They say the only constant in life is change and that’s as true for blogs as anything else. After almost a dozen years blogging here on WordPress.com as Another Fine Mesh, it’s time to move to a new home, the … Continue reading →

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Welcome to the 500th edition of This Week in CFD on the Another Fine Mesh blog. Over 12 years ago we decided to start blogging to connect with CFDers across teh interwebs. “Out-teach the competition” was the mantra. Almost immediately … Continue reading →

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Automated design optimization is a key technology in the pursuit of more efficient engineering design. It supports the design engineer in finding better designs faster. A computerized approach that systematically searches the design space and provides feedback on many more … Continue reading →

The post Create Better Designs Faster with Data Analysis for CFD – A Webinar on March 28th first appeared on Another Fine Mesh.

It’s nice to see a healthy set of events in the CFD news this week and I’d be remiss if I didn’t encourage you to register for CadenceCONNECT CFD on 19 April. And I don’t even mention the International Meshing … Continue reading →

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Some very cool applications of CFD (like the one shown here) dominate this week’s CFD news including asteroid impacts, fish, and a mesh of a mesh. For those of you with access, NAFEM’s article 100 Years of CFD is worth … Continue reading →

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

This week’s aggregation of CFD bookmarks from around the internet clearly exhibits the quote attributed to Mark Twain, “I didn’t have time to write a short letter, so I wrote a long one instead.” Which makes no sense in this … Continue reading →

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

Blue-green algae bloom in Lake Erie’s summer conditions. Unfortunately for those looking to spend summer on the water, the dominant organism in this bloom produces a toxin that “can cause liver damage, numbness, dizziness, and vomiting.” Bloom season can last from late June into October, depending on the how many nutrients get washed into the lake and when wind mixes the lake water in the fall. A new hyperspectral instrument aboard NASA’s PACE spacecraft will identify bloom species from space, helping scientists track, understand, and predict blooms like these. (Image credit: W. Liang; via NASA Earth Observatory)

This surreal image comes from an aurora on Halloween 2013. Photographer Ole C. Salomonsen captured it in Norway during one of the best auroral displays that year. The shimmering green and purple hues are the glow of oxygen and nitrogen in the upper atmosphere reacting to high-energy particles streaming in from the solar wind. These geomagnetic storms can disrupt GPS satellites, compromise radio communication, and even corrode pipelines, but they also create these stunning nighttime displays. (Image credit: O. Salomonsen; via APOD)

When surface tension varies along an interface, fluids move from regions of low surface tension to higher surface tension, a behavior known as the Marangoni effect. Here, a drop of (dyed) water is placed on glycerol. The two fluids are miscible, but water has much a lower viscosity and density yet a higher surface tension. The drop’s interface quickly becomes unstable; viscous fingers form along the edge as the less viscous water pushes into the more viscous glycerol. Eventually, the surface-tension-driven Marangoni flow breaks those fingers off into lip-like daughter drops. The researchers also show how the interplay between viscosity and surface tension affects the size of fingers that form by varying the water/glycerol concentration. (Image and video credit: A. Hooshanginejad et al.)

First spotted by humanity in 1664, Jupiter‘s Great Red Spot is a seemingly endless storm. Strictly speaking, there is debate as to whether observations prior to 1831 were of the same storm, but there’s no denying that the storm has raged unabated since regular observations began in the first half of the nineteenth century. Despite its longevity, the Great Red Spot is not unchanging. Overall, its major axis is shrinking, making the storm more circular over time. The storm also has a 90-day cycle in which its size, shape, and brightness vary, as seen below. Researchers note that the changes are relatively subtle — at least to the eye — but now that they’ve been identified, it may be possible to use amateur astronomers’ data to track these variations more closely. (Image credits: GRS – K. Gill/NASA, snapshots – A. Simon et al.; research credit: A. Simon et al.; via Gizmodo)

Plasma lighters — as their name indicates — use plasma in place of burning butane. Plasma — our universe’s most common state of matter — is a gas that’s been stripped of its electrons, ionizing it so that it’s electrically and magnetically active. In these lighters (as well as other plasma generators), a high-voltage current jumps between two nodes to ignite the spark. In effect, it’s a tiny lightning bolt you can hold in your hand. (Though I don’t recommend that you try to literally hold it; plasma burns suck.) (Video and image credit: J. Rosenboom; via Nikon Small World in Motion)

On a rainy autumn day, Agorastos Papatsanis headed to the forest in search of fungi. There he captured this fairytale-like scene with falling rain and drifting spores. Near the forest floor, any breeze is slight, so mushrooms use their own humidity to move air and spread their spores. As water evaporates from the mushroom’s cap, it cools the air nearby, causing it to spread outward. Since that water vapor is lighter than air, it rises, too, carrying the mushroom’s spores along with it. (Image credit: A. Papatsanis; via Wildlife PotY)

Figure 1: Automated gerotor pump meshing with GridPro’s structured multiblock grid generator.

1454 words / 7 minutes read

Automated hexahedral mesher empowers engineers to effortlessly scrutinize the flow behaviour, vividly understand the change in flow with the change in clearance gap, and explicitly bring out the differences in the gerotor design variant’s performances.

Introduction

The unique characteristics of gerotor pumps have made them a widely used pumping device in various industries. They are compact, reliable, and inexpensive, making them a cost-effective option for fluid transfer applications. Additionally, they offer high tolerance to fluid contamination, aeration, and cavitation. By providing excellent flow control, minimal flow pulsation and low noise, they have a strong footprint in the aerospace, automotive and manufacturing sectors.

The aerospace industry uses them for cooling, lubrication, and fuel boost and transfer processes. In manufacturing, they are used for dosing, filling, dispensing, and coating applications. Gerotor pumps are also extensively used in the automotive, agriculture, and construction fields, particularly for low-pressure applications. With the progress of technology, gerotor pumps are finding new applications in the life science, industrial, and mechanical engineering sectors.

Driving Forces for Gerotor Pump Research

This expansion in applicability across various industries is driving the gerotor pump research for further improvement. Also, the growing environmental concern in various industries is creating a need for newer applications, which demand pumps that can improve their efficiency. Gerotor pumps, with their simple design, have presented themselves as an attractive option for these newer applications. However, the increasing demand for pumps that meet stringent specifications and shorter design cycles necessitates a cost-effective design process that can lead to optimal performance and efficiency.

This has driven further research on gerotor pumps, focusing on improving design through numerical simulation, allowing designers to identify potential performance issues and optimize their designs before building physical prototypes. By leveraging this approach, researchers are leading the way towards more efficient and reliable gerotor pump designs that meet the growing demand for pump applications in various industries.

CFD in Gerotor Pump Design: Advantages and Constraints

CFD is an essential tool for the design and optimization of gerotor pumps. CFD simulations accurately predict the effect of cavitation and fluid-body interaction on performance by providing a detailed description of the fluid’s behaviour inside the pump. Due to its accuracy, CFD is often used as a benchmark for pump experiments when no experimental comparison data is available.

However, there are certain challenges in using CFD for gerotor pump design. The CFD process requires large simulation time and memory requirements, and there is a need to re-mesh the entire domain at each angular step. Further, meshing the inter-teeth clearance and constantly changing fluid domain could be a challenging task.

These constraints can delay the design verification stage, making the process time-consuming. The design engineer must mesh the volume chambers each time the design changes and perform a time-consuming simulation. In most cases, the simulation of a geometric configuration takes up to a day to generate results. This workflow hinders the effectiveness of rapid design methodologies or the easy testing of a large number of geometric configurations of the pump in a reasonable time.

Gerotor Pump Meshing Challenge

The primary focus of research w.r.t meshing positive displacement machines is the development of methodologies to support rapid simulation of any geometry. Efforts are made to develop meshing methods to automatically generate high-resolution grids with optimal cell size and high quality without human intervention.

However, gerotor pump meshing is challenging due to the rotating and deforming fluid volumes created during their working cycle. The rapid transformation of the deforming fluid zone from a large pocket region to a narrow passage makes meshing extremely difficult, w.r.t maintaining cell resolution, cell quality and mesh size. Trying to attain one of these meshing objectives results in the failure of the other. On top of this, coming up with a meshing procedure to avoid human intervention further ups the difficulty levels.

Additionally, the tight clearance space, which plays a significant role in determining volumetric efficiency, presents another obstacle for CFD simulations. These clearances are extremely small, often in the range of a few microns, and impact various aspects of the pump’s performance, such as flow leakage, flow ripple, cavitation, pressure lock, torque, and power. Out of these, the flow ripple parameter is significantly affected by the design of the tip and side gaps. A high ripple in the outlet flow can cause high levels of vibration and noise in the pump.

Hence it is critically important to accurately represent these narrow gaps with high-resolution, high-quality meshes to bring out their effects in high clarity. Low-resolution coarse grids will decrease the accuracy and may lead to over or underestimation of the flow variables. Maintaining a certain mesh quality is also important, as it enables CFD to easily analyse variations in clearances and other tendencies.

Commonly Adopted Meshing Techniques

Various meshing techniques have been employed over time to discretize the gerotor fluid space. Among them, overlapping meshing methods, deform and remesh methods and customised structured meshing are the most popular ones.

Overlapping meshing methods, including the overset and immersed boundary methods, are frequently used. Although they are quick to generate, they often fall short of properly resolving the boundary layer and narrow clearance gaps while also employing an excessive number of cells.

The deform and remesh method is another popular approach that offers automation but often generates grids with a large cell count. Unfortunately, these methods can cause interpolation errors and stability issues while running the CFD solvers.

While manual customised grid generation methods provide the best mesh in terms of cell quality and grid size, they demand excessive time and human effort to generate the mesh. Unlike the generic moving mesh methods, such as the immersed boundary method, manual gridding approaches, such as the structured moving/sliding methods, accurately represent the dynamic gaps.

Structured Moving/sliding mesh approach

In the structured moving/sliding mesh approach, the fluid volume of the rotor chamber is isolated from the stationary fluid volumes related to the suction and delivery port. The rotor volume is topologically similar to a ring, making it easy to create an initial structured mesh for this shape. This zone being an extrudable domain, a 2D grid is created, which is later extruded to get a 3D mesh.

The stationary fluid volumes of the suction and delivery port are meshed using unstructured approaches. They are linked to the rotor mesh volume via non-conformal interfaces.

When the inner gear surface shifts to a new position, the mesh on the surface does not simply move with it. Instead, the mesh “slides” on the inner gear surface while adjusting to conform to the new clearance between the inner and outer gear surfaces. Simultaneously, the interface connections between the rotor volume and other fluid volumes are updated. These meshing steps ensure good resolution of the clearance space while maintaining good cell quality.

Single Topology Approach of GridPro

GridPro addresses the gerotor pump meshing challenge with its unique single-topology multi-configuration approach. To start with, for a given instance of the inner and the outer gear position, a 2D wireframe topology is built. Since the meshing zone is 2.5D in nature, a grid in 2D is good enough, which is later extruded in the perpendicular direction to get the 3D grid. The 2D topology acts as a template, to be later used repeatedly to generate mesh for all instances of the inner and outer gear positioning.

An automated python script ensures the grids for all angular steps are generated in an automated, hands-free environment. The script rotates the inner and outer gear at a user-specified angular step of 0.1 degrees and gives out a grid with consistent mesh quality. Since the topology is the same, the mesh generated for each angular step is practically the same. This particular aspect brings in significant positive benefits when compared to an unstructured re-meshing approach where the cell count and connectivity are completely different from one angular step grid to another.

This consistency in grids generated for all instances of the gear position aids in generating superior flow field simulation results. The automated meshing environment saves time and human effort and provides the much-needed trust of the design engineer in the simulated CFD data.

Conclusion

Engineers can enhance their workflow for 3D CFD analyses of gerotor pumps with an automated hexahedral mesher. It will empower engineers to effortlessly scrutinize the flow behaviour inside the working chambers, vividly understand the change in flow physics with variation in clearance gap, and explicitly bring out the differences in parametric design variant’s performances.

More importantly, an automated mesher brings the engineers’ focus back to the design aspects of the pump rather than on the meshing.

Further Reading

• Automation of Hexahedral Meshing for Scroll Compressors
• Turbopumps – A Unique Rotating Machine
• Efficient Meshing of Turbine Blade Cooling Holes!
• The Importance of Flow Alignment of Mesh

Reference

1. “A Study on Effect of Operating Conditions on Gerotor Pump Performance”, Tarique Hussain et al., Defence Science Journal, Vol. 72, No. 2, March 2022, pp. 146-150.
2. “A CFD-Based Comparison of Different Positive Displacement Pumps for Application in Future Automatic Transmission Systems”, Thomas Lobsinger et al., Energies 2021, 14, 2501.
3. “A CFD model for orbital gerotor motor”, H Ding et al., 26th IAHR Symposium on Hydraulic Machinery and Systems, 2012.
4. “Computational fluid dynamics and particle image velocimetry assisted design tools for a new generation of trochoidal gear pumps”, M Garcia-Vilchez et al., Advances in Mechanical Engineering, 2015, Vol. 7(7).
5. “A Virtual Prototype for Fast Design and Visualization of Gerotor Pumps“, Juan Pareja-Corcho et al , Applied. Sciences. 2021, 11, 1190.
6. “Three Dimensional Simulation of Gerotor with Deforming Mesh by using OpenFOAM”, Robert Castilla et al, Universitat Politècnica de Catalunya, LABSON Department of Fluid Mechanics, Colom, 11, ES-08222 Terrassa, Spain.

