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Level set topology optimization with nodally integrated reproducing kernel particle method
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Level set topology optimization with nodally integrated reproducing kernel particle method

Andreas Neofytou, Tsung-Hui Huang, Sandilya Kambampati, Renato Picelli, Jiun-Shyan ChenH. Alicia Kim
Computer Methods in Applied Mechanics and Engineering, 卷.385, 114016
11/2021

摘要

Design-depended Level set topology optimization Naturally stabilized nodal integration Reproducing kernel particle method Stress-based Computational Mechanics Mechanics of Materials Mechanical Engineering Physics and Astronomy (all) Computer Science Applications
A level set topology optimization (LSTO) using the stabilized nodally integrated reproducing kernel particle method (RKPM) to solve the governing equations is introduced in this paper. This methodology allows for an exact geometry description of a structure at each iteration without remeshing and without any interpolation scheme. Moreover, useful characteristics of the RKPM such as the easily controlled order of continuity and the ability to freely place particles in a design domain wherever needed are illustrated through stress based and design-dependent surface loading examples. The numerical results illustrate the effectiveness and robustness of the methodology with good optimization convergence behavior and ability to handle large topological changes. Furthermore, it is shown that different particle distributions can be used to increase efficiency without additional complexity.

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https://doi.org/10.1016/j.cma.2021.114016檢視
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