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.
Skip Nav Destination
Article navigation
January 2013
Research-Article
Efficient Global Surrogate Modeling for Reliability-Based Design Optimization Available to Purchase
Barron J. Bichon,
Barron J. Bichon
Senior Research Engineer Materials Engineering Department, Mechanical Engineering Division, Southwest Research Institute
, San Antonio, TX 78238
e-mail: [email protected]
Search for other works by this author on:
Michael S. Eldred,
Michael S. Eldred
Distinguished Member of Technical Staff Optimization and Uncertainty Quantification Department, Sandia National Laboratories
, Albuquerque, NM 87185
e-mail: [email protected]
Search for other works by this author on:
Sankaran Mahadevan,
Sankaran Mahadevan
John R. Murray Sr. Professor of Engineering Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Vanderbilt University
, Nashville, TN 37235
e-mail: [email protected]
Search for other works by this author on:
John M. McFarland
John M. McFarland
Research Engineer Materials Engineering Department, Mechanical Engineering Division, Southwest Research Institute
, San Antonio, TX 78238
e-mail: [email protected]
Search for other works by this author on:
Barron J. Bichon
Senior Research Engineer Materials Engineering Department, Mechanical Engineering Division, Southwest Research Institute
, San Antonio, TX 78238
e-mail: [email protected]
Michael S. Eldred
Distinguished Member of Technical Staff Optimization and Uncertainty Quantification Department, Sandia National Laboratories
, Albuquerque, NM 87185
e-mail: [email protected]
Sankaran Mahadevan
John R. Murray Sr. Professor of Engineering Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Vanderbilt University
, Nashville, TN 37235
e-mail: [email protected]
John M. McFarland
Research Engineer Materials Engineering Department, Mechanical Engineering Division, Southwest Research Institute
, San Antonio, TX 78238
e-mail: [email protected]Contributed by Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received April 25, 2011; final manuscript received September 12, 2012; published online December 12, 2012. Assoc. Editor: Christiaan J. J. Paredis.
J. Mech. Des. Jan 2013, 135(1): 011009 (13 pages)
Published Online: December 12, 2012
Article history
Received:
April 25, 2011
Revision Received:
September 12, 2012
Citation
Bichon, B. J., Eldred, M. S., Mahadevan, S., and McFarland, J. M. (December 12, 2012). "Efficient Global Surrogate Modeling for Reliability-Based Design Optimization." ASME. J. Mech. Des. January 2013; 135(1): 011009. https://doi.org/10.1115/1.4022999
Download citation file:
Get Email Alerts
Related Articles
Versatile Formulation for Multiobjective Reliability-Based Design Optimization
J. Mech. Des (November,2006)
Reliability-Based Design Optimization of Complex Problems With Multiple Design Points via Narrowed Search Region
J. Mech. Des (June,2020)
Hybrid Analysis Method for Reliability-Based Design Optimization
J. Mech. Des (June,2003)
Related Proceedings Papers
Related Chapters
A PSA Update to Reflect Procedural Changes (PSAM-0217)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
STRUCTURAL RELIABILITY ASSESSMENT OF PIPELINE GIRTH WELDS USING GAUSSIAN PROCESS REGRESSION
Pipeline Integrity Management Under Geohazard Conditions (PIMG)
The Type-Variety Principle, Mathematical Models and Algorithms for the Optimization of the Reliability of Series–Parallel and Parallel–Series Systems, the Elements of Which Allow Two Types of Failures
Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection 1: Stochastic Modeling