Predictive Modeling for INDYCAR’s Driver-in-the-Loop Aerodynamic Simulators

This presentation recording was made at NAFEMS Americas Seminar "Engineering Analysis & Simulation in the Automotive Industry: Creating the Next Generation Vehicle Accurate Modelling for Tomorrow's Technologies".

The automotive engineering community is now confronting the largest technology transformation since its inception. This includes the electrification of powertrains for more efficient consumption and cleaner emissions, the reinvention of the battery with fast wireless charging capabilities and finally the advent of a fully autonomous vehicle. Compounding to these technology changes, the automotive companies design verification process is moving away from a major reliance on physical testing to almost a full virtual simulation product verification process. The challenges to the automotive engineers are enormous and require a significant increase in the upfront use of numerical simulation capabilities, methods and processes such they’re able to efficiently design, manufacture and deliver these very innovative technologies to the market in greater speeds than ever before.)

Resource Abstract

INDYCAR’s adoption of modeling and simulation has been instrumental in improving race speed, safety and entertainment over the years. Improvements to aerodynamic kits prescribed to the racing teams is constant, with the objective of reducing vehicle drag and increasing speed, while balancing competition between leading and trailing vehicles.



INDYCAR continues to push aerodynamic improvements, and with help of it’s research partners ARC, R Systems & Dell EMC and Parallel Works, it is pioneering new methods to enable more exhaustive modeling, near-real-time predictions of outputs and ultimately, driver-in-the-loop simulators whereby vehicle changes can be physically tested far before an aerodynamic kit is specified.



This presentation will showcase the beginning of these cutting-edge innovations, including the development of parametric models for exploring vehicle draft position, creation of response surfaces for key metrics, and the integration of these response surfaces into predictive methods that will eventually enable the near-real-time driver-in-the-loop simulators.