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

By X. Chen, Matteo Diez, M. Kandasamy, Zhiguo Zhang, Emilio Fortunato Campana, Frederick Stern. 2015

117 citations recorded by the source corpus.

Advances 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/5bd75fdecfd31dba2e354f645c467bd4e470f03c