Development of a computational efficient tool for robust structural optimization

• Published in: Engineering computations Vol. 32; no. 2; pp. 258 - 288
• Main Authors: Motta, Renato de Siqueira, Afonso, Silvana Maria Bastos, Lyra, Paulo Roberto, Willmersdorf, Ramiro Brito
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 20-04-2015
• Subjects: Aerospace engineering; Approximation; Collocation methods; Computation; Computing time; Design optimization; Engineering; Expected values; Mathematical models; Methodology; Methods; Multiple objective analysis; Optimization; Parameter sensitivity; Parameter uncertainty; Probabilistic methods; Process parameters; Random variables; Reduced order; Reduced order models; Reduction; Robust design; Searching; Software; Standard deviation; Uncertainty; Uncertainty propagation; Probabilistic collocation method; Robust optimization; Reduced basis method; Multiobjective optimization
• ISSN: 0264-4401; 1758-7077
• DOI: 10.1108/EC-06-2013-0172

Purpose
– Optimization under a deterministic approach generally leads to a final design in which the performance may degrade significantly and/or constraints can be violated because of perturbations arising from uncertainties. The purpose of this paper is to obtain a better strategy that would obtain an optimum design which is less sensitive to changes in uncertain parameters. The process of finding these optima is referred to as robust design optimization (RDO), in which improvement of the performance and reduction of its variability are sought, while maintaining the feasibility of the solution. This overall process is very time consuming, requiring a robust tool to conduct this optimum search efficiently.
Design/methodology/approach
– In this paper, the authors propose an integrated tool to efficiently obtain RDO solutions. The tool encompasses suitable multiobjective optimization (MO) techniques (encompassing: Normal-Boundary Intersection, Normalized Normal-Constraint, weighted sum method and min-max methods), a surrogate model using reduced order method for cheap function evaluations and adequate procedure for uncertainties quantification (Probabilistic Collocation Method).
Findings
– To illustrate the application of the proposed tool, 2D structural problems are considered. The integrated tool prove to be very effective reducing the computational time by up to five orders of magnitude, when compared to the solutions obtained via classical standard approaches.
Originality/value
– The proposed combination of methodologies described in the paper, leads to a very powerful tool for structural optimum designs, considering uncertainty parameters, that can be extended to deal with other class of applications.