Let’s get Navier-Stoked because it’s Friday and time to review recent news from the world of computational fluid dynamics. I want to draw your attention to a few specific articles beginning with the importance of stupidity. In case you couldn’t … Continue reading →

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

The times of drawing ships by hand are over as CAD vessel designs become more complex to reduce fuel emissions. Though maritime transport is one of the most energy-efficient modes of transportation, it is also a large and growing source … Continue reading →

The post Webinar: Making the Shipping Sector Greener by Leveraging CFD first appeared on Another Fine Mesh.

Hi, I’m Reza Djeddi and I’m a Lead CFD Application Engineer at Cadence Design Systems. Some of my friends call me “The Jedi” because of how they think my last name is pronounced and the fact that I’m a big … Continue reading →

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

This week’s agglomeration of CFD flotsam and jetsam is chock full of wonderful images including an “image of the week” that qualifies for “image of the year.” There are several articles involving ML including one that’s cautionary. There are also … Continue reading →

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

Through further enhancements to our Flashpoint goal-oriented and intelligent automatic surface meshing framework and a new automatic volume meshing mode, one can continue to build upon established best practices throughout all stages of the meshing process. In addition, we have … Continue reading →

The post Webinar: Accelerated CFD Meshing with Fidelity Pointwise first appeared on Another Fine Mesh.

Predicting aerodynamic flow physics around automotive vehicles is a complex endeavor, often requiring engineers to balance cost and accuracy. While steady-state approaches (such as RANS) are attractive for their low computational cost, they usually fail to predict all flow phenomena … Continue reading →

The post Webinar: Predicting Aerodynamic Flow Around Automotive Vehicles first appeared on Another Fine Mesh.

In Susi Sie’s “Haut” the camera seems to fly over ever-shifting landscapes. In reality, these are macro images, created (I think) by dyes and patterns atop a water bath. But they look like vistas we could find on Earth or Mars — giant dune fields, calving glaciers, and river-divided canyons. For something similar in color, check out Roman De Giuli’s “Geodaehan.” (Video credit: S. Sie)

In a typical wind farm, each wind turbine aligns itself to the local wind direction. In an ideal world where every turbine was completely independent, this would maximize the power produced. But with changing wind directions and many turbines, it’s inevitable that upstream wind turbines will interfere with the flow their downstream neighbors see.

So, instead, a research team investigated how to optimize the collective output of a wind farm. Their strategy involved intentionally misaligning the upstream wind turbines to improve conditions for downstream turbines. They found that the loss in power generation by upstream turbines could be more than recovered by improved performance downstream.

After testing their models over many months in an actual wind farm, they reported that their methodology could, on average, increase overall energy output by about 1.2 percent. That may sound small, but the team estimates that if existing wind farms used the method, it would generate additional power equivalent to the needs of 3 million U.S. households. (Image credit: N. Doherty; research credit: M. Howland et al.; via Boston Globe; submitted by Larry S.)

Parallel lines of cumulus clouds stream over the Labrador Sea in this satellite image. These cloud streets are formed when cold, dry winds blow across comparatively warm waters. As the air warms and moistens over the open water, it rises until it hits a temperature inversion, which forces it to roll to the side, forming parallel cylinders of rotating air. On the rising side of the cylinder, clouds form while skies remain clear where the air is sinking. The result are these long, parallel cloud bands. (Image credit: J. Stevens; via NASA Earth Observatory)

Europa is an ocean world trapped beneath an ice shell tens of kilometers thick. To better understand what we might find in those oceans, researchers turn to analogs here on Earth, looking at Antarctica’s ice shelves. Beneath those shelves, ice forms via two mechanisms: the first, congelation ice, freezes directly onto the existing ice-water interface. The second, frazil ice, forms crystals in supercooled water columns, which drift upward in buoyant currents and settle on the ice shelf like upside-down snow (pictured above).

Based on Europa’s conditions, the researchers conclude that congelation ice would gradually thicken the ice shell as the moon’s interior cools. But in areas where the shell is thinned by local rifts and Jovian tidal forces, frazil ice is likely to form. (Image credit: H. Glazer; research credit: N. Wolfenbarger et al.; via Physics World)

Two spheres dropped into water next to one another form asymmetric cavities. A single ball’s cavity is perfectly symmetric, and so are two spheres’, provided they are far enough apart. But for close impacts, the spheres influence one another, creating a mirror image. The same asymmetric cavity also forms when a sphere is dropped near a wall. In fluid dynamics, this trick — using two mirrored objects in place of a wall — is used to make calculating certain flows easier! (Image credit: A. Kiyama et al.)

These recent composite images from the James Webb Space Telescope show Jupiter in stunning infrared detail. They’re the result of several images taken in different infrared bands, then combined and rendered in visible light. In general, the redder colors show longer wavelengths and the bluer ones show shorter wavelengths.

Jupiter’s cloud bands appear in beautiful detail. The Great Red Spot looks white in infrared. And the planet’s polar auroras shine bright in both images. The wide-angle shot additionally shows two of Jupiter’s moons and the planet’s rings, which are a million times fainter than the planet itself. If you look carefully, you may also see faint points of light in the lower half of the image. These are likely distant galaxies “photobombing” Jupiter’s close-up. (Image credit: NASA/ESA/Jupiter ERS Team 1, 2; via Colossal)

Figure 1: Hexahedral mesh for an aircraft icing surface.

1228 words / 6 minutes read

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

Introduction

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

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

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

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

Ice Accretion Analysis

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

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

Complex Geometric Shapes

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

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

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

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

Wide-Ranging Geometric Scales

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

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

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

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

Horns and Crevices

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

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

Boundary-Layer Mesh

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

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

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

Meshing Transient Ice Accumulation

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

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

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

Parting Remarks

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

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

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

Further Reading

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

References

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

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

804 words / 4 minutes read

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

Introduction

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

Scroll Compressor CFD Challenges

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

Geometric Challenges for meshing

Deforming Flow Field:

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

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

Flank Clearance:

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

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

Axial Clearance:

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

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

Tip Seal Modeling:

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

Discharge Check Valve Modeling:

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

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

Influence of Mesh Element Type

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

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

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

Parting Remarks

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

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

References

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

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

1167 words / 5 minutes read

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

Introduction

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

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

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

Meshing Strategy

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

Mesh Topology

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

Advantages of Topology based Meshing:

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

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

Flank Clearance and it’s Meshing Needs

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

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

Solid Scroll Meshing for FSI

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

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

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

Automation and Optimization of Scroll Compressor

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

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

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

The key features are:

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

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

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

Parting Remarks

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

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

Contact GridPro

References

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

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

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

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About GridPro Version 8.1 

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

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

Here is a quick Preview of the Major Features:

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

Major Highlights of Version 8.1

License Monitoring System

Network / Float Licenses

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

Node-locked / Served Licenses

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

Smart Face Groups to Enhance user workflow in GridPro

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

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

Topology now has Face Display for Better Visualization

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

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

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

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

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

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

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

Supported Platforms

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

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

GridPro AZ will be discontinued from version 9 onwards.

Download

GridPro Version 8.1 can be downloaded by registering here.

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

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

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Figure 1: Structured multi-block grid for turbopumps.

1050 words / 5 minutes read

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

The Rocket Challenge

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

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

Failed Missions

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

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

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

Why Turbopump in Rockets?

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

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

Challenges in Turbopump Design

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

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

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

Importance of Inducer in Turbopumps

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

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

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

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

Parting Remarks

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

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

Further Readings

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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Figure 1: Structured multi-block meshing for aircraft vortex generators – the nacelle strakes.

1100 words / 5 minutes read

Introduction

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

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

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

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

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

Effectiveness of strake installation

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

Parametric design of nacelle strake

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

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

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

Parting thoughts

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

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

Further Reading

1. Engine Nacelle Aerodynamics
2. Role of Vortex Generators in Diffuser S-Ducts of Aircraft

References

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

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

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

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

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

What we learn from the aerodynamics:

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

Stallion 3D strengths are:

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

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

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

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

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

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

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

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

Figure 2. Enstrophy histories in a p-refinement study

Happy 2021!

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

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

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

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

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

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

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

(a) hpMusic 3rd Order, 9.6M DOFs

(b) Commercial Solver, 2nd Order, 28.7M DOFs

Figure 1. Comparison of Q-criterion and Schlieren  

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

Happy 2018!     

Tamara Gammaidoni

We’re thrilled to announce that Tamara Gammaidoni, a graduate student at the Università degli Studi di Perugia, has won the 2022 CONVERGE Academic Competition. The competition challenged students to design and run a novel CONVERGE simulation that demonstrated significant engineering knowledge, accurately reflected the real world, and represented progress for the engineering community.

