scholarly journals A new regularization method for dynamic load identification

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042093128 ◽  
Author(s):  
Linjun Wang ◽  
Yang Huang ◽  
Youxiang Xie ◽  
Yixian Du

Dynamic forces are very important boundary conditions in practical engineering applications, such as structural strength analysis, health monitoring and fault diagnosis, and vibration isolation. Moreover, there are many applications in which we have found it very difficult to directly obtain the expected dynamic load which acts on a structure. Some traditional indirect inverse analysis techniques are developed for load identification by measured responses. These inverse problems about load identification mentioned above are complex and inherently ill-posed, while regularization methods can deal with this kind of problem. However, most of regularization methods are only limited to solve the pure mathematical numerical examples without application to practical engineering problems, and they should be improved to exclude jamming of noises in engineering. In order to solve these problems, a new regularization method is presented in this article to investigate the minimum of this minimization problem, and applied to reconstructing multi-source dynamic loads on the frame structure of hydrogenerator by its steady-state responses. Numerical simulations of the inverse analysis show that the proposed method is more effective and accurate than the famous Tikhonov regularization method. The proposed regularization method in this article is powerful in solving the dyanmic load identification problems.

2013 ◽  
Vol 22 (7) ◽  
pp. 1062-1076 ◽  
Author(s):  
Xingsheng Sun ◽  
Jie Liu ◽  
Xu Han ◽  
Chao Jiang ◽  
Rui Chen

2011 ◽  
Vol 19 (6) ◽  
pp. 765-776 ◽  
Author(s):  
Linjun Wang ◽  
Xu Han ◽  
Jie Liu ◽  
Xiaoqiao He ◽  
Fen Huang

2020 ◽  
Vol 10 (18) ◽  
pp. 6348 ◽  
Author(s):  
Jinhui Jiang ◽  
Hongzhi Tang ◽  
M Shadi Mohamed ◽  
Shuyi Luo ◽  
Jianding Chen

We introduce the augmented Tikhonov regularization method motivated by Bayesian principle to improve the load identification accuracy in seriously ill-posed problems. Firstly, the Green kernel function of a structural dynamic response is established; then, the unknown external loads are identified. In order to reduce the identification error, the augmented Tikhonov regularization method is combined with the Green kernel function. It should be also noted that we propose a novel algorithm to determine the initial values of the regularization parameters. The initial value is selected by finding a local minimum value of the slope of the residual norm. To verify the effectiveness and the accuracy of the proposed method, three experiments are performed, and then the proposed algorithm is used to reproduce the experimental results numerically. Numerical comparisons with the standard Tikhonov regularization method show the advantages of the proposed method. Furthermore, the presented results show clear advantages when dealing with ill-posedness of the problem.


2021 ◽  
Vol 156 ◽  
pp. 107586 ◽  
Author(s):  
Linjun Wang ◽  
Yunlong Peng ◽  
Youxiang Xie ◽  
Baojia Chen ◽  
Yixian Du

2018 ◽  
Vol 76 (4) ◽  
pp. 741-759 ◽  
Author(s):  
Linjun Wang ◽  
Jinwei Liu ◽  
Youxiang Xie ◽  
Yuantong Gu

2020 ◽  
Vol 18 (1) ◽  
pp. 1685-1697
Author(s):  
Zhenyu Zhao ◽  
Lei You ◽  
Zehong Meng

Abstract In this paper, a Cauchy problem for the Laplace equation is considered. We develop a modified Tikhonov regularization method based on Hermite expansion to deal with the ill posed-ness of the problem. The regularization parameter is determined by a discrepancy principle. For various smoothness conditions, the solution process of the method is uniform and the convergence rate can be obtained self-adaptively. Numerical tests are also carried out to verify the effectiveness of the method.


Sign in / Sign up

Export Citation Format

Share Document