Robust optimization using NURBs based metamodels

dc.contributor.advisorCrawford, Richard H.en
dc.creatorAjetunmobi, Abiola Morufen
dc.date.accessioned2016-08-09T16:18:20Z
dc.date.available2016-08-09T16:18:20Z
dc.date.issued2007-08
dc.description.abstractThe subject of uncertainty is a prevalent factor in engineering and design. Real-world engineering systems are susceptible to uncontrollable dynamics or variations that influence their real-time performance and long-term consistency or reliability. Therefore designers and engineers desire to deliver system solutions that are both optimal and dependable. Robust design, in particular robust optimization has emerged as a promising methodology to address the problems of dealing with system uncertainty. The goal of robust optimization is to arrive at the optimized system configuration for a design objective (performance/objective function) that is tolerant to uncertain system variables through a strategy of minimizing the sensitivity of the system’s performance to the uncertain variables. The robust optimization approach creates representations of system perturbations/randomness, and develops measures of randomness and the designer’s risk aversion tolerance which are incorporated into identifying a robust optimal solution. This thesis presents a method for robust optimization that identifies robust regions and eliminates non-robust regions based on evaluations that estimate the gradients of the performance space topology across subspaces of NURBs based metamodel representations of a system’s design space. The thesis advances a new approach towards exploiting design space by searching for sections that could potentially hold robust solutions through analysis of the gradients across proximate clusters of control points in the control point networks inherent in NURBs metamodels and selectively optimizing only within the section(s) with the desired sensitivity profile to uncover robust optimal solutions. The HyPerROB algorithm is implemented in C++ and tested to prove the validity of its results in comparison to alternative methods in literature. This robust optimization framework is applied to formulate unconstrained robust optimization problems from three test functions and a constrained robust optimization problem from a practical engineering design problem.en
dc.description.departmentMechanical Engineeringen
dc.format.mediumelectronicen
dc.identifierdoi:10.15781/T26688J8Cen
dc.identifier.urihttp://hdl.handle.net/2152/39348en
dc.language.isoenen
dc.relation.ispartofUT Electronic Theses and Dissertationsen
dc.rightsCopyright © is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.rights.restrictionRestricteden
dc.subjectSystem uncertaintyen
dc.subjectRobust designen
dc.subjectRobust optimizationen
dc.titleRobust optimization using NURBs based metamodelsen
dc.typeThesisen
dc.type.genreThesisen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Scienceen

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