CN115130244B - Method and device for selecting target wheel-rail irregularity spectrum for dynamic simulation of wheel-rail system - Google Patents

Abstract

The invention provides a method and a device for selecting a target wheel-rail irregularity spectrum for wheel-rail system dynamic simulation, wherein the method comprises the following steps: establishing a vehicle-track coupling vertical dynamic model based on preset vehicle parameters and track parameters; selecting a target long-wave irregularity spectrum from a plurality of pre-acquired long-wave irregularity spectra according to a preset standard track quality index management value; fitting the target long wave irregularity spectrum with a plurality of preset short wave irregularity spectra respectively to obtain a plurality of fitting results; obtaining the vibration acceleration of the wheel corresponding to each wheel-rail irregularity spectrum based on the fitting results and the vehicle-rail coupling vertical dynamic model; and determining a target wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations. In the method, the target wheel-rail irregularity spectrum is determined based on the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum, and the wheel-rail irregularity spectrum can be reasonably selected according to the actual line state, so that the wheel-rail contact response is accurately predicted.

Description

Method and device for selecting target wheel-rail irregularity spectrum for dynamic simulation of wheel-rail system

Technical Field

The present invention relates to the technical field of wheel-rail system dynamic response prediction, and in particular to a method and device for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system.

Background Art

At present, rail transit brings convenience to people while also bringing a series of vibration effects. Among them, the vibration generated by wheel-rail contact will have a certain impact on vehicles, tracks, etc. At present, there are two main ways to obtain the dynamic response of the wheel-rail system, one is on-site monitoring, and the other is numerical simulation prediction. If you want to study the vibration response of the wheel-rail system under a specific working condition, the on-site measurement method consumes more manpower and material resources, while the numerical simulation method is more convenient. Among them, the wheel-rail unevenness spectrum is a very important part of the dynamic simulation of the wheel-rail system. The wheel-rail unevenness spectrum is divided into long-wave unevenness spectrum and short-wave unevenness spectrum. If you want to accurately simulate and predict the wheel-rail dynamic response, the selection of the wheel-rail unevenness spectrum must conform to the actual state of the line. Therefore, it is very necessary to select a wheel-rail unevenness spectrum that conforms to the actual line operation state for the dynamic simulation of the wheel-rail system.

At present, the existing technology uses the American six-level spectrum as the long-wave unevenness spectrum and the short-wave unevenness spectrum in simulation calculations. First, the spectral lines of the American six-level spectrum belong to the long-wave unevenness spectrum and cannot be used as the short-wave unevenness spectrum; secondly, the selection of the short-wave unevenness spectrum should correspond to the actual line. This method does not reasonably select the wheel-rail unevenness spectrum according to the actual line status, and cannot accurately predict the wheel-rail contact response.

Summary of the invention

The object of the present invention is to provide a method and device for selecting wheel-rail irregularity spectrum for dynamic simulation of wheel-rail system, so as to reasonably select the irregularity spectrum according to the actual track state, thereby accurately predicting the wheel-rail contact response.

The present invention provides a method for selecting a wheel-rail irregularity spectrum for dynamic simulation of a target wheel-rail system, the method comprising: establishing a vehicle-rail coupled vertical dynamics model based on preset vehicle parameters and track parameters; selecting a target long-wave irregularity spectrum from a plurality of pre-acquired long-wave irregularity spectra according to a preset standard track quality index management value; fitting the target long-wave irregularity spectrum with a plurality of preset short-wave irregularity spectra to obtain a plurality of fitting results; obtaining wheel vibration accelerations corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-rail coupled vertical dynamics model; wherein each short-wave irregularity spectrum is respectively combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; determining the target wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations

Furthermore, the step of selecting a target long-wave unevenness spectrum from a plurality of long-wave unevenness spectra acquired in advance according to a preset standard track quality index management value includes: calculating the track quality index management values corresponding to each of the long-wave unevenness spectra acquired in advance; comparing each track quality index management value with the standard track quality index management value to obtain a plurality of first comparison results; and selecting the target long-wave unevenness spectrum from a plurality of long-wave unevenness spectra according to the plurality of first comparison results.

Furthermore, the track quality index management values corresponding to each long-wave irregularity spectrum include: left high and low, right high and low, left track direction, right track direction, track gauge, level, and triangular pit.

Furthermore, based on multiple fitting results and the vehicle-track coupled vertical dynamics model, the steps of obtaining the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum include: performing Fourier transform on each fitting result to generate an irregularity space sample corresponding to each fitting result; inputting each irregularity space sample into the vehicle-track coupled vertical dynamics model to obtain the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum.

Furthermore, based on the multiple wheel vibration accelerations, the step of determining the target wheel-rail irregularity spectrum includes: calculating the difference between the amplitude of each wheel vibration acceleration and the amplitude of the actual wheel vibration acceleration acquired in advance, to obtain multiple difference results; determining the wheel vibration acceleration corresponding to the result with the smallest value among the multiple difference results as the target wheel vibration acceleration; and determining the wheel-rail irregularity spectrum corresponding to the target wheel vibration acceleration as the target wheel-rail irregularity spectrum.

Furthermore, the method also includes: obtaining the wheel-rail force corresponding to each wheel-rail irregularity spectrum based on multiple fitting results and the vehicle-track coupled vertical dynamics model; inputting each wheel-rail force as an excitation into a pre-established track-tunnel-soil coupled dynamic simulation analysis model to obtain the tunnel wall vibration source intensity corresponding to each wheel-rail irregularity spectrum.

Furthermore, the vehicle-track coupled vertical dynamics model includes: a vehicle vertical model and a track model; wherein the vehicle vertical model simulates the vehicle as a half-vehicle system with a secondary suspension; and the track model is simulated using a long-pillow embedded integral ballast bed.

