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The weekend is nigh and if you’re in the U.S. the long Memorial Day weekend beckons and if you work for a top 100 best place to work company the extra long weekend has already started. What better way to … Continue reading
The post This Week in CFD first appeared on Another Fine Mesh.
These past three weeks of CFD news threaten to turn this regular post into This Month in CFD if the burst of business travel doesn’t abate. But the more content the merrier. For those of you up for some fluids … Continue reading
The post This Week in CFD first appeared on Another Fine Mesh.
It’s been three weeks since the last collection of CFD news and notes was published so you can only imagine the backlog. This summer looks to be the return of a lot of in-person conferences and I am looking forward … Continue reading
The post This Week in CFD first appeared on Another Fine Mesh.
This is the first CFD news roundup of 2022 Q2 and it comes with the implication – or at least hope – that everyone’s Q1 was productive. We’re meshing a lot of rotating machinery this week, we have several job … Continue reading
The post This Week in CFD first appeared on Another Fine Mesh.
This week’s compilation of CFD news is highly image-oriented which might make it easier to read and maybe more enjoyable. But don’t skip over the event news, several CFD jobs, mesh generation resources, and your assigned reading for hypersonics. Shown … Continue reading
The post This Week in CFD first appeared on Another Fine Mesh.
Welcome to the “ides of March” roundup of CFD happenings. If you’re not currently enjoying spring break, check your knives at the door and dive into advice for engineering students from Jousef Murad that I couldn’t resist commenting on. Speaking … Continue reading
The post This Week in CFD first appeared on Another Fine Mesh.
Today’s video is something a little different. Rather than looking at fluids and their physics directly, we’ll take a step back and think about how people relate to the subject. This short film, “Volcano Pilot,” follows Haraldur Unason Diego as he reflects on his life’s work. It’s a beautiful and moving glimpse of the life and philosophy of a small aircraft pilot. Many people never have the opportunity to see the world from cockpit of a Cessna or similar small aircraft, and I think there are few experiences that can better connect someone to the fluids-in-action that is aviation. (Image and video credit: M. Aberra et al.)
How does water drip, drip, dripping onto stones erode a crater? Water is so much more deformable that it seems impossible for it to wear harder materials away, even over thousands of impacts. To investigate this, a team of researchers developed a new measurement technique: high-speed stress microscopy. In the process, they found that water owes its incredible erosive power to three factors: 1) The drop’s impact creates surface shock waves along the material, which helps increase erosive power; 2) After the shock wave passes, a decompression wave in the material helps loosen surface matter; and 3) The spreading drop sends a non-uniform wave of stress across the material that simultaneously presses and scrubs at the surface. Together, these factors enable simple, repetitive droplet impacts to wear away at hard surfaces. (Image credit: cottonbro; research credit: T. Sun et al.; via Cosmos; submitted by Kam-Yung Soh)
Sandwich a viscous fluid between two transparent plates and then inject a second, less viscous fluid. This is the classic set-up for the Saffman-Taylor instability, a well-studied flow in which the interface between the two fluids forms a wavy edge that develops into fingers. Despite its long history, though, there is still more to learn, as shown in this video. Here, researchers alternately injected a dyed and undyed version of the less viscous fluid. The result (Image 3) is a set of concentric dye rings that show how the fluid moves far from the fingers along the edge. Notice that the waviness of the fingers appears in the flowing fluid well before it approaches the interface. (Image and video credit: S. Gowan et al.)
When droplets coalesce, they perform a wiggly dance, gyrating as the capillary waves on their surface interfere. When the droplets have matching surface tensions, like the two water droplets in the animation on the lower left, the coalescence dance is symmetric. But for differing droplets, like the water and ethanol droplets merging on the lower right, coalescence is decidedly asymmetric.
The asymmetry arises from the droplets’ different surface tensions. The size and speed of the capillary waves that form on a droplet depend on surface tension, so droplets of different liquids have inherently different capillary waves. During merger, the interference of these capillary waves causes the asymmetry we see. (Image credit: top – enfantnocta, coalescence – M. Hack et al.; research credit: M. Hack et al.)
The mathematics of fluid dynamics still have many unknowns, which makes them an attractive playground for mathematicians of all stripes. One perennial area of interest is the Euler equations, which describe an ideal (i.e., zero viscosity), incompressible fluid. Mathematicians suspect that these equations may produce impossible answers — vortices with infinite velocities, for example — under just the right circumstances, but so far no one has been able to prove the existence of such singularities.
A recent Quanta article delves into this issue and the race between researchers using traditional methods and those using new deep learning techniques. Will the singularities be found and who will get there first? It’s well worth a read, whether theoretical mathematics is your thing or not. (Image credit: S. Wilkinson; see also Quanta; submitted by Jo V.)
Fly over a Martian crater in this incredibly detailed 8K video built from Mars Reconnaissance Orbiter imagery. Like Earth’s deserts, Mars is largely shaped by wind, and we get some fantastic views of sand ripples in this flyover. For reference, the vertical scale covered in the video image is roughly 1 kilometer. It’s pretty astounding to see this kind of detail from a spacecraft 250 kilometers away! (Video and image credit: S. Doran/NASA; via Colossal)
nproc = mpi_comm_size !How many processes myid = mpi_comm_rank !My rank among processes !Loop over all processes for i = 1 : nprocs !My communication partner at the i-th stage myp = modulo(i-myid-1,nproc) if (myid>myp) then !The process with higher rank sends and then receives mpi_send mpi_recv elseif (myid<myp) then !The process with lower rank receives and then sends mpi_recv mpi_send else !This is me, no send or recv actually needed endif endfor
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.
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.
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.
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.
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.
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.
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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The post Challenges in Meshing Scroll Compressors appeared first on GridPro Blog.
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.
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 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.
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:
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.
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.
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:
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.
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
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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The post Automation of Hexahedral Meshing for Scroll Compressors appeared first on GridPro Blog.
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.
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.
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.
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.
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.
There are many such scenarios where the user is provided feedback on the operations visually.
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:
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.
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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The post GridPro Version 8.1 Released appeared first on GridPro Blog.
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.
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.
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.
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.
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.
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.
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.
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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The post Turbopumps – A Unique Rotating Machine appeared first on GridPro Blog.
Figure 1: Structured multi-block meshing for aircraft vortex generators – the nacelle strakes.
1100 words / 5 minutes read
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.
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.
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.
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.
1. “Modelling the aerodynamics of propulsive system integration at cruise and high-lift conditions”, Thierry Sibilli, PhD Academic Year: 2011-2012, Cranfield University.
2. “CFD Prediciton of Maximum Lift Effects on Realistic High-Lift-Commercial-Aircraft-Configurations within the European project EUROLIFT II”, H. Frhr. v. Geyr et al, Second Symposium “Simulation of Wing and Nacelle Stall”, June 22nd – 23rd, 2010, Braunschweig, Germany.
3. “Navier-Stokes Analysis of a High Wing Transport High-Lift Configuration With Externally Blown Flaps”, Jeffrey P. Slotnick et al, NASA.
4. “Numerical Research of the Nacelle Strake on a Civil jet“, Wensheng Zhang et al, 28TH International Congress of the Aeronautical Sciences, ICAS 2012.
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The post Aircraft Vortex Generators – The Nacelle Strakes appeared first on GridPro Blog.
Figure 1: Structured multi-block grid for an aircraft intake S-Duct with vortex generators.
2300 words / 11 minutes read
Highly offset modern-day S-ducts need flow control mechanisms like vortex generators or synthetic jets to control the flow. The general consensus among researchers is that RANS solvers are in-capable of meeting the challenges of S-duct internal flow fields, while DES is considered the most promising candidate. Lastly, structured grids are observed to provide superior flow field prediction for S-ducts on a fraction of a grid size than that needed for unstructured grids.
