This week’s hoard of CFD miscellany includes several items that will require a deep breath and some time to digest. There’s an online interactive text on linear algebra, the International Simulation Olympiad for academia, a very cool video visualization of … Continue reading →

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

So many in-person CFD events are coming up in the first half of the year that’s what this edition of This Week in CFD leads with [count the grammar errors in that sentence]. Not to be outdone, the Image of … Continue reading →

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

Welcome to the first, almost not published, edition of This Week in CFD for 2023. I write “almost not published” because WordPress is having some serious problems today so I’m just publishing what I’ve got before I lose any more … Continue reading →

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

If you enjoy seeing CFD in action, this week’s news has applications galore including some non-traditional uses. Be certain to browse Digital Engineering’s Technology Outlook edition to learn what they’re thinking about the coming year. Software releases and job openings … Continue reading →

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Ever wish you could slow down or stop time so you could really enjoy something? This week’s article includes several items that can serve as a time sink. The “image of the week” deserves detailed inspection. Soccer fans (aka futbol … Continue reading →

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Here’s a harvesting of CFD news for the first Friday of December. It’s a little light because your editor has been operating at less than full capacity all week but nonetheless you’ll see a lot of cool applications, event news, … Continue reading →

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Evaporation is a well-known effect in soap films and bubbles. It’s responsible for the ever-changing thickness reflected in the film’s many colors. But evaporation does more than change the bubble’s thickness: it affects its temperature, too. Just as sweat evaporating off our skin cools us, the soap film’s evaporation makes it cooler than the surrounding air.

Researchers found that their soap films could be as much as 8 degrees Celsius cooler than the surrounding air! They also found that the film’s glycerol content affect how much cooler the soap film is; films with more glycerol had higher temperatures, which could impact their overall stability. (Image credit: E. Škof; research credit: F. Boulogne et al.; via APS Physics)

When a partially-air-filled drop hits a surface, it splashes and rebounds in a complex fashion. This video breaks down the physics of the process. Upon impact, a lamella spreads, eventually becoming wavy and unstable along its rim. At the same time, a counterjet forms, growing until it pierces the remaining bubble of the drop. The jet continues to stretch upward due to its momentum, pinching off and forming wobbly satellite drops that finally fall back to the surface. (Image and video credit: D. Naidu and S. Dash)

Many theories posit the physical and chemical origins of life. In the short film “aBiogenesis”, CGI artist Markos Kay imagines one such theory — the lipid world theory — in which cellular life began as a soup contained within immiscible fatty membranes. Chemicals trapped within these vesicles interacted and ultimately formed the building blocks of life as we know it, including RNA. Kay’s interpretation is a beautiful exploration of this intersection of physics, chemistry, and biology. (Image and video credit: M. Kay; via Colossal)

A lucky encounter led the Perseverance rover to record the first-ever sound of a dust devil on Mars. The rover happened to have its microphone on (something that only happens a few minutes every month) just as a dust devil swept directly over the rover. Check out the video above to see and hear what Perseverance captured.

Using the rover’s instrumentation, researchers worked out that the dust devil was at least 118 meters tall and about 25 meters wide. The team was even able to determine the density of dust in the vortex from the sound of individual grain impacts captured in the acoustic signal! Serendipitous as the experience was, planetary scientists may now look to include microphones on more missions, since we now know how to get useful meteorological data from them. (Video credit: JPL-Caltech/NASA; image credit: LPL/NASA; research credit: N. Murdoch et al.; via AGU Eos; submitted by Kam-Yung Soh)

Physicist Silke Weinfurtner studies fluids, not for themselves, but for what they can teach us about black holes, cosmic inflation, and quantum gravity. Black holes are notoriously difficult to study directly, but, mathematically speaking, it’s possible to set up a fluid system that behaves in the same way a black hole does. The result is a bathtub-like arrangement with a central vortex, seen above. And within this “bathtub,” Weinfurtner and her colleagues can directly measure sound waves equivalent to Hawking radiation, the theoretical means by which black holes emit heat. Learn more about these analogue gravity experiments in her interview over at Quanta Magazine. (Image credit: P. Ammon; via Quanta Magazine; submitted by clogwog)

Marsh grass shrimp, despite their small size, are zippy swimmers. They move using a series of closely-spaced legs that stroke asynchronously. Researchers found that the flexibility and stiffness of the legs are critical for the shrimp’s efficiency. During the power stroke, the shrimp’s leg is held stiff, maximizing the force it’s able to transfer to the water. But during the forward-moving recovery stroke, the shrimp bends its legs almost horizontal and presses both legs in the pair together tightly. This action minimizes the area of the leg pair and reduces the drag they cause as they move into position for the next stroke. (Image, video, and research credit: N. Tack et al.; via Ars Technica; submitted by Kam-Yung Soh)

Figure 1: Flow-aligned mesh around an MDA -3 element configuration.

                                                                                                                                                                                                                              1350 words / 7 minutes read

Introduction

In the rapid world of product design, CFD simulations are expected to generate quick results. Quick results mean faster grid generation, which inevitably leads to a loss of attention to subtle gridding details. One such critically important gridding aspect that most CFD practitioners have less appreciation for is that of alignment of the grid to the flow.

Three aspects of gridding dictate the final solver solution outcome – grid quality, mesh resolution and grid alignment. Most grid generators pay attention to the first two aspects of mesh cell quality and refinement but ignore grid line alignment to the flow. This is understandable as rapid domain filling algorithms like unstructured meshing and Cartesian will not be able to meet the meshing criteria of flow alignment, as these algorithms are inherently handicapped to do so. Only inside the boundary layer, where they adopt stacking of prism or hexahedral cells, is some flow alignment achieved. Currently, only the structured multi-block technique is capable of orienting the grid cells to the flow inside the boundary layer padding as well as outside.

It is critically essential that CFD practitioners know how alignment or non-alignment of the grid to flow, how the presence of different degrees of mesh singularities affects the flow field and how grid alignment to high gradient flow phenomena like shock influences the final solution outcome. This article attempts to address these meshing aspects.

A Gridding Experiment to Demonstrate the Need for Alignment of Grid to Flow:

The importance of grid cell orientation w.r.t to the flow can be demonstrated with a simple convective-diffusive flow in a square domain. Figures 2 and 3 show the errors produced due to different orientations of the cells to the flow direction.

If we have two velocities, V1 and V2, flowing on a structured mesh in the direction of the grid lines, the solution will be completely conformal without any diffusion or numerical error, as shown in Figure 2a. This is true, even for a grid where the mesh lines are not oriented in the direction of the coordinate system, as illustrated in Figure 2b.

However, if we have an unstructured mesh or a structured mesh, but the flow is not aligned, then there is diffusion taking place. The amount of diffusion depends on differencing scheme used in the flow solver and on the size of the mesh. The finer the mesh, the lower the diffusion. But, never the less, it still exists.

Effect of Grid Singularities

A grid singularity is nothing but a grid point in 2-Dimension where more or less than four grid lines radiate from a point. Singularities exist in large numbers in unstructured meshes and in very small numbers in multi-block meshes for complex configurations.

Results from the gridding experiment on singularities show that the error magnitudes are least for lesser singularities ( 3-way singularity) while it is high for larger singularities like an 8-way singularity, as shown in Figures 5 and 6.

A closer review of the results shows that the results for 3- and 5- way singularity grids are quite acceptable and actually are as good as the results from the non-singular grids from the same grid generator.

Hex Cells in Cartesian and Structured Grids are Not the Same

Though both Cartesian grids and the classical structured grids use hexahedral cells, the effect of the grid on the flow solver output is not the same. The subtle difference in the alignment of the cells and the need for interpolation in Cartesian grids show up in the computed results. In a Cartesian grid, the grid lines are aligned to the regular Cartesian coordinates, while the grid lines in structured grids are aligned to the geometric body and the flow field.

Figure 7 illustrates the computed species mass fraction and temperature distribution for a CFD simulation involving fuel injection in a combustor of a hypersonic vehicle. As shown in Figure 7a, the Cartesian interpolation leads to dramatic spurious oscillations for the species mass fraction, especially at small stoichiometric scalar dissipation rate. On the other hand, structured curvilinear meshes show a very smooth interpolation without any oscillation. Similar results can be seen in the computed temperature distribution in Figure 7b. As V. E. Terrapon, the author of the research work [ref 1], says,

“The small additional lookup cost in a curvilinear mesh is largely compensated by a much smoother interpolation.”

Flow Aligned Mesh for Boundary Layer Capturing

The boundary layer, which is home to wall-bounded viscous flows, experiences high gradients. To capture the high gradients, finely stacked flow-aligned cells are required. Maintaining cell orthogonality w.r.t to the wall is another key factor in boundary layer generation. So, to maintain optimal cell count and yet finely resolve the boundary layer, stretched elements in the form of prisms or hexahedral cells are preferred. For the same reason, even the hybrid unstructured meshing approach adopts stacked prism cells in the viscous padding, as stacking high aspect ratio tetrahedral is not preferred due to deterioration in cell skewness.

Orderly arranged flow-aligned mesh in the boundary layer are critical and essential as it aids in the accurate representation of its profile, leading to accurate predictions of wall shear stress, surface pressure and also the effect of adverse pressure gradients and forces.

Further, at very high Mach numbers in the supersonic or hypersonic flow regimes, the laminar to turbulent boundary layer transition and shock boundary layer interactions significantly influence aircraft aerodynamic characteristics. They affect the thermal processes, the drag coefficient and the vehicle lift-to-drag ratio. Hence, it is critical essentially to pay attention to how well the cells are arranged in the boundary layer padding.

Flow Aligned Mesh for Shock Capturing

To capture the effects of high gradient flow phenomena like shocks on the flow field downstream, it is essential to align the grid lines to the shock shape and have refined cells.

For this, hexahedral meshes are better suited. They can be tailored to the shock pattern and can be made finer in the shock normal direction or can be adaptively refined. This not only brings the captured shock thickness closer to its physical value but also allows for the improvement of the solution quality by aligning the faces of the control volumes with the shock front. Aligned grids reduce the numerical errors induced by the captured shock waves and thereby significantly enhance the computed solution quality in the entire region downstream of the shock.

Grid alignment is necessary for both oblique and normal bow shock. Grid studies have shown that solver convergence is extremely sensitive to the shape of the O-grid at the stagnation point. Matching the edge of the O-grid with the curved standing shock and maintaining cell orthogonality at the walls was found to be necessary to get good convergence.

Also, grid misalignment is observed to generate non-physical waves, as shown in Figure 10. For CFD solvers with low numerical dissipation, a strong shock generates spurious waves when it goes through a ‘cell step’ or moves from one cell to another. Such numerical artefacts can be avoided, or at least the strength of the spurious waves can be minimized by reducing the cell growth ratio and cell misalignment w.r.t the shock shape.