The present invention provides a target wheel-rail irregularity spectrum selection device for dynamic simulation of a wheel-rail system, the device comprising: a model establishment module, used for establishing a vehicle-rail coupling vertical dynamics model based on preset vehicle parameters and track parameters; a selection module, used for selecting a target long-wave irregularity spectrum from a plurality of pre-acquired long-wave irregularity spectra according to a preset standard track quality index management value; a fitting module, used for fitting the target long-wave irregularity spectrum with a plurality of preset short-wave irregularity spectra respectively to obtain a plurality of fitting results; an acquisition module, used for obtaining wheel vibration accelerations corresponding to each wheel-rail irregularity spectrum respectively based on the plurality of fitting results and the vehicle-rail coupling vertical dynamics model; wherein each short-wave irregularity spectrum is respectively combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; and a determination module, used for determining the target wheel-rail irregularity spectrum based on a plurality of wheel vibration accelerations.

The present invention provides an electronic device, which includes a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement any of the above-mentioned target wheel-rail irregularity spectrum selection methods for dynamic simulation of a wheel-rail system.

The present invention provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any of the above-mentioned target wheel-rail irregularity spectrum selection methods for dynamic simulation of a wheel-rail system.

The present invention provides a method and device for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system, the method comprising: establishing a vehicle-rail coupling vertical dynamics model based on preset vehicle parameters and track parameters; selecting a target long-wave irregularity spectrum from a plurality of pre-acquired long-wave irregularity spectra according to a preset standard track quality index management value; fitting the target long-wave irregularity spectrum with a plurality of preset short-wave irregularity spectra to obtain a plurality of fitting results; obtaining the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-rail coupling vertical dynamics model; determining the target wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations. In the method, the target wheel-rail irregularity spectrum is determined based on the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum, and the wheel-rail irregularity spectrum can be reasonably selected according to the actual line state, thereby accurately predicting the wheel-rail contact response.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of a method for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system provided by an embodiment of the present invention;

FIG2 is a schematic diagram of a vertical model of a subway vehicle provided by an embodiment of the present invention;

FIG3 is a schematic diagram of a track model provided by an embodiment of the present invention;

FIG4 is a flow chart of another method for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system provided by an embodiment of the present invention;

FIG5 is a schematic diagram of a long-wave height irregularity time history curve provided by an embodiment of the present invention;

FIG6 is a schematic diagram of a shortwave high and low unevenness time history curve provided by an embodiment of the present invention;

FIG7 is a schematic diagram of wheel vertical vibration acceleration obtained by simulating different wheel-rail irregularities provided by an embodiment of the present invention;

FIG8 is a schematic diagram of a track-tunnel-soil ABAQUS finite element model provided by an embodiment of the present invention;

FIG9 is a schematic diagram of the frequency division vibration level of a tunnel wall under different wheel-rail irregularities provided by an embodiment of the present invention;

FIG10 is a schematic structural diagram of a target wheel-rail irregularity spectrum selection device for dynamic simulation of a wheel-rail system provided by an embodiment of the present invention;

FIG. 11 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

DETAILED DESCRIPTION

The technical solution of the present invention is clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

The newly added rail transit operation length in my country exceeds 2,000 kilometers, and the total operation mileage exceeds 5,700 kilometers. At present, there are two main ways to obtain the dynamic response of the wheel-rail system. One is on-site monitoring, and the other is numerical simulation prediction. Since the on-site measurement method consumes more manpower and material resources, scholars generally use more convenient numerical simulation methods to study the vibration response of the wheel-rail system. Among them, wheel-rail unevenness is a very important part of the dynamic simulation of the wheel-rail system. Wheel-rail unevenness is divided into long-wave unevenness and short-wave unevenness. It is generally believed that the wavelength is greater than 1m is long-wave unevenness, and the wavelength is less than 1m is short-wave unevenness. Under the stimulation of wheel-rail unevenness, the vehicle-track coupling system will produce random vibrations, which on the one hand affects the comfort of passengers and the stability of cargo transportation, and on the other hand affects the fatigue damage of vehicle structural components, fastener systems, etc., and finally the line deformation accumulates, which in turn aggravates the deterioration of the track geometry and increases the dynamic response of the wheel-rail system. Therefore, if you want to accurately simulate and predict the dynamic response of the wheel-rail system, it is very necessary to select a wheel-rail unevenness spectrum that conforms to the actual line operation state for the dynamic simulation of the wheel-rail system.

At present, the existing technology has proposed a method of using the American six-level spectrum as the long-wave and short-wave unevenness excitation in simulation calculations. First, the spectral line wavelength of the American six-level spectrum is 1.524~304.8m, which belongs to long-wave unevenness and cannot be used for short-wave unevenness; secondly, the selection of short-wave unevenness should be consistent with the on-site unevenness. Therefore, the short-wave unevenness selected by this method is unreasonable, and the unevenness is not reasonably selected according to the actual line operation status, and the wheel-rail contact response cannot be accurately predicted.

To facilitate understanding of this embodiment, a method for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system disclosed in an embodiment of the present invention is first introduced in detail. As shown in FIG1 , the method comprises the following steps:

Step S102: establishing a vehicle-track coupled vertical dynamics model based on preset vehicle parameters and track parameters.