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Figure 1: Block Adapted Shock Fitted Structured Grid for Hypersonic Simulations for Orion Reentry Capsule Configuration.

1483 words / 7 minutes read

Discover the advancements in flow feature-aligned structured grid generation for hypersonic simulations with strong shocks. This article explores the innovative shock-fitting feature in GridPro, which aligns mesh blocks with shock contours to improve accuracy and reduce computational costs. Learn how this new technique outperforms traditional methods by simplifying grid alignment and enhancing shock capture, making it ideal for complex geometries with multiple shocks.

Introduction

Computational fluid dynamics is critically essential and highly recommended for predicting the aerothermal environment of reentry vehicles experiencing hypersonic flow. In these regimes, shock waves are a dominant flow phenomenon. It is needless to say capturing these shock waves to the finest level possible is critically essential for accurately predicting the hypersonic flow field. Traditionally, two distinct approaches, known as shock fitting and shock capturing, have been widely used to handle such discontinuities.

Shock Capturing involves implicit handling of shocks through numerical schemes that can deal with discontinuities without explicitly locating them. They employ artificial viscosity or flux limiters to stabilize the solution and prevent non-physical oscillations. However, they may produce smeared shock profiles and require fine grids to achieve higher accuracy, potentially increasing computational costs.

Shock Fitting, on the other hand, explicitly tracks the position of shock waves within the CFD domain. It treats the shock as a moving boundary within the domain, solving additional equations to update its position and speed. This approach provides a sharp and accurate representation of shocks without the smearing effects seen in shock capturing. However, it is more complex to implement, requiring additional equations for shock dynamics and frequent grid adjustments to accommodate moving shocks.

To sum up, shock capturing is robust, versatile and easier to apply to a wide range of problems, albeit with potential accuracy trade-offs. while on the other hand, shock fitting offers superior accuracy for specific applications but at the cost of increased complexity and implementation effort.

To tackle the challenge of accurately representing shock features, engineers at GridPro have developed a new feature, which enables shock-fitted structured grid generation by aligning grid blocks with the shock contour. This article provides an in-depth discussion of this novel method.

Workflow of the Block-Adapted Shock-Fitting Approach

The computational fluid dynamics workflow begins with the creation of a base structured multi-block mesh for the desired geometry and conducting an initial CFD simulation using this mesh. Following this, post-processing is carried out to extract an iso-contour Mach sheet that passes through the shock.

This Mach iso-contour surface serves as a reference to realign the mesh blocks. By adjusting the topology to align the blocks with the shock, a new mesh is generated with cells more effectively positioned to capture the thin, three-dimensional shock. This updated grid is then used for a second simulation, and the process is repeated until the user achieves the desired level of accuracy in the results.     

Generating the Baseline Mesh

To commence the simulation, an initial structured grid must be created. The baseline mesh adopts an analytical sphere as its outer domain, with no specialized adjustments made to accommodate shock features. This streamlined approach allows for swift setup of the initial grid, requiring minimal effort and time investment. Furthermore, maintaining symmetry along the X-Y plane aids in reducing grid points, thereby enhancing simulation efficiency.

Initial Solution Generation

Following the structured grid generation, the flow simulation is conducted using MISTRAL, a Navier-Stokes solver tailored for reacting flows.

Initially, the solution is computed on an Euler grid, without implementing specialized boundary layer clustering for the capsule wall.

This simplification is deliberate, aimed at minimizing computational time, as the primary focus of the initial iteration is the extraction of the shock surface.

Shock Detection and Mach Iso-Contour Extraction

The process of detecting and extracting shocks commences with the post-processing of the MISTRAL solution to derive the Mach distribution in the flow domain. The location of the bow shock is determined by selecting a percentage of the freestream Mach number, typically ranging between 90% to 95%. Subsequently, a Mach iso-contour sheet is extracted from the flow solution utilizing the Paraview visualization package. Following the extraction, the Mach sheet is saved as an STL file and imported into GridPro for further processing.

Shock Contour Smoothing

Given the coarse resolution of the bow shock on the initial grid, the Mach iso-surface may display roughness. To address this, a smoothing process becomes imperative. Utilizing GridPro’s built-in subdivision scheme, the extracted Mach contour is smoothed and enhanced to make it more suitable for subsequent stages of the shock-capturing procedure.

The Block-Adapted Shock-Fitting Process

With the shock contour sheet in hand, we’re ready to delve into the actual shock-capturing process. Firstly, the tool automatically pinpoints the block faces closest to the extracted shock contour sheet. Next, those faces proximate to the shock undergo splitting and a buffer layer of the block is created around the shock. Notably, this splitting operation maintains the integrity of the block structure, relieving the user from the burden of resolving any ensuing issues.

Following this, blocks lying beyond the buffer layer are automatically deleted (as shown in Figure 7a). Next, a new outer domain surface, encapsulating the capsule is generated by scaling up the shock contour sheet by a small percentage.

Consequently, the outer faces of the buffer layer blocks serve as the boundary faces of this new outer envelope, establishing a zone characterized by shock-aligned grid lines. A detailed view of the mesh obtained after the initial shock-fitting iteration is presented in Figure 7b.

It’s important to note that the decision to split the topology hinges on the specifics of each case. In scenarios where there’s no need to reduce the computational domain’s size, this step may be bypassed. However, in instances like the one described here, where pinpointing the shock’s location and the primary flow physics region is challenging before simulation, employing this process can significantly slash the computational domain by over half. Such reduction translates into substantial savings in computational time and resources.

Shock-Fitted CFD Solution

The Mach contour image in Figure 8 clearly demonstrates that the shock is significantly crisper and closer to the body. The cells are noticeably better aligned with both the general flow direction and the shock’s location. Remarkably, just one shock-fitting iteration was sufficient to achieve a good solution, highlighting the efficiency and effectiveness of the block-adapted shock alignment method.

The computed results are compared to data from Reference, which utilizes the US3D code. Figure 9 compares surface pressure variation along the symmetry line (in the z direction) of the capsule. The maximum pressure error is less than 1%, which is well within acceptable standards, validating the quality of the obtained solution.

Case 2: Space Launch System

The next test case considered to validate the tool was the leading nose region of the Space Launch System. Two structured grids were generated- a baseline grid (0.7 million) and a shock-fitted grid (0.762 million) and CFD computations were done at Mach 5. Figure 10 shows the grids and the improvement in the flow field with the block-adapted shock alignment method.

Case 3: Hypersonic Simulations for a Blunt Body with Fin

The third test case involves hypersonic simulations for a blunt body configuration at Mach 20. Here also 2 grids – Baseline (5.92 million) and shock-fitted grids (5.61 million) were employed. Figure 11 below shows the crisp representation of the bow shock with block-adapted shock-fitted grids.

Advantages of the Block-Adapted Shock-Fitting Approach for Hypersonic Simulations

The block-adapted shock alignment approach is straightforward to implement, requiring fewer re-meshing and CFD simulation iterations compared to other shock-fitting procedures or adaptive shock-capturing methods. Additionally, it only increases the cell count of the base grid by a smaller amount.

This method can be seamlessly integrated into the existing GridPro-CFD solver-post-processor loop without any modifications. The base-structured hexahedral meshes, with their inherently low dissipation properties, enhance shock capture accuracy. By using the shock surface to identify shock-interfering blocks and refining these blocks through wrapping, the resulting grid is sufficiently dense and aligned with the shock contour to capture it accurately. Typically, executing this loop for one or two iterations is sufficient.

A key advantage of this approach is the minimal increase in cell count and the presence of one-to-one connected cells. Due to the limited number of iterative loops and uni-directional cell refinement, the increase in cell count remains marginal. Importantly, any computational fluid dynamics solver compatible with hexahedral meshes can utilize the shock-fitted grids, as one-to-one cell connectivity with neighbouring cells is consistently maintained.

Conclusion

A workflow for shock-fitting grid generation has been developed and rigorously tested, proving effective in accurately capturing shocks. Demonstrated through the Orion re-entry capsule and SLS rocket test cases, this new mesh generation process can rapidly produce accurate CFD estimates for hypersonic geometries. It stands as a viable and promising alternative to traditional shock-fitting or shock-capturing mesh generation methods. The efficacy of these novel approaches is evident, showcasing significant improvements in the flow field due to the highly precise representation of the shock contour.

Further Reading

• Know your mesh for Hypersonic Intake CFD Simulations
• Meshing of Rocket Engine Nozzles for CFD
• Spiked Blunt Bodies for Hypersonic Flights
• Know Your Mesh for Reentry Vehicles
• Supersonic Parachutes for Reentry Vehicles

References

• “A Shock-Fitting Technique For Hypersonic Flows Using Hexahedral Meshes“, Karan H Baliga et al, 25th AIAA International Space Planes and Hypersonic Systems and Technologies Conference, May 28 – June 1, 2023, Bengaluru, Karnataka, India.
• “Development of the Orion Crew Module Static Aerodynamic Database, Part I: Hypersonic”, Bibb, K., Brauckmann, G., Walker, E., and Robinson, P., 29th AIAA Applied Aerodynamics Conference, 10 Aug 2012.
• “Development of the US3D Code for Advanced Compressible and Reacting Flow Simulations”, Candler, G. V., Johnson, H. B., Nompelis, I., Gidzak, V. M., Subbareddy, P. K., and Barnhardt, M., 53rd AIAA Aerospace Sciences Meeting, 3 Jan 2015.

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Figure 1: GridPro Version 9 Feature Image.

1532 words / 7 minutes read

Introduction

In the ever-evolving landscape of grid generation, the goal of having an autonomous and reliable CFD simulation is the driving force behind progress. We’re thrilled to announce the release of GridPro Version 9, a major update that brings many new features, improvements, and powerful tools to empower users to achieve this vision. This release marks a significant milestone in our commitment to automate structured meshing. We have two new verticals released along with GridPro Version 9:

1. HexaMesh Volute – Vertical product that automatically meshes compressor, turbine, and pump volutes in less than 15 minutes.
2. HexaMesh Nuclear Rods – Vertical product to mesh nuclear rods with or without wires in PWR reactors.

This article presents a few highlights. To learn more about other features packed in Version 9, Check out the release notes and What’s New.

CAD Creation and Integration

To align ourselves with CFD_Vision_2030_Roadmap, we now use ESP as our modelling environment. With the introduction of this new environment, we aim to provide an adequate linkage between GridPro and the upfront CAD system. We have implemented a host of CAD creation tools, which enables users to create basic geometries in GridPro using the CAD panel.

[ New Feature ]: Grouping and Labelling of CAD Faces for Enhancing Workflow

As the first linking step in any Upfront CAD package, GridPro can import the labelling and grouping from any CAD package upstream through our improved STEP file format. The labels created in CAD software can be edited or inherited as surface and boundary labels in the mesh exported from GridPro, creating a seamless integration with the solver downstream.

[ New Feature ]: CAD Label Based Surface Splitting

In GridPro Version 9, labelling/grouping can also be used to split the underlying surface mesh. In the previous version, surfaces were split to improve the volume mesh quality based on the feature angles, but now users can also split surfaces by utilizing surface labels/groups. This saves time and reduces the manual effort required to select multiple surfaces for splitting purposes.

New Blocking Creation and Editing Features

[ New Feature ]: Block Once to Mesh all Similar Geometries

One of the significant challenges with traditional structured meshing is to dynamically update 3D blocking in the design and analysis of many engineering applications. Especially in scenarios involving shape optimization, moving boundary problems, etc. This requires the user to regenerate the blocks for every design change. It is a tedious and very time-consuming task to recreate a block structure after every design iteration.

GridPro, being a topology-based mesher, could readily accommodate geometry variations without any additional changes to the blocking. However, when geometries have non-uniform scaling, the parts of the block topology have to be moved close to the geometry to be mapped. This could become a time-consuming process, but with the introduction of the Block Mapping tool, the mapping can be done with a few clicks.

[ New Feature ]: Advanced Topology Block Smoother for Faster Meshing

GridPro’s flexible topology paradigm enables users to create blocks without any restrictions. This sometimes results in the user creating poorly shaped blocks. Though the mesh generation engine smooths the poorly shaped blocks, it increases the mesh convergence time. With the new topology, smoother, irregularly placed blocking is now repositioned to provide a better intuition and speed up the meshing time. With this new feature, the time for grid generation is significantly reduced by an order of magnitude in many cases.

