Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics t...Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics theory and the K-FWH acoustic equation,a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs.A component optimization method is proposed as a possible solution to the problemof aerodynamic drag and noise in high-speed pantographs.The results of the study indicate that the panhead,base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs.Therefore,a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise.By optimizing the cross-sectional shape of the strips and insulators,the drag and noise caused by airflow separation and vortex shedding can be reduced.The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest.Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph.In addition,the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise.In contrast to the original pantograph design,the improved pantograph shows a 21.1%reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise.展开更多
针对轴承故障诊断中传统脉冲量化指标性能受限,无法正确指示在强背景噪声掩盖下的轴承故障频带的难题,提出了重加权平方包络负熵(reweighted negentropy of the squared envelope,RNSE)和重加权平方包络谱负熵(reweighted negentropy of...针对轴承故障诊断中传统脉冲量化指标性能受限,无法正确指示在强背景噪声掩盖下的轴承故障频带的难题,提出了重加权平方包络负熵(reweighted negentropy of the squared envelope,RNSE)和重加权平方包络谱负熵(reweighted negentropy of the squared envelope spectrum,RNSES),它们不仅能够在无周期先验知识情况下保持对故障周期性脉冲敏感性,而且对于随机脉冲也有较强的鲁棒性。进一步地,为提取轴承振动信号中的故障特征,基于RNSE和RNSES的加权平均值提出了重加权信息图(reweighted infogram,Rinfogram)算法。利用轴承故障仿真信号和高速列车牵引电机轴承台架试验信号证明Rinfogram算法能够在强噪声干扰下成功识别故障频带,对于随机脉冲干扰具有很好的鲁棒性,其故障特征提取效果优于基于谱峭度的Kurtogram和传统Infogram,从而提高了轴承故障诊断的准确性。展开更多
基金supported by National Natural Science Foundation of China(12372049)Science and Technology Program of China National Accreditation Service for Confor-mity Assessment(2022CNAS15)+1 种基金Sichuan Science and Technology Program(2023JDRC0062)Independent Project of State Key Laboratory of Rail Transit Vehicle System(2023TPL-T06).
文摘Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics theory and the K-FWH acoustic equation,a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs.A component optimization method is proposed as a possible solution to the problemof aerodynamic drag and noise in high-speed pantographs.The results of the study indicate that the panhead,base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs.Therefore,a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise.By optimizing the cross-sectional shape of the strips and insulators,the drag and noise caused by airflow separation and vortex shedding can be reduced.The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest.Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph.In addition,the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise.In contrast to the original pantograph design,the improved pantograph shows a 21.1%reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise.
文摘针对轴承故障诊断中传统脉冲量化指标性能受限,无法正确指示在强背景噪声掩盖下的轴承故障频带的难题,提出了重加权平方包络负熵(reweighted negentropy of the squared envelope,RNSE)和重加权平方包络谱负熵(reweighted negentropy of the squared envelope spectrum,RNSES),它们不仅能够在无周期先验知识情况下保持对故障周期性脉冲敏感性,而且对于随机脉冲也有较强的鲁棒性。进一步地,为提取轴承振动信号中的故障特征,基于RNSE和RNSES的加权平均值提出了重加权信息图(reweighted infogram,Rinfogram)算法。利用轴承故障仿真信号和高速列车牵引电机轴承台架试验信号证明Rinfogram算法能够在强噪声干扰下成功识别故障频带,对于随机脉冲干扰具有很好的鲁棒性,其故障特征提取效果优于基于谱峭度的Kurtogram和传统Infogram,从而提高了轴承故障诊断的准确性。