Check out the importance of flow alignment and comparison on various grid types for an airfoil and Onera M6 wing.

Do Mesh Still Play a Critical Role in CFD?

Conclusion

For ultra-accurate CFD results, flow alignment of grids is a must. It is a subtle detail in grid generation which can make a mammoth difference in the computed solution. Out of all the gridding methodologies developed to date, structured hexahedral meshing is the best candidate for the job. Whether it is near the wall in the boundary layer or in the interior of the domain to discretize shocks, structured meshes optimally align to the flow features and helps to avoid dissipation or numerical errors.

To sum up, if accurate CFD results are the top priority in your CFD cycle, then having flow-aligned grids is your secret recipe.

To know about generating flow-aligned meshes in GridPro, contact us at: support@gridpro.com.

Further Reading

• Do Mesh Still Play a Critical Role in CFD?
• The Art and Science of Meshing Airfoil
• Is Grid Generation an Art or Science?
• Nesting Your Way to Mesh Multi-Scale CFD Simulation!

References

1. “A flamelet-based model for supersonic combustion”, V. E. Terrapon et al, Center for Turbulence Research Annual Research Briefs, 2009.
2. “HEC-RAS 2D – AN ACCESSIBLE AND CAPABLE MODELLING TOOL“, C. M. Lintott Beca Ltd, Water New Zealand’s 2017 Stormwater Conference.
3. “Effect of Grid Singularities on the Solution Accuracy of a CAA Code”, R. Hixon et al, 41st Aerospace Sciences Meeting and Exhibit, 6-9 January 2003, Reno, Nevada.
4. “Challenges to 3D CFD modelling of rotary positive displacement machines”, Prof Ahmed Kovacevic, SCORG Webinar.
5. “Experimental Study of Hypersonic Fluid-Structure Interaction with Shock Impingement on a Cantilevered Plate”, Gaetano M D Currao, PhD Thesis, March 2018.

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Figure 1: Hexahedral mesh for an aircraft icing surface.

1228 words / 6 minutes read

Complex ice shapes make generating well-resolved mesh extremely difficult, CFD practitioners make geometric and meshing compromises to understand the effect of Ice accretion on UAVs.

Introduction

Flying safely and reliably depends on how well icing conditions are managed. Atmospheric icing is one of the main reasons for the operational limitations, Icing disturbs the aerodynamics and limits the flight capabilities such as range and duration. In some scenarios, it can even lead to crashes.

Icing has been under research for manned aircraft since the 1940s. However, the need to understand icing effects for different flying scenarios in unmanned aerial vehicles (UAVs) or drones has reignited the research. Drones are used for a wide range of applications like package delivery, military, glacier studies, pipeline monitoring, search and rescue, etc.

The well-understood icing process of manned civil and military aircraft does not hold good for most UAVs. UAVs fly at a lower air speed and are smaller in size. They operate at a low Reynolds Number in the range of 0.1-1.0 million as against manned aviation which fly at Reynolds Numbers of the order of 10-100 million. This huge difference necessitates the need to gain a better understanding of the icing process at low Reynolds numbers.

CFD simulation of aircraft ice accretion is a natural choice for researchers due to its cost-effective approach when compared to flight testing. In this article, we will discuss how researchers navigate through geometry and meshing challenges to understand the icing effects.

Ice Accretion Analysis

Icing analysis covers a large variety of physical phenomena. From droplet or ice crystal impact on cold surfaces to solidification process at different scales. Ice accumulation degrades aerodynamic performances such as the lift, drag, stability and stall behaviour of lifting surfaces by modifying the leading-edge geometry and the state of the boundary layer downstream. This results in premature and highly undesirable flow separation.

Such flow transition and turbulently active regions need well-resolved grids. However, the complex icing undulations make meshing very hard, forcing the CFD practitioners to face geometric and meshing challenges.

Complex Geometric Shapes

Icing develops different kinds of geometric features such as conic shapes, jagged ridges, narrow, deep valleys and concave regions. In 3D, the spanwise variation of these features creates further complexities.

Geometric simplification is more often done while attempting 3D simulations. Even though fine resolution 3D scanned ice feature data is available, incapability to create quality normal wall resolved cells compels CFD practitioners to either simplify the ice features or settle down for some kind of inviscid simulation without capturing the viscous effects. Figure 4 shows such a compromised unstructured mesh without viscous padding for a DLES simulation. Figure 5 shows the extraction of a smoothened and simplified ice geometry from an actual icing surface.

It is extremely difficult to mesh such realistic ice shapes for any mesh generation algorithm let alone the aspect of mesh quality.

As a compromise, the sub-scale surface roughness is smoothened out and is not captured. As a consequence, the turbulence effects due to sub-scale geometric features get ignored.

Wide-Ranging Geometric Scales

Ice features range widely in geometric scales. For, e.g., ice horns can be as big as 1-2 centimetres, while sub-scale surface roughness can be as small as a few microns.

The level of deterioration in performance is directly related to the ice shapes and to the degree of aerodynamic flow disruption they rake up. Sub-scale ice surface roughness triggers laminar to turbulent transition while large size ice-horns cause large-scale separation.

Meshing such wide-ranging geometric scales poses a few challenges. Firstly, they will need a massive number of cells to capture the micron-level features, directly posing a challenge to the computational power and considerable time for both meshing and CFD.

Literature review shows that certain CFD practitioners, foreseeing these challenges, settle down for 2D simulations to avoid computationally expensive 3D simulations. Even at the 2D level, finer ice-roughness features are smoothened to make viscous padding creation more manageable.

Horns and Crevices

Crevices and concave regions are home to re-circulation flows. These viscous regions need finely resolved unit aspect ratio cells to capture them. But since many grid generators find it difficult to mesh these regions, the crevices are removed and replaced by a small depression.

Aft of the horns, large-scale wakes are created, which are highly unsteady and three-dimensional in nature. Also, with an increase in the angle of attack, these turbulent features grow in size and start to extend further in the normal and axial direction w.r.t the wing surface. In concave regions and narrow crevices, recirculation flows can be observed.

Boundary-Layer Mesh

The boundary layer padding needs to have a good wall-normal resolution with first spacing equivalent to Y+ not more than 1. The rough ice surfaces aggravate flow separation and adequate viscous padding with a uniform number of layers with orthogonal cells is necessary at all locations.

Growing wall-normal quadrilateral or hexahedral cells from the ice walls for the entire region is a challenge since the crevices are very narrow with irregular protrusions, and generating continuous viscous padding causes cells to collapse one over the other.

To overcome this some grid generators resort to partial normal wall padding to the extent the local geometry permits and quickly transition to unstructured meshing, as shown in Figure 9a.

Meshing Transient Ice Accumulation

Research has shown that airframe size and air speed are two main important parameters influencing ice accretion.

One of the icing simulation requirements is computing ice accumulation for a finite time period spanning 15 to 20 minutes. Multiple CFD simulations are done for different chord lengths and air velocities. As one can perceive, this is a numerically intensive job requiring automated geometry building and mesh generation. In such studies, it is necessary to generate new mesh for every minute or even less to make a CFD run for newer instances of ice deposition.

With each time step the shape of the ice-feature changes and with time, they take fairly complex shapes with horns and crevices, making local manual intervention an inevitable necessity.

Parting Remarks

For the safe operation of UAVs without an icing protection system, the common solution is to ground the aircraft when icing conditions prevail. This limitation can be overcome by having a better de-icing system. Through CFD analysis of ice accretion at different atmospheric conditions, the amount of optimal onboard electrical power needed to do de-icing can be known.

However, accurate CFD analysis hinges on precise capturing of the ice features by the mesh. A meshing system which can aptly meet this requirement without making geometric or meshing compromises is the need of the hour.

For structured meshing needs for icing analysis reach out to GridPro, please contact: gridpro@gridpro.com.

Further Reading

• Nesting Your Way to Mesh Multi-Scale CFD Simulation
• The Art and Science of Meshing Airfoil
• Know Your Mesh for Reentry Vehicles
• Role of Vortex Generators in Diffuser S-Ducts of Aircraft

References

1.”Comparison of LEWICE 1.6 and LEWICE/NS with IRT Experimental Data from Modern Airfoil Tests“, William B. Wright, Mark G. Potapczuk.
2. “Geometry Modeling and Grid Generation for Computational Aerodynamic Simulations around Iced Airfoils and Wings“, Yung K. Choo, John W. Slater, Mary B. Vickerman, Judith F. VanZante.
3. “COMPUTATIONAL MODELING OF ROTOR BLADE PERFORMANCE DEGRADATION DUE TO ICE ACCRETION“, A Thesis in Aerospace Engineering, Christine M. Brown, The Pennsylvania State University The Graduate School, December 2013.
4. ” ICE INTERFACE EVOLUTION MODELLING ALGORITHMS FOR AIRCRAFT ICING“, SIMON BOURGAULT-CÔTÉ, Thesis, UNIVERSITÉ DE MONTRÉAL, 2019.
5. “Atmospheric Ice Accretions, Aerodynamic Icing Penalties, and Ice Protection Systems on Unmanned Aerial Vehicles“, Richard Hann, PhD Thesis,  Norwegian University of Science and Technology, July 2020.
6. “Icing on UAVs“, Richard Hann, NASA Seminar.
7. https://www.ntnu.no/blogger/richard-hann/
8. https://uavicinglab.com/
9. “An Integrated Approach to Swept Wing Icing Simulation“, Mark G. Potapczuk et al, Presented at 7th European Conference for Aeronautics and Space Sciences Milan, Italy, July 3-6, 2017.

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Figure 1: Structured multi-block mesh for scroll compressors with tip seal.

804 words / 4 minutes read

Scroll compressors with deforming fluid space, narrow flank, and axial clearance pose immense meshing challenges to any mesh generation technique.

Introduction

Scroll compressors and expanders have been in extensive usage in refrigeration, air-conditioning, and automobile industries since the 1980s. A slight improvement in scroll efficiency results in significant energy savings and reduction in pollution on the environment. It is therefore important to minimize frictional power loss at each pair of the compressor elements and also the fluid leakage power loss at each clearance between the compressor elements. So developing ways to minimize leakage losses is essential to improve scroll performance.

Scroll Compressor CFD Challenges

Unlike other turbomachines like compressors and turbines, Positive Displacement (PD) machines like scroll suffer from innovative designs and performance enhancements. This is mainly due to difficulties in applying CFD to these machines because of the challenges in meshing , fluid real equations and long computational time.

Geometric Challenges for meshing

Deforming Flow Field:

The fluid flow is transient and the flow volume changes with time (Figure 3). The fluid is compressed and expanded as it passes through different stages of the compression process. The mesh for the fluid space should be able to ‘follow’ the deformation imposed by the machine without losing its quality.

When the deformation is small, the initial mesh maintains cell quality, however, for large deformations, mesh quality deteriorates and collapses near the contact points between the stator and moving parts.

Flank Clearance:

The narrow passage between the stationary and moving scroll in the radial direction is called the Flank clearance. A clearance of [~ 0.05 mm] is generally used to avoid contact, rub and tear.

Adequately resolving this clearance with a fine mesh is one of the key factors in obtaining an accurate CFD simulation. However, the narrowness of this gap poses meshing challenges for many grid generators.

Axial Clearance:

The narrow passage between the stationary and moving scroll in the axial direction is called the Axial clearance. The axial clearance is about one thousand of the axial scroll plate height, which is much smaller than the flank clearance.

The gap actually forces to have separate zones of mesh in some cases. Adequate resolution of axial clearance gaps is also equally important since it leads to inaccurate flow field prediction.

Tip Seal Modeling:

Tip seals are used to reduce axial leakages which are caused due to wear and tear. The tip seals influence the mass flow rate of the fluid. Modeling internal leakages with tip seals would require many numerical techniques ranging from fluid-structure interaction to special treatments for thermal deformation and tip seals efficiency.

Discharge Check Valve Modeling:

Valves called reed valves are installed at the discharge to prevent reverse flow. Understanding the dynamics of the check valves is important because they significantly influence scroll efficiency and noise levels. The losses at the discharge can significantly reduce the overall efficiency.

However, modeling the valve with appropriate simplification is a challenge for any meshing technique.

Influence of Mesh Element Type

A lot of different meshing methods have been employed from tetrahedral to hexahedral to polyhedral cells to discretize the fluid passage. However, researchers who tend to weigh more on the accuracy of the solution tend to weigh more to mesh with structured hexahedral cells.

Hexahedral meshing outweighs other element types w.r.t grid quality, domain space discretization efficiency, solution accuracy, solver robustness, and convergence levels.

One of the reasons why structured hexahedral mesh offers better accuracy is that it can be squeezed without deteriorating the cell quality. This allows to place, a large number of mesh layers in the narrow clearance gap. Better resolution of the critical gap results in better CFD prediction.

Parting Remarks

Understanding the key meshing challenges before setting forth to mesh scrolls is very essential. Becoming aware of the regions that pose difficulties to mesh and regions that strongly influence the accuracy of the CFD prediction is critically important. More importantly, which meshing approach to pick – structured, unstructured, or cartesian also influence the quality and accuracy of your CFD prediction.

In the next article on Automating meshing for scroll compressors, we discuss, how we can mesh scroll compressors in GridPro.

References

1.“Study on the Scroll Compressors Used in the Air and Hydrogen Cycles of FCVs by CFD Modeling”, Qingqing ZHANG et al, 24th International Compressor Engineering Conference at Purdue, July 9-12, 2018.
2. “Numerical Simulation of Unsteady Flow in a Scroll Compressor”, Haiyang Gao et al, 22nd International Compressor Engineering Conference at Purdue, July 14-17, 2014.
3. “Novel structured dynamic mesh generation for CFD analysis of scroll compressors”, Jun Wang et al, Proc IMechE Part A: J Power and Energy 2015, Vol. 229(8), IMechE 2015.
4. “Modeling A Scroll Compressor Using A Cartesian Cut-Cell Based CFD Methodology With Automatic Adaptive Meshing”, Ha-Duong Pham et al, 24th International Compressor Engineering Conference at Purdue, July 9-12, 2018.
5. “3D Transient CFD Simulation of Scroll Compressors with the Tip Seal”, Haiyang Gao et al, IOP Conf. Series: Materials Science and Engineering 90 (2015) 012034.
6.“CFD simulation of a dry scroll vacuum pump with clearances, solid heating and thermal deformation”, A Spille-Kohoff et al, IOP Conf. Series: Materials Science and Engineering 232 (2017).
7.  “Structured Mesh Generation and Numerical Analysis of a Scroll Expander in an Open-Source Environment”, Ettore Fadiga et al, Energies 2020, 13, 666.
8. “Analysis of the Inner Fluid-Dynamics of Scroll Compressors and Comparison between CFD Numerical and Modelling Approaches”, Giovanna Cavazzini et al, Energies 2021, 14, 1158.
9. “FLOW MODELING OF SCROLL COMPRESSORS AND EXPANDERS”, by George Karagiorgis, PhD- Thesis, The City University, August 1998.
10. “Heat Transfer and Leakage Analysis for R410A Refrigeration Scroll Compressor“, Bin Peng et al, ICMD 2017: Advances in Mechanical Design pp 1453-1469.
11. “Implementation of scroll compressors into the Cordier diagram“, C Thomas et al, IOP Conf. Series: Materials Science and Engineering 604 (2019) 012079.

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Figure 1: Structured multi-block mesh for scroll compressors.

1167 words / 5 minutes read

Developing a three-dimensional mesh of a scroll compressor for reliable Computational Fluid Dynamics (CFD) Analysis is challenging. The challenges not only demand an automated meshing strategy but also a high-quality structured hexahedral mesh for accurate CFD results in a shorter turnaround time.

Introduction

The geometric complexities of Meshing Scroll Compressors discussed in our previous article give us a window into the need for creating a high-quality structured mesh of scroll compressors.

A good mesher should handle the following challenges in a positive displacement machine:

• The continuously deforming pocket volume.
• Since its a complex and time-dependent fluid dynamic phenomena. The mesher should be able to accurately “follow” the deformation imposed by the machine moving part without losing the mesh quality.
• The mesh should not suffer quality decay, or have uncontrolled mesh refinements and mesh collapses near contact points between the stator and moving parts, etc.
• Should offer higher accuracy of numerical simulations and the short simulation turn-around time.

Meshing Strategy

The scroll compressor fluid mesh region on a given plane is a helical passage, with varying thickness, expanding, and contracting based on the crank angle and the fluid domain is topologically a rectangular passage. So we use the same approach as that of meshing a rectangle for the Scroll Compressor.

Mesh Topology

One of the main obstacles for simulation in scroll compressors is the generation process of dynamic mesh in fluid domains, especially in the region of flank clearance. The topology based approach offers a perfect solution for such scenarios. Primarily because the deforming fluid domain in the Scroll compressor does not change the topology of the fluid region.

Advantages of Topology based Meshing:

• At each time step, when the orbiting rotor moves to a new position, the new mesh is generated without any user intervention.

• The block built becomes a template for a new variation of the scroll rotors, this makes it ideal for optimization and even meshing variable thickness scroll compressors.
• Since meshes share the same topology, i.e. the number of blocks and their connectivity and cells remain the same, which avoids the need for interpolation of results. The computational effort is significantly reduced and the mesh quality is high, leading to reliable CFD analysis.

Flank Clearance and it’s Meshing Needs

The flank clearance could reduce to as low as 0.05 mm and an adequate resolution of the flank clearance with low skewness is the key reason for better prediction of performance by structured meshes when compared to unstructured meshes.

The dynamic boundary conforming algorithm of GridPro moves the blocks into the compressed space automatically and generates the mesh. The smoother ensures that the mesh has a homogenous mesh distribution and is orthogonal. Orthogonality is another important mesh quality metric that sets structured meshes against moving mesh approaches. Orthogonality improves the numerical accuracy, stability of the solution and prevents numerical diffusion.

Solid Scroll Meshing for FSI

Understanding the heat transfer towards and inside the solid components is important since the heat transfer influences the leakage gap size. Heat transfer analysis is especially required in vacuum pumps where the fluid has low densities and low mass flow rates.

One of the major drawbacks of scroll compressors is the high working temperature (maximum temperature of up to 250 degrees Celsius is reported [Ref 3]). The higher temperatures increase excessively the thermal expansion of scroll spirals, leading to significant increments of internal leakages and thereby affecting the efficiency.

A mesh created for conjugate heat transfer has to model the in-between compression chamber, the scrolls and the convective boundary condition at the outer surface of the scrolls. This type of mesh enables to get consistent temperatures in the solids, to calculate the thermal deformation of the scrolls.

Automation and Optimization of Scroll Compressor

Even though scroll compressors enjoy a high volumetric efficiency in the range of 80-95%, there is still room for improvements. Optimization of the geometric parameters is necessary to reduce the performance degradation due to leakage flows in radial and axial clearances.

CFD as a design tool plays a significant role in optimizing scroll geometry. The major advantage of a 3D CFD simulation combined with fluid-structure interaction (FSI) is that the 3D geometry effect is directly considered. This makes CFD analysis highly suitable for the optimization of the design.

GridPro provides an excellent platform for automating hexahedral meshing through because of its working principle and the python based API.

The key features are:

• Quick set up of a CFD model from CAD geometry.
• Parametric design of geometry can be incorporated into the same blocking and can be used even for variable thickness scrolls.
• The mesh at each time interval is of high quality with orthogonal cells and even distribution.
• The other advantage of this strategy is that it is respectful of the space conservation law while conserving mass, momentum, energy, and species.

Since GridPro offers both process automation through scripting and API level automation. The automation can either be triggered outside of a CAD environment or inside the CAD environment.

This flexibility provides companies and researchers to develop full-scale meshing automation with GridPro while the user only interacts with CAD / CFD or any software connector platform.

Parting Remarks

The generation of a structured mesh for the entire scroll domains, including the port region, is a very challenging task. It could be very difficult to model narrow gaps and complex features of the geometry. However, with GridPro’s template-based approach and dynamic boundary conforming technology the setup is reduced to a few specifications and the user can develop his own automation module for structured hexahedral meshing.

If scroll compressor meshing is your need and you are looking out for solutions. Feel free to reach out to us at: support@gridpro.com

Contact GridPro

References

1.”Analysis of the Inner Fluid-Dynamics of Scroll Compressors and Comparison between CFD Numerical and Modelling Approaches“, Giovanna Cavazzini et al, Advances in Energy Research: 2nd Edition, 2021.

2. “Structured Mesh Generation and Numerical Analysis of a Scroll Expander in an Open-Source Environment”, Ettore Fadiga et al, Energies 2020, 13, 666.

3. “Waste heat recovery for commercial vehicles with a Rankine process“, Seher, D.; Lengenfelder, T.; Gerhardt, J.; Eisenmenger, N.; Hackner, M.; Krinn, I., In Proceedings of the 21st Aachen Colloquium on Automobile and Engine Technology, Aachen, Germany, 8–10 October 2012; pp. 7–9.

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About GridPro Version 8.1 

The GridPro version 8.1 release marks the completion of yet another endeavor to provide a feature-rich, powerful and reliable package to the Structured meshing software to the CAE community.

In every cycle of development, we fulfill the feature requests from our users, improve workflow challenges and democratize the feature to enable newer users to transition without much learning. Along the way, we are improvising on the performance of the tool with the increasing demand to handle challenging geometries in meshing.

