Accelerating design optimisation via principal components' analysis

Abstract

In the last 15 years, automatic design optimisation has been the subject of ever growing interest, thanks to the development of ever more reliable analysis software, efficient optimisation methods and powerful computers. Even in industrial environments, design optimisation is extensively used in a number of disciplines, such as aerodynamics, structures and many more applications. Better designs in shorter times and clearer trade-offs between different objectives and disciplines are the most generally recognised advantages.22 The parameterisation represents the " critical enabling factor" for an efficient exploration of the design space: it is essential to ensure that the parameterisation scheme is able to cover all feasible designs, in order not to lose potentially good designs, but also that the minimum possible number of parameters is used, since these affect the size of the design space and thus the convergence time of the optimiser. Irrespective of the specific parameterisation technique, the optimisation of complex products is likely to translate into a large design space and an a priori reduction is not advisable, as this could reduce the generality of the paramenterisation and lead to the loss of potentially good designs. Kipouros et al.16 have presented a method for selecting only the most significant components based on the results of a preliminary optimisation run. In the present study, a method based on Principal Components' Analysis is introduced: given a generic parameterisation, this allows an optimal representation to be derived that will facilitate a faster and more complete exploration of the design space. The advantages of this approach are demonstrated through two optimisation test cases from the design of a core compression system for a three-spool modern turbofan engine.