计算力学学报2025，Vol.42Issue(5)：744-750,7.DOI:10.7511/jslx20240627001
基于RIDLA的卫星承重结构螺栓连接非线性滞回建模
Nonlinear hysteresis modeling of bolt connections in satellite load-carrying structures using a Residual Improvement Deep Learning Algorithm
摘要
Abstract
Accurately constructing the nonlinear hysteresis loop model at the bolt connection is crucial for the vibration reduction and safety performance evaluation of a satellite load-carrying structure.Traditional time-domain analysis methods of computational models require substantial time costs,and typical data-driven models struggle to construct high-precision hysteresis models.To address these challenges,a novel Residual Improvement Deep Learning Algorithm(RIDLA)is proposed for constructing the hysteresis loop model of displacement and force at the bolt connection.The algorithm fully leverages the capacity of Long Short-Term Memory(LSTM)neural networks to fit nonlinear relationships in time series.It adopts an innovative approach by creating a multi-level residual improvement deep learning model that iteratively refines predictions based on measured responses,resulting in highly accurate modeling of hysteresis at bolt connections.The performance of the RIDLA method is validated using experimental data from cyclic loading of a subcomponent of a satellite load carrying structure.The findings demonstrate that RIDLA achieves highly accurate predictions of the displacement and force hysteresis loop at the bolt connection.Additionally,the RIDLA method could be applied to predict the dynamic responses of other complex non-linear systems.关键词
承重结构/螺栓连接/滞回模型/残差改进/深度学习Key words
load-carrying structure/bolt connection/hysteresis model/residual improvement/deep learning引用本文复制引用
顾乃建,刘坤,武文华,郭杏林..基于RIDLA的卫星承重结构螺栓连接非线性滞回建模[J].计算力学学报,2025,42(5):744-750,7.基金项目
国家重点研发计划(2021YFA1003501) （2021YFA1003501）
航空科学基金(2022Z061001)资助项目. （2022Z061001）
