High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm

Abstract

AbstractAdvances in high-fidelity shape optimization for industrial problems are presented, based on geometric variability assessment and design-space dimensionality reduction by Karhunen–Loève expansion, metamodels and deterministic particle swarm optimization (PSO). Hull-form optimization is performed for resistance reduction of the high-speed Delft catamaran, advancing in calm water at a given speed, and free to sink and trim. Two feasible sets (A and B) are assessed, using different geometric constraints. Dimensionality reduction for 95% confidence is applied to high-dimensional free-form deformation. Metamodels are trained by design of experiments with URANS; multiple deterministic PSOs achieve a resistance reduction of 9.63% for A and 6.89% for B. Deterministic PSO is found to be effective and efficient, as shown by comparison with stochastic PSO. The optimum for A has the best overall performance over a wide range of speed. Compared with earlier optimization, the present studies provide an additional resistance reduction of 6.6% at 1/10 of the computational cost.Keywords: shape optimizationdimensionality reductionKarhunen–Loève expansionsurrogate-based optimizationparticle swarm optimization AcknowledgementsURANS computations were performed at the NAVY DoD Supercomputing Research Centre. KLE computations were performed at the DLTM (Liguria District of Marine Technology) HPC facility.FundingThe present research is supported by the Office of Naval Research [grant N00014-11-1-0237] and Office of Naval Research Global [NICOP grant N62909-11-1-7011], under the administration of Dr Ki-Han Kim and Dr Woei-Min Lin, and by the Italian Flagship Project RITMARE, coordinated by the Italian National Research Council and funded by the Italian Ministry of Education, within the National Research Program 2011–2013. The first author is also grateful for support from the China Scholarship Council (CSC) [grant 201206160070].