of event news in which we see things straddling the line between in-person and … Continue reading →

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

Hi, I’m Marc Tombroff and I’m a VP Research & Development CFD at Cadence. I am an engineer and since my time at the engineering school I have always been impressed by the technology, its innovation drivers with the feeling … Continue reading →

The post I’m Marc Tombroff and This Is How I Mesh first appeared on Another Fine Mesh.

The ekranoplan was a massive, Soviet-era aircraft that relied on ground effect to stay aloft. In this video, RC pilots test out their own homemade version of the craft, including some neat flow visualization of the wingtip vortices. When an aircraft (or, for that matter, a bird) flies near the ground, it experiences less drag than at higher altitudes. This happens primarily because of the ground’s effect on wingtip vortices.

In normal flight, the vortices from an aircraft’s wingtips create a downwash that reduces the wing’s overall lift. But in ground effect, the vortices cannot drift downward as they normally do. Instead, they spread apart from one another, thereby reducing the drag caused by downwash from the aircraft. The end result is better performance, though it comes with added risk since there’s very little time to correct an error when flying at an altitude less than half the aircraft’s wingspan. (Video and image credit: rctestflight; submitted by Simplicator)

I love a good crossover between fluid dynamics and something unexpected. Fiber artist Megan Zaniewski uses thread-painting techniques to embroider ducks, frogs, otters, and other animals as they appear both above and below water. I am blown away by how she captures the movement and turbulence of water in these pieces! Just look at that spectacular frog splash. You can find lots more of her art on her Instagram. (Image credit: M. Zaniewski; via Colossal)

As levels of air pollution rise, so does the incidence of pulmonary diseases like asthma. Treatments for these diseases largely rely on inhalers containing drug particles that need to be carried into the small bronchi of the lungs. To better understand how the process works, researchers used computational fluid dynamics to simulate how air and particles travel through the human respiratory tract.

The team found that larger particles tended to get stuck in the mouth instead of making it down into the lungs. This problem was made worse at high inhalation rates because the particles’ inertia was too large for them to make the sharp turn down into the trachea. In contrast, smaller particles could travel down into the lungs and into the smaller branches there before settling. The authors concluded that inhalers should use fine drug particles to maximize delivery into the lungs. They also note that adjusting inhalers to deliver more medication to the lungs may also lower the overall price because less of the dosage gets wasted in the patient’s mouth.

Of course, the study’s results also serve as a warning about the dangers of air pollution from fine particulates. Here in Colorado, our summers are punctuated with wildfire smoke, much of it in the form of tiny particles about the same size as the drug particles in this study. If fine drug particles are effective at making it into the smaller branches of our lungs, so are those pollutants. That’s a good reason to stay inside in smoky conditions or use a high-quality N-95 mask while out and about. (Image credit: coltsfan; research credit: A. Tiwari et al.; via Physics World; submitted by Kam-Yung Soh)

The fish-shaped Gluggle Jug makes an impressive set of sounds when tilted for pouring. Steve Mould explores their origin in this video. When liquid is poured from a container, air needs a path in to replace the poured liquid. You’re likely most familiar with this from long-necked bottles, where trying to pour the liquid too quickly results in a glug-glug noise as air bubbles periodically force their way through the bottle neck. The same thing happens in the Gluggle Jug, particularly at the joint between the tail and body of the pitcher. The volume and resonance of the jug’s sounds comes from the shape; the open mouth of the container amplifies the sound of bubbles popping back from the tail region. (Image and video credit: S. Mould)

Robust, self-cleaning surfaces are a holy grail for many engineers, but they’re tough to achieve. One necessary ingredient for a self-cleaning surface is the ability to shed water, which is why superhydrophobic coatings and surface treatments are popular. Here, researchers prompt their droplets to move at speeds up to 16 cm/s by dropping them onto a thin layer of heated oil.

Longtime readers will no doubt be reminded of self-propelling Leidenfrost drops, but this situation is not quite the same. In general, the oil layer suppresses the Leidenfrost effect. Instead, the oil heats the drop, evaporating its vapor. A bubble of vapor will nucleate at a random location in the droplet and eject itself, pushing the drop in the opposite direction. Because of the disruption caused by that ejection, new bubbles will preferentially form at the same spot, providing an ongoing supply of vapor that keeps the drop sliding in the same direction. It’s like a miniature rocket zooming along the oil film! (Image and research credit: V. Leon and K. Varanasi; via APS Physics)

Hydrostatic pressure in a fluid is based on the fluid’s depth. You’ll rarely see a more dramatic example of that power than with a water release from a dam. Here we see the outlet of the Verbund Hydro Power dam in Austria. With 190 meters of water behind the dam, the outlet jet is massive. It moves 20,000 liters of water per second at a speed of 50 meters per second. Imagine what it would be like to stand next to that! (Image and video credit: Discovery UK; submitted by Olwyn B.)

Figure 1: Structured multi-block meshing for a nacelle.

1100 words / 5 minutes read

Introduction

In modern transport aircraft, underwing engine nacelle installation is the most common design choice. Here, the engine nacelles which are tightly coupled with the wing have a huge impact on the maximum lift and stall angle of the wing. With the usage of larger by-pass ratio engines over the years, the adverse effects of the nacelle on the wing’s performance have increased dramatically, especially so when the high lift devices are deployed.

The nacelles hamper the wing’s desired performance by triggering premature massive flow separation on the main element and decrease the CL_max and stall angle. We cover more of this is in our earlier article Engine Nacelle Aerodynamics.

In order to attenuate the negative influence of nacelle, most aircraft manufacturers employ strakes or more popularly known as chines at appropriate locations on the nacelle.

Nacelle strakes are small delta-shaped or triangular panel sheets positioned strategically on the nacelle to induce longitudinal vortices. In short – vortex generators mounted on nacelles are called strakes.

Usually, a pair of strakes are mounted on the nacelles to generate additional vortices to control the flow separation on the wing. Depending on the mounting location and the nacelle-pylon-wing flow field, the generated strake vortices can avoid the generation of slower nacelle vortex or sometimes even interact with nacelle vortex and increase their axial core speed. Thus they affect the position and strength of the installation vortices leading to an increase in maximum achievable lift. Since strakes directly influence the wing’s lift generation capabilities, their design demands careful attention.

Effectiveness of strake installation

For underwing nacelle configurations without strakes, at alphas near to stall, a large zone of low energy flow gets set above the main wing. The creation of this low energy zone is due to the nacelle blocking the flow from passing over the upper surface of the wing at high alphas. Any further increase in the angle of attack results in premature flow separation.

Parametric design of nacelle strake

The effectiveness of the strakes is directly related to the strake’s geometry and installation location. The strength and trajectory of the strake vortex depend on the strake area, deflection angle, axial position, and azimuth location.

Also, strake 2 and strake 3 have the same exact position, but strake 3 has an area that is two-thirds that of strake 2. Since the location is the same, there is hardly any difference in lift coefficients between strake 2 and 3, before stall. However, after the stall, the strake with a smaller area (strake 3) produces an abrupt drop in lift coefficient.

With these observations, we can conclude that the axial positioning of the strake determines the circumferential component of the flow, which in turn determines the strake’s local angle of attack. For a fixed azimuth positioning, the local alpha is a key factor influencing the strength of the vortex. In turn, strake’s vortex strength is a key factor in strake’s effectiveness in delaying the stall.

Parting thoughts

Even though strakes are proven devices to enhance lift for underwing mounted nacelle configurations, they are observed to be less effective for larger UHBR nacelles. For larger bypass ratio engines, they are unable to energize the flow sufficiently and make the flow remained attached to the wing surface. For such nacelles, researchers are working on developing active flow control devices such as pulsed jet blowing to control flow separation.

Nevertheless, strakes which are successfully deployed by all aircraft manufacturers around the world for many decades, will continue to be in use for small and medium-sized aircraft because of their simplicity, cost-effectiveness, and more importantly for their effectiveness in controlling the flow.

Further Reading:
Article: Engine Nacelle Aerodynamics

References

1. “Modelling the aerodynamics of propulsive system integration at cruise and high-lift conditions”, Thierry Sibilli, PhD Academic Year: 2011-2012, Cranfield University.

2. “CFD Prediciton of Maximum Lift Effects on Realistic High-Lift-Commercial-Aircraft-Configurations within the European project EUROLIFT II”, H. Frhr. v. Geyr et al, Second Symposium “Simulation of Wing and Nacelle Stall”, June 22nd – 23rd, 2010, Braunschweig, Germany.

3. “Navier-Stokes Analysis of a High Wing Transport High-Lift Configuration With Externally Blown Flaps”, Jeffrey P. Slotnick et al, NASA.

4. “Numerical Research of the Nacelle Strake on a Civil jet“, Wensheng Zhang et al, 28TH International Congress of the Aeronautical Sciences, ICAS 2012.

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The post Influence of Vortex Generators in Aircraft Aerodynamics. appeared first on GridPro Blog.

Figure 1: Structured multi-block grid for an aircraft intake S-Duct.

2300 words / 11 minutes read

Highly offset modern-day S-ducts need flow control mechanisms like vortex generators or synthetic jets to control the flow. The general consensus among researchers is that RANS solvers are in-capable of meeting the challenges of S-duct internal flow fields, while DES is considered the most promising candidate. Lastly, structured grids are observed to provide superior flow field prediction for S-ducts on a fraction of a grid size than that needed for unstructured grids.

Introduction

The design of an optimized intake-duct is a trade-off between multiple requirements. This includes high-pressure recovery, low installation drag, low radar, and noise signatures, as well as minimum weight and cost. These requirements are driving the development of modern fighter aircraft and UAVs towards compact designs. This in turn has resulted in having a high degree of offset between the intake and engine face, leading to the design of highly bend S-ducts or serpentine ducts.

However, higher bend ducts lead to larger flow separation and vortex formation. With the compact nature of the duct, the available duct length is not good enough for the diffusion and dissipation of these secondary flows. This results in total pressure loss and higher flow non-uniformity at the engine fan face. This is something unpleasant, as flow distortion at the inlet can lead to reduced stability margin for the compressor or fan. Further, inlet distortion can cause high cycle fatigue resulting in increased maintenance costs, loss of aircraft operability, and even catastrophic loss of the aircraft.

CFD as a design tool is used extensively for accurate aerodynamic prediction of highly offset diffuser shapes. Attention is paid to understanding the variation in distortion levels and pressure recovery at the AIP with changes in duct shape. CFD is quite handy in studying the parametric variations of vortex generators and synthetic jets. Figure 3 shows the reduction in flow separation with the introduction of vortex generators.

Passive flow control by vortex generators

The effectiveness of the VGs depends on a number of parameters, but the main ones are the height of the VG, the orientation angle of the VG relative to the free stream and the number of VGs used. Hence testings’ are done by tweaking these parameters to study their influence on the flow.

Effect of Number of VGs: The placement of the VGs starts from the symmetry plane. The first one is placed near the symmetry plane and subsequent ones are placed in the circumferential direction at constant x. As can be observed in Figure 3, the flow quality improves with the usage of additional VGs in the form of diminishing of low Mach number region and the recovery of the pressure loss.

Active flow control by synthetic Jets

Passive flow control techniques have been in existence for decades, but active flow control (AFC) methods like constant or pulsed air jets are a new budding research area. Here, the improvement in flow efficiency is accomplished through a delay in flow separation.

Figure 6 shows AFC configurations for constant blowing jets with three slit areas with different widths. Studies show that improvement in recovery and reduction of distortion happens with an increase in the mass flow rate of the jets. However, beyond a certain value, a further increase in mass flow rate makes the AFC less effective. Best performance is shown by the smaller area and higher jet velocity configuration. A maximum pressure recovery of 1.15% and distortion reduction by 60-80 percent have been reported.