The design of an optimized intake-duct is a trade-off between multiple requirements. This includes high-pressure recovery, low installation drag, low radar, and noise signatures, as well as minimum weight and cost. These requirements are driving the development of modern fighter aircraft and UAVs towards compact designs. This in turn has resulted in having a high degree of offset between the intake and engine face, leading to the design of highly bent S-ducts or serpentine ducts.
However, higher bend ducts lead to larger flow separation and vortex formation. With the compact nature of the duct, the available duct length is not good enough for the diffusion and dissipation of these secondary flows. This results in total pressure loss and higher flow non-uniformity at the engine fan face. This is something unpleasant, as flow distortion at the inlet can lead to reduced stability margin for the compressor or fan. Further, inlet distortion can cause high cycle fatigue resulting in increased maintenance costs, loss of aircraft operability, and even catastrophic loss of the aircraft.
CFD as a design tool is used extensively for accurate aerodynamic prediction of highly offset diffuser shapes. Attention is paid to understanding the variation in distortion levels and pressure recovery at the AIP with changes in duct shape. CFD is quite handy in studying the parametric variations of vortex generators and synthetic jets. Figure 3 shows the reduction in flow separation with the introduction of vortex generators.
The effectiveness of the VGs depends on a number of parameters, but the main ones are the height of the VG, the orientation angle of the VG relative to the free stream and the number of VGs used. Hence testings’ are done by tweaking these parameters to study their influence on the flow.
Effect of Number of VGs: The placement of the VGs starts from the symmetry plane. The first one is placed near the symmetry plane and subsequent ones are placed in the circumferential direction at constant x. As can be observed in Figure 3, the flow quality improves with the usage of additional VGs in the form of diminishing of low Mach number region and the recovery of the pressure loss.
Passive flow control techniques have been in existence for decades, but active flow control (AFC) methods like constant or pulsed air jets are a new budding research area. Here, the improvement in flow efficiency is accomplished through a delay in flow separation.
Figure 6 shows AFC configurations for constant blowing jets with three slit areas with different widths. Studies show that improvement in recovery and reduction of distortion happens with an increase in the mass flow rate of the jets. However, beyond a certain value, a further increase in mass flow rate makes the AFC less effective. Best performance is shown by the smaller area and higher jet velocity configuration. A maximum pressure recovery of 1.15% and distortion reduction by 60-80 percent have been reported.
Accurate prediction of large separated flow regions such as seen in S-ducts by CFD is very difficult. Currently, efforts are put to evaluate and understand the capabilities and limitations of RANS, URANS, and DES methods in modeling the flow physics and performance characteristics of highly offset intake diffusers.
The general consensus among researchers is that RANS solvers are in-capable of meeting the challenges of S-duct internal flow fields. URANS shows better predictions capabilities by capturing the time-evolution of the flow. Further research and development are needed to make it more accurate. DNS or LES methods at least in principle are capable to make accurate predictions of the flow physics to the required level. However, being computationally expensive, they are still not feasible. Only DES – Detached Eddy Simulation, is considered among many researchers as the potential candidate for predicting unsteady flow fields, involving high levels of turbulence and occurrences of instantaneous flow phenomena.
DES, which is nothing but a hybrid of RANS-LES turbulence methods has computational costs which are quite acceptable. DES offers a nice balance by providing the physical accuracy of the LES with the cost-effectiveness of the RANS. Since building a DES code involves, only coupling the pre-existing RANS and LES code, they reduce the time and cost needed for development.
This bifurcation of the regions is essential because there are major differences between RANS and LES arising due to the different turbulence modeling approaches. LES resolves the larger scales of the flow and models only the smaller scales. While, RANS on the other hand does not explicitly resolve any scales, but calculates the mean flow quantities and models the turbulent scales.
Since in LES, the flow turbulent scales are explicitly resolved, the generated eddy viscosity is smaller compared to that in RANS. As a result, LES reduces the viscous dissipation and diffusion in the flow, thereby allowing weaker flow structures to sustain in the solution.
Another difference between RANS and LES is the grid dependency. In RANS, the turbulence model is based on flow quantities and is similar for every grid, while in LES, the filter width is directly dependent on the grid-spacing. What this means is that any grid refinement in LES not only influences the numerical accuracy, but also the subgrid-scale turbulence model. As a consequence, unlike in RANS, grid-refinement beyond a point of convergence in LES will not produce the same solution.
When we consider standard DES in general, it can be noticed that there is a large dependency on the RANS part of the simulation, which requires a tangential grid spacing on the wall to be greater than the local boundary layer thickness. Sticking to this gridding requirement may be very difficult inside the duct. In such circumstances, if the switching to LES occurs inside the RANS boundary layer, then there will be an underestimation of the skin friction coefficient.
So, in order to overcome this grid-induced separation, newer approaches like DDES – Delayed Detached Eddy Simulation and ZDES – Zonal Detached Eddy Simulation have been developed. In DDES, the switch to LES mode is delayed while in ZDES, the RANS and DES zones are selected individually, to have clarity of the role of each region. These steps are undertaken to avoid ‘model stress depletion’ and grid-induced flow separation.
In RANS alone, the predicted flow distortion and pressure recovery depend highly on turbulence modeling. Depending on the turbulence model chosen, the total pressure recovery is reported to vary from 0.1% to -1.8%, while flow distortion is observed to differ widely from +37% to +126%.
However, many major differences exist between the numerical results and experiments. The region with total pressure losses doesn’t conform with the experiments. The distortion parameter is systematically overestimated, with the DES solution differing from experiments by a larger value than RANS, as DES overestimates the separated flow region. Also, DES shows a delay in the development of the instabilities in the shear layer.
Just like the need to pick the right solver type, there is a need to pick the right grid type to do accurate CFD prediction for S-ducts. Studies have shown that grid generation both in structured and unstructured ways for S-ducts is quick, efficient, and reliable. However, structured meshes tend to achieve superior performances than high-quality unstructured meshes says a research study by ANSA, BETA CAE system.
Solution differences between the structured medium grid and fine grid were observed to be very small. Even though the grid size nearly doubles in between the medium and fine grids, the AIP back pressure was seen to make only a marginal variation of 0.05% between the two grids. On the other hand, notable differences were noticed between the unstructured medium grid and fine grid simulation results. The difference in predicted backpressure between the two grids is about 1%.
When the flow field as generated by equivalently resolved unstructured and structured grids was compared, the differences were significant. To make the grids more relatable to each other, the fundamental cell size was kept the same for the two different mesh setups. Tetrahedral cells being isotropic in nature needs more mesh cells to achieve a roughly equivalent sized structured grid cell.
Thus, the structured mesh outperforms the unstructured hybrid grid despite having a far lower cell count. The structured grid with a cell count of 4.2 million predicts a more greatly resolved flow solution than its equivalent unstructured counterpart. Interestingly, to achieve a similarly resolved flow solution, the unstructured approach demanded a grid of size 31.2 million.
What we can conclude from these gridding experiments is that, at least for flows in S-duct with significant shear, an order of magnitude more unstructured cells are needed to match an equivalent structured grid in terms of solution accuracy and flow field resolution. Even with VGs, structured meshes require far fewer cells. Thus, structured meshes require lesser computational resources to predict higher-quality flow fields than higher-density unstructured grids.
This brings us to the end of this article. Complete elimination of flow distortions in S-duct at all flight conditions is impossible. The use of flow control mechanisms like vortex generators or synthetic jets is the way to go forward in dealing with flow separations.
