A method and system for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel

Abstract

This invention provides a method and system for extracting micro-pressure wave sound sources at the tunnel exit of high-speed trains, relating to the field of railway tunnel technology. The method includes: acquiring time-domain data of micro-pressure waves at different locations at the tunnel exit when a train enters the tunnel based on numerical simulation; setting multiple time windows of different lengths based on the pressure amplitude and preset energy proportion of the micro-pressure waves; collecting pulsating pressure time series in the tunnel exit area within different time windows; mapping the pulsating pressure time series of different time windows onto an acoustic grid, calculating the far-field sound pressure level, and extracting the corresponding acoustic parameters; establishing response curves of acoustic parameters versus sound pressure level under different time windows, comparing them with experimental data from a live train to obtain the optimal time window; and extracting the micro-pressure wave sound source based on the optimal time window. This invention, by optimizing the time window, avoids insufficient energy extraction or sound source averaging, enhances the contribution of the main impulse, and improves the accuracy of noise prediction results.

Description

Technical Field

[0001] This invention relates to the field of railway tunnel technology, and more specifically, to a method and system for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel. Background Technology

[0002] When a high-speed train enters a tunnel, the air inside the tunnel is compressed by the train, generating an initial compression wave. This initial compression wave propagates forward within the tunnel at approximately the local speed of sound and excites a typical nonlinear pulse wave, known as a micro-pressure wave, at the tunnel exit. This pulse wave propagates at high speed to the external environment outside the tunnel entrance, forming far-field sound radiation and inducing a sonic boom, which has a serious impact on surrounding building structures, the ecological environment, and residents' lives.

[0003] To accurately predict tunnel exit noise induced by micro-pressure waves, current research generally employs numerical simulation-based methods for sound source extraction and sound radiation analysis. However, micro-pressure waves exhibit strong non-steady-state characteristics and time-domain compression properties, with their main energy concentrated within tens of milliseconds, manifesting as a typical pulse signal characterized by short-duration, strong impacts and rapid decay over time. Because the duration of this pulse wave in the time domain is extremely short, the selection of the time window during sound source extraction has a decisive impact on the final noise results.

[0004] Current research often employs empirical rules to set fixed time sampling windows. For example, a fixed time length is set to extract pressure signals 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 an underestimation of the sound pressure level; if the time window is too long, background disturbances or low-frequency wake waves are introduced into the sound source region, weakening the dominant role of the main pulse in sound radiation, leading to the averaging of noise signals, spectral broadening, and frequency shift. These problems are particularly prominent when predicting far-field noise from micro-pressure waves, causing significant frequency shifts, amplitude instability, and even spurious peaks in the prediction results, making it difficult to match actual measurements and limiting the reliability of numerical prediction methods in engineering applications. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for extracting micro-pressure wave sound sources at the exit of high-speed train tunnels, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0006] In a first aspect, this application provides a method for extracting the sound source of micro-pressure waves at the exit of a high-speed train tunnel, comprising:

[0007] 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.

[0008] Based on the pressure amplitude of the micro-pressure wave and the preset energy ratio, multiple time windows of different lengths are set;

[0009] Time series of pulsating pressures were collected from the surface of the tunnel exit area and the entire exit space within different time windows.

[0010] 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.

[0011] 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;

[0012] Micro-pressure wave sound source extraction based on the optimal time window.

[0013] Secondly, this application also provides a micro-pressure wave sound source extraction system for high-speed train tunnel exits, comprising:

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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;

[0019] The extraction module is used to extract micro-pressure wave sound sources based on the optimal time window.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention improves the accuracy of noise prediction by optimizing the time window setting to avoid insufficient energy extraction or sound source averaging, thereby enhancing the contribution of the main impulse. The method is logically clear, easy to implement, and can be integrated into existing CFD+CAA (Computational Aeroacoustics) workflows without relying on additional hardware measurements. It is applicable to various operating conditions such as different train speeds, tunnel structures, and blockage ratios, facilitating engineering implementation.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. 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] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0034] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0035] To accurately predict tunnel exit noise induced by micro-pressure waves, current research generally employs numerical simulation-based methods for sound source extraction and sound radiation analysis. Mainstream methods include the Lighthill analogy, the FW-H equation and its integral form, or more precise nonlinear acoustic methods. These methods all require the unsteady pulsating pressure field generated during train operation as the input source, particularly the extraction of the pulsating pressure time series near the tunnel exit as the sound source for subsequent sound field radiation calculations. However, micro-pressure waves exhibit strong non-steadiness and time-domain compression characteristics; their main energy is concentrated within tens of milliseconds, manifesting as a short-duration, strong impact, rapidly decaying pulse signal. Because this pulse wave has an extremely short duration in the time domain, the selection of the time window during sound source extraction has a decisive impact on the final noise results.

[0036] Example 1

[0037] See Figure 1 This embodiment provides a method for extracting the sound source of micro-pressure wave at the exit of a high-speed train tunnel, including steps S100, S200, S300, S400, S500, and S600.

[0038] S100. Based on numerical simulation, obtain time-domain data of micro-pressure waves at different locations at the tunnel exit when the train enters the tunnel;

[0039] A numerical calculation model of the coupled flow field between a high-speed train and a tunnel was established. When the initial compression wave generated by the high-speed train entering the tunnel propagates to the tunnel exit, time-domain data of micro-pressure waves at different locations at the tunnel exit were obtained. This data contains the main fluctuation process of the micro-pressure waves.