[ New Feature ]: Block Adaptation to Shock

To improve hypersonic simulation workflows, GridPro introduces a Shock Alignment feature. This innovation adapts the grid blocks to the shock formed in a baseline solution. By splitting blocks in the shock region and aligning the grid normal to the shock surface, the algorithm enhances simulation accuracy and accelerates convergence. This advancement allows users to achieve faster and more precise results, optimizing their computational fluid dynamics analyses. With GridPro’s Shock Alignment, engineers and researchers can tackle complex hypersonic flows more efficiently and reliably. (Check out the paper published in the AIAA Hypersonics Conference: A Shock Fitting Technique For Hypersonic Flows Using Hexahedral Meshes.)

[ New Feature ]: Clamped Nesting for Enhanced Mesh Refinement

In GridPro, we have combined the benefits of unstructured meshing like local refinement, mesh adaptation, and multi-scale meshing by adapting the multi-block structure. This is done with a feature called Nesting, In this version we have released another flavour of nesting called the clamped nest. Clamped nesting aggressively refines the mesh near the geometry while coarsening it outside the region. This technique is particularly effective in creating highly refined regions, especially for LES and DNS simulations.

[ Enhancement ]: Quick Blocking for Regular Patterns Using the Array-Block Feature

To speed up the block creation time for repeated geometries, an Array-block replication option is introduced. This provides the capability to replicate a topology in multiple directions. This tool is particularly advantageous when dealing with similar geometric patterns or shapes. Instead of creating the topology individually for each pattern, users can generate one pattern and seamlessly replicate it across self-similar geometric patterns in three different directions. Utilizing the Array feature, users can create blocking for a single periodic section and extend it in the X, Y, and Z directions.

[ Enhancement ]: Generate, View, and Check Higher-Order Meshes

Starting now, the UI enables the creation of higher-order meshes with ease. Users can choose their preferred higher-order format – quadratic, cubic, or quartic – and the tool will automatically adjust the density to the nearest multiple of the selected order for seamless mesh generation.

Users can also import internally or externally generated higher-order grids into the UI for visualization and quality assessment. The meshes can be observed in various modes, including Only Edges, Edges with Corners, Edges with Nodes, and Edges with All Nodes, allowing for a comprehensive examination.

Moreover, users can evaluate mesh quality parameters such as the Jacobian of the higher-order elements and compare them to the native linear mesh for detailed analysis.

[ New Feature ]: Eliminating Negative Volumes with Local Block Smoothing.

The local block smoothing feature introduced in GridPro Version 9 provides the user a way to eliminate negative volumes ( folds) in the generated mesh locally. The local smoothing is a post-processing step which can be done in the grid to either improve the grid locally or to eliminate negative volumes.

The smoothing feature offers two schemes: Transfinite Interpolation (TFI) and Partial Differential Equation (PDE) smoothing. TFI-based smoothing is computationally less intensive, while PDE-based smoothing, despite its higher computational cost, proves more effective in areas with high curvature, producing meshes with fewer folds.

[ Enhancement ]: GUI Access to Grid Schedule to Speedup Mesh Smoothing

In version 9, we provide GUI options to harness the control features in the Grid Schedule function. These are designed to accelerate mesh smoothing by leveraging the multi-grid capabilities of structured meshes. Particularly beneficial for large topologies, the approach involves initially running the topology at a lower density. Once the corners are approximately smoothed and positioned, the smoother can be executed for higher densities, contributing to accelerated grid convergence.

Users can insert additional steps to further customize the smoothing process, effectively breaking down the process into multiple stages. After each step, the smoothing computations automatically resume from the previous state, ensuring a seamless and efficient progression.

[ Enhancement ]: Enhanced Cut Plane Tool to Create Multiple Sectional Views

Now, users can generate multiple Cut Planes, allowing them to create sectional views at various locations and directions. The Cut Plane, employed to clip a portion of a surface or grid, facilitates the examination of its interior, mainly when the area to be meshed is situated inside the surfaces. The enhanced feature of utilizing more than one Cut Plane significantly simplifies the assessment of topology and mesh in complex areas.

[ Enhancement ]: Python-based CAD and Meshing API

In version 9, we introduced the CAD and Meshing API with Python 3 support, empowering users to automate the meshing workflow with greater control. The updated API provides a comprehensive set of commands, including preprocessing and postprocessing operations. Repetitive tasks and batch operations can also be automated. This significantly reduces the user’s time spent on meshing and enhances productivity, particularly for new designs that follow similar workflows.

The new APIs can be tightly integrated with any CAD or optimization system, making them an excellent tool for automating topologically similar geometries. By leveraging the API, users can streamline their design process, ensuring efficient and consistent high-quality meshes and CFD results across different designs.

How to Get Your Hands on GridPro Version 9

Upgrading to GridPro Version 9 ! Existing users can easily upgrade to the latest version, while new users can explore the enhanced capabilities by downloading the software from https://www.gridpro.com.

Visit our official website gridpro.com to download the latest version of GridPro!

To see these features in action, visit our Youtube Channel: GridPro Version 9 New Features Playlist.

Conclusion

As we continue to evolve and innovate, GridPro Version 9 reflects our commitment to providing you with the best tools and features to ease the workflow of mesh generation and accuracy of your CFD simulations. We believe these new features and tools will increase reliability and change how you mesh.

Further Reading

• Automated Structured Meshing of Nuclear Fuel Rod Assembly
• Automation of Hexahedral Meshing for Scroll Compressors

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Figure 1: Turbine blade with winglet tips. Image source – Ref [1].

                                                                                                                                                                                                                              702 words / 3 minutes read

Winglet Tips are effective design modifications to minimize tip leakage flow and thermal loads in turbine blades. A reduction in leakage losses of up to 35-45 % has been reported.

Introduction

The design and optimization of gas turbines is a crucial aspect of the energy industry. One aspect that has gained significant attention in recent years is the issue of tip leakage flow in gas turbines. Tip clearances, which are provided between the turbine blade tip and the stationary casing, allow free rotation of the blade and also accommodate mechanical and thermal expansions.

However, this narrow space becomes instrumental in the leakage of hot gases when the pressure difference between the pressure side and the suction side of the flow builds up. This is undesirable as it reduces the turbine efficiency and work output. According to some studies, tip leakage loss could account for one-third of the total aerodynamic loss in turbine rotors. Further, leakage flows bring in extra heat, which raises the blade tip metal temperature, thereby increasing the tip thermal load.

It is, therefore, essential to cool the blade tip and seal the leakage flow. Over the years, various design features have been proposed as a solution. One of the promising features employed in tip design is the use of winglets.

Turbine Blade Winglet Tips

Winglet tips comprise of a blade tip with a central cavity and an outward extension of the cavity rim called the winglet. Different variants are developed based on the outward extent of the winglet, the length of the winglet and the location of the winglet. Figure 3 shows three winglet variants derived from the base geometry of the tip with a cavity. The first two have winglets on the suction side with different lengths, while the third one has a small winglet on the suction side as well as on the pressure side.

Tip Flow Structure

The flow pattern within the cavity of the winglet-cavity tip is similar to that in the cavity tip. On the blade pressure surface, the flow accelerates toward the trailing edge. On the blade suction surface, the flow accelerates till 60 percent of the tip chord and then decelerates toward the trailing edge. Near the leading edge of the blade tip, the flow enters the tip gap and impinges on the cavity floor of the tip, enhancing the local heat transfer. Then, a vortex forms along the suction side squealer. The vortex within the cavity is called a “cavity vortex.” It is also observed that the flow separates at the pressure-side tip edge, and most of the fluid exits the tip gap straight after entering the tip gap from the pressure-side inlet. Nevertheless, some fluid entering the tip gap mixes with the cavity vortex first and then exits the tip gap. The tip leakage flow exiting the gap rolls up to form a tip leakage vortex.

Meshing Winglet Tips

While generating meshes for leakage flow simulations, having a fine mesh in the leakage gap is critical. The narrow gap should be finely resolved with at least 40-50 layers of cells. In the tangential direction across the tip gap, 30 – 40 layers of cells are required to capture the winglet width and 150-160 cell layers to capture the tip gap from the suction side to the pressure side. Such a fine-resolution structured mesh will lead to a total cell count of about 7 to 9 million.

The boundary layer should be fully resolved with an estimated Y+ less than 1, using a slow cell growth rate of 1.1 to 1.2. Grid refinement studies with grids varying from 6 to 10 million have shown to decrease the tip average heat transfer coefficient by about 1.8 to 1.9% with every 2 million increase in cell count.

Conclusion

The average tip heat transfer coefficient (HTC) and total tip head load increase with an increase in tip gap. HTC is observed to be high on the pressure side winglet due to flow separation reattachment and also high on the side surface of the suction side winglet due to impingement of the tip leakage vortex.

Tip winglets are found to decrease tip leakage losses. Because of the long distance between the two squealer rims, the flow mixing inside the cavity is enhanced, and the size of the separation bubble at the top of the suction side squealer is increased, effectively reducing leakage loss. In a low-speed turbine, the winglet cavity tip is observed to reduce loss by 35-45% compared to a flat tip. When it comes to thermal performance, the tip gap size becomes a major influencing factor.

Other Articles in the Series

• Innovative Turbine Blade Tips to Reduce Tip Leakage Flow.
• Effective Control of Tip Vortices Using Squealer Tips.
• Honeycomb Tips to Reduce Turbine Leakage Flows.

Further Reading

• The Art and Science of Meshing Turbine Blades
• Efficient Meshing of Turbine Blade Cooling Holes!
• Turbopumps – A Unique Rotating Machine

Reference

1. “Heat Transfer of Winglet Tips in a Transonic Turbine Cascade”, Fangpan Zhong et al., Article in Journal of Engineering for Gas Turbines and Power · September 2016.
2. “Tip gap size effects on thermal performance of cavity-winglet tips in transonic turbine cascade with endwall motion”, Fangpan Zhong et al., J. Glob. Power Propuls. Soc. | 2017, 1: 41–54.
3. “Turbine Blade Tip External Cooling Technologies”, Song Xue et al., Aerospace 2018, 5, 90.
4. “Aero-Thermal Performance of Transonic High-Pressure Turbine Blade Tips“, Devin Owen O’Dowd, St John’s College, PhD Thesis, Department of Engineering Science, University of Oxford, 2010.

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Figure 1: Turbine blade with squealer tips.

400 words / 2 minutes read

Revolutionary turbine blade tip designs are critical for minimizing tip leakage losses in gas turbines. Several innovative designs, such as flat tips, squealer tips, tips with winglets and honeycomb cavities, have shown the potential to mitigate this problem.

Importance of Reducing Blade Tip Leakage Losses in Turbine Blades

Turbine efficiency and performance can be improved by minimizing tip leakage losses in the blades. These losses are an unavoidable result of the flow passing through the narrow gap between the blade tip and shroud, mixing with the main flow and causing disruption in the flow pattern. The proportion of tip leakage losses to total aerodynamic loss can be as high as one-third, a significant amount that can reduce turbine output.

By reducing tip leakage losses, a turbine can operate more efficiently, resulting in lower fuel consumption, fewer emissions, and ultimately lower operating costs. In addition, it can prolong the lifespan of the turbine by reducing wear and tear on the blades and other components. Therefore, minimizing tip leakage losses is essential for optimizing the turbine’s performance and ensuring its long-term sustainability.

The Impact of Blade Tip Clearance on Gas Turbine Performance

Gas turbines use tip clearances between the turbine blade tip and the stationary casing to prevent rubbing and accommodate expansions. Unfortunately, these clearances create aerodynamic losses and leakage, which reduce turbine efficiency and work output. Leakage through the clearance also adds extra heat, increasing the tip metal temperature and thermal load. In high-performance turbines, the tip leakage flow is intense, significantly impacting turbine performance. As a result, developing new or enhanced designs that cool the blade tip and seal the leakage flow is crucial. Proper tip clearance control is vital to optimizing gas turbine performance and output.

Improving Turbine Efficiency: Exploring Innovative Blade Tip Design Features

Over the years, various tip design features have been proposed as a solution, like flat tips, squealer tips, tips with winglets and honeycomb cavities, etc. Unfortunately, less clarity exists in understanding the dominant flow structure, affecting these tip designs’ aerodynamic benefits. Hence, numerical and experimental studies are conducted to study the effects of different tip designs on the aerodynamic performance and cooling requirements.

To learn more about innovative tip designs and their impact on turbine performance, consider reading these three articles:

• Effective Control of Tip Vortices Using Squealer Tips.
• Cooling the Hot Turbine Blade with Winglet Tips.
• Honeycomb Tips to Reduce Turbine Leakage Flows.

