A surrogate-based reliability analysis method of the motion of large flexible space structures

TL;DR

This paper introduces a Bayesian support vector regression-based surrogate modeling approach to efficiently and accurately assess the reliability of large flexible space structures, crucial for satellite stability.

Contribution

It develops a surrogate model using Bayesian SVR for reliability analysis of LFSS, reducing computational costs while maintaining high accuracy.

Findings

High accuracy in reliability assessment demonstrated

Significant reduction in computational cost achieved

Method applicable to various LFSS types

Abstract

Satellites and their instruments are subject to the motion stability throughout their lifetimes. The reliability of the large flexible space structures (LFSS) is particularly important for the motion stability of satellites and their instruments. In this paper, the reliability analysis of large flexible space structures is conducted based on Bayesian support vector regression (SVR). The kinematic model of a typical large flexible space structure is first established. Based on the kinematic model, the surrogate model of the motion of the large flexible space structure is then developed to further reduce the computational cost. Finally, the reliability analysis is conducted using the surrogate model. The proposed method shows high accuracy and efficiency for the reliability assessments of the typical large flexible space structure and can be further developed for other LFSS.