CN

Gabriel JieGAO JIE

Associate professor    Supervisor of Doctorate Candidates    Supervisor of Master's Candidates

  • Professional Title:Associate professor
  • Gender:Male
  • Status:Employed
  • Department:School of Aeronautics and Astronautics
  • Education Level:Postgraduate (Doctoral)
  • Degree:Doctoral Degree in Philosophy

Paper Publications

Current position: 英文主页 > Scientific Research > Paper Publications

J. Wu, J. Zhu, J. Gao, L. Gao, H. Liu, A CAD-oriented parallel-computing design framework for shape and topology optimization of arbitrary structures using parametric level set, Comput. Methods Appl. Mech. Eng. 431 (2024) 117292. https://doi.org/10.1016/j.cma.2024.117292.

Release time:2024-08-11
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Journal paper
Journal:
Computer Methods in Applied Mechanics and Engineering
Included Journals:
SCI
Affiliation of Author(s):
华中科技大学
Discipline:
Engineering
First-Level Discipline:
Mechanics
Funded by:
This work is partially supported by the National Natural Science Foundation of China (No. 52105255,
Page Number:
117292
Key Words:
High-resolution topology optimization Parametric level set method Parallel computation Large-scale structures CAD
DOI number:
10.1016/j.cma.2024.117292.
Abstract:
Recently, the high-resolution topology optimization to promote engineering applicability has gained much more attentions. However, an accurate and highly-efficient design framework for implementing shape and topology optimization of engineering structures with integration of CAD model is still in demand. In the current work, the critical intention is to develop a CAD-oriented parallel-computing design framework for arbitrary structures, where the Parametric Level Set Method (PLSM) is employed for shape and topology optimization. Firstly, an implicit identification model is constructed for generating a signed distance field using the vertex and normal information from the ‘STL’ file of engineering structures. The signed distance field is combined with the compactly supported radial basis functions (CSRBFs) to solve the initial level set function with a parametrization. This method is applied to present all domains, including design domains, Neumann boundary domains, Dirichlet boundary domains, and non-design domains. Secondly, the CPU parallel strategy is considered for allocating partitions of structural stiffness matrix in finite element analysis to different CPU cores for the parallel-computing to save computation costs. Thirdly, a parallel-computing design formulation is developed for performing shape and topology optimization of arbitrary structures, in which the partitioned terms of all design variables and stiffness matrix are concurrently computed on each CPU core. Finally, several classic benchmarks and the critical engineering structure of Virtual Reality (VR) glass part with extremely complex geometries, are discussed to demonstrate the effectiveness and efficiency of the proposed design framework.
Links to published journals:
https://www.sciencedirect.com/science/article/pii/S0045782524005486