Optimization of Aerospace Structures under Uncertainty using an Iterative Distribution Evolutionary Algorithm

Abstract

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. An Iterative Differential Evolutionary Algorithm is proposed for the optimized identification of sets of parameter values for a system whose analysis is very time demanding and for which it is difficult to identify and define the optimum set. The algorithm is an evolutionary technique and reduces the number of evaluations required for both numerical and experimental systems thanks to an iterative procedure. It exploits geometrical considerations to distribute the points to be investigated in the considered parameter space, and SVD/metamodeling based techniques to further decrease the computational burden. The developed strategy aims to minimize the probability of failure, guaranteeing a reliable optimum, providing an understanding of the acceptable range of uncertainties and keeping robustness. The approach is validated considering the optimization of landing gear designs minimizing the probability of occurrence of shimmy phenomena during ground manoeuvres. Nomenclature B, Bmtrack of the main assembly and distance between the nose and the axis of the main assembly d direction of interest F * set of tangency parameters F opt optimum nominal parameter value g limit state function L Lift LoI locus of interest P maxi maximum range of variation for the ith considered parameters T ol , T ol p tolerances adopted in the algorithm W vertical load acting on the landing gear structure W weight of the aircraft x vector of design parameters * Marie Curie early stage PhD researcher, ALPES Project,