Evaluation of the distortion parameter results from steady and dynamic simulations at the engine face reveal that only dynamic simulations can provide the correct assessment of the performance parameters considering the distortion limits as required by the engine manufacturers. Compared to RANS, DES results, in general, are in accordance with the experiments, but in the future, its capabilities need to be further enhanced to improve its accuracy to the level required by industrial standards.
Physical time step size and grid resolution have an important role in the outcome of the computational results. For DES simulations, a strong association exists between the grid and the ability of the algorithm to correctly manage the varying turbulent scales.
Structured meshes are reported to provide superior solutions compared to unstructured meshes. Needing only a fraction of the cell count as needed by unstructured meshes, structured grids are observed to provide a better-resolved flow field and tend to take far lesser computational resources.
1. “Numerical Simulations of Flow Through an S-Duct”, Pravin Peddiraju, Arthur Papadopoulos, Vangelis Skaperdas, Linda Hedges, BETA CAE Systems, 6th BETA CAE International Conference.
2. “CFD Simulation of Serpentine S-Duct with Flow Control”, Lie-Mine Gea, 51st AIAA/SAE/ASEE Joint Propulsion Conference, July 27-29, 2015, Orlando, FL.
3. “CFD Validation and Flow Control of RAE-M2129 S-Duct Diffuser Using CREATE-AV Kestrel Simulation Tools”, Pooneh Aref et al, Aerospace 2018, 5, 31.
4. “Numerical simulations for high offset intake diffuser flows”, T.M. Berens et al, NLR-TP-2014-096.
5. “A Multi-objective shape optimization of an S-Duct intake through NSGA-II genetic algorithm”, Aurora Rigobello, 5 Dicembre 2016, Universita’ Degli Studi Di Padova.
6. “S-Duct Inlet Design for a Highly Maneuverable Unmanned Aircraft”, Jacob Brandon, Thesis, The Ohio State University, 2020.
7. “Effectiveness of a Serpentine Inlet Duct Flow Control Scheme at Design and Off-Design Simulated Flight Conditions”, Angela C. Rabe, Doctor Of Philosophy In Mechanical Engineering, Virginia Polytechnic Institute and State University, August 1, 2003.
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The post Role of Vortex Generators in Diffuser S-Ducts of Aircraft appeared first on GridPro Blog.
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:
Stallion 3D strengths are:
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:
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.
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Figure 1. Various discretization stencils for the red point |
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p = 1 |
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p = 2 |
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p = 3 |
CL
| CD
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p = 1 | 2.020 | 0.293 |
p = 2 | 2.411 | 0.282 |
p = 3 | 2.413 | 0.283 |
Experiment | 2.479 | 0.252 |
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!
[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.
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.
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.
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.
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 cases2,6 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.
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?
[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.
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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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.
The extreme conditions in internal combustion (IC) engines make it important to understand the thermal and structural stresses experienced by critical components of the device. In the cylinder, studying the temperature distribution and the thermal stresses caused by combustion is essential to determine the durability of the engine. For such conjugate heat transfer (CHT) problems, where the heat transfer occurs simultaneously within and between the fluid and solid regions, CONVERGE offers a novel time control approach to accelerate the simulations without decreasing accuracy: super-cycling.
In super-cycling, CONVERGE alternates between two solution methods. First, CONVERGE runs a fully-coupled fluid-solid transient solver to convergence. Then, CONVERGE uses the solution of that solver to update the boundary conditions for a steady-state solid-only simulation. The solid-only results feed back new boundary condition information to the transient solver. This alternating process repeats until the global simulation is complete. Super-cycling also makes it possible to map heat transfer results from one cylinder to all the other cylinders in the geometry, obviating the need to model combustion in all cylinders and further accelerating the simulation.
Here, we’ll briefly look at a study demonstrating how CONVERGE can accurately predict the temperature distribution in the engine head and block. For more details, please see our white paper on the topic.
In this study, the geometry is half of a V6 engine, including the engine head and block. We solve the case by two different approaches which are described below.
Because of the different time-scales of combustion and heat transfer in the engine’s solid components compared to heat transfer in the coolant, CONVERGE’s methodology involves running two separate simulations and mapping results between the two in an iterative process to obtain converged temperature predictions. This procedure allows for optimal settings for both the coolant and the chamber simulations to enhance the overall simulation accuracy and speed.
In this option, the first simulation is of the coolant flow and the heat transfer between the coolant and the solid materials of the engine head. The second simulation is a CHT simulation that models the combustion process and heat transfer between the combustion chamber and the engine block and head. Figure 1(a) shows the engine coolant system, and Figure 1(b) shows the geometry for the combustion simulation.
The second approach also iterates between two simulations. To efficiently determine the requisite solid temperature field, we first perform simulations to model combustion in the engine cylinder. These simulations may take longer to run. We then perform a CHT simulation to model heat transfer between the coolant and the solid material.
The results from both of the iterative approaches were in good agreement with experimental data. The normalized surface temperature distribution results are shown in Figure 2. These approaches combine the accurate and detailed simulation of combustion kinetics with CHT to develop a realistic temperature distribution in the solid components of an engine. With CONVERGE, engineers can efficiently include the details of gas flow, combustion, and coolant flow to predict the temperature field for optimal design of solid engine components without the additional complexity of building a mesh. To learn more, please check out our CHT white paper!
Significant cycle-to-cycle variations (CCV) in internal combustion (IC) engines can lead to undesirable effects like noise and vibration, engine damage, and poor drivability. It is important for engineers to estimate quantities such as peak cylinder pressure, combustion duration, and coefficient of variance of indicated mean effective pressure (IMEP) to design better engines. Moderating CCV can open doors to many advanced combustion concepts, such as low-temperature combustion strategies, to reduce emissions and increase efficiency.
To accurately estimate CCV, you need to perform many engine cycle simulations—on the order of 100 consecutive cycles. Typically, simulating one engine cycle that follows our recommended best practices in CONVERGE takes a few hours with sufficient computational resources. Continuing that simulation for 100 consecutive cycles is a painstaking process (on the order of a few months) and hence a computationally expensive one.
Is there an alternate method to capture CCV?
The answer is yes! We know that long runtimes are unacceptable for many industry research timelines, and so we have applied an alternate method, called the concurrent perturbation method (CPM), to capture CCV in CONVERGE. This method was first proposed and published by Ameen et al. (2016)1 at Argonne National Laboratory.
What is the concurrent perturbation method?
Instead of solving 100 cycles consecutively, with CPM, CONVERGE solves 100 cycles concurrently. Given sufficient computational resources, CPM reduces the overall turnaround time to the time taken to simulate one engine cycle. At this point, you might be asking yourself how it is possible to run the cycles concurrently when the result of one cycle can be determined only after knowing the results of the cycle preceding it.
This is where the perturbation in CPM comes into play. We start by simulating one or more engine cycles to wash out the homogenized initial conditions that are defined while setting up the case. The combustion event and exhaust process of the first cycle(s) produces a representative velocity, pressure, temperature, and species field. The outcome of the initial cycle(s) is used to initialize each of the concurrent cycles, which are set up as independent cases. Each individual cycle’s flow field is then perturbed in order to yield a distinct cycle as the simulation proceeds (Figure 1). We apply only a miniscule perturbation to each flow field so as to not significantly change it. The perturbation is simply a noise field applied on top of the velocity field. The solution naturally develops into a different realization due to the chaotic nature of the combustion system.
What do the results show?
Figure 2 shows a comparison of the cylinder pressure obtained from consecutively and concurrently run simulations performed by Probst et al. (2020).2 The results are similar, and the predicted pressure lies within the maximum and the minimum pressure cycle of the measured data.
