摘要
为了优化螺旋桨因负荷过重导致的空泡问题,基于进化计算的思想提出了一种改进螺旋桨参数设计的方法。该方法利用淌水实验数据和专家知识对螺旋桨综合性能进行仿真实验优化,并通过遗传算法得到满足优化目标的螺旋桨参数的帕累托解集,并通过蒙特卡洛树搜索出解集中的最优个体。实验结果表明,提出的方法能够显著提高螺旋桨的空泡起始航速,同时降低最大航速工况下螺旋桨的脉动压力。
In order to optimize the cavitation problem of propellers caused by overload,a method is proposed in this paper to improve the propeller parameter design on the basis of the concept of evolutionary computation.In this method,the comprehensive performance of propellers is simulated and optimized by leveraging water flowing experimental data and expert knowledge,and the Pareto solution set of propeller parameters that realizes the optimization aim is obtained by the Genetic algorithm.Finally,Monte Carlo tree is used to search the optimal individual in the solution set.The experimental results show that the method proposed in this paper can significantly improve the cavitation initial speed of propellers and reduce the fluctuating pressure of propellers at the working condition of maximum speed.
出处
《工业控制计算机》
2022年第7期75-76,共2页
Industrial Control Computer
关键词
进化计算
螺旋桨设计
多目标参数优化
遗传算法
蒙特卡洛树
evolutionary computation
propeller design
multiple objective parameter optimization
genetic algorithm
Monte Carlo tree
