Multi-Objective Optimization of Aircraft Wing Beam Cross-Sections Using Reinforcement Learning

Le résumé fourni par la source

The aircraft wing beam serves as the primary load-bearing structure of an aircraft, and the quality of its cross-section design significantly impacts the aircraft’s performance, safety, and economic efficiency. Traditional design methods for wing beams often rely on experience and trial-and-error processes, which are not only time-consuming and labor-intensive but also result in low design efficiency and difficulty in achieving optimal results. Furthermore, conventional optimization algorithms lack transferability, requiring re-optimization for each design iteration. In contrast, reinforcement learning enhances the design process through trial-and-error learning and feedback mechanisms, improving both efficiency and quality. This study proposes a reinforcement learning-based multi-objective optimization design method for aircraft wing beam structures. The method facilitates multi-objective optimization, enables the storage of design knowledge, and allows for the future expansion of the design space.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.