A Configuration-Driven Modal Optimization Method for Thin-Walled Structures Using a Surrogate-Assisted Evolutionary Algorithm with an Iterative Update Kriging Model

34 Pages Posted: 1 Aug 2024

See all articles by Hu Yujia

Hu Yujia

University of Shanghai for Science and Technology

Gao Jingkun

affiliation not provided to SSRN

Zhao Haolan

University of Shanghai for Science and Technology

Weidong Zhu

University of Maryland Baltimore County

Abstract

A new configuration-driven modal optimization method is proposed in this work by utilizing the sensitivity of a thin-walled structure's modal characteristics to its configuration. Unlike the conventional technical roadmap in modal optimization of thin-walled structures, an adjustable configuration function is used to design undeformed configurations of thin-walled structures and to achieve the increase of certain natural frequencies of a thin-walled structure while other important non-designed natural frequencies minimally change. The weak form quadrature element method is used to solve modal characteristics of thin-walled structures with arbitrary undeformed configurations under natural coordinates. A surrogate-assisted evolutionary algorithm based on a novel iterative update Kriging model is adopted to enhance the optimization efficiency. As applications, modal crossings induced by undeformed configurations are studied in detail, and modal optimization problems of two kinds of thin-walled structures are given to assess its validity and effectiveness.

Keywords: modal optimization, weak form quadrature element method, Kriging model, Thin-walled structure

Suggested Citation

Yujia, Hu and Jingkun, Gao and Haolan, Zhao and Zhu, Weidong, A Configuration-Driven Modal Optimization Method for Thin-Walled Structures Using a Surrogate-Assisted Evolutionary Algorithm with an Iterative Update Kriging Model. Available at SSRN: https://ssrn.com/abstract=4913252

Hu Yujia (Contact Author)

University of Shanghai for Science and Technology ( email )

Gao Jingkun

affiliation not provided to SSRN ( email )

Zhao Haolan

University of Shanghai for Science and Technology ( email )

Weidong Zhu

University of Maryland Baltimore County ( email )

Baltimore, Maryland
United States

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