Bow Optimization of a Polar Vessel Based on Resistance Approximation Model

Excellent resistance performance constitutes a longstanding objective in ship design and serves as a primary means of reducing operational costs. Polar transport vessels operate along trade routes that traverse both open water and ice-covered regions. Consequently, hull form optimization for such vessels must comprehensively address resistance performance in both environments to achieve the overarching goal of minimizing operational expenditures. This study undertakes hull form optimization for a polar oil tanker, with the objective of reducing resistance in open water while maintaining favorable ice resistance characteristics. Parametric modeling is implemented by selecting five key geometric parameters: waterline curvature of the forward shoulder, waterline angle, stem angle, curvature of bow section lines, and width of the forward skeg. An improved uniform experimental design is adopted to create sample hull configurations. On the basis of CFD-calculated open-water resistance, a neural network approximation model is constructed and combined with a multi-island genetic algorithm to search for optimal bow shapes. Nonlinear finite element simulations, validated against model test data of the initial vessel, are used to assess the ice resistance of optimized hulls. Under the constraint that the displacement and other main dimensions remain nearly unchanged, the open-water resistance at the design speed decreases by 7.62%. The optimized bow achieves an approximate 3.12% reduction in ice resistance within the current precision range of the numerical method.