Railway vehicle dynamics simulation method and system based on parameter change probability distribution

Abstract

The invention provides a rail vehicle dynamics simulation method and system based on parameter change probability distribution, and the method comprises the steps: obtaining a variable combination sample through a traversal combination mode based on a wheel rail profile of a to-be-evaluated motor train unit trailer and a variable Latin hypercube sampling sample, the variable Latin hypercube sample is constructed by test data of a plurality of bogie system random variables of different types; according to the variable combination sample, assigning corresponding elements in a dynamic simulation model to obtain a new dynamic simulation model; and performing dynamic simulation on the to-be-evaluated motor train unit trailer according to the new dynamic simulation model to obtain a dynamic evaluation index result corresponding to the to-be-evaluated motor train unit trailer. According to the method, the data calculation amount in the simulation process is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of railway vehicles, and in particular to a railway vehicle dynamics simulation method and system based on parameter change probability distribution. Background Art

[0002] In the bogie system of high-speed railway vehicles, the radial stiffness of the swing arm node, the axial stiffness, and the damping force exhibited by various shock absorbers (including anti-snaking shock absorbers, primary vertical shock absorbers, secondary vertical shock absorbers, and secondary lateral shock absorbers) when reaching the critical speed are not fixed calibration values. These force values ​​are affected by the installation process accuracy and the actual service conditions of the vehicle, and usually fluctuate within a specific range. At the same time, the wheel-rail profile will also undergo random changes after a shaping cycle. In this environment where multiple variables work together, the operating safety, driving stability, and ride stability of high-speed vehicles will be affected accordingly.

[0003] When performing vehicle dynamics simulation, the existing technology can usually only set the radial stiffness of the swing arm node in the bogie system, the axial stiffness, and the damping force of various shock absorbers (including anti-snaking shock absorbers, primary vertical shock absorbers, secondary vertical shock absorbers, and secondary lateral shock absorbers) at key speeds to preset specific values. This approach results in the vehicle dynamics evaluation indicators being based on a single result of these specific values, and it is impossible to directly obtain the distribution range of these evaluation indicators and their probability density distribution function. If you try to simulate by comprehensively traversing all possible combinations of these damping forces to obtain a more comprehensive dynamic performance evaluation, it will require extremely large computer resources.

[0004] Therefore, there is an urgent need for a rail vehicle dynamics simulation method and system based on the probability distribution of parameter changes to solve the above problems. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a rail vehicle dynamics simulation method and system based on parameter change probability distribution.

[0006] The present invention provides a rail vehicle dynamics simulation method based on parameter change probability distribution, comprising: Based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, a variable combination sample is obtained by traversing the combination method, wherein the variable Latin hypercube sampling sample is constructed by test data of random variables of multiple bogie systems of different types; According to the variable combination sample, assign values ​​to corresponding elements in the dynamics simulation model to obtain a new dynamics simulation model; According to the new dynamics simulation model, dynamics simulation is performed on the EMU trailer to be evaluated to obtain dynamics evaluation index results corresponding to the EMU trailer to be evaluated.

[0007] According to a rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention, the multiple bogie system random variables include test data of multiple dynamic parameter types, and the multiple dynamic parameter types include radial static stiffness of axle box swing arm node, axial static stiffness of axle box swing arm node, first speed damping force of anti-snaking damper, second speed damping force of anti-snaking damper, first speed damping force of primary vertical damper, second speed damping force of primary vertical damper, first speed damping force of secondary vertical damper, second speed damping force of secondary vertical damper, first speed damping force of secondary lateral damper and second speed damping force of secondary lateral damper.

