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Efficient frequency response and its direct sensitivity analyses for large-size finite element models using Krylov subspace-based model order reduction
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  • Efficient frequency response and its direct sensitivity analyses for large-size finite element models using Krylov subspace-based model order reduction
  • Efficient frequency response and its direct sensitivity analyses for large-size finite element models using Krylov subspace-based model order reduction
저자명
Han. Jeong-Sam
간행물명
Journal of mechanical science and technology
권/호정보
2012년|26권 4호|pp.1115-1126 (12 pages)
발행정보
대한기계학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

In this paper, we examine an efficient calculation of the approximate frequency response (FR) for large-size finite element (FE) models using the Krylov subspace-based model order reduction (MOR) and its direct design sensitivity analysis with respect to design variables for sizing. Information about both the FR and its design sensitivity is necessary for typical gradient-based optimization iterations; therefore, the problem of high computational cost may occur when FRs of a large-size FE models are involved in the optimization problem. In the method suggested in this paper, reduced order models, generated from the original full-order FE models through the Arnoldi process, are used to calculate both the FR and FR sensitivity. This maximizes the speed of numerical computation of the FR and its design sensitivity. Assuming that the Krylov basis vectors remain constant with respect to the perturbation of a design variable, the FR sensitivity analysis is performed in a more efficient manner. As numerical examples, a car body with 535,992 degrees of freedom (DOF) and a $6{ imes}6$ micro-resonator array with 368,424 DOF are adopted to demonstrate the numerical accuracy and efficiency of the suggested approach. Using the reduced-order models, we found that the FR and FR sensitivity are in a good agreement with those using the full-order FE model. The reduction in computation time is also found to be significant because of the use of Krylov subspace-based reduced models.