arrow
返回

Novel operator-splitting methods for real-time dynamic substructure testing

delete2023-11-01
delete0
PRE
AI
许
许国山 (Guoshan Xu) *
Z
Zhen Wang
DOI:10.1016/j.ymssp.2023.110727delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Improved and corrected Operator-Splitting (OS) methods are proposed for Real-Time Dynamic Substructure Testing (RTDST) in this paper. Firstly, the theory and the stability of two proposed OS methods are investigated. Furthermore, the effectiveness of two proposed OS methods is validated by RTDSTs with one pure mass specimen and one mass-stiffness specimen. By using the predictor velocity and acceleration as well as the correction force approximations, the improved OS method and the corrected OS method are explicit methods for RTDST. It is shown from the analytical and experimental results that the improved OS method is unconditionally stable for linear type and softening type stiffness and damping systems and conditionally stable for hardening type stiffness and damping system if the mass ratio of the experimental substructure to the numerical substructure is less than 1. In contrast, the improved OS method is unsuitable for RTDST if the mass ratio is larger than 1. The corrected OS method is unconditionally stable for the linear type and softening type stiffness and damping systems and conditionally stable for hardening type stiffness and damping systems. The stability of the corrected OS method is better than the improved OS method, which indicates the advantage of the corrected OS method over the improved OS method.
Keyword:
Operator-splitting method
Real-time dynamic substructure testing
Stability
Explicit method
Mass ratio

期刊

Mechanical Systems and Signal Processing 封面图
Mechanical Systems and Signal Processing
IF:
8.9
论文数:
1.3W
被引数:
6.6W

机构

H
harbin institute of technology
学者数:
8.0W
论文数: 6.6W
被引数: 66
引用论文

引用论文

Valeur de service et compétence
err2000-01-01
err0
PREAI
errPhilippe Zarifian
err分享
err收藏
err分享
err收藏
University Students and Their Ability to Perform Self-Regulated Online Learning Under the COVID-19 Pandemic
err2022-03-09
err0
errOAAI
errBlanka Klimova; Katarina Zamborova; Anna Cierniak-Emerych; Szymon Dziuba
err分享
err收藏
err分享
err收藏
err分享
err收藏
学者 查看更多内容