Additionally, Probst et al. found that starting the concurrent cycle simulations at intake valve opening (IVO) is sufficient to yield distinct and valid cycles. In contrast, when running cycles consecutively, it is necessary to simulate the full cycle. The required core hours for concurrently run cycles, as a result, are fewer than for consecutively run cycles. So, by concurrently running cases, multiple engine cycles can be completed in far less wall-clock time and with fewer core-hours compared to consecutive simulations.
Are you ready to try CPM to speed up your projects? Check out the video below to learn how CPM works, how to set up CPM in CONVERGE, and the conditions in which it will work best.
References
[1] Ameen, M., Yang, X., Kuo, T., and Som, S., “Parallel methodology to capture cyclic variability in motored engines”, International Journal of Engine Research, 18(4), 366-377, 2016. DOI: 10.1177/1468087416662544
[2] Probst, D., Wijeyakulasuriya, S., Pomraning, E., Kodavasal, J., Scarcelli, R., and Som, S., “Predicting Cycle-to-Cycle Variation With Concurrent Cycles In A Gasoline Direct Injected Engine With Large Eddy Simulations”, Journal of Energy Resources Technology, 142(4), 2020. DOI: 10.1115/1.4044766
Tarique Shaikh joined Convergent Science in March 2018, after earning his master’s degree from the Technical University of Munich Asia. In his three and a half years with us, Tarique made a significant and lasting impact on both the company and his coworkers. Ashish Joshi, general manager of Convergent Science India, grew to know Tarique well on a professional and a personal level:
“Tarique joined Convergent Science as an applications engineer in our India office to support customers in a variety of industries. He worked on several interesting simulations, including one of flow over an entire city. This was a new application for Convergent Science, and his work in this area was greatly appreciated. Tarique, however, didn’t want to be a ‘Jack of all trades’, but instead a ‘master of one’. It was then that I recommended him for the Gas Turbine Applications team.”
The Gas Turbine Applications team at Convergent Science studies key issues related to the safety, operability, and environmental impact of aviation engines. The team simulates phenomena including lean blow-off, a scenario in which an airplane engine goes out; high altitude relight, which investigates how to relight an engine that goes out mid-flight; and methods for reducing harmful emissions from gas turbine engines. As the leader of the Gas Turbine Applications team, Scott Drennan worked closely with Tarique:
“Tarique worked in the Gas Turbine Applications group for a little over three years. In that time, Tarique developed a deep understanding of gas turbine combustion and became a valued member of the team. Specifically, he handled the creation of marketing materials to demonstrate CONVERGE’s capabilities for hydrogen combustion in gas turbines. He developed a simulation of fuel switchover from methane to hydrogen in a gas turbine combustor. In addition, he generated comparison videos of lean blow-off (LBO) for methane and hydrogen, demonstrating CONVERGE’s ability to use the SAGE detailed chemistry solver to simulate a dynamic (changing) operating condition. Tarique also developed a microturbine ignition marketing animation and helped on ignition modeling with a commercial customer. Tarique was a key contributor in a recent commercial evaluation with an aviation engine manufacturer using a combination of high-fidelity models for turbulence, heat transfer, combustion, and emissions in CONVERGE, and comparing the results to experimental wall temperature and emissions data for NOx, CO, and soot. Recently, Tarique was working on validation of our Thickened Flame Model (TFM) in a commercial combustor and testing CONVERGE’s hybrid turbulence model on wall cooling validation cases.
Tarique was a thoughtful, smart, hardworking engineer and a key contributor to the Gas Turbine Applications team. He will be greatly missed.”
Tarique’s legacy at Convergent Science extends far beyond his technical contributions. He was a kind and caring person, with whom his colleagues greatly enjoyed working. Many of his coworkers from the Convergent Science India office and the Gas Turbine team wished to provide their thoughts and reflections on knowing and working with Tarique:
“I remember one time when the Convergent Science owners asked me who my favorite colleague from the India team was. It was very difficult to give just one name, but Tarique was definitely one of my favorites. We would discuss many topics apart from work: food, religion, politics, and fitness, just to name a few. One thing we bonded over was our love for biryani.”
–Ashish Joshi, General Manager, India office
“While pursuing my master’s degree, Tarique and I spent a lot of time together since we both belonged to the Aerodynamics and Fluid Mechanics lab at the Technical University of Munich (TUM). We were both from the Indian subcontinent, so we were hardly prepared for the European winters. During those harsh winter days in Munich, Tarique used to invite a few of us to his place for delicious dinners, which he prepared with such fervor. Cooking for friends and family was one of the things that Tarique dearly loved. As international students, a lot of us struggled with cooking, but Tarique tried to motivate us to cook. Whenever we felt homesick, he was always there to cook some of the most delicious meals I had during my student days. Those memories with Tarique are something I will cherish all my life. We will miss you, Tarique.”
–Harshan Arumugam, Business Development Manager, India office
“Tarique had a great love for food. Whenever we traveled together, he wanted to have a scrumptious meal; I really enjoyed seeing his joy for food. During our visit to the IIT Madras Gas Turbine Conference, he twice had me run to the nearby store to get cans of coke so he could have them with his five-course meal.”
–Abhishek Sinha, Sr. Business Development Manager, India office
“Tarique was a very friendly person who was always willing to offer help. He was good at technical discussions and meticulous at his work. In one of our first interactions, we bonded over the fact that he was originally from Sawantwadi, a place close to where I’m from.”
–Viraj Shirodkar, Research Engineer, Software Development, India office
“Tarique was a cleanliness freak. I remember when I visited his house for the first time, no one would believe that it was a bachelor’s home. He proudly showed us the different kinds of vacuum cleaners he used to clean his house. On my first day in the office, he was annoyed when I mistakenly took his chair and removed its plastic cover. Everyone joked that Tarique would be very angry. Little did I know that he was a sweetheart and took everything as a good sport. I’m glad we crossed each others’ paths in life.”
–Apurva Bhagat, Research Engineer, Software Development, India office
“Tarique made a courageous decision when he voluntarily switched to the Gas Turbine team, because I believe this to be one of the most challenging applications of CFD. Needless to say, in only a short time, he turned out to be one of the most valuable members of the team. One of his qualities that I admired the most was that whenever we discussed a problem, he was never hesitant to take out a pen and a piece of paper and immerse himself in analytical equations.
If there is one word that can describe Tarique, it is no doubt ‘caring’. If anyone around him was in need of any kind of help, be it professional or personal, you could always count on him to be the first one to offer help. On more occasions than I can remember, he offered me rides, especially when I had a leg procedure and had difficulty walking. His nurturing nature was further evident from his ardent love for plants, of which he had a great collection at home. I have always taken inspiration from him and hope to be able to integrate these qualities of his into my life.”
–Geet Nautiyal, Research Engineer, QA, India office
“It is very difficult to write about Tarique in the past tense, but time doesn’t care for feelings and emotions. Tarique—the name itself is a treasure trove of memories. When I joined Convergent Science India back in 2018, it was Tarique who trained me on CONVERGE Studio and the solver. Tarique helped me a lot in setting up a fixed inlaid mesh for a rectangular bluff body for turbulence validation in 2019. Our last work-related interaction was when he helped correct the heat transfer validation slides.
Tarique and I shared a passion for general knowledge and current affairs, which led to many funny conversations. Our company trek to the Singahad fort was an epic one, in which Tarique set off quickly ahead of us, but ended up last to 900m. I used to call him ‘Gibraltar’, which is the Spanish version of the Arabic name Jabel al Tarique (Rock of Tarique). Tarique, my friend, why so early?”