The S-duct’s challenge to the CFD community

Accurate prediction of large separated flow regions such as seen in S-ducts by CFD is very difficult. Currently, efforts are put to evaluate and understand the capabilities and limitations of RANS, URANS, and DES methods in modeling the flow physics and performance characteristics of highly offset intake diffusers.

The general consensus among researchers is that RANS solvers are in-capable of meeting the challenges of S-duct internal flow fields. URANS shows better predictions capabilities by capturing the time-evolution of the flow. Further research and development are needed to make it more accurate. DNS or LES methods at least in principle are capable to make accurate predictions of the flow physics to the required level. However, being computationally expensive, they are still not feasible. Only DES – Detached Eddy Simulation, is considered among many researchers as the potential candidate for predicting unsteady flow fields, involving high levels of turbulence and occurrences of instantaneous flow phenomena.

DES, which is nothing but a hybrid of RANS-LES turbulence methods has computational costs which are quite acceptable. DES offers a nice balance by providing the physical accuracy of the LES with the cost-effectiveness of the RANS. Since building a DES code involves, only coupling the pre-existing RANS and LES code, they reduce the time and cost needed for development.

This bifurcation of the regions is essential because there are major differences between RANS and LES arising due to the different turbulence modeling approaches. LES resolves the larger scales of the flow and models only the smaller scales. While, RANS on the other hand does not explicitly resolve any scales, but calculates the mean flow quantities and models the turbulent scales.

Since in LES, the flow turbulent scales are explicitly resolved, the generated eddy viscosity is smaller compared to that in RANS. As a result, LES reduces the viscous dissipation and diffusion in the flow, thereby allowing weaker flow structures to sustain in the solution.

Another difference between RANS and LES is the grid dependency. In RANS, the turbulence model is based on flow quantities and is similar for every grid, while in LES, the filter width is directly dependent on the grid-spacing. What this means is that any grid refinement in LES not only influences the numerical accuracy, but also the subgrid-scale turbulence model. As a consequence, unlike in RANS, grid-refinement beyond a point of convergence in LES will not produce the same solution.

When we consider standard DES in general, it can be noticed that there is a large dependency on the RANS part of the simulation, which requires a tangential grid spacing on the wall to be greater than the local boundary layer thickness. Sticking to this gridding requirement may be very difficult inside the duct. In such circumstances, if the switching to LES occurs inside the RANS boundary layer, then there will be an underestimation of the skin friction coefficient.

So, in order to overcome this grid-induced separation, newer approaches like DDES – Delayed Detached Eddy Simulation and ZDES – Zonal Detached Eddy Simulation have been developed. In DDES, the switch to LES mode is delayed while in ZDES, the RANS and DES zones are selected individually, to have clarity of the role of each region. These steps are undertaken to avoid ‘model stress depletion’ and grid-induced flow separation.

In RANS alone, the predicted flow distortion and pressure recovery depend highly on turbulence modeling. Depending on the turbulence model chosen, the total pressure recovery is reported to vary from 0.1% to -1.8%, while flow distortion is observed to differ widely from +37% to +126%.
However, many major differences exist between the numerical results and experiments. The region with total pressure losses doesn’t confirm with the experiments. The distortion parameter is systematically overestimated, with the DES solution differing from experiments by a larger value than RANS, as DES overestimates the separated flow region. Also, DES shows a delay in the development of the instabilities in the shear layer.

Which grid type is better for S-duct?

Just like the need to pick the right solver type, there is a need to pick the right grid type to do accurate CFD prediction for S-ducts. Studies have shown that grid generation both in structured and unstructured ways for S-ducts are quick, efficient, and reliable. However, structured meshes tend to achieve superior performances than high-quality unstructured meshes says a research study by ANSA, BETA CAE system.

Solution differences between the structured medium grid and fine grid were observed to be very small. Even though the grid size nearly doubles in between the medium and fine grids, the AIP back pressure was seen to make only a marginal variation of 0.05% between the two grids. On the other hand, notable differences was noticed between the unstructured medium grid and fine grid simulation results. The difference in predicted backpressure between the two grids is about 1%.

When the flow field as generated by equivalently resolved unstructured and structured grids was compared, the differences were significant. To make the grids more relatable to each other, the fundamental cell size was kept the same for the two different mesh setups. Tetrahedral cells being isotropic in nature needs more mesh cells to achieve a roughly equivalent sized structured grid cell.

Thus, the structured mesh outperforms the unstructured hybrid grid despite having a far lower cell count. The structured grid with a cell count of 4.2 million predicts a more greatly resolved flow solution than its equivalent unstructured counterpart. Interestingly, to achieve a similarly resolved flow solution, the unstructured approach demanded a grid of size 31.2 million.

What we can conclude from these gridding experiments is that, at least for flows in S-duct with significant shear, an order of magnitude more unstructured cells are needed to match an equivalent structured grid in terms of solution accuracy and flow field resolution. Even with VGs, structured meshes require far fewer cells. Thus, structured meshes require lesser computational resources to predict higher-quality flow fields than higher density unstructured grids.

Parting Remarks

This brings us to the end of this article. Complete elimination of flow distortions in S-duct at all flight conditions is impossible. The use of flow control mechanisms like vortex generators or synthetic jets is the way to go forward in dealing with flow separations.

Evaluation of the distortion parameter results from steady and dynamic simulations at the engine face reveal that only dynamic simulations can provide the correct assessment of the performance parameters considering the distortion limits as required by the engine manufacturers. Compared to RANS, DES results, in general, are in accordance with the experiments, but in the future, its capabilities need to be further enhanced to improve its accuracy to the level required by industrial standards.

Physical time step size and grid resolution have an important role in the outcome of the computational results. For DES simulations, a strong association exists between the grid and the ability of the algorithm to correctly manage the varying turbulent scales.

Structured meshes are reported to provide superior solutions compared to unstructured meshes. Needing only a fraction of the cell count as needed by unstructured meshes, structured grids are observed to provide a better-resolved flow field and tend to take far lesser computational resources.

References:

1. “Numerical Simulations of Flow Through an S-Duct”, Pravin Peddiraju, Arthur Papadopoulos, Vangelis Skaperdas, Linda Hedges, BETA CAE Systems, 6th BETA CAE International Conference.

2. “CFD Simulation of Serpentine S-Duct with Flow Control”, Lie-Mine Gea, 51st AIAA/SAE/ASEE Joint Propulsion Conference, July 27-29, 2015, Orlando, FL.

3. “CFD Validation and Flow Control of RAE-M2129 S-Duct Diffuser Using CREATE-AV Kestrel Simulation Tools”, Pooneh Aref et al, Aerospace 2018, 5, 31.

4. “Numerical simulations for high offset intake diffuser flows”, T.M. Berens et al, NLR-TP-2014-096.

5. “A Multi-objective shape optimization of an S-Duct intake through NSGA-II genetic algorithm”, Aurora Rigobello, 5 Dicembre 2016, Universita’ Degli Studi Di Padova.

6. “S-Duct Inlet Design for a Highly Maneuverable Unmanned Aircraft”, Jacob Brandon, Thesis, The Ohio State University, 2020.

7. “Effectiveness of a Serpentine Inlet Duct Flow Control Scheme at Design and Off-Design Simulated Flight Conditions”, Angela C. Rabe, Doctor Of Philosophy In Mechanical Engineering, Virginia Polytechnic Institute and State University, August 1, 2003.

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The post Role of CFD Solvers and Structured Meshes in Highly Offset S-Ducts appeared first on GridPro Blog.

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

2200 words / 11 minutes read

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

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

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

Introduction

The geometry of the fuel rod bundles with helically wrapped wires is complex. The helical nature of the wire along the rod length makes meshing challenging for both structured and unstructured meshing algorithms. Adding to the complexity are the presence of high acute angle wire-rod junction and the small gaps between the wire and neighbouring rod. The geometric complexities mentioned above are intimidating enough for a user to look for solutions other than structured mesh. But as we discussed in our previous article, Structured meshes seem to be the Holy grail for Thermal Hydraulics of the Nuclear Fuel Rods. Empathising with the needs of the community, we at GridPro have developed an automation script that focuses on providing a solution that will provide the user the highest quality mesh with minimal input. Our goal was  to build a solution that would be

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

Though the above objectives are conflicting in nature, we wanted to provide a solution that would require minimal input and have the turnaround time as that of an unstructured mesher and provide all the benefits of a structured mesh-like high quality, Multi-Grid Scalability, and user control in terms of mesh size and refinement. Over the years, with GridPro our goal is to provide a platform for automating Structured Multi-Block Meshing. For self-replicable configurations like nuclear fuel rod bundles, it is possible to nearly reach these ideal requirements using the tools in GridPro. The following sections elaborate on this aspect in greater detail.

Automation

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

The automation script requires the following inputs,

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

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

Scalability

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

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

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

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

Local grid-refinement

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

When the flow trips over the wire placed across its flow path vortices are generated. Fine mesh points are needed to discretise the region in the near vicinity of the wire, especially in the wake region where the vortices are present. Also, the location where the wire comes in contact with the rod is susceptible to shoot-up in temperature. Such hot spot regions need high-resolution meshes to accurately predict the local temperature. Figure 7 shows the local grid refinement at the wire-rod junction. Local refinement by Enriching in GridPro ensures that the refinement is contained locally and is not allowed to propagate to the larger domain. Figure 8: Accurate geometric capturing of the thin wire with an optimal number of cells. Figure 8, shows the local refinement by Enrichment along the entire length of the rod, in the near vicinity of the wire. The ratio of rod diameter to wire diameter is nearly 20:1. The mesh element size needed to discretise also varies by the same ratio. Enrichment in GridPro ensures appropriate resolution of the thin wire and smooth transition to the bigger cells used to discretise the rods without abnormal shoot-up in cell count.

Optimising cell count

From a numerical point of view, hexahedral elements are the most efficient elements. They consume the least memory and computing time per element. The grid built using hex elements are well aligned to the flow and hence well adapted for long and thin shear layers on the wall and in the wake. Compared to the unstructured grids, to fill a volume of space with a fixed edge length, the hex meshing approach needs the least number of cells. One of the major advantages of hex meshing is its ability to generate high aspect ratio grids without any deterioration in cell quality. This ability, unlike in the unstructured approach helps in generating grids with directional refinement. In the case of fuel rod meshing, this is a powerful asset, as it helps to reduce the cell count in the rod axial direction and more optimally refine the grid in the other two directions.

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

Quality

Generating meshes with high quality is always the goal of a CFD Analyst who is looking beyond pretty images. Hence, ensuring cell quality parameters like skew, aspect ratios, face warpage, negative volumes, right-handedness is very essential before performing large and complex CFD analyses like nuclear-fuel rod subassemblies. In GridPro, internally the algorithm ensures that essential quality criteria are met. For example, the algorithm strives to place cells adjacent to the wall as orthogonally as possible and also maintain the cell angles larger than 20 degrees and lesser than 160 degrees. Management of the cell aspect ratios is easy and efficient. By varying the edge density, the cell aspect ratio can be modified. Block smoothing algorithm ensures that there is a smooth variation of cells in the domain and the cell aspect-ratio are kept in the range of 10-50 – a range well acceptable to most CFD solvers to obtain good solution convergence.

The generated blocks are not rigid. Automatic block smoothing ensures the gradual transition of the cell size from the region of high density to coarser regions and avoids jumps in cell size. The algorithm tries to ensure that the growth rate in adjacent cell volume is always lesser than 2. Further, block smoothening ensures maintenance of grid quality even in narrow gaps and high acute regions. In GridPro, gaps as small as 4 microns have been meshed with cell quality well within acceptable limits. Solutions obtained on grids generated with these tools are also of superior quality. Blocks are placed aligned to the fluid flow. The presence of hexahedral cells aligned to the predominant flow direction ensures a drastic reduction in discretization errors. Further, the blocks generated in GridPro have a one-to-one connected interface. Since there are no non-matching grid interfaces, there is no degradation in the flow prediction.

Versatility

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

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

Figures 13b and 14b shows the second case of displaced wire, where the wire is offset by a larger margin resulting in the creation of an acute-angle intersection between wire and rod. The geometric acuteness is accurately captured with fine grid points. Sometimes, studies are done with the wire physically not touching the rod. Figure 14a shows a mesh for such a geometric variant with a small gap. Accurate capturing of these narrow gaps with high-resolution grids having high cell quality is key to obtaining accurate reliable solutions. Tools in GridPro help to meet the meshing requirement of all the possible variants regularly used in the nuclear fuel rod assembly simulations.

Conclusion

With this, we come to the end of this Part 3 in the series on Nuclear fuel rods. For adequate resolution of the geometry and flow field, meshes with a sufficiently large number of cells are essential. Since, the number of elements is proportional to storage requirements and computing time, for many large-scale 3D problems like nuclear fuel rod subassemblies, Engineers usually end up compromising between desired accuracy level and the number of cells. Using GridPro, the need to make such a compromise can be eliminated. Optimised grids can be automatically generated for wire-wrapped nuclear fuel-rod bundles in no time and with ease. Whether it is a 7-rod bundle or a 217-rod bundle the time and effort are just the same.

Case Studies

CFD computational studies using GridPro’s structured multi-block meshes for wire-wrapped nuclear fuel rod bundles have been made by TerraPower . Here is the link to the case study. Fuel_Rods_TerraPower_Casestudy

Nuclear Fuel Rods Series

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

References

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

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The post Automated Structured Meshing of Wire-Wrapped Fuel Rods appeared first on GridPro Blog.

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

1910 words / 9 minutes read

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

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

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

Introduction

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

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

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

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

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

Geometric Approximations

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

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

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

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

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

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

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

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

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

Gridding Requirements

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

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

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

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

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

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

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

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

Challenges in modeling a full-scale 217 fuel pin bundle

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

Parting thoughts

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

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

References:

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

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The post Role of Structured and Unstructured Meshes in Nuclear Fuel Rods CFD appeared first on GridPro Blog.

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

1700 words / 8 minutes read

Intro

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

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

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

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

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

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

 Need for Thermal-Hydraulic analysis in FBR

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

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

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

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

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

Wire-Wrapped Fuel Rods

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

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

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

Flow Field characteristics

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

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

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

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

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

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

Parting Thoughts

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

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

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

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

References:

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

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

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

2231 words / 11 minutes read

Introduction

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

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

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

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

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

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

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

Video 1: Why bypassing of air necessary?

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

Aircraft engine nacelles

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

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

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

Parameters affecting interference phenomena

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

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

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

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

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

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

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

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

Below wing nacelle integration – cruise condition

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

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

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

High lift propulsion system integration

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

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

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

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

The vortex system

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

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

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

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

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

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

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

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

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

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

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

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

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

Nacelle strakes

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

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

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

Parting Remarks

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

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

References

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

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• 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

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

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

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

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

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

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

Figure 2. Enstrophy histories in a p-refinement study

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.

• Ability to handle complex geometries, and ease of mesh generation
• Robustness for a wide variety of flow problems
• Scalability on supercomputers

(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!     

Significant cycle-to-cycle variations (CCV) in internal combustion (IC) engines can lead to undesirable effects like noise and vibration, engine damage, and poor drivability. It is important for engineers to estimate quantities such as peak cylinder pressure, combustion duration, and coefficient of variance of indicated mean effective pressure (IMEP) to design better engines. Moderating CCV can open doors to many advanced combustion concepts, such as low-temperature combustion strategies, to reduce emissions and increase efficiency. 

To accurately estimate CCV, you need to perform many engine cycle simulations—on the order of 100 consecutive cycles. Typically, simulating one engine cycle that follows our recommended best practices in CONVERGE takes a few hours with sufficient computational resources. Continuing that simulation for 100 consecutive cycles is a painstaking process (on the order of a few months) and hence a computationally expensive one.

Is there an alternate method to capture CCV?

The answer is yes! We know that long runtimes are unacceptable for many industry research timelines, and so we have applied an alternate method, called the concurrent perturbation method (CPM), to capture CCV in CONVERGE. This method was first proposed and published by Ameen et al. (2016)¹ at Argonne National Laboratory.

What is the concurrent perturbation method?

Instead of solving 100 cycles consecutively, with CPM, CONVERGE solves 100 cycles concurrently. Given sufficient computational resources, CPM reduces the overall turnaround time to the time taken to simulate one engine cycle. At this point, you might be asking yourself how it is possible to run the cycles concurrently when the result of one cycle can be determined only after knowing the results of the cycle preceding it. 

This is where the perturbation in CPM comes into play. We start by simulating one or more engine cycles to wash out the homogenized initial conditions that are defined while setting up the case. The combustion event and exhaust process of the first cycle(s) produces a representative velocity, pressure, temperature, and species field. The outcome of the initial cycle(s) is used to initialize each of the concurrent cycles, which are set up as independent cases. Each individual cycle’s flow field is then perturbed in order to yield a distinct cycle as the simulation proceeds (Figure 1). We apply only a miniscule perturbation to each flow field so as to not significantly change it. The perturbation is simply a noise field applied on top of the velocity field. The solution naturally develops into a different realization due to the chaotic nature of the combustion system. 

What do the results show?

Figure 2 shows a comparison of the cylinder pressure obtained from consecutively and concurrently run simulations performed by Probst et al. (2020).² The results are similar, and the predicted pressure lies within the maximum and the minimum pressure cycle of the measured data.

Additionally, Probst et al. found that starting the concurrent cycle simulations at intake valve opening (IVO) is sufficient to yield distinct and valid cycles. In contrast, when running cycles consecutively, it is necessary to simulate the full cycle. The required core hours for concurrently run cycles, as a result, are fewer than for consecutively run cycles. So, by concurrently running cases, multiple engine cycles can be completed in far less wall-clock time and with fewer core-hours compared to consecutive simulations.

Are you ready to try CPM to speed up your projects? Check out the video below to learn how CPM works, how to set up CPM in CONVERGE, and the conditions in which it will work best. 

References

[1] Ameen, M., Yang, X., Kuo, T., and Som, S., “Parallel methodology to capture cyclic variability in motored engines”, International Journal of Engine Research, 18(4), 366-377, 2016. DOI: 10.1177/1468087416662544

[2] 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

Tarique Shaikh joined Convergent Science in March 2018, after earning his master’s degree from the Technical University of Munich Asia. In his three and a half years with us, Tarique made a significant and lasting impact on both the company and his coworkers. Ashish Joshi, general manager of Convergent Science India, grew to know Tarique well on a professional and a personal level:

“Tarique joined Convergent Science as an applications engineer in our India office to support customers in a variety of industries. He worked on several interesting simulations, including one of flow over an entire city. This was a new application for Convergent Science, and his work in this area was greatly appreciated. Tarique, however, didn’t want to be a ‘Jack of all trades’, but instead a ‘master of one’. It was then that I recommended him for the Gas Turbine Applications team.”

The Gas Turbine Applications team at Convergent Science studies key issues related to the safety, operability, and environmental impact of aviation engines. The team simulates phenomena including lean blow-off, a scenario in which an airplane engine goes out; high altitude relight, which investigates how to relight an engine that goes out mid-flight; and methods for reducing harmful emissions from gas turbine engines. As the leader of the Gas Turbine Applications team, Scott Drennan worked closely with Tarique:

“Tarique worked in the Gas Turbine Applications group for a little over three years. In that time, Tarique developed a deep understanding of gas turbine combustion and became a valued member of the team. Specifically, he handled the creation of marketing materials to demonstrate CONVERGE’s capabilities for hydrogen combustion in gas turbines. He developed a simulation of fuel switchover from methane to hydrogen in a gas turbine combustor. In addition, he generated comparison videos of lean blow-off (LBO) for methane and hydrogen, demonstrating CONVERGE’s ability to use the SAGE detailed chemistry solver to simulate a dynamic (changing) operating condition. Tarique also developed a microturbine ignition marketing animation and helped on ignition modeling with a commercial customer. Tarique was a key contributor in a recent commercial evaluation with an aviation engine manufacturer using a combination of high-fidelity models for turbulence, heat transfer, combustion, and emissions in CONVERGE, and comparing the results to experimental wall temperature and emissions data for NOx, CO, and soot. Recently, Tarique was working on validation of our Thickened Flame Model (TFM) in a commercial combustor and testing CONVERGE’s hybrid turbulence model on wall cooling validation cases. 

Tarique was a thoughtful, smart, hardworking engineer and a key contributor to the Gas Turbine Applications team. He will be greatly missed.”

Tarique’s legacy at Convergent Science extends far beyond his technical contributions. He was a kind and caring person, with whom his colleagues greatly enjoyed working. Many of his coworkers from the Convergent Science India office and the Gas Turbine team wished to provide their thoughts and reflections on knowing and working with Tarique:

“I remember one time when the Convergent Science owners asked me who my favorite colleague from the India team was. It was very difficult to give just one name, but Tarique was definitely one of my favorites. We would discuss many topics apart from work: food, religion, politics, and fitness, just to name a few. One thing we bonded over was our love for biryani.”

–Ashish Joshi, General Manager, India office

“While pursuing my master’s degree, Tarique and I spent a lot of time together since we both belonged to the Aerodynamics and Fluid Mechanics lab at the Technical University of Munich (TUM). We were both from the Indian subcontinent, so we were hardly prepared for the European winters. During those harsh winter days in Munich, Tarique used to invite a few of us to his place for delicious dinners, which he prepared with such fervor. Cooking for friends and family was one of the things that Tarique dearly loved. As international students, a lot of us struggled with cooking, but Tarique tried to motivate us to cook. Whenever we felt homesick, he was always there to cook some of the most delicious meals I had during my student days. Those memories with Tarique are something I will cherish all my life. We will miss you, Tarique.”

–Harshan Arumugam, Business Development Manager, India office

“Tarique had a great love for food. Whenever we traveled together, he wanted to have a scrumptious meal; I really enjoyed seeing his joy for food. During our visit to the IIT Madras Gas Turbine Conference, he twice had me run to the nearby store to get cans of coke so he could have them with his five-course meal.”

–Abhishek Sinha, Sr. Business Development Manager, India office

“Tarique was a very friendly person who was always willing to offer help. He was good at technical discussions and meticulous at his work. In one of our first interactions, we bonded over the fact that he was originally from Sawantwadi, a place close to where I’m from.”

–Viraj Shirodkar, Research Engineer, Software Development, India office

“Tarique was a cleanliness freak. I remember when I visited his house for the first time, no one would believe that it was a bachelor’s home. He proudly showed us the different kinds of vacuum cleaners he used to clean his house. On my first day in the office, he was annoyed when I mistakenly took his chair and removed its plastic cover. Everyone joked that Tarique would be very angry. Little did I know that he was a sweetheart and took everything as a good sport. I’m glad we crossed each others’ paths in life.”

