Artificial Intelligence and Machine Learning in Aerodynamics

This case study details the collaboration between Toyota Gazoo Racing (TGR) and Muttii to enhance aerodynamic processes through the implementation of artificial intelligence and machine learning (AI/ML). TGR, responsible for designing race cars for the World Endurance Championship, faced challenges in analyzing large volumes of data generated from wind tunnel and computational fluid dynamics (CFD) testing. The partnership aimed to leverage AI/ML to identify trends and improve performance more efficiently. Muttii's AI/ML technology was tasked with analyzing 240GB of data from CFD tests to isolate areas for improvement in TGR's new GR010 Hybrid race car. The initial success demonstrated the potential of AI as a development tool, leading to further training of the AI/ML software to handle terabytes of data. The integration of this solution is expected to significantly reduce engineering hours spent on data analysis, allowing TGR to focus on other performance enhancements, ultimately creating a more efficient development system.