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Bigger Sims Win the Race http://www.deskeng.com/de/bigger-sims-win-the-race/ By Alex Read In auto racing, bigger is better. Nowhere is this more true than in the world of motor-sport computational fluid dynamics (CFD) where teams regularly push the envelope by running simulations involving hundreds of millions of cells. CFD, a mainstay of Formula 1 (F1) racing, is increasingly being used by NASCAR teams for aerodynamics, underhood thermal management, and engine simulations. STAR-CCM+, CD-adapco’s new technology solution, is enabling teams in Formula 1 and NASCAR to create, set up, run, and postprocess bigger, and of course better, flow and thermal simulations.

Bigger Sims Win the Race

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Bigger Sims Win the Race

http://www.deskeng.com/de/bigger-sims-win-the-race/

By Alex Read

In auto racing, bigger is better. Nowhere is this more true than in the world of motor-sport computational fluid dynamics (CFD) where teams regularly push the envelope by running simulations involving hundreds of millions of cells.

CFD, a mainstay of Formula 1 (F1) racing, is increasingly being used by NASCAR teams for aerodynamics, underhood thermal management, and engine simulations.STAR-CCM+, CD-adapco’s new technology solution, is enabling teams in Formula 1 and NASCAR to create, set up, run, and postprocess bigger, and of course better, flow and thermal simulations.

Why so big?

Race teams use CFD as an aid and extension to physical testing. It is used to optimize expensive wind-tunnel time by identifying the best designs to work with, and to provide information that is difficult to obtain via testing. For NASCAR underhood thermal management, this can take the form of detailed visualizations of the flow and thermal field in the engine compartment. For external aerodynamics, it may be the car’s front and rear down-force balance when a driver drafts another.Race teams’ other requirements, common to all simulation engineers, are accurate results and rapid and robust model turnaround. This presents a particular challenge in areas like external aerodynamics where components demonstrate a strong interdependence. For example, the performance of the rear wing of an F1 car will vary depending on the setup for the components in front of it — and vice-versa.F1 teams opt for running detailed models of the full car to ensure accurate aerodynamic resolution for all parts of the car. Similarly, in NASCAR, the interaction between cars — aerodynamic and even physical contact — is a key part of racing. Because cars are bumper to bumper and door handle to door handle much of the time, understanding the effect this has on front and rear down-force and airflow to the engine compartment can make the difference between success and failure.Historically, this need for more and more data has presented problems: is my computer big enough to store and solve for hundreds of millions of cells? If it is, is my CFD tool sufficiently adept at using this enormous computing resource — with hundreds of processors operating in parallel — to let me create, set up, run, and postprocess my case within a reasonable timeframe?Users as partnersCD-adapco has a proud history of solving these problems for race teams. It has supplied CFD tools to, among others, the current double World Championship-winning ING-Renault F1 team since its inception in 2001. Now, the company’s latest offering, STAR-CCM+, has been specifically designed for motor-sport CFD, with regular evaluation and specification being carried out by top motor-sport teams during its development.“Around five years ago we were starting to develop STAR-CCM+ from a blank sheet of paper,” explains Richard Johns, CD-adapco’s director for the automotive industry, “which allowed us to use everything we’d learnt in the previous 20 years of developing and using CFD, as well as the latest computing technology. In addition, at CD-adapco, we see our users as partners and not just clients. As well as using our own know-how, our motor sport partners were integral in defining STAR-CCM+’s specification and reviewing its progress.”The result is a code that’s revolutionizing motor sport CFD, enabling teams to run ever more detailed models, with ever more computational cells, while shortening case turnaround times.

