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Reliability Based Design Optimization by Using a SLP Approach and Radial Basis Function Networks
Örebro University, School of Science and Technology, Örebro University, Sweden. Department of Mechanical Engineering.ORCID iD: 0000-0001-6821-5727
2016 (English)In: Proceedings of the ASME Design Engineering Technical Conference and Computers and Information in Engineering Conference, 2016: Vol. 2B, New York, USA: American Society of Mechanical Engineers (ASME) , 2016, 309-318 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper reliability based design optimization by using radial basis function networks (RBFN) as surrogate models is presented. The RBFN are treated as regression models. By taking the center points equal to the sampling points an interpolation is obtained. The bias of the network is taken to be known a priori or posteriori. In the latter case, the well-known orthogonality constraint between the weights of the RBFN and the polynomial basis functions of the bias is adopted. The optimization is performed by using a first order reliability method (FORM)-based sequential linear programming (SLP) approach, where the Taylor expansions are generated in intermediate variables defined by the iso-probabilistic transformation. In addition, the reliability constraints are expanded at the most probable points which are found by using Newton's method. The Newton algorithm is derived by proposing an in-exact Jacobian. In such manner, a FORM -based LP-formulation in the standard normal space of problems with non-Gaussian variables is suggested. The solution from the LP problem is mapped back to the physical space and the suggested procedure continues in a sequence until convergence is reached. This is implemented for five different distributions: normal, lognormal, Gumbel, gamma and Weibull. It is also presented how the FORM-based SLP approach can be corrected by using second order reliability methods (SORM) and Monte Carlo simulations." In particular the SORM approach of Hohenbichler is studied. The outlined methodology is both efficient and robust. This is demonstrated by solving established benchmarks as well as finite element problems.

Place, publisher, year, edition, pages
New York, USA: American Society of Mechanical Engineers (ASME) , 2016. 309-318 p.
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:oru:diva-56176DOI: 10.1115/DETC2016-59522ISI: 000393363400027Scopus ID: 2-s2.0-85007576223ISBN: 978-0-7918-5011-4 (print)OAI: oai:DiVA.org:oru-56176DiVA: diva2:1078959
Conference
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (ASME 2016), Charlotte, North Carolina, USA, August 21–24, 2016
Available from: 2017-03-07 Created: 2017-03-07 Last updated: 2017-03-07Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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