The above-mentioned vehicle-track coupling vertical dynamics model includes: a vehicle vertical model and a track model; wherein, the vehicle vertical model is determined according to the specific vehicle type, and can be a subway vehicle vertical model, a high-speed rail vehicle vertical model, etc. Taking a subway type B vehicle as an example, in specific implementation, a numerical simulation model (equivalent to the above-mentioned vehicle-track coupling vertical dynamics model) is established in MATLAB mathematical software according to the vehicle-track coupling dynamics theory. The specific subway type B vehicle parameters (equivalent to the above-mentioned preset vehicle parameters) and track model parameters (equivalent to the above-mentioned preset track parameters) are shown in Tables 1 and 2:

Table 1 Parameters of Metro Type B Vehicle

Table 2 Track model parameters

When taking a subway B-type vehicle as an example, the above vehicle vertical model can be determined as a subway vehicle vertical model, which simulates the vehicle as a half-vehicle system with a two-stage suspension; as shown in Figure 2 below, the system can fully reflect the body's heave (Z _c ) and nodding (β _c ) motion, the front and rear bogies' heave (Z _t1 , Z _t2 ) and nodding (β _t1 , β _t2 ) motion, and the four wheels' vertical motion (Z _wi , i=1-4), with a total of 10 degrees of freedom. The above track model can be simulated using a long-pillow embedded integral roadbed, which can be composed of rails, fasteners, concrete ballastless roadbed plates and concrete bases, as shown in Figure 3.

Step S104: selecting a target long-wave roughness spectrum from a plurality of long-wave roughness spectra acquired in advance according to a preset standard track quality index management value.

The above-mentioned multiple long-wave unevenness spectra obtained in advance can be other long-wave unevenness spectra such as the US five-level spectrum, the US six-level spectrum, the German high interference spectrum, the German low interference spectrum and the high-speed rail spectrum. The above-mentioned preset standard track quality index management value actually refers to the track quality index management value under different design speed levels proposed in the "Rules for the Repair of Conventional Railway Lines" (GW 102-2019), as shown in Table 3:

Table 3 Track quality index management values

In the above Table 3, when the speed level is V≤80km/h, the left high and low track quality index management value is 2.2~2.5mm, which means that during the line acceptance stage, the left high and low track quality index management value cannot exceed 2.2mm, and during the line operation stage, the left high and low track quality index management value cannot exceed 2.5mm.

Since the actual line is accepted and inspected according to the track quality index management values given in the "Rules for Repair of Conventional Railway Lines" (GW 102-2019), it is reasonable to select long-wave irregularity according to the track quality index management values in Table 3 above in the simulation calculation.

In specific implementation, according to the track quality index management value in Table 3 above, a long wave unevenness spectrum can be selected from other long wave unevenness spectra such as the US five-level spectrum, the US six-level spectrum, the German high interference spectrum, the German low interference spectrum and the high-speed rail spectrum as the target long wave unevenness spectrum.

Step S106, fitting the target long-wave roughness spectrum with a plurality of preset short-wave roughness spectra to obtain a plurality of fitting results.

The preset multiple short-wave roughness spectra may be other short-wave roughness spectra such as iron science spectrum, ISO 3095-2005 roughness spectrum and ISO3095-2013 roughness spectrum. In actual implementation, different short-wave roughness can be selected from the multiple short-wave roughness spectra, and then fit with the selected target long-wave roughness spectrum to form multiple roughness power spectrum density curves (equivalent to the multiple fitting results).

Step S108, based on the multiple fitting results and the vehicle-track coupled vertical dynamics model, the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum is obtained.

Each short-wave irregularity spectrum is respectively combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; that is, each wheel-rail irregularity spectrum includes the target long-wave irregularity spectrum and one of the multiple short-wave irregularity spectra. For example, one wheel-rail irregularity spectrum may be a German low-interference spectrum (corresponding to the target long-wave irregularity spectrum) + an ISO 3095-2013 roughness spectrum (corresponding to one short-wave irregularity spectrum).

Step S110: determining a target wheel-rail irregularity spectrum based on a plurality of wheel vibration accelerations.

The target wheel-rail irregularity spectrum selection method for the above-mentioned wheel-rail system dynamic simulation includes: establishing a vehicle-rail coupling vertical dynamics model based on preset vehicle parameters and track parameters; selecting a target long-wave irregularity spectrum from a plurality of pre-acquired long-wave irregularity spectra according to a preset standard track quality index management value; fitting the target long-wave irregularity spectrum with a plurality of preset short-wave irregularity spectra to obtain a plurality of fitting results; obtaining the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-rail coupling vertical dynamics model; wherein each short-wave irregularity spectrum is combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; and determining the target wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations. In this method, the target wheel-rail irregularity spectrum is determined based on the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum, and the wheel-rail irregularity spectrum can be reasonably selected according to the actual line state, thereby accurately predicting the wheel-rail contact response.

The embodiment of the present invention further provides another method for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system. The method is implemented on the basis of the method in the above embodiment. As shown in FIG4 , the method comprises the following steps:

Step S202: establishing a vehicle-track coupled vertical dynamics model based on preset vehicle parameters and track parameters.

Step S204: Calculate the track quality index management value corresponding to each long-wave irregularity spectrum acquired in advance.

The track quality index management values corresponding to each of the above-mentioned long-wave irregularity spectra include: left high-low, right high-low, left track direction, right track direction, track gauge, level, and triangular pit.

The track quality index (TQI) is an evaluation and management value for long-wave irregularity. The calculation formula for the track quality index is given in the Technical Specifications for Dynamic Acceptance of High-Speed Railway Projects (TB 10761-2013), as shown below:

Wherein, _σi is the standard deviation of various geometric deviations, and various geometric deviations can be left high-low, right high-low, left track direction, right track direction, track gauge, level and triangular pit (mm). The calculation formula of _σi is as follows:

为各项几何偏差在单元区段中x_ij的算术平均值(mm)；x_ij为采样点的幅值；N为采样点的个数(200m单元区段，一般0.25m取一个采样点)。in:

is the arithmetic mean (mm) of various geometric deviations in the unit section _xij ; _xij is the amplitude of the sampling point; N is the number of sampling points (200m unit section, generally one sampling point is taken every 0.25m).