–Akshay Iyer, Research Engineer, Validation, India office
“Tarique was a valuable member of our India team. Although we did not have the pleasure of interacting with him as much as some in the company, his contributions did not go unnoticed. He was a hardworking engineer who tackled some of the most challenging problems with our software. During our first Indian CONVERGE User Conference in Bengaluru, Tarique was tasked with providing a demo of our software to a large audience of CFD enthusiasts. This was a critical part of introducing many Indian engineers to CONVERGE, and Tarique did a wonderful job. He will be missed.”
–Dan Lee, Eric Pomraning, Keith Richards, Kelly Senecal, and Rainer Rothbauer, Convergent Science Owners, U.S. and Europe offices
“Tarique was an invaluable member of the Convergent Science family and the Gas Turbine team. Over the last year and a half, we had grown to be more than just colleagues—we had become good friends. He was kind, amicable, and easy to talk to. I never got to meet Tarique in person, but we worked closely every day. He has left a lasting impression through his dedication, hard work, and team spirit. He approached his work with determination and diligence, and he was a quick learner who had a huge appetite for knowledge. He was always eager to pick up tasks that needed research and reading. He was a true team player who always put the goals of the team first, and he was always very accommodating and helpful. Scheduling meetings between India and the U.S. offices is always a challenge due to the time difference. I was humbled that Tarique always insisted I choose a time convenient for me because I had a small child at home. The work he did as part of the Gas Turbine team helped immensely in the support and evaluation efforts of gas turbine customers and in the continuous improvement of the CONVERGE solver and graphical user interface. Some of his work, like the microturbine and annular combustor example cases, will be used by the team for years to come. Tarique will be greatly missed.”
–Gaurav Kumar, Principal Engineer, U.S. office
“Working from the U.S., I was never able to meet Tarique in person, instead working closely with him via Zoom meetings, phone calls, etc. Even through these somewhat impersonal media, it was clear that Tarique was a thoroughly kind, trustworthy, and caring person and a sharp engineer to boot. If I ever needed to dive deep into a challenging problem or diagnose a tricky issue, Tarique was the first person I turned to. He approached his work with diligence, determination, positivity, and attention to detail, and I learned a lot from watching him attack problems without ever giving up. On days when our shared projects weren’t going the best, we would often meet well past midnight U.S. time. These interactions were always lighter, and we would joke our way through to finding a solution to whatever was stumping us. I will miss him.”
–Gabe Jacobsohn, Research Engineer, U.S. office
Tarique touched many of our lives during his time at Convergent Science, and he will continue to have a presence at our company. Tarique’s father donated his collection of CFD books to Convergent Science, which we are using to make a small memorial library at our India office. In addition, Tarique had a penchant for gardening and a love for plants, and one of his plants will now brighten our India office’s reception area. We are grateful for the opportunity to keep Tarique’s memory alive with these donations. We will miss him greatly.
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.
This video will demonstrate time animation using the Probe to Create Time Series Plot tool as an example. We will create an XY line plot on top of a contour plot, link the frames by solution time, and provide time animation export recommendations.
Starting with a simple 2D transient plot, our goal is to create a probe over time plot which will graph the data in a separate XY line frame.
First, select Tools > Probe to Create Time Series Plot, and click on the plot in a region of interest.
This creates an XY line plot on screen.
Second, select Frame>Frame Linking… and you can see that both frames are linked by solution time. If we un-toggle this option for either of the plots, they will not play concurrently. Leave these frames linked by solution time.
Notice also that the XY plot has a Solution Time x-axis marker. Turn this on and off in the plot with a macro command. See this example for how to use that macro. If you are interested in creating an XY line plot that will draw the line as time progresses, see this other macro file on our GitHub (.mcr) as an example.
Third, move the XY Line plot on top of the 2D contour plot. Give it a transparent background by double-clicking the edge of the Frame to bring up the Edit Active Frame dialog. Toggle off Show background.
In an earlier video, we demonstrated placing text annotations on the plot. Here, we input the current solution time dynamic text on the plot in scientific notation, and increase or decrease the precision.
Now we are ready to export an animation of our plot. In some cases, you may want to create an .mp4 animation file directly using the Tecplot 360 GUI. However, for large data sets, we recommend exporting a sequence of images (demonstrate on screen and make sure to click antialiasing). This option can be found in the time animation details dialog after clicking export to file, and selecting Export format:
Exporting a sequence of high-quality images provides flexibility. It allows you to manually step through each image.
Additionally, using a tool such as ImageMagick or FFmpeg (executable shipped with Tecplot 360), you can create a variety of animations with different framerates, in formats like .mp4 or .gif. You can also take advantage of our GitHub script, to automate this process with Python.
If you want to export images more quickly, you can automate the process in parallel, see this parallel image creator script for more details.
This concludes this tutorial on working with transient data. Thank you for watching.
The post Plotting Transient Data with XY Line Plots and Exporting Animations appeared first on Tecplot.
At blueOASIS, Tecplot 360 is an essential tool for Making the World Green and the Oceans Blue. Either in the CFD simulation of offshore energy harvesting devices (wind, wave and current) and their impact on the maritime life; or other projects such as:
All our Renewables and Ocean Sustainability projects generate large amounts of raw data which require an efficient, yet powerful tool to generate comprehensive, easy interpretable and fine-looking results. Our tool of choice is Tecplot 360.
Figure 1. Maritime & Naval applications: validation of limiting streamlines vs experimental data for a model-scale KVLCC2 ship. Effect of turbulence modelling (reference).
The engineers at blueOASIS have gained 20 years of experience using both open-source and commercial visualization packages, and still use both types of tools. But one of the key advantages of Tecplot 360 is the simplicity and intuitive character of its graphical user interface (GUI), which enables new users to quickly start using it. Another feature that sets Tecplot 360 apart from other solutions is its scripting capabilities using the Python API. PyTecplot provides a platform for simple scripts that can be executed within the GUI or in batch, which harness the full functionality of the software in streamlining complex workflows, saving valuable time in projects.
Figure 2. Renewable Energy: simulation of a floating offshore wind platform. Surface grid, free-surface iso-surface and vorticity distribution at symmetry plane.
Obviously, Tecplot 360 also excels at data visualization; It provides control and flexibility over the resulting image, vector, or animation in a user-friendly manner. Multiple views of the same data are easily set-up using separate frames, which can be either independent or dependent from one another and are fully customizable. Another advantage of Tecplot 360 is having several visualization modes, including XY plots, which can be easily overlapped and even time-synchronized with regular 2D/3D views. These functionalities enable the creation of data-rich graphics, suitable for reports and presentations.
Renewable Energy Video: Simulation of a free-floating offshore wind platform. Geometry, free-surface iso-surface colored with x-velocity component. It includes platform attitude time history xy plots.
The crucial CFD-oriented operations embedded into Tecplot 360 are vital for our Renewables and Ocean Sustainability projects. Tasks that are tedious and unreliable in open-source tools are simplified and reliable in Tecplot 360. One example is the easy calculation and setup of surface limiting streamlines (or shear-stress lines). Because this is an important fluid mechanics feature to analyze flow patterns, recirculation and separation areas, and drive geometry optimization, it is just one of the many crucial features for any CFD practitioner. But there are more examples, such as one-button contour plots with colors and lines, tangent vectors in slices, arbitrary slice shape creation, volume streamlines control and accuracy, memory management, and more.
Figure 3. Maritime & Naval applications: simulation of a propelled appended ship including free-surface effects. Geometry, free-surface iso-surface, slice at symmetry place of the x-component of the velocity field and surface limiting streamlines.
