TSI metamodels-based multi-objective robust optimization

Abstract

Purpose

This paper aims to deal with an efficient strategy for robust optimization when a large number of uncertainties are taken into account.

Design/methodology/approach

ANOVA analysis is used in order to perform a variance-based decomposition and to reduce stochastic dimension based on an appropriate criterion. A massive use of metamodels allows reconstructing response surfaces for sensitivity indexes in the design variables plan. To validate the proposed approach, a simplified configuration, an inverse problem on a 1D nozzle flow, is solved and the performances compared to an exact Monte Carlo reference solution. Then, the same approach is applied to the robust optimization of a turbine cascade for thermodynamically complex flows.

Findings

First, when the stochastic dimension is reduced, the error on the variance between the reduced and the complete problem was found to be roughly estimated by the quantity (1−T¯ _TSI )×100, where T¯ _TSI is the summation of TSI concerning the variables respecting the TSI criterion. Second, the proposed strategy allowed obtaining a converged Pareto front with a strong reduction of computational cost by preserving the same accuracy.

Originality/value

Several articles exist in literature concerning robust optimization but very few dealing with a global approach for solving optimization problem affected by a large number of uncertainties. Here, a practical and efficient approach is proposed that could be applied also to realistic problems in engineering field.

Citation

Marco Congedo, P., Geraci, G., Abgrall, R., Pediroda, V. and Parussini, L. (2013), "TSI metamodels-based multi-objective robust optimization", Engineering Computations, Vol. 30 No. 8, pp. 1032-1053. https://doi.org/10.1108/EC-01-2012-0012