Before arriving at the track on race day, TRD evaluates a race vehicle’s chassis by performing wind tunnel, shaker, kinematics, compliance, and hundreds of other tests in the laboratory. These evaluations help TRD
consider the millions of possible combinations of vehicle components and recommend ways to improve vehicle performance and drivability.

The challenge, says Essma, is to make the vast quantities of data produced by these tests meaningful: “These days, you can gather more data than you can store on a hard drive, but you need to make that data useful by correlating individual metrics with on-track performance and driver feedback.”

TRD used MATLAB and Statistics and Machine Learning Toolbox™ to process, analyze, visualize, and share data generated from laboratory testing and simulations and apply the results to improve performance.

"MATLAB often helps us correct a deficiency on the vehicle, respond to driver feedback, or address the conditions of the track with five changes as opposed to 20," Essma says.

Using MATLAB, TRD performs detailed tire analysis for each race track. Essma and his team import data from a commercial lap simulation program into a tire analysis routine written with MATLAB, where they process and present data to help understand how the tires will perform in the upcoming race.

"There isn’t a piece of data that goes into a simulation program that is not processed with MATLAB," explains Essma. "From fitting the aerodynamic maps to analyzing tire models, MATLAB handles all our preprocessing and postprocessing tasks."

TRD used Statistics and Machine Learning Toolbox to develop the Experimental Design Tool (EDT), which uses Design of Experiments to help define the most significant test points to acquire during on-track or laboratory test runs. They also used the EDT with simulation programs to batch process thousands of simulation runs for different attributes of performance, and then automatically aggregate and analyze the data. Response surfaces are then fit to results to evaluate which setup changes had the most effect on performance.

The TRD team created graphical user interfaces (GUIs) to make engineering tools accessible to end users and plans to distribute the GUIs to other groups using MATLAB Compiler™. They also plan to use Optimization Toolbox™ to balance multiple design tradeoffs.