Moreover, the easily accessible online tutorials and training sessions enable us to continuously learn more on how to fully take advantage of Tecplot 360’s functionalities. blueOASIS uses both open-source tools and Tecplot 360, which means we can combine the best features of both worlds. Currently, we are working on the coupling between Tecplot 360/PyTecplot and Blender, a popular high-level rendering open-source tool, to create (even) more realistic pictures and animations.
Figure 4. Current/tidal turbine: limiting streamlines and q-factor iso-surface showing the cells used.
All in all, Tecplot 360/PyTecplot is the preferred choice of blueOASIS’s for data treatment and visualization in Renewables and Ocean Sustainability projects, since it provides a complete and efficient sustainable solution on our quest to seek new solutions for old problems.
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The post A Tight Cooperation Towards Ocean Sustainability appeared first on Tecplot.
Key Frame Animations allow you to easily animate a smooth progression through two or more specified views (key frames) and export them as an animation. You can zoom, rotate, and translate in these animations. In addition, you can create “fly around” movies or first-person animations. The animation you see here explores a “fly-around” of air flowing through a duct.
To export an animation, change the Destination from “On Screen” to “To File”. Then select the [Animate All] or [Animate Selected] to open the Export Options dialog. We recommend exporting to MPEG-4 format as it offers a high quality to file-size ratio, and it can play-back on many media players.
Key Frames are not saved with the layout, so to save your key frame views, go to File > Save Animation File, to choose a location to save your *.keyframe.
To reopen your plot after restarting Tecplot 360, open your saved layout and then in the Key Frame Animation dialog, click File > Open Animation File and select the saved key frames.
The Key Frame Animation tool can also be helpful for users that want to save and return to multiple different views. Instead of creating an animation, you can simply append any important views you want of your layout and save them in a *.keyframe file. In the Key Frame dialog, to get back to a saved view, select the view and click [Apply]. This will snap the selected frame to your saved view.
For more details, especially for concurrent time animation, check-out the documentation in the User’s Manual.
Thank you for watching!
Get a Free Trial of Tecplot 360
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If you’re anything like me, you probably played with LEGOs as a kid (and probably continue to play with LEGOs as an adult).
I love LEGOs for the fact that you can take just a few building blocks and make nearly anything you want out of them. But LEGOs can be frustrating when you can’t find the one piece you need for your project.
In my years at Tecplot I’ve started to think of Tecplot 360 and PyTecplot like LEGOs. There are a lot of building blocks that can help you do many types of visualization and analysis. Some building blocks are easy to find (like slices, iso-surfaces, and streamtraces), but other building blocks are a bit harder to find (like spanwise integrations and time averages).
Thankfully Tecplot has a team that can help you find these build blocks if you’re having a hard time finding them yourself (kind of like my mom, who could always find the LEGO piece when I needed it).
We recently had a user contact us wanting to “extract a line that represents the minimum pressure location as a function of Z.” Whew – now that’s a building block that’s hard to find. Sometimes you need to build your own – so that’s what Tecplot support engineers did for this user.
This user has Tecplot for Barracuda to visualize their results from Barracuda Virtual Reactor. They want to understand where the minimum pressure is throughout the height of their cyclone. With Tecplot 360 you can easily create a series of slices (as pictured at left) to visually understand the minimum pressure location. But it can be challenging to see this visually since the pressure values vary so much in the vertical direction you get little variation across each slice.
So you can see the need for a more quantifiable approach.
PyTecplot, the Python API of Tecplot 360, gives you direct access to the raw data in your simulation results. Using PyTecplot we were able to extract the minimum pressure location at defined Z-locations within the volume. With the set of XYZ minimum pressure locations we were then able to generate a new zone which is the “line that represents the minimum pressure location as a function of Z.”
Sure, this took a few lines of Python (about 30 lines), but through the power of Tecplot 360, PyTecplot, and an amazing technical support staff, we were able to use the building blocks provided by Tecplot products to build an analysis routine that we hadn’t seen before.
An example of the script is on the Tecplot GitHub site. And the next time you’re working on an analysis problem and can’t find the LEGO piece you need, make sure to call mom, er, I mean support@tecplot.com
The post Tecplot 360 and PyTecplot are like LEGOs appeared first on Tecplot.
In this video, we will give a simple demonstration of plotting surface slices, extracting them, and creating an XY line plot from the results.
First, we’ll open our data set. In this demonstration, we are using the Onera wing example found in the Tecplot 360 installation directory. This data set has Pressure Coefficient variable values on the wing surface, and I want to plot a Y-slice of this data in an XY plot.
We can toggle on the contour layer and display the Pressure Coefficient.
Toggle on Slices in the plot sidebar to place a slice on the plot; Use the slice placement tool to place a slice on the wing surface.
This isn’t in the orientation or slice type that we want, so we will open the Slice Details dialog to adjust it:
Now create a new Frame, and change to the XY Line plot type, and note the option of creating new mappings for all linear zones. This option is useful if multiple slices are extracted. For the X axis we’ll choose the normalized X variable which I calculated beforehand. For more information on how I calculated this normalize variable, see our Getting Started Guide. For the Y axis we can select the Pressure Coefficient variable. We could name the XY mapping something more useful, but we will take advantage of an intrinsic variable later in the Mapping Style dialog.
After the XY line mappings have been created, I want to edit one of my axes. I want to reverse the Y-variable direction because a y-axis ranging from positive to negative values is typical for a Cp line plot. To reverse the Y axis direction, double click the axis in the plot, or select Plot>Axis, and toggle on Reverse axis direction in the Range tab of the Y1 axis.
Now, tile the frames so that they do not overlap (Frames>Tile Frames…)
And now we have our XY line plot showing the extracted slice from the wing surface. I can change the line color to (blue) and turn-on the line legend. To change the map name to the zone name, in the Mapping Style dialog we can right-click on the mapping and choose zone name by double-clicking on it:
This concludes the tutorial on surface slice extraction. Thank you for watching.
For more advanced information on how to perform other data extractions, check out our other videos linked below.
The post Extracting Surface Slices in XY Line Data appeared first on Tecplot.
Is there a trial version of the software?
Yes! Here are links to request a free trial of 360, FieldView, and RS
Where can I apply for a Suite license?
You can get the Application for Academic pricing and request a quote on our Academic Suite webpage. Or simply contact us – email sales@tecplot.com or call 1.800.763.7005 .
Where can I learn more about the Tecplot Academic Suite?
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Ansys has announced that it will acquire Zemax, maker of high-performance optical imaging system simulation solutions. The terms of the deal were not announced, but it is expected to close in the fourth quarter of 2021.
Zemax’s OpticStudio is often mentioned when users talk about designing optical, lighting, or laser systems. Ansys says that the addition of Zemax will enable Ansys to offer a “comprehensive solution for simulating the behavior of light in complex, innovative products … from the microscale with the Ansys Lumerical photonics products, to the imaging of the physical world with Zemax, to human vision perception with Ansys Speos [acquired with Optis]”.
This feels a lot like what we’re seeing in other forms of CAE, for example, when we simulate materials from nano-scale all the way to fully-produced-sheet-of-plastic-scale. There is something to be learned at each point, and simulating them all leads, ultimately, to a more fit-for-purpose end result.
Ansys is acquiring Zemax from its current owner, EQT Private Equity. EQT’s announcement of the sale says that “[w]ith the support of EQT, Zemax expanded its management team and focused on broadening the Company’s product portfolio through substantial R&D investment focused on the fastest growing segments in the optics space. Zemax also revamped its go-to-market sales approach and successfully transitioned the business model toward recurring subscription revenue”. EQT had acquired Zemax in 2018 from Arlington Capital Partners, a private equity firm, which had acquired Zemax in 2015. Why does this matter? Because the path each company takes is different — and it’s sometimes not a straight line.
