电动垂直起降飞行器（eVTOL）可以在城市内提供便捷的点对点飞行，有望革命性的改变城市居民的出行方式，然而与常规飞机相比它在耐撞性设计方面存在诸多新挑战。对此本研究对滑橇式起落架和吸能元件进行适坠性优化设计，并应用于整机进行适坠性分析，并进行了多角度多速度的离轴坠撞仿真。同时鉴于坠撞仿真耗时多、不利于优化设计的问题，提出了使用机器学习技术预测滑橇式起落架应力、吸能元件吸能效率的方法，并且开发出结合遗传算法的优化方法。通过研究大幅提高了滑橇式起落架、吸能元件、以及eVTOL整机的适坠性能，初步掌握各离轴坠撞参数对乘员安全的威胁程度，并能在误差可接受范围内实时预测滑橇式起落架应力和吸能元件的吸能效率。
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 skid landing gear and energy-absorbing components, followed by comprehensive analysis, including multi-angle, multi-speed off-axis crash simulations. Due to the time-consuming nature of crash simulations, which limits optimization, the study introduced a machine-learning approach to predict stress on the skid landing gear and the energy absorption efficiency of the energy absorber. An optimization method incorporating genetic algorithms was also developed. The results showed a significant enhancement in the crashworthiness of the skid landing gear, energy absorber, and the whole eVTOL. Moreover, preliminary threat levels of various off-axis crash parameters to passenger safety was identified and real-time prediction tools for stress and energy absorption efficiency were introduced, maintaining accuracy within acceptable margins.
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