Structural Optimization of Tilt-Duct Aircraft Based on Deep Reinforcement Learning

The transition to green aviation, particularly in Urban Air Mobility (UAM), demands highly energy-efficient aircraft like electric tilt-ducts. A central challenge is designing structures that are lightweight enough for both vertical takeoff-landing (VTOL) and high-speed cruise. This study tackles the complex structural optimization problem arising from these multi-modal demands, where design variables are often a mix of continuous and discrete parameters. We develop a deep reinforcement learning (DRL) framework based on the Proximal Policy Optimization (PPO) algorithm to efficiently navigate this design space without explicit gradients. When applied to a critical load-bearing component, our framework discovered designs with significant mass reduction that satisfied all constraints across flight modes. On a benchmark wing, it achieved a 17.28% mass reduction while satisfying constraints for both VTOL and cruise modes, and showed good generalization capability. This approach marks a step toward automated, intelligent design for sustainable aircraft.