Efficient ship hull multi-objective optimization method considering ice resistance and calm water resistance
[Objective]To address the impact of ice-covered environments on ship performance and the lim-itations of traditional optimization methods based on empirical formulas for ice resistance,a precise ship design optimization method based on CFD&DEM is proposed to optimize both ice resistance and calm water resistance.[Methods]First,calm water resistance and ice resistance are calculated based on the CFD and CFD&DEM methods,and an innovative hybrid multi-island genetic algorithm(HMIGA)is introduced to simulate realistic ice fields.Next,an efficient surrogate model is established using XGBoost,followed by the execution of the NSGA-III algorithm for optimization.Finally,the method is validated using the KCS stand-ard model.[Results]The results show that the optimized ship design achieves a 10.58%reduction in ice res-istance and a 2.32%reduction in calm water resistance.The optimized ship experiences lower peak loads and further reduces ice resistance by generating waves to push away floating ice.[Conclusions]The proposed method comprehensively considers the effects of flow field and ice field randomness on the optimization res-ults,leading to more accurate and effective improvements in ship ice resistance and calm water resistance.The introduction of HMIGA and XGBoost enhances the practical application of the method,providing valuable guidance for the future optimization design of ships operating in ice-covered environments.
naval architecturehull form optimization designmultiobjective optimizationcomputational fluid dynamicsdiscrete element methodhybrid multi-island genetic algorithmensemble learning
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