Maio 2014 vol. 1 num. 1 - 10th World Congress on Computational Mechanics
Full Article - Open Access.
ROBUST OPTIMIZATION USING REDUCED-ORDER MODELING FOR NON-LINEAR STATIC TRUSS SYSTEM
In this work a design optimization tool to obtain robust optimum designs of trusses under nonlinear conditions is described and implemented. The robustness measures considered here are the expected value and standard deviation of the function involved in the optimization problem. To calculate such quantities, we employ two nonintrusive uncertainty propagation analysis techniques that exploit deterministic computer models: Monte Carlo (MC) method and Probabilistic Collocation Method (PCM). When using these robustness measures combined, the search of optimal design appears as a robust multi-objective optimization (RMO) problem. To overcome the time consuming problem inherent in a RMO problem a model reduction technique using the proper orthogonal decomposition (POD) method will be employed to provide fast outputs for nonlinear analysis of trusses. A structural sizing optimization (SSO) algorithm incorporating such procedure in the structural and sensitivity stochastic analyses will be used to obtain efficient optimal trusses design. Optimization studies will be conducted for trusses problems considering different loads level, exploring the material plasticity. Comparisons will be conducted with the SSO approach via traditional FEM and via POD.
Palavras-chave: Robust Optimization, Multi-Objective Optimization, Proper Orthogonal Decomposition, Nonlinear Static Problem, Probabilistic Collocation Method,
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Motta, Renato de S.; Afonso, Silvana M. B.; "ROBUST OPTIMIZATION USING REDUCED-ORDER MODELING FOR NON-LINEAR STATIC TRUSS SYSTEM", p. 3569-3580 . In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1].
São Paulo: Blucher,
ISSN 2358-0828, DOI 10.5151/meceng-wccm2012-19472
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