Hull form design optimization of twin-skeg fishing vessel for minimum resistance based on surrogate model. (September 2018)

• Record Type: Journal Article
• Authors: Lin, Yan; He, Jingyi; Li, Kai
• Is Part Of: Advances in engineering software. Volume 123(2018)
• Page Start: 38
• Page End: 50
• Publication Date: 2018-09
• Subjects: Twin-skeg -- Resistance optimization -- CFD -- Kriging modeling method -- Multi-objective optimization; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.05.010
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A resistance optimization method for twin-skeg ship is proposed. Surrogate model is used to investigate the effect of changing design parameters. Multiple speed cases are considered to enhance the practicality. Skeg shape is controlled by several design parameters. Small amount of calculation with good convergence property. Abstract: Twin-skeg ship has better hydrodynamic performances than regular ship, however, it is still difficult to obtain an accurate relationship between skeg design and overall hydrodynamic performances. Resistance optimization is the major concern of developing twin-skeg ship. This paper proposes a combined approach for hull form design optimization of twin-skeg ship by using computational fluid dynamics (CFD) calculation and surrogate model. Main design parameters of skeg geometry and arrangement could be determined from the design domain by using the proposed method. Parametric modeling technology is adopted for performing design evaluations in an automatic manner with different design parameter combinations. A twin-skeg fishing vessel is selected as research object. In the proposed method, the sample set for constructing surrogate models is generated by using Optimal Latin Hypercube Sampling (OLHS) method, the corresponding responses are calculated through CFD simulations, and then the surrogate models are constructed by using Kriging modeling method, which represent the mathematical relationship between input design variables (skeg shape design variables) and output objective functions (resistance values under four different working conditions). The functional analysis of variance (ANOVA) is performed to investigate how much influence the design variables have on the objective functions. Finally, a multi-objective evolutionary algorithm (NSGA-II) is used to obtain the optimal solution, which shows 5.4% average decrease in the total resistance than the original design. The CFD calculation results of the optimal solution show that the proposed method can achieve minimum resistance design with high accuracy and low time cost.