Method and system for extracting micro-pressure wave sound source at tunnel exit of high-speed train

Abstract

The invention provides a high-speed train tunnel exit micro-pressure wave sound source extraction method and system, and relates to the technical field of railway tunnels, and the method comprises the steps: obtaining the time domain data of micro-pressure waves at different positions of a tunnel exit based on numerical simulation when a train enters the tunnel; setting a plurality of time windows with different lengths based on the pressure amplitude of the micro-pressure wave and a preset energy ratio; in different time windows, pulsating pressure time sequences of the tunnel exit area are collected; mapping the pulsating pressure time sequences of different time windows into an acoustic grid, calculating a far-field sound pressure level, and extracting corresponding acoustic parameters; establishing an acoustic parameter-sound pressure level response curve under different time windows, and comparing the acoustic parameter-sound pressure level response curve with current vehicle experiment data to obtain an optimal time window; and performing micro-pressure wave sound source extraction based on the optimal time window. By optimizing the time window, insufficient energy extraction or sound source averaging is avoided, the contribution of the main pulse is enhanced, and the accuracy of a noise prediction result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of railway tunnels, in particular to a high-speed train tunnel exit micro-baro-wave sound source extraction method and system. BACKGROUND

[0002] When a high-speed train enters a tunnel, the air in the tunnel is initially compressed due to the train, generating an initial compression wave. This initial compression wave propagates forward in the tunnel at approximately the local sound speed and excites a typical nonlinear pulse wave at the tunnel exit, known as a micro-baro-wave. This pulse wave propagates rapidly to the external environment of the tunnel portal, forming a far-field sound wave radiation and inducing an "sound blast" phenomenon, which has a serious impact on surrounding buildings, the ecological environment, and residents' lives.

[0003] In order to accurately predict the tunnel exit noise induced by the micro-baro-wave, current research generally uses numerical simulation methods for sound source extraction and sound radiation analysis. However, the micro-baro-wave has strong non-stationary and time-domain compression characteristics, with its main energy concentrated within tens of milliseconds, exhibiting a typical pulse signal characterized by short-time strong impact and rapid decay over time. Due to the extremely short duration of this pulse wave in the time domain, the selection of the time window during the sound source extraction process has a decisive impact on the final noise results.

[0004] Current research often uses empirical rules to set a fixed time sampling window. For example, a fixed time length is set to extract the pressure signal at the tunnel exit. However, this approach has significant limitations: if the time window is too short, it may not fully cover the main pulse energy, resulting in sound source energy loss and underestimation of the sound pressure level; if the time window is too long, it will introduce background disturbances or low-frequency tail waves into the sound source interval, weakening the dominant role of the main pulse in sound radiation, leading to noise signal averaging, spectral broadening, and main frequency shift; these problems are particularly pronounced when predicting the far-field noise of the micro-baro-wave, resulting in significant frequency shift and unstable amplitude in the prediction results, and even false peaks, making it difficult to match actual measurements and limiting the reliability of numerical prediction methods in engineering applications. SUMMARY

[0005] The present application aims to provide a high-speed train tunnel exit micro-baro-wave sound source extraction method and system to improve the above problems. To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] In a first aspect, the present application provides a high-speed train tunnel exit micro-baro-wave sound source extraction method, comprising:

[0007] Based on numerical simulation, time-domain data of micro-baro-waves at different positions of the tunnel exit when the train enters the tunnel is obtained;

[0008] Based on the pressure amplitude of the micro-baro-wave and the preset energy proportion, a plurality of time windows with different lengths are set;

[0009] collecting fluctuating pressure time series of the surface of the tunnel exit area and the entire exit space in different time windows;

[0010] mapping the fluctuating pressure time series of different time windows into an acoustic grid, calculating the far-field sound pressure level, and extracting the corresponding acoustic parameters;

[0011] establishing the response curve of the acoustic parameters-sound pressure level under different time windows, comparing with the existing vehicle experimental data, and obtaining the optimal time window;

[0012] performing microbarom wave sound source extraction based on the optimal time window.

[0013] In a second aspect, the application further provides a high-speed train tunnel exit microbarom wave sound source extraction system, comprising:

[0014] The first acquisition module is configured to acquire microbarom wave time domain data of different positions of the tunnel exit when the train enters the tunnel based on numerical simulation;

[0015] The setting module is configured to set a plurality of time windows with different lengths based on the pressure amplitude of the microbarom wave and a preset energy proportion;

[0016] The acquisition module is configured to collect fluctuating pressure time series of the surface of the tunnel exit area and the entire exit space in different time windows;

[0017] The calculation module is configured to map the fluctuating pressure time series of different time windows into an acoustic grid, calculate the far-field sound pressure level, and extract the corresponding acoustic parameters;

[0018] The comparison module is configured to establish the response curve of the acoustic parameters-sound pressure level under different time windows, compare with the existing vehicle experimental data, and obtain the optimal time window;

[0019] The extraction module is configured to perform microbarom wave sound source extraction based on the optimal time window.

[0020] The application has the following beneficial effects:

[0021] The application optimizes the time window setting, avoids insufficient energy extraction or sound source averaging, enhances the contribution of the main pulse, and improves the accuracy of the noise prediction result. The method has clear logic and is easy to implement, can be integrated into the existing CFD+CAA (Computational Aeroacoustics) process, and does not rely on additional hardware measurement. It is suitable for different train speeds, tunnel structures, blockage ratios and other operating conditions, and is convenient for engineering promotion.

[0022] Other features and advantages of the application will be described in the following description, and some will become apparent from the description, or will be understood by those skilled in the art from the description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel, according to an embodiment of this application.

[0025] Figure 2 This is a numerical simulation model diagram of an embodiment of this application;

[0026] Figure 3 These are schematic diagrams of the cross-section and longitudinal section of the tunnel in this application;

[0027] Figure 4 These are time history curves of micro-pressure waves at different locations at the tunnel exit in the embodiments of this application;

[0028] Figure 5 This is a schematic diagram illustrating the time window setting and sliding method in an embodiment of this application;

[0029] Figure 6 This is a comparison diagram of the far-field sound pressure spectrum under different time windows in the embodiments of this application;

[0030] Figure 7 This is a comparison chart of the calculation results of the time window setting method in the embodiments of this application and the actual vehicle test data;

[0031] Figure 8 This is a structural diagram of a micro-pressure wave sound source extraction device for a high-speed train tunnel exit, according to an embodiment of this application.

[0032] The diagram is labeled as follows: 1-Train model; 2-Tunnel body; 3-Micro-pressure wave sound source area; 4-Inlet air domain; 5-Outlet air domain; 800-High-speed train tunnel outlet micro-pressure wave sound source extraction equipment; 801-Processor; 802-Memory; 803-Multimedia component; 804-I / O interface; 805-Communication component. Detailed Implementation

[0033] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0034] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0035] In order to accurately predict the tunnel exit noise induced by microbarom waves, the current research generally adopts the numerical simulation method to extract the sound source and analyze the sound radiation. The mainstream methods include the Lighthill acoustic analogy method, the Ffowcs Williams-Hawkings (FW-H) equation and its integral form, or a higher precision nonlinear acoustic method. The common point of these methods is that the unsteady pulsating pressure field generated during the train operation is taken as an input source term, especially the pulsating pressure time series near the tunnel exit is extracted as a sound source for subsequent sound field radiation calculation. However, the microbarom wave has strong non-stationary and time-domain compression characteristics, and its main energy is concentrated in tens of milliseconds, which is a typical pulse signal with short-time strong impact and rapid decay over time. Since the duration of the pulse wave in the time domain is extremely short, the selection of the time window in the sound source extraction process has a decisive influence on the final noise result.