“We are incredibly proud of the work produced by the students competing in this year’s CONVERGE Academic Competition,” said Hannah Leystra, University Relationship Specialist and competition director at Convergent Science. “Tamara’s winning project is especially noteworthy. She took on a challenging, globally relevant problem and developed a thoughtful and insightful simulation.”

Tamara is pursuing a master’s degree in mechanical engineering. “CFD is something that always fascinated me since the beginning of my studies,” she said. “Fortunately in my master’s program, we have a professor of fluid dynamics, Michele Battistoni, who introduced us to CONVERGE.”

For the academic competition, Tamara investigated an air-cooled battery pack (Figure 1). “The automotive industry is rapidly evolving, and much of the focus is on electric vehicles,” Tamara said. She was first introduced to the problem of battery thermal management a few years ago on her Formula SAE UniPG Racing Team, where part of their pilot project was studying battery cooling for an electric vehicle. Properly cooling a battery pack is key for optimal performance and to ensure safe operating conditions.

“The main goal of my project was to determine which parameters most affect the simulation. Because simulating a real battery pack can be time consuming and expensive, it’s important to know which parameters are useful to change and optimize,” Tamara said.

The battery pack geometry, material properties, and thermal data used as inputs came from an experimental setup by the University of Singapore.¹ Tamara conducted a steady-state simulation using CONVERGE’s super-cycling feature to reduce the computational cost. In addition, Tamara employed Adaptive Mesh Refinement to capture large gradients in the temperature field, and she applied fixed embedding around the battery cells and copper bus bars to increase the simulation accuracy. 

With this case setup, Tamara explored several different parameters, starting with the wall treatment for the battery surfaces and for the surrounding casing. Based on the y+ value, she concluded that enhanced wall treatment would be the most accurate model for the battery surfaces. Indeed, when she compared the results between standard wall treatment and enhanced wall treatment, she found that standard wall treatment under-predicted the average temperature of the battery cells (Figure 2). 

For the casing, Tamara tested three different boundary conditions: convection, adiabatic, and law of the wall. Figure 3 shows that these boundary conditions had a significant effect on the battery temperature. For this simulation, Tamara determined that the convection boundary condition was the most realistic.

Next, Tamara investigated the effect of grid size on her results. She determined that a base grid size of 0.004×0.004×0.004 m provided sufficient resolution, as her results didn’t change significantly with a more refined grid. In addition, Tamara tested out CONVERGE’s inlaid meshing feature to add a boundary layer mesh around the battery cells. She found that the inlaid mesh approach didn’t provide additional benefits for her case, so she opted to stick with fixed embedding. 

Having determined the optimal parameters for her case, Tamara compared the average battery cell temperatures obtained from her CONVERGE simulation with experimental measurements from the University of Singapore.¹ As you can see in Figure 4, the data matched well across a range of mass flow rates.

The video below, provided courtesy of Tamara, presents a visual overview of her simulation, including a closer look at the geometry, mesh, and temperature and velocity results.

“Overall, this project provides useful information on how to make a valid model to simulate an air-cooled battery pack,” Tamara said. “In the future, the simulation could be improved by adopting the anisotropic conductivity and electric potential models.” 

This work was made possible by Prof. Michele Battistoni and Dr. Jacopo Zembi, who introduced Tamara to CONVERGE and gave her the opportunity to run her simulation on the Università degli Studi di Perugia cluster. 

Tamara is now beginning to work on her master’s thesis, which will focus on conjugate heat transfer modeling of an electric motor using CONVERGE. We look forward to seeing more of her impressive work in the future!

To receive updates about upcoming CONVERGE Academic Competitions, please email capcompetition@convergecfd.com. 

References

[1] Saw, L.H., Ye, Y., Tay, A.A.O., Chong, W.T., Kuan, S.H., and Yew, M.C., “Computational Fluid Dynamic and Thermal Analysis of Lithium-Ion Battery Pack With Air Cooling,” Applied Energy, 177, 783-792, 2016. DOI: 10.1016/j.apenergy.2016.05.122

I grew up in a port town on Lake Superior, the largest of the Great Lakes. As a kid, I would watch, spellbound, as cargo ships pulled into the harbor, amazed by their sheer looming size. As an adult, I’m no less in awe. These great metal giants can be laden with a hundred thousand tons of goods and conveyed through the water by propellers as tall as two- or three-storey buildings—truly a marvel of engineering. Moreover, cargo ships are vital to the global economy, transporting some 90% of goods worldwide. Shipping is by far the greatest enabler of global trade, but the industry is having to contend with tightening emissions regulations. To both meet demand and comply with legislation, designing increasingly efficient and effective marine propulsion systems is imperative—and one of the core factors in ship performance is the propeller.

CONVERGE CFD software offers many advantages for analyzing and optimizing propeller designs. With fully autonomous meshing, CONVERGE quickly generates a high-quality computational mesh for even the most complex propeller geometry. CONVERGE employs a stationary mesh that is regenerated locally at each time step to seamlessly accommodate the propeller motion. In addition, CONVERGE includes robust models for multi-phase flows, fluid-structure interaction, and cavitation—all the tools you need to assess propeller performance. 

In this article, we’ll discuss how we applied CONVERGE to several different propeller cases. We’ll start with validation of CONVERGE’s steady-state and transient modeling capabilities on the Potsdam propeller test case (PPTC), in which the propeller is fully submerged. Then, we’ll apply CONVERGE to a more physically complex surface-piercing propeller simulation.

PPTC: Steady-State Analysis

For our first validation case, we ran a steady-state simulation of the SVA Potsdam controllable pitch propeller VP1304 (Figure 1) and compared the results to published experimental data.¹ The experimental measurements were obtained from open water tests carried out in a towing tank with a constant propeller rotational speed of 15 rps. The inflow velocity was varied to test different advance coefficients (J), and the thrust and torque of the blades were measured for each run. From these measurements, the open water characteristics were established as the thrust coefficient (K_T), torque coefficient (K_Q), and open water efficiency (η₀).

For our CONVERGE simulations, we employed the k-ω SST turbulence model, velocity-based Adaptive Mesh Refinement (AMR), and the multiple reference frame (MRF) approach for the moving geometry. 

Figure 2 shows the experimental and numerical thrust coefficients, torque coefficients, and open water efficiencies for varying advance coefficients. The CONVERGE results match the experimental data well across the full range of advance coefficients. 

PPTC: Transient Analysis

Following the steady-state PPTC validation, we ran a transient simulation to compare the predicted velocity field with published experimental measurements.² Laser Doppler velocimetry (LDV) measurements were carried out in a cavitation tunnel, and the velocity was measured at several locations. 

CONVERGE’s autonomous meshing allowed us to resolve the rotating propeller geometry. As with the steady-state case, we used the k-ω SST turbulence model and velocity-based AMR. In the video below (Figure 3), the isosurfaces represent vorticity, and the mesh is shown on a plane perpendicular to the axis of the propeller. You can see how CONVERGE’s autonomous meshing accommodates the propeller motion and how AMR adjusts the resolution to capture the velocity field. 

Figure 4 shows the experimental and numerical velocity results for two different radial positions at the plane x/D = 0.2. CONVERGE accurately captures the axial, tangential, and radial velocity trends. 

Surface-Piercing Propeller

Having validated that CONVERGE can accurately predict key performance factors for a submerged propeller, we then moved on to a more complicated scenario: a surface-piercing propeller. For this study, we used the same SVA Potsdam controllable pitch propeller VP1304 geometry. We took advantage of CONVERGE’s volume of fluid (VOF) approach, solved using the individual species solution method, to simulate the multi-phase flow. In addition, we applied a surface compression technique to track the air-water interface. As with the previous cases, we relied on CONVERGE’s autonomous meshing for easy case setup and AMR to help capture the important physical phenomena. In the video below (Figure 5), you can see that CONVERGE is able to capture the complex wake structure and maintain a sharp air-water interface.

Conclusion

This study validated CONVERGE’s modeling capabilities for propeller simulations and demonstrated the software’s utility for complex cases. Incorporating CONVERGE’s fluid-structure interaction and cavitation modeling will enable more holistic studies of ship propulsion and propeller wear. Overall, CONVERGE is a powerful tool for assessing propeller performance, and autonomous meshing makes it easy to test different propeller designs. If you’re interested in trying CONVERGE for your own propeller simulations, contact us below!

References

[1] Barkmann, U.H., “Potsdam Propeller Test Case (PPTC): Open Water Tests With the Model Propeller VP1304,” Schiffbau-Versuchsanstalt Potsdam GmbH Report 3752, 2011. https://www.sva-potsdam.de/wp-content/uploads/2016/04/SVA_report_3752.pdf

[2] Mach, K.-P., “Potsdam Propeller Test Case (PPTC) – LDV Velocity Measurements With the Model Propeller VP1304,” Schiffbau-Versuchsanstalt Potsdam GmbH Report 3754, 2011. https://www.sva-potsdam.de/wp-content/uploads/2016/03/SVA-report-3754.pdf

Author:
Eileen Wagner
Research Engineer

Interest in home improvement has soared since the start of the pandemic, along with demand for the requisite tools. Saws, drills, sanders, and routers—what kind of motor do they use? Ideally, one that is powerful, easy to control, lightweight, affordable, robust, and low maintenance. In practice, no single motor meets all of these requirements.