At present, the long-wave unevenness spectrum suitable for China's high-speed railway is the high-speed railway spectrum, but for conventional railways, there is no corresponding long-wave unevenness spectrum in China. Assuming that the line under the design speed is a subway line with a design speed of 80 km/h (corresponding to the above conventional railway), it can be assumed that five long-wave unevenness spectra are pre-acquired, namely the US five-level spectrum, the US six-level spectrum, the German high interference spectrum, the German low interference spectrum and the high-speed railway spectrum. In actual implementation, according to the above calculation formula of σ _i , the TQI management values of the left high-low, right high-low, left track direction, right track direction, track gauge, level and triangular pit of each of the above five long-wave unevenness spectra are calculated respectively. Specifically, each long-wave unevenness spectrum selects a length of 2000m, of which 200m is a group of TQI. Tables 4 and 5 show the calculation results of the TQI management values of the German low interference spectrum and the US six-level spectrum respectively:

Table 4 TQI management values of seven indicators of Germany's low interference spectrum (2000m)

Table 5 TQI management values of seven indicators in the US six-level spectrum (2000m)

Similarly, the TQI management values of the 2000m American five-level spectrum, the German high interference spectrum and the high-speed rail spectrum are calculated in the same way, and the TQI management values of the five long-wave unevenness spectra are shown in Table 6:

Table 6 Comparison of TQI management values of seven indicators (2000m)

Left High Low Right High Low Left track Right track gauge level Triangle Pit American Five-Level Spectrum 3.2～4.7 3.1～4.7 2.8～5.0 2.6～5.0 1.7～2.9 1.7～2.8 1.2～1.8 American Sixth Grade 2.1～3.6 2.3～3.6 2.0～3.4 2.2～3.5 1.3～2.3 1.6～2.0 1.0～1.9 German High Interference Spectrum 2.6～4.8 2.6～4.4 2.0～3.0 2.0～3.0 0.4～0.7 1.8～3.0 0.3～0.5 German low interference spectrum 1.8～2.4 1.8～2.4 1.3～2.0 1.3～2.0 0.4～0.5 1.2～1.7 0.3～0.4 High-speed rail spectrum 0.5～0.6 0.5～0.7 0.5～0.6 0.4～0.7 0.3～0.5 0.4～0.6 0.3～0.4

Step S206: Compare each track quality index management value with the standard track quality index management value to obtain a plurality of first comparison results.

Step S208 : selecting a target long-wave roughness spectrum from a plurality of long-wave roughness spectra according to the plurality of first comparison results.

In specific implementation, the TQI management values of the seven indicators of the five spectra in Table 6 above can be compared with the TQI management values of different design speed levels given in the "Rules for Repair of Conventional Railway Lines" (GW 102-2019) (as shown in Table 3). According to multiple comparison results, a reasonable long-wave unevenness spectrum is selected for the line at different design speeds. The seven TQI management values corresponding to the selected long-wave unevenness spectrum must simultaneously meet the seven TQI management values in Table 3. The selection of long-wave unevenness spectra at different design speeds is shown in Table 7 below.

Table 7 Selection of long wave irregularity spectra at different design speeds

In actual implementation, there may be many long-wave roughness spectra that meet the seven TQI management values corresponding to a certain speed level in Table 3. Table 7 only provides one long-wave roughness spectrum that meets the seven TQI management values corresponding to each speed level.

For a subway line with a design speed of 80 km/h, the seven TQI management values corresponding to the selected target long-wave unevenness spectrum shall all meet the seven TQI management values corresponding to the speed level of V≤80 km/h in Table 3. Among them, the TQI management values of left high-low, right high-low, left track direction, right track direction, track gauge, horizontal, and triangular pit corresponding to the speed level of V≤80 km/h in Table 3 are 2.2-2.5 mm, 2.2-2.5 mm, 1.8-2.2 mm, 1.8-2.2 mm, 1.4-1.6 mm, 1.7-1.9 mm, and 1.9-2.1 mm, respectively. The TQI management values of left high-low, right high-low, left track direction, right track direction, track gauge, horizontal, and triangular pit corresponding to the German low interference power spectrum are 1.8-2.4 mm, 1.8-2.4 mm, and 1.8-2.4 mm, respectively. mm, 1.3～2.0mm, 1.3～2.0mm, 0.4～0.5mm, 1.2～1.7mm, 0.3～0.4mm; for the German low interference power spectrum, during the line operation stage, the corresponding left high-low and right high-low maximum values are both 2.4mm, which is less than the left high-low and right high-low maximum values of 2.5mm corresponding to the speed level of V≤80km/h in Table 3, and the corresponding left track direction and right track direction maximum values are both 2.0mm, which is less than the left high-low and right high-low maximum values of 2.5mm in Table 3. The maximum values of the left and right rail directions corresponding to the speed level of V≤80km/h in Table 3 are 2.2mm, and the corresponding maximum value of the track gauge is 0.5mm, which is smaller than the maximum value of the track gauge of 1.6mm corresponding to the speed level of V≤80km/h in Table 3. The corresponding maximum value of the horizontal track is 1.7mm, which is smaller than the maximum value of the horizontal track of 1.9mm corresponding to the speed level of V≤80km/h in Table 3. The corresponding maximum value of the triangular pit is 0.4mm, which is smaller than the maximum value of the triangular pit of 2.1mm corresponding to the speed level of V≤80km/h in Table 3. Therefore, it is reasonable to select the German low interference spectrum as the target long-wave unevenness spectrum during the operation stage.