–Apurva Bhagat, Research Engineer, Software Development, India office

“Tarique made a courageous decision when he voluntarily switched to the Gas Turbine team, because I believe this to be one of the most challenging applications of CFD. Needless to say, in only a short time, he turned out to be one of the most valuable members of the team. One of his qualities that I admired the most was that whenever we discussed a problem, he was never hesitant to take out a pen and a piece of paper and immerse himself in analytical equations.

If there is one word that can describe Tarique, it is no doubt ‘caring’. If anyone around him was in need of any kind of help, be it professional or personal, you could always count on him to be the first one to offer help. On more occasions than I can remember, he offered me rides, especially when I had a leg procedure and had difficulty walking. His nurturing nature was further evident from his ardent love for plants, of which he had a great collection at home. I have always taken inspiration from him and hope to be able to integrate these qualities of his into my life.”

–Geet Nautiyal, Research Engineer, QA, India office

“It is very difficult to write about Tarique in the past tense, but time doesn’t care for feelings and emotions. Tarique—the name itself is a treasure trove of memories. When I joined Convergent Science India back in 2018, it was Tarique who trained me on CONVERGE Studio and the solver. Tarique helped me a lot in setting up a fixed inlaid mesh for a rectangular bluff body for turbulence validation in 2019. Our last work-related interaction was when he helped correct the heat transfer validation slides. 

Tarique and I shared a passion for general knowledge and current affairs, which led to many funny conversations. Our company trek to the Singahad fort was an epic one, in which Tarique set off quickly ahead of us, but ended up last to 900m. I used to call him ‘Gibraltar’, which is the Spanish version of the Arabic name Jabel al Tarique (Rock of Tarique). Tarique, my friend, why so early?”

–Akshay Iyer, Research Engineer, Validation, India office

“Tarique was a valuable member of our India team. Although we did not have the pleasure of interacting with him as much as some in the company, his contributions did not go unnoticed. He was a hardworking engineer who tackled some of the most challenging problems with our software. During our first Indian CONVERGE User Conference in Bengaluru, Tarique was tasked with providing a demo of our software to a large audience of CFD enthusiasts. This was a critical part of introducing many Indian engineers to CONVERGE, and Tarique did a wonderful job. He will be missed.”

–Dan Lee, Eric Pomraning, Keith Richards, Kelly Senecal, and Rainer Rothbauer, Convergent Science Owners, U.S. and Europe offices

“Tarique was an invaluable member of the Convergent Science family and the Gas Turbine team. Over the last year and a half, we had grown to be more than just colleagues—we had become good friends. He was kind, amicable, and easy to talk to. I never got to meet Tarique in person, but we worked closely every day. He has left a lasting impression through his dedication, hard work, and team spirit. He approached his work with determination and diligence, and he was a quick learner who had a huge appetite for knowledge. He was always eager to pick up tasks that needed research and reading. He was a true team player who always put the goals of the team first, and he was always very accommodating and helpful. Scheduling meetings between India and the U.S. offices is always a challenge due to the time difference. I was humbled that Tarique always insisted I choose a time convenient for me because I had a small child at home. The work he did as part of the Gas Turbine team helped immensely in the support and evaluation efforts of gas turbine customers and in the continuous improvement of the CONVERGE solver and graphical user interface. Some of his work, like the microturbine and annular combustor example cases, will be used by the team for years to come. Tarique will be greatly missed.”

–Gaurav Kumar, Principal Engineer, U.S. office

“Working from the U.S., I was never able to meet Tarique in person, instead working closely with him via Zoom meetings, phone calls, etc. Even through these somewhat impersonal media, it was clear that Tarique was a thoroughly kind, trustworthy, and caring person and a sharp engineer to boot. If I ever needed to dive deep into a challenging problem or diagnose a tricky issue, Tarique was the first person I turned to. He approached his work with diligence, determination, positivity, and attention to detail, and I learned a lot from watching him attack problems without ever giving up. On days when our shared projects weren’t going the best, we would often meet well past midnight U.S. time. These interactions were always lighter, and we would joke our way through to finding a solution to whatever was stumping us. I will miss him.”

–Gabe Jacobsohn, Research Engineer, U.S. office

Tarique touched many of our lives during his time at Convergent Science, and he will continue to have a presence at our company. Tarique’s father donated his collection of CFD books to Convergent Science, which we are using to make a small memorial library at our India office. In addition, Tarique had a penchant for gardening and a love for plants, and one of his plants will now brighten our India office’s reception area. We are grateful for the opportunity to keep Tarique’s memory alive with these donations. We will miss him greatly.

Amid the COVID-19 pandemic, determining how to safely reopen schools, colleges, and universities has been a primary focus. A number of studies conducted this past year have investigated airflow and ventilation in classrooms, airborne pathogen transport, and how masks affect pathogen transmission. The consensus of these studies is that wearing masks and social distancing in a well-ventilated room decrease the risks of transmitting COVID-19. However, implementing these practices can be tricky in certain circumstances, in particular in music schools.

Music schools often have small classrooms where students and instructors meet for lessons and practice sessions. In a small space, social distancing can be difficult or impossible, and wearing masks is often not an option for students who sing or play wind instruments. In addition, singing and playing wind instruments increases the rate at which potentially virus-laden particles are introduced into the environment. 

Given these factors, how can we make music classrooms safer for music students and instructors? One possible solution is portable air purifiers, which have the potential to improve ventilation and filter out viral aerosols. However, the World Health Organization (WHO) and the Centers for Disease Control don’t currently have guidelines on how best to use air purifiers or exactly how much safer they make a classroom.

To fill in these gaps, Sai Ranjeet Narayanan, a graduate researcher in the Department of Mechanical Engineering at the University of Minnesota, and his advisor, Dr. Suo Yang, teamed up with the University of Minnesota’s School of Music to investigate the potential of portable air purifiers to make music rooms safer. While their study focused specifically on music classrooms, the implications of the research are much broader.

“We started this project right in the middle of the pandemic,” Sai said, “and we could see straight away that the outcomes of this project could significantly help not only the music school, but any enclosed space, such as other types of classrooms, offices, or hospitals.”

Computational fluid dynamics (CFD) was Sai’s tool of choice for this study. To ensure the simulation results were as representative and applicable as possible, Sai modeled his geometry on a standard classroom at the School of Music that is used for one-on-one tutoring sessions or solo practice sessions (Figure 1).

CONVERGE is designed so that you can run a simulation with exactly as much detail as your analysis requires (geometric complexity, spatial and temporal resolution, etc.). For this simulation, Sai took advantage of CONVERGE’s meshing capabilities to embed a fine mesh in certain parts of the domain, such as near the inlets, outlets, and the region in front of the aerosol emitter (i.e., the student). In addition, Sai used CONVERGE’s Adaptive Mesh Refinement to add cells when and where they were needed to capture the important flow phenomena in the room.

Sai simulated a variety of scenarios common to the music classroom, including a student singing, a student playing a wind instrument, and a student playing piano. He investigated three different parameters: (1) the effect of the air purifier on the room’s ventilation rate, (2) the best location to place the air purifier in the classroom, and (3) the effect of the aerosol injection rate on the aerosol airborne suspension rate and surface deposition rate. 

Effect of the Air Purifier on Ventilation Rate

To study the effect of the air purifier on aerosol removal and ventilation rate, Sai looked at a case in which a student alone in a classroom sings for 10 minutes and then leaves the room for 25 minutes. 

Figures 2 and 3 show the effect of the air purifier on the airflow in the room. In the case without an air purifier (Figure 2), the airflow is driven by the building’s HVAC system. In Figure 3, you can see how the streamlines deviate once the air purifier is introduced and drives the airflow.

To quantify the effect of the air purifier, Sai calculated the number of aerosols removed with the air purifier and compared it to the number removed without an air purifier (Figure 4). In the case with an air purifier, the number of aerosols removed is two orders of magnitude higher than the baseline case.

To decrease chances of COVID transmission in a room, the WHO recommends a ventilation rate of at least 288 m³/h per person. Without an air purifier, the ventilation rate in the room due to the HVAC system is about 166 m³, significantly less than the WHO’s recommendation. With an air purifier, however, the overall ventilation rate increases to approximately 488 m³/h, far exceeding the WHO’s recommended value.

Finally, Sai found that with an air purifier, 97% of airborne aerosols are removed 25 minutes after injection stops (i.e., when the student leaves the room). This suggests that enforcing a break of 25 minutes between uses will make the music classroom much safer for the next student.

Location of the Air Purifier

In order to achieve the maximum impact of the air purifier, you need to determine the best location to place it inside the classroom. To investigate this, Sai studied a scenario in which a student is playing a wind instrument and an instructor is standing on the opposite side of the room. Figure 5 shows the different air purifier placements that were tested (no air purifier, elevated left purifier, ground purifier, elevated right purifier) and the deposition trends for each position. As you can see, both the elevated left purifier and the ground purifier show similar trends to the case with no purifier, although the ground purifier shows an overall reduction in deposition. The elevated right purifier, however, shows a very different pattern, indicating the air purifier significantly affects the airflow streamlines in this position.

Next, Sai quantified the number of airborne aerosols for all four purifier locations. In Figure 6, you can see that the ground purifier case results in the lowest number of suspended aerosols. The elevated right purifier case actually increases the number of aerosols compared to the baseline case, because it disrupts the natural airflow from the HVAC system. This demonstrates how important the placement of the air purifier is—if placed in the wrong location, the air purifier can make the room more dangerous. Overall, Sai determined that the best location for the air purifier is on the ground near the injection source.

The video below shows the difference in aerosol cloud profiles between the case without an air purifier and the case when the air purifier is in the optimal location. 

Injection Rate

Finally, Sai investigated the effect of injection rate on both airborne aerosols and surface deposition. He considered three scenarios that exhibit different injection rates: (1) a student playing a wind instrument, (2) a student singing while wearing a surgical face mask, and (3) a student playing piano while wearing a cloth face mask. No air purifiers were included in these cases. 

The effects of injection rate on the deposited and airborne aerosols are shown in Figure 7. Sai found that both the airborne suspension rate and the surface deposition rate increased linearly with the injection rate. 

“Discovering these trends were linear is important because it means we can predict the aerosol suspension and deposition rates for different injection rates without having to conduct a full simulation,” Sai said. “This can also be extended to any geometry, so it’s not limited to just this scenario.”

Conclusions

Sai’s studies produced highly practical, applicable results. He found that an air purifier can significantly help with ventilation in enclosed spaces, and determined the amount of time needed between sessions in the music room for it to be safe to use again. In addition, he determined the optimal location to place the air purifier for maximum benefit, and discovered a linear correlation between aerosol injection rate and aerosol suspension and deposition rates. While Sai looked at a specific music room, this same case setup can be used for different geometries. 

“Working on a project such as this really does feel like you’re contributing to the community,” Sai said. “You’re helping the music school make decisions about their safety guidelines, and the results could be extended beyond the music school to other scenarios. It was a very rewarding project.”

Currently, Sai is working on applying this technique to simulate an orchestra in an orchestra hall. With multiple musicians performing on stage with different kinds of wind instruments, he is investigating how the aerosols circulate on stage to determine which students will be at risk. 

Overall, CFD is a great tool to help make our communities safer as we work on reopening society and navigating the post-pandemic world. If you’re interested in learning more about Sai’s research, you can check out his paper here.

About the CONVERGE Academic Program

The CONVERGE Academic Program empowers students, professors, and academic researchers around the world to advance science and technology. Convergent Science offers exclusive CONVERGE license deals for academic research—free in the United States and Europe—along with free support, training, and resources. Academic researchers are leveraging CONVERGE’s unique capabilities to study everything from gas turbines and internal combustion engines to wind turbines and heart valves. Learn more!

