为了实现太空探索的新愿景，研制中的新一代载人飞船的容量有大幅度的提高，这将造成其着陆时产生更大的冲击负载。引入气囊缓冲系统可以大幅度衰减飞船着陆过程中的冲击加速度，但是加大了系统设计和分析的难度。文章利用长短时记忆(Long Short Term Memory，LSTM)网络算法，基于有限元分析生成的数据集，训练了载人飞船气囊缓冲系统着陆冲击响应的代理模型，可快速预测飞船质心处的加速度曲线。对比有限元分析和代理模型的预测结果表明:基于LSTM的代理模型可以快速预测在飞船随体坐标系下的冲击响应，尤其是在预测沿飞船旋转轴的纵向加速度和垂直于旋转轴的横向加速度方面表现出色;代理模型与有限元分析的相对误差约为 10%，但是其预测速度快了约 105 倍。对比结果充分证明了文中提出的代理模型可以有效缩短此类刚柔耦合复杂系统的设计周期，提高工程计算的效率。
Predicting Impact Responses of the Spacecraft Soft Landing on the Airbag System by the Long Short-Term Memory Network
To realize the new vision of space exploration,the capacity of the new generation of manned spacecraft is significantly increased,which results in greater impact load during landing.The airbag cushioning system can substantially attenuate the impact acceleration but increases the difficulty of system design and analysis.This paper utilizes the long short-term memory network and the dataset generated by finite element analysis to train a surrogate model for quickly predicting the impact acceleration of the spacecraft when soft landing on the complex airbag cushioning system.Comparison of the prediction results between the finite element analysis and the surrogate model shows that the LSTM-based surrogate model can quickly predict the impact acceleration under the body fixed coordinate system of the spacecraft,especially in the prediction of the longitudinal acceleration along the spacecraft's rotation axis and the transverse acceleration perpendicular to the rotation axis.The relative error between the surrogate model and the finite element analysis is about 10%,but the prediction speed is 100,000 times faster.It is fully proved that the proposed surrogate model can effectively accelerate the design cycle of such rigid-flexible coupled complex systems and improve the efficiency of engineering calculations.
manned spacecraftairbag cushionlong short-term memory networkdeep learningsurrogate model
NETL
NSTL
万方数据
