Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints

TL;DR

This paper presents a transfer learning-enhanced deep reinforcement learning approach for optimizing airfoil geometry considering aerodynamic performance and structural constraints, outperforming traditional methods in efficiency and results.

Contribution

It introduces a novel transfer learning-enhanced DRL methodology for airfoil optimization that efficiently balances aerodynamic and structural criteria.

Findings

DRL outperforms Particle Swarm Optimization in efficiency and aerodynamic gains

Transfer learning reduces computational resources while maintaining performance

The method achieves comparable results to standard DRL with less training time

Abstract

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.