References

Narayanan, S.R. and Yang, S., “Airborne Transmission of Virus-Laden Aerosols Inside a Music Classroom: Effects of Portable Purifiers and Aerosol Injection Rates,” Physics of Fluids, 33, 2021. DOI: 10.1063/5.0042474

Author:
Jameil Kolliyil
Engineer, Documentation

In the quest to reduce carbon dioxide emissions, the world is searching for more environmentally friendly modes of transportation. Aided by policy decisions and massive improvements in battery technology, each year of the last decade has witnessed a year-over-year increase in the market share of electric vehicles¹. In 2010, approximately 17,000 electric cars were on the world’s roads. By 2019, that number had grown to 7.2 million²! Due to their high energy density, capacity, and excellent cycling performance, lithium-ion batteries are used to power most of these vehicles. However, lithium-ion batteries have lower thermal stability than other rechargeable batteries, leading to potential safety issues, such as thermal runaway and the subsequent release of flammable gases. With more electric vehicles on the road, you may have noticed a few news reports about battery fires. Just in the last couple of years, several auto manufacturers have had to recall their electric vehicles because of battery fire issues. If the world is to embrace electric vehicles with open arms, such safety concerns must be addressed. Let’s see how CONVERGE can help you simulate, study, and design safer lithium-ion battery packs.

Battery Cooling

Temperature is a critical factor that impacts the performance of lithium-ion batteries. Generally, the acceptable operating temperature range for lithium-ion batteries is -20°C to 60°C (-4°F to 140°F)³. As cell components provide resistance to the flow of current, the cell heats up even under normal operation. Therefore, cooling is necessary for safe operation. With its robust conjugate heat transfer modeling, CONVERGE is well-suited to simulate a wide variety of cooling methods for battery packs. Figure 1 shows the temperature contours from a simulation of an air-cooled lithium-ion battery pack. Notice that the cells are unevenly cooled with this cooling strategy, increasing the risk of potential hazards. Also note CONVERGE’s inlaid meshing at each cell boundary, providing better accuracy with a reduced cell count.

Thermal Runaway

Once a cell heats up to approximately 80°C to 90°C (176°F to 194°F), the solid electrolyte interphase (SEI) layer starts decomposing, and the cell starts self-heating⁴. If the temperature continues to increase, this initiates a sequence of exothermic reactions, which can lead to thermal runaway (where the cell temperature increases uncontrollably). Thermal runaway can also be initiated due to mechanical stress (imagine a metal piece penetrating the battery pack during a car crash) or operational stress (when the battery is aggressively charged, discharged, or overcharged). Once thermal runaway has been initiated, exothermic reactions continue until all the energy in the cell is released. This makes it extremely difficult to completely stop the reactions once they are underway. While tackling battery fires, firefighters often have to cool the battery for hours while monitoring the temperature to make sure that all reactions have died down. Naturally, over the years, several studies have been conducted to study thermal runaway in lithium-ion cells. Ren et al., 2018⁴ carried out one such study where they heated a lithium-ion battery to 150°C in an accelerated rate calorimeter (ARC; blue line in Figure 2) to initiate thermal runaway, and monitored the temperature. To calculate the heat released due to thermal runaway in CONVERGE, the SAGE detailed chemistry solver is employed to solve an Arrhenius-style reaction mechanism for pseudo-species representing the cell components. The chemistry solver is highly efficient and can provide results for such mechanisms in a matter of minutes. Figure 2 shows a comparison of the temperature profile of their experimental results and simulation results from CONVERGE.

Vent Gas Analysis

After the onset of self-heating and continued temperature rise, the electrolyte begins to break down at around 100°C (212°F), releasing several flammable gases like hydrogen, methane, and ethane⁴. The exact composition of the vented gas varies depending on the state of charge of the battery and the battery chemistry. With CONVERGE’s SAGE detailed chemistry solver you can simulate this complex process to assess the risk of fire in the battery pack. The chemistry solver is fully coupled to the flow solver for accurate and efficient simulation of venting, ignition, and combustion processes. Figure 3 shows a lithium-ion battery pack simulation where gases are released from a cell undergoing thermal runaway. Isosurfaces of the flammability limits of released methane and hydrogen are highlighted for easy analysis. When an ignition source was introduced inside the battery pack, the resulting flame (depicted in red in Figure 3) quickly burned through the available oxygen. Once the flame exits the battery pack there is much more available oxygen for the combustion process, leading to more substantial heat release.

Conclusion

The flow, heat transfer, and chemistry models in CONVERGE provide highly accurate, reliable, and fast results for battery simulations. Conventional battery design processes rely heavily on conducting “trial-and-error” tests to ensure operability and safety. By including CONVERGE simulations in this development process, you can evaluate and optimize several battery pack designs, and then build and test only the most promising ones. Using simulations in this manner leads to the development of more efficient and safer batteries—and safer batteries mean safer electric vehicles. 

Learn more about using CONVERGE for emobility modeling here. Are you interested in using CONVERGE for your battery simulations? Get in touch with us here!

References

[1] Woodward, M., Walton, B., Hamilton, J., “Electric vehicles: Setting a course for 2030,” https://www2.deloitte.com/us/en/insights/focus/future-of-mobility/electric-vehicle-trends-2030.html, accessed on Mar 3, 2021.

[2] International Energy Agency (IEA), “Global EV Outlook 2020,” https://www.iea.org/reports/global-ev-outlook-2020, accessed on Mar 3, 2021.

[3] Ma, S., Jiang, M., Tao, P., Song, C., Wu, W., Deng, T., Shang, W., “Temperature effect and thermal impact in lithium-ion batteries: A review,” Progress in Natural Science: Materials International, 28, 653-666, 2018. DOI: 10.1016/j.pnsc.2018.11.002

[4] Ren, D., Liu, X., Feng, X., Lu, L., Ouyang, M., Li, J., He, X., “Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components,” Applied Energy, 228, 633-644, 2018. DOI: 1.1016/j.apenergy.2018.06.126

From the Argonne National Laboratory + Convergent Science Blog Series

As the coronavirus vaccine continues to be administered around the world, you’ve likely heard about the challenges associated with distributing the vaccine to remote areas. You may not have heard, however, how a particular technology is aiding the distribution efforts: drones. Autonomous drones are able to reach remote areas that may not have reliable infrastructure and deliver hundreds of vaccine doses to hospitals or temporary mobile clinics. Drones will not be able to single-handedly solve the problem of vaccine accessibility, but they are already making a difference.

The medical field isn’t the only industry making use of drones—they are becoming increasingly common for defense, agriculture, construction, package delivery, photography, videography, and environmental applications. The drone market is expected to grow rapidly over the next decade as drones become integrated into more aspects of our lives. With a greater number of drones in the sky comes the need to ensure their safety and reliability. 

Engineers in the Computational Multi-Physics Research Section at Argonne National Laboratory are putting their skills to use to develop computational fluid dynamics (CFD) models to help design capable drones.

“CFD is beneficial for designing drones, because we can obtain answers quickly,” said Dr. I-Han Liu, Postdoctoral Researcher at Argonne. “For example, we can predict the aerodynamic coefficients and quickly gather results for different flight conditions and different geometries, instead of conducting wind tunnel tests or actual flight tests, which can save a lot of costs and time in the design and development process.”

However, simulating drones is computationally intensive because of their large domains, moving geometries, and complex physics. The Argonne engineers took advantage of CONVERGE 3.0’s excellent load balancing and parallel scaling to run their drone simulations in a reasonable amount of time. To develop models that can be applied to a range of drones, Dr. Liu and Dr. Roberto Torelli, Research Scientist at Argonne, investigated two different types of drones: fixed-wing and multicopter.

Fixed-Wing Drone

The computational multi-physics group at Argonne has a long history of modeling automotive systems, particularly internal combustion engines and injection systems, but external aerodynamics was new territory. Before they jumped into modeling an entire drone, they simulated standardized airfoils known as NACA airfoils (from the name of the National Advisory Committee for Aeronautics, who developed and defined them). This activity ensured the team had a good understanding of the relevant physics. With that validation complete, they moved on to simulating a fixed-wing drone.

Dr. Liu and Dr. Torelli simulated the Pioneer RQ-2A drone, which was used for military operations in the 1980s and ‘90s, including reconnaissance, surveillance, target acquisition, and assessing battle damage. They chose this drone because there is a substantial amount of experimental data available to validate their numerical results. The Pioneer geometry is shown in Figure 1.

The Argonne engineers used incompressible, transient, unsteady Reynolds-Averaged Navier-Stokes (RANS) modeling to simulate the drone.¹ As you can see in Figure 2, they applied fixed embedding to refine the mesh near the walls to accurately model the flow around the aircraft. In addition, they employed CONVERGE’s Adaptive Mesh Refinement (AMR) to dynamically refine the mesh in the wake region to capture gradients in the flow velocity. 

Dr. Liu and Dr. Torelli computed lift, drag, and moment coefficients to characterize the fixed-wing drone and compared the results to experimental wind tunnel data. As you can see in Figure 3, the results match quite well. The Argonne engineers also analyzed the vortex structures in the wake. At a 14-degree tilt angle, flow separation occurs, vortices originate from the wing tips, and vortex shedding is generated by the wing surfaces and the fuselage (Figure 4).

Quadcopter

Following the fixed-wing drone studies, Dr. Liu and Dr. Torelli moved on to simulating a quadcopter, i.e., a multicopter with four propellers. They modeled the DJI Phantom 3 drone, which is a recreational quadcopter used for photography. You can see the geometry in Figure 5. 

Multicopters pose an additional simulation challenge compared to fixed-wing drones. “If you think of a quadcopter, you have a series of spinning propellers that are moving at each time step and interacting with the computational grid,” said Dr. Torelli. “This complicates how we handle the solution of the flow field because you need to account for the propellers moving into a new computational domain. CONVERGE allowed us to tackle this problem with its cut-cell approach, in which the mesh is redefined at every time step by calculating the intersections of the base grid with the geometry.”

To start with, Dr. Liu and Dr. Torelli simulated a single quadcopter propeller.² They tested three different turbulence models: k-ω shear stress transport (SST), Spalart-Allmaras (SA), and a detached eddy simulation (DES) model. To model the near-wall boundary flow, they embedded a fine mesh around the propeller, and they used AMR to capture the vortex structures in the wake.

With this method, Dr. Liu and Dr. Torelli calculated both the thrust force and moment versus the propeller rotation speed. As you can see in Figure 6, the trends for both parameters matched well with available experimental data. 

Next, the Argonne engineers simulated the entire quadcopter geometry, using a DES turbulence model, fixed-mesh embedding, and AMR². They calculated the pressure coefficient on the surface of the drone, and looked at the Q-criterion to visualize the vortex structures in the wake. Figure 7(a) shows high-pressure regions that were observed when the propeller tips rotated over the surface of the quadcopter. In Figure 7(b), you can see the helix vortex ropes that are generated by the propeller tips as the propellers rotate.

Significance

The results obtained via CFD can be incorporated into vehicle system simulations to investigate questions like how the drone will interact with the environment, whether a given drone will be able to accomplish its mission with its onboard battery, or if a certain drone can complete a new task assigned to it mid-mission. This cross-platform integration is what the Argonne engineers have planned for the future.

“The next step for my research will be trying to connect the CFD simulations with the dynamic system team to further help design the drones,” said Dr. Liu. “My CFD simulations can map data across different ranges, like different flying speeds or tilted angles, and provide them with comprehensive, accurate data they can use to design the control system.”

This research will help not only to create more efficient vehicles, but also to ensure that future drones will be able to complete their often critical missions, like delivering vaccines to communities in need.

