Efficient Global Surrogate Modeling for Reliability-Based Design Optimization

Author:

Bichon Barron J.1,Eldred Michael S.2,Mahadevan Sankaran3,McFarland John M.4

Affiliation:

1. Senior Research Engineer Materials Engineering Department, Mechanical Engineering Division, Southwest Research Institute, San Antonio, TX 78238 e-mail:

2. Distinguished Member of Technical Staff Optimization and Uncertainty Quantification Department, Sandia National Laboratories, Albuquerque, NM 87185 e-mail:

3. John R. Murray Sr. Professor of Engineering Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235 e-mail:

4. Research Engineer Materials Engineering Department, Mechanical Engineering Division, Southwest Research Institute, San Antonio, TX 78238 e-mail:

Abstract

Determining the optimal (lightest, least expensive, etc.) design for an engineered component or system that meets or exceeds a specified level of reliability is a problem of obvious interest across a wide spectrum of engineering fields. Various formulations and methods for solving this reliability-based design optimization problem have been proposed, but they typically involve accepting a tradeoff between accuracy and efficiency in the reliability analysis. This paper investigates the use of the efficient global optimization and efficient global reliability analysis methods to construct surrogate models at both the design optimization and reliability analysis levels to create methods that are more efficient than existing methods without sacrificing accuracy. Several formulations are proposed and compared through a series of test problems.

Publisher

ASME International

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials

Reference54 articles.

1. Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions;AIAA J.,2008

2. Efficient Surrogate Modeling for Reliability Analysis and Design,2010

3. Eigenvector Dimension Reduction Method for Sensitivity-Free Uncertainty Quantification;Struct. Multidiscip. Optim.,2008

4. Design Under Uncertainty Employing Stochastic Expansion Methods;Int. J. Uncertainty Quantification,2011

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