Here is a quick Preview of the Major Features:

• New License Monitoring System for Network license users.
• Automatic grouping of Boundary faces for quicker workflow.
• New Face display for better understanding of Topology.
• Faster and Robust block extrusion for Tubular geometries ( ducts, arteries, volutes, etc).

Major Highlights of Version 8.1

License Monitoring System

Network / Float Licenses

The License Management System now has GUI access to most of the features that a user or a system admin would look for. The License Manager GUI now displays all the license-related information. When the user loads the license file and starts the license manager, all the initialization process is done before the license manager is started. The license manager also displays the number of licenses used and the MAC id/ hostname of the user using the license.

Node-locked / Served Licenses

The client license management system is now packaged along with the GUI. When the GUI is opened for the first time the license popup appears where the user is asked to upload the license and Initialise. The initialization process runs in the background and opens up the GUI. This process irons out the need to go through a list of specific commands listed in section 9.11 of the utility manual.

Smart Face Groups to Enhance user workflow in GridPro

The quest to improve user experience and provide easy access to the entities continues. The current version has made a major stride in this direction. From version 8.1 onwards the user has a list of smart selections of face groups available as a part of the Selection Panel. From the blocking, the algorithm calculates the boundary faces and smart groups, based on certain checks. These face groups are displayed and the user can select a single group or a combination of groups to progress in further modifying the structure or assigning to surfaces.

The selection pane also has a temporary selection group to provide flexibility in the workflow. In the past, the user had to select a group to select the entities in the GL. However, the present version enables the users with an alternative workflow where they can right-click and drag in the GL to select faces /blocks. These selected blocks/faces/edges/corners are stored in the Selection Group. It is overwritten when the next selection is made. However, the user has an option to move the selection into one of the permanent groups.

Topology now has Face Display for Better Visualization

The topology now has a Face display along with the corners and edges. The face display now helps the user to have a better perception of the faces and blocks both displayed in the GL and grouped in individual groups. To reassure the user of the topology entities selected, the display mode is automatically changed to face display mode in the following scenarios.

• User selects corners and edges into a group.
• Wrap displays the new faces created after an operation.
• Copy shows the blocks that are created when a face/s are created.
• Extrude displays the output blocks created.

There are many such scenarios where the user is provided feedback on the operations visually.

Fast Blocking for Tubular Geometries (Arteries, Ducts, etc)

The improved centreline evaluation tool is now robust and fast. This speeds up the topology building for geometries like pipes and human arteries and ducts. The algorithm extrudes the given input along the centreline of the geometry resection the change in cross-sectional area change. The algorithm is now available under extrude option in the GUI.

For more details about the new features, enhancements, and bug fixes please, refer to:

• What’s New.
• Release Notes.
• Videos of New Feature Applications.

Supported Platforms

GridPro WS works on Windows 7 and above, Ubuntu 12.04 and above, Rhel 5.6 and above, MacOS 10 and above.

The support for the 32-bit platform has been discontinued for all operating systems.

GridPro AZ will be discontinued from version 9 onwards.

Download

GridPro Version 8.1 can be downloaded by registering here.

All tutorials can be found in the Doc folder in the GridPro Installation directory. Alternatively, it can be downloaded from the link here.

All earlier software versions can be found in the Download sections.

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Figure 1: Structured multi-block grid for turbopumps.

1050 words / 5 minutes read

Turbopumps help rockets achieve high power to weight ratio by feeding pressurized propellant to the rocket’s combustion chamber. The success of rocket launch missions is heavily influenced by the design of inducers in the turbopumps. 

The Rocket Challenge

Human conquest of space has advanced at full speed over the last few years. According to Forbes, there are 10,000 space companies globally. This massive growth has triggered competition in the global space transportation business which is fueling innovations. Reports indicate that nearly 59 % of rocket launch failures are due to propulsion system failures. In this article, we will discuss how the industry is focused on increasing reliability and reducing the cost of development of launch vehicles by improving the design of liquid-propellent rocket engines.

The main reason for propulsion system failure is due to instabilities in the combustor or turbopump. It is estimated that up to 50% of a rocket development program’s cost goes into the design and development of turbopumps.

Failed Missions

Despite many years of extensive research, unsteady cavitation instabilities in turbopumps are a significant problem and are not entirely understood. Further, there are no well-established procedures for predicting its onset during the early design phase.

Cavitation instabilities that can trigger severe load and vibrations within turbopumps cause engine thrust fluctuations and sometimes even total mechanical failure. Historically, cavitation instabilities have caused failed missions in almost all rocket development programs, including Apollo (NASA), Space Shuttle main engines (NASA), Fastrac (NASA), Vulcain (ESA), and LE-7 (JAXA).

Hence, it is critical to identify the mechanisms governing cavitation instabilities to pave the way for building principle-based design guidelines for inducers to suppress cavitation instabilities in turbopump. The beneficial outcome of this exercise will be – more affordable, reliable, and higher-performance turbopumps.

Why Turbopump in Rockets?

Liquid oxidizers and fuels like hydrogen or methane must be fed into the combustion chamber at a higher pressure than the existing chamber at a sufficient flow rate. It is done either by having pressurized tanks or by using a pump. Pressurized tanks tend to be heavy and bulky and are less preferred since they add to the overall rocket system weight.

On the other hand, turbopumps serve as a better alternative due to their compact nature and low weight. Rockets can achieve a high power-to-weight ratio since turbopumps only need a lightweight, low-pressure feed tank.

Challenges in Turbopump Design

One of the main goals of a rocket designer is to stretch the maximum possible delivery payload. Maintaining high thrust chamber pressure and reducing the inert weight of the rocket to a minimum can help achieve this goal. Reduction in system weight is possible by lowering the turbopump size and mass. But, to maintain the same pressure and flow rate, the turbopump needs to run at a high rotational speed. Unfortunately, running at high speed leads to cavitation problems. Coming up with ways to mitigate this issue is a critical design challenge.

The second design challenge arises when the turbopumps are expected to work in off-design conditions. Such a need arises because rocket engines often face varying thrust requirements during their flight. For example, the designer needs to make appropriate design decisions to alleviate the problems of vibrations due to cavitation when the liquid pressure is lowered below the vapour pressure limits. Hence, coming up with ways to reduce performance degradation under cavitation conditions is essential.

Other design challenges which come in more significant magnitudes, unlike in compressors include, high radial and axial thrusts, leakages, increased disk friction, etc. It is up to the designer to develop tricks to manage the tradeoffs and make specific design choices to overcome these problems.

Importance of Inducer in Turbopumps

Designing small and compact turbopumps rotating at high speed can reduce the total weight of rockets. However, at higher speeds, cavitation onsets, causing machine noise and vibration, erosion, loss of head and efficiency, etc.

An anti-cavitation component called the inducer is axially placed upstream of the impeller to overcome these challenges. The inducer, acting as a pre-pump, increases the pressure of the fluid by a sufficient amount to minimize cavitation and improve the performance of the impeller. They are sometimes expected to sacrifice themselves to safe-guard the impeller blade from cavitation.

Unlike the impeller, the inducer blades are fewer in number and are lengthier and wider. Further, they have larger stagger angles, increasing pitch between blades, high blade solidity, and usually small angles of incidences.

With these unique features, inducer blades have minimal blockage due to cavitation, thereby allowing them to operate under very low suction pressure conditions without deteriorating the pump performance. In general, inducers have a minimal effect on the efficiency and head of the pump but offer a dramatic impact on the cavitation performance. Further, they reduce noise and vibration. But more importantly, inducers decrease the pump’s critical NPSH by more than three times.

Parting Remarks

Cavitation surge and inlet backflows are inevitable in turbopumps. All we can think of is finding ways to suppress them to some extent. Suppression can be done by using an obstruction plate or by connecting a smaller diameter suction pipe upstream of the inducers. Backflow suppression helps to narrow the onset range of cavitation surge. Even if they occur, their amplitudes are weakened and subdued by the suppression devices. This helps in achieving improved surge performance.

However, these two suppression techniques are effective when the flow rates are healthy but show their limitations at extremely low flow rates. Researchers recommend combining these suppressing methods with inducer blade shapes suitable for reducing inlet backflows for such extreme conditions.

Further Readings

1. Meshing of Rocket Engine Nozzles for CFD
2. Spiked Blunt Bodies for Hypersonic Flights
3. Know Your Mesh for Reentry Vehicles

References

1. “Studies of cavitation characteristics of inducers with different blade numbers“, Lulu Zhai et al., AIP Advances 11, 085216; 12 August 2021.

2. “Numerical and experimental study of cavitating flow through an axial inducer considering tip clearance“, Rafael Campos-Amezcua et al., Proc IMechE Part A: J Power and Energy 227(8) 858–868, IMechE 2013.

3. “Suction Performance and Cavitation Instabilities of Turbopumps with Three Different Inducer Design“, Tatsuya Morii et al., International Journal of Fluid Machinery and Systems, Vol. 12, No. 2, April-June 2019.

4. “A Study on the Design of LOx Turbopump Inducers“, Lucrezia Veggi et al., International Symposium on Transport Phenomena and Dynamics of Rotating Machinery Maui, Hawaii, December 16-21, 2017.

5. “Study on Hydraulic Performances of a 3-Bladed Inducer Based on Different Numerical and Experimental Methods“, Yanxia Fu et al., Hindawi Publishing Corporation International Journal of Rotating Machinery, Volume 2016, Article ID 4267429.

6. “Study on inducer and impeller of a centrifugal pump for a rocket engine turbopump“, Soon-Sam Hong et al., Proc IMechE Part C: J Mechanical Engineering Science 227(2) 311–319, IMechE 2012.

7. “Turbopump Design: Comparison of Numerical Simulations to an Already Validated Reduced-Order Model“, A Apollonio et al., Journal of Physics: Conference Series 1909 (2021) 012029, ISROMAC18.

8. “Effect of leading-edge sweep on the performance of cavitating inducer of LOX booster turbopump used in semi cryogenic engine“, Arpit Mishra et al., IOP Conf. Series: Materials Science and Engineering 171 (2017).

9. “Design and Analysis of a High Speed, High-Pressure Peroxide/RP-1 Turbopump“, William L. Murray et al., AIAA paper.

10. “A Body Force Model for Cavitating Inducers in Rocket Engine Turbopumps“, William Alarik Sorensen et al., MS Thesis, Massachusetts Institute of Technology, September 2014.

11. “Rocket engine inducer design optimization to improve its suction performance“, M. J. Lubieniecki, M S Thesis, Delft University of Technology, 7 December 2018.

12.” Modeling Rotating Cavitation Instabilities in Rocket Engine Turbopumps“, Adam Gabor Vermes, M S Thesis, Delft University of Technology.

