A multi-discretization scheme for topology optimization based on the parameterized level set method

Author:

Wei PengORCID,Liu Yang,Li ZuyuORCID

Abstract

In the framework of the parameterized level set method, the structural analysis and topology representation can be implemented in a decoupling way. A parameterized level set function, typically, using radial basis functions (RBFs), is a linear combination of a set of prescribed RBFs and coefficients. Once the coefficients are determined, the theoretical level set function is determined. Exploiting this inherent property, we propose a multi-discretization method based on the parameterized level set method. In this approach, a coarse discretization is applied to do the structural analysis whereas another dense discretization is employed to represent the structure topology. As a result, both efficient analysis and high-resolution topological design are available. Note that the dense discretization only accounts for a more precise and smooth description of the theoretical level set function rather than introduce extra design freedom or incur interference to structural analysis or the optimization process. In other words, this decoupling way will not add to the computational burden of structural analysis or result in non-uniqueness of converged results for a particular analysis setting. Numerical examples in both two-dimension and three-dimension show effectiveness and applicability of the proposed method.

Funder

the National Natural Science Foundation of China

State Key Laboratory of Structural Analysis for Industrial Equipment

Publisher

EDP Sciences

Subject

Control and Optimization,Modeling and Simulation

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Topology optimization design of microstructures with zero Poisson's ratio;International Journal for Simulation and Multidisciplinary Design Optimization;2024

2. Topology Optimization for Steady-State Navier-Stokes Flow Based on Parameterized Level Set Based Method;Computer Modeling in Engineering & Sciences;2023

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