Determination of Natural Frequencies and Mode Shapes of Structures Using Subspace Iteration Method with Accelerated Starting Vectors
Byoung‐Wan Kim, Sang-Won Cho, Chun‐Ho Kim, Inwon Lee
Abstract
Byoung‐Wan Kim, Sang-Won Cho, Chun‐Ho Kim, Inwon Lee
Abstract
Natural frequencies and mode shapes of structures are determined from eigenvalue analysis. This paper proposes a subspace iteration method with an accelerated Lanczos starting subspace for the efficient eigenvalue analysis of structures. The proposed method uses accelerated Lanczos vectors as starting vectors in order to reduce the number of subspace iterations. Accelerated Lanczos starting vectors are generated by employing the repeated forward reduction and back substitution. The proposed method has less computing time than the subspace iteration method with a conventional Lanczos starting subspace when the number of required eigenpairs is relatively small. The efficiency of the proposed method is verified through numerical examples.
OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Natural frequencies and mode shapes of structures are determined from eigenvalue analysis. This paper proposes a subspace iteration method with an accelerated Lanczos starting subspace for the efficient eigenvalue analysis of structures. The proposed method uses accelerated Lanczos vectors as starting vectors in order to reduce the number of subspace iterations. Accelerated Lanczos starting vectors are generated by employing the repeated forward reduction and back substitution. The proposed method has less computing time than the subspace iteration method with a conventional Lanczos starting subspace when the number of required eigenpairs is relatively small. The efficiency of the proposed method is verified through numerical examples.
Key concepts: Lanczos resampling, Subspace topology, Eigenvalues and eigenvectors, Lanczos algorithm, Power iteration, Iterative method, Applied mathematics, Algorithm