[0036] Embodiment 1

[0037] With reference to Figure 1 , the embodiment provides a high-speed train tunnel exit microbarom wave sound source extraction method, comprising steps S100, S200, S300, S400, S500, S600;

[0038] S100, obtaining microbarom wave time-domain data at different positions of the tunnel exit when the train enters the tunnel based on numerical simulation;

[0039] A high-speed train-tunnel coupled flow field numerical calculation model is established. When the initial compression wave generated by the high-speed train entering the tunnel propagates to the tunnel exit, microbarom wave time-domain data at different positions of the tunnel exit is obtained, which contains the main fluctuation process of the microbarom wave;

[0040] S200, set a plurality of time windows with different lengths based on the pressure amplitude of the microbarom wave and the preset energy proportion;

[0041] The pressure amplitude at the tunnel exit 0m is obtained as a reference value;

[0042] The pressure amplitude of the microbarom wave is positively correlated with the contained energy, and the pressure amplitude at the tunnel exit 0m is defined as the reference value p0;

[0043] The microbarom wave energy proportion at different times at the tunnel exit is calculated based on the reference value;

[0044] The microbarom wave energy proportion is defined as: p i is the pressure value at different times at the tunnel exit;

[0045] The time point corresponding to the reference value is taken as the center of the time window, and time windows with different lengths are intercepted, each time window corresponding to a preset different microbarom wave energy proportion. For example, the microbarom wave energy proportions of 85%, 90%, 95% and 98% are selected to obtain time windows W1-W4. As shown in Figure 5 .

[0046] S300, in different time windows, collect the fluctuating pressure time series p(t) at the surface of the tunnel exit area and the entire exit space;

[0047] The surface of the tunnel exit area includes the wall surface, wall surface and rail surface adjacent to the tunnel exit, and the collected signal contains the main pulse information of the microbarom wave, which is the basic data for sound source calculation;

[0048] S400, map the fluctuating pressure time series of different time windows to the acoustic grid, calculate the far-field sound pressure level, and extract the corresponding acoustic parameters;

[0049] The acoustic grid is constructed according to the physical structure of the tunnel exit;

[0050] The acoustic grid is the carrier of the sound source information. At the beginning, the sound source information exists in the fluid grid. Only by mapping the sound source information in the fluid grid to the acoustic grid, can the subsequent acoustic calculation be carried out;

[0051] The fluctuating pressure time series in the fluid grid is mapped to the acoustic grid through the acoustic model to obtain the far-field sound pressure time series of each grid element;

[0052] Acoustic model such as numerical propagation method extracts sound source information (usually time domain or frequency domain data) from fluid simulation, interpolates the sound source information to the acoustic grid, numerically solves the simplified control equation such as linear Euler equation (LEE) or acoustic wave equation (APE) on the acoustic grid, and simulates the process of sound wave propagation from near field to far field.

[0053] The far field sound pressure level is calculated based on the time series of far field sound pressure;

[0054] The time series of far field sound pressure is subjected to fast Fourier transform and converted into 1 / 3 octave spectrum, and the corresponding acoustic parameters are extracted based on the 1 / 3 octave spectrum;

[0055] The 1 / 3 octave spectrum is a spectrum diagram with 1 / 3 octave center frequency as the abscissa and sound pressure level (dB) as the ordinate, which is easier to understand and simulates the auditory response of the human ear; wherein the total sound pressure level and other information can be extracted.

[0056] The acoustic parameters also include spectral characteristics, and the extraction method can also be:

[0057] The fluctuating pressure is taken as the sound source term, and the fluid motion equation is converted into the acoustic wave equation through the Lighthi l l equation;

[0058] The boundary element method is used to solve the acoustic wave equation to obtain the far field sound pressure level;

[0059] The far field sound pressure level is subjected to Fourier transform to obtain the frequency domain signal, and the spectral characteristics including the dominant frequency band, peak frequency, etc. are extracted.