[0008] According to a rail vehicle dynamics simulation method based on parameter variation probability distribution provided by the present invention, the variable combination samples are obtained by traversing the combination method based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, including: Determining the variable distribution range of the bogie system random variable of each of the dynamic parameter types according to the minimum value and the maximum value corresponding to the test data of the bogie system random variable of each of the dynamic parameter types; According to the variable distribution range of the bogie system random variable of each of the dynamic parameter types, a range equalization sub-interval of the bogie system random variable of each of the dynamic parameter types is constructed, and based on the number of test data corresponding to the bogie system random variable within the range equalization sub-interval, a probability density function corresponding to the bogie system random variable of each of the dynamic parameter types is calculated; wherein the range equalization sub-interval is obtained by equally dividing the variable distribution range of the bogie system random variable; According to the variable distribution range and the probability density function, the variable distribution range of the bogie system random variable of each of the dynamic parameter types is segmented according to the number of sub-interval segmentations to obtain a plurality of sub-intervals corresponding to the bogie system random variables of each of the dynamic parameter types, wherein the probability of the bogie system random variables of each of the dynamic parameter types falling into each of the sub-intervals is equal, and the number of sub-interval segmentations is determined by the number of variable Latin hypercube sampling samples; Based on a sampling value obtained by randomly sampling each subinterval of the bogie system random variable of each dynamic parameter type, an initial sampling matrix is ​​constructed, wherein each row of the initial sampling matrix corresponds to a sampling value of the bogie system random variable of a different dynamic parameter type in each subinterval; Based on the number of subinterval divisions, construct a Latin hypercube matrix of a preset specification, wherein the elements in the Latin hypercube matrix are natural numbers greater than or equal to 1 and less than or equal to the number of subinterval divisions; According to the Latin hypercube matrix, the initial sampling matrix is ​​adjusted to obtain the variable Latin hypercube sampling samples, wherein each column of the variable Latin hypercube sampling samples is a sampling sample; Based on the traversal combination method, a set of variable combination samples is constructed by combining the wheel profile and the rail profile in the wheel-rail profile with the sampling samples, and all the wheel profiles, the rail profiles and the variable Latin hypercube sampling samples are traversed to obtain all the variable combination samples.

[0009] According to a rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention, the corresponding elements in the dynamics simulation model are assigned values ​​according to the variable combination sample to obtain a new dynamics simulation model, including: Based on the subintervals corresponding to the random variables of the bogie system in the variable combination sample, corresponding elements in the dynamic simulation model are assigned values ​​according to the random variables of the bogie system in the variable combination sample to obtain the new dynamic simulation model.

[0010] According to a rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention, the dynamics simulation of the EMU trailer to be evaluated is performed according to the new dynamics simulation model to obtain the dynamics evaluation index result corresponding to the EMU trailer to be evaluated, including: According to the new dynamic simulation model, a dynamic simulation is performed on the EMU trailer to be evaluated to obtain an initial dynamic time domain signal corresponding to the EMU trailer to be evaluated, wherein the initial dynamic time domain signal includes time domain signals of a derailment coefficient, a wheel load reduction rate, a wheel-rail vertical force, a wheel axle lateral force, a vehicle body lateral acceleration, and a vehicle body vertical acceleration; performing a first low-pass filtering process on the time domain signals corresponding to the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force respectively, performing smoothing process on the time domain signals of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force after the first low-pass filtering based on a preset window, and obtaining evaluation index results of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force respectively according to preset percentiles in the time domain signals of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force after the smoothing process; A second low-pass filtering process is performed on the time domain signal of the wheel axle lateral force, and the time domain signal of the wheel axle lateral force after the second low-pass filtering is smoothed based on a preset window, and an evaluation index result of the wheel axle lateral acceleration is obtained according to a preset percentile in the time domain signal of the wheel axle lateral force after the smoothing process.

[0011] According to a rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention, the method further includes: Performing Fourier transformation on the time domain signal of the vehicle body lateral acceleration to obtain a vehicle body lateral acceleration amplitude spectrum; Obtaining an evaluation index result of the lateral stability of the vehicle body according to the vehicle body lateral acceleration amplitude spectrum and the frequency range corresponding to the vehicle body lateral acceleration amplitude spectrum; Performing Fourier transformation on the time domain signal of the vehicle body vertical acceleration to obtain a vehicle body vertical acceleration amplitude spectrum; An evaluation index result of the vertical stability of the vehicle body is obtained according to the vehicle body vertical acceleration amplitude spectrum and the frequency range corresponding to the vehicle body vertical acceleration amplitude spectrum.

[0012] The present invention also provides a rail vehicle dynamics simulation system based on parameter change probability distribution, comprising: A variable combination sample acquisition module is used to acquire variable combination samples by traversing the combination method based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, wherein the variable Latin hypercube sampling sample is constructed from the test data of random variables of multiple bogie systems of different types; A dynamics simulation model generation module is used to assign values ​​to corresponding elements in the dynamics simulation model according to the variable combination samples to obtain a new dynamics simulation model; The simulation module is used to perform dynamic simulation on the EMU trailer to be evaluated according to the new dynamic simulation model to obtain the dynamic evaluation index results corresponding to the EMU trailer to be evaluated.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the rail vehicle dynamics simulation method based on parameter change probability distribution as described in any one of the above-mentioned methods is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for simulating rail vehicle dynamics based on parameter change probability distribution as described in any one of the above is implemented.