Further Reading

• Efficient Meshing of Turbine Blade Cooling Holes!
• The Art and Science of Meshing Turbine Blades
• Influence of Meshes on Hydraulic Turbines CFD

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Figure 1: Structured mesh for honeycomb cells using GridPro.

                                                                                                                                                                                                                    475 words / 2 minutes read

Honeycomb tips offer a promising solution for mitigating tip leakage flows in turbines. These novel tip designs have shown to be highly effective in reducing tip leakage mass flow rate and aerodynamic losses, outperforming conventional flat tips.

Introduction

The design of turbine blades, particularly the tips, plays a significant role in determining the efficiency and performance of turbines. Although an unavoidable issue, tip leakage flow can lead to reduced tip loads and increased aerodynamic losses through flow separation and mixing. With the increasing loads handled by high-performance turbines, the intensity of tip leakage flow becomes even more pronounced. To address this, new designs or improvements to existing designs are necessary. One promising solution is using honeycomb tips, which have proven to be effective in reducing the negative effects of tip leakage flow. This article will delve into the advantages of honeycomb tips and how they can improve the performance of high-performance turbines.

Honeycomb Tips

The concept of honeycomb tips is a relatively new and innovative approach to blade tip design. This design features multiple hexagonal cavities on the blade tip, usually numbering 60-70 and with a depth of a few millimeters.

Research has revealed that honeycomb tips have favourable characteristics for inhibiting tip leakage flow. Part of the leakage flow enters the hexagonal cavities and creates small vortices. These vortices mix with the upper leakage fluid, which leads to an increase in resistance in the clearance space, suppressing the tip leakage flow and reducing the size and intensity of the leakage vortex. Additionally, the use of honeycomb tips allows for a tighter build clearance compared to a flat tip, as contact is less damaging.

Honeycomb Tip Design Variants

There are various modifications to the standard design of honeycomb tips. One such variation involves injecting coolant from the bottom of each hexagonal cavity to enhance heat transfer at the blade tip. Simulations have demonstrated that this design results in a reduction of up to 26.7% in the tip leakage mass flow rate and a decrease of approximately 4.6% in the total pressure loss coefficient compared to a flat tip.

Another variation of honeycomb tips is the composite honeycomb tip, which features an additional lower frustum in addition to the upper hexagonal prism. Studies have indicated that this design leads to a reduction of up to 16.81% in the tip leakage mass flow rate and a decrease of 5.49% in losses.

Thus, both variations of honeycomb tips show great promise in significantly enhancing the performance of turbine blades.

Meshing the Honeycomb Tips

To effectively capture the effects of these tiny honeycomb cavities, finely resolved mesh is essential. The boundary layer needs to be fully resolved with Y+ around 1. With 60-70 cavities, the total cell count could reach 5-6 million, with each cavity requiring about 0.0075 million cells for proper discretization. Such a fine mesh is necessary to precisely capture the tip vortex and the small cavity vortices, as well as the interactions between them.

Conclusion

Honeycomb tips offer a promising solution for mitigating tip leakage flows in turbines. These novel tip designs have proven to be highly effective in reducing the tip leakage mass flow rate and aerodynamic losses, outperforming conventional flat tips. The cavity vortices created within the honeycomb design effectively control the leakage flow, making it a viable option for various clearance gaps. Overall, honeycomb tips have the potential to significantly improve the performance of turbines.

Other Articles in the Series

• Innovative Turbine Blade Tips to Reduce Tip Leakage Flow.
• Effective Control of Tip Vortices Using Squealer Tips.
• Cooling the Hot Turbine Blade with Winglet Tips.

Further Reading

• The Art and Science of Meshing Turbine Blades
• Efficient Meshing of Turbine Blade Cooling Holes!

References

1. “Effect of clearance height on tip leakage flow reduced by a honeycomb tip in a turbine cascade”, Fu Chen et al., Proc IMechE Part G: J Aerospace Engineering 0(0) 1–13.
2. “Effect of cooling injection on the leakage flow of a turbine cascade with honeycomb tip”, Yabo Wang et al., Applied Thermal Engineering 133 (2018) 690–703.
3. “Parameter optimization of the composite honeycomb tip in a turbine cascade”, Yabo Wang et al., Energy, 22 February 2020.

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VisualFoil Plus is a version of VisualFoil that has a built-in compressible flow solver for transonic and supersonic airfoil analysis.  As VisualFoil Plus is currently not in active development, the perpetual license is only $189.

Learn more 👉 https://www.hanleyinnovations.com/air_16.html

VisualFoil Plus has the following features:

1. An extensive airfoil library for simulation, comparing, graphing and printing.
2. An intuitive user interface where you can export airfoil ordinates, and performance results
3. Compare the aerodynamic performance of multiple airfoil on a single graph
4. Work and learn within an intuitive graphical user interface.

The picture above, shows referrals the solution of the NACA 0012 airfoil at a Mach number of 0.825.
Please visit us at https://www.hanleyinnovations.com/air_16.html for more information.

Here are instructions on how to import a surface CSV file from Stallion 3D into ParaView using the Point Dataset Interpolator:*

In Stallion 3D

• Export the computational fluids dynamics (CFD) surface data as a CSV file
• Export the volume data also as a CSV file

Open ParaView.

• Click "File" > "Open".
• Navigate to your CSV file and select it.
•  Click "OK".

Convert CSV to points:

•  In the "Pipeline Browser", click on the CSV file.
• -Click "Filters" > "Alphabetical" > "Table To Points".
• -Set "X Column", "Y Column", and "Z Column" to the appropriate column names in your CSV file.
• Click " OK".

Load the target surface mesh:

• Click "File" > "Open".
• Navigate to your surface mesh file (e.g., VTK, STL, OBJ) and select it.
• Click "OK".

 Apply Point Dataset Interpolator:

• Click on the "Table To Points" output in the "Pipeline Browser".
• Click "Filters" > "Alphabetical" > "Point Dataset Interpolator".
• Set "Source" to the target surface mesh.
• Set "Input" to the "Table To Points" output.
• Click "Apply".

5. Visualize:

• Click on the "Point Dataset Interpolator" output in the "Pipeline Browser".
• The interpolated data should now be mapped onto the surface mesh.
• Use ParaView's rendering and visualization options to create the desired visual representation.

Take flight with your next project! Hanley Innovations offers powerful software solutions for airfoil design, wing analysis, and CFD simulations.

Here's what's taking off:

• MultiElement Airfoils: Master the interaction of flaps, slats, and spoilers for optimal performance. Analyze isolated or multiple airfoils, compressible or incompressible flow.
• 3DFoil & MultiSurface Aero: Design and analyze 3D wings, hydrofoils, and other surfaces with ease. Vortex lattice method delivers rapid results for lift, drag, and stability.
• Stallion 3D: Dive into accurate CFD simulations. User-friendly, cost-effective, and MS Windows compatible. Grid generation, graphs, tables – everything you need in one package.

Hanley Innovations: Empowering engineers, students, and enthusiasts to turn aerodynamic dreams into reality.

Ready to soar? Visit www.hanleyinnovations.com and take your designs to new heights.

Stay tuned for more updates!

#airfoil #cfd #wingdesign #aerodynamics #iAerodynamics

Stallion 3D has updated features for accurate analysis

• Export results to CSV files and then into Paraview (see picture above)
• Faster numerical convergence
• Compute lift, drag and moment coefficients for your unique vehicle shape and determine its performance
• Analyze wing, bodies and other shapes with high fidelity RANS CFD and predict stall
• Find stability derivatives
• Add the effect of prop-wash using the built-in propeller models (actuator disks)
• Start your analysis with the Stallion 3D built-in wing editor and thousands of airfoils

Stallion 3D 5.0  provides state of the art external aerodynamic analysis of your 3D designs directly on your Windows PC or Laptop.  It will run on Windows 7 to 11. 

Find out more by visiting https://www.hanleyinnovations.com/stallion3d.html

Why use 3DFoil 🤔

There are several reasons why you might use 3DFoil for wing analysis:

• Accuracy: 3DFoil is known for its accuracy in predicting the lift, drag, and moments of 3D wings. This is due to its use of a novel numerical algorithm that couples the vortex lattice method with a linear strength panel method.
• Speed: 3DFoil is a very fast software package, making it ideal for parametric studies and optimization.
• Ease of use: 3DFoil has a user-friendly interface that makes it easy to set up and run simulations.
• Affordability: 3DFoil is relatively affordable, making it a good option for students and hobbyists.

Here are some specific examples of when you might want to use 3DFoil:

• To design a new wing for an aircraft or other vehicle
• To analyze the performance of an existing wing design
• To troubleshoot problems with an existing wing design
• To optimize a wing design for a specific performance goal
• To educate yourself about the principles of wing aerodynamics

If you are looking for an accurate, fast, and easy-to-use wing analysis software package, 3DFoil is a good option to consider.

Here are some additional benefits of using 3DFoil:

• 3DFoil can be used to analyze wings with taper, twist, dihedral angles, and sweep.
• 3DFoil can be used to analyze wings with multiple airfoils.
• 3DFoil provides a variety of visualization options, including contour plots and graphs.
• 3DFoil is actively developed and supported by Hanley Innovations.

Overall, 3DFoil is a good choice for wing analysis if you are looking for an accurate, fast, and easy-to-use software package.

More information about 3DFoil can be found at: https://www.hanleyinnovations.com/3dfoil.html

 In the computation of turbulent flow, there are three main approaches: Reynolds averaged Navier-Stokes (RANS), large eddy simulation (LES), and direct numerical simulation (DNS). LES and DNS belong to the scale-resolving methods, in which some turbulent scales (or eddies) are resolved rather than modeled. In contrast to LES, all turbulent scales are modeled in RANS.

Another scale-resolving method is the hybrid RANS/LES approach, in which the boundary layer is computed with a RANS approach while some turbulent scales outside the boundary layer are resolved, as shown in Figure 1. In this figure, the red arrows denote resolved turbulent eddies and their relative size. 

Depending on whether near-wall eddies are resolved or modeled, LES can be further divided into two types: wall-resolved LES (WRLES) and wall-modeled LES (WMLES). To resolve the near-wall eddies, the mesh needs to have enough resolution in both the wall-normal (y+ ~ 1) and wall-parallel directions (x+ and z+ ~ 10-50) in terms of the wall viscous scale as shown in Figure 1. For high-Reyolds number flows, the cost of resolving these near-wall eddies can be prohibitively high because of their small size. 

In WMLES, the eddies in the outer part of the boundary layer are resolved while the near-wall eddies are modeled as shown in Figure 1. The near-wall mesh size in both the wall-normal and wall-parallel directions is on the order of a fraction of the boundary layer thickness. Wall-model data in the form of velocity, density, and viscosity are obtained from the eddy-resolved region of the boundary layer and used to compute the wall shear stress. The shear stress is then used as a boundary condition to update the flow variables.    

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:
Elizabeth Favreau
Marketing Writing Team Lead

In today’s energy and transportation industries, sprays and combustion are at the heart of many of our most relied-upon technologies. From internal combustion engines to gas turbines to burners, better understanding the fundamental physical processes that drive these devices can help us make them more efficient and more sustainable in the future.

Professor Noah Van Dam’s Multi-Phase and Reacting Flows Laboratory at the University of Massachusetts Lowell is dedicated to studying and characterizing these processes through computational fluid dynamics (CFD) modeling. A CFD aficionado since his undergraduate days, Prof. Van Dam was introduced to CONVERGE during his postdoctoral studies at Argonne National Laboratory, where he focused on the effects of fuel properties on engine performance. When he started his own lab at UMass Lowell, he continued to use CONVERGE through the CONVERGE Academic Program, which provides licenses, training, and support for academic research. 

Starting at the Very Beginning: Simulating Spray G

CONVERGE wasn’t the only thing Prof. Van Dam carried over into his lab at UMass Lowell—he also continued his research on spray and combustion modeling. When Aman Kumar joined Prof. Van Dam’s lab in 2020 as a graduate research assistant, he began conducting detailed numerical studies of the Engine Combustion Network (ECN) Spray G injector. He was interested in understanding how the injector geometry and the location of the spray plume affected downstream conditions and overall engine performance.

“I’ve been focusing on fundamental studies, because everything starts right at the beginning with how you are injecting the fuel and how the mixture is being developed,” Aman said. “If the mixture is homogenous, the fuel-air mixture will burn at reduced combustion temperature and the engine will produce lower amounts of NOx, soot, and particulate matter emissions.”

