颗粒阻尼器耗能特性及振动抑制研究  

Study on Energy Dissipation Characteristics and Vibration Suppression of Particle Damper

作　　者：邵敏强[1] 宋杰[1] 姚鹏 滕汉东[1] Shao Minqiang;Song Jie;Yao Peng;Teng Handong(State Key Laboratory of Mechanics and Control for Aerospace Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;AVIC Jincheng Nanjing Engineering Institute of Aircraft System,Nanjing 211106,China)

机构地区：[1]南京航空航天大学航空航天结构力学及控制全国重点实验室,江苏南京210016 [2]航空工业金城南京机电液压工程研究中心,江苏南京211106

出　　处：《航空科学技术》2023年第6期86-94,共9页Aeronautical Science & Technology

基　　金：航空科学基金(20200028052013)。

摘　　要：机电控制系统受振动影响易发生故障,严重影响飞行安全,本文通过颗粒阻尼器对机电控制系统进行振动抑制研究,采用离散元仿真方法研究阻尼器的耗能变化规律与振动幅值、振动频率和颗粒数量的影响关系,并通过BP神经网络对颗粒阻尼器耗能数据进行训练和预测;通过机电控制器的随机振动试验,验证离散元仿真结论与BP神经网络预测模型的准确性。结论表明,离散元仿真在振动频率20~40Hz、激励幅值2~16mm范围内,其他条件一定时,阻尼器耗能随频率和幅值的增大而增大,随颗粒填充率先增大后减小,在57%~70%填充率范围内具有最佳耗能效果;在机载系统随机振动试验中,颗粒阻尼器填充率处于30%~90%范围内均表现出较好的振动抑制效果。仿真和试验结果对颗粒阻尼器在机电控制系统中进一步应用具有指导意义。Mechanical and electrical control system is prone to failure due to vibration,which seriously affects flight safety.In this paper,particle damper is used to study the vibration suppression of electromechanical control system.Discrete element simulation method is used to study the energy dissipation law of the damper and the influence on vibration amplitude,vibration frequency and particle number.BP neural network is used to train and predict the energy dissipation data of the particle damper.Through random vibration test of electromechanical controller,the accuracy of discrete element simulation conclusion and BP neural network prediction model is verified.The results show that discrete element simulation is performed in the range of vibration frequency 20~40Hz and amplitude 2~16mm.And when other conditions are fixed,the energy dissipation of the damper increases with the increase of frequency and amplitude,the energy dissipation of the damper increases first and then decreases with the particle filling.The best energy dissipation effect is obtained at the filling rate of 57%~70%.The particle damper has a good vibration reduction effect when the filling rate is between 30%and 90%.Combined with BP neural network energy dissipation prediction model and experiment of particle damper,it can provide guidance for the design and application of particle damper effectively.

关 键 词：机载设备 颗粒阻尼器 离散单元法 BP神经网络 随机振动 

分 类 号：TH703[机械工程—仪器科学与技术]