Germain Racing radically improved the aerodynamics of their NASCAR race car using SimScale CFD

simulations run time was less than 7 hours

Run time per simulation

mesh of 28 million cells

Mesh cells

Germain Racing logo

Germain Racing is an American professional stock car racing team that exclusively competes in the Monster Energy NASCAR Cup Series. The team races the No. 13 GEICO Chevrolet Camaro ZL1 and is currently upgrading its cars to meet the new 2019 NASCAR design requirements.

The Challenge

Optimizing the Race Car’s Design and Reducing Drag for Higher Speed

Stock car racing is an exciting field, inspiring many especially with the fraternity of mechanical engineers and racers taking part in competitions around the globe. In modern racing, many advanced technologies are available, and it is therefore imperative to be up-to-date to keep a competitive edge. In race car dynamics, even a small design change can shave off seconds that matter in a race. With the new aerodynamic package released by NASCAR for next year, more design changes than ever will be needed for racers to compete and comply with racing regulations.

Germain Racing is using the SimScale cloud-based simulation platform to optimize their car design and reduce drag for higher speed.

CFD analyis of a race car for NASCAR, streamlines
Flow recirculation and local vortices visualization around the vehicle

This is done by computing and observing the airflow as it moves. SimScale can also provide the drag of each car design or of individual components, which helps designers track their progress with every design improvement.

Airflow and pressure distribution around the car determined with SimScale are used to compare designs both qualitatively and quantitatively.

SimScale is very quick and easy to use. It gives us a relatively low-cost way of examining the car’s behavior, with very little wind tunnel time. Onshape allows us to make surface changes very quickly, and import our native CAD files directly to SimScale.

Matt Borland

Crew Chief at Germain Racing

How they solved it with SimScale

Improving the Race Car’s Aerodynamics with CFD Simulation

The model was created in the cloud using Onshape, directly supported from the SimScale interface. A highly optimized mesh was generated to capture the turbulent airflow around the vehicle. The final mesh had around 28 million cells.

Each simulation was run on a 96-core machine and typically for less than 7 hours.

total pressure coefficient race car, CFD analysis
Total pressure coefficient on the car

It was possible to perform multiple simulation runs in parallel, with different incident angles to mimic the car driving around a curve. Hence, the same vehicle speed was defined in multiple directions, which is typical for analysing race car dynamics. This means that within a single working day, multiple results can be analyzed and design decisions can be made.

The results

Optimized Downforce and Overall Drag Reduced

It was noticed that small vortices were formed in different local regions around the vehicle with different designs. There were also large recirculation areas behind and under the car.

Very high drag was noticed on the rear spoiler/vertical wing and also on the closed grill in front of the vehicle. Additionally, an interesting discovery made by SimScale predicted higher drag in certain sections of the wheels when they were angled in different directions. This gave a good indication of how the design could be optimized and where focus was required.

Velocity distribution over the solar panels and terrain
Germain Racing car (photo courtesy of Tami Pope)

SimScale was used to understand how the car was producing downforce and to optimise this while simultaneously reducing the overall drag. The end-to-end workflow from CAD to airflow to even drag results, went from being months to just a single day using SimScale for simulation instead of traditional, desktop-based CAE software.

SimScale predicted higher drag in certain sections of the wheels when they were angled in different directions. This gave a good indication of how the design could be optimized and where focus was required.

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