In his studies, Aman experimented with both Reynolds-Averaged Navier-Stokes (RANS)¹ and large eddy simulation (LES)² modeling frameworks. He looked at various parameters, including having the injector tip geometry drawn in the cylinder head versus not including it, initializing the parcels at the counterbore exit versus the nozzle exit, using an experimentally derived rate of injection versus reading the injector flow parameters from a volume of fluid (VOF) simulation of the internal injector flow, and the use of a nominal versus x-ray scanned injector geometry. He compared spray penetration and other global parameters to experimental data. Figure 1 shows vapor and liquid penetration length plots for eight RANS simulation cases compared to experimental data. The different cases resulted in only slightly different penetration lengths, and the CONVERGE simulations matched well with the experimental data. Figure 2 shows a comparison of projected liquid volume fraction for the RANS and LES cases. While the RANS simulation captures the global spray behavior, the LES simulation better captures the local turbulent flow features. 

The Future of Energy: Simulating Ammonia Sprays

Following their Spray G studies, Aman and Prof. Van Dam turned their attention to alternative fuels, in particular ammonia. 

“Our future energy requirements need to move in a direction where we’re reducing the net greenhouse gases that we are emitting from transportation and other energy systems. Alternative fuels, such as ammonia, is one pathway that has been proposed, and it’s one that is looking more and more like it is going to be a fruitful avenue for research and actual production,” explained Prof. Van Dam.

The properties of ammonia, however, differ significantly from traditional hydrocarbon fuels. For example, liquid ammonia sprays are more likely to undergo flash boiling under most engine operating conditions, which could necessitate new injection strategies. Aman used CONVERGE to study how well current spray models can capture liquid ammonia spray behavior.³

He used a RANS turbulence model with two different simulation methods: a VOF approach for in-nozzle simulations and a Lagrangian-Eulerian (LE) parcel-based approach for downstream simulations. For the LE simulations, Aman also tested two different methods of initializing the spray parcels: one-way coupling using the results from the in-nozzle simulations and a prescribed rate-of-injection (ROI) method. 

Figure 3 compares experimental images with simulated ammonia sprays using the in-nozzle VOF approach at different pressure ratios. The CONVERGE simulations are able to capture the widening of the spray plume as the spray begins to undergo flash boiling at higher pressure ratios. 

Aman found that for a non-flashing case, the two LE modeling frameworks best captured the liquid penetration lengths, whereas the VOF in-nozzle method performed the best for the flash-boiling case. A significant amount of ammonia vapor is produced inside the counterbore geometry which can be seen easily in CFD simulations but is difficult to capture in experiments. He and his lab are continuing their studies into ammonia sprays and are working to further improve the existing spray models to robustly capture ammonia’s flash boiling behavior.

Turning Up the Heat: Ammonia/Hydrogen Combustion

As mentioned earlier, fuel injection is only the beginning of the story in an IC engine. Continuing on downstream, Prof. Van Dam is also investigating the combustion of alternative fuels. For these studies, Prof. Van Dam teamed up with other researchers including Prof. Dimitris Assanis at Stony Brook University with the goal of gaining a better understanding of ammonia/hydrogen combustion.

As combustible fuels go, both ammonia and hydrogen come with some challenges. Ammonia is hard to ignite and has a very low flamespeed. On the other hand, hydrogen is very reactive and burns very rapidly. 

“By mixing hydrogen and ammonia, we can mitigate some of the issues of each individual fuel and create a blended fuel that behaves much more closely to our current hydrocarbon fuels. We have a lot of experience with hydrocarbon fuels, and so it’s much easier for us to design engines for fuels that behave similarly,” said Prof. Van Dam.

In their collaborative study,⁴ the researchers from UMass Lowell and Stony Brook first tested several different chemical kinetic mechanisms for ammonia/air and ammonia/hydrogen/air combustion to determine which mechanism best matched available experimental data for laminar flamespeed and ignition delay. They then took the best performing mechanism and ran 3D CFD simulations in CONVERGE to study the combustion characteristics. Figure 4 shows a visual comparison of the flame from experimental Schlieren images and the CFD results for ammonia/air combustion. The simulations show similar flame shapes as the experiment at each time step for all three equivalence ratios. 

The researchers discovered that compared to ammonia/air combustion, the ammonia/hydrogen/air combustion resulted in a faster flame that was less dependent on the spark event and did not experience buoyancy effects. They concluded that ammonia/hydrogen mixtures demonstrate complementary combustion characteristics that could lead to improved performance for engine applications.⁴

The group from UMass Lowell and Stony Brook are continuing their research into ammonia combustion, which you can look forward to in an upcoming paper at the 2024 ICE Forward Conference.⁵

A Taste of Salt Air: Simulating a Swirl Burner for Marine Propulsion

Alternative fuels aren’t the only pioneering technology that Prof. Van Dam’s lab is researching—they are also helping to develop reliable propulsion systems for the next generation of unmanned surface vessels. In a collaborative project with the U.S. Office of Naval Research, Prof. Van Dam’s group is investigating how burners operating in marine environments are affected by intaking salty air.

Undergraduate researcher Colin Wildman began working on this project when he joined Prof. Van Dam’s lab in 2022. The first step for the project was to test different diesel fuel surrogates in a swirl burner to determine which one most accurately represented the flame shapes and emissions of the experimental setup. Using the SAGE detailed chemistry solver and LES turbulence modeling, Colin tested five different diesel fuel surrogates (Surrogate A, Surrogate B, T15, T15 + CH5, and T20).⁶ He also tested a more computationally efficient RANS turbulence model, using Surrogate A, to see if that would give them reasonable results in a shorter amount of time. Figure 5 shows the temperature contours of the resulting flames for the different surrogates they tested. The RANS model produced a smoother, more cylindrical flame shape compared to the LES simulations, which more accurately captured the intricate flame structures. Because of this, they decided to stick with LES modeling with diesel Surrogate A. The next step in this project is to introduce salt into the flame and see how that affects combustion and emissions production. 

Growing as Researchers

When Colin joined Prof. Van Dam’s lab, he had no experience with CFD software. By watching our on-demand training courses, getting hands-on experience with CONVERGE, and working with our support engineers, he was able to become a proficient and independent CFD user. 

“The CONVERGE Academic Program, with the training videos and support, has helped me grow into a student that’s independent. It’s kind of a happy memory for me thinking that when I started, I had no idea what I was doing. And now I’m independent, running cases on my own. Now when we have new students join the lab, I’m the one that shows them the ropes,” Colin said. 

The goal of the CONVERGE Academic Program is to equip students and other academic researchers with the tools and skills they need to succeed in academia and beyond. Academic users get access to the full-featured CONVERGE package, which helps prepare them for a smooth transition to a career in industry after graduation. Stories like Colin’s emphasize that with the right resources and support, learning an advanced CFD software and conducting impactful, cutting-edge research is well within your reach. 

To learn more about the CONVERGE Academic Program, visit our webpage or fill out this form to get in touch with our academic specialists!

References

[1] Kumar, A. and Van Dam, N., “Study of Injector Geometry and Parcel Injection Location on Spray Simulation of the Engine Combustion Network Spray G Injector,” Journal of Engineering for Gas Turbines and Power, 145(7), 2023. DOI: 10.1115/1.4062414

[2] Kumar, A., Boussom, J.A., and Van Dam, N., “Large-Eddy Simulation Study of Injector Geometry and Parcel Injection Location on Spray Simulation of the Engine Combustion Network Spray G Injector,” Journal of Engineering for Gas Turbines and Power, 146(8), 2024. DOI: 10.1115/1.4063957

[3] Kumar, A. and Van Dam, N., “Liquid Ammonia Sprays for Engine Applications,” ILASS-Americas 34th Annual Conference on Liquid Atomization and Spray Systems, Ithaca, NY, United States, May 19–22, 2024.

[4] Shaalan, A., Nasim, M.N., Mack, J.H., Van Dam, N., and Assanis, D., “Understanding Ammonia/Hydrogen Fuel Combustion Modeling in a Quiescent Environment,” ASME 2022 ICE Forward Conference, ICEF2022-91185, Indianapolis, IN, United States, Oct 16–19, 2023. DOI: 10.1115/ICEF2022-91185

[5] Mathai, J.R., Rana, S., Shaalan, A., Nasim, M.N., Trelles, J.P., Mack, J.H., Assanis, D., and Van Dam, N., “Numerical Study of Buoyancy and Flame Characteristics of Ammonia-Air Flames,” 2024 ASME ICE Forward Conference, ICEF2024-141569, San Antonio, TX, United States, Oct 20–23, 2024. (Forthcoming)
[6] Wildman, C., Fernandez, J., and Van Dam, N., “Low-Pressure Swirl Burner for Marine Propulsion Applications,” 2023 CONVERGE CFD Conference, Online, Sep 26–28, 2023.

Author:
Elizabeth Favreau
Marketing Writing Team Lead

The need to reduce emissions from the transportation sector has spawned a new era of evolution and development in the automotive industry. Many countries around the world have identified electric vehicles as a crucial piece of the decarbonization puzzle, and as with any emerging technology (or, more precisely in the case of electric vehicles, reemerging), safety is a primary concern. 

While statistically the safety of electric vehicles is on par with conventional powertrains, battery thermal runaway and thermal propagation have been thrust into the spotlight as a potential hazard. In the worst-case scenarios, thermal propagation can lead to battery fires or explosions, which pose a threat to vehicle occupants and can release toxic gases into the environment.

Convergent Science recently teamed up with IAV to take on the problem of thermal runaway propagation. IAV is an international company based in Berlin, Germany, that has been developing technical solutions for the automotive industry for over forty years. They are dedicated to providing the best expertise and methodologies to their customers to help them tackle challenging engineering problems, such as thermal propagation in electric vehicle batteries.

“There are many reasons why it’s important to study thermal propagation,” says Dr. Alexander Fandakov, who leads the R&D team working on battery electric vehicle powertrain development at IAV. “First and foremost is the safety of the vehicle, but another big reason is legislation.”

Many jurisdictions around the world have enacted legislation stipulating that if damage to the electric vehicle battery is imminent, there must be sufficient time for drivers and passengers to stop and exit the vehicle before thermal propagation occurs. For example, UN regulations require that vehicle occupants receive a signal “5 minutes prior to the presence of a hazardous situation inside the passenger compartment caused by thermal propagation”.¹ Moreover, there has been discussion in some jurisdictions about significantly increasing the duration of time required between thermal runaway and thermal propagation, which would essentially mean that no thermal propagation would be allowed.

To meet these legislative requirements, manufacturers must conduct extensive testing of their battery modules or packs under different conditions to evaluate the risk of thermal runaway and devise methods to mitigate thermal propagation. Extensive testing, however, doesn’t come cheap.

“The problem with electric vehicle battery development is that when you want to perform testing related to thermal propagation, you generally need at least a module, or the entire battery pack, which typically are not available until a late stage of the development process. And when looking into thermal propagation, you have to consider different boundary conditions, and then you basically put the battery pack in the trash after the test. So these tests are very, very expensive,” Alexander explains, “and the implementation of additional propagation mitigation measures based on the test results are typically anything but straightforward at this late development stage.”

It follows naturally, then, that if you can cut down on the number of physical tests, you can save a significant amount of time and money. This is where computational fluid dynamics (CFD) comes into play. CFD allows engineers to simulate battery packs with different chemistries, materials, and configurations under different conditions to virtually assess the efficacy of thermal propagation mitigation strategies. To be an effective development tool, however, you need to have a predictive CFD code—which is why IAV elected to use CONVERGE.

“CONVERGE uses a physics-based approach to model 3D thermal runaway and thermal propagation,” says Kislaya Srivastava, Principal Engineer at Convergent Science. “This means that we don’t rely on experimental profiles, instead using chemical reaction mechanisms coupled with high-fidelity models to predict the thermal runaway behavior.”

IAV and Convergent Science worked together to develop and validate a numerical approach in CONVERGE to simulate thermal propagation, starting with modeling the thermal runaway kinetics of different battery chemistries, then using the validated kinetic mechanisms to predict the 3D spatial temperature distributions and heat transfer in battery systems employing a variety of different materials. In this blog post, we’ll take a look at an overview of the team’s 3D modeling work; for details on the experimental work and more in-depth information on the simulation studies, please refer to Sens et al. 2024.²

Single-Cell Studies

The team from IAV and Convergent Science first used CONVERGE to conduct single-cell tests of different lithium-ion battery chemistries, including nickel manganese cobalt (NMC) and lithium iron phosphate (LFP), as well as a sodium-ion battery (SIB). Figure 1 shows the single-cell geometry, including clamps, used for the 3D CONVERGE simulations.

The cell is modeled as a single solid with an applied anisotropic thermal conductivity along the direction of the cell layers, and interfaces are defined between the components depicted in Figure 1 to allow heat transfer between them. The team used established thermal runaway mechanisms available in CONVERGE and calibrated them to accurately represent the thermal abuse within the cell.

In this blog post, we’ll focus on an NMC811 cell, for which the team employed the Ren mechanism.³ The team calibrated the mechanism using experimental data from a constant heating test, then validated the NMC model with the calibrated mechanism against experimental data from heat-wait-seek and nail penetration tests. 