Brushed DC motors are commonly used in tools because they are cheap and the speed and torque can be easily adjusted. However, they require frequent maintenance due to brush wear. Brushless motors are another option, and they provide high efficiency and power density. A drawback of brushless motors is that they rely on rare-earth permanent magnets, which are costly, not suitable for high speed rotation, and susceptible to damage at high temperatures. These limitations have spurred a renewed interest in alternative designs, including switched reluctance machines.

Though switched reluctance motors (SRMs) date back to the mid-nineteenth century, they never gained widespread use. A major reason for that was the lack of precise controllers. Now, with the availability of improved electronics, SRMs are getting a second look. SRMs are unique in that the windings are placed on the stator instead of the rotor. This enables a simplified, rugged design and low-cost manufacturing. In addition, they lack permanent magnets, which helps to further reduce costs. To operate, the current in the stator windings is switched from pole to pole, generating a rotating magnetic field. The rotor poles seek to align with the moving magnetic field and follow the path of least reluctance to drive motor spinning. One potential problem with SRMs is low torque density, which can be overcome by increasing the current and, inadvertently, heat. Because heat can degrade the windings and insulation and, over time, reduce performance and efficiency, accurate simulations are crucial for guiding the development of improved designs.

What improvements can be made to the conventional SRM to efficiently power a hand-held tool? In this blog post, we present the results of a collaborative effort between JMAG, the Shibaura Institute of Technology, and Convergent Science to evaluate a self-cooling SRM. This motor has a non-axisymmetric salient pole rotor with five poles and a segmented stator with six slots. The spinning rotor generates wind to cool the stator and windings. No fan is required due to the self-cooling effect, enabling an increase in motor volume and torque while retaining a small size. The motor was designed and characterized at Shibaura.¹ JMAG was used to calculate electromagnetic (copper and iron) losses, which were applied as heat sources in CONVERGE to predict the temperature rise in the solid components and model the self-cooling effect of rotor spinning.

CONVERGE is well-suited to electric motor cooling simulations. CONVERGE’s coupling with JMAG enables seamless import of the NASTRAN geometry file that includes the computed electromagnetic losses. Conjugate heat transfer modeling is highly efficient with transient super-cycling time control and completes in a fraction of the time required for a fully transient calculation. And finally, CONVERGE offers superior grid control capabilities with autonomous meshing and Adaptive Mesh Refinement. These features allow high-speed motion of the rotor and dynamic air flow patterns to be captured with ease.

The copper loss in the windings and the iron losses in the stator and rotor (calculated in JMAG) were modeled as volumetric heat sources in CONVERGE. A separate case where a 12A current was applied to the windings in the absence of rotor spinning was simulated to determine the contact resistance between the windings and the stator (Figure 1).

Next, the self-cooling effect simulated in CONVERGE was compared to experimental results. In the high load case, self-cooling is minimal, while under low load, the effect is more pronounced. In all cases, the CONVERGE simulations match the experimental measurements within 5°C (Figure 2).

What about air flow? In this animation, the air flow path in the wake region is depicted with velocity vectors (Figure 3). Air enters from the radial direction and flows through the stator slots to cool the stator and windings. Conjugate heat transfer modeling with transient super-cycling depicts the increasing temperature of the solid stator during sustained motor operation.

The market for electric motors is expanding rapidly. Meeting this demand will require ongoing innovation—both a massive challenge and opportunity. With the combined capabilities of CONVERGE and JMAG, you’ll be equipped with powerful and efficient tools to drive the transition to a more electrified future.

Ready to simulate electric motor cooling? Contact us today!

References

1.   Koinuma, K., Aiso, K., and Akatsu, K., “A Novel Self Cooling SRM for Electric Hand Tools,” 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Portland, OR, United States, Sep 23–27, 2018. DOI: 10.1109/ECCE.2018.8557901

Author:
Jameil Kolliyil
Engineer, Documentation

Wind energy has emerged as one of the major types of renewable energy sources in recent times. In the United States alone, the past decade has seen a 15% growth per year in wind power capacity, making wind energy the largest source of renewable power in the U.S.¹ And as the market for wind energy grows, wind turbines and farms are subsequently becoming larger (some of these wind turbines are over 100 meters tall!). With such large turbine structures, in addition to wake effects from other wind turbines, the effect of the atmospheric boundary layer (or ABL, which is the lowest part of the atmosphere directly influenced by the earth’s surface) also becomes significant. The turbulence in the ABL can affect the efficiency and lifetime of wind farms, and the wake flows from the farms can alter the structure of the ABL. So when designing and optimizing large wind farms, you have to consider the complex interaction between the farm and the ABL. 

So the all-important question is whether computational fluid dynamics can help you design better wind farms. Can it? Well, the short answer is yes, but the biggest hurdle to performing full-scale CFD simulations becomes immediately apparent: the massive disparity in length and time scales. In a comprehensive simulation of such systems, you will have length scales ranging from millimeters, corresponding to the thickness of the boundary layer on the turbine rotor, to tens of kilometers, corresponding to the size of a wind farm. Simulations of this magnitude would be expensive and resource-intensive, to put it mildly. The industry has therefore turned to actuator models. These models replace the rotor blade with lines (actuator line model) or discs (actuator disc model) that impose body forces corresponding to blade loading on the flow field. Meanwhile, a three-dimensional Navier-Stokes solver is used to simulate the flow field. This circumvents the need for a fine mesh around the rotor blade while maintaining adequate refinement to capture turbulence and wake characteristics. Developed in 2002 by Sorensen and Shen², the actuator line model (ALM) has gained popularity in recent years and has been extensively used in wind turbine simulations. The main challenges in developing ALM involve determining the relative velocity at each discrete point on the actuator line and deciding how to project the aerodynamic forces back onto the flow field. State-of-the-art ALM codes use an interpolation method (where velocity is interpolated from nearby fluid points to AL points) or an integral method (where a force projection weighted velocity integral is used to retrieve the free upwind velocity) for calculating the relative velocity. A Gaussian function is used for projecting the aerodynamic forces onto the fluid flow.

At Convergent Science, we’re constantly pushing the envelope and looking to improve existing models. Dr. Shengbai Xie (Principle Research Engineer at Convergent Science) published a research paper in which he employed alternate velocity-sampling and force projection functions. Instead of interpolating velocity from nearby fluid points, Dr. Xie’s approach used a Lagrangian-averaged velocity sampling technique. Instead of a Gaussian force projection function, he used a piecewise function³. He implemented these modifications in CONVERGE CFD software and simulated a 5MW NREL (National Renewable Energy Laboratory) reference wind turbine⁴. Figure 1 shows steady-state rotor power and torque predictions from other ALM implementations, Dr. Xie’s approach, and reference curve from Jonkman et al., 2009⁴. 

As you can see in Figure 1, Dr. Xie’s novel approach produces a better match to the reference curve when compared to the interpolation and integral methods. Dr. Alessandro Bianchini’s Wind Section group at the University of Florence has already employed this new approach to simulate a DTU 10 MW reference wind turbine; Figure 2 shows an animation from their work. You can find more information about the new approach and detailed comparisons with other ALM implementations in Dr. Xie’s research paper here. 

CONVERGE’s trademark autonomous meshing, Adaptive Mesh Refinement (AMR), and smooth handling of moving geometries make it uniquely suitable for simulating wind turbines. Check out our wind turbine webpage to see how CONVERGE can bolster your wind turbine simulations! 

References

[1] Wind Energy Technologies Office, “Advantages and Challenges of Wind Energy”, https://www.energy.gov/eere/wind/advantages-and-challenges-wind-energy, accessed on Aug 10, 2021.

[2] Sorensen, J. N., Shen, W. Z., “Numerical modelling of wind turbine wakes,” J. Fluids Eng., 124, 393-399, 2002. DOI: 10.1115/1.1471361

[3] Xie, S., “An actuator-line model with Langrangian-averaged velocity sampling and piecewise projection for wind turbine simulations,” Wind Energy, 1-12, 2021. DOI: 10.1002/we.2619

[4] Jonkman, J., Butterfield, S., Musial, W., Scott, G., “Definition of a 5-MW reference wind turbine for offshore system development,” NREL, 2009, https://www.nrel.gov/docs/fy09osti/38060.pdf

Co-Author:
Scott Drennan
Director of Aftertreatment Applications

Co-Author:
Pengze Yang
Senior Research Engineer

Prevention of solid deposit formation in urea/Selective Catalytic Reduction (SCR) aftertreatment systems is a primary concern for design engineers. There are significant resource and reputation costs associated with urea deposits if they arise in the field. It is imperative that aftertreatment system designers evaluate and mitigate the potential for urea deposit formation.