Step S210 , fitting the target long-wave roughness spectrum with a plurality of preset short-wave roughness spectra to obtain a plurality of fitting results.

Step S212, performing Fourier transform on each fitting result to generate a non-smooth space sample corresponding to each fitting result.

The above-mentioned roughness space samples include long-wave roughness space samples and short-wave roughness space samples. The long-wave roughness space samples can also include long-wave high and low roughness space samples (equivalent to long-wave high and low roughness time-history curves), and the short-wave roughness space samples can also include short-wave high and low roughness space samples (equivalent to short-wave high and low roughness time-history curves); among which the high and low roughness time-history curves of the US five-level spectrum, the US six-level spectrum, the German high interference spectrum, the German low interference spectrum and the high-speed rail spectrum are shown in Figure 5; the high and low roughness time-history curves of the rail science spectrum, the ISO 3095-2005 roughness spectrum and the ISO 3095-2013 roughness spectrum are shown in Figure 6.

Step S214: input each irregularity spatial sample into the vehicle-track coupled vertical dynamics model to obtain the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum.

Step S216, calculating the difference between the amplitude of each wheel vibration acceleration and the amplitude of the actual wheel vibration acceleration acquired in advance, and obtaining a plurality of difference results.

Step S218: The wheel vibration acceleration corresponding to the result with the smallest value among the multiple difference results is determined as the target wheel vibration acceleration.

Step S220: determining the wheel-rail irregularity spectrum corresponding to the target wheel vibration acceleration as the target wheel-rail irregularity spectrum.

In specific implementation, different irregularity spatial samples are respectively input into the vehicle-track coupled dynamics model, and the wheel vibration acceleration under the excitation of long-wave + different short-wave irregularity spectra is calculated. The calculated wheel vibration acceleration amplitude is compared with the amplitude of the measured wheel vibration acceleration, and a group of long-wave + short-wave irregularity spectra that best matches (closest to) the measured wheel vibration acceleration is selected. This group of long-wave + short-wave irregularity spectra is the wheel-rail irregularity spectrum that best conforms to the actual line (equivalent to the above-mentioned target wheel-rail irregularity spectrum).

In order to better understand the above embodiment, a subway line with a design speed of 80 km/h is still taken as an example below. The target wheel-rail irregularity spectrum is selected according to the above method for selecting the target wheel-rail irregularity, and the environmental vibration source intensity is calculated.

It can be seen from Table 7 above that the German low-interference spectrum can be selected as the target long-wave unevenness spectrum for subway lines with a design speed of 80 km/h. The short-wave spectra commonly used at home and abroad are the China Railway Science Spectrum and the ISO 3095-2013 roughness spectrum. These two short-wave spectra and the long-wave German low-interference spectrum are selected to generate a complete wheel-rail unevenness spectrum (working conditions 2 and 3 in Table 8), and a set of wheel-rail unevenness spectra with extremely good short-wave unevenness spectra are generated (working condition 1 in Table 8). In order to prove that the wheel vibration acceleration is mainly determined by the short-wave unevenness spectrum, another set of wheel-rail unevenness spectra with long waves as the US five-level spectrum and short waves as the Railway Science spectrum is added (working condition 4 in Table 8). All working conditions are shown in Table 8:

Table 8 Unsmooth working condition settings

Working conditions Long wave is not smooth Shortwave is not smooth 1 German low interference spectrum 0.01ISO 3095-2013 (almost no shortwave) 2 German low interference spectrum ISO 3095-2013 Roughness Spectrum 3 German low interference spectrum Iron Science 4 American Five-Level Spectrum Iron Science

The simulated wheel vertical vibration acceleration corresponding to different wheel-rail irregularity spectra (conditions 1, 2, 3 and 4 in Table 8) is shown in Figure 7; the wheel vertical vibration acceleration amplitude corresponding to different wheel-rail irregularity spectra is shown in Table 9:

Table 9 Amplitude of wheel vertical vibration acceleration corresponding to different unevenness

Working conditions Wheel-rail unevenness (long wave + short wave) Wheel vertical vibration acceleration amplitude 1 German low interference spectrum + 0.01ISO 3095-2013 roughness spectrum 2.6m/s ² 2 German low interference spectrum + ISO 3095-2013 roughness spectrum 25.5m/s ² 3 German low interference spectrum + iron science spectrum 69.1m/s ² 4 American five-level spectrum + iron science spectrum 69.5m/s ²

According to the results in Table 9, the wheel vertical vibration acceleration under different wheel-rail irregularity spectra is mainly determined by the short-wave irregularity spectra. When the long-wave irregularity spectra are the same, different short-wave irregularities will have a greater impact on the wheel vibration acceleration.

Through vehicle-track coupling dynamics calculation, the wheel vibration acceleration peak value measured on site is compared with the wheel vibration acceleration amplitude obtained by numerical calculation (working conditions 2 and 3 in Table 9). Assuming that the measured vibration acceleration amplitude is 40.5m/s2, the difference between the wheel vibration acceleration amplitude corresponding to working condition 2 in Table 9 and the measured vibration acceleration amplitude is 15m/s2, and the difference between the wheel vibration acceleration amplitude corresponding to working condition 3 in Table 9 and the measured vibration acceleration amplitude is 28.6m/s2. Since 15m/s2 is less than 28.6m/s2, the wheel vibration acceleration amplitude corresponding to working condition 2 is the target wheel vibration acceleration, and the wheel-rail unevenness spectrum corresponding to the target wheel vibration acceleration, the German low interference spectrum + ISO 3095-2013 roughness spectrum (target wheel-rail unevenness spectrum), is the wheel-rail unevenness spectrum that conforms to the actual situation on site.

