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arXiv 2302.14175physics.app-ph
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用于分析无限和有限系统的动态力学超材料的降阶建模

Reduced Order Modeling of Dynamic Mechanical Metamaterials for Analysis of Infinite and Finite Systems

Weidi Wang, Alireza Amirkhizi

AI总结:

本研究提出一种广义降阶建模方法,利用少量参数和小矩阵快速准确计算动态力学超材料在低频下的动力学特性,有效简化建模并辅助设计优化。

摘要:

动态力学超材料是由周期性微结构组成的人工介质,旨在调控波的传播。由于几何和材料参数所跨越的设计空间广阔,对这些材料进行建模和设计在计算上可能极具挑战性。本研究旨在开发一种广义的降阶建模方法,使用有限数量的参数和小型矩阵,快速且准确地确定低频范围内的MM动力学特性。将MM单元胞视为结构元件和离散自由度的集合,其有效刚度和惯性通过基于从少量特征研究模拟导出的连续介质结果来优化能量准则而确定。该方法提供了MM系统的参数化和离散化表示,从而能够快速准确地计算重复单元胞周期阵列的特征研究结果,以及有限尺寸阵列的动态响应。该方法的高计算效率和物理准确性将有助于简化建模过程,并辅助设计发现与优化,特别是与科学机器学习和数据驱动技术相结合时。

英文摘要:

Dynamic mechanical metamaterials (MMs) are artificial media composed of periodic micro-structures, designed to manipulate wave propagation. Modeling and designing these materials can be computationally demanding due to the broad design space spanned by a range of geometric and material parameters. This work aims to develop a generalized reduced order modeling (ROM) approach for determining MM dynamics in low frequency ranges with accuracy and speed, using a limited number of parameters and small matrices. The MM unit cells are treated as assemblies of structural elements and discrete degrees of freedom, whose effective stiffness and inertia are determined by optimizing energy criteria based on continuum results derived from a small number of eigen-study simulations. This proposed approach offers a parameterized and discretized representation of MM systems, which leads to fast and accurate computation of eigen-study results for periodic arrays of repeating unit cells, as well as dynamic responses for finite-sized arrays. The high computational efficiency and physical accuracy of this method will help to streamline the modeling process and aid in design discovery and optimization, especially in combination with scientific machine learning and data-driven techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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