Though we colloquially refer to “urea deposits”, the actual deposit species are byproducts of urea decomposition. Within a narrow temperature range, ammelide and cyanuric acid (CYA) form hard crystalline deposits of considerable size on the walls of the exhaust system. These crystalline structures are exceedingly difficult to remove once they form, and the deposits decompose only at very high temperatures. Computational fluid dynamics (CFD) tools can provide valuable design information to mitigate urea deposit formation if the tool is both accurate and fast enough to meet tight design schedules.

Urea deposits form over a long period of operation. Some deposits are not even visible for up to five minutes of operation time, and many experiments consider runs of more than one hour. Running CFD for one hour of simulated time is not currently feasible. However, we can get closer to a fully developed film prediction and steady formation rate of deposits with a few minutes of simulation time. Unfortunately, even simulating several minutes with traditional CFD approaches is unacceptably expensive in a production environment, perhaps taking many weeks. Of course, an overnight run would be best, but a valuable result would be worth a few days of wall-clock time. How do we speed things up? How do we ensure that our result will be a valuable one? CONVERGE offers two key answers to those questions.

First Principles: Urea Decomposition Chemistry

A urea-water solution (UWS), also called Diesel Exhaust Fluid (DEF), is injected upstream of the SCR catalyst as a feedstock for the ammonia needed to reduce NOx. At exhaust gas temperatures, after the solvent water evaporates, the urea thermally decomposes into ammonia (NH3) and isocyanic acid (HNCO), as seen on the left side of Figure 1. The HNCO then hydrolyzes into ammonia and water, either before the SCR or inside of it. The most common method of modeling urea decomposition is to treat the urea, after the evaporation of water, as a molten solid that decomposes into gaseous ammonia and HNCO¹. This decomposition happens in both droplets and films.

Unfortunately, urea films at temperatures from 130°C to 165°C can form crystalline deposits that require very high temperatures to remove. Deposit removal is accomplished through decomposition, as shown in the right side of Figure 1. Biuret is formed first, which then converts into CYA and ammelide. These latter species require temperatures as high as 360°C to decompose.

Challenges of Urea Deposit Risk Assessment

Traditionally, deposit formation has not been simulated directly due to long simulation times and the lack of accurate deposit kinetics that we mentioned earlier. Many aftertreatment system modelers use a CFD approach that focuses on assessing the risk of urea deposit formation based on film temperatures and other local film conditions. Urea deposit risk models are highly empirical, requiring extensive tuning of key urea film parameters such as temperature, shear, etc. The payoff: after investing significant time in tuning the parameters, users can predict which walls are at risk for deposit formation. There is no information provided on the growth rate, shape, or composition of the deposit, or on other key parameters designers need to know. At Convergent Science, we invested in integrating an accurate detailed decomposition mechanism for urea into CONVERGE, then worked to accelerate simulation speeds to make direct prediction of urea deposits possible at reasonable runtimes.

CONVERGE’s detailed decomposition mechanism for urea was originally developed by our partners at IFP Energies nouvelles. Prior validation work by IFPEN has shown the urea mechanism to be accurate in several fundamental validation cases (e.g., heated decomposition, or single drop and spray ammonia conversion²).

CONVERGE’s detailed urea decomposition model has been used successfully to determine where deposits will form on a commercial validation case for a medium-duty diesel engine with Isuzu-Americas, shown in Figure 2³. Isuzu had a wide range of experimental data on deposits, and CONVERGE was used to determine which of the cases formed deposits of the crystalline species biuret, ammelide, and CYA. Isuzu was able to determine the composition of the deposits, with the ratio of CYA to ammelide being an important parameter. These experimental results for ratio of CYA to ammelide in the deposit are well predicted by the CONVERGE detailed decomposition simulation for the location of the sample.

Now that we have a fully-coupled modeling capability with accurate deposit chemistry, spray-wall interactions, and conjugate heat transfer for accurate metal and film temperatures, it’s time to speed things up to address the long runtimes needed in deposit simulations.

Speedup: Fixed Flow Advantages

We can harness our understanding of typical urea/SCR systems to optimize our solution strategy and provide a dramatic simulation speedup. The DEF injection duration is relatively short compared to the pulse frequency, and the spray momentum flux is very small compared to the gas momentum flux. Therefore, the gas flow is relatively consistent in between the spray pulses. CONVERGE has implemented what we call a fixed flow solver approach to take advantage of this quasi-steady-state behavior. This technique exploits the disparity in time scales, solving the full spray and Navier-Stokes equations only during the spray pulse. The flow state is then fixed for the interval between sprays. Simulations for urea/SCR applications are sped up many times with fixed flow, achieving up to 30 seconds per day on a typical commercial aftertreatment system (e.g., approximately 2 million cells on 96 processors).

Such an achievement in speedup must be properly validated to determine the effects on accuracy. The most common validation case to demonstrate accuracy in urea spray, splash, film heat transfer and evaporation, and metal temperature prediction is the Birkhold filming spray-wall validation case⁴. This case has a pulsed urea/water spray impinging on a thin flat metal plate, with hot air flowing above and below the plate. A thermocouple is located at the leading edge of the film pool. Successful validation of this case requires prediction of the initial slope of the temperature drop during dry cooling, capturing the temperature when films begin to form with a rapid temperature drop, and the final temperature as the film becomes fully developed. The CONVERGE fixed flow results for the Birkhold filming case are shown on the left side of Figure 3, and we see good agreement on all three key behaviors⁵. Note that achieving this accuracy requires some initial tuning of the Kuhnke splash model constant. However, once tuned, the same model constants produced accurate results for the Birkhold non-filming cases.

Putting it Together: Speed and Accurate Deposit Chemistry

Detailed decomposition of urea in CFD offers the promise of moving from empirical predictions of risk to actual predictions of deposit formation through detailed chemistry. CONVERGE’s detailed decomposition model for urea has been validated against fundamental urea validation cases^2,6 and in commercial cases³.

A recent validation of CONVERGE’s urea deposit prediction ability was conducted with some experimental data from Prof. Deutschmann at Karlsruhe University⁷. In this experiment, three different exhaust gas temperatures and DEF spray conditions were operated for many minutes (see Figure 4). The available experimental data in this study include the outer wall temperature of the exhaust pipe, the shape of the deposit, and the chemical composition of the deposit. Unfortunately, no information was available on the mass of the deposit formed.

The CONVERGE deposit simulations included fixed flow for speed and the accurate spray-wall interaction, conjugate heat transfer with super-cycling, and the decomposition of urea mechanism for accuracy of wall film temperature and deposit chemistry. The simulations predicted the outer wall temperature quite well, including the location and shape of the wall film (see Figure 5). The predictions of the crystalline deposit species also matched quite nicely with the experiment. CONVERGE achieved 30 seconds of simulation time per day for this nearly 2 million-cell model when coupled with the fixed flow approach.

The next step in urea deposit predictions is to achieve deposit growth estimates based on accurate deposit species growth rates in a fully-developed urea film. The growth rates of species such as biuret, ammelide, and CYA must be calculated at their exact location, allowing prediction of the shape, size, and weight of the deposit. This deposit growth projection capability must predict the mass, location, and speciation of deposits over many minutes or hours in duration. Many commercial customers are now using CONVERGE to conduct urea deposit predictions and for comparison to their experimental data.

It is important that the urea decomposition model be fully coupled with the spray, film, gas, and metal models to obtain the accurate film conditions that are required to correctly predict the chemical kinetics. The main drawback of this full coupling had been the computational cost associated with the film kinetic calculation.

Summary

CONVERGE’s detailed decomposition model is now delivering the same simulation speed as the lower-fidelity molten-solid model and urea deposit risk approach. The detailed decomposition model is a direct and fully coupled calculation of the deposit species of interest (i.e., CYA, ammelide, and biuret). Therefore, it is a more fundamental approach, requiring little or no tuning for accurate predictions of urea deposits. The urea deposit risk model was once the state of the art, and it delivered value to design engineers, but we now have something better that is just as fast. Why just assess risk when you can know when, where, and what kind of deposits are formed?

References

[1] Quan, S., Wang, M., Drennan, S., Strodtbeck, J., and Dahale, A., “A Molten Solid Approach for Simulating Urea-Water Solution Droplet Depletion,” ILASS Americas 27th Annual Conference on Liquid Atomization and Spray Systems, Raleigh, NC, United States, May 17–20, 2015.

