Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
This addresses aerodynamic shape optimization for engineering applications, but it is incremental as it builds on existing DRL and transfer learning techniques.
The paper tackled aerodynamic airfoil optimization with structural constraints by introducing a transfer learning-enhanced deep reinforcement learning method, which outperformed Particle Swarm Optimization in computational efficiency and aerodynamic improvement, achieving comparable performance with substantial resource savings.
The main objective of this paper is to introduce a transfer learning-enhanced deep reinforcement learning (DRL) methodology that is able to optimise the geometry of any airfoil based on concomitant aerodynamic and structural integrity criteria. To showcase the method, we aim to maximise the lift-to-drag ratio $C_L/C_D$ while preserving the structural integrity of the airfoil -- as modelled by its maximum thickness -- and train the DRL agent using a list of different transfer learning (TL) strategies. The performance of the DRL agent is compared with Particle Swarm Optimisation (PSO), a traditional gradient-free optimisation method. Results indicate that DRL agents are able to perform purely aerodynamic and hybrid aerodynamic/structural shape optimisation, that the DRL approach outperforms PSO in terms of computational efficiency and aerodynamic improvement, and that the TL-enhanced DRL agent achieves performance comparable to the DRL one, while further saving substantial computational resources.