Crashworthiness analysis and optimization for eVTOL vehicles

Electric Vertical Take-Off and Landing (eVTOL) vehicles could transform urban transportation by enabling convenient point-to-point flights. However, their crashworthiness design presents unique challenges compared to conventional aircraft. This study optimized the crashworthiness of the skid landing gear and energy-absorbing components, followed by comprehensive analysis, including multi-angle, multi-speed off-axis crash simulations. Considering the time-consuming nature of crash simulations, which limits optimization, we introduce a machine learning method to predict stress on the skid landing gear and the energy absorption efficiency of the energy absorber. An optimization method incorporating genetic algorithms is developed. The results show a significant enhancement in the crashworthiness of the skid landing gear, energy absorber, and the whole eVTOL. Moreover, the preliminary threat level of various off-axis crash parameters to passenger safety is identified and real-time prediction tools for stress and energy absorption efficiency are introduced, maintaining accuracy within acceptable margins.