MULTI-OBJECTIVE HULL-FORM OPTIMIZATION OF A SWATH CONFIGURATION VIA DESIGN-SPACE DIMENSIONALITY REDUCTION, MULTI-FIDELITY METAMODELS, AND SWARM INTELLIGENCE

被引：0

作者：

Pellegrini, Riccardo ^{[1
]}

Serani, Andrea ^{[1
]}

Harries, Stefan ^{[2
]}

Diez, Matteo ^{[1
]}

机构：

[1] CNR, CNR, INSEAN, Marine Technol Res Inst, Rome, Italy

[2] Friendship Syst AG, Potsdam, Germany

来源：

VII INTERNATIONAL CONFERENCE ON COMPUTATIONAL METHODS IN MARINEENGINEERING (MARINE2017) | 2017年

关键词：

Simulation-based design; Hull-form optimization; Design-space dimensionality reduction; Karhunen-Loeve expansion; Multi-fidelity metamodels; Multi-objective particle swarm optimization;

D O I：

暂无

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

A multi-objective simulation-based design optimization (SBDO) is presented for the resistance reduction and displacement increase of a small water-plane area twin hull (SWATH). The geometry is realized as a parametric model with the CAESES software, using 27 design parameters. Sobol sampling is used to realize design variations of the original geometry and provide data to the design-space dimensionality reduction method by Karhunen-Loeve expansion. The hydrodynamic performance is evaluated with the potential flow code WARP, which is used to train a multi-fidelity metamodel through an adaptive sampling procedure based on prediction uncertainty. Two fidelity levels are used varying the computational grid. Finally, the SWATH is optimized by a multi-objective deterministic version of the particle swarm optimization algorithm. The current SBDO procedure allows for the reduction of the design parameters from 27 to 4, resolving more than the 95% of the original geometric variability. The metamodel is trained by 117 coarse-grid and 27 fine-grid simulations. Finally, significant improvements are identified by the multi-objective algorithm, for both the total resistance and the displacement.

引用

页码：95 / 106

页数：12