Figure 2 compares the CONVERGE results with measurement data from three thermocouple positions for the constant heating and heat-wait-seek tests. Figure 3 shows the results of the nail penetration test, comparing CONVERGE with measurement data from the most representative thermocouple position. The CONVERGE results match well with the experimental data in all three cases, demonstrating that the calibrated mechanism is able to represent the thermal runaway behavior of the cell for different initiation methods, thus confirming its predictivity.

Thermal Propagation Simulations

With the single-cell validation completed, the team moved on to conduct thermal propagation studies in a seven-cell module, as shown in Figure 4. They employed the same calibrated Ren mechanism for the NMC811 cell chemistry from their single-cell studies. They looked at several different scenarios, including where the space around the cells within the housing was filled with either nitrogen or air, and the application of an insulating inter-cell element or immersion oil cooling to delay thermal propagation.

“In addition to the thermal runaway chemistry, CONVERGE offers a number of features that make these simulations possible,” says Kislaya. “CONVERGE’s autonomous meshing easily handles the complex battery pack geometries with no user meshing time, and Adaptive Mesh Refinement dynamically adjusts the mesh throughout the simulation to capture the complex physical phenomena at a lower computational cost. In addition, CONVERGE’s conjugate heat modeling allows us to analyze heat transfer between the solid and fluid domains, and its multi-phase modeling capabilities enable us to investigate liquid cooling techniques.”

Figure 5 shows the results for a case with air surrounding the cells and no thermal insulation applied. Thermal runaway is initiated in the center cell (cell 7) via nail penetration; the adjacent cells also go into thermal runaway immediately after the nail penetration occurs. CONVERGE is able to capture the timing and duration of the thermal propagation very well. While the predicted peak temperatures are lower than the measured peak values, the measured peak temperatures are considered mainly as gas temperatures and thus cannot be directly compared with the solid cell surface temperatures obtained from the simulation. The solid cell surface temperatures drive the processes inside the cell that ultimately result in thermal runaway, and the simulation captures these important values.

After validating the CONVERGE model for a case with immediate thermal propagation, IAV turned their attention to mitigation strategies. In this post, we’ll focus on the adoption of an insulating inter-cell element, but the results of oil cooling can also be found in Sens et al. 2024.² 

“One way you can prevent heat from transferring from one cell to another is by inserting a foam, for example, that is a thermal insulator between the cells,” says Alexander. “But such a foam also has challenges because it has an impact on the overall weight of the battery, it has an impact on cost, and so on. It’s a very complex problem, and that’s why it is crucial to be able to investigate different types of inter-cell materials with simulation.”

Figure 6 shows the impact of adding an inter-cell element on thermal propagation. Thermal runaway is once again triggered in the center cell (cell 7) via nail penetration. As you can see, the addition of the inter-cell element significantly delays thermal propagation to the adjacent cells. Overall, the CONVERGE simulations are able to represent well the progression of the thermal propagation, especially considering the immense complexity of the events occurring in the experimental setup that are not considered in these simulations, such as mechanical deformation, material melting, and material ejection out of the battery. The deviation between the measured and simulated total propagation duration is approximately 10%. 

Bringing It All Together

This successful collaboration brought together IAV’s extensive industry expertise and state-of-the-art testing facilities with CONVERGE’s predictive simulation capabilities. Together, IAV and Convergent Science developed and validated a numerical model to study thermal runaway and thermal propagation in battery modules. In the future, this methodology can be applied to different battery chemistries, module configurations, and thermal propagation mitigation strategies. Having a powerful and efficient method to study thermal propagation is a game-changer for industry, enabling manufacturers to meet legislative requirements and ensure the safety of electric vehicles for consumers, all while saving time and reducing development costs.

Learn more about this collaborative work in our joint webinar: A Cool Take on Hot EV Batteries: Navigating Thermal Propagation With CFD Based on Thermal Runaway Kinetics Modeling.

References

[1] United Nations, “UN Regulation No 100 – Uniform Provisions Concerning the Approval of Vehicles With Regard to Specific Requirements for the Electric Power Train,” E/ECE/Rev.2/Add.99/Rev.3.

[2] Sens, M., Fandakov, A., Mueller, K., von Roemer, L., Woebke, M., Tourlonias, P., Mueller, T., Burton, T., Srivastava, K., and Senecal, P.K., “From Thermal Runaway to No Thermal Propagation,” 45th International Vienna Motor Symposium, Vienna, Austria, Apr 24–26, 2024.

[3] Ren, D., Liu, X., Feng, X., Lu, L. Ouyang, M., Li, J., and He, X., “Model-Based Thermal Runaway Prediction of Lithium-Ion Batteries From Kinetics Analysis of Cell Components,”Applied Energy, 228, 633-644, 2018.

If you’ve ever talked to someone who works at Convergent Science, you will undoubtedly have heard us extolling the virtues of CONVERGE’s autonomous meshing. Got a complicated geometry? No problem! Moving boundaries? Easy! No time to waste on meshing? We’ve got you covered!

This enthusiasm, we would argue, is not unwarranted—CONVERGE’s autonomous meshing strategy was truly a novel innovation. So much so that when CONVERGE was first released, the Convergent Science founders were met with more than a little skepticism. As Convergent Science Co-Founder Kelly Senecal puts it, “Nobody believed us.” The founders had to prove the worth of this new feature, asking companies to provide their hardest geometry so they could see for themselves that, in a matter of minutes, the geometry could be up and running in CONVERGE.

Fast forward 16 years, and CONVERGE’s autonomous meshing has become the industry gold standard. As other CFD solvers are releasing their own versions of automated meshing, I wanted to find out what it is that makes CONVERGE’s autonomous meshing different. To do so, I sat down and talked with Kelly, one of the original developers of CONVERGE and, arguably, the number one fan of autonomous meshing. 

To start off, what exactly is autonomous meshing?

Autonomous meshing is truly automated meshing, in the sense that the user just has to supply a few parameters in the user interface, and all the actual meshing is done at runtime by CONVERGE. So it takes the meshing completely out of the hands of the user. You still have control over the mesh, though. As a user, you can define fixed embedding regions if you know ahead of time that you want to have fine resolution near a boundary, for example. CONVERGE also uses Adaptive Mesh Refinement—at every time-step the code is intelligently figuring out where mesh is needed and where mesh can be removed based on the flow physics to be very efficient with the cell count.

What prompted you, Keith, and Eric to develop an automated meshing approach?

We spent a lot of time during our graduate school days, and during the early days of Convergent Science back when it was a consulting company, making meshes for people in a code called KIVA. And even though we could do it relatively quickly—we had created tools to help us—it could still take days or even weeks to make a mesh for a complicated geometry. And when you’re an engineer, you want to spend your time running your CFD simulations, analyzing results, and using them to make design decisions; you don’t want to spend all your time making meshes. So that’s what motivated us. We thought there had to be a better way. 

What was the process like writing the code for autonomous meshing?

That’s a good question. Scary? Because we didn’t know if it was going to work or not. And we had some missteps. We originally based the code around an immersed boundary method, as opposed to the modified cut-cell Cartesian approach we use now. We thought the immersed boundary method could work, and we got something running—not quickly exactly, it probably took a year and a half to get the code up and running for a 3D engine simulation. But we realized it wasn’t going to work because it was hard to get that approach to conserve robustly. So we had to scrap essentially all the code we had written and go to this new approach. So that was a bit scary. And we still weren’t sure the new method was going to work. Originally, the automated meshing took minutes or even hours to create the mesh in the solver. We get that question a lot: “Doesn’t this take forever?” And originally, yes it did. One of the real breakthroughs we came up with was making that process almost instant. It adds very little time to the overall CFD calculation, even though we’re remaking the mesh entirely at each time-step. Once we had that eureka moment, we knew we were onto something big. And so it went from scary to very exciting. 

What makes CONVERGE’s autonomous meshing capabilities unique?

A lot of CFD codes these days throw around the automated meshing terminology fairly loosely, I would say. There are different levels of automated meshing out there, and how truly automated it is depends on a lot of factors, like how complicated your geometry is and whether or not you have moving boundaries. There are some codes that can do automated meshing for certain cases, but it’s really hard to be able to have automated meshing work in general for all cases. But that’s what we have. We have yet to find a case where we throw a geometry at CONVERGE and it isn’t able to mesh it. And so that’s what makes us unique—we have truly autonomous meshing for every case, no matter how complicated the geometry or the motion profiles are. 

What kinds of practical benefits do companies see as a result of CONVERGE’s autonomous meshing?

Really it’s about efficiency. Maybe you have a design out in the field that is having problems, and you want to use CFD to figure out what happened. Or maybe you want to design a brand new flow device from scratch using CFD. In the past, you’d spend a lot of time just making your mesh. And once you’ve made your mesh, the next question is, “How do you know if it’s fine enough? How do you know that you’re grid-converged?” It’s very hard to answer that question with traditional meshing techniques, because it’s so difficult to make the mesh in the first place, you’re probably not going to want to make another one. With autonomous meshing in CONVERGE, it’s very easy to make multiple meshes and show grid convergence. So again, it makes the process much more efficient. It also gives you confidence in your solutions because you can very easily double or triple the resolution and see how that affects your answer. Of course, you still have to run those simulations, so that takes some computer time. But the actual engineer time is minimal. So it’s much more efficient, you’re more confident in your solutions, and you get more accurate results. And in the end, that leads to a better design.

What applications benefit the most from autonomous meshing?

The ones that benefit the most are cases with complicated geometries and moving boundaries. That’s what’s hardest to do traditionally in CFD, and most real fluid devices are complicated. There are approaches that can handle moving geometries, but a lot of them add numerical error because you’re deforming the mesh near the boundary, for example. Whereas with our autonomous meshing technique, we recreate the mesh at every time-step while the motion is occurring, so we avoid those numerical artifacts. Autonomous meshing can also handle large differences in scales—maybe you have really tiny channels in your geometry as well as very large areas. CONVERGE can handle those very different scales efficiently, automatically putting fine resolution in the small channels and very coarse resolution in the large areas. So varying scales, complicated geometries, and moving boundaries benefit the most. But again, even simple geometries benefit because you’re not spending any time making the mesh. 

Are there any new meshing features currently in the works for CONVERGE?

In version 3.0, we released something called inlaid meshing. It’s not required for any simulation, but if you want to add a boundary layer mesh or a non-Cartesian mesh in a portion of your domain, you can do that through inlaid meshing. We already have all the tools implemented in the code to read those meshes and have them interface with the traditional cut-cell Cartesian mesh. What we’re working on now is automating the inlaid mesh generation similar to how we automate our traditional CONVERGE meshing. When this feature is implemented, the inlaid meshing will also be fully autonomous. 

If someone is interested in trying autonomous meshing out for themselves, what should they do?
Reach out to us! We have a variety of licensing options available, whether you want to use CONVERGE for commercial purposes, to conduct academic research, or to learn a new skill for your resume. We also offer on-demand licensing and access to computing hardware through our cloud computing platform, CONVERGE Horizon. We would love to work with you to find the right license for your needs so you can experience the power of autonomous meshing for yourself!

Co-Author:
Adam Dempsey

Assistant Professor, Marquette University

Co-Author:
Jared Zeman

Graduate Research Assistant, Marquette University

Co-Author:
Osama Nsaif

Graduate Research Assistant, Marquette University

The heartbeat of the global economy is commercial vehicles, and as the economy grows, so does the demand for on-road trucks, off-road construction equipment, and agricultural vehicles. These vehicles are almost entirely powered by compression ignition engines using fossil diesel fuel. In the face of the global climate crisis, this presents a real challenge: how do we provide efficient and productive commercial vehicle powertrains while reducing criteria and greenhouse gas (GHG) emissions? Full electrification of these vehicles faces many hurdles, such as cost, weight, operating hours, lack of infrastructure, and time to implementation. Thus, the most pragmatic and impactful way to reduce emissions in the near term is by using lower carbon intensity fuels, such as ethanol, methanol, natural gas, propane, hydrogen, or ammonia. 

Using these fuels in heavy-duty engines as a substitute for diesel fuel is very challenging, because these fuels are poor direct replacements for diesel fuel. These fuels all have very low cetane numbers, which means they are very hard to autoignite and more suitable to spark ignition (SI) engines. But SI engines are NOT suited to heavy-duty applications because of the knock-limited peak torque, potential for catastrophic pre-ignition when highly boosted, poor torque density, poor torque response, low thermal efficiency, high exhaust temperatures, and high heat rejection. The combustion process used in conventional diesel engines is lean, mixing-controlled combustion (MCC). It is highly desirable for heavy-duty vehicles to use an engine that employs this combustion strategy—regardless of the fuel—because the engine will maintain the performance and operational characteristics of a diesel engine, such as high efficiency, no fear of knock or pre-ignition, snap torque, high torque at low speed, low cyclic variability, and robust combustion. An engine that has these characteristics, we like to say, “runs like a diesel”, which all stems from the mixing-controlled combustion process. Thus, an innovative combustion system is needed that will allow low-cetane fuels to ignite readily and be used in a non-premixed MCC strategy, just like the diesel engine today.

