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<p>With the development of aerospace industry, the complexity and large size of spacecraft bring huge time cost for simulation. The damping characteristics of materials and the influence of solar heat flux will further lead to a decrease in computational efficiency. In this study, a model order reduction method for viscoelasticity-thermal coupled multibody systems is proposed. The Absolute Nodal Coordinate Formulation (ANCF) and the Kelvin–Voigt viscosity model are used to build the system equation of motion. The Proper Orthogonal Decomposition (POD) method is introduced to reduce the system degrees of freedom. The computational efficiency is further improved by dividing the system motion process into multiple linearized intervals based on the first-order Taylor expansion. To improve the robustness of the reduced-order model, a multi-parameterized reduced-order model is constructed based on Grassmann manifold interpolation theory. Three numerical examples are presented as verification and validation. Results show that the proposed method is able to quickly predict the response of the viscoelasticity-thermal coupled systems with uncertain parameters.</p>

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Multi-parametric model order reduction of viscoelasticity-thermal coupled multibody system based on POD

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Qinglong Tian,
  • Hongyu Sun,
  • Chengyu Pan,
  • Zuqing Yu

摘要

With the development of aerospace industry, the complexity and large size of spacecraft bring huge time cost for simulation. The damping characteristics of materials and the influence of solar heat flux will further lead to a decrease in computational efficiency. In this study, a model order reduction method for viscoelasticity-thermal coupled multibody systems is proposed. The Absolute Nodal Coordinate Formulation (ANCF) and the Kelvin–Voigt viscosity model are used to build the system equation of motion. The Proper Orthogonal Decomposition (POD) method is introduced to reduce the system degrees of freedom. The computational efficiency is further improved by dividing the system motion process into multiple linearized intervals based on the first-order Taylor expansion. To improve the robustness of the reduced-order model, a multi-parameterized reduced-order model is constructed based on Grassmann manifold interpolation theory. Three numerical examples are presented as verification and validation. Results show that the proposed method is able to quickly predict the response of the viscoelasticity-thermal coupled systems with uncertain parameters.