Stochastic optimization methods for ship resistance and operational efficiency via CFD

被引:40
作者
Diez, Matteo [1 ,2 ]
Campana, Emilio F. [1 ]
Stern, Frederick [2 ]
机构
[1] CNR, Marine Technol Res Inst, CNR INSEAN, Rome, Italy
[2] Univ Iowa, IIHR Hydrosci & Engn, Iowa City, IA 52242 USA
关键词
Hydrodynamic optimization; Reliability-based robust design optimization; Uncertainty quantification; Resistance; Operability (operational efficiency); URANS (unsteady Reynolds-averaged Navier-Stokes); SHAPE OPTIMIZATION; DESIGN OPTIMIZATION; INTERFERENCE; SIMULATIONS; FORMULATION; UNCERTAINTY;
D O I
10.1007/s00158-017-1775-4
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Hull-form stochastic optimization methods are presented and evaluated for resistance reduction and operational efficiency (operability), addressing stochastic sea state and operations. The cost/benefit analysis of the optimization procedure is presented by comparison of four hierarchical problems, from stochastic most general to deterministic least general. The parent hull is a high-speed catamaran, with geometrical constraints for maximum variation of length, beam, draft, and displacement. Problem 1 is used as a benchmark for the evaluation of the other problem formulations and is defined as a multi-objective stochastic optimization for resistance and operability, considering stochastic sea state and speed, but limited to head waves. Problem 2 is a multi-objective stochastic optimization for resistance and motions at fixed sea state and speed. Problem 3 is a multi-objective deterministic optimization for resistance and motions using a single regular wave at fixed speed. Problem 4 is a single-objective deterministic optimization for calm-water resistance at fixed speed. The design optimization is based on hull-form modifications by the Karhunen-LoSve expansion of a free-form deformation, URANS-based CFD simulations, regular wave approximations for irregular waves, metamodels and multi-objective particle swarm. The design optimization achieves an 8.7, 23, 53, and 10% average improvements for problems 1, 2, 3, and 4, respectively. Comparing to problem 1, problem 2, 3, 4 optimized designs have average performances 1, 2.1 and 1.7% worse, respectively. The most efficient problem, from the computational cost/benefit viewpoint, is problem 3. Nevertheless, problem 1 is needed to evaluate and compare the stochastic performance of the designs and finally assess the optimization cost/benefit.
引用
收藏
页码:735 / 758
页数:24
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