[0015] The rail vehicle dynamics simulation method and system based on parameter change probability distribution provided by the present invention perform rail vehicle dynamics simulation by analyzing the probability distribution characteristics of random changes in the dynamic parameters of the bogie components, and then obtain the distribution range and probability density function of the railway vehicle dynamics evaluation index, so that the rail vehicle dynamics simulation results are more accurate, and the amount of data calculation in the simulation process is also reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic flow chart of a rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention; Figure 2 A schematic diagram of the distribution range of the lateral stability index provided by the present invention; Figure 3 A schematic diagram of the structure of a rail vehicle dynamics simulation system based on parameter change probability distribution provided by the present invention; Figure 4 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. 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.

[0019] Since the radial stiffness and axial stiffness of the swing arm node of the railway vehicle bogie system, as well as the damping force of the anti-snaking damper, the first vertical damper, the second vertical damper and the second lateral damper at critical speeds are affected by the installation process and service conditions, the dynamic-related parameters of these components are not calibrated values ​​and usually vary within a certain range; at the same time, the wheel-rail profile also changes randomly within a shaping cycle. Under the combined influence of these variable changes, the operation safety, stability and stability of high-speed vehicles need to be confirmed, that is, the operation safety evaluation indicators (derailment coefficient, wheel weight reduction rate, wheel axle lateral force), stability evaluation indicators (frame lateral acceleration), and stability evaluation indicators (lateral stability, vertical stability) need to meet the relevant prescribed parameter ranges.

[0020] Since the radial stiffness and axial stiffness of the swing arm node of the railway vehicle bogie system, as well as the damping forces of the anti-snaking damper, the first-order vertical damper, the second-order vertical damper, and the second-order lateral damper at critical speeds are often distributed within a certain range, it is not easy to directly obtain the distribution range of the operation safety evaluation indicators (derailment coefficient, wheel weight reduction rate, wheel axle lateral force), stability evaluation indicators (frame lateral acceleration), and stability evaluation indicators (lateral stability, vertical stability), and on-site testing requires huge time, manpower and economic costs. Therefore, vehicle dynamics simulation is an important means to obtain the distribution range and probability distribution form of dynamic indicators.

[0021] The prior art can only set the radial stiffness and axial stiffness of the swing arm node, as well as the damping force of the anti-snaking damper, the first vertical damper, the second vertical damper, and the second lateral damper at the critical speed to specific values ​​to carry out vehicle dynamics simulation, resulting in the vehicle dynamics evaluation index being a specific value, and the distribution range and probability density distribution function of the vehicle dynamics evaluation index cannot be obtained. If the radial stiffness and axial stiffness of the swing arm node, as well as the damping force of the anti-snaking damper, the first vertical damper, the second vertical damper, and the second lateral damper at the critical speed are traversed, carrying out vehicle dynamics simulation will consume a very large amount of computer resources. In view of the problems existing in the above-mentioned prior art, the present invention provides a rail vehicle dynamics simulation method that takes into account the probability distribution characteristics of the dynamic parameter changes, thereby solving the problem of large simulation workload in the prior art.

[0022] Figure 1 A schematic diagram of the flow chart of the rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention, such as Figure 1 As shown, the present invention provides a rail vehicle dynamics simulation method based on parameter change probability distribution, comprising: Step 101, based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, obtain the variable combination sample by traversal combination method, wherein the variable Latin hypercube sampling sample is constructed by test data of random variables of multiple bogie systems of different types.

[0023] The wheel-rail profile combination mode can be the specific shape and matching mode of the contact surface between the wheel and the track of the EMU trailer (i.e., the carriage without the power unit). The design of the wheel-rail profile has an important impact on the running stability, noise, wear and ride comfort of the train. In the present invention, the EMU trailer to be evaluated can be equipped with a variety of different wheel-rail profile combinations, which are determined based on different design standards, material selection or operating environment requirements.

[0024] After determining a variety of wheel-rail profile combinations, it is necessary to collect corresponding data samples for each mode, and these data samples are used for subsequent analysis and evaluation to understand the performance of different wheel-rail profile combinations in actual operation. Specifically, in the present invention, variable combination samples refer to a data set composed of test data of multiple bogie system random variables of different types in variable Latin hypercube sampling samples under each wheel-rail profile combination, and these variables include but are not limited to parameters of the bogie system (such as stiffness and damping characteristics of the suspension system), geometric dimensions of wheels, geometric characteristics of tracks, etc.

[0025] The bogie system is an important part of the EMU trailer. It is responsible for supporting the carriage, guiding the vehicle along the track, and absorbing vibration and shock during operation. When constructing the variable combination sample, different types of bogie systems were considered, and some parameters or variables were randomly selected within a certain range to simulate various situations that may be encountered in actual operation, thereby improving the accuracy and reliability of the analysis results.

[0026] Specifically, in the present invention, one turning and repair cycle tests Wheel profile, one grinding cycle tested rail profile, then all wheel profiles match the rail profiles to form Then, all the bogie system samples (i.e. random variables) are respectively matched with all wheel-rail profile combinations ( ) are combined to form the final sample library, that is, the variable combination samples corresponding to different wheel-rail profile combinations are obtained, a total of indivual.

[0027] For example, in one embodiment, one turning and repairing cycle tests three wheel profiles, and one grinding cycle tests two rail profiles, then all wheel profiles are matched with each rail profile to form six wheel-rail profile matching combinations. Further, all bogie suspension parameter sampling samples (the bogie system random variables in this embodiment are 11) are combined with all wheel-rail profile matching combinations to form a final sample library, with a total of 66 variable combination samples.

[0028] Step 102: assign values ​​to corresponding elements in the dynamics simulation model according to the variable combination sample to obtain a new dynamics simulation model.

[0029] In the present invention, in the dynamic simulation, various parameters of the bogie system have uncertainty or randomness, and these factors can be regarded as random variables. The variable combination samples are selected from these random variables according to different wheel-rail profile combinations for subsequent simulation analysis.

[0030] In the present invention, the dynamics simulation model is a structure for storing and managing data required in the simulation process, which includes various parameters related to the bogie system and their corresponding values.

[0031] The random variables of the bogie system include but are not limited to mass, stiffness, damping coefficient, etc. These variables may follow a certain probability distribution (such as normal distribution, uniform distribution, etc.) in practical applications.

[0032] Latin Hypercube Sampling (LHS) is a technique for generating multidimensional random samples, which can generate uniformly distributed sample points within a given variable distribution range, thereby effectively exploring the parameter space. The variable Latin hypercube sampling sample is a sample matrix generated by the LHS method. In the present invention, the variable combination sample generated by Latin hypercube sampling can assign values ​​to the elements in the dynamic simulation model, thereby mapping each sample point (i.e., the value of a set of random variables) to the corresponding position in the dynamic simulation model, so that the new dynamic simulation model contains multiple simulation scenarios based on different random variable combinations, and each scenario corresponds to a specific set of parameter values.

[0033] Step 103: Performing dynamic simulation on the train-train trailer to be evaluated according to the new dynamic simulation model to obtain dynamic evaluation index results corresponding to the train-train trailer to be evaluated.

[0034] In the present invention, a series of evaluation index results related to the dynamic performance of the EMU trailer can be obtained through dynamic simulation. These indicators include but are not limited to: wheel-rail force (such as vertical force, lateral force), derailment coefficient, wheel weight reduction rate and stability index, etc., which reflect the operating stability of the EMU trailer under different working conditions and are crucial to ensuring the safety of the EMU.

[0035] The rail vehicle dynamics simulation method based on parameter change probability distribution provided by the present invention performs rail vehicle dynamics simulation by analyzing the probability distribution characteristics of random changes in dynamic parameters of bogie components, and then obtains the distribution range and probability density function of railway vehicle dynamics evaluation indicators, so that the rail vehicle dynamics simulation results are more accurate, and the amount of data calculation in the simulation process is also reduced.

[0036] On the basis of the above embodiments, the multiple bogie system random variables include test data of multiple types of dynamic parameters, and the multiple types of dynamic parameters include the radial static stiffness of the axle box arm node, the axial static stiffness of the axle box arm node, the first speed damping force of the anti-snaking shock absorber, the second speed damping force of the anti-snaking shock absorber, the first speed damping force of the primary vertical shock absorber, the second speed damping force of the primary vertical shock absorber, the first speed damping force of the secondary vertical shock absorber, the second speed damping force of the secondary vertical shock absorber, the first speed damping force of the secondary lateral shock absorber and the second speed damping force of the secondary lateral shock absorber.