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 Stallion 3D is an aerodynamics analysis software package that can be used to analyze golf balls in flight. The software runs on MS Windows 10 & 11 and can compute the lift, drag and moment coefficients to determine the trajectory.  The STL file, even with dimples, can be read directly into Stallion 3D for analysis.

What we learn from the aerodynamics:

• The spinning golf ball produces lift and drag similar to an airplane wing
• Trailing vortices can be seen at the "wing tips"
• The extra lift helps the ball to travel further

Stallion 3D strengths are:

• The built-in Reynolds Averaged Navier-Stokes equations provide high fidelity CFD solutions
• The grid is generated automatically 
• Built-in  menus are used to specify speed, angle, altitude and even spin
• Built-in visualization
• The numbers are generated to compute the trajectory of the ball
• The software runs on your laptop or desktop under Windows 7, 10 and 11

• 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

During the past summer, AIAA successfully organized the 4th High Lift Prediction Workshop (HLPW-4) concurrently with the 3rd Geometry and Mesh Generation Workshop (GMGW-3), and the results are documented on a NASA website. For the first time in the workshop's history, scale-resolving approaches have been included in addition to the Reynolds Averaged Navier-Stokes (RANS) approach. Such approaches were covered by three Technology Focus Groups (TFGs): High Order Discretization, Hybrid RANS/LES, Wall-Modeled LES (WMLES) and Lattice-Boltzmann.

The benchmark problem is the well-known NASA high-lift Common Research Model (CRM-HL), which is shown in the following figure. It contains many difficult-to-mesh features such as narrow gaps and slat brackets. The Reynolds number based on the mean aerodynamic chord (MAC) is 5.49 million, which makes wall-resolved LES (WRLES) prohibitively expensive.

The geometry of the high lift Common Research Model

University of Kansas (KU) participated in two TFGs: High Order Discretization and WMLES. We learned a lot during the productive discussions in both TFGs. Our workshop results demonstrated the potential of high-order LES in reducing the number of degrees of freedom (DOFs) but also contained some inconsistency in the surface oil-flow prediction. After the workshop, we continued to refine the WMLES methodology. With the addition of an explicit subgrid-scale (SGS) model, the wall-adapting local eddy-viscosity (WALE) model, and the use of an isotropic tetrahedral mesh produced by the Barcelona Supercomputing Center, we obtained very good results in comparison to the experimental data. 

At the angle of attack of 19.57 degrees (free-air), the computed surface oil flows agree well with the experiment with a 4th-order method using a mesh of 2 million isotropic tetrahedral elements (for a total of 42 million DOFs/equation), as shown in the following figures. The pizza-slice-like separations and the critical points on the engine nacelle are captured well. Almost all computations produced a separation bubble on top of the nacelle, which was not observed in the experiment. This difference may be caused by a wire near the tip of the nacelle used to trip the flow in the experiment. The computed lift coefficient is within 2.5% of the experimental value. A movie is shown here.        

Comparison of surface oil flows between computation and experiment

Comparison of surface oil flows between computation and experiment

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.

Author:
Sankalp Lal
Senior Research Engineer

Seeking to reduce local exhaust emissions and embrace sustainable transport, governments across continents have undertaken multiple initiatives to promote the adoption of battery electric vehicles (BEVs). The result: a rapid acceleration in the number of BEVs on the roads. But with an increasing number of BEVs, stories of electric vehicle fires began to trickle in and were quickly picked up by national news organizations. These fires usually originate from batteries that attain elevated temperatures due to thermal failure, mechanical failure, short-circuiting, or physical damage. These thermal runaway events are not only dangerous and toxic, but also extremely difficult to extinguish, posing a serious safety concern for passengers and bystanders.

Introduction of Battery Regulations

To eradicate battery fires, some governments have introduced regulations that require more stringent battery testing prior to approval for use in road BEVs. The Ministry of Road Transport and Highway in India has implemented AIS-156 and AIS-038 (Rev 2), and the European Union has implemented ECE R100 Rev2. The regulations for both jurisdictions are quite similar. Both sets of regulations require a thermal propagation test to be performed on a battery pack to ensure that no fire or explosion results from a thermal runaway incident triggered by a short circuit.

This push from the governments means that manufacturers cannot just evaluate battery pack operation under normal conditions. Manufacturers will now have to ensure that during a thermal runaway event, propagation to other cells is prevented, reaction gases are contained in the pack, and the pack can withstand the associated high pressure. Designing a robust battery pack can take many iterations of building and testing prototypes—a potentially long and expensive process to find the final design!

Using CONVERGE: The Better Route

To help manufacturers reduce the expense of testing prototypes, we have equipped CONVERGE, our flagship simulation software, with best-in-class capabilities to evaluate battery cooling, predict thermal runaway propagation, and model gas venting in any Li-ion battery pack design. All of this analysis can take place before constructing a physical product! Simulating designs in CONVERGE will help to filter out the inefficient cell arrangements and battery designs, reducing the number of prototypes that must be built for testing.

We discussed in more detail how CONVERGE can help you simulate, study, and design safer batteries in the first blog post of our CONVERGE for Batteries series. Our next installment will cover a case study we conducted in collaboration with Renault Group to predict thermal runaway propagation in one of their battery packs. Stay tuned!

To learn more about CONVERGE’s modeling capabilities for emobility, join us for the 2023 CONVERGE User Conference–India! The conference features industry presentations on simulating electric motors and batteries with CONVERGE and a hands-on emobility workshop. Find more details and registration here!

Author:
Wendy Lovinger
Marketing Writer

On November 4, 2022, Convergent Science broke ground on construction of its second office building in Madison, Wisconsin, just down the street from its World Headquarters. “For a company whose employees once fit in a small broom closet, we now need quite a bit of space,” said Kelly Senecal, Co-Owner and Vice President of Convergent Science. “We’re expanding outside of our original office building here in Madison. We want to make sure that each of our employees has their own space, their own office.”

The new building is expected to be finished in the summer of 2023 and will feature 43 individual offices, a dedicated training room for CONVERGE users, a recording studio, an employee gym, and more accommodations for bikes, including an inside bike rack. “We’re really excited to get moving on this project,” said Keith Richards, Co-Owner and Vice President of Convergent Science. “It’s been two years that we’ve been trying to expand our office space. It’s good to finally get the project going.”

In order to properly inaugurate construction on the new building, all employees were invited to put on a hard hat and take a swing at the old building. “We want our employees to feel part of the process,” said Eric Pomraning, Co-Owner and Vice President of Convergent Science. “We’ve had employees give us input on the design of the building. Some people are going to be moving in, it’s going to be their new office, their home office so to speak. We want them to feel a bit of ownership in the process and have fun.”

All three Madison owners joined in on the demolition party. “I’m just glad I didn’t injure myself, to be honest,” said Kelly.

Designing an efficient and effective compressor requires a detailed understanding of the device’s internal flow field. Experimental measurements provide useful information, but they have some disadvantages: building prototypes for testing is time-consuming and expensive, there is limited space inside the machine to insert probes, and the probes themselves can alter the flow field.

Daikin Industries, a leading manufacturer of advanced, high-quality air conditioning systems, uses CFD to help fill in those gaps. Simulation provides a more comprehensive picture of the internal workings of the compressor, outputting information that’s difficult to obtain experimentally. 

Recently, Daikin integrated CONVERGE into their development process to help them analyze their swing compressor design. Because of its autonomous meshing, CONVERGE saves Daikin engineers more than two weeks per simulation compared to their previous CFD software, allowing them to more rapidly iterate on designs. 

Daikin’s swing compressor is similar to a traditional rotary compressor, but it exhibits less leakage and can achieve higher efficiencies. The geometry of the swing compressor is shown in Figure 1. 

Daikin takes advantage of CFD to identify and solve problems in their compressors during the design phase. However, their previous CFD software package had some limitations, such as not allowing for moving boundaries and only supporting single-phase simulations. With the increasing complexity of compressors, they needed a CFD solver capable of handling more complex physics. Thus, they turned to CONVERGE.

One of the big draws of CONVERGE was its autonomous meshing, which eliminates all manual meshing time and easily accommodates moving boundaries. CONVERGE’s novel Cartesian cut-cell approach produces a high-quality mesh that minimizes grid-related error. Furthermore, CONVERGE offers built-in fluid-structure interaction (FSI) modeling capabilities, including a 1D beam model ideal for simulating reed valves at a relatively low cost.

Daikin employed these features to validate that CONVERGE can accurately capture critical parameters including pressure and valve lift. They performed simulations for two different operating conditions: 28 rps and 110 rps. 

Figure 2 shows the CONVERGE results for pressure and valve lift compared to experimental measurements for both operating conditions. The CONVERGE results indicate very good agreement in the pressure rise during compression, the pressure peak at valve opening, and the pressure fluctuation frequency and amplitude during the discharge event. In addition, the CONVERGE results demonstrate a valve opening timing very similar to the measurements, as well as the maximum valve lift, valve lift fluctuation frequency, and amplitude during the discharge event. The discrepancy that occurs around 310 crank angle degrees is most likely a result of the modeled gap flow at the point where the vane tip passes over the pressure transducer location.

Daikin Industries confirmed that CONVERGE can accurately simulate their swing compressor while also saving them a significant amount of time—upwards of two weeks per simulation. In addition, CONVERGE’s FSI modeling allows them to capture the motion of the swing compressor’s reed valve.

Next, Daikin plans to incorporate oil and phase change phenomena into their simulations to create a comprehensive model of their swing compressor. Ultimately, using CONVERGE will allow Daikin to account for the complex physics of their real-world compressor in their simulations to more effectively analyze and enhance the performance of their product.

Interested in using CONVERGE for your compressor simulations? Contact us today!

References

Kawabata, S., Deguchi, R., and Matsuura, H., “Calculation of Internal Flow in a Compressor With Valve Motion,” 26th International Compressor Engineering Conference at Purdue, West Lafayette, IN, United States, Jul 10–14, 2022.

Author:
Wendy Lovinger
Marketing Writer

This year, Convergent Science turns 25 years old. On December 7, 1997, the seven original founders of Convergent Science—including current co-owners and co-vice presidents Keith Richards, Kelly Senecal, Eric Pomraning, and Dan Lee—filled out the paperwork to incorporate Convergent Thinking, LLC, and paid the couple hundred dollar fee (although, as with all company lore, there is some disagreement over the exact amount).

Seven young University of Wisconsin-Madison engineering graduate students set out that day on a snowy trek to a government office building. 25 years later, three have since moved on to other careers, while four are still with the company, still good friends, making major business decisions together as co-VPs. “People have told us, you need a CEO,” said Kelly. “You need a single person who sits at the top and makes all of the final decisions. And maybe it’s because we started out as an LLC, which is a partnership, and we were structured as a partnership when we incorporated, we kind of kept that structure.”

