Multi-fidelity model and reduced-order method for comprehensive hydrodynamic performance optimization and prediction of JBC ship

Abstract

The multi-fidelity Co-Kriging surrogate model can be applied to combine the accuracy advantage of high-fidelity sample evaluation with the efficiency advantage of low-fidelity sample evaluation. In this paper, the hull form optimization process for resistance and wake performance of a Japan bulk carrier (JBC) hull at design speed is given in detail, where the evaluation results from medium and coarse grids are regarded as the high- and low-fidelity data, respectively. 60 high-fidelity hydrodynamic evaluations have been done to construct the Kriging model, while the Co-Kriging model uses 30 high-fidelity and 60 low-fidelity evaluations with a 25% reduction of the total computation time. The optimization results show that, for the total drag, the Kriging-based optimal hull has a 4.57% reduction, while the Co-Kriging-based optimal hull has 5.67%; for the axial wake fraction reduction at the propeller disk, the Kriging-based optimal hull has a 7.20% reduction, while the Co-Kriging-based optimal hull has 10.37%. Furthermore, in the latter stage of hull form optimization, dimensionality reduction field learning can be performed to fully use the viscous-flow-based calculation results. An accurate and efficient viscous-flow-based wake field learning method is proposed based on the Kriging model and the Proper Orthogonal Decomposition (POD) method with qualitative and quantitative error analysis, which can guide the sensitivity analysis of the design variables, the selection of the design variables and spaces, and new flow field prediction for comprehensive hull form performance optimization.