ROBUST OPTIMIZATION USING REDUCED-ORDER MODELING FOR NON-LINEAR STATIC TRUSS SYSTEM

Abstract. In this work a design optimization tool to obtain robust optimum designs of trusses under nonlinear conditions is described and implemented. The robustness measures con-sidered 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 opti-mization (RMO) problem. To overcome the time consuming problem inherent in a RMO prob-lem 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 sensitiv-ity 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