How does a company survive to be 25 years old? In the current business environment, when so many startups flame out quickly, what lessons can be learned from the success of Convergent Science? One of the keys is luck. More than that, though, the key is knowing how and when to capitalize on that luck.

From the beginning, Convergent Science faced its share of obstacles. The owner-engineers spent seven years building a computational fluid dynamics (CFD) code designed to model internal combustion engines, and it was ready for release in 2008. At the time, several automakers were facing bankruptcy. “The automotive industry was hit pretty hard,” said Keith. “That’s right when we released CONVERGE, and it felt like the world was crashing down around us.”

But there was a silver lining in the market crash, at least for Convergent Science. “It turned out to be one of the best possible times to start selling CONVERGE,” continued Keith. “We didn’t have to convince any of the major automotive companies that they needed to be doing CFD more efficiently. They knew that their financial difficulties were rooted in the fact that it was taking them too long to do research, and there was too much effort involved in developing new products.”

CONVERGE, a CFD software designed to eliminate all user meshing time and accelerate iterative prototyping, was quickly embraced by the automotive industry. Major U.S. growth spawned from there.

In 2009, the four U.S. owners were approached by Rainer Rothbauer, now co-owner and geschäftsführer of the European branch of Convergent Science, when he was working at the Southwest Research Institute in San Antonio, Texas. One of the first users of CONVERGE (or the very first, depending on who you’re talking to), Rainer was interested in distributing the software in Europe. He spoke to Dan first. “One of the first calls I had with Dan, I said, ‘Wow, this guy can talk,’” said Rainer. “And I still think he can talk pretty well, but I also know now that he knows CFD very well and is a fantastic friend.” In 2010, Rainer returned home to Austria and started Ignite3D, a CONVERGE distributor. In 2014, Ignite3D became Convergent Science GmbH, allowing the company to better expand its operations by hiring support and sales staff specifically for the European market.

Around the same time Rainer began distributing CONVERGE in Europe, the owners also started targeting the Japanese market. It was a hard sell. An initial distributor did not work out. “We struggled selling our software in that area due to non-technical reasons such as language barriers, cultural differences, and time zones,” said Dan. “We needed to have a local representative.” In 2013, the opportunity arose to partner with IDAJ to distribute CONVERGE in Japan, South Korea, and China, and the owners seized upon it. The partnership with IDAJ was a boon for Convergent Science, and the Asian market continues to contribute significantly to the company’s revenue.

More international opportunities presented themselves. In 2017, Convergent Science India, LLP, opened in Pune under the leadership of Ashish Joshi, who had worked with CONVERGE in his previous position at CEI. “We realized that a lot of our clients have an international presence in India,” said Eric. “In order to support them better, we really needed to have local support. An opportunity opened up for Ashish to start a branch in India for us. That’s been very successful. Now we have 20-some people in that branch in India, and we’re continuing to grow it.”

25 years ago when they were trudging through the snow, Kelly, Dan, Eric, and Keith could not have imagined that one day they would be co-owners of a company with branches in Madison, Wisconsin; Detroit, Michigan; New Braunfels and Houston, Texas; Linz, Austria; and Pune, India. Or that their software would be used all over the world in industry and academia. If you ask them, they will admit that they never wrote up a business plan. However, they have been lucky to get some good business advice along the way, some of which they did listen to. Eric remembers being told, “Choose your partners carefully.” “Maybe I didn’t choose my partners carefully,” he said, “but I got lucky. I probably didn’t take that very seriously when we were starting out, but I was very fortunate that my partners ended up being very good people and good people to be in business with.”

And it has worked out well. The owners have transitioned the company from a handful of graduate students coding together in a broom closet into a successful international business. “At Convergent Science, we’ve been fortunate enough to always experience significant growth in our software revenue,” said Dan. “To continue that growth, we’re going to have to solve more problems in more application areas. We need to continue to hire the best people, including individuals that have an expertise in applications that are new to Convergent Science. We need to partner with world-leading organizations, research labs, and universities, and continue to promote CONVERGE and extend the value statement into applications that are new to us.”

Emissions regulations around the globe are becoming increasingly stringent. To design compliant internal combustion (IC) engines, optimizing every component of the engine is key and every small gain in efficiency counts. Since their invention more than a century ago, IC engines have come a long way. Today’s IC engines are advanced technologies, but there’s still room for improvement—and new, innovative methods can help us achieve those improvements more efficiently.

Convergent Science, Polaris, and Oracle Cloud teamed up to put some of the latest technologies to the test, combining machine learning (ML), high-performance computing, and computational fluid dynamics (CFD) for a geometry optimization study. The team was led by Jacob Hanson, Senior Powertrain CFD Engineer at Polaris, Dan Probst, Senior Principal Engineer at Convergent Science, and Arnaud Froidmont, HPC Solution Architect at Oracle Cloud. 

To test this methodology, the team tackled a relatively simple exhaust port optimization. As Jacob says: “Every engine has an exhaust port, and every engine needs an optimized exhaust port.”

Figure 1 shows a diagram of the optimization process the team undertook. First, they conducted a large design of experiments (DoE) study on CONVERGE Horizon, Convergent Science’s new cloud computing service. The DoE provided insight into how varying different exhaust port geometry parameters affected the exhaust efficiency. 

The results of the DoE were then used to train a machine learning (ML) algorithm, which the team used to predict the optimal geometry configuration of the exhaust port. The predicted best case was run in CONVERGE to confirm an increase in efficiency. The results of the study demonstrate that this combination of cutting-edge technologies provides a fast, cost-effective approach for geometry optimization.

Part 1: Design of Experiments Study

Geometry Parameterization

The first step of the DoE was to parameterize the exhaust port geometry. Jacob and his colleagues at Polaris decided on five different parameters to vary: top angle, seat angle, seat diameter, throat angle, and bottom angle (Figure 2). They also specified realistic ranges of values for each parameter based on their current exhaust port geometry.

Trying every possible combination of parameter values would be unrealistic, so the team chose to run 256 cases that spanned the ranges set by Polaris. To select which parameter combinations to test, the team employed Latin hypercube sampling, which produces a quasi-random sample that better captures the underlying data distribution than simple random sampling.

With the parameter values selected, the next step was to generate CAD files of the exhaust port geometry for each case. Creating 256 different geometries can be a daunting task, so the team automated the process using a script in Creo. 

CONVERGE Simulations

To set up the cases for evaluation in CONVERGE, the team once again took advantage of automation. CONVERGE Studio offers a scripting capability that allows users to write custom scripts to automate routine tasks, such as setting up dozens of exhaust port cases. The script automatically flagged the boundaries, set the valve lift, and ran a diagnostic check to ensure the case was ready to go. 

“It was exciting to automate most of the approach used in this design study as it saved a lot of time and avoided potential errors if all the case setups were done manually,” says Dan.

Next came the task of running the cases in CONVERGE. DoEs are an ideal use case for high-performance computing, and so the team turned to CONVERGE Horizon. This cloud service for CONVERGE users provides affordable, on-demand access to the latest Oracle Cloud hardware. Arnaud took charge of running the cases concurrently on Oracle Cloud’s bare metal servers. He ran them in two batches on 128 nodes, with one case per node (128 cores/node). Running all 256 cases took less than one day!

“Running CONVERGE on Oracle Cloud provides the benefit of running jobs in parallel and only paying for the usage,” Arnaud says. “Selecting the right hardware configuration gave us the ideal ratio between time and cost. Using the integration with the scheduler, the infrastructure is created and terminated on demand and automatically. Since jobs are independent, multiple regions can be used in case of capacity constraints for extremely large DoEs.”

The results of the DoE can be seen in Figure 3. Polaris was looking to minimize the pumping work required by the exhaust process. With that success metric in mind, nine cases performed better than the baseline case (blue dot), i.e., the current exhaust port geometry derived from decades of experiments and manual iterations. The best case (#248) and the worst case (#197) from the DoE are shown in Figure 3 for reference. 

Part 2: Machine Learning + Optimization

Using the wealth of data from the DoE, Dan trained an ML emulator. 90% of the data was used to train the emulator, and the remaining 10% was used to test the emulator. 

“Since you never know what algorithm will best represent the data, we used an ensemble approach to train and rank multiple ML algorithms,” says Dan. As you can see in Figure 4, the ML predictions match well with the simulation data.

The team then used the ML algorithm to predict what combination of parameters would minimize the pumping work. They took the predicted best case and ran it in CONVERGE. While the case did outperform the best case from the DoE, the decrease in pumping work was not as large as predicted. 

Dan added this new case back into the ML emulator as a training point and once again had the algorithm predict the best case. The process resulted in only a minimal improvement, suggesting that the team had found an exhaust port geometry configuration that was very near the optimum.

Conclusions

The optimization study resulted in a small (0.5%) but significant improvement in exhaust efficiency to an engine built on decades of accumulated knowledge. This gain was achieved in a matter of days through an optimization process that would have taken months using more traditional methods. Conducting an experimental optimization would have cost on the order of 100 times more than this approach, accounting for both software and hardware costs. 

Even using simulations, executing such a large DoE would have taken several months if the simulations were run in serial. CONVERGE Horizon offers competitive prices for top-of-the-line Oracle Cloud hardware, enabling highly scalable and affordable high-performance computing. In addition, the autonomous meshing and scripting capabilities in CONVERGE made it significantly easier to set up such a large number of cases.

“This kind of optimization study has a lot of value in industry,” says Jacob. “Adding high-performance computing and machine learning, we can do this in a really timely and cost-efficient manner. These methods blow traditional optimizations out of the water.”

Polaris will be using the results from this study to inform their upcoming production engine design. Overall, this study demonstrates how you can take advantage of advanced technology to rapidly optimize a system and achieve meaningful, real-world improvements.

Learn more about the process of running a large DoE on Oracle Cloud’s hardware in their blog!

Many entrepreneurs dream of creating a product that will revolutionize an industry. Starting off with an idea and a vision, only a small fraction manage to bring their dream to fruition. Sometimes, however, equipped with the right tools and a solution to a pressing need, an entrepreneur can transform an industry and usher in a new era of technology.

Working in the HVAC industry, Greg Kemp, founder and CEO of TORAD Engineering, together with partner Joe Orosz, a veteran HVAC compressor engineer, saw room for improvement: many compressors were overly complex and difficult to manufacture. They had a solution: create a new compressor technology that was simpler, lower cost, and easier to manufacture—a compressor that would simultaneously help shift the industry to low-global warming potential (GWP) refrigerants. The technology? A spool compressor.

“The spool compressor is a high-displacement-density machine for ultra-low GWP refrigerants,” Greg said. “The spool compressor provides a lower cost and higher efficiency alternative than legacy technologies for use in the 10–100 hp range with medium-pressure, ultra-low GWP refrigerants.”

