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Citation:
　Wenjing Lyu and Weilin Luo.Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy[J].Journal of Marine Science and Application,2014,(3):274-280.[doi:10.1007/s11804-014-1257-7]
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Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

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Title:
Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy
Author(s):
Wenjing Lyu and Weilin Luo
Affilations:
Author(s):
Wenjing Lyu and Weilin Luo
College of Mechanical Engineering and Automation, Fuzhou University, Fujian 350108, China
Keywords:
hybrid optimization strategy;  automatic optimization platform;  underwater robot lines;  hydrodynamic numerical simulation;  computational fluid dynamics
分类号:
-
DOI:
10.1007/s11804-014-1257-7
Abstract:
In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body’s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

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Last Update: 2014-10-16