[2] Habchi, C., Quan, S., Drennan, S.A., and Bohbot, J., “Towards Quantitative Prediction of Urea Thermo-Hydrolysis and Deposits Formation in Exhaust Selective Catalytic Reduction (SCR) Systems,” SAE Paper 2019-01-0992, 2019. DOI: 10.4271/2019-01-0992

[3] Sun, Y., Sharma, S., Vernham, B., Shibata, K., and Drennan, S., “Urea Deposit Predictions on a Practical Mid/Heavy Duty Vehicle After Treatment System,” SAE Paper 2018-01-0960, 2018. DOI: 10.4271/2018-01-0960

[4] Birkhold, F., Meingast, U., Wassermann, P., and Deutschmann, O., “Modeling and Simulation of the Injection of Urea-Water-Solution for Automotive SCR DeNOx-Systems,” Applied Catalysis B: Environmental, 70, 119-127, 2007. DOI: 10.1016/j.apcatb.2005.12.035

[5] Maciejewski, D., Sukheswalla, P., Wang, C., Drennan, S.A., and Chai, X., “Accelerating Accurate Urea/SCR Film Temperature Simulations to Time-Scales Needed for Urea Deposit Predictions,” SAE Paper 2019-01-0982, 2019. DOI: 10.4271/2019-01-0982

[6] Ebrahimian, V., Nicolle, A., and Habchi, C., “Detailed Modeling of the Evaporation and Thermal Decomposition of Urea-Water Solution in SCR Systems,” AIChE Journal, 58(7), 1998-2009, 2011. DOI: 10.1002/aic.12736

[7] Brack, W., Heine, B., Birkhold, F., Kruse, M., and Deutschmann, O., “Formation of Urea-Based Deposits in an Exhaust System: Numerical Predictions and Experimental Observations on a Hot Gas Test Bench,” Emission Control Science and Technology, 2, 115-123, 2016. DOI: 10.1007/s40825-016-0042-2

[8] Yang, P. and Drennan, S., “Predictions of Urea Deposit Formation With CFD Using Autonomous Meshing and Detailed Urea Decomposition,” SAE Paper 2021-01-0590, 2021. DOI: 10.4271/2021-01-0590

2021 was a complicated year. The second full year of the pandemic offered reasons for hope and optimism, along with times of hardship and uncertainty. I sincerely hope that this next year is a turning point in the pandemic and that we see significant improvement around the world.

Despite the continuing pandemic, there have been exciting developments and opportunities for Convergent Science this past year. We are releasing CONVERGE 3.1, a major version of our software that includes many new features and enhancements. We strengthened relationships with our partners and collaborators, and forged new ones with universities around the world through our CONVERGE Academic Program. We were honored to receive several awards, and we have pushed further into new market segments and application areas. All the while, we have continued to strive to improve CONVERGE in a way that best meets your simulation needs and to provide our customers with the best possible support.

CONVERGE 3.1 Release

We’re pleased to be releasing a new major version of our software: CONVERGE 3.1. During development of this version, we focused on expanding CONVERGE’s physical modeling capabilities, improving user experience, and simplifying the workflow for advanced simulations. We added several new volume of fluid (VOF) modeling approaches for multi-phase flows that reduce numerical diffusion at fluid interfaces and enable you to simulate the separation of phases or immiscible liquids under the influence of gravity. CONVERGE 3.1 also offers implicit fluid-structure interaction (FSI) modeling, which increases the stability of the solver when simulating floating objects or simulating fluids and solids with similar densities. To complement this capability, CONVERGE 3.1 contains tools to generate realistic wind and wave fields. This set of features opens the door to many offshore and marine applications, such as floating offshore wind turbines and boat or ship hulls.

CONVERGE 3.1’s multi-stream simulation capability allows you to apply different solver settings and physical models to different regions of the domain. Using the multi-stream approach, you can model complex, multi-physics problems in a single simulation, which offers a simpler workflow than running multiple independent simulations. Another workflow enhancer in 3.1 is the ability to couple CONVERGE with ParaView Catalyst to perform in situ post-processing of your simulation results. You’ll find many other enhancements in CONVERGE 3.1, including moving inlaid meshes, the capability to simulate solid particles, and more flexibility for wall motion. We’re very excited about this new release, and we think it will greatly benefit users across many application areas.

Award-Winning Collaborations

At Convergent Science, we’re dedicated to creating innovative tools and methods that industry can leverage to accelerate the development of cutting-edge technology. We couldn’t achieve this goal without the invaluable collaborations we have with world-class institutions and companies. This year, several of our collaborative projects were recognized for their merit and contributions to the research community and society at large.

TCF Award

This summer, Convergent Science and Argonne National Laboratory were awarded funding through the U.S. Department of Energy’s 2021 Technology Commercialization Fund (TCF) to continue developing a deep learning framework called ChemNODE, which accelerates detailed chemistry CFD simulations for reacting flows. The goal of ChemNODE is to enable engineers to use detailed mechanisms that, compared to skeletal mechanisms, provide more predictive results for combustion simulations, without incurring such a large computational expense. 

HPCwire Awards

In the fall, Convergent Science received two 2021 HPCwire Awards. 

With Aramco Research Center – Detroit and Argonne National Laboratory, we received the 2021 Editors’ Choice Award for Best Use of HPC in Industry. We were recognized for our work using high-performance computing and CONVERGE simulations to evaluate engine cold-start operations, during which the majority of emissions are formed in modern vehicles. We achieved a 26% improvement in combustion efficiency at cold conditions for a heavy-duty engine.

The second HPCwire Award we received was for a collaborative project with Argonne National Laboratory and Parallel Works. Together, we have been developing an automated machine learning-genetic algorithm (ML-GA) approach to accelerate design optimization and virtual prototyping. We coupled ML-GA with CONVERGE to perform a design optimization of a gasoline compression ignition engine and found that this approach sped up the process by ten times compared to the industry standard.

Convergent Science Partner Updates

Another key way we are able to deliver top-notch products to our customers is through our partnerships. In 2021, we strengthened our partnership with Tecplot as we work to provide a seamless simulation workflow from pre- to post-processing. Tecplot for CONVERGE is included with a CONVERGE license, and now users have the convenient option to buy a full Tecplot 360 license directly from our Convergent Science sales team. 

This year we also introduced GT-CONVERGE, a specialized version of CONVERGE that is fully integrated into GT-SUITE. GT-CONVERGE replaced our previous GT-SUITE product, CONVERGE Lite, and offers many more features and greater functionality, including conjugate heat transfer, a steady-state solver, automatic export of 3D visualization slices, enhanced wall models, and much more. 

Computational Chemistry Consortium

Another collaborative effort, the Computational Chemistry Consortium (C3) concluded Phase 1 of operations in 2021, culminating in the public release of C3MechV3.3. C3Mech is a new detailed kinetic model for surrogate fuels consisting of 3,761 species and 16,522 reactions. It contains chemistry for small species such as hydrogen, syngas, natural gas, and methanol; important surrogate fuel components for gasoline, diesel, and jet fuel; and NOx and PAHs. The mechanism represents the first time that the combustion community has developed and validated a mechanism combining small, intermediate, and large species in a self-consistent, comprehensive, and hierarchical way. C3Mech will help facilitate the study of low-carbon, carbon-neutral, and carbon-free fuels, which are going to play a critical role in the decarbonization of industry. If you’re interested in checking out the mechanism, it will soon be available to download on the C3 website.

CONVERGE Academic Program

At Convergent Science, we have always been strong believers in the importance of training the next generation of engineers, and we greatly value our relationships with universities and other academic institutions. Now, we have dedicated personnel to help cultivate these relationships. Our goal with the CONVERGE Academic Program is to make it easier for students around the world to access our software and to better support them throughout their academic journey. 

This year, we also launched the CONVERGE Academic Competition, a simulation competition for students around the world. We’re challenging participants to design and execute a novel CONVERGE simulation that doesn’t just look nice, but also accurately captures the relevant physics of their system. We’re looking forward to seeing the creative simulations the competitors come up with, and we’re excited to showcase their work when the winners are announced next summer!

2021 Global CONVERGE User Conference

This year we held the first-ever global edition of our CONVERGE User Conference, with the goal of exposing attendees to research they might not otherwise come across. To accommodate attendees in different time zones, we hosted each of the four presentation sessions twice. In addition, we offered attendees the option to watch the presentations on-demand, and we also unveiled on-demand CONVERGE training. Each day of the conference, our support engineers hosted office hours so attendees could meet one-on-one with a CONVERGE expert to get answers to any questions they had. The event was a great success, with more than 400 attendees from six continents and nearly 30 countries. While we hope future user conferences can once again take place in person, we were thrilled to be able to host this virtual global event.

