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LMP2 Car Setup Optimization

Please enjoy the 2-minute summary gallery below, or scroll further to explore my work in greater detail

I believe that change is not something to be simply dealt with; rather, change is something to take charge of and make into your own opportunity. The 2020 COVID-19 pandemic shook the world, and I went into quarantine with my family for over 5 months. I consider myself lucky to have been blessed with all that time alone in a position free of responsibility. This time was mine to make the most of, and this is one of the significant projects I undertook during that period.

Project Background

On April 9th, Danny Nowlan, Director of ChassisSim Technologies, announced the launch of the ChassisSim Online Race Engineering Competition. In this competition, for a small entrance fee you were given a vehicle and circuit model, as well as a temporary license of ChassisSim, good for 100 simulations. The car in question was an LMP2 race car, pictured below, running on the Circuit 24 Hours of Le Mans. Competitors are free to adjust the setup of the car within a set of regulations, and whoever could achieve the fastest lap time in 100 iterations would win a cash prize. My friend Derek Moore and I decided to team up and tackle this project together (Spoiler alert, in case you don't read to the end - we finished quite well!). On this page you will learn about some tools that I developed to aid in our work, as well as a summary of the tuning decisions we made together to optimize our lap time. 

The Ligier JS P217 as replicated in the model provided to entrants.


Vehicle Characterisation

Before using our limited reserve of simulations, we agreed to use the provided parameters to understand as much about the vehicle as possible. Anything we learn could help inform where to begin with setup changes.

The first thing I did was to create a suspension setup table based on the parameters provided in ChassisSim. This would enable us to visualize the effects that suspension adjustments have on overall vehicle characteristics. The baseline setup is pictured in the table below. Some initial details that stood out were the differences in ride frequency and damping ratio. Another significant detail was the 63% front static lateral load transfer distribution, which is significantly higher than the front weight distribution (46%). It is discrepancies such as these that can point us in the right direction to determine where to focus our setup changes.

In addition, Derek used his custom kinematics software to evaluate the front and rear suspension pickup points. A brief evaluation showed that the sign of the front camber gain rate was opposite to that of the rear, and the front end was gaining positive camber over bump. This was a fast catch that could have potentially cost several precious runs to uncover.

Finally, ChassisSim included aero maps for lift, drag, and center of pressure as a function of front and rear ride height. This created an opportunity for a head start on setting ride heights for maximum aero performance without having to use a single simulation run! Starting from a framework initially developed for the vehicle tradespace analysis project, I was able to create a tool to evaluate the suspension and aerodynamic interactions of the car.

Aero Platform Optimization

As mentioned earlier, included in the vehicle parameters were ride height maps allowing me to track various aerodynamic characteristics:

Another important set of information was a bump-profile provided for the track. This enabled me to capture the spectral density of the track surface:

Taking the parameters from the setup sheet I built earlier, I simulated the vehicle using a 4 degree of freedom ride model (unsprung masses, body height, pitch angle). By capturing the partial differentials of the aero maps, I was able to linearize the change in downforce, drag, and pitch moment at any single operating point. These effects could be represented as spring elements, and incorporated into the ride model. Using a state space representation, I was then able to simulate the model travelling over the above track surface at a constant speed, constructing frequency response plots from the output signals: