Reliability-based design optimization with confidence level under input model uncertainty due to limited test data

Authors:
Yoojeong Noh;K. K. Choi;Ikjin Lee;David Gorsich;David Lamb
Affiliations:
Department of Mechanical & Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, USA 52242;Department of Mechanical & Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, USA 52242;Department of Mechanical & Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, USA 52242;US Army RDECOM/TARDEC, Warren, USA 48397-5000;US Army RDECOM/TARDEC, Warren, USA 48397-5000
Venue:
Structural and Multidisciplinary Optimization
Year:
2011

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Abstract

For obtaining a correct reliability-based optimum design, the input statistical model, which includes marginal and joint distributions of input random variables, needs to be accurately estimated. However, in most engineering applications, only limited data on input variables are available due to expensive testing costs. The input statistical model estimated from the insufficient data will be inaccurate, which leads to an unreliable optimum design. In this paper, reliability-based design optimization (RBDO) with the confidence level for input normal random variables is proposed to offset the inaccurate estimation of the input statistical model by using adjusted standard deviation and correlation coefficient that include the effect of inaccurate estimation of mean, standard deviation, and correlation coefficient.