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An Adaptive Frequency Sampling Algorithm for Dynamic Condensation-Based Frequency Response Analysis

Author

Listed:
  • Jaehun Lee

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul 04620, Republic of Korea)

  • Younggeun Park

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul 04620, Republic of Korea)

  • Yeji Lee

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul 04620, Republic of Korea)

  • Seongmin Chang

    (School of Mechanical Engineering, Chungnam National University, Daejeon 34134, Republic of Korea)

Abstract

This paper proposed an efficient and adaptive frequency sampling algorithm for frequency response analysis using dynamic condensation-based reduced-order modeling. For the degree of freedom-based model reduction method, the reduced-order basis becomes a frequency-dependent matrix since the relationship between master and slave degrees of freedom stems from partial equations of a second-order dynamical system. Such frequency-dependency makes the analysis inefficient for investigating the frequency response of the system. Considering that the coverage of a local reduced-order basis at a single frequency varies depending on the frequency, a new frequency sampling algorithm was proposed with a strategy of constructing multiple local reduced-order models (ROMs) at sample frequencies. For adaptive sampling, the frequency range of a local ROM was evaluated, and a new sample was added if there was a gap between two adjacent ROMs. As a result, the accuracy of the local ROM can be estimated, and the efficiency in the online stage was greatly enhanced. The proposed method was verified by performing frequency response analysis with several numerical examples, including a large-scale structural and dynamic system.

Suggested Citation

  • Jaehun Lee & Younggeun Park & Yeji Lee & Seongmin Chang, 2023. "An Adaptive Frequency Sampling Algorithm for Dynamic Condensation-Based Frequency Response Analysis," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2683-:d:1170187
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    References listed on IDEAS

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    1. Jonggeon Lee & Euiyoung Kim & Jaehun Lee, 2022. "Data Reconstruction-Based Two-Step Non-Intrusive Reduced-Order Modeling Using Fourier Transform and Interpolations," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
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