[0060] S500, the response curve of the acoustic parameter-sound pressure level under different time windows is established, and the optimal time window is obtained by comparing with the experimental data of the existing vehicle;

[0061] The response curve of frequency-sound pressure level under different time windows is established, and the maximum sound pressure and total sound pressure are calculated; at the same time, the prediction difference of microbar wave noise under multiple different time windows can be evaluated;

[0062] Obtain the experimental data of the existing vehicle, which includes the maximum sound pressure and total sound pressure of the experiment; the experimental conditions of the existing vehicle are the same as the numerical simulation conditions;

[0063] The maximum sound pressure and total sound pressure under different time windows are compared with the experimental data of the existing vehicle, and the time window with the minimum error is taken as the optimal time window.

[0064] S600, microbar wave sound source extraction based on the optimal time window.

[0065] Embodiment 2

[0066] A method for extracting the sound source of micro-pressure waves at the exit of a high-speed train tunnel, such as Figure 2 As shown, a numerical calculation model of the coupled flow field between a high-speed train and a tunnel is established. This model includes a train model 1, the tunnel body 2, a micro-pressure wave sound source region 3, an inlet air domain 4, and an outlet air domain 5. The train model 1 is located within the inlet air domain 4, with the train nose 50m from the tunnel entrance. It travels at a constant speed of 350km / h through a 500m long tunnel with a cross-sectional area of ​​100m². 2 The tunnel is a double-track tunnel with a tunnel blockage ratio of 0.24. The radius of the inlet air domain 4 is 30m, the radius of the micro-pressure wave sound source region 3 is 50m, and the outlet air domain 5 is a quarter-sphere with a radius of 300m. The high-speed train model uses a 3-car CR400AF train with a total length of 78m.

[0067] Figure 3 The diagram shows the cross-section and longitudinal section of the tunnel. When the initial compression wave generated by the high-speed train entering the tunnel from the air domain propagates to the tunnel exit, data collection points are set up at different locations (0m, 10m, 20m, 30m, 40m, 50m) from the tunnel exit to record micro-pressure wave time history data, such as... Figure 4 As shown in the figure, the micro-pressure waves at 0m, 20m, and 50m are 351Pa, 68Pa, and 24Pa, respectively. This waveform serves as the basis for subsequent time window selection, and the signal within the main peak interval contains the primary acoustic energy information.

[0068] Based on the characteristics of micro-pressure wave pulse waveforms, multiple pressure-symmetrical time windows (such as W1-W4) are set with the pulse peak value at 0m from the tunnel exit as the center. Figure 4 As shown. The length and start and end positions of the time window are determined based on the preset micropressure wave energy ratio. The time window is set according to the micropressure wave energy ratio formula. Where p0 is the pressure amplitude at 0m from the tunnel exit, p i Table 1 shows the pressure values ​​at the tunnel exit at different times. Time windows W1-W4 correspond to 85%, 90%, 95%, and 98% of the micropressure wave energy, respectively. Specific information about the time windows is shown in Table 1.

[0069] Table 1 Information on different time window divisions

[0070] Time window number Window length / s Starting position / s Ending position / s Microbar wave energy proportion / % W1 0.35 2.36 2.71 85 W2 0.40 2.33 2.73 90 W3 0.48 2.28 2.76 95 W4 0.69 2.14 2.83 98

[0071] Based on different time windows, time-domain information of the surface of the micro-pressure wave sound source area at the tunnel exit and the entire exit space within different window ranges is collected, and this sound source information is mapped to an acoustic grid for far-field noise radiation calculation. Figure 6 The figure shows the calculated sound pressure level spectrum distribution at 20m from the tunnel exit under different time windows. Detailed sound pressure level information is shown in Table 2.Figure 6 As can be seen from Table 2, if the time window is too short, some of the micropressure wave energy will be lost; if the time window is too long, destructive interference and energy dilution will occur in the part far from the peak pulse. Therefore, the time window is not better the longer it is, but should be matched with the effective duration of the pulse signal.

[0072] Table 2 Sound pressure level distribution at 20m under different time windows.

