Study On The Characteristics Of Trackside Acoustic Flow Field Of High-Speed Train Under The Influence Of Crosswind

The rail acoustic detection technology employs an array of acoustic sensors,data acquisition and processing systems,to collect and analyze acoustic signals,effectively achieving early warning of bearing faults.However,during the detection process,crosswind,as a common environmental factor,has not been taken into account.In reality,crosswind affects the propagation of sound waves in media,leading to errors in the signals received by the sensor array.Such errors may result in false alarms or failure of warning.Therefore,this paper explores the characteristic laws of the rail acoustic field under the influence of crosswind on high-speed trains.This study focuses on several aspects,including: firstly,building the potential flow model and acoustic finite element model of a high-speed train.In the modelling process,simplified train models are employed to increase computational efficiency by neglecting components that have insignificant impacts on the accuracy of calculations.Additionally,an unstructured mesh partition method is used to achieve better discretization effects.Next,the automatic matching of sound radiation boundary conditions(AML)technology is implemented to simulate and obtain the distribution map of the trackside sound field of the high-speed train.Secondly,a mathematical calculation model is established based on the combined effects of the speed,wind direction angle,and crosswind speed of the train on synthetic winds to explore the regularity of the influence of speed,wind direction angle,and crosswind speed on the sound field characteristics.Moreover,the effects of the acoustic frequency parameters on the trackside sound field are further explored by changing the acoustic frequency parameters.Finally,the arrangement of acoustic sensors is studied by solving the frequency response curve of the trackside acoustic sensor array’s sound pressure.This includes changing the number of sensors,the spacing between sensors,and the height of the sensors.In addition to maintaining the original arrangement of sensors,the delay processing of the sound pressure signal received by the sensor was performed by calculating the sound pressure gradient to enhance the practicality of the method.Through the aforementioned research,characteristic patterns of the trackside acoustic field under crosswind conditions have been identified,allowing for an increase of up to 13.15 d B in the maximum detectable sound pressure level range,as well as an increase of up to 100 Hz in the maximum detectable frequency range,effectively reducing the negative impact of crosswinds on the trackside acoustic detection system.Furthermore,it was found that at different vehicle speeds,sound pressure values at the same location gradually increase with increasing vehicle speed,while larger wind direction angles could hinder the reception of acoustic signals and the accurate identification of malfunctioning sound sources.This study provides valuable insights into the accurate diagnosis of trackside acoustic detection systems.