Greg set out to create a compressor with a simple, compact design, consisting of only a few major components to reduce the complexity and manufacturing costs. At the same time, he wanted his design to be highly scalable to broaden its applicability across capacity ranges. Figure 1 shows the spool compressor he invented.

For the next step in creating a revolutionary compressor, the team at TORAD wanted to optimize their design for maximum efficiency and ensure its compatibility with low-pressure, ultra-low-GWP refrigerants. This is where having the right tools comes into play.

Computational fluid dynamics (CFD) is a powerful tool for compressor design. CFD saves you time and money by allowing you to virtually test different designs before building a physical prototype. You can gain insight into global parameters including mass flow rate and power consumption, and analyze noise, vibration, thermal design, and leakage. With fully autonomous meshing, CONVERGE CFD software significantly simplifies the case setup process, enabling an even faster turnaround on results.

“We use CONVERGE extensively for modeling the compression process, specifically trying to maximize the efficiency,” Greg said.

For their simulation studies, Greg and his team focused on the compressor geometry and the discharge process, which is regulated by an array of valves. The CONVERGE simulations revealed some previously unknown flow losses that occurred when the vane tip passed over the valves.

“Working with the CONVERGE team and modeling our valves, we were able to come up with a configuration that would offer a significant improvement in the efficiency of the machine because of the improved valve operation,” Greg said.

The engineers at TORAD built a couple of prototypes to test the new valve configuration. The experimental results matched well with the CFD predictions, confirming an increase in efficiency.

“Those gains actually put us in a position to meet critical market hurdles relative to performance utilizing the low-GWP refrigerant R1234ze,” Greg said.

Greg’s journey with CONVERGE began when the TORAD team was looking for a CFD solver to help them analyze their spool compressor design. After researching CFD vendors, TORAD selected two companies, Convergent Science being one of them, to conduct benchmark studies. Both software packages replicated the experimental results to their satisfaction, but they were also evaluating another critical factor: usability.

“We determined that it was just a lot easier to go about setting up CONVERGE than anything else we saw,” Greg said.

TORAD settled on CONVERGE as their CFD software of choice, and Greg began the process of learning how to use it. He worked closely with the Convergent Science Applications team, meeting with them up to 2–3 times per week.

“They were so good and so responsive at sitting down with me and working through issues and explaining things where I had questions,” Greg said. “It was not typical of what I’ve seen with other software packages. There was no shortage of time they were willing to spend. There was no clock running, ticking off how many hours I’m using.”

After about three months of regular training and support sessions, Greg was able to set out on his own and run simulations by himself. Greg will tell you that he’s not a CFD expert, but with CONVERGE, he’s able to create a case setup template that makes it, if not trivial, at least straightforward to set up a new simulation.

In addition to the relatively simple case setup, TORAD has been pleased with how well the CONVERGE results match the experimental data for critical parameters (e.g., localized pressures, overall efficiency, and volumetric efficiency). The plot in Figure 2 shows an example of measured pressure values versus the simulation results.

Incorporating CFD into their development workflow, the team at TORAD has achieved some impressive improvements to their already impressive technology.

“It’s not an over-exaggeration to say that we owe the success we’ve had over the last year in obtaining higher efficiency results to the work we’ve done with CONVERGE,” Greg said. “CONVERGE is a strategic development tool for TORAD. We are now able to model and evaluate ideas and optimize configurations before building hardware, which saves us time and money.”

TORAD’s spool compressor is still in development, with production expected to begin in the next couple of years. The team is currently working on modeling their spool compressor with ultra-low GWP refrigerants like R1234ze. If the results they’ve seen during the development phase are any indication, the spool compressor is going to make a big splash (with little carbon footprint) when it hits the market.

To learn more about how CONVERGE was used to simulate the TORAD spool compressor, register for our upcoming webinar!

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.

A license booster for engineers who want maximum throughput at minimum cost.

Batch-mode is a term nearly as old as computers themselves. Despite its age, however, it is representative of a concept that is as relevant today as it ever was, perhaps even more so: headless (scripted, programmatic, automated, etc.) execution of instructions. Lots of engineering is done interactively, of course, but oftentimes the task is a known quantity and there is a ton of efficiency to be gained by automating the computational elements. That efficiency is realized ten times over when batch-mode meets parallelization – and that’s why we thought it was high-time we offered a batch-mode licensing model for Tecplot 360’s Python API, PyTecplot. We call them “batch-packs.”

Tecplot 360 Batch-Packs

Tecplot 360 batch-packs work by enabling users to run multiple concurrent instances of our Python API (PyTecplot) while consuming only a single license seat. It’s an optional upgrade that any customer can add to their license for a fee. The benefit? The fee for a batch-pack is substantially lower than buying an equivalent number of license seats – which makes it easier to justify outfitting your engineers with the software access they need to reach peak efficiency.

Batch-Packs Explained

Here is a handy little diagram we drew to help explain it better:

Each network license allows ‘n’ seats. Traditionally, each instance of PyTecplot consumes 1 seat. Prior to the 2022 R2 release of Tecplot 360 EX, licenses only operated using the paradigm illustrated in the first two rows of the diagram above (that is, a user could check out up to ‘n’ seats, or ‘n’ users could check out a single seat). Now customers can elect to purchase batch-packs, which will enable each seat to provide a single user with access to ‘m’ instances of PyTecplot, as shown in the bottom row of the figure.

Batch-Pack Benefits

In addition to a cost reduction (vs. purchasing an equivalent number of network seats), batch-pack licensees will enjoy:

• Reduced license contention. Since each user is guaranteed “m” PyTecplot instances they can run post-processing jobs in parallel without fear of their job failing due to license contention.
• Faster turnaround times by running your post-processing jobs in parallel across multiple nodes of an HPC, or even on a single workstation. Running across multiple nodes may help alleviate memory limitations for large datasets.

Learn More

We’re excited to offer this new option and hope that our customers can make the most of it.

• Contact us by emailing sales@tecplot.com.
• See a full explanation in the Webinar Tecplot 360 2022 Release 2.
• Read the Tecplot 360 2022 R2 Release Notes (PDF).

The post Introducing 360 “Batch-Packs” appeared first on Tecplot.

In this release, we are very excited to offer “Batch-Pack” licensing for the first time. A Batch-Pack license enables a single user access to multiple concurrent batch instances of our Python API (PyTecplot) while consuming only a single license seat. This option will reduce license contention and allow for faster turnaround times by running jobs in parallel across multiple nodes of an HPC. All at a substantially lower cost than buying additional license seats.

Data courtesy of ZJ Wang, University of Kansas, visualization by Tecplot.

Webinar Agenda for 360 2022 R2

• Tecplot at a Glance
• Tecplot 360 Suite of Tools [02:11]
• Overview of What’s New in Tecplot 360 2022 R2 [03:15]
• Batch-Packs [04:25]
• Critical Bug Fixes [8:29]
• Loader Updates [11:16]
• TecIO Updates [15:37]
• Platform Updates [17:15]
• Higher-Order Element Technology Preview [18:50]
• Questions & Answers [27:26]

Resources

• Python Multiprocessing Accelerates Your CFD Analysis Time
• Analyze Your Time-Dependent Data 6 Times Faster
• What is TecIO?
• Learn more about Batch-Packs
• Tecplot Knowledge Base

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The Rainbow Colormap Sucks and Here’s Why…

If you care about how you present your data and how people perceive your results, stop reading and watch this talk by Kristen Thyng on YouTube. Seriously, I’ll wait, I’ve got the time.

Why Colormaps are Important

Which colormap you choose, and which data values are assigned to each color can be vitally important to how you (or your clients) interpret the data being presented. To illustrate the importance of this, consider the image below.

Figure 1. Visualization of the Southeast United States. [4]

Why You Should Consider Perceptually Uniform Colormaps

Before I explain what a perceptually uniform colormap is, let’s start with everyone’s favorite: the rainbow colormap. We all love the rainbow colormap because it’s pretty and is recognizable. Everyone knows “ROY G BIV” so we think of this color progression as intuitive, but in reality (for scalar values) it’s anything but.

Consider the image below, which represents the “Estimated fraction of precipitation lost to evapotranspiration”. This image makes it appear that there’s a very distinct difference in the scalar value right down the center of the United States. Is there really a sudden change in the values right in the middle of the Great Plains? No – this is an artifact of the colormap, which is misleading you!

Figure 2. This plot illustrates how the rainbow colormap is misleading, giving the perception that there is a distinct different in the middle of the US, when in fact the values are more continuous. [2]

Comparison of Perceptually Uniform and Rainbow Colormaps

So let’s dive a little deeper into the rainbow colormap and how it compares to perceptually uniform (or perceptually linear) colormaps.

Consider the six images below, what are we looking at? If you were to only look at the top three images, you might get the impression that the scalar value has non-linear changes – while this value (radius) is actually changing linearly. If presented with the rainbow colormap, you’d be forgiven if you didn’t guess that the object is a cone, colored by radius.

Figure 3. An example of how the rainbow colormap imparts information that does not actually exist in the data.

So why does the rainbow colormap mislead? It’s because the color values are not perceptually uniform. In this image you can see how the perceptual changes in the colormap vary from one end to the other. The gray scale and “cmocean – haline” colormaps shown here are perceptually uniform, while the rainbow colormap adds information that doesn’t actually exist.

Figure 4. Visualization of the perceptual changes of three colormaps. [5]

So which colormap should I use?

Well, that depends. Tecplot 360 and FieldView are typically used to represent scalar data, so Sequential and Diverging colormaps will probably get used the most – but there are others we will discuss as well.

Sequential colormaps

Sequential colormaps are ideal for scalar values in which there’s a continuous range of values. Think pressure, temperature, and velocity magnitude. Here we’re using the ‘cmocean – thermal’ colormap in Tecplot 360 to represent fluid temperature in a Barracuda Virtual Reactor simulation of a cyclone separator.

Diverging Colormaps

Diverging colormaps are a great option when you want to highlight a change in values. Think ratios, where the values span from -1 to 1, it can help to highlight the value at zero.

The diverging colormap is also useful for “delta plots” – In the plot below, the bottom frame is showing a delta between the current time step and the time average. Using a diverging colormap, it’s easy to identify where the delta changes from negative to positive.

Qualitative Colormaps

If you have discrete data that represent things like material properties – say “rock, sand, water, oil”, these data can be represented using integer values and a qualitative colormap. This type of colormap will do good job in supplying distinct colors for each value. An example of this, from a CONVERGE simulation, can be seen below. Instructions to create this plot can be found in our blog, Creating a Materials Legend in Tecplot 360.

