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Tire Modelling and Selection

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

Tires are one of the most important components of any vehicle, as they represent the only point of contact with the ground. Any force accelerating the vehicle is transmitted through the tires, making them the critical element in maintaining safety, performance and stability during vehicle operation. Thus it is of the utmost importance to understand the behavior of the tire and how it changes with various operating conditions. In this page, I will cover the steps I took to model tire behavior, and the analysis in selecting a tire for the Clemson Formula SAE vehicle.

Tire Modelling Strategy

Maximizing the performance of the tire is crucial for unlocking the performance of the car

Clemson University is part of the Formula SAE Tire Testing Consortium. This is a service provided by Calspan that grants students access to tire force and moment testing data for a range of FSAE tires, at a significantly discounted rate. Obtaining this data is critical to be able to capture and predict tire behavior. Since this data is protected intellectual property, all figures in this page will have normalized, unlabeled units.

To process the data, I pull it into MATLAB. Data sets can contain different procedures sweeping through various parameters. The first step is to filter out unnecessary data and isolate only the information of interest. The variables I am most often concerned with are inflation pressure, normal load, inclination angle, tire slip, and resulting force generated.

The next step is to normalize the data for the vertical load (Fz) on the tire. Seeing the plot below on the left, it is evident that there is a significant amount of noise in the Fz channel. A key characteristic of tires is a phenomenon known as "Normal Load Sensitivity", where effective coefficient of the tire drops with increased load. This is a significant effect, and if you are trying to evaluate a tire at a specific set of conditions it is important to filter out the variation.

Noise in the normal load channel can be clearly seen here

This demonstrates the importance of correcting for normal load variation

Once the full data set was properly treated, I isolated individual slip angle sweeps, such as the sample sweep above. Each sweep captures a unique combination of load, pressure, and inclination angle. Once every sweep was isolated, they could be smoothed to filter out the remaining noise, and stitched back together to create a tire performance surface, like the ones pictured below. This process was repeated for all outputs of interest, especially lateral force, longitudinal force, and self-returning moment.