【摘要】 论文采用RNSGA-Ⅱ-SBJG优化算法,开展考虑兼顾推进效率、空泡、激振力及桨叶强度等要求的螺旋桨优化设计。该算法基于实数编码的非支配排序多目标遗传算法RNSGA-Ⅱ,并引入跳变基因算子以提升基因多样性、加快寻优进程。此外,还引入灰色关联度分析方法,通过计算Pareto解集与理想解的关联度,对优化解集进行评价排序。应用上述方法,对某集装箱船螺旋桨进行了船桨匹配和船机桨匹配情况下的优化设计。数值结果表明,Pareto解集分散良好,基于灰色关联度分析的优化解评价方法能够有效和合理地选取优化设计方案。更多还原

【Abstract】 Algorithm RNSGA-Ⅱ-SBJG is a variant of the real-coded non-dominated sorting genetic algorithm(RNSGA) and is adopted to optimize propeller design with consideration of requirements in propulsive efficiency, cavitation, excitation force and blade strength. Simulated binary jumping gene(SBJG) operator is introduced to improve genetic diversity and expedite searching process. To determine the optimal design from the Pareto solution set, a grey relational analysis approach is employed. The proposed method is applied to optimize the design of a container-ship propeller under the requirement of hull-propeller matching and hull-engine-propeller matching respectively. Numerical results indicate that the Pareto solutions are well dispersed, and optimal solutions can be effectively identified by the grey relational analysis.更多还原