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

• Part 1: The Collaboration Effect: A Decade of Innovation
• Part 2: The Collaboration Effect: Advancing Engines Through Simulation & Experimentation
• Part 3: The Collaboration Effect: Developing a New Generation of Gas Turbine & Rotating Detonation Engines

References

[1] Liu, I.-H., Torelli, R., Prabhakar, N., and Karbowski, D., “CFD Modeling of Unmanned Aerial Systems With Cut-Cell Grids and Adaptive Mesh Refinement,” AIAA SciTech Forum and Exposition 2020, AIAA 2020-0538, Orlando, FL, United States, Jan 6–10, 2020. DOI: 10.2514/6.2020-0538
[2] Liu, I.-H. and Torelli, R., “Numerical Characterization of a Multi-Copter Using Moving Boundaries and Cut-Cell Grids,” 2021 AIAA Aviation Forum, Online, Aug 2–6, 2021. (accepted)

Co-Author:
Elizabeth Favreau
Senior Marketing Communications Writer

Until a few years ago, I never thought much about diesel engines. In the United States, very few passenger cars have diesel engines, and while I was aware that they were used in vehicles like long-haul trucks, mostly I was just glad that I didn’t have to pay for the pricier diesel fuel when I filled my tank with gasoline.

It wasn’t until I started working at Convergent Science that I really considered how much of the world is powered by diesel engines. Not only are diesel cars more common in other countries, but diesel engines also enable trade, the distribution of goods, and the construction of new buildings and infrastructure by powering ships, trucks, and construction equipment. The diesel engine has been, and continues to be, instrumental in shaping society—pretty amazing, right?

However, working at Convergent Science, I also started to think more about emissions. Of course, I knew that reducing emissions from vehicles was incredibly important. But, I thought, isn’t that what electric cars are for? Now, electric cars make sense in certain sectors, but heavy-duty vehicles are a different story. Moving heavy-duty vehicles requires significant power, and today’s battery technology isn’t a practical solution. So what can we do to reduce emissions from heavy-duty vehicles while also ensuring that they can still perform their vital functions?

On the other side of the world from the Convergent Science World Headquarters, Annabelle Evans, an undergraduate at the University of New South Wales (UNSW), was considering this very problem. For her honors thesis project, she teamed up with Professor Evatt Hawkes and his research group to investigate a potential solution: hydrogen. I’ll turn it over to Annabelle to tell us about her research!

Co-Author:
Annabelle Evans Undergraduate Researcher, University of New South Wales

Hydrogen is a promising alternative fuel for internal combustion engines. It has the potential to be produced renewably, and its only emission is water (theoretically, at least). In addition, hydrogen has a superior energy density compared to batteries, making it an attractive option for heavy-duty applications.

However, if you just put hydrogen into a conventional compression ignition engine, you’re going to run into trouble. Hydrogen has a higher autoignition temperature than diesel, so it’s difficult to ignite hydrogen by compression alone. My research group is working on an engine that will use a little bit of diesel, which will ignite under compression, to act a bit like a match for the hydrogen.

Hydrogen-diesel dual-fuel engines are capable of being significantly cleaner and more efficient than traditional diesel engines, but careful consideration must go into their design. Hydrogen has a higher adiabatic flame temperature than diesel, which can lead to higher NOx emissions, and extreme temperatures can cause substantial heat losses, reducing the efficiency of the engine.

In order to design an optimal hydrogen-diesel dual-fuel engine, you need to understand the mechanisms driving the NOx emissions and heat losses. I set out to investigate these phenomena using computational fluid dynamics simulations. Simulations are cheaper and easier to run than experiments, and they give you more data than you can get experimentally. With my CONVERGE simulations, I could track the temperature, pressure, and mixture composition at many locations inside the engine.

I used CONVERGE’s detailed chemistry solver and high-fidelity emissions models to simulate a dual-fuel engine with different combinations of hydrogen and diesel [1]. I varied the hydrogen fraction from 0% to 90% and assessed the NOx emissions and heat losses for each condition. In my simulations, I assumed the hydrogen was evenly mixed with the air inside the cylinder when the diesel was injected at the top of the compression stroke.

I found that NOx emissions varied with hydrogen fraction, as shown in Figure 1. After initially increasing, NOx emissions began to decrease as the hydrogen fraction grew to more than 50%.

We believe the reason that NOx emissions rise initially is because hydrogen’s high flame temperature increases the combustion temperature, which leads to more NOx formation. When you reach a certain level of hydrogen addition, however, you see the fuel and air mixing more evenly before combustion, which reduces the NOx emissions.

Our research group is also running experiments on the dual-fuel hydrogen engine concept, but they have not yet tested hydrogen fractions above 50%. However, these simulation results are a promising indicator that future experiments may show a decrease in NOx emissions with greater hydrogen fractions.

In terms of heat losses, I found that there were three main contributing factors: combustion phasing, the equivalence ratio near the cylinder walls, and turbulence. The heat transfer was concentrated primarily in a specific region around the edge of the piston, as you can see in Figure 2. Much of the injected diesel ends up in these regions, which causes high temperatures. In addition, the narrow “squish zone” above the piston rim generates turbulence, which promotes heat transfer.

Thanks, Annabelle! Understanding the reasons behind NOx emissions and heat loss is critical for designing highly efficient, low-emissions hydrogen engines. Annabelle’s data provides insight into the optimal ratio of hydrogen to diesel, as well as the information necessary to begin minimizing heat losses and NOx emissions. Hydrogen offers a path to greener heavy-duty vehicles, and Annabelle’s research brings us one step closer to a cleaner transportation future.

To learn more about Annabelle’s research, check out her SAE paper here!

About the CONVERGE Academic Program

The CONVERGE Academic Program empowers students, professors, and academic researchers around the world to advance science and technology. Convergent Science offers exclusive CONVERGE license deals for academic research, along with free support, training, and resources. Academic researchers are leveraging CONVERGE’s unique capabilities to study everything from gas turbines and internal combustion engines to wind turbines and heart valves. Learn more!

References

[1] Evans, A., Wang, Y., Wehrfritz, A., Srna, A., Hawkes, E., Liu, X., Kook, S., and Chan, Q.N., “Mechanisms of NOx Production and Heat Loss in a Dual-Fuel Hydrogen Compression Ignition Engine,” SAE Technical Paper 2021-01-0527, 2021. DOI: 10.4271/2021-01-0527

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.

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.

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.

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.

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.

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.

Improved Performance, Flexibility, and Resource Management in Tecplot 360 2021 Release 2

BELLEVUE, WA (October 21, 2021) – Tecplot, Inc. has announced the general availability of Tecplot 360 2021 Release 2.

Tecplot 360 is the leading independent CFD post-processor on the market today. Our 2021 R2 release reaffirms our commitment to providing the best technical data visualization and analysis software available by providing quality of life improvements, performance enhancements, and new ways to manipulate your data and plots.

“The improvements in this release add up to ease-of-use and a big timesaving for our customers.” says Scott Fowler, Tecplot 360 Product Manager.

Get all the details of this release.

Quality of Life Improvements:

• Labelling a plot is more flexible with the ability for users to edit the contour legend header without editing the variable names. This update supports the inclusion of auxiliary data and other text modifiers supported by Tecplot 360.
• Users can now opt to show zones only at specified solution time, which ensures the correct zones are displayed.
• Support for logarithmic time scales has been added to correctly cluster solution time steps.
• Divide-by-zero errors can be ignored with a simple toggle during analysis.

Performance Improvements:

• Temporary disk storage usage has been reduced by 99.6%! This reduction has resulted in up to a 20% performance improvement when creating movies with transient data.
• Initializing contour levels for transient datasets with many zones consistently showed up to a 23.8x speedup over previous versions.

Loader Improvements:

• The Fluent CFF (HDF5) loader is supported on CentOS and RedHat7 and includes face-neighbor connections.
• The Plot3D loader treats function files as transient if they appear to have a time value in the file name.

Details of this release are on the Tecplot website.

Download Tecplot 360

This new release is available for download as Free Trial Software, or for customers through the Customer Portal.

About Tecplot 360

Tecplot 360 is a suite of CFD visualization and analysis tools that can handle large data sets, automate workflows, and visualize parametric results. Three powerful modules in this one tool include:

• Tecplot Chorus – an analytics tool for exploring large datasets composed of multiple solutions or experiments.
• SZL Server – a client-server module for accessing data remotely.
• PyTecplot – a Python API for automating workflows.

Integrated XY, 2D and 3D plotting speeds data analysis and increases productivity. Easy-to-use, fast and memory efficient, Tecplot 360 produces visually powerful output to help engineers and scientists communicate CFD, other simulation and test data results to clients and stakeholders.

The Tecplot 360 suite of tools are available for customers on TecPLUS maintenance service and anyone who downloads a free trial of the software.

Special pricing is available for Academic users upon request, see Tecplot for Academics.

About Tecplot, Inc.

Tecplot, an operating company of Toronto-based Constellation Software, Inc. (CSI), is the leading independent developer of visualization and analysis software for engineers and scientists. CSI is a public company listed on the Toronto Stock Exchange (TSX:CSU). CSI acquires, manages, and builds software businesses that provide mission-critical solutions in specific vertical markets.

Tecplot visualization and analysis software allows customers using desktop computers and laptops to quickly analyze and understand (local or remote) information hidden in complex data and communicate their results to others via professional images and animations. The company’s products are used by more than 47,000 technical professionals around the world.

Contact

Margaret Connelly
Marketing Manager, Tecplot

pr@tecplot.com
(425) 653-1200

The post Improved Performance and Resource Management in Tecplot 360 2021 R2 appeared first on Tecplot.

Take a tour of Tecplot 360 2021 R2 with Product Manager, Scott Fowler, and his team.

Learn more about Tecplot 360

Agenda:

• 03:36 – Overview of what’s new in Tecplot 360 2021 R2
• 04:50 – Improved performance and resource management
• 07:05 – Expanded support for transient data
• 14:30 – Transient data zone visibility is now configurable
• 22:17 – User Interface (GUI) creature comforts
• 25:09 – Loader and library changes
• 26:11 – Platform support changes
• 29:04 – What’s coming in 2022
• 32:37 – Q&A

For all changes in Tecplot 360, see the release notes.

Q&A from the Webinar

Is it possible to use multiple CPUs or GPUs to speed up post processing?

Tecplot 360 is a shared memory application that uses OpenGL for rendering, which will use your graphics card. That means when you are post-processing, all the CPU cores available on a single machine will be used. We don’t do distributed memory parallel processing.

If you have transient data with one file per time step, you can distribute that work among multiple machines. On an HPC, you could submit a batch job where each node on your HPC would load one time step of your data and generate an image. So, you can get parallel processing done on an HPC.

You can also do it on a single machine using multiple instances of Tecplot 360. But you have to be careful because you may run into resource contention if you have multiple instances running at the same time on the same machine.

In Tecplot 360, when you’re working with isosurfaces, slices, or streamtraces, those are fully parallelized and will use all your CPU cores.

You can use PyTecplot and the Python multiprocessing library to launch multiple instances of Tecplot 360 simultaneously to improve the speed of post processing. Here’s a webinar to show you how, Analyze Your Time-Dependent Data 6 Times Faster. There is no load balancing so you have to be careful with the resources available on your machine.

Is this release (Tecplot 360 2021 R2) supported on the Mac M1 chips or Mac OS M1 version?

Tecplot 360 will run on Macs with the M1 chip, but it’s going to run in compatibility mode. We haven’t done any performance testing to see if there’s a degraded performance running in that mode, so your mileage may vary there. We are looking into native support for Mac M1. The GUI (graphical user interface) library we use, QT, recently came out with native M1 support.  This will be an active area of research for us in the next few months.