On-Demand CONVERGE Training

As I mentioned above, we introduced a new resource for CONVERGE users at our fall conference: on-demand training. Both introductory and advanced training courses are available on the Convergent Science Hub, and we’ll keep adding and updating courses as we go. We hope this convenient option helps you get up and running with CONVERGE on your own schedule—and our Support team is always available if you have questions. We’ll continue to offer live training throughout the year as well, virtually at the beginning of 2022 and hopefully (!) in person later in the year. 

Convergent Science Around the Globe

The primary mission of Convergent Science is twofold: (1) help current clients run the best CFD simulations possible, and (2) discover other industries that can benefit from CONVERGE’s unique combination of features. Our offices around the world are dedicated to fulfilling both parts of this mission.

In Europe, we’ve had a great year for bringing on new clients in a variety of industries, who plan to use CONVERGE for a broad array of applications: oil and gas, hydrogen injectors and engines, vacuum pumps, compressors and engines for refrigeration applications, fuel cells, marine technology, construction and agricultural engines, redesigning racing engines as motorsports move to renewable fuels, and more. We attended a wide variety of conferences, both virtually and in person, that covered topics ranging from tunnel safety to space propulsion to compressors. Our European team grew, and we expanded our office space to accommodate more growth in the future.

This year, our India branch celebrated its four-year anniversary. Our team in India continued to grow, gaining seven new employees in 2021. The team is busy exploring how to most effectively apply CONVERGE to applications such as motor cooling, battery thermal runaway, flexible fuel engines, pumps, and more. In addition, the India office is working to bridge the gap between industry and academia by helping students gain exposure to simulation software.

In the United States, our world headquarters in Madison, Wisconsin continued to thrive, with more than a dozen new hires this year. We’re continuing to branch out into exciting application areas including hydrogen, aerospace, batteries, biomedical applications, and renewable energy. With our dedicated university relations team, we strengthened our relationships with existing academic users and forged many new relationships as well. In 2021, we gained more than 180 new academic users in North and South America across 36 different labs and 14 universities. 

Our partners at IDAJ continue to provide excellent support to CONVERGE users in China, Korea, and Japan. Major areas of focus for IDAJ include hydrogen engines and non-engine applications such as rotating machinery, battery burning, and spray painting. They hosted their popular IDAJ Conference Online 2021, which garnered over 2,800 attendees. In addition, we worked with IDAJ to port CONVERGE on Fugaku, the world’s fastest supercomputer. IDAJ demonstrated CONVERGE on Fugaku by running high-fidelity combustion simulations using large eddy simulations (LES) and detailed chemistry. 

A Look Forward

Despite the ongoing challenges of the pandemic, 2021 has been a successful year, and we’re looking forward to new opportunities in 2022. While virtual events have been a great way to connect during the pandemic, they just aren’t the same as seeing your colleagues face-to-face. We hope to be able to hold our next user conference in person and to attend more in-person tradeshows in the new year. We’re also looking forward to our next CONVERGE release—we have many great features under development, and we can’t wait to share them with you. We’re excited to continue to delve into new application areas and to strengthen our collaborations and partnerships. Above all, we look forward to helping you run novel simulations and providing you with the tools you need to create next-generation technology.

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.

Try Tecplot 360 and PyTecplot for Free! Image courtesy of blueOASIS (case study).

Agenda

• Tecplot 360 with PyTecplot [0:0:57]
• Why use PyTecplot? [0:02:19]
• Customer Examples [0:04:10]
• PyTecplot Requirements & Installation [0:07:45]
• Running PyTecplot on the Command Line [0:10:44]
• Common Problems [0:14:49]
• Running PyTecplot: Batch vs Connected [0:19:19]
• Running PyTecplot: Jupyter Notebooks [0:25:40]
• Recording Scripts [0:47:09]
• PyTecplot Documentation & Resources [0:47:44]
• Q&A [0:50:00]

Tecplot 360 with PyTecplot

PyTecplot is the Python API to automate and analyze your data with Tecplot 360, our tool for data visualization.

Tecplot 360 is our flagship software and has long been trusted as a multi-purpose post-processor by engineers and scientists. 360’s  clear visualization of complex data removes any doubt in your designs or analyses. It can create visually impressive 3D plots, and create 2D and XY plots for detailed engineering decision making. Large billion-cell datasets are efficiently handled  to visualize your simulated or experimental data from multiple industry-standard data formats.

The built in CFD Analyzer toolkit also gives you the option of performing integrations, extracting key flow features such as vortex cores and shock surfaces, calculating new variables, and more, for additional analysis of your data.

PyTecplot allows you to automate 360 with the Python language. Use your own installation of 64-bit Python, install the PyTecplot Python API, and then create scripts that control an active session of Tecplot 360, or run in batch mode – which is especially useful if you want to incorporate this with other Python scripts as part of a larger workflow.

Why use PyTecplot?

Tecplot 360 is capable of powerful data analysis on its own and has a custom scripting language known as the Tecplot Macro Language. However, there is a benefit to using PyTecplot:

Automate your work in Tecplot 360 with Python, a widely used scripting language.

• Actions that are tedious in the Tecplot 360 user interface can be performed quickly with a PyTecplot Python script.
• For those who are new to Tecplot 360, the Tecplot Macro Language allows you to automate workflows as well, but rather than learning a scripting language specific to Tecplot 360 you can use Python, which is already used for many other tools and you may have as part of your workflow as well.

Directly access your data for analysis. Certain types of data analysis can be cumbersome, or not possible, in either the Tecplot 360 user interface or with the Tecplot Macro Language. Python libraries like NumPy or SciPy allow you to analyze and manipulate your data in ways that are not possible using only Tecplot 360: arrays, statistical analysis, and more.

Use your own installation of Python 64 bit.

Debug with Python IDEs. Tecplot 360 has a built-in debugger for the Tecplot Macro language, but with PyTecplot you can use your preferred Python IDE (Integrated Development Environment) to debug your script.

Customer Examples

1. A Tight Cooperation Towards Ocean Sustainability by blueOASIS
2. Analyzing Bubble Characteristics in Simulations of Fluidized Beds by CPFD Software
3. Thermal Visualization of a Self-Sustaining Green House by MSOL, UC Berkeley
4. Thermal Field Visualization for Uncertainty Quantification by MSOL, UC Berkeley
5. Flow Dynamics of Liquid Jet Irrigation by CFD Research Group, RMIT University
6. Tecplot Visualization Helps Understand the Carbon Cycle by UMCES, University of Maryland

PyTecplot Documentation & Resources

• PyTecplot Docs
• PyTecplot Video Tutorials & Webinars
• Getting Started with PyTecplot Scripting
• Blogs & Articles about PyTecplot
• PyTecplot Resources on GitHub
• Tecplot Technical Support

PyTecplot Script Examples on GitHub

• Minimum Pressure Location: MinimumPressureLocation.ipynb
• Hello World: 00_hello_world.py (also in the python examples folder within the Tecplot 360 installation directory)
• Timestep Delta: 05_timestep_delta.py

We highly recommend using PyTecplot if you want to get more out of your work with Tecplot 360, and to get to the answers that you need faster.

Try Tecplot 360 for Free

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Tecplot 360 vs. ParaView for CONVERGE Data

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

CONVERGE users are fortunate to have access to ‘Tecplot for CONVERGE’ (TfC) as part of their CONVERGE license. However, for users who are looking for more power than what TfC offers out of the box (i.e. batch-processing), the most popular options are Tecplot 360 or ParaView.

TfC, 360, and ParaView each include a direct data reader for CONVERGE post*.h5 files, which means that with these post-processors there’s no need to run post_convert. By avoiding running post_convert you’re saving yourself time and disk space.

Experiment

Since many CONVERGE users need to create movies of their results, we chose a reasonably large, multi-cycle internal combustion engine (ICE) simulation. This dataset is composed of 1278 timesteps, totaling 169Gb on disk.

The goal is to capture the execution time and peak RAM required to create a plot which consists of a slice colored by temperature and an iso-surface of the flame front (temperature = 1700). We repeated this for each time step in the data series for a total of 1278 images produced. Figure 1 shows an example of the plot:

Figure 1: Example Image from ICE Simulation Showing Temperature & Flame Front Iso-Surface

Setup

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

All tests were run unattended in batch mode (using PyTecplot for Tecplot 360, and pvbatch for ParaView 5.10). We used the Python memory-profiler utility to capture timing and RAM information.

Figure 2: Animation of ICE Simulation Created with Tecplot 360 Image Exports (For animation speed and file size, every 4^th image was used to create this animation)

Results

Serial Execution

The graph below shows the outcome of our tests running the processes serially (i.e. producing 1 image at a time).

Figure 3: 360 vs ParaView: Execution Time & Peak RAM Usage

See the PyTecplot script on our GitHub.

For the generated plot, Tecplot 360 uses multi-threading for a number of operations such as: deriving node-located values from the cell-centered Temperature value (a pre-requisite for iso-surface creation), slice creation, and iso-surface creation.

