A Need For Speed: Enhancing F1 Race Cars with a Novel Computational Fluid Dynamics and Machine Learning Method
DOI:
https://doi.org/10.54097/hset.v49i.8388Keywords:
Computational fluid dynamics, Machine learning, Rear wing, Formula 1, Backpropagation algorithm, Artificial neural networks, Fuel consumption, Drag reduction, Downforce production.Abstract
Formula One (F1) engineers dedicate their lives to finding tenths of a second in car performance. But, the technical complexities of F1 cars must now also make inroads to increasing sustainability because fuel consumption in F1 is a major problem. Furthermore, F1 teams with lower budgets now need to find ways to be more efficient in aerodynamic testing to match the richer teams’ expensive equipment. This would improve the competitiveness of F1 and please 100 million fans around the world. Computer-aided design to model Formula One race cars for aerodynamic testing, computational fluid dynamics to test 90 different airfoils and simulation setups as well as the finalized model car against the control car, and machine learning to quickly predict the results of other simulations I have not conducted yet with high accuracy rates in a very little amount of time.
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