[0073] Time window number Peak frequency / Hz Maximum sound pressure level / dB Total sound pressure level / dB W1 4 125.5 127.3 W2 4 127.7 129.0 W3 4 130.8 131.9 W4 4 126.4 128.7

[0074] The same time window setting method was used to calculate the micro-pressure wave noise generated by a high-speed train traveling at 293 km / h through a long tunnel, and the data were compared with the micro-pressure wave noise data from a live test of the Wan'an Tunnel on the Beijing-Hong Kong High-Speed ​​Railway. Figure 7 As shown. Detailed sound pressure level information is shown in Table 3. From Figure 7 As shown in Table 3, different time windows do not change the spectral distribution and dominant frequency of the micro-pressure wave noise; they only affect the maximum and total sound pressure levels. The maximum and total sound pressure levels calculated using time window W3 are closest to the data from the on-vehicle test, with errors of less than 1 Pa for both. The spectral distribution of the sound pressure levels under different time windows is consistent with the on-vehicle test data within the 10 Hz range. However, the sound pressure levels are significantly lower than those in the on-vehicle test beyond 10 Hz, mainly because the on-vehicle test inevitably includes background noise. Figure 7 The results in Table 3 further verify the effectiveness of the above-mentioned time window optimization strategy in micropressure wave noise prediction. In particular, the calculation results obtained by using time window W3 (containing 95% of the micropressure wave energy) are more accurate.

[0075] Table 3 Comparison of sound pressure distribution at 20m under different time windows and on-vehicle test data

[0076] Time window number Peak frequency / Hz Maximum sound pressure / Pa Total sound pressure / Pa W1 4 9.9 17.3 W2 4 10.1 17.7 W3 4 10.4 18.5 W4 4 10.3 18.1 On-road test data 4 11.9 19.2

[0077] Example 3

[0078] This embodiment provides a micro-pressure wave sound source extraction system for high-speed train tunnel exits, including:

[0079] The first acquisition module is used to acquire time-domain data of micro-pressure waves at different locations at the tunnel exit when the train enters the tunnel, based on numerical simulation.

[0080] The setting module is used to set multiple time windows of different lengths based on the pressure amplitude of the micro-pressure wave and the preset energy ratio.

[0081] The acquisition module is used to acquire the time series of pulsating pressure on the surface of the tunnel exit area and throughout the entire exit space within different time windows.

[0082] a calculating module, configured to map the pulsating pressure time series of different time windows into an acoustic grid, calculate a far-field sound pressure level, and extract corresponding acoustic parameters;

[0083] a comparing module, configured to establish a response curve of the acoustic parameters-sound pressure level under different time windows, compare with existing vehicle experimental data, and obtain an optimal time window;

[0084] an extracting module, configured to extract a microbaric wave sound source based on the optimal time window.

[0085] As an optional implementation, the setting module comprises:

[0086] a first obtaining unit, configured to obtain a pressure amplitude at the tunnel exit 0 m as a reference value;

[0087] a first calculating unit, configured to calculate a microbaric wave energy proportion at different moments at the tunnel exit based on the reference value;

[0088] a first intercepting unit, configured to take a time point corresponding to the reference value as a center of a time window, intercept time windows of different lengths, and each time window corresponds to a preset different microbaric wave energy proportion.

[0089] As an optional implementation, the calculating module comprises:

[0090] a first constructing unit, configured to construct an acoustic grid according to a physical structure of the tunnel exit;

[0091] a first mapping unit, configured to map pulsating pressure time series in a fluid grid into the acoustic grid through an acoustic model, to obtain a far-field sound pressure time series of each grid unit;

[0092] a second calculating unit, configured to calculate a far-field sound pressure level based on the far-field sound pressure time series;

[0093] a first extracting unit, configured to perform a fast Fourier transform on the far-field sound pressure time series, convert into a 1 / 3 octave spectrum, and extract corresponding acoustic parameters based on the 1 / 3 octave spectrum.

[0094] As an optional implementation, the comparing module comprises:

[0095] a third calculating unit, configured to establish a response curve of frequency-sound pressure level under different time windows, calculate a maximum sound pressure and a total sound pressure;

[0096] a second obtaining unit, configured to obtain existing vehicle experimental data, the existing vehicle experimental data comprising the maximum sound pressure and the total sound pressure of the experiment;

[0097] The first comparison unit is used to compare the maximum sound pressure and total sound pressure under different time windows with the experimental data of the actual vehicle, and the time window with the smallest error is taken as the optimal time window.

[0098] Example 4

[0099] Corresponding to the above method embodiments, this embodiment also provides a device for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel. The device for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel described below can be referred to in correspondence with the method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel described above.

[0100] Figure 8 This is a block diagram illustrating a micro-pressure wave sound source extraction device 800 at the exit of a high-speed train tunnel, according to an exemplary embodiment. Figure 8 As shown, the high-speed train tunnel exit micro-pressure wave sound source extraction device 800 includes a processor 801 and a memory 802. The high-speed train tunnel exit micro-pressure wave sound source extraction device 800 may also include one or more of the following: a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805. The processor 801 controls the overall operation of the high-speed train tunnel exit micro-pressure wave sound source extraction device 800 to complete all or part of the steps in the aforementioned high-speed train tunnel exit micro-pressure wave sound source extraction method. The memory 802 stores various types of data to support the operation of the high-speed train tunnel exit micro-pressure wave sound source extraction device 800. This data may include, for example, commands for any application or method operating on the high-speed train tunnel exit micro-pressure wave sound source extraction device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0101] The multimedia component 803 can include a screen and an audio component. The screen, for example, can be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component can include a microphone for receiving external audio signals.

[0102] The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component further includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which can be a keyboard, a mouse, a button, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the high-speed train tunnel exit micro-bar wave sound source extraction device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0103] Embodiment 4

[0104] Corresponding to the above method embodiment of the high-speed train tunnel exit micro-bar wave sound source extraction method, the present embodiment also provides a readable storage medium. The readable storage medium described below can be mutually corresponding to the high-speed train tunnel exit micro-bar wave sound source extraction method described above.

[0105] A readable storage medium, the readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above high-speed train tunnel exit micro-bar wave sound source extraction method embodiment.

[0106] The readable storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0107] It should be noted that, in the present document, relational terms such as "first" and "second", and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0108] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel, characterized in that, include: Time-domain data of micro-pressure waves at different locations at the tunnel exit when a train enters the tunnel were obtained based on numerical simulation. Based on the pressure amplitude of the micro-pressure wave and the preset energy ratio, multiple time windows of different lengths are set; Time series of pulsating pressures were collected from the surface of the tunnel exit area and the entire exit space within different time windows. The pulsating pressure time series of different time windows are mapped onto an acoustic grid to calculate the far-field sound pressure level and extract the corresponding acoustic parameters. Response curves of acoustic parameters and sound pressure levels under different time windows were established and compared with experimental data from actual vehicles to obtain the optimal time window; Micro-pressure wave sound source extraction based on the optimal time window.

2. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, Based on the pressure amplitude of the micro-pressure wave and the preset energy ratio, multiple time windows of different lengths are set, including: The pressure amplitude at 0m from the tunnel exit is used as the baseline value. The proportion of micro-pressure wave energy at different times at the tunnel exit was calculated based on the benchmark value. Using the time point corresponding to the baseline value as the center of the time window, time windows of different lengths are extracted, and each time window corresponds to a preset micropressure wave energy ratio.

3. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, The pulsating pressure time series of different time windows are mapped onto an acoustic mesh to calculate the far-field sound pressure level and extract the corresponding acoustic parameters, including: An acoustic grid is constructed based on the physical structure of the tunnel exit; The pulsating pressure time series in the fluid grid is mapped to the acoustic grid using an acoustic model to obtain the far-field sound pressure time series of each grid cell; The far-field sound pressure level was calculated based on the far-field sound pressure time series. The far-field sound pressure time series is subjected to fast Fourier transform and converted into a 1 / 3 octave band spectrum. The corresponding acoustic parameters are extracted based on the 1 / 3 octave band spectrum.

4. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, Response curves of acoustic parameters versus sound pressure level under different time windows were established and compared with experimental data from actual vehicles to obtain the optimal time window, including: Establish frequency-sound pressure level response curves under different time windows, and calculate the maximum sound pressure and total sound pressure. Acquire test data from existing vehicles, including the maximum sound pressure level and total sound pressure level of the test. The maximum sound pressure level and total sound pressure level under different time windows were compared with the experimental data of the actual vehicle, and the time window with the smallest error was taken as the optimal time window.

5. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, The pulsating pressure time series of different time windows are mapped onto an acoustic mesh to calculate the far-field sound pressure level and extract the corresponding acoustic parameters, including: By using pulsating pressure as the sound source term, the fluid motion equation is transformed into an acoustic wave equation through the Lighthil equation. The boundary element method is used to solve the acoustic wave equation to obtain the far-field sound pressure level; The frequency domain signal is obtained by performing a Fourier transform on the far-field sound pressure level, and the spectral features are extracted.

6. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, The method further includes: The numerical simulation parameters are perturbed, including mesh size, boundary conditions, and experimental parameters. The calculation is repeated a preset number of times under disturbance, and the fluctuation range of the optimal window is statistically analyzed; Calculate window reliability based on the fluctuation range of the optimal window.

7. A system for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel, characterized in that, include: The first acquisition module is used to acquire time-domain data of micro-pressure waves at different locations at the tunnel exit when the train enters the tunnel, based on numerical simulation. The setting module is used to set multiple time windows of different lengths based on the pressure amplitude of the micro-pressure wave and the preset energy ratio. The acquisition module is used to acquire the time series of pulsating pressure on the surface of the tunnel exit area and throughout the entire exit space within different time windows. The calculation module is used to map the pulsating pressure time series of different time windows onto the acoustic grid, calculate the far-field sound pressure level, and extract the corresponding acoustic parameters. The comparison module is used to establish the response curves of acoustic parameters and sound pressure levels under different time windows, compare them with the experimental data of the actual vehicle, and obtain the optimal time window; The extraction module is used to extract micro-pressure wave sound sources based on the optimal time window.

8. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, The setting module includes: The first acquisition unit is used to acquire the pressure amplitude at 0m of the tunnel exit as a reference value. The first calculation unit is used to calculate the proportion of micro-pressure wave energy at different times at the tunnel exit based on the benchmark value. The first interception unit is used to intercept time windows of different lengths with the time point corresponding to the reference value as the center of the time window, and each time window corresponds to a preset different micro-pressure wave energy ratio.

9. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, The computing module includes: The first building block is used to construct an acoustic mesh based on the physical structure of the tunnel exit; The first mapping unit is used to map the pulsating pressure time series in the fluid grid to the acoustic grid through the acoustic model, so as to obtain the far-field sound pressure time series of each grid unit. The second calculation unit is used to calculate the far-field sound pressure level based on the far-field sound pressure time series. The first extraction unit is used to perform a fast Fourier transform on the far-field sound pressure time series and convert it into a 1 / 3 octave spectrum, and extract the corresponding acoustic parameters based on the 1 / 3 octave spectrum.

10. The method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel according to claim 1, characterized in that, The comparison module includes: The third calculation unit is used to establish the frequency-sound pressure level response curves under different time windows and to calculate the maximum sound pressure and total sound pressure. The second acquisition unit is used to acquire on-site vehicle test data, which includes the maximum sound pressure level and total sound pressure level of the test. The first comparison unit is used to compare the maximum sound pressure and total sound pressure under different time windows with the experimental data of the actual vehicle, and the time window with the smallest error is taken as the optimal time window.

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