ParaView does not publish a list of which filters are multi-threaded, but “all common filters are [multi-threaded]” ^[1,2]

So, serial here means that we loaded the series of CONVERGE post*.h5 files and ran through the animation, creating an image one timestep at a time.

For the HDF5 dataset performance tests, we discovered that processing with Tecplot 360 in minimized memory mode gave the fastest processing time at 1.99 hours, ~5.6 seconds per image. Tecplot 360 in minimized memory mode also held the least amount of RAM at 2.7 Gb peak RAM usage. Note, Tecplot 360 defaults to keeping 30-70% of the data that it’s loaded in RAM in the event that this data may need to be re-rendered. Once the RAM meets the 70% threshold, Tecplot 360 will offload stored memory until it reaches 30%. This behavior is handy in the GUI when moving back and forth between time-steps. Setting Tecplot 360 to minimize memory mode prevents it from saving data from previous time steps in RAM.

Did we run ParaView using MPI? – The current recommendation is to only run ParaView using MPI when you can distribute the data ^[3]. CONVERGE data is single block, so it’s not ideal for distribution. Furthermore, the ParaView CONVERGE reader does not read in parallel ^[4]. As such, we did not run ParaView using MPI.

Parallel Execution

Another benefit of PyTecplot (i.e. batch mode) is that image export scripts can be written to use multiple concurrent PyTecplot processes—slashing the time to process images. We utilized our ParallelImageCreator.py PyTecplot script (located on our GitHub — see the documentation for examples) which processes multiple timesteps simultaneously to put more cores to work. In the plot below you can see how running even just two concurrent processes can drastically reduce the processing time:

Figure 4: Total time to Execute and RAM Usage against # of Parallel 360 Processes

Our script, running with 16 cores, took an average of ~1.2 seconds per image—this is 4.8 times faster than Tecplot 360’s fastest run without parallelizing the image exports and 10.7 times faster than ParaView! However, this comes at a significant cost in terms of RAM (34.7 vs 2.74 Gb, 12.6 times more RAM). We observed nearly identical speed with half the RAM when parallelizing with 8 processors vs. 16.  With parallel processes you can get hardware contention (which is why we don’t see a linear improvement in performance), so it may take some experimentation with your data to find the ideal amount of concurrency to use.

Once the images were created, we recommend using tools such as ImageMagick or FFmpeg (executable shipped with Tecplot 360) where you can stitch together the exported images. You can create numerous animation types, such as .mp4, .gif, or .avi, and also tune your settings by adjusting the framerates for example. You can also take advantage of our GitHub script, to automate this process with Python.

All in all, with Tecplot 360’s batch mode, you have choices — if you have a single license seat, you can create fast animations, and with multiple cores and licenses, you can create images even faster. Let us know if you want to talk about ways we can supercharge your image processing!

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References

The post Which is Faster? appeared first on Tecplot.

Flow Visualization of Nasal Irrigation Leads to New Squeeze Bottle Design

Nasal saline irrigation is a therapy technique where a liquid solution cleans the nasal passages. It can be used to alleviate congestion and sinus irritation by forcing the saline through the nasal cavity washing out thick or dry mucus, and pollutants such as pollen, and dust particles.

Squeeze bottles deliver the solution into the nasal cavity as liquid jets, which have been shown to reduce allergic rhinitis symptoms and can be used by patients with good compliance and minimal side effects.[1] However, the reasons why squeeze bottles were effective for nasal irrigation were not well known from an engineering perspective.

Kiao Inthavong and his CFD Research Group (RMIT University) team wanted to better understand the flow dynamics of liquid jet irrigation. Using CT scans of actual human patients from ENT clinicians, his team reconstructed a computational model of a human nasal cavity, and applied the multiphase Volume Of Fluid (VOF) approach to track the liquid-air interface of the nasal irrigation within the human nasal cavity domain.

Fig 1. A) Computational model of the nasal cavity with the squeeze bottle inserted into the nostril. B) The internal nasal cavity with colors depicting some anatomical features, including the frontal sinus (green), sphenoid sinus (blue), maxillary sinus (yellow), and the nasopharynx (dark grey)

The simulations are inherently transient applied with small time steps, and with multiphase flows, double precision was used. This led to huge datasets per study. Tecplot 360’s ability to handle large datasets allowed the entire set of results to be loaded and the time-dependent data to be processed efficiently.

“We were impressed by the powerful capabilities of Tecplot 360 to handle the dataset”, Kiao remarked. “Furthermore, we used the varied automation tools available, including PyTecplot connected with Python, and Macro Scripts, which has become part of our established workflow.”

The flow visualization from CFD modelling with the VOF method revealed the liquid jet behavior within a human nasal cavity. The overflow and flooding effects helped increase sinonasal surface coverage, residence times across the mucosal surfaces, and shearing force of irrigation.

Fig 2. Liquid surface coverage inside the nasal cavity viewed from the top of the head for three patients that had undergone FESS (Functional Endoscopic Sinus Surgery). The blue color represents the liquid region.

The applied industry related research project outcomes were published in two leading journals, the International Forum of Allergy and Rhinology[2], and Journal of Biomechanics[3] . Later, in collaboration with ENT Technologies, the results were used by the company to redesign their squeeze bottle. The new larger volume bottle and ergonomically designed received huge success anecdotally by patients and the new bottles are expected to come out to market.

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[1] Piromchai P, Kasemsiri P, Reechaipichitkul W. Squeeze Bottle Versus Syringe Nasal Saline Irrigation for Persistent Allergic Rhinitis – a Randomized Controlled Trial. Rhinology. 2020 Oct 1;58(5):460-464. doi: 10.4193/Rhin19.308. PMID: 32427228

[2] Inthavong, K., Shang, Y., Wong, E. and Singh, N., 2020, January. Characterization of Nasal Irrigation Flow from a Squeeze Bottle Using Computational Fluid Dynamics. In International Forum of Allergy & Rhinology (Vol. 10, No. 1, pp. 29-40).

[3] Shrestha, K., Salati, H., Fletcher, D., Singh, N. and Inthavong, K., 2021. Effects of Head Tilt on Squeeze-bottle Nasal Irrigation–A Computational Fluid Dynamics Study. Journal of Biomechanics, 123, p.110490.

The CFDResearch group is a collaborative research space at RMIT University in Australia, led by Dr Kiao Inthavong. The group uses CFD and experimental flow visualization techniques to investigate multiphase flows in the respiratory system, indoor airflow behavior, and fluid-particle dynamics. We use Tecplot 360 as the primary post processing tool to analyze complex data from CFD simulations. The main features we use are automation through Macros and Python scripting to handle large data sets, helping to generate streamlines, contours, and overlaying them onto the shaded geometries for visualization.

In 2017, we initiated a research program that involved a cross-disciplinary engineering-clinician program on “Engineering Solutions Impacting Clinical Outcomes (ESICO)” that led to the foundation of SCONA (Society of Computational Fluid Dynamics of the Nose and Airway). Throughout this research program, we have helped clinicians to understand nasal diseases, their therapy through drug delivery, and support treatment planning in functional rhinosurgery through CFD visualization with Tecplot 360 to generate images and animations and its interpolation function to compare data sets.

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In this video, we will explain Pages and Frames in Tecplot 360.

When first opening Tecplot 360, a single frame is visible where you can load a data set and create a plot. We can create multiple frames in the workspace, but Tecplot 360 also provides pages to help organize and present your visualizations!

A page is a container for any number of frames, and each frame is a container for a single plot.

With multiple pages, users can create multiple workspaces within a single layout—like a spreadsheet with multiple sheets or a PowerPoint with multiple slides.

For instance, in this layout (image above), the plot has five frames. One frame shows the overall view of the 3D plot for context. Another frame is a magnified view to visualize the red, blue, and green points of interest in the volume. And the three line plots display the variable values for each of the points of interest over time in separate frames.

Creating a New Page

Now, if we wanted to focus on a different aspect of our dataset without losing these views, we could either open a new instance of Tecplot 360 or create a new page within this layout. Creating a new page is beneficial because:

• You don’t need to create another layout file to save the visualization.
• Creating a page doesn’t consume another license of Tecplot 360.
• It is an easy way to bookmark a visualization of interest, and then continue exploring the data.
• And it also doesn’t load data twice—the datasets can be shared between pages.

In this 2^nd page, we’ve focused our visualization on a holistic view of contoured slices. The 3^rd page focuses on fluid velocity and the flow of particles. The 4^th page visualizes the Particle Volume Fraction as an iso-surface, and the last page shows a simple plot of particles against a black background.