Circular (Phase) Colormaps

Perhaps infrequently used, but still important to point out is the “phase” colormap. This is particularly useful for values which are cyclic – such as a theta value used to represent wind direction in this FVCOM simulation result. If we were to use a simple sequential colormap (inset plot below) you would observe what appears to be a large gradient where the wind direction is 360^o vs. 0^o. Logically these are the same value and using the “cmocean – phase” colormap allows you communicate the continuous nature of the data.

Purposeful Breaks in the Colormap

There are times when you want to force a break in a continuous colormap. In the image below, the colormap is continuous from green to white but we want to ensure that values at or below zero are represented as blue – to indicate water. In Tecplot 360 this can be done using the “Override band colors” option, in which we override the first color band to be blue. This makes the plot more realistic and therefore easier to interpret.

Best Practices

• Avoid red and green in the same plot. About 1 in 12 men are color blind, with red-green color blindness being the most common [7].
• Use different colormaps for different data and objects. Using colors that are associated with the physical object or property can help make the visualization more intuitive. For example, blue hues for rain and ice, green hues for algae, yellow and orange hues for heat.
• Don’t like our colormaps? Create your own! Tecplot 360 allows you to supply your own custom colormaps as well as change which colormap is default. [1]

References

1. https://kb.tecplot.com/2019/12/18/setting-custom-color-maps-as-defaults/
2. https://eagereyes.org/basics/rainbow-color-map
3. https://pdfs.semanticscholar.org/ee79/2edccb2c88e927c81285344d2d88babfb86f.pdf
4. https://flowingdata.com/2008/04/29/why-should-engineers-and-scientists-care-about-color-and-design/
5. https://youtu.be/o9KxYxROSgM
6. https://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf
7. https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/color-blindness
8. https://medium.com/nightingale/color-in-a-perceptual-uniform-way-1eebd4bf2692

The post Colormaps appeared first on Tecplot.

I’m Raja Olimuthu, Product Manager for Tecplot RS. My team is excited to announce the RS 2022 R1 release – we are eager for you to start taking advantage of all the newest features. Here is a quick overview of the most significant updates in the 2022 R1 release.

Timestep Access for Equations

Users can now “freeze” a dynamic variable at a specific timestep and use that value in a grid equation. For example, if you want to include Pressure at timestep 3 in an equation, you can now do that with the following example syntax: {NEWVARIABLE}={PRESSURE[3]-{PRESSURE}. In this equation, the pressure value at the 3^rd timestep is held as a constant while the 2^nd pressure value in the equation remains a dynamic variable, and will change as you advance the timestep. In order to determine the current timestep #, simply hover over the date next to the VCR controls, and the number will be displayed.

Grid Frame Ordering

When comparing a large number of grid solutions, users found it very difficult to control the ordering of the frames (in previous versions, you had to load the grids in the order that you wanted them displayed). We remedied that by building a new UI in the Compare Grid Data dialog that will allow you to easily activate the comparison grids you wish to view and re-order the frames by using the arrows or by simply dragging the grid solution to the desired spot

(BETA): Bubble Plot Guided Tutorial

In this release, we are rolling out a beta version of the Guided Tutorials. Customers have been asking us to build something embedded in RS that will guide them through complex operations and workflows step by step. For the first tutorial, we decided to walk users through setting up bubble plots, which is an extremely valuable spatial view, and can be used to understand things like the proportion of water/oil at each well, or even visually understand a history match.

Try it out and give us feedback. In future versions, our vision is to have a full catalog of multiple guided tutorials. If you have ideas of workflows you’d like to see, shoot us an email! We hope this can be a valuable learning tool for both new and seasoned RS users.

Please contact me directly at r.olimuthu@tecplot.com if you would like a 1:1 demonstration of RS 2022 R1. For those of you who do not have access to Tecplot RS, I’ll be happy to set you up with an evaluation version of our software. Looking forward to hearing from you.

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Designating an RLM License Server

To use a network license for one or more Tecplot products, you must install the Reprise License Manager (RLM), our third-party license manager, on a computer on your network. The computer with RLM installed will become the license server. You must choose a single machine to be your network’s license server even if you have licenses for multiple Tecplot products. To install and configure RLM, you must have Administrator privileges on the computer you designate as the license server. We do not recommend installing multiple RLM programs on a license server.

Installing RLM and Creating a Network License

1. Download the RLM installer for your operating system from https://my.tecplot.com/portal/product-releases/rlm. Install RLM on your designated license server.
2. Create your own license key from our Customer Portal, MyTecplot. To do this, locate your myhostids.txt file in the RLM installation directory. For example, on Windows, myhostids.txt file can be found in the directory: C:\Program Files\Tecplot\RLM for Tecplot Products. Then, go to my.tecplot.com > Licenses and Keys, select the appropriate license, and create a new key by uploading the myhostids.txt to the website.
3. Once you have a license key, add the license key file to the RLM installation directory, and make sure it is in the same location as the teclmd.set file. You can install as many license key files as you’d like, and there is no naming convention needed. RLM will read as a license any files with a .lic extension.
4. Finally, click Reread/Restart on the RLM license administration webpage.

More detailed information on this entire process can be found in the RLM Installation Guide.

Note: If RLM is Already Installed for Other Products

If you already have RLM installed for other products, you do not need to download the RLM installer. The minimum files needed are the teclmd.set file and a license key file. These files must be placed into your existing RLM installation directory.

You can download a teclmd.set file, and other installation files such as the gethostids executable and the teclmd options file from MyTecplot RLM Product Releases. More details on installing a Tecplot license on an existing RLM install can be found in this Knowledge Base article. After installing the Tecplot files, restart the server to begin serving the Tecplot licenses.

Thank you for watching the tutorial for setting up RLM for a Tecplot network license server. If you have questions or need help, please contact support@tecplot.com.

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Performance Testing with PLOT3D data

At Tecplot we know your time is important to you and that you have choices when it comes to post-processing. So we’ve done some (more) testing to help you decide which post-processor will perform the best with your data. Of course, performance is dependent on several factors – and which data type you are using is an important one. For today’s post we’ll be diving into performance with PLOT3D data.

One of the primary producers of PLOT3D data is NASA’s Overflow code, which is the code that produced the data we tested with in this post.

Experiment

For this test we used 46 timesteps in a transient simulation of a wind turbine. These total 118 Gb on disk and 2.18 trillion elements. The final timestep alone is 20.9 Gb (grid and solution), composed of 5863 zones, and has a total of 263 million elements.

In this experiment we load the data, compute Q-Criterion, draw an isosurface at Q=0.001, and export the image. We repeat this for each grid/solution pair in the time series and capture the execution time and the maximum RAM consumed.

It’s important to note that we set the Tecplot 360 Load-On-Demand setting to “Minimize Memory Use.” The 360 default setting allows up to 70% consumption of available RAM before unloading the data from RAM. Retaining data in RAM improves performance when moving back and forth between timesteps – but for batch operations this is unnecessary.

Figure 1: Images of the wind turbine results for FieldView, 360, and ParaView (PLOT3D data)

Setup

We conducted our experiments on a Windows 10 machine with 32 logical cores, 128 Gb RAM, and a NVIDIA Quadro K4000 graphics card. The data was stored locally on a spinning hard drive to avoid any slowdowns due to network traffic.

The machine was accessed via Remote Desktop and all tests were run unattended in batch mode using:

• PyTecplot for Tecplot 360 2022 R1
• FVX script in batch with FieldView 21
• pvbatch.exe for ParaView 5.10

We used the Python memory-profiler utility to capture timing and RAM information.

The Windows utility RamMap.exe was used to clear the disk cache before each run. Clearing the disk cache ensured all tests were fair between the post-processors. This also more closely simulates an end-user experience when opening their simulation results for the first time after the simulation is complete. We then ran each test three times, clearing the disk cache each time. Timing results are an average of the three runs.

Summary Results

FieldView 21 proved to be the fastest post-processor in our test: 53% faster than both ParaView and Tecplot 360.

Figure 2: FieldView 21 proves to be the fastest and most RAM efficient post-processor for this test

When comparing peak RAM consumption by each post-processor, we found that FieldView also used the least amount of RAM at 22 Gb. 360, while no faster (nor slower) than ParaView, used 37% less RAM than ParaView, at 26 Gb. ParaView proved to be the least RAM efficient, consuming 42 Gb peak RAM in this test.

We should note that the peak RAM for all post-processors occurred at the final time-step, since that was by far the largest pair of data files in this simulation.

Conclusion

If you’re an Overflow user and want the fastest and most memory-efficient post-processor, you should use FieldView. If you’re a 360 user, you’re still getting a fairly memory efficient post-processor.

Thankfully, academics don’t need to choose between 360 and FieldView – you get access to both in the Tecplot Academic Suite.

Appendix

Here we dive into the RAM profile over time for each of the post-processors in this test. The plots presented below were produced using the Python memory-profiler module. This module tracks both the execution time and RAM consumption of a process.

Each of the plots below illustrate how the post-processors load data into RAM and subsequently discard data from RAM when it’s no longer needed.

ParaView

In this result, we see that ParaView took ~2505 seconds and used a peak of ~42 Gb RAM

Figure 3: RAM vs. Time for ParaView 5.10 (via pvbatch.exe)

It’s important to note that we started a separate ‘pvbatch.exe’ instance for each grid/solution pair. We did this because ParaView does not easily handle PLOT3D files in which the grid varies over time. With 360 and FieldView we were able to use a single application instance for the entire time series. As you can see in this plot, the RAM consumption of pvbatch.exe goes to zero between each grid/solution file pair, since the executable exits and then restarts for the next timestep. pvbatch.exe utilizes multi-threading for most computations.

Tecplot 360

This plot shows that 360 took ~2500 seconds and used a peak of ~26 Gb RAM. Recall that 360 was run using a single process to load each grid/solution file pair. You can see how 360 unloads the data that it no longer needs as we advance through the timesteps. Also recall that the default setting in 360 is to retain data in RAM up to the 70% of RAM threshold – but for our test we used the Minimize Memory Use strategy to observe the true RAM requirements. For batch operations we suggest end-users use this setting.

Figure 4: RAM vs. Time for Tecplot 360 2022 R1 (via PyTecplot)

FieldView

It’s important to note that the plot below does not represent the true RAM profile for FieldView. We ran FieldView MPI-parallel, which spawns additional processes. We used Python memory-profiler with the –include-children command line option, which is supposed to track all child processes, but we found that this did not yield correct results. Due to this, we visually monitored Windows Task Manager and recorded the peak observed RAM.

FieldView took ~1575 seconds. The RAM observed here is from the controller process, not the worker processes. Actual observed peak RAM was ~22 Gb (only ~3 Gb RAM was recorded by memory-profiler)

Figure 5: RAM vs. Time for FieldView 21 (RAM is from the controller process only)

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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

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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.

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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.