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Title page for ETD etd-07022003-191128

Type of Document Dissertation
Author Padmanabhan, Dhanesh
Author's Email Address
URN etd-07022003-191128
Title Reliability-Based Optimization for Multidisciplinary System Design
Degree Doctor of Philosophy
Department Aerospace and Mechanical Engineering
Advisory Committee
Advisor Name Title
John E. Renaud Committee Member
Michael M. Stanisic Committee Member
Stephen M. Batill Committee Member
Timothy C. Ovaert Committee Member
  • Multidisciplinary Design Optimization
  • Structural Reliability
  • Reliability-Based Optimization
Date of Defense 2003-06-18
Availability unrestricted
Reliability-Based Optimization (RBO) for engineering design deals mainly with two

design attributes, namely the merit, for example cost, and the reliability of the design. In

this work the class of design problems which are considered, are designs characterized

by a minimum merit function and that satisfy certain reliability constraints. The reliability

constraints are typically constraints on the probabilities of failure due to component

failure events or a system failure event. These are obtained using standard reliability analysis

techniques such as First Order Reliability Method (FORM), Second Order Reliability

Methods (SORM) and Monte Carlo Simulation (MCS) techniques.

The reliability analysis and RBO are very expensive for multidisciplinary systems

consisting of various disciplines that are dependent on each other or coupled, for example,

an aeroelastic structure. Hence, the primary goal of the research is to develop

efficient methodologies that perform RBO for multidisciplinary systems. The methodologies

considered incorporates a Concurrent Subspace Optimization technique that allows

concurrent design optimization in each discipline. The methodologies also incorporate

approximation concepts to reduce the computational costs. There are essentially two

methodologies, one that uses a traditional reliability analysis method and the other that

uses a new reliability analysis method geared towards reduction of computational expenses

for coupled multidisciplinary problems. A new reliability analysis tool based on Trust Region methods was developed for the latter case. Both methodologies were applied

to multidisciplinary test problems and about 20%-30% computational savings were


A second goal of the research was to investigate the use of Monte Carlo Simulation

(MCS) techniques for reliability analysis in RBO, that are more accurate but more expensive

than FORM or SORM. In this work, conditional expectation MCS was selected

over indicator-based MCS techniques based on smoothness criteria and the availability

of analytic sensitivities. A MCS-based RBO methodology was developed and successfully

implemented to problems with both component and series failure events. It was

observed that designs with significantly lower merit functions were obtained for the application

problems considered, compared to a FORM-based RBO approach. It was also

observed that the computational costs were extremely high for one of the application problems.

Some suggestions for future research are made regarding development of efficient

methodologies for the MCS-based RBO.

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