Bookmarking a View

If we wanted to bookmark a view of interest as a page, for example the moment when the particles make their way to the bottom wall of the collection chamber, we can do so by copying the frame (control-c or right-clicking on the edge of the frame), creating a new page, and then pasting the frame into the new page (control-v or Edit>Paste). No new data is loaded because the dataset is shared between frames. Data sharing occurs when copying and pasting a frame, or when creating a new page and updating the plot type from Sketch to any other plot type which shares data from a previous frame.

Finally, we can rename the Page and zoom-in on the Frame.

This concludes our video on Pages and Frames in Tecplot 360. Thank you for watching!

Try Pages and Frames for yourself!

Use your own data or the sample data included with your free trial.

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The post Using Pages and Frames in Tecplot 360 appeared first on Tecplot.

When it comes to PIV, Tecplot 360 is the real MVP.

PIV, or Particle Image Velocimetry, is a method of experimental flow visualization that uses seeding particles and some form of camera or optics to measure 2- or 3-dimensional vector fields. It can be an extremely powerful way to capture “real life” (in quotes due to the errors and assumptions inherent in any experiment!) flow physics so as not to be fully reliant on computational methods. But, just as with computational methods, the output of the experiment is a ton of numerical data – what then?

Enter Tecplot 360, which is used in conjunction with every major PIV system to visualize and analyze the recorded data. 360 has everything you might need to get the most out of your PIV data; it can handle 3D data, 2D data, XY plots, animations, text & graphical annotations, and can even compare your PIV data against CFD results for the same experiment.

3D Data

For 3D measurements, like tomographic PIV, 360 can produce high-quality movies for transient cases. In steady-state cases, 360 can create plots of amazing quality.

3D model plus PIV slices

TomoPIV Animation from La Vision

You can easily combine data from arbitrary sources with 360. One option is to compare PIV slices which have been measured at different heights with the corresponding 3D CAD model. The import of the CAD model is done with 360’s STL loader.

3D Model combined with PIV planes, done with Dantec Dynamics, data from Valeo

2D Data

The classic output from a PIV system is 2D measurement data.

Here again you may have transient cases that you might wish to animate. Another great feature of 360 is the extraction of point probes over time. You may wish to do a Fourier transform to understand fluctuations better. Or perhaps you will want to extract profile lines in stationary and transient cases and perform some statistical analysis (if not already given by your PIV system) such as the calculation of mean values, measurement of turbulent energy, etc. With 360 any of these analyses or visualization tasks are easy to perform out of the box.

2D Droplet Visualization Vectorfield

XY Plots

XY plots are often used to create various line plots from extracted profile data. Here is an example from the 2D animation for the first timestep.

XY Profile Lines

Animations

High-quality animations from several different types of plots are easily created with 360. You also have the freedom to arrange animations over multiple frames, allowing multiple animations to play in a synchronized manner, as in the animations above.

Text or Graphical Annotations

360 allows the user to anchor text or geometric annotations (vectors, lines, circles, rectangles, …) to specific locations in your plot. This helps the audience to understand your work better by adding context to your visuals.

2D PIV Data Combined with 3D Simulation Data

Oftentimes CFD groups work closely together with PIV or other measurement groups. 360 is perfectly able to load all the different data types. You can compare the data, create difference plots, show the PIV slices in 3D together with CFD results, etc.

Suffice to say, if you have a PIV visualization or analysis task in mind the odds are good that 360 can help you accomplish it.

Tips and Tricks for PIV Post-Processing

PIV systems can produce a wide variety of data formats. Fortunately, 360 makes it simple to handle most formats, and even for more esoteric data formats there is usually a path to load your data. Briefly we’ll describe some common workflows, but if you need more help, please don’t hesitate to contact our fantastic support team (support@tecplot.com).

#1 The ideal case:

If your data is already in Tecplot format via export or data loaders (for example, via Dantec, La Vision, TSI), you won’t have to do much. You may wish to scale your data or rotate/translate it to align with your preferred reference frame. If so, read case #4 below.

#2 The 2D measurement data is ASCII, but contains only points, not the structure as an IJ-ordered matrix:

Use the General Text loader and, if needed, give the data a structure.

• Triangulation. In 2D mode, Data > 2D triangulation. This will create a triangular mesh connecting the data points.
• Linear interpolation: First, triangulate the data (this is required for linear interpolation, but not required if you use inverse-distance interpolation). Now, go to 2D mode, select Data > Create Zone > Rectangular, in order to create a IJ-ordered mesh matrix. Here you interpolate, Data > Interpolate > Linear, in order to interpolate the source zone data in the triangulation to the rectangular, IJ-ordered zone.

#3 The data is transient, but time information is missing:

We have a tool which assigns time information to the zones. After loading your data, you might see that the time slider in the Plot sidebar is greyed out. In the Zone Style, you will see all timesteps as single zones. If you want to assign solution times to your data, select Data > Edit Time Strands which allows you to assign the correct timesteps.

#4 The data must be rotated, translated, or scaled:

• To scale the data, go to Data > Alter > Specify Equations and use an assignment (= “Equation”), such as x = x/1000 to convert millimeters to meters in the x-direction.
• To translate the data, go to Data > Alter > Specify Equations and use an assignment like x = x + 1.2 to translate the data 1.2 units in the positive x-axis direction.
• To rotate the data, you will use in 2D mode: Data > Alter > Axial Rotation. Be sure that you correctly assigned the vector field first (simply activate the vector layer). This will rotate the vector field accordingly.

#5 Data is loaded, but more variables must be calculated:

To compute new variables from your existing dataset, use “Specify Equations” for simple cases. Alternatively, you can calculate CFD Analyze variables with Analyze > Field Variables to select the correct vector field first, and then choose the desired computation (i.e. Analyze > Calculate Variables > Velocity Magnitude). The full spectrum of possibilities is available using 360 together with PyTecplot, which is 360’s Python API. Use the Python API to compute more complex variables, such as the standard deviation of velocity in each timestep compared to the mean velocity over time, or multidimensional Fourier transforms.

#6 You find you must perform certain tasks repeatedly:

Use either 360’s intrinsic macro language or PyTecplot to perform repeated tasks programmatically. Either of these options allows a full automation and adaption to your workflow.

Learn More and Try Tecplot 360 For Free!

Again, please feel free to ask the Support Team for help with these tasks or anything else! They are eager to help! Also, take a look at our existing tutorial videos to gain further insight into the wide range of 360’s capabilities.

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The post For PIV, Tecplot 360 is the Real MVP appeared first on Tecplot.

In this webinar you’ll learn what’s new in Tecplot 360 2022 R1 as well as important platform and Python version support changes.  We will also introduce our Higher-Order Element Technology Preview, which is available as a separate download.  See how Tecplot, Inc. is helping usher in the future of CFD by supporting higher-order elements.

Highlights of the 360 Release

• LaTeX support in Contour Legends
• TecIO performance improvements
• Data Loader updates
  • Ansys Fluent 2022 Common Fluids Format (HDF5) support
  • Abaqus 2022 (.odb files)
  • HDF5 loader performance (nearly 10x faster!)
  • EnSight loader updates
• Platform updates
  • CentOS 8 is no longer supported
  • PyTecplot 1.5.0 released. Python 3.7 or greater is a hard requirement.  Note that we officially support the latest Python release and the two previous (currently 3.8, 3.9, 3.10)

• See all updates in our Release Notes (PDF)

Get a Free Trial

Update Your Software

Higher-Order Element Support

The Higher-Order Element Technology Preview is available as a separate download. Log in to MyTecplot and check it out!

Download Beta Here

The post What’s New in Tecplot 360 2022 R1 appeared first on Tecplot.

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

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

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Let's first address the elephant in the room - it's been a while since the last Caedium release. The multi-substance infrastructure for the Conjugate Heat Transfer (CHT) capability was a much larger effort than I anticipated and consumed a lot of resources. This lead to the relative quiet you may have noticed on our website. However, with the new foundation laid and solid we can look forward to a bright future.

Conjugate Heat Transfer Through a Water-Air Radiator
Simulation shows separate air and water streamline paths colored by temperature

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It turns out that Computational Fluid Dynamics (CFD) has a key role to play in determining the behavior of long extinct creatures. In a previous, post we described a CFD study of parvancorina, and now Pernille Troelsen at Liverpool John Moore University is using CFD for insights into how long-necked plesiosaurs might have swum and hunted.

CFD Water Flow Simulation over an Idealized Plesiosaur: Streamline VectorsIllustration only, not part of the study

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Fossilized imprints of Parvancorina from over 500 million years ago have puzzled paleontologists for decades. What makes it difficult to infer their behavior is that Parvancorina have none of the familiar features we might expect of animals, e.g., limbs, mouth. In an attempt to shed some light on how Parvancorina might have interacted with their environment researchers have enlisted the help of Computational Fluid Dynamics (CFD).

CFD Water Flow Simulation over a Parvancorina: Forward directionIllustration only, not part of the study

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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.