Fast Collaborative Multi-Objective Optimization for Hydrodynamic
 Based on Kriging Surrogate Model

doi:10.16183/j.cnki.jsjtu.2018.06.006

Abstract

Abstract: The wave drag, heave and pitch performance of the ship at design speed are optimized by introducing Kriging surrogate model. During the optimizing process, hull lines are optimized by utilizing the non-dominated sorting genetic algorithm and surface modification functions based on Rankine source method and 3-D Green function method. In addition, the high-precision Kriging surrogate model is applied to approximate optimization objectives in order to solve the time-consuming problem in the iterative process. Numerical results show that the present optimization approach can be used to optimize ship hull forms for reducing drag and improving seakeeping performance, and Kriging surrogate model can improve optimization efficiency considerably. Finally, the drag optimization results at model scale λ=31.599 are validated via CFD method based on Reynolds-averaged Navier-Stokes (RANS) equations.

Key words: multi-objective optimization, Kriging surrogate model, drag, seakeeping, Reynolds-averaged Navier-Stokes (RANS)

CLC Number:

U 661.3

WANG Gangcheng，MA Ning，GU Xiechong. Fast Collaborative Multi-Objective Optimization for Hydrodynamic Based on Kriging Surrogate Model[J]. Journal of Shanghai Jiaotong University, 2018, 52(6): 666-673.

References

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Fast Collaborative Multi-Objective Optimization for Hydrodynamic Based on Kriging Surrogate Model

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