Ansys says the transaction is not expected to have a material impact on its 2021 financial results.
Last year Sandvik acquired CGTech, makers of Vericut. I, like many people, thought “well, that’s interesting” and moved on. Then in July, Sandvik announced it was snapping up the holding company for Cimatron, GibbsCAM (both acquired by Battery Ventures from 3D Systems), and SigmaTEK (acquired by Battery Ventures in 2018). Then, last week, Sandvik said it was adding Mastercam to that list … It’s clearly time to dig a little deeper into Sandvik and why it’s doing this.
First, a little background on Sandvik. Sandvik operates in three main spheres: rocks, machining, and materials. For the rocks part of the business, the company makes mining/rock extraction and rock processing (crushing, screening, and the like) solutions. Very cool stuff but not relevant to the CAM discussion.
The materials part of the business develops and sells industrial materials; Sandvik is in the process of spinning out this business. Also interesting but …
The machining part of the business is where things get more relevant to us. Sandvik Machining & Manufacturing Solutions (SMM) has been supplying cutting tools and inserts for many years, via brands like Sandvik, SECO, Miranda, Walter, and Dormer Pramet, and sees a lot of opportunity in streamlining the processes around the use of specific tools and machines. Light weighting and sustainability efforts in end-industries are driving interest in new materials and more complex components, as well as tighter integration between design and manufacturing operations. That digitalization across an enterprise’s areas of business, Sandvik thinks, plays into its strengths.
According to info from the company’s 2020 Capital Markets Day, rocks and materials are steady but slow revenue growers. The company had set a modest 5% revenue growth target but had consistently been delivering closer to 3% — what to do? Like many others, the focus shifted to (1) software and (2) growth by acquisition. Buying CAM companies ticked both of those boxes, bringing repeatable, profitable growth. In an area the company already had some experience in.
Back to digitalization. If we think of a manufacturer as having (in-house or with partners) a design function, which sends the concept on to production preparation, then to machining, and, finally, to verification/quality control, Sandvik wants to expand outwards from machining to that entire world. Sandvik wants to help customers optimize the selection of tools, the machining strategy, and the verification and quality workflow.
The Manufacturing Solutions subdivision within SMM was created last year to go after this opportunity. It’s got 3 areas of focus: automating the manufacturing process, industrializing additive manufacturing, and expanding the use of metrology to real-time decision making.
The CGTech acquisition last year was the first step in realizing this vision. Vericut is prized for its ability to work with any CAM, machine tool, and cutting tool for NC code simulation, verification, optimization, and programming. CGTech is a long-time supplier of Vericut software to Sandvik’s Coromant production units, so the companies knew one another well. Vericut helps Sandvik close that digitalization/optimization loop — and, of course, gives it access to the many CAM users out there who do not use Coromant.
But verification is only one part of the overall loop, and in some senses, the last. CAM, on the other hand, is the first (after design). Sanvik saw CAM as “the most important market to enter due to attractive growth rates – and its proximity to Sandvik Manufacturing and Machining Solutions’ core business.” Adding Cimatron, GibbsCAM, SigmaTEK, and Mastercam gets Sandvik that much closer to offering clients a set of solutions to digitize their complete workflows.
And it makes business sense to add CAM to the bigger offering:
To head off one question: As of last week’s public statements, anyway, Sandvik has no interest in getting into CAD, preferring to leave that battlefield to others, and continue on its path of openness and neutrality.
And because some of you asked: there is some overlap in these acquisitions, but remarkably little, considering how established these companies all are. GibbsCAM is mostly used for production milling and turning; Cimatron is used in mold and die — and with a big presence in automotive, where Sandvik already has a significant interest; and SigmaNEST is for sheet metal fabrication and material requisitioning.
One interesting (to me, anyway) observation: 3D Systems sold Gibbs and Cimatron to Battery in November 2020. Why didn’t Sandvik snap it up then? Why wait until July 2021? A few possible reasons: Sandvik CEO Stefan Widing has been upfront about his company’s relative lack of efficiency in finding/closing/incorporating acquisitions; perhaps it was simply not ready to do a deal of this type and size eight months earlier. Another possible reason: One presumes 3D Systems “cleaned up” Cimatron and GibbsCAM before the sale (meaning, separating business systems and financials from the parent, figuring out HR, etc.) but perhaps there was more to be done, and Sandvik didn’t want to take that on. And, finally, maybe the real prize here for Sandvik was SigmaNEST, which Battery Ventures had acquired in 2018, and Cimatron and GibbsCAM simply became part of the deal. We may never know.
This whole thing is fascinating. A company out of left field, acquiring these premium PLMish assets. Spending major cash (although we don’t know how much because of non-disclosures between buyer and sellers) for a major market presence.
No one has ever asked me about a CAM roll-up, yet I’m constantly asked about how an acquirer could create another Ansys. Perhaps that was the wrong question, and it should have been about CAM all along. It’s possible that the window for another company to duplicate what Sandvik is doing may be closing since there are few assets left to acquire.
Sandvik’s CAM acquisitions haven’t closed yet, but assuming they do, there’s a strong fit between CAM and Sandvik’s other manufacturing-focused business areas. It’s more software, with its happy margins. And, finally, it lets Sandvik address the entire workflow from just after component design to machining and on to verification. Mr. Widing says that Sandvik first innovated in hardware, then in service – and now, in software to optimize the component part manufacturing process. These are where gains will come, he says, in maximizing productivity and tool longevity. Further out, he sees, measuring every part to see how the process can be further optimized. It’s a sound investment in the evolution of both Sandvik and manufacturing.
We all love a good reinvention story, and how Sandvik executes on this vision will, of course, determine if the reinvention was successful. And, of course, there’s always the potential for more news of this sort …
I missed this last month — Sandvik also acquired Cambrio, which is the combined brand for what we might know better as GibbsCAM (milling, turning), Cimatron (mold and die), and SigmaNEST (nesting, obvs). These three were spun out of 3D Systems last year, acquired by Battery Ventures — and now sold on to Sandvik.
This was announced in July, and the acquisition is expected to close in the second half of 2021 — we’ll find out on Friday if it already has.
At that time. Sandvik said its strategic aim is to “provide customers with software solutions enabling automation of the full component manufacturing value chain – from design and planning to preparation, production and verification … By acquiring Cambrio, Sandvik will establish an important position in the CAM market that includes both toolmaking and general-purpose machining. This will complement the existing customer offering in Sandvik Manufacturing Solutions”.
Cambrio has around 375 employees and in 2020, had revenue of about $68 million.
If we do a bit of math, Cambrio’s $68 million + CNC Software’s $60 million + CGTech’s (that’s Vericut’s maker) of $54 million add up to $182 million in acquired CAM revenue. Not bad.
More on Friday.
CNC Software and its Mastercam have been a mainstay among CAM providers for decades, marketing its solutions as independent, focused on the workgroup and individual. That is about to change: Sandvik, which bought CGTech late last year, has announced that it will acquire CNC Software to build out its CAM offerings.
According to Sandvik’s announcement, CNC Software brings a “world-class CAM brand in the Mastercam software suite with an installed base of around 270,000 licenses/users, the largest in the industry, as well as a strong market reseller network and well-established partnerships with leading machine makers and tooling companies”.
We were taken by surprise by the CGTech deal — but shouldn’t be by the Mastercam acquisition. Stefan Widing, Sandvik’s CEO explains it this way: “[Acquiring Mastercam] is in line with our strategic focus to grow in the digital manufacturing space, with special attention on industrial software close to component manufacturing. The acquisition of CNC Software and the Mastercam portfolio, in combination with our existing offerings and extensive manufacturing capabilities, will make Sandvik a leader in the overall CAM market, measured in installed base. CAM plays a vital role in the digital manufacturing process, enabling new and innovative solutions in automated design for manufacturing.” The announcement goes on to say, “CNC Software has a strong market position in CAM, and particularly for small and medium-sized manufacturing enterprises (SME’s), something that will support Sandvik’s strategic ambitions to develop solutions to automate the manufacturing value chain for SME’s – and deliver competitive point solutions for large original equipment manufacturers (OEM’s).”
Sandvik says that CNC Software has 220 employees, with revenue of $60 million in 2020, and a “historical annual growth rate of approximately 10 percent and is expected to outperform the estimated market growth of 7 percent”.
No purchase price was disclosed, but the deal is expected to close during the fourth quarter.
Sandvik is holding a call about this on Friday — more updates then, if warranted.
Bentley continues to grow its deep expertise in various AEC disciplines — most recently, expanding its focus in underground resource mapping and analysis. This diversity serves it well; read on.
In Q2,
Unlike AspenTech, Bentley’s revenue growth is speeding up (total revenue up 21% in Q2, including a wee bit from Seequent, and up 17% for the first six months of 2021). Why the difference? IMHO, because Bentley has a much broader base, selling into many more end industries as well as to road/bridge/water/wastewater infrastructure projects that keep going, Covid or not. CEO Greg Bentley told investors that some parts of the business are back to —or even better than— pre-pandemic levels, but not yet all. He said that the company continues to struggle in industrial and resources capital expenditure projects, and therefore in the geographies (theMiddle East and Southeast Asia) that are the most dependent on this sector. This is balanced against continued success in new accounts and the company’s reinvigorated selling to small and medium enterprises via its Virtuosity subsidiary — and in a resurgence in the overall commercial/facilities sector. In general, it appears that sales to contractors such as architects and engineers lag behind those to owners and operators of commercial facilities —makes sense as many new projects are still on pause until pandemic-related effects settle down.
One unusual comment from Bentley’s earnings call that we’re going to listen for on others: The government of China is asking companies to explain why they are not using locally-grown software solutions; it appears to be offering preferential tax treatment for buyers of local software. As Greg Bentley told investors, “[d]uring the year to date, we have experienced a rash of unanticipated subscription cancellations within the mid-sized accounts in China that have for years subscribed to our China-specific enterprise program … Because we don’t think there are product issues, we will try to reinstate these accounts through E365 programs, where we can maintain continuous visibility as to their usage and engagement”. So, to recap: the government is using taxation to prefer one set of vendors over another, and all Bentley can do (really) is try to bring these accounts back and then monitor them constantly to keep on top of emerging issues. FWIW, in the pre-pandemic filings for Bentley’s IPO, “greater China, which we define as the Peoples’ Republic of China, Hong Kong and Taiwan … has become one of our largest (among our top five) and fastest-growing regions as measured by revenue, contributing just over 5% of our 2019 revenues”. Something to watch.
The company updated its financial outlook for 2021 to include the recent Seequent acquisition and this moderate level of economic uncertainty. Bentley might actually join the billion-dollar club on a pro forma basis — as if the acquisition of Seequent had occurred at the beginning of 2021. On a reported basis, the company sees total revenue between $945 million and $960 million, or an increase of around 18%, including Seequent. Excluding Seequent, Bentley sees organic revenue growth of 10% to 11%.
Much more here, on Bentley’s investor website.
We still have to hear from Autodesk, but there’s been a lot of AECish earnings news over the last few weeks. This post starts a modest series as we try to catch up on those results.
AspenTech reported results for its fiscal fourth quarter, 2021 last week. Total revenue of $198 million in DQ4, down 2% from a year ago. License revenue was $145 million, down 3%; maintenance revenue was $46 million, basically flat when compared to a year earlier, and services and other revenue was $7 million, up 9%.
For the year, total revenue was up 19% to $709 million, license revenue was up 28%, maintenance was up 4% and services and other revenue was down 18%.
Looking ahead, CEO Antonio Pietri said that he is “optimistic about the long-term opportunity for AspenTech. The need for our customers to operate their assets safely, sustainably, reliably and profitably has never been greater … We are confident in our ability to return to double-digit annual spend growth over time as economic conditions and industry budgets normalize.” The company sees fiscal 2022 total revenue of $702 million to $737 million, which is up just $10 million from final 2021 at the midpoint.
Why the slowdown in FQ4 from earlier in the year? And why the modest guidance for fiscal 2022? One word: Covid. And the uncertainty it creates among AspenTech’s customers when it comes to spending precious cash. AspenTech expects its visibility to improve when new budgets are set in the calendar fourth quarter. By then, AspenTech hopes, its customers will have a clearer view of reopening, consumer spending, and the timing of an eventual recovery.
Lots more detail here on AspenTech’s investor website.
Next up, Bentley. Yup. Alphabetical order.
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
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
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
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
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
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
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.
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:
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:
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:
Temparature (K) | Viscosity (Pa.s) |
200 |
0.000012924 |
400 | 0.000022217 |
600 | 0.000029602 |
800 | 0.000035932 |
1000 | 0.000041597 |
1200 | 0.000046812 |
1400 | 0.000051704 |
1600 | 0.000056357 |
1800 | 0.000060829 |
2000 | 0.000065162 |
This data is the dynamics viscosity of nitrogen N2 pulled from the NIST database for 0.101 MPa. (Note that in these ranges viscosity should be only temperature dependent).
Step 2: Use python to fit the data
If you are unfamiliar with Python, this may seem a little foreign to you, but python is extremely simple.
First, we need to load the necessary packages (here, we’ll load numpy, scipy.optimize, and matplotlib):
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
Now we define the sutherland function:
def sutherland(T, As, Ts):
return As*T**(3/2)/(Ts+T)
Next we input the data:
T=[200,
400,
600,
800,
1000,
1200,
1400,
1600,
1800,
2000]
mu=[0.000012924,
0.000022217,
0.000029602,
0.000035932,
0.000041597,
0.000046812,
0.000051704,
0.000056357,
0.000060829,
0.000065162]
Then we fit the data using the curve_fit function from scipy.optimize. This function uses a least squares minimization to solve for the unknown coefficients. The output variable popt is an array that contains our desired variables As and Ts.
popt = curve_fit(sutherland, T, mu)
As=popt[0]
Ts=popt[1]
Now we can just output our data to the screen and plot the results if we so wish:
print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')
xplot=np.linspace(200,2000,100)
yplot=sutherland(xplot,As,Ts)
plt.plot(T,mu,'ok',xplot,yplot,'-r')
plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])
plt.show()
Overall the entire code looks like this:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def sutherland(T, As, Ts):
return As*T**(3/2)/(Ts+T)
T=[200, 400, 600,
800,
1000,
1200,
1400,
1600,
1800,
2000]
mu=[0.000012924,
0.000022217,
0.000029602,
0.000035932,
0.000041597,
0.000046812,
0.000051704,
0.000056357,
0.000060829,
0.000065162]
popt, pcov = curve_fit(sutherland, T, mu)
As=popt[0]
Ts=popt[1]
print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')
xplot=np.linspace(200,2000,100)
yplot=sutherland(xplot,As,Ts)
plt.plot(T,mu,'ok',xplot,yplot,'-r')
plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])
plt.show()
And the results for nitrogen gas in this range are As=1.55902E-6, and Ts=168.766 K. Now we have our own coefficients that we can quantify the error on and use in our academic research! Wahoo!
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.
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!
You can download the script here:
https://github.com/curiosityFluids/curiosityFluidsAirfoilMesher
Here you will also find a template based on the airfoil2D OpenFOAM tutorial.
(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
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.
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:
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:
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
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.