用迁移学习增强的强化学习，高效优化机翼气动与结构性能。
Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
- 结合迁移学习的深度强化学习，同时优化机翼气动与结构强度。
- 相比粒子群算法，计算效率更高，升阻比提升更显著。
- 迁移学习策略大幅节省计算资源，适合工程实际应用。
本文提出一种融合迁移学习的深度强化学习方法，用于在满足结构约束条件下优化任意机翼几何形状。目标是在最大化升阻比 $C_L/C_D$ 的同时，保持机翼最大厚度等结构完整性指标。通过对比多种迁移学习策略训练强化学习智能体，并与传统的无梯度优化方法粒子群优化（PSO）进行比较。结果表明，该方法能实现纯气动及气动-结构联合优化；深度强化学习在计算效率和气动性能提升上优于PSO；采用迁移学习增强的强化学习模型性能接近标准DRL，但显著降低了计算开销。
原文摘要 · Abstract (English)
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.
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