Are there any changes to PyTecplot? Is it compatible with Homebrew?

Yes, there are a few API changes to PyTecplot. PyTecplot 1.4.2 has been uploaded to the PyPI servers.The changes parallel some of the new capabilities in Tecplot 360:

• Transient zone visibility
• Updates to the AVI contour legend header and the time clustering options
• Minor documentation changes

Is it compatible with Homebrew? We have made no attempt to intentionally make it compatible with Homebrew in this release.

Can I Use LaTeX with the Contour Legend?

Unfortunately, the answer is no, we don’t support LaTeX in the contour legend. But this update to use custom text is a steppingstone to supporting of LaTeX in the contour legend – so it is on our roadmap! We’ve also talked about LaTeX support in the axes titles. You can look forward to that in the next couple of releases.

By the way, support for LaTeX is one of our most requested items! It’s fun to see how many people are passionate about using LaTeX.

To get notified of upcoming releases and webinars, subscribe to Tecplot!

Who do I contact if I have more questions?

For technical support contacts in your area, visit our support page.

The post What’s New in Tecplot 360 2021 Release 2 appeared first on Tecplot.

Isosurfaces are a 3D tool in Tecplot 360 that show the value as well as the shape of your flow. In a previous video tutorial, we demonstrated Placing Slices on your plot. Slices are helpful not only for visualizing your flow and seeing what’s going on in your domain, but they are a useful tool for figuring out where to put an isosurface (as well as where to Add Streamtraces).

Placing Isosurfaces

In this plot, I want to define an isosurface using the RHO variable and show that isosurface at a specific value. To do that, first open the Isosurface Details dialog.

To find a good value of RHO, use the Probe tool and probe on an area of interest.

I can Zoom in closer on the plot with the Probe tool still active by clicking and holding the middle mouse button. Probe by clicking at a point of interest and view the variables in the Probe sidebar. Copy and paste the value into The Isosurface Details dialog Value 1. Then toggle on the isosurfaces in the plot sidebar.

Power Switches

Note that the toggles in the Plot sidebar are power switches that turn on and off plot features. For example, if I had multiple isosurface groups or multiple slice groups displayed, I would turn on and off all slices and isosurfaces by toggling the switches. Individual groups can be toggled on and off in the Isosurface Details dialog.

Styling Isosurfaces

Now I have an isosurface going through my domain at a location of interest. The isosurface can be made translucent by right-clicking on the isosurface and setting the translucency (to 30) in the context menu.

There are two ways to adjust the look of your isosurface.

1. The Context Menu – by right-clicking on the isosurface to open the context menu where the mesh, vectors, shade, and translucency can be adjusted.
2. Or the Isosurface Details dialog – Double-click on the isosurface to open this dialog to adjust the Contour, Shade, Mesh, Vectors, or to Animate.

Animating Through Time

To animate through time, click the video-like play control in the Plot sidebar.

Extracting Isosurfaces

Normally, isosurfaces are derived from the dataset “on the fly” and do not add any data to the dataset. To extract existing isosurfaces to Tecplot zones, allowing you to retain them even if the contour variable is changed, select Extract>Iso-Surfaces from the Data menu.

Once extracted, you can also write the new Isosurface Zone to a separate data file under the File dropdown menu.

Conclusion

This concludes the tutorial for working with Isosurfaces. Thank you for watching.

Online Resources

• Tecplot 360 User’s Manual, Section 16 Iso-surfaces
• Getting Started Guide

The post Working with Isosurfaces – Tecplot Tutorial appeared first on Tecplot.

Engineers from Convergent Science are studying the effect of obstacles on the time taken for deflagration to detonation transition (DDT) in various configurations (blockage ratio and stagger ratio). Ideally one would like to delay DDT. This study could help understand how blockages can be used to control DDT.

Deflagration describes combustion at a velocity slower than the speed of sound. You see deflagration typically as common flame in everyday life, like in a fireplace or on burning candle. Detonation describes combustion at a higher velocity than the speed of sound. You see this in explosions that release shockwaves, such as a fighter jet breaking the sound barrier or a gas leak in a mine or a factory.

To quantify the results of the simulations, they wanted to compute the flame area (represented by an isosurface at 1800 degrees) and the flame speed (determined by the max x-position over time, dX_max/dt). Measuring flame area gives information on flame wrinkling which they wanted to correlate with flame speed. They also wanted to see how flame wrinkling changes as flame transitions from deflagration to detonation.

This guide explains the techniques and features used in Tecplot 360 and PyTecplot to extract this information from the simulation results.

Data and scripts for this case can be downloaded here: FlameFrontAnalysis.tar. To run the scripts, be sure to unpack Data.tar.gz to a sub-directory called Data first.

Computing the flame area and flame speed using Tecplot 360 and PyTecplot

Computing Flame Area and Flame Speed Using Tecplot 360 and PyTecplot

The two quantities we need to compute from this dataset are flame area and flame speed. First, we need to define the flame front. Once we have the flame front defined, we can compute the flame area and flame speed.

Computing Flame Area

Flame area is relatively simple – you can use Analyze > Perform Integration > Length/Area/Volume to compute the area of the isosurface. Recall that Tecplot 360 performs calculations on Zones – so we must first extract the isosurface to zones before we can use the integration feature. So, the rough steps are:

1. Define an isosurface at temperature = 1800, which defines the flame front
2. Data > Extract > Iso-Surfaces Over Time
3. Analyze > Perform Integration
   1. Choose Length/Area/Volume
   2. Integrate by Time Strands and select the strand associated with the isosurface you extracted.
   3. Check the Plot Results As toggle
   4. Press Integrate

Computing Flame Area

At this point you will have a new frame with a line plot that represents the flame area. Select this frame and save this data to a data file using File > Write Data…

Flame Area

Computing Flame Speed

The flame speed is defined as dX_max/dt. Tecplot 360 cannot compute this equation with its built-in equation processing, so we’ll use the PyTecplot scripting language to not only extract the maximum X-position from the flame front at each timestep, but also compute the flame speed. Once we’ve computed the results, we’ll save the results to a Tecplot ASCII file.

The PyTecplot script below will connect to a live, running instance of Tecplot 360 to extract the information. There are a couple prerequisites to running this script:

1. Ensure you have Python 3 and PyTecplot installed
2. Save the script (below) to a file (for example, compute_flame_speed.py)
3. Enable PyTecplot connections via Scripting > PyTecplot Connections…
4. Ensure that the frame with the CONVERGE data is the active frame. As you can see, the script below queries for the dataset associated with the active frame.

Once you’ve satisfied the pre-requisites, simply run the script:

> python -O compute_flame_speed.py

Python Script

import tecplot as tp
tp.session.connect()

ds = tp.active_frame().dataset

iso_zones = ds.zones("Iso*")

times = []
maxx = []
speed = []
# Assuming that the zones are given in time order
for z in iso_zones:
    maxx.append(z.values("X").max())
    times.append(z.solution_time)
    if len(maxx) == 1:
        speed.append(0)
    else:
        dx = (maxx[-1] - maxx[-2])
        dt = (times[-1] - times[-2])
        speed.append(dx/dt)

# Save the results to a Tecplot data file. We do this by creating a
# new dataset in Tecplot 360 with the results and saving to a file.
new_frame = tp.active_page().add_frame()
ds = new_frame.create_dataset("Flame Speed", var_names=["Solution Time", "Flame Speed"])
zone = ds.add_ordered_zone("Flame Speed", shape=len(times))
zone.values("Solution Time")[:] = times
zone.values("Flame Speed")[:] = speed
tp.data.save_tecplot_ascii("flame_speed.dat", frame=new_frame)
# We’ve saved the data to disk and no longer need the frame around
tp.active_page().delete_frame(new_frame)

Plotting the Results

We now have one data file with the flame area results and another file with the flame speed results. To plot the results, simply create a new frame in Tecplot 360, then load the two files together.

By default, Tecplot 360 plots only the results for the first zone, so you’ll have to use the Mapping Style dialog to plot the data of interest. As seen in the image below, we’ve created two separate line maps, each plotting different data. Because flame area and flame speed have very different magnitude, we’re using the Y2 axis to plot the flame speed.

Mapping Style dialog in Tecplot 360

To create the final plotted result, we’ve added a line legend and modified the first line map name from “Result” to “Flame Area.” We’ve also renamed the Y1 axis title to use “Flame Area.”

Final plotted result in Tecplot 360

Summary

In this how-to guide you learned the following:

• Loading CONVERGE data.
• Defining and extracting an isosurface.
• Use of Length/Area/Volume integration.
• Use of PyTecplot to extract data and to create new results.
• Plotting of resulting data.

Appendix – Automating the Entire Process

Note that this entire process can be fully automated using PyTecplot, so that the resulting data and plots can be created by running a single script. See the FlameFrontAnalysis.py script in the downloadable package FlameFrontAnalysis.tar, which shows how we automated the entire process. The script uses a slightly different method to collect the flame area – rather than having Perform Integration create a new plot, this script uses Perform Integration to compute and collect the flame area at each timestep. In the end this script saves the flame area and flame speed results to a file and creates a Tecplot 360 layout file for viewing the results.

Try Tecplot 360 and PyTecplot for Free

The post Flame Front Analysis in Tecplot 360 appeared first on Tecplot.

Bubbles in fluidized beds are (mostly) pockets of fluid traveling upward through the particle bed.

Specific features of Tecplot for Barracuda covered will include Extract Blanked Zones, Extract Connected Regions, and Perform Integration. We’ll demonstrate how to use these features directly within the Tecplot for Barracuda GUI, and also how to automate the process with PyTecplot (included with the full version of Tecplot 360).

Download the data Used in this webinar: cpfd.bubbles.data.zip

Webinar Agenda

• 0:01:27;00 – Overview of Tecplot, Inc.
• 0:03:34;00 – CPFD and Barracuda Virtual Reactor
• 0:04:26;21 – Introducing gas-particle fluidized bed systems
• 0:08:37;00 – Measuring bubble characteristics experimentally
• 0:13:54;10 – Extracting bubble characteristics from simulation results
• 0:15:47;20 – Extracting bubble characteristics in the Tecplot for Barracuda GUI
  • 0:22:35;05 – Extract Blanked Zones
  • 0:23:30;02 – Extract Connected Regions
  • 0:25:45;23 – Perform Integration
• 0:26:47;28 – Automating and Extending the process with PyTecplot
• 0:32:59;00 – Q&A

Webinar Q&A

Webinar questions are answered by hosts Sam Clark, Tecplot for Barracuda Product Manager, and Scott Fowler, Tecplot 360 Product Manager.

If you calculate the total bubble volume using volume fraction, what would be the error relative to this method?

Sam Clark: I think the crux of this question is related to your grid resolution. In our demo the grid was relatively course. We wanted something that would run fast and would be easy for the purpose of illustration. But I think probably the limiter on your accuracy could be your cell size, and so just keep that in mind. When we’re looking at this volume fraction data, each cell in the Barracuda simulation has a single value for the particle volume fraction at any given point in time, so that’s really where your resolution will be limited.

Would it be possible to map a z location onto the bubbles?

Sam Clark: It’s often helpful to see how the bubbles grow versus height. I think it would be easy to grab the average z location on each bubble and record that as well.

Scott Fowler: The Integrate panel we used for computing the volume also can compute averages. A version of the script that Sam and I worked on earlier also computed average velocities. You could see the velocity of the bubbles, and Sam also took it a step further to put in an algorithm for computing a diameter of those bubbles. He saw that some of them were oblong, so not exactly circular, but you can make some assumptions about bubble shapes.