Using the CONVERGE computational fluid dynamics (CFD) modeling software, our engine combustion research group at Marquette University has been working to develop such an innovation known as prechamber enabled mixing-controlled combustion (PC-MCC). Illustrated in Figure 1, the concept uses a conventional compression ignition engine with high-pressure direct injection and adds an actively fueled prechamber igniter. The igniter contains a fuel injector, a spark plug, a small prechamber volume, and orifice passageways between the prechamber and main chamber. The high-pressure direct injector and prechamber injector use the same low-cetane fuel source. Figure 2 shows the operational strategy of PC-MCC with ethanol fuel compared to conventional diesel combustion (CDC). During the compression stroke, the prechamber is fueled with ethanol, while air from the main chamber is forced into the prechamber by piston motion. Closely coupled to the direct injection timing near top dead center, the prechamber is sparked, and the prepared charge is burned by rapid flame propagation. This combustion process elevates the pressure of the prechamber and promotes hot jet flames that are ejected into the main chamber. The penetrating jets impinge and subsequently ignite the direct-injected ethanol fuel, which would otherwise not autoignite. 

As shown in Figure 3, the direct-injected ethanol, once ignited by the prechamber jet flames, burns in a mixing-controlled manner with a rate of combustion like that of diesel fuel. This is the ultimate goal: to allow the engine to “run like a diesel” by reproducing the diesel engine combustion process, but running on low-cetane fuels like ethanol, methanol, or even hydrogen and ammonia. 

An animation of the CFD-predicted PC-MCC combustion process with ethanol fuel is illustrated in Figure 4. The prechamber jet flames are ejected toward the direct-injected ethanol fuel, igniting the ethanol fuel sprays very quickly and establishing a mixing-controlled, diffusion-style combustion process that is typical of a modern diesel engine.

This concept is currently under development with the assistance of two federal grants. The first is from a United States Department of Energy Vehicle Technologies Office award (DE-EE0009872), where the concept is being developed to convert diesel engines to be flex-fuel and run on gasoline/ethanol, while maintaining performance and dramatically reducing GHG emissions. The second is from the Advanced Research Projects Agency Energy’s (ARPA-e) REMEDY program (DE-AR0001528), which aims to reduce methane emissions, a powerful GHG, from natural gas engines by radically changing the combustion process to PC-MCC. The institutions working on these projects together with Marquette University are John Deere, Mahle Powertrain, the University of Wisconsin-Madison, Czero, ClearFlame Engines, and the Missouri Corn Merchandising Council. The CFD modeling has led to several publications by the team, showing how the CONVERGE simulations were used to determine the prechamber’s characteristics—such as volume, number of holes, hole size, and jet targeting—and the general PC-MCC operating strategy for prechamber fueling, injection timing, spark timing, and direct injection timing [1-5].

The modeling tools provided by CONVERGE were quintessential for performing the detailed CFD modeling needed to develop this advanced combustion concept. CONVERGE’s automatic mesh generation dramatically reduces the simulation setup time and complexity, allowing for rapid simulation development and analysis with confidence in the meshing strategy. Further refinement to the mesh is achieved through fixed grid embedding in user-defined regions of interest within the domain and CONVERGE’s Adaptive Mesh Refinement (AMR), which is able to resolve cell-to-cell gradients in temperature and velocity. AMR is especially useful when modeling the combustion process within the prechamber, the resultant high-intensity jets, and subsequent jet-spray induced combustion process. The predicted combustion process is captured using CONVERGE’s detailed chemical kinetics solver SAGE, which is fully coupled to the flow solution for accurate results and efficient solution times. 

Based on the CFD modeling, a prototype PC-MCC engine was constructed and tested separately on pure ethanol fuel and natural gas, demonstrating a robust mixing-controlled combustion process with both fuels and highlighting the fuel-agnostic nature of the technology. Photos of the prototype hardware and recorded test data are shown in Figure 5. The tests with the prototype PC-MCC hardware corroborates the findings from the CONVERGE CFD simulations: that PC-MCC can be a fuel-agnostic, low-carbon engine technology for the future of heavy-duty engines, both on-road and off-road and for stationary power generation.  

 References

[1] Dempsey, A., Chowdhury, M., Kokjohn, S., and Zeman, J., “Prechamber Enabled Mixing Controlled Combustion – A Fuel Agnostic Technology for Future Low Carbon Heavy-Duty Engines,” SAE Paper 2022-01-0449, 2022. DOI: 10.4271/2022-01-0449

[2] Zeman, J., Yan, Z., Bunce, M., and Dempsey, A., “Assessment of Design and Location of an Active Prechamber Igniter to Enable Mixing-Controlled Combustion of Ethanol in Heavy-Duty Engines,” International Journal of Engine Research, 24(9), 4226-4250, 2023. DOI: 10.1177/14680874231185421

[3] Zeman, J., and Dempsey, A., “Characterization of Flex-Fuel Prechamber Enabled Mixing-Controlled Combustion With Gasoline/Ethanol Blends at High Load,” Journal of Engineering for Gas Turbines and Power, 146(8), 2024. DOI: 10.1115/1.4064453

[4] Nsaif, O., Kokjohn, S., Hessel, R., and Dempsey, A., “Reducing Methane Emissions From Lean Burn Natural Gas Engines With Prechamber Ignited Mixing-Controlled Combustion,” Journal of Engineering for Gas Turbines and Power, 146(6), 2024. DOI: 10.1115/1.4064454[5] Zeman, J., and Dempsey, A., “Numerical Investigation of Equivalence Ratio Effects on Flex-Fuel Mixing Controlled Combustion Enabled by Prechamber Ignition,” Applied Thermal Engineering, 249, 2024. DOI: 10.1016/j.applthermaleng.2024.123445.

Author:
Allie Yuxin Lin
Marketing Writer

Imagine you’re a CFD engineer and you want to run a combustion simulation for a certain kind of reacting flow device. But before you can do that, you need to find a chemical mechanism that can mathematically represent the chemistry within the reacting fluid. So you scour the available literature to find published mechanisms from third parties that fit your case conditions. This time-consuming and inefficient process prompted us, and other like-minded individuals across academia and industry, to seek a more consolidated alternative.

The Computational Chemistry Consortium (C3), the brainchild of Convergent Science owners Kelly Senecal, Dan Lee, Eric Pomraning, and Keith Richards, was established with the goal of creating a comprehensive and detailed mechanism that would serve as an all-inclusive solution for fuel combustion chemistry. Creating this repository of mechanisms would also help us investigate and develop alternative fuels to create more sustainable technologies. Professor Henry Curran from the University of Galway leads the consortium from the technical side, working with research groups whose respective areas of expertise complement each other, including the University of Galway, Lawrence Livermore National Laboratory, Argonne National Laboratory, Politecnico di Milano, and RWTH Aachen University. 

The summer of 2018 marked a milestone in combustion chemistry, as C3 officially kicked off. Following the directional guidance from a diverse group of industry partners, C3 develops chemical mechanisms that include pollutant chemistry like PAH and NOx, creates tools for generating surrogate and multi-fuel mechanisms, and improves reduction and merging tools. C3 operates with a top-down approach, featuring one large mechanism from which users can extract the specific chemistry for their fuel. This method allows C3’s technical team to validate the mechanism as a whole, rather than combine many small, independently-validated mechanisms. In December 2021, C3 published the first version of their mechanism, making it widely available to the combustion community. Since then, the mechanism has been integrated into our software, allowing you to combine the flexibility of C3 with the power of CONVERGE. 

To generate your fuel chemistry mechanism with CONVERGE, start by identifying all the individual components for your fuel surrogate. CONVERGE offers a surrogate blender tool where you can specify fuel properties such as viscosity, H/C ratio, octane number, distillation data, and ignition delay. The blender tool will then use mixing rules to match the specified fuel properties and come up with a fuel surrogate. Alternatively, the experienced user may choose to handpick certain fuel species according to information laid out in a test fuel’s spec sheet. 

After you’ve identified your fuel surrogate, you can use the extraction tool in CONVERGE Studio, which was designed specifically for the purpose of extracting fuel chemistry from the parent C3 mechanism. 

In most cases involving traditional hydrocarbon fuels, your extracted mechanism will have hundreds to thousands of species, which is far too many to use for a 3D CFD simulation. To ensure computational efficiency while maintaining solution accuracy, you should reduce your mechanism to a manageable size using CONVERGE’s mechanism reduction process.

A key component of this process in CONVERGE is the analysis of autoignition, extinction, speciation, and/or laminar flamespeed simulations. Therefore, before you can begin your reduction process, you must consider the specific conditions of your engine/combustor under which these simulations are evaluated. These operating conditions include pressure, unburnt temperature, equivalence ratio, and EGR fractions. For example, if your mechanism is meant to be used for a diesel engine simulation, you must select a pressure range from the start of injection to peak cylinder pressure. 

To reduce the number of species, a directed relation graph (DRG) will be constructed and error propagation (DRGEP) can be added for further precision. The DRGEP methodology works to remove species and corresponding reactions within the user-specified error bounds of ignition delay, extinction, speciation, and/or laminar flamespeed. Once the number of species is ~500, sensitivity analysis (SA) can be added to the existing DRGEP methodology for further reduction of species. The optimal resulting mechanism will have calculations that fall within a user-specified range and a reduced number of species, making these mechanisms practical for 3D combustion simulations. 

When you have obtained the optimal reduced mechanism, the reaction rates of the most sensitive reactions can be tuned to match specific targets of this mechanism to those of the parent mechanism. Similarly to the reduction process, these targets are speciation, extinction, laminar flamespeed, and/or ignition delay. You can tune your mechanism using CONVERGE’s mechanism tuning tools, such as NLOPT, an open-source library for nonlinear local and global optimization; the MONTE-CARLO method, which uses randomization to solve problems that may be deterministic in principle; or CONGO, CONVERGE’s in-house genetic algorithm optimization tool. These methods focus on the pre-exponential factor, A, or the activation energy in the Arrhenius reaction equation. 

After completing these steps, your reduced chemical mechanism is ready to be run in 3D CFD simulations. The flexibility, versatility, and ingenuity of C3 simplifies the process of modeling both traditional and alternative fuels in a variety of applications where combustion is involved. With C3, your days of manually searching through the literature for a specific mechanism are over. Welcome to a new era of ease! 

If you would like to help set the direction of future C3 efforts and have access to our mechanisms before they are publicly available, we invite you to join our consortium. To learn more, please contact C3 Director Dr. Kelly Senecal at senecal@fuelmech.org.

Author:
Allie Yuxin Lin
Marketing Writer

Introduction and Motivation

The trials of climate change and humanity’s desire to mitigate our carbon footprint is motivating the research and development of renewable technologies for the transportation and energy sectors. Hydrogen is a promising technology, with the potential to address issues in energy security, pollution, emissions reduction, and sustainability. Hydrogen is carbon free, abundant, and can be stored as a gas or a liquid, making it an important player in the transition toward a cleaner planet.

However, the challenges associated with devising safe and reliable storage methods delay the increase of hydrogen production. Hydrogen’s highly diffusive and corrosive nature makes it prone to leaking, while its unique thermodynamic properties, such as the negative Joule-Thompson effect, require engineers to rethink traditional storage infrastructure. Hydrogen’s low compressibility and extreme sensitivity to the environment mean tanks should both be strong enough to withstand high pressures and flexible enough to handle large temperature fluctuations. Tank design should also account for hot pockets formed due to the increased pressure when hydrogen is compressed, which could damage the tank’s structural integrity.

Computational fluid dynamics (CFD) can help overcome some of the hurdles associated with hydrogen storage. CFD provides insight into the behavior of various fluids and gasses in different environments, so it can be used to optimize the design of hydrogen fuel systems, including fuel cells, storage tanks, and delivery systems. CFD can be used to identify areas of improvement in the fuel system design and help pinpoint potential safety hazards.

CONVERGE for Hydrogen Storage Simulation: A Case Study

CONVERGE is a powerful CFD software whose unique capabilities make it advantageous for simulating hydrogen storage. Autonomous meshing removes the mesh generation bottleneck, while Adaptive Mesh Refinement (AMR) continuously adjusts the mesh throughout the simulation. Conjugate heat transfer (CHT) modeling solves for the heat exchange between the fluids and solids in the system. Additionally, CONVERGE provides multiple turbulence models to efficiently capture the flow dynamics within the storage unit.

Validating CONVERGE With the HyTransfer Project

The HyTransfer project¹ was funded by the European Union to study the physics in the hydrogen filling process, with the hopes of providing guidelines on how to achieve an efficient filling strategy. Using the framework and the experimental data publicly available for the HyTransfer project, we performed a validation study to showcase the value of CONVERGE in hydrogen storage.

