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RBDO with Non-Gaussian Variables by using a LHS- and SORM-based SLP approach and Optimal Regression Models
Jönköping University, School of Engineering, JTH. Research area Product Development - Simulation and Optimization.
2012 (English)Conference paper, (Other academic)
Abstract [en]

A general sequential linear programming (SLP) approach for reliability based design optimization (RBDO) with non-Gaussian random variables is presented. The RBDO problems are formulated by using optimal regression models (ORM) as surrogate models and S-optimal design of experiments (DoE). The S-optimal DoE is obtained by maximizing the average mean of the distances between the nearest neighbors. Finite element simulations are performed for the S-optimal DoE and corresponding ORM are obtained by a genetic algorithm. In such manner not only optimal regression coefficients are generated but also optimal rational base functions. The RBDO problems are solved by introducing intermediate variables defined by the iso-probabilistic transformation at the most probable point. By using these variables in the Taylor expansions, a corresponding deterministic linear programming problem is derived, which is corrected by applying second order reliability methods (SORM) as well as Monte Carlo simulations. For low target values on the reliability crude Monte Carlo simulations are used, but for high targets a Latin hypercube sampling (LHS) approach is utilized. The implementation of the suggested sampling- and SORM-based SLP approach is efficient and robust. This is demonstrated by presenting trade-off curves between the objective function, constraints, variables and the target of reliability.

Place, publisher, year, edition, pages
2012.
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:hj:diva-19436OAI: oai:DiVA.org:hj-19436DiVA: diva2:552805
Conference
3rd International Conference on Engineering Optimization, Rio de Janeiro, Brazil, 1-5 July
Available from: 2012-09-17 Created: 2012-09-17 Last updated: 2012-10-12Bibliographically approved

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Strömberg, Niclas
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CiteExportLink to record
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