首页|基于机器学习的船舶水润滑轴承结构多目标优化研究

基于机器学习的船舶水润滑轴承结构多目标优化研究

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水润滑轴承是船舶轴系安全稳定运转的重要支撑部件,通过优化轴承结构以提升轴承性能是保证船舶安全航行的有效措施.采用PSO-BP神经网络建立水润滑轴承承载力和摩擦力预测模型,应用NSGA-Ⅱ以预测模型承载力最大和摩擦力最小为优化目标,优化预测模型的输入值使目标函数达到最优,得到轴承的Pareto解,通过TOPSIS方法选取Pareto解集的最优非劣解.结果表明:优化后的轴承承载力较原始设计值提高了 38.17％,摩擦力降低了 2.23％;预测模型的优化结果与将优化参数输入仿真模型计算得到的结果相比,误差小于7％.
Multi-objective Optimization Study of Water-lubricated Bearing Structure for Ships Based on Machine Learning
Water-lubricated bearing is an important supporting component for the safe and stable operation of ship shaft systems,and it is an effective measure to ensure the safe navigation of ships and improve bearing performance.The PSO-BP neural network is introduced to establish an agent model of load-carrying capacity and friction force of water-lubricated bearings.The NSGA-Ⅱ algorithm is applied to take the maximum load-carrying capacity output and the minimum friction force output of the agent model.Then the input of the agent model is optimized,and the Pareto solution of the bearings is obtained.The optimal compromise solution in the Pareto solution set is selected using the TOPSIS method.The results show that the optimized bearing load carrying capacity is increased by 38.17％,and friction force is reduced by 2.23％compared with the original design of the bearing.The percentage difference in optimization results is less than 7％between the agent and the simulation model.

water-lubricated bearingsstructural optimizationPSO-BP neural networknon-dominated sorting genetic algorithm-Ⅱtechnique for order preference by similarity to ideal solution

刘辉、于鹏法、陈紫起

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大连理工大学船舶工程学院,大连 116024

水润滑轴承 结构优化 PSO-BP神经网络 NSGA-Ⅱ TOPSIS方法

中国博士后科学基金资助项目中国博士后科学基金资助项目国家资助博士后研究人员计划项目

2023TQ00412023M7404771GZC20230347

2024

中国造船
中国造船工程学会

中国造船

CSTPCD北大核心
影响因子：0.81
ISSN：1000-4882
年,卷(期)：2024.65(4)