The Hexagon 36 L Type IV tank consists of a polymer liner encased in a composite wrapping, providing the necessary structural length to withstand large pressures up to 70 MPa. A liner thickness of 4 mm and an injector diameter of 10 mm were chosen for the study. In line with existing literature,^2,3,4 we simulated half the horizontal tank domain, reducing overall computational cost. We provided the inlet mass flow rate profile and monitored the development of the tank pressure from the initial state.

CONVERGE Simulation Setup

CONVERGE’s graphical user interface, CONVERGE Studio, allows users to take advantage of a wide variety of geometry manipulation and repair tools during case setup. Since hydrogen’s behavior is known to deviate from ideal gas representations, CONVERGE provides the option to use real gas properties.

With the density-based PISO solver, we were able to achieve rapid convergence while simultaneously capturing the heat fluxes between different regions with CONVERGE’s CHT analysis.

As the injector diameter used for hydrogen tanks typically ranges from 3–10 mm, the incoming flow velocity can reach up to 300 m/s. High jet penetration plays a critical role in maintaining circulation within the tank, and mesh embedding used in conjunction with CONVERGE’s AMR can capture the jet profile with a high degree of accuracy.

To get the appropriate jet penetration and reduce the spreading over-prediction, the RNG k-epsilon constant in the turbulence model was modified according to past research.^2,3,4

Our Results

Figure 1 shows the velocity contours of the hydrogen jet during the filling process. The velocity decreases rapidly as the filling progresses due to the compression of hydrogen. The flapping motion of the jet is related to flow circulation within the tank, which is important when considering the redistribution of thermal gradients. In our study, this circulation caused hot pockets to form near the injection side of the tank.

The temperature profiles within the tank agree with the experimental data (Figure 2), demonstrating CONVERGE can capture the complex thermodynamics of hydrogen. The reading at thermocouple 5 (TT5) is higher than thermocouple 1 (TT1) because long filling times can cause thermal stratification. As the filling progresses, the jet velocity decreases and the circulation within the tank stabilizes.

The ideal material for a hydrogen storage tank is lightweight with excellent thermal integrity and strength. CONVERGE’s CHT analysis enables engineers to assess the thermal gradients within the tank’s structure and helps in the proper selection of tank materials. Figure 3 shows the predicted temperature at the liner-composite interface compared to the experiment, demonstrating CONVERGE can also be used to capture the tank’s internal thermal behavior.

Toward a More Sustainable Future: Concluding Remarks

Using CONVERGE, we simulated the flow dynamics and thermal behavior during the hydrogen filling process; our results aligned well with previous experimental data.¹ We assessed major flow features and identified recirculating vortical structures caused by the fluctuating behavior of the jet. CONVERGE accurately captured temperature profiles inside the tank, and its CHT capabilities predicted the liner-composite interface temperature.

The ease of use, flexibility, accuracy, and rich set of features make CONVERGE a highly effective tool for studying hydrogen tank storage. Check out our white paper, “Exploring Hydrogen Tank Filling Dynamics,” to learn more about how CONVERGE is helping engineers tackle an important challenge of the modern era!

References

[1] Ravinel, B., Acosta, B., Miguel, D., Moretto, P., Ortiz-Cobella, R., Janovic, G., and van der Löcht, U., “HyTransfer, D4.1 – Report on the experimental filling test campaign,” 2017.

[2] Melideo, D. and Baraldi, D., “CFD analysis of fast filling strategies for hydrogen tanks and their effects on key-parameters,” International Journal of Hydrogen Energy, 40, 735-745, 2015.

[3] Melideo, D., Baraldi, D., Acosta-Iborra, A., Cebolla, R.O., and Moretto, P., “CFD simulations of filling and emptying of hydrogen tanks,” International Journal of Hydrogen Energy, 42, 7304-7313, 2017.

[4] Gonin, R., Horgue, P., Guibert, R., Fabre, D., and Bourget, R., “A computational fluid dynamic study of the filling of a gaseous hydrogen tank under two contrasted scenarios.” International Journal of Hydrogen Energy, 47(55), 23278-23292, 2022.

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 common question from Tecplot 360 users centers around the hardware that they should buy to achieve the best performance. The answer is invariably, it depends. That said, we’ll try to demystify how Tecplot 360 utilizes your hardware so you can make an informed decision in your hardware purchase.

Let’s have a look at each of the major hardware components on your machine and show some test results that illustrate the benefits of improved hardware.

Test data

Our test data is an OVERFLOW simulation of a wind turbine. The data consists of 5,863 zones, totaling 263,075,016 elements and the file size is 20.9GB. For our test we:

• Load the data.
• Compute Q-Criterion.
• Display an iso-surface of Q-Criterion (the resulting iso-surface consists of 32,248,635 triangular elements).
• Export an image to PNG format.

The test was performed using 1, 2, 4, 8, 16, and 32 CPU-cores, with the data on a local HDD (spinning hard drive) and local SSD (solid state disk). Limiting the number of CPU cores was done using Tecplot 360’s ––max-available-processors command line option.

Data was cleared from the disk cache between runs using RamMap.

Machine Specs

• Windows 10
• 32 logical (16 physical) CPU cores. Intel Xeon E5-2650 v2 @ 2.60GHz
• ATA ST2000DM001 Spinning Hard Disk
• ATA INTEL SSDSC2BA40 Solid State Disk
• Intel Gigabit Ethernet Adapter
• 128GB DDR3 RAM
• Nvidia Quadro K4000 graphics card

Disk

Advice: Buy the fastest disk you can afford.

In order to generate any plot in Tecplot 360, you need to load data from a disk. Some plots require more data to be loaded off disk than others. Some file formats are also more efficient than others – particularly file formats that summarize the contents of the file in a single header portion at the top or bottom of the file – Tecplot’s SZPLT is a good example of a highly efficient file format.

We found that the SSD was 61% faster than the HDD when using all 32 CPU-cores for this post-processing task.

All this said – if your data are on a remote server (network drive, cloud storage, HPC, etc…), you’ll want to ensure you have a fast disk on the remote resource and a fast network connection.

With Tecplot 360 the SZPLT file format coupled with the SZL Server could help here. With FieldView you could run in client-server mode.

CPU

Advice: Buy the fastest CPU, with the most cores, that you can afford. But realize that performance is not always linear with the number of cores.

Most of Tecplot 360’s data compute algorithms are multi-threaded – meaning they’ll use all available CPU-cores during the computation. These include (but are not limited to): Calculation of new variables, slices, iso-surfaces, streamtraces, and interpolations. The performance of these algorithms improves linearly with the number of CPU-cores available.

You’ll also notice that the overall performance improvement is not linear with the number of CPU-cores. This is because loading data off disk becomes a dominant operation, and the slope is bound to asymptote to the disk read speed.

You might notice that the HDD performance actually got worse beyond 8 CPU-cores. We believe this is because the HDD on this machine was just too slow to keep up with 16 and 32 concurrent threads requesting data.

It’s important to note that with data on the SSD the performance improved all the way to 32 CPU-cores. Further reinforcing the earlier advice – buy the fastest disk you can afford.

RAM

Advice: Buy as much RAM as you need, but no more.

You might be thinking: “Thanks for nothing – really, how much RAM do I need?”

Well, that’s something you’re going to have to figure out for yourself. The more data Tecplot 360 needs to load to create your plot, the more RAM you’re going to need. Computed iso-surfaces can also be a large consumer of RAM – such as the iso-surface computed in this test case.

If you have transient data, you may want enough RAM to post-process a couple time steps simultaneously – as Tecplot 360 may start loading a new timestep before unloading data from an earlier timestep.

The amount of RAM required is going to be different depending on your file format, cell types, and the post-processing activities you’re doing. For example:

• A structured dataset will require less RAM than an unstructured dataset because structured data has implicit cell connectivity, while unstructured data has an explicit cell connectivity, which requires more RAM. For example, we compared a 100 million cell structured vs equivalent unstructured dataset, plotting one slice and one iso-surface. The peak RAM required by Tecplot 360 2022 R2 in this case was as such:
  • Structured dataset: 2.1GB RAM
  • Unstructured dataset: 8.8GB RAM
• A simple plot of your surface data colored by a scalar is going to require less RAM than computing Q-Criterion and rendering an iso-surface. Why? Because computing Q-Criterion requires loading the volume data, plus several scalars. And then plotting the iso-surface requires the generation of new data in RAM.

When testing the amount of RAM used by Tecplot 360, make sure to set the Load On Demand strategy to Minimize Memory Use (available under Options>Performance).

This will give you an understanding of the minimum amount of RAM required to accomplish your task. When set to Auto Unload (the default), Tecplot 360 will maintain more data in RAM, which improves performance. The amount of data Tecplot 360 holds in RAM is dictated by the Memory threshold (%) field, seen in the image above. So you – the user – have control over how much RAM Tecplot 360 is allowed to consume.

Graphics Card

Advice: Most modern graphics cards are adequate, even Intel integrated graphics provide reasonable performance. Just make sure you have up to date graphics drivers. If you have an Nvidia graphics card, favor the “Studio” drivers over the “Game Ready” drivers. The “Studio” drivers are typically more stable and offer better performance for the types of plots produced by Tecplot 360.

Many people ask specifically what type of graphics card they should purchase. This is, interestingly, the least important hardware component (at least for most of the plots our users make). Most of the post-processing pipeline is dominated by the disk and CPU, so the time spent rendering the scene is a small percentage of the total.

That said – there are some scenes that will stress your graphics card more than others. Examples are:

• Showing lots of spherical scatter symbols
• Many iso-surfaces or a complex iso-surface

Note that Tecplot 360’s interactive graphics performance currently (2023) suffers on Apple Silicon (M1 & M2 chips). The Tecplot development team is actively investigating solutions.

Summary

As with most things in life, striking a balance is important. You can spend a huge amount of money on CPUs and RAM, but if you have a slow disk or slow network connection, you’re going to be limited in how fast your post-processor can load the data into memory.

So, evaluate your post-processing activities to try to understand which pieces of hardware may be your bottleneck.

For example, if you:

• Load a lot of timesteps and render simple objects like slices or just surfaces, your process is dominated by I/O – consider a fast disk or network connection.
• Have a process that is compute heavy – like creating complicated iso-surfaces, computing new variables, or doing interpolations – consider more CPU cores.
• Render a lot of images for a single dataset – for example multiple view angles of the same dataset, your process will spend a lot of time rendering – consider a higher-end GPU.

And again – make sure you have enough RAM for your workflow.

Try Tecplot 360 for Free

The post What Computer Hardware Should I Buy for Tecplot 360? appeared first on Tecplot Website.

Members of Tecplot 360 & FieldView teams exhibit together at AIAA SciTech 2023. From left to right: Shane Wagner, Charles Schnake, Scott Imlay, Raja Olimuthu, Jared McGarry and Yves-Marie Lefebvre. Not shown are Scott Fowler and Brandon Markham.

It’s been a pleasure seeing two groups that were once competitors come together as a team, learn from each other and really enjoy working together.

– Yves-Marie Lefebvre, Tecplot CTO & FieldView Product Manager.

Our customers have seen some of the benefits of our merger in the form of streamlined services from the common Customer Portal, simplified licensing, and license renewals. Sharing expertise and assets across teams has already led to the faster implementation of modules such as licensing and CFD data loaders. By sharing our development resources, we’ve been able to invest more in new technology, which will soon translate to increased performance and new features for all products.

Many of the improvements are internal to our organization but will have lasting benefits for our customers. Using common development tools and infrastructure will enable us to be as efficient as possible to ensure we can put more of our energy into improving the products. And with the backing of the larger organization, we have a firm foundation to look long term at what our customers will need in years to come.

We want to thank our customers and partners for their support and continued investment as we endeavor to create better tools that empower engineers and scientists to discover, analyze and understand information in complex data, and effectively communicate their results.

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The post FieldView joins Tecplot.com – Merger Update appeared first on Tecplot Website.

The post Faster Visualization of Higher-Order Finite-Element Data appeared first on Tecplot Website.

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

Get a Free Trial   Update Your Software

The post Webinar: Tecplot 360 2022 R2 appeared first on Tecplot Website.

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 Website.

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

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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.

If you found this useful, and have the need for more, visit www.stfsol.com. One of STF Solutions specialties is providing our clients with custom software developed for their needs. Ranging from custom CFD codes to simpler targeted codes, scripts, macros and GUIs for a wide range of specific engineering purposes such as pipe sizing, pressure loss calculations, heat transfer calculations, 1D flow transients, optimization and more. Visit STF Solutions at www.stfsol.com for more information!

Disclaimer: This calculator is for educational purposes and is free to use. STF Solutions and curiosityFluids makes no guarantee of the accuracy of the results, or suitability, or outcome for any given purpose.