[0037] In the present invention, the random variables in the bogie system include the radial static stiffness and axial static stiffness of the axle box swing arm node, the critical speed damping force of the anti-snaking damper (including the first speed damping force of the anti-snaking damper and the second speed damping force of the anti-snaking damper), the critical speed damping force of the first-series vertical damper (including the first speed damping force of the first-series vertical damper and the second speed damping force of the first-series vertical damper), the critical speed damping force of the second-series vertical damper (including the first speed damping force of the second-series vertical damper and the second speed damping force of the second-series vertical damper), and the critical speed damping force of the second-series lateral damper (including the first speed damping force of the second-series lateral damper and the second speed damping force of the second-series lateral damper), wherein the critical speed is the speed corresponding to the specific damping force generated by the shock absorber, and the damping force of the critical speed represents the damping force corresponding to the critical speed generated by the shock absorber during the vibration process. For example, the first speed damping force of the anti-snaking damper is the damping force of the anti-snaking damper. The damping force of the anti-snaking damper is the damping force when the vibration speed of the anti-snaking damper reaches 0.01m / s, the second speed damping force of the anti-snaking damper is the damping force when the vibration speed of the anti-snaking damper reaches 0.02m / s, the first speed damping force of the primary vertical damper is the damping force when the vibration speed of the primary vertical damper reaches 0.1m / s, the second speed damping force of the primary vertical damper is the damping force when the vibration speed of the primary vertical damper reaches 0.2m / s, and the first speed damping force of the secondary vertical damper is the damping force when the vibration speed of the secondary vertical damper reaches 0.01m / s. The first speed damping force is the damping force when the vibration speed of the secondary vertical shock absorber reaches 0.1m / s, the second speed damping force of the secondary vertical shock absorber is the damping force when the vibration speed of the secondary vertical shock absorber reaches 0.3m / s, the first speed damping force of the secondary lateral shock absorber is the damping force when the vibration speed of the secondary lateral shock absorber reaches 0.1m / s, and the second speed damping force of the secondary lateral shock absorber is the damping force when the vibration speed of the secondary lateral shock absorber reaches 0.3m / s.

[0038] On the basis of the above embodiment, the variable combination samples are obtained by traversing the combination mode based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, including: Determining the variable distribution range of the bogie system random variable of each of the dynamic parameter types according to the minimum value and the maximum value corresponding to the test data of the bogie system random variable of each of the dynamic parameter types; According to the variable distribution range of the bogie system random variable of each of the dynamic parameter types, a range equalization sub-interval of the bogie system random variable of each of the dynamic parameter types is constructed, and based on the number of test data corresponding to the bogie system random variable within the range equalization sub-interval, a probability density function corresponding to the bogie system random variable of each of the dynamic parameter types is calculated; wherein the range equalization sub-interval is obtained by equally dividing the variable distribution range of the bogie system random variable; According to the variable distribution range and the probability density function, the variable distribution range of the bogie system random variable of each of the dynamic parameter types is segmented according to the number of sub-interval segmentations to obtain a plurality of sub-intervals corresponding to the bogie system random variables of each of the dynamic parameter types, wherein the probability of the bogie system random variables of each of the dynamic parameter types falling into each of the sub-intervals is equal, and the number of sub-interval segmentations is determined by the number of variable Latin hypercube sampling samples; Based on a sampling value obtained by randomly sampling each subinterval of the bogie system random variable of each dynamic parameter type, an initial sampling matrix is ​​constructed, wherein each row of the initial sampling matrix corresponds to a sampling value of the bogie system random variable of a different dynamic parameter type in each subinterval; Based on the number of subinterval divisions, construct a Latin hypercube matrix of a preset specification, wherein the elements in the Latin hypercube matrix are natural numbers greater than or equal to 1 and less than or equal to the number of subinterval divisions; According to the Latin hypercube matrix, the initial sampling matrix is ​​adjusted to obtain the variable Latin hypercube sampling samples, wherein each column of the variable Latin hypercube sampling samples is a sampling sample; Based on the traversal combination method, a set of variable combination samples is constructed by combining the wheel profile and the rail profile in the wheel-rail profile with the sampling samples, and all the wheel profiles, the rail profiles and the variable Latin hypercube sampling samples are traversed to obtain all the variable combination samples.

[0039] In the present invention, the test data of the radial static stiffness of the swing arm node is The maximum and minimum values ​​of and , then the variable distribution range of the radial static stiffness of the swing arm node is Then, in and insert Points , thus The average is equal subