Predicting Impact Responses of the Spacecraft Soft Landing on the Airbag System by Simulation-Based Deep Learning Models

EasyChair Preprint 13584

Abstract

This study proposes simulation-based deep learning models for fast predicting the impact accelerations of the spacecraft during soft landing on the complex airbag landing system. The finite element model was constructed to generate the dataset with multiple inputs and outputs. The deep learning models were developed under the framework of the multi-layer perceptron (MLP) and the convolutional neural network (CNN), respectively. We first trained separate MLP models for different impact accelerations and then organized one CNN model with time-stepping to output all accelerations simultaneously.  By comparing predictions on longer time series, the CNN model demonstrates better extrapolation than the MLP model. The results indicate that deep learning models can improve the efficiency of system design optimization and real-time prediction.

Keyphrases: Airbag landing system, Crew module, finite element analysis, impact dynamics, machine learning, neural network, spacecraft

@booklet{EasyChair:13584,
  author    = {Xinyi Shen and Caishan Liu},
  title     = {Predicting Impact Responses of the Spacecraft Soft Landing on the Airbag System by Simulation-Based Deep Learning Models},
  howpublished = {EasyChair Preprint 13584},
  year      = {EasyChair, 2024}}