Can you provide the Python Script used in this demonstration?

Yes, and here is the link: cpfd.bubbles.data.zip

Do we have to create a 2D slice first to analyze bubble volumes with Tecplot?

Scott Fowler: And the answer is no. In the demo, we used the full volume cells for analyzing the bubble volumes. If you wanted to create a 2D slice first, you could, but in this case, we did it with the full volume cells.

Sam Clark: For most Virtual Reactor users, you’re probably modeling big three-dimensional systems that are maybe 10 meters in diameter and 20 meters tall. For the most part, you will be interested in bubbles that are inside the bed and are three-dimensional diameter or volume. That’s totally possible using the method that Scott showed in the demo. You would get the three-dimensional volume that the bubble occupies within the 3D space. Again, we used a 2D example for the sake of illustration.

Why did the particle volume fraction range from zero to 0.5 versus zero to one?

Sam Clark: This is a fundamental behavior of the way that particles pack. Most particles can only pack up to a fraction of about 0.6 and there’s always some gas space in between. And so that’s why even the highest red color on our volume fraction scale was probably around 0.6, rather than going all the way up to one, which would indicate a solid block of particles with no gas in between.

Can you make more than one variable per bubble at the same time? And if so, could you plot bubble velocity as a function of bubble size?

Sam Clark: Yes, you can do this. It’s like the previous question about attaching the z position or the elevation of the bubbles. Whatever information you’re interested in can generally be calculated through the PyTecplot script. So, if you wanted bubble velocity, bubble size, bubble z position, those sorts of things are possible to extract.

Scott Fowler: I think it’s also important to note that computing custom values, where Tecplot 360 doesn’t have a built-in capability, can be done with PyTecplot scripting. Sam came up with a heuristic for determining the bubble diameter and added that capability to the script.

If you can imagine it, you can pretty much do it. The Python layer really has opened up Tecplot 360 from being a visualization tool to a robust analysis tool. The connection with being able to access your raw data with Python, opens many interesting workflows, such as this one.

How do I get the add-on to separate the blank regions as separate zones?

Sam Clark: If you want this capability, you need to upgrade to Barracuda version 21.0.1, which we released in August 2021.

Scott Fowler: The extract connected regions capability was introduced as a macro function and Python function in Tecplot 360 2020 R2. It is not yet available in the user interface, but it is available in the scripting language.

What is the maximum number of clusters or smallest size of particles that we can do corresponding simulation by Barracuda? And what are the heating mechanisms in Barracuda? [40:58]

Sam Clark: This is a Barracuda Virtual Reactor capability question. In general, people model very, very large systems with Barracuda. There’s no limit on the number of real particles that you can model. Essentially Barracuda models particles as clouds of particles, so it groups them for the sake of the numerical method. With the latest GPU cards and the latest computers, people are running Barracuda models that might have something like 20 million to 30 million clouds. We’ve run simulations up to a hundred million clouds, but typically you would need a system with a lot of memory RAM and large GPU cards to do that. So, I would say something like 20 to 30 million clouds is not uncommon these days.

The smallest size of particles? Again, there’s no inherent limitation in the code, but most people are doing something like particles in the range of maybe 10-micron diameter to a thousand-micron diameter. That’s the most common range of particle sizes that people tend to be simulating.

Heating mechanisms implemented in Barracuda have conduction and convection between the particle phase and the gas phase, and then between the gas phase and any thermal walls. And then we also have some radiation models. If you’re running systems at very high temperature or with particles that are very different temperature than the surrounding fluid, those radiation models can be enabled in the software.

Scott Fowler: We have found that post-processing about 1 to 5 million particles tends to start taxing commercial grade graphics cards, especially if you’re using spheres as a way of representing those particles. We recommend using the point representation, especially when you have many particles. Sam, I believe Tecplot for Barracuda, you do that by default with some of those quick macros. Is that right?

Sam Clark: Yes, exactly. For quickly analyzing the results and getting those engineering conclusions, by default we render all the clouds or particles as points. And then if you want to make a really awesome looking animation, and you can afford the extra processing time, you can render them as spheres. But most of our analysis is done in the points view.

Is possible to carry out other type of non-uniform airflow distribution such as a spouted bed?

Yes, this is possible to do with Virtual Reactor, and a number of people have simulated spouted beds in the past.

Can bubble characteristics be extracted from simulation results of pressure fluctuations, is mesh size a limiting factor?

I don’t think you would be able to derive bubble characteristics from pressure fluctuation data alone, though perhaps there is some method I’m not familiar with that attempts to do this. Most people tend to deal with particle volume fraction data directly when calculating bubble sizes from simulation results. Mesh size is a limiting factor, for example, you cannot generally resolve bubbles that are smaller than the cell sizes in your model.

Talk about options for running in the Cloud.

Scott Fowler: Tecplot 360 can be run on cloud resources. When running on virtual machines (read: the cloud) we require access to a license server. To learn more you can read about setting up a post-processing environment on AWS.

It is also possible to run Virtual Reactor in the cloud. For example, see this case study.

Shape of the bubble? How does the vertical pierced length, measured by probes, compare with the CFD bubble diameter?

This is not something we’ve studied yet. However, it would be possible to simulate the data from optical probes by using data points in a Barracuda model, and compare those results with the bubble diameter results calculated from the Tecplot techniques discussed in this webinar.

Is it possible to incorporate particle shrinkage/breakage in solid-gas fluidized bed simulation?

Virtual Reactor has the capability of modeling changes in particle size due to chemistry, for example, particles can shrink if they are being consumed through chemistry or grow if a solid material is being deposited onto the particle through chemistry. We don’t currently have the capability of modeling particle breakage through impact, though.

How do I measure the following bubble parameters (fraction, diameter and velocity) in a gas fluidized bed using Tecplot for Barracuda?

Scott Fowler: In this demo we used Tecplot 360’s integration capability to compute the volume of the bubble, but you could also use integration to compute the average Z-velocity if that’s of interest.

The post Analyzing Bubble Characteristics in Simulations of Fluidized Beds appeared first on Tecplot.

In this video we will demonstrate how to export images and animations from Tecplot 360.

Once you have your plot looking the way you want it, you can show it off by exporting your plots as images or animations.

Image Export Options

You’ll see the image export options under File > Export. This dialog has multiple vector and raster image export options. Vector formats such as EPS or PostScript are ideal for line plots, while raster formats are ideal for 2D and 3D plots – especially plots using continuous contouring or translucency. PNG is the recommended raster format today due to its combination of high image quality and small size on disk. You can specify the region to export from the following:

• Current frame – exports the frame highlighted in your plot.
• All frames – exports all frames on your plot.
• Work area – exports everything within the grey workspace.

The default in Tecplot 360 is to export all frames.

You can specify several other details for your export:

• Specify the exact width of your image. There is a blog on our website titled Best Formats for your Tecplot Images that talks more about image export formats.
• Toggle on anti-aliasing for smoother lines and text in the final output. A value of three should be sufficient for most images. Anti-aliasing requires more graphics resources which can cause the image generation to take longer.
• Full details for exporting images can be found in the Tecplot 360 User’s Guide, Section 25 (html)

Animation Export Options for Unsteady Simulations

There are other export options available for unsteady or transient simulations. Open the Time Animation Details dialog by clicking the Solution time gear icon in the Plot sidebar. Then click the filmstrip icon that will open the animation Export dialog. You can choose one of the video formats, or you can export as a sequence of images. This is the same export dialog we used to export a single image, and the same options are available.

Clicking on the Animate drop-down main menu gives a list of the animation options in the dataset. You can see that in this dataset, we have animation options for iso-surfaces, slices, and streamtraces. Each of the dialogs launched from here will also have the filmstrip icon as seen previously in the Time Animation Details dialog. There are also options for Time because this is a transient dataset. And last, there is an option for Key Frame Animation, which allows you to append multiple 3D Cartesian views. Key Frame Animation is a great way to show off a cool 3D perspective change in our dataset.

One last export option is a simple copy and paste. Select one or more frames, and copy using right-click or control-C. Then use control-V to paste the image or images into a Word document or PowerPoint presentation.

This concludes the tutorial on exporting images and animations. Thank you for watching!

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The post Exporting Tecplot Images and Animations appeared first on Tecplot.

There is an interesting new trend in using Computational Fluid Dynamics (CFD). Until recently CFD simulation was focused on existing and future things, think flying cars. Now we see CFD being applied to simulate fluid flow in the distant past, think fossils.

CFD shows Ediacaran dinner party featured plenty to eat and adequate sanitation

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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

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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

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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

read more

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

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The recent attempt to break the 2 hour marathon came very close at 2:00:24, with various aids that would be deemed illegal under current IAAF rules. The bold and obvious aerodynamic aid appeared to be a Tesla fitted with an oversized digital clock leading the runners by a few meters.

2 Hour Marathon Attempt

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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:

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 . 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.

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:

Gradient of Unstructured DataSet:

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:

Horizontal Knife Edge

Vertical Knife Edge

Now how about ShadowGraph?

The process of computing the shadowgraph field is very similar. However, recall that shadowgraph visualizes the Laplacian of the density field. BUT THERE IS NO LAPLACIAN CALCULATOR IN PARAVIEW!?! Haha no big deal. Just remember the basic vector calculus identity:

Therefore, in order for us to get the Shadowgraph image, we just need to take the Divergence of the Synthetic Schlieren vector field!

To do this, we just have to use the Gradient of Unstructured DataSet tool again:

This time, Deselect “Compute Gradient” and the select “Compute Divergence” and change the Divergence array name to Shadowgraph.

Visualized in black and white, we get a very realistic looking synthetic Shadowgraph image:

Shadowgraph Image

So what do the values mean?

Now this is an important question, but a simple one to answer. And the answer is…. not much. Physically, we know exactly what these mean, these are: Schlieren is the gradient of the density field in one direction and Shadowgraph is the laplacian of the density field. But what you need to remember is that both Schlieren and Shadowgraph are qualitative images. The position of the knife edge, brightness of the light etc. all affect how a real experimental Schlieren or Shadowgraph image will look.

This means, very often, in order to get the synthetic Schlieren to closely match an experiment, you will likely have to change the scale of your synthetic images. In the end though, you can end up with extremely realistic and accurate synthetic Schlieren images.

Hopefully this post will be helpful to some of you out there. Cheers!

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:

It is also often simplified (as it is in OpenFOAM) to:

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:

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.

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 (
https://www.linkedin.com/feed/update/urn:li:groupPost:1920608-6518408864084299776/?commentUrn=urn%3Ali%3Acomment%3A%28groupPost%3A1920608-6518408864084299776%2C6518932944235610112%29&replyUrn=urn%3Ali%3Acomment%3A%28groupPost%3A1920608-6518408864084299776%2C6518956058403172352%29).

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.

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!

Download

You can download the script here:

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

LeadingEdgeGrading: Grading from the 1/4 chord position to the leading edge

TrailingEdgeGrading: Grading from the 1/4 chord position to the trailing edge

inletGradingFactor: This is a grading factor that modifies the the grading along the inlet as a multiple of the leading edge grading and can help improve mesh uniformity

trailingBlockAngle: This is an angle in degrees that expresses the angles of the trailing edge blocks. This can reduce the aspect ratio of the boundary cells at the top and bottom of the domain, but can make other mesh parameters worse.

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!

Comments and bug reporting encouraged!

DISCLAIMER: This script is intended as an educational and productivity tool and starting point. You may use and modify how you wish. But I make no guarantee of its accuracy, reliability, or suitability for any use. This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via http://www.openfoam.com, and owner of the OPENFOAM®  and OpenCFD®  trademarks.

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.