Electric vertical take-off and landing (eVTOL) aircraft are transforming urban air mobility (UAM) by providing efficient, low-emission, and rapid transit in congested cities. However, ensuring safe and stable landings remains a critical challenge, particularly in constrained urban environments with variable wind conditions. This study investigates the landing aerodynamics of a multi-rotor eVTOL using the lattice Boltzmann method (LBM), a computational approach well-suited to complex boundary conditions and parallel processing. This analysis examines the ground effect, descent speed, and crosswind influence on lift distribution and stability. A rooftop landing scenario is also explored, where half of the rotors operate over a rooftop while the rest remain suspended in open air. Results indicate that rooftop landings introduce asymmetric lift distribution due to crosswind and roof-induced flow circulation, significantly increasing rolling moment compared to ground landings. These findings underscore the role of descent speed, crosswinds, and landing surface geometry in eVTOL aerodynamics, particularly the heightened risk of rollover in rooftop scenarios.

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.