Step S222: based on the multiple fitting results and the vehicle-track coupled vertical dynamics model, the wheel-rail forces corresponding to each wheel-rail irregularity spectrum are obtained.

Step S224, input each wheel-rail force as an excitation into a pre-established track-tunnel-soil coupling dynamic simulation analysis model to obtain the tunnel wall vibration source intensity corresponding to each wheel-rail irregularity spectrum.

The wheel-rail irregularity spectrum will have a great impact on the environmental vibration source intensity. If it is not selected properly, it may result in excessive vibration reduction investment or reconstruction costs that do not meet the vibration standards. In actual implementation, the wheel-rail irregularity spectrum (German low interference spectrum + ISO 3095-2013 roughness spectrum) that conforms to the actual situation on site can be used as the wheel-rail irregularity spectrum that conforms to the actual situation on site required for the subsequent wheel-rail system dynamic simulation, thereby calculating a smaller tunnel wall vibration source intensity.

Specifically, the wheel-rail irregularity spectrum (German low interference spectrum + ISO 3095-2013 roughness spectrum) that meets the actual on-site conditions is re-input into the vehicle-track coupling dynamics model, and the wheel-rail system dynamic response (equivalent to the above-mentioned wheel-rail force) that meets the actual line operation state is calculated. The wheel-rail system dynamic response is input into the pre-established track-tunnel-soil coupling dynamic simulation analysis model to obtain the corresponding tunnel wall vibration source strength. The tunnel wall vibration source strength has a small value, and therefore has a good shock absorption effect.

Based on the tunnel structure of a certain line and the stratigraphic data of the area where the line is located, the finite element simulation software ABAQUS is used to establish a track-tunnel-soil coupling dynamic simulation analysis model. Different uneven spatial samples are input into the vehicle-track coupling dynamic model, and the wheel-rail force under the excitation of the long wave + different short wave uneven spectrum is calculated. The calculated wheel-rail force is input as excitation into the ABAQUS finite element model (equivalent to the above-mentioned track-tunnel-soil coupling dynamic simulation analysis model) for simulation, thereby simulating the tunnel wall vibration source strength (tunnel wall vibration acceleration). Table 10 gives the dimensions and material parameters of the track-tunnel-soil coupling dynamic simulation analysis model. In the prediction and analysis of environmental vibration caused by trains, the influence of soil parameters on the calculation results is also very important. This application can set the parameters of each soil layer in the finite element model according to the geological survey data of the area where the line is located, as shown in Table 11:

Table 10 ABAQUS finite element model simulation parameters

project type Main parameters Track bed type Double-block ballastless track C40 Concrete Tunnel Segments C50 Segment thickness 300mm Tunnel radius Inner diameter 2.8m, outer diameter 3.1m Tunnel depth 18m

Table 11 Strata distribution parameters of the subway line

In actual implementation, in order to avoid the reflection of elastic waves caused by load excitation (equivalent to the above wheel-rail force) on the model boundary, infinite element boundaries are used on both sides away from the tunnel, with the upper surface of the soil layer as a free boundary and the bottom surface as a fixed boundary, as shown in Figure 8 Track-tunnel-soil ABAQUS finite element model (equivalent to the above track-tunnel-soil coupled dynamic simulation analysis model). The track-tunnel-soil coupled finite element model uses the wheel-rail force time history results calculated in the vehicle-track coupled dynamic model to simulate the vibration of the tunnel wall caused by the train operation. According to the requirements of the "Technical Guidelines for Environmental Impact Assessment of Urban Rail Transit" (HJ 453-2018) (the tunnel wall vibration source strength measurement point should be 1.25m±0.25m above the rail top surface), this simulation selects observation points according to this specification.

When the train runs at a speed of 80 km/h, the tunnel wall vibration source intensity under the action of different wheel-rail irregularity spectra is obtained through simulation calculation, and its frequency division vibration level is shown in Figure 9:

According to Figure 9, when the long-wave irregularities are the same, the vibration levels of the tunnel wall at low frequencies are not much different, but different short-wave irregularities will lead to large differences in the vibration levels above 10 Hz. The tunnel wall vibration source strength under different wheel-rail irregularity spectra is shown in Table 12:

Table 12 Tunnel wall vibration source strength under different short-wave irregularities

According to Table 12, it can be seen that the tunnel wall vibration source strength caused by different short-wave unevenness spectra is quite different. This is because the main frequency of the tunnel wall vibration acceleration appears in 50-80Hz. The vibration in this frequency range is determined by the short-wave unevenness, so the short-wave unevenness directly determines the size of the tunnel wall vibration source strength. Under the same long-wave unevenness conditions, the tunnel wall vibration source strength caused by different short-wave unevenness differs by 9-24dB, which is close to 2-4 vibration reduction levels, which has a great impact on the vibration reduction design cost.

In summary, the selection of wheel-rail irregularity spectrum directly affects the vibration of vehicles, tracks, and even tunnels. Only by reasonably selecting the wheel-rail irregularity spectrum in the wheel-rail dynamics system according to the line operation status can we accurately predict the dynamic response of the wheel-rail system, accurately evaluate passenger comfort, scientifically assess structural fatigue damage, accurately predict environmental vibration and noise, and provide economical control measures.

The target wheel-rail irregularity spectrum selection method for the above-mentioned wheel-rail system dynamic simulation first calculates the TQ management values of various long-wave irregularity spectra, and then selects the long-wave irregularity spectrum that is more in line with the actual line maintenance and operation status by comparing them with the TQI management values at different design speed levels in the "Rules for Repair of Conventional Railway Lines" (GW 102-2019). This method of selecting the long-wave irregularity spectrum conforms to the maintenance and management specifications of the actual line and has a high degree of fit with the actual line status.

