  Aerodynamic design optimisation plays a crucial role in improving the
performance and efficiency of automotive vehicles. This paper presents a novel
approach for aerodynamic optimisation in car design using deep reinforcement
learning (DRL). Traditional optimisation methods often face challenges in
handling the complexity of the design space and capturing non-linear
relationships between design parameters and aerodynamic performance metrics.
This study addresses these challenges by employing DRL to learn optimal
aerodynamic design strategies in a voxelised model representation. The proposed
approach utilises voxelised models to discretise the vehicle geometry into a
grid of voxels, allowing for a detailed representation of the aerodynamic flow
field. The Proximal Policy Optimisation (PPO) algorithm is then employed to
train a DRL agent to optimise the design parameters of the vehicle with respect
to drag force, kinetic energy, and voxel collision count. Experimental results
demonstrate the effectiveness and efficiency of the proposed approach in
achieving significant results in aerodynamic performance. The findings
highlight the potential of DRL techniques for addressing complex aerodynamic
design optimisation problems in automotive engineering, with implications for
improving vehicle performance, fuel efficiency, and environmental
sustainability.
