基于粒子群的船舶动力机械空蚀优化控制
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摘要
空蚀直接影响船舶动力机械的寿命,为了解决当前船舶动力机械空蚀控制精度低的难题,提出了基于粒子群算法的船舶动力机械空蚀优化控制方法,首先对当前船舶动力机械空蚀优化控制研究现状进行分析,并采集船舶动力机械空蚀的历史数据,然后采用神经网络对船舶动力机械空蚀的历史数据进行学习,找到船舶动力机械空蚀变化特点,并引入粒子群算法对神经网络的参数进行优化,最后进行船舶动力机械空蚀优化控制的仿真测试。结果表明,本文方法可以描述船舶动力机械空蚀变化的特点,可以提高船舶动力机械空蚀预测精度,有利于制定船舶动力机械空蚀优化控制方案。
Cavitation directly affects the life of ship power machinery. In order to solve the problem of low control precision of current ship power machinery cavitation erosion control, an optimization control method based on particle swarm optimization(PSO) is proposed for ship power machinery cavitation erosion control. First, the current research status of ship power machinery cavitation optimization control is analyzed, and ships are adopted. The historical data of the cavitation erosion of power machinery, then the neural network is used to study the historical data of the cavitation erosion of the ship's power machinery, to find the characteristics of the cavitation erosion of the ship power machinery, and to optimize the parameters of the neural network by the particle swarm optimization algorithm. Finally, the simulation test of the optimization control of the cavitation erosion of the ship's power machinery is carried out. The results show that this method can describe the characteristics of the cavitation erosion of ship power machinery, and can improve the prediction precision of the cavitation erosion of the ship power machinery, and is beneficial to the formulation of the optimal control scheme for the cavitation erosion of the ship's power machinery.
Cavitation directly affects the life of ship power machinery. In order to solve the problem of low control precision of current ship power machinery cavitation erosion control, an optimization control method based on particle swarm optimization(PSO) is proposed for ship power machinery cavitation erosion control. First, the current research status of ship power machinery cavitation optimization control is analyzed, and ships are adopted. The historical data of the cavitation erosion of power machinery, then the neural network is used to study the historical data of the cavitation erosion of the ship's power machinery, to find the characteristics of the cavitation erosion of the ship power machinery, and to optimize the parameters of the neural network by the particle swarm optimization algorithm. Finally, the simulation test of the optimization control of the cavitation erosion of the ship's power machinery is carried out. The results show that this method can describe the characteristics of the cavitation erosion of ship power machinery, and can improve the prediction precision of the cavitation erosion of the ship power machinery, and is beneficial to the formulation of the optimal control scheme for the cavitation erosion of the ship's power machinery.
引文
[1]陈源华,张月雷,严新平.工程船舶动力机械状态监测与故障诊断现状及发展[J].中国设备工程,2010,12(5):7-10.
[2]朱丽娟,董秀琦,王宏伟,等.流体机械的空蚀机理及其防护[J].东方电机,2000,25(4):56-60.
[3]庞佑霞,李彬,刘厚才,等.流体机械叶轮常用材料冲蚀与空蚀交互磨损特性研究[J].润滑与密封,2013,38(12):23-26.
[4]袁丹青,陈向阳,白滨,等.水力机械空化空蚀问题的研究进展[J].排灌机械,2009,27(4):269-271.
[5]吴小珊,张步涵,袁小明,等.求解含风电场的电力系统机组组合问题的改进量子离散粒子群优化方法[J].中国电机工程学报,2013,33(4):45-51.
[2]朱丽娟,董秀琦,王宏伟,等.流体机械的空蚀机理及其防护[J].东方电机,2000,25(4):56-60.
[3]庞佑霞,李彬,刘厚才,等.流体机械叶轮常用材料冲蚀与空蚀交互磨损特性研究[J].润滑与密封,2013,38(12):23-26.
[4]袁丹青,陈向阳,白滨,等.水力机械空化空蚀问题的研究进展[J].排灌机械,2009,27(4):269-271.
[5]吴小珊,张步涵,袁小明,等.求解含风电场的电力系统机组组合问题的改进量子离散粒子群优化方法[J].中国电机工程学报,2013,33(4):45-51.