In view of the fact that there are currently no management regulations for short-wave unevenness spectra in my country, and the actual measurement of short-wave unevenness spectra is relatively time-consuming and labor-intensive, the target wheel-rail unevenness spectrum selection method for the dynamic simulation of the wheel-rail system is to select different short-wave unevenness spectra and long-wave fitting into multiple unevenness power spectrum density curves after determining a reasonable long-wave unevenness spectrum, generate unevenness space samples that vary with line mileage through Fourier transform, input different unevenness space samples into the vehicle-track coupling dynamic model, calculate the wheel vibration acceleration under the excitation of long-wave + different short-wave unevenness spectra, and compare the calculated wheel vibration acceleration amplitude with the measured wheel vibration acceleration amplitude. , based on this, the wheel-rail unevenness spectrum is determined, and a group of wheel-rail unevenness spectra closest to the wheel vibration acceleration amplitude is selected. The long-wave + short-wave wheel-rail unevenness spectrum is the wheel-rail unevenness spectrum that matches the actual line operation status, and the short-wave in the group of wheel-rail unevenness spectra closest to the wheel vibration acceleration amplitude is the short-wave unevenness to be selected. This method of calculating the wheel vibration acceleration caused by the determined long-wave + different short-wave unevenness spectra and comparing it with the wheel vibration acceleration measured on site to select the short-wave unevenness spectrum closest to the working condition is both economical and convenient, the selected wheel-rail unevenness has a good match with the on-site wheel-rail unevenness spectrum status, and saves time and effort.

The wheel-rail irregularity spectrum selection method for the wheel-rail system dynamic simulation, after determining the long-wave irregularity spectrum and short-wave irregularity spectrum that meet the actual line operation status, inputs the irregularity excitation into the vehicle-track coupling dynamic model, and calculates the wheel-rail system dynamic response that meets the actual line operation status. This method of reasonably selecting the wheel-rail irregularity spectrum according to the actual line status can accurately predict the wheel-rail contact response.

The embodiment of the present invention further provides a device for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system, as shown in FIG10 , the device comprising: a model building module 30, for building a vehicle-rail coupled vertical dynamics model based on preset vehicle parameters and track parameters; a selection module 31, for selecting a target long-wave irregularity spectrum from a plurality of pre-acquired long-wave irregularity spectra according to a preset standard track quality index management value; a fitting module 32, for fitting the target long-wave irregularity spectrum with a plurality of preset short-wave irregularity spectra, respectively, to obtain a plurality of fitting results; an acquisition module 33, for obtaining the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-rail coupled vertical dynamics model; wherein each short-wave irregularity spectrum is respectively combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; and a determination module 34, for determining the target wheel-rail irregularity spectrum based on a plurality of wheel vibration accelerations.

The above-mentioned target wheel-rail irregularity spectrum selection device for dynamic simulation of wheel-rail system comprises: establishing a vehicle-rail coupling vertical dynamics model based on preset vehicle parameters and track parameters; selecting a target long-wave irregularity spectrum from a plurality of long-wave irregularity spectra acquired in advance according to a preset standard track quality index management value; fitting the target long-wave irregularity spectrum with a plurality of preset short-wave irregularity spectra to obtain a plurality of fitting results; obtaining the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-rail coupling vertical dynamics model; and determining the wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations. In the device, the target wheel-rail irregularity spectrum is determined based on the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum, and the wheel-rail irregularity spectrum can be reasonably selected according to the actual line state, thereby accurately predicting the wheel-rail contact response.

Furthermore, the selection module is also used to: calculate the track quality index management values corresponding to each of the long-wave unevenness spectra acquired in advance; compare each track quality index management value with the standard track quality index management value to obtain multiple first comparison results; and select a target long-wave unevenness spectrum from a plurality of long-wave unevenness spectra based on the multiple first comparison results.

Furthermore, the track quality index management values corresponding to each long-wave irregularity spectrum include: left high and low, right high and low, left track direction, right track direction, track gauge, level, and triangular pit.

Furthermore, the acquisition module is also used to: perform Fourier transform on each fitting result to generate a roughness space sample corresponding to each fitting result; input each roughness space sample into the vehicle-track coupled vertical dynamics model to obtain the wheel vibration acceleration corresponding to each wheel-rail roughness spectrum.

Furthermore, the determination module is also used to: calculate the difference between the amplitude of each wheel vibration acceleration and the amplitude of the actual wheel vibration acceleration obtained in advance, and obtain multiple difference results; determine the wheel vibration acceleration corresponding to the result with the smallest value among the multiple difference results as the target wheel vibration acceleration; determine the wheel-rail irregularity spectrum corresponding to the target wheel vibration acceleration as the target wheel-rail irregularity spectrum.

Furthermore, the device also includes: obtaining the wheel-rail force corresponding to each wheel-rail irregularity spectrum based on multiple fitting results and the vehicle-track coupled vertical dynamics model; inputting each wheel-rail force as an excitation into a pre-established track-tunnel-soil coupling dynamic simulation analysis model to obtain the tunnel wall vibration source intensity corresponding to each wheel-rail irregularity spectrum.

Furthermore, the vehicle-track coupled vertical dynamics model includes: a vehicle vertical model and a track model; wherein the vehicle vertical model simulates the vehicle as a half-vehicle system with a secondary suspension; and the track model is simulated using a long-pillow embedded integral ballast bed.

