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Acta Mechanica Solida Sinica

, Volume 29, Issue 1, pp 31–45 | Cite as

A Comparison of Deterministic, Reliability-Based Topology Optimization under Uncertainties

  • Qinghai Zhao
  • Xiaokai Chen
  • Zhengdong Ma
  • Yi Lin
Article

Abstract

Reliability and optimization are two key elements for structural design. The reliability-based topology optimization (RBTO) is a powerful and promising methodology for finding the optimum topologies with the uncertainties being explicitly considered, typically manifested by the use of reliability constraints. Generally, a direct integration of reliability concept and topology optimization may lead to computational difficulties. In view of this fact, three methodologies have been presented in this study, including the double-loop approach (the performance measure approach, PMA) and the decoupled approaches (the so-called Hybrid method and the sequential optimization and reliability assessment, SORA). For reliability analysis, the stochastic response surface method (SRSM) was applied, combining with the design of experiments generated by the sparse grid method, which has been proven as an effective and special discretization technique. The methodologies were investigated with three numerical examples considering the uncertainties including material properties and external loads. The optimal topologies obtained using the deterministic, RBTOs were compared with one another; and useful conclusions regarding validity, accuracy and efficiency were drawn.

Key Words

reliability-based design optimization topology optimization first-order reliability method (FORM) stochastic response surface method sparse grid method 

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Copyright information

© The Chinese Society of Theoretical and Applied Mechanics and Technology 2016

Authors and Affiliations

  • Qinghai Zhao
    • 1
  • Xiaokai Chen
    • 1
  • Zhengdong Ma
    • 2
  • Yi Lin
    • 3
  1. 1.Collaborative Innovation Center of Electric Vehicles in Beijing, School of Mechanical EngineeringBeijing Institute of TechnologyBeijingChina
  2. 2.Department of Mechanical EngineeringUniversity of MichiganUSA
  3. 3.Beijing Automotive Technology CenterBeijingChina

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