A new method for computation of eigenvector derivatives with distinct and repeated eigenvalues in structural dynamic analysis. (July 2018)

• Record Type: Journal Article
• Authors: Li, Zhengguang; Lai, Siu-Kai; Wu, Baisheng
• Is Part Of: Mechanical systems and signal processing. Volume 107(2018)
• Page Start: 78
• Page End: 92
• Publication Date: 2018-07
• Subjects: Real symmetric eigensystems -- Distinct and repeated eigenvalues -- Eigenvector derivatives -- Singularity -- Finite element model; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.01.003
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A new approach is presented to compute eigenvector derivatives for the real symmetric eigensystems. Highly accurate approximations to the eigenvector derivatives can be obtained within a few iterations. The method is applicable for both cases of simple and repeated eigenvalues. The method is suitable for finite element models with large sparse matrices. Abstract: Determining eigenvector derivatives is a challenging task due to the singularity of the coefficient matrices of the governing equations, especially for those structural dynamic systems with repeated eigenvalues. An effective strategy is proposed to construct a non-singular coefficient matrix, which can be directly used to obtain the eigenvector derivatives with distinct and repeated eigenvalues. This approach also has an advantage that only requires eigenvalues and eigenvectors of interest, without solving the particular solutions of eigenvector derivatives. The Symmetric Quasi-Minimal Residual (SQMR) method is then adopted to solve the governing equations, only the existing factored (shifted) stiffness matrix from an iterative eigensolution such as the subspace iteration method or the Lanczos algorithm is utilized. The present method can deal with both cases of simple and repeated eigenvalues in a unified manner. Three numerical examples are given to illustrate the accuracy and validity of the proposed algorithm. Highly accurate approximations to the eigenvector derivatives are obtained within a few iteration steps, making a significant reduction of the computational effort. This method can be incorporated into a coupled eigensolver/derivative software module. In particular, it is applicable for finite element models with large sparse matrices.