Aerodynamic optimization of high-lift devices using a 2D-to-3D optimization method based on deep reinforcement learning and transfer learning

Abstract

The computational fluid dynamics is the main method to evaluate the aerodynamic performance for the optimization of high-lift devices. Currently, the direct three-dimensional (3D) optimization requires significant computational resources. Additionally, the commonly used heuristic algorithms can not extract the experience of two-dimensional (2D) optimization to accelerate the 3D optimization process. In order to resolve these issues, a novel 2D-to-3D optimization method based on the coupling of Deep Reinforcement Learning (DRL) and Transfer Learning (TL) is proposed to conduct the aerodynamic optimization of the 3D high-lift devices and tested on the NASA Trap Wing model. The 2D optimization is first carried out, and then its neural networks in DRL and the optimal configuration are extracted by TL to turn into the 3D optimization. Compared with the direct 3D optimization, the proposed 2D-to-3D optimization method can result in improved aerodynamic performance for the same computational cost, or can save 51%-81% of the computational cost to obtain a similar performance.