为了提高基于深度强化学习的气动外形优化效率,提出基于代理模型的深度强化学习气动优化框架。深度强化学习存在样本利用率低的问题,在气动优化中需要大量使用流场数值模拟来获取样本数据,通过引入代理模型,能够快速生成样本数据,减小对流场数值模拟的依赖,提高气动优化效率。在跨声速翼型减阻优化设计中,仅使用深度强化学习和使用基于代理模型的深度强化学习优化方法都能获得最优解,但代理模型的使用能够使流场数值仿真次数减少67%,显著提升优化效率,在翼型反设计中,基于代理模型的深度强化学习优化方法得到的反设计翼型和目标翼型压力分布以及翼型形状均吻合较好,进一步验证了提出的基于代理模型的深度强化学习气动优化框架的有效性。

To enhance the efficiency of aerodynamic shape optimization based on deep reinforcement learning (DRL), a DRL-based aerodynamic optimization framework incorporating surrogate models is proposed. Deep reinforcement learning suffers from low sample efficiency, in aerodynamic optimization, it requires extensive use of flow field numerical simulations to obtain sample data. However, high-precision flow field numerical simulations come with a substantial computational cost in terms of time. By introducing surrogate models, it can rapidly generate sample data, reduce the dependence on high-precision flow field numerical simulations, and improve the efficiency of aerodynamic shape optimization. Taking the transonic airfoil drag reduction optimization design and inverse design as examples, the effectiveness of the DRL-based aerodynamic optimization framework incorporating surrogate models is analyzed. In transonic airfoil drag reduction optimization design, both DRL and DRL based on surrogate models achieve optimal solutions, eliminate the shock waves. However, the use of surrogate models reduces the number of high-precision flow field numerical simulations by 67%, significantly improving optimization efficiency. In airfoil inverse design, the pressure distribution and shape of the inversely designed airfoil obtained through DRL based on surrogate models show good agreement with the target airfoil. Both examples illustrate the effectiveness of the proposed DRL-based aerodynamic optimization framework incorporating surrogate models.