[0040] S200: Based on the pressure amplitude of micro-pressure waves and the preset energy ratio, multiple time windows of different lengths are set;

[0041] The pressure amplitude at 0m from the tunnel exit is used as the baseline value.

[0042] The pressure amplitude of micro-pressure waves is positively correlated with the energy they contain. The pressure amplitude at 0m from the tunnel exit is defined as the reference value p0.

[0043] The proportion of micro-pressure wave energy at different times at the tunnel exit was calculated based on the benchmark value.

[0044] The energy percentage of micropressure waves is defined as follows: p i Pressure values ​​at the tunnel exit at different times;

[0045] Using the time point corresponding to the baseline value as the center of the time window, time windows of different lengths are extracted, each corresponding to a preset micropressure wave energy percentage. For example, in this embodiment, time windows W1-W4 are selected with micropressure wave energy percentages of 85%, 90%, 95%, and 98%. Figure 5 As shown.

[0046] S300. Collect the time series p(t) of pulsating pressure on the surface of the tunnel exit area and in the entire exit space within different time windows.

[0047] The surface of the tunnel exit area includes the wall, rail surface, and adjacent tunnel exit surfaces. The collected signals contain the main pulse information of the micro-pressure wave, which is the basic data for sound source calculation.

[0048] S400: 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;

[0049] An acoustic grid is constructed based on the physical structure of the tunnel exit;

[0050] The acoustic mesh is the carrier of sound source information. Initially, the sound source information exists in the fluid mesh. Only by mapping the sound source information in the fluid mesh to the acoustic mesh can subsequent acoustic calculations be performed.

[0051] 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;

[0052] Acoustic models, such as numerical propagation methods, extract sound source information (usually time-domain or frequency-domain data) from fluid simulations, interpolate this sound source information onto an acoustic grid, and numerically solve simplified governing equations such as the linear Euler equation (LEE) or acoustic wave equation (APE) on the acoustic grid to simulate the process of sound waves propagating from the near field to the far field.

[0053] The far-field sound pressure level was calculated based on the far-field sound pressure time series.

[0054] 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.

[0055] The 1 / 3 octave band spectrum is a spectrum plot with the center frequency of the 1 / 3 octave band as the horizontal axis and the sound pressure level (dB) as the vertical axis. This spectrum is easier to understand because it simulates the auditory response of the human ear; information such as the total sound pressure level can be extracted from it.

[0056] Acoustic parameters also include spectral features, which can also be extracted using the following methods:

[0057] By using pulsating pressure as the sound source term, the fluid motion equation is transformed into an acoustic wave equation through the Lighthill equation.

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

[0059] The frequency domain signal is obtained by performing a Fourier transform on the far-field sound pressure level, and the spectral features, including the dominant frequency band and peak frequency, are extracted.

[0060] S500: Establish response curves of acoustic parameters and sound pressure levels under different time windows, compare them with experimental data from actual vehicles, and obtain the optimal time window;

[0061] Establish frequency-sound pressure level response curves under different time windows, and calculate the maximum sound pressure and total sound pressure; at the same time, it can evaluate the prediction differences of micro-pressure wave noise under multiple different time windows;

[0062] Obtain real-vehicle test data, including the maximum sound pressure level and total sound pressure level of the test; the real-vehicle test conditions are the same as the numerical simulation conditions.

[0063] 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.

[0064] S600: Extraction of micro-pressure wave sound sources based on the optimal time window.

[0065] Example 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] 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] 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 actual 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 actual vehicle test data within the 10 Hz range. However, the sound pressure levels are significantly lower than those in the actual vehicle test beyond 10 Hz, mainly because the actual 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] W1 4 9.9 17.3 W2 4 10.1 17.7 W3 4 10.4 18.5 W4 4 10.3 18.1 Current vehicle 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] 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.

[0083] 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;

[0084] The extraction module is used to extract micro-pressure wave sound sources based on the optimal time window.

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

[0086] The first acquisition unit is used to acquire the pressure amplitude at 0m of the tunnel exit as a reference value.

[0087] 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.

[0088] 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.

[0089] As an optional implementation, the computing module includes:

[0090] The first building block is used to construct an acoustic mesh based on the physical structure of the tunnel exit;

[0091] 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.

[0092] The second calculation unit is used to calculate the far-field sound pressure level based on the far-field sound pressure time series.

[0093] 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.

[0094] As an optional implementation, the comparison module includes:

[0095] 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.

[0096] 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.

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

[0102] The received audio signal can be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting the audio signal. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the high-speed train tunnel exit micro-pressure 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 thereof, is used. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0103] Example 4

[0104] Corresponding to the above embodiment of the method for extracting the micro-pressure wave sound source at the exit of a high-speed train tunnel, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in correspondence with the above-described method for extracting the micro-pressure wave sound source at the exit of a high-speed train tunnel.

[0105] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described embodiment of the method for extracting micro-pressure wave sound sources at the exit of a high-speed train tunnel.

[0106] Specifically, the readable storage medium can be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0108] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the 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 micropressure 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. 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 the acoustic grid, the far-field sound pressure level is calculated, and the corresponding acoustic parameters are extracted. 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, 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.

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, 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. Obtain 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.

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, 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 Lighthill 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.

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 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.

6. 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 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. Each time window corresponds to a preset micro-pressure wave 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.

7. The micro-pressure wave sound source extraction system for high-speed train tunnel exit according to claim 6, 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.

8. A micro-pressure wave sound source extraction system for high-speed train tunnel exits according to claim 6, 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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