Technology in the sim processMuch of the focus on CFD codes has been on the time it takes for them to solve the Navier-Stokes equations for a large number of computational cells. Of equal importance is the time required to create, set up, and postprocess the case, which can take days or even weeks.A key technology in STAR-CCM+ is the process by which it does this for very large cases. First, all the steps of the simulation process are integrated into one tool: from CAD geometry to postprocessing. This saves a considerable amount of time as there is no requirement to export and import large data sets and there is no requirement to respecify parameters in different tools or when running iterative design studies.Second, it uses the latest software technology: a client-server architecture. The server runs on a parallel cluster, distributing the work of processing hundreds of millions of cells, while the light Java client only passes along the information it needs. What this means is that cases can be set up, run, and postprocessed using the light client while the server makes full use of parallel hardware, thereby significantly cutting model turnaround time.Pushing the limitsSo far, it’s been possible to simulate 40 cars at once with a mesh count of one billion polyhedral cells: the equivalent of several billion tetrahedral cells. The purpose of the simulation was twofold: to simulate the complex interaction between multiple cars in close proximity and to see how far we could push STAR-CCM+ on existing hardware.The starting point for the calculation was creating a mesh around a single car, providing the baseline drag, lift, and yaw force values. A second car was then introduced and analyses were performed with the cars directly in line, and with an offset as if one was beginning an overtaking maneuver.The drivers’ goal when drafting is to reduce the drag on both cars, making them collectively faster. If the second car is in the correct position, it has the effect of increasing the pressure at the rear of the lead car, reducing its overall drag. However, the effects are highly dependent on the car positions. At times, the drag on the second car

is reduced as the first car deflects the air over it. In other configurations the drag force on the rear car is actually greater than that on the lead car as it sits in the dirty, highly turbulent wake. Handling is also affected as the front and rear lift on each varies with the car positions and strong yaw forces occur on the rear car when in the offset position.These simulations provided the aerodynamicists with detailed visualization of the complex flow patterns around the cars.The model was then extended to evaluate what happens in highly complex race conditions, with many cars unevenly spaced and positioned. Engineers use a building-block approach whereby one car and its close proximity to another are meshed, then that mesh is copied and pasted with an offset to produce two cars side-by-side or nose-to-tail. A spacer mesh section is created to vary the distance between the vehicles. Using this technique, analysts create a model of 40 unevenly distributed cars with a total mesh count of one billion cells. Although this is impressive by today’s standards, according to Johns, multibillion cell calculations will be commonplace in the not-too-distant future.“As the hardware vendors continue to produce even bigger and faster machines,” Johns says, “so the model sizes ]that] race teams want to run increases. It’s our goal to make sure STAR-CCM+ can efficiently handle these enormous calculations. So far we’ve made it to one billion, we don’t see any reason why — in the future — we can’t go much larger than that.”

More than just flowOf course, aerodynamics is only one application area for motor-sport CFD. NASCAR teams are increasingly using CFD for underhood thermal management simulations to gain detailed insight into the flow and thermal fields. Here, the CFD tool must be able to automatically mesh highly complex geometries and deal with the additional physics required to efficiently model fans and heat exchangers, and convective and radiative heat transfer.“When developing STAR-CCM+,” says Johns “]the automotive powertrain] was one of our target applications. Its combination of the latest generation of our surface wrapper and dedicated underhood models mean it’s much more than just a flow code.”At the heart of STAR-CCM+ is an automated process that links a powerful surface wrapper to CD-adapco’s unique meshing technology.

The surface wrapper significantly reduces the number of man-hours spent on surface cleanup and, for problems that involve large assemblies of complex geometry parts, reduces the entire meshing process to hours instead of days. The surface wrapper works by “shrink-wrapping” a high-quality triangulated surface mesh onto any geometrical model, closing holes in the geometry and joining disconnected and overlapping surfaces, providing a single manifold surface that can be used to automatically generate a computational mesh without user intervention. STAR-CCM+ also has specialist models for efficient representation of fans and heat exchangers.Staying ahead of the packIn motor-sport CFD from F1 to NASCAR bigger is better. As teams continue to push the envelope of CFD so the tools they use need to adapt to their requirements. STAR-CCM+ is one of those tools helping race teams run bigger models faster and ultimately to stay at the front of the pack.More InformationCD-adapcoMelville, NY cd-adapco.com

Alex Read studied engineering and CFD at the University of Leeds, UK, and wrote his thesis on simulation of vehicle aerodynamics. He is now the engineering manager of CD-adapco responsible for supporting clients in the UK, Scandinavia, and Holland in their efforts to simulate vehicle aerodynamics and aeroacoustics. To comment on this article, send an e-mail to [email protected].