The target wheel-rail irregularity spectrum selection device for wheel-rail system dynamic simulation provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the target wheel-rail irregularity spectrum selection method embodiment for the aforementioned wheel-rail system dynamic simulation. For the embodiment of the target wheel-rail irregularity spectrum selection device for wheel-rail system dynamic simulation, reference may be made to the corresponding contents in the embodiment of the target wheel-rail irregularity spectrum selection method for the aforementioned wheel-rail system dynamic simulation.

An embodiment of the present invention also provides an electronic device, as shown in Figure 11, the electronic device includes a processor 130 and a memory 131, the memory 131 stores machine executable instructions that can be executed by the processor 130, and the processor 130 executes the machine executable instructions to implement the target wheel-rail irregularity spectrum selection method for the above-mentioned wheel-rail system dynamic simulation.

Furthermore, the electronic device shown in FIG. 11 further includes a bus 132 and a communication interface 133 , and the processor 130 , the communication interface 133 and the memory 131 are connected via the bus 132 .

Among them, the memory 131 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 133 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 132 can be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one bidirectional arrow is used in Figure 11, but it does not mean that there is only one bus or one type of bus.

The processor 130 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 130. The above processor 130 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131 and completes the steps of the method of the aforementioned embodiment in combination with its hardware.

An embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the target wheel-rail irregularity spectrum selection method for dynamic simulation of the wheel-rail system. The specific implementation can be found in the method embodiment and will not be repeated here.

The target wheel-rail irregularity spectrum selection device method and device for dynamic simulation of a wheel-rail system provided in an embodiment of the present invention include a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for selecting a target wheel-rail irregularity spectrum for dynamic simulation of a wheel-rail system, characterized in that the method comprises: Based on the preset vehicle parameters and track parameters, a vehicle-track coupled vertical dynamics model is established; According to the preset standard track quality index management value, a target long-wave irregularity spectrum is selected from a plurality of long-wave irregularity spectra acquired in advance; Fitting the target long-wave roughness spectrum with a plurality of preset short-wave roughness spectra to obtain a plurality of fitting results; Based on the plurality of fitting results and the vehicle-track coupled vertical dynamics model, the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum is obtained; wherein each short-wave irregularity spectrum is combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; Based on the plurality of wheel vibration accelerations, a target wheel-rail irregularity spectrum is determined. 2. The method according to claim 1, characterized in that the step of selecting a target long-wave roughness spectrum from a plurality of long-wave roughness spectra acquired in advance according to a preset standard track quality index management value comprises: Calculating the track quality index management value corresponding to each of the long-wave irregularity spectra acquired in advance; Comparing each of the track quality index management values with the standard track quality index management value to obtain a plurality of first comparison results; The target long-wave roughness spectrum is selected from a plurality of the long-wave roughness spectra according to the plurality of the first comparison results. 3. The method according to claim 2 is characterized in that the track quality index management values corresponding to each of the long-wave irregularity spectra include: left high and low, right high and low, left track direction, right track direction, track gauge, level, and triangular pit. 4. The method according to claim 1, characterized in that the step of obtaining the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-track coupled vertical dynamics model comprises: Performing Fourier transform on each of the fitting results to generate an uneven space sample corresponding to each of the fitting results; Each of the irregularity spatial samples is input into the vehicle-track coupled vertical dynamics model to obtain the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum. 5. The method according to claim 1, characterized in that the step of determining the target wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations comprises: Calculating the difference between the amplitude of each wheel vibration acceleration and the amplitude of the actual wheel vibration acceleration acquired in advance to obtain a plurality of difference results; Determine the wheel vibration acceleration corresponding to the result with the smallest value among the plurality of difference results as the target wheel vibration acceleration; The wheel-rail irregularity spectrum corresponding to the target wheel vibration acceleration is determined as the target wheel-rail irregularity spectrum. 6. The method according to claim 1, characterized in that the method further comprises: Based on the plurality of fitting results and the vehicle-track coupled vertical dynamics model, the wheel-rail force corresponding to each wheel-rail irregularity spectrum is obtained; Each of the wheel-rail forces is input as an excitation into a pre-established track-tunnel-soil coupling dynamic simulation analysis model to obtain the tunnel wall vibration source intensity corresponding to each wheel-rail irregularity spectrum. 7. The method according to claim 1, characterized in that the vehicle-track coupled vertical dynamics model comprises: a vehicle vertical model and a track model; Among them, the vehicle vertical model is a model that simulates the vehicle as a half-vehicle system with a secondary suspension; the track model is simulated by a long-pillow embedded integral roadbed. 8. A device for selecting target wheel-rail irregularity spectrum for dynamic simulation of wheel-rail system, characterized in that the device comprises: A model building module, used to build a vehicle-track coupled vertical dynamics model based on preset vehicle parameters and track parameters; A selection module is used to select a target long-wave irregularity spectrum from a plurality of long-wave irregularity spectra acquired in advance according to a preset standard track quality index management value; A fitting module, used for fitting the target long-wave roughness spectrum with a plurality of preset short-wave roughness spectra to obtain a plurality of fitting results; An acquisition module is used to obtain the wheel vibration acceleration corresponding to each wheel-rail irregularity spectrum based on the plurality of fitting results and the vehicle-track coupled vertical dynamics model; wherein each short-wave irregularity spectrum is combined with the target long-wave irregularity spectrum to obtain each wheel-rail irregularity spectrum; The determination module is used to determine a target wheel-rail irregularity spectrum based on the plurality of wheel vibration accelerations. 9. An electronic device, characterized in that it comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the target wheel-rail irregularity spectrum selection method for wheel-rail system dynamic simulation according to any one of claims 1 to 7. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the target wheel-rail irregularity spectrum selection method for wheel-rail system dynamic simulation according to any one of claims 1 to 7.

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