Multi-object aerodynamic design optimization using deep reinforcement learning

Aerodynamic design optimization is a key aspect in aircraft design. The further evolution of advanced aircraft derivatives requires a powerful optimization toolbox. Reinforcement learning (RL) is a powerful optimization tool but has rarely been utilized in the aerodynamic design. It can potentially obtain results similar to those of a human designer, by accumulating experience from training. In this work, a popular RL method called proximal policy optimization (PPO) is proposed to investigate multi-object aerodynamic design optimization. By observing the aerodynamic performances of different airfoils, the PPO updates a reasonable policy to generate the optimal airfoils in a single step. In a Pareto optimization problem with constraints, the PPO requires only 15% of the computational time of the non-dominated sorted genetic algorithm (II) to achieve the same accuracy. The results from testing show that the agent learns a policy that can achieve similar to 4.3%-10.1% improvements of the aerodynamic performance compared with the results of baseline. (C) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

The study introduces a method using proximal policy optimization (PPO) for multi-object aerodynamic design optimization, which shows higher efficiency and accuracy compared to traditional algorithms.