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Reliability-Based Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition
Engineering Mechanics Institute Annual Conference (EMI) (2014)
  • Xuchun Ren, Georgia Southern University
  • Vaibhav Yadav, San Diego State University
  • Sharif Rahman, University of Iowa
This article presents a new method for reliability-based design optimization (RBDO) of complex engineering systems [1]. The method, capable of solving component- and system-level RBDO problems, involves (1) an adaptive-sparse polynomial dimensional decomposition (AS-PDD) of a high-dimensional stochastic response [2] for reliability analysis, (2) a novel integration of AS-PDD and score functions for design sensitivity analysis, and (3) standard gradient-based optimization algorithms, encompassing a multi-point, single-step design process.  Both the failure probability and its design sensitivities are determined concurrently from a single stochastic simulation or analysis.  Precisely for this reason, the multi-point, single-step framework of the proposed method affords the ability of solving industrial-scale problems with large design spaces.  Numerical results stemming from mathematical functions or elementary engineering problems indicate that the new method provides more accurate or computationally efficient design solutions than existing methods.  Furthermore, shape design of a 79-dimensional engine bracket was performed, demonstrating the power of the new method to tackle practical RBDO problems.
  • Reliability-based design optimization,
  • Adaptive-sparse polynomial,
  • Dimensional decomposition
Publication Date
August 7, 2014
Hamilton, Ontario, Canada
Citation Information
Xuchun Ren, Vaibhav Yadav and Sharif Rahman. "Reliability-Based Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition" Engineering Mechanics Institute Annual Conference (EMI) (2014)
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