Journal article
High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm
Engineering optimization, Vol.47(4), pp.473-494
04/03/2015
DOI: 10.1080/0305215X.2014.895340
Abstract
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.
Details
- Title: Subtitle
- High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm
- Creators
- Matteo Diez - University of IowaXi Chen - Huazhong University of Science and TechnologyManivannan Kandasamy - University of IowaZhiguo Zhang - Huazhong University of Science and TechnologyEmilio F Campana - National Research CouncilFrederick Stern - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Engineering optimization, Vol.47(4), pp.473-494
- DOI
- 10.1080/0305215X.2014.895340
- ISSN
- 0305-215X
- eISSN
- 1029-0273
- Publisher
- Taylor & Francis
- Language
- English
- Date published
- 04/03/2015
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984196515402771
Metrics
23 Record Views