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An accurate method for evaluating brake cooling and calculating cooling time in brake systems is critical for automotive development. Traditional 3D Computational Fluid Dynamics (CFD) simulations, which involve wind tunnel modeling of an entire car and cooling process, are computationally expensive and time-consuming. Furthermore, early-stage virtual development lacks reliable validation benchmarks. This study addresses these challenges by employing AI-driven Bayesian optimization for brake cooling performance, focusing on relative improvements rather than absolute results. Using AI models, such as Gaussian processes, is motivated by their ability to perform sub-second evaluations of new designs, enabling comprehensive exploration of the design space.

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Brake Cooling Optimization Using AI: A Case Study on Audi SUVs

  • 会议论文

  • 发表日期：

    2026年

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  • Farhad Nazarpour,
  • John Perry,
  • Sam Lishak,
  • Parsa Vatani,
  • Nico Haag,
  • Harry Softley-Graham,
  • Paul Furnival,
  • Amir Vaziri

摘要

An accurate method for evaluating brake cooling and calculating cooling time in brake systems is critical for automotive development. Traditional 3D Computational Fluid Dynamics (CFD) simulations, which involve wind tunnel modeling of an entire car and cooling process, are computationally expensive and time-consuming. Furthermore, early-stage virtual development lacks reliable validation benchmarks. This study addresses these challenges by employing AI-driven Bayesian optimization for brake cooling performance, focusing on relative improvements rather than absolute results. Using AI models, such as Gaussian processes, is motivated by their ability to perform sub-second evaluations of new designs, enabling comprehensive exploration of the design space.