Aerodynamic design optimization based on multi-attribute structured hybrid direct-search application to industrial problems

Abstract

The present Thesis tackles the problem of aerodynamic shape optimization, particularly in the case of big changes of geometry. The approach proposed is a Multi-Objective Structured Hybrid Direct Search and this Thesis presents the MOST-HDS model developed for this purpose. This model is a general, automatic, flexible and robust methodology which is applicable to many different fields of aerodynamic optimization and which combines elements of gradient, genetic and swarm search. MOST-HDS is applied to two relevant and significantly different industrial cases: the design of closed wind tunnels and the inlet duct design of industrial boilers used in combined cycle power plants. The results obtained with the optimization methodology proposed show significant performance improvements over traditional designs and, moreover, innovative and non-conventional designs are obtained for certain cases, which also outperform current design guidelines. A comparison of MOST-HDS and surrogate-based optimization (using response surfaces) is presented and the advantages and limitations of each approach are discussed in detail. Finally, the algorithm developed for this Thesis is also applied to a well-known and challenging mathematical test problem (the WFG test suite) and compared to a popular, advanced Multi-Objective Evolutionary Algorithm, the NSGA-II. The results are very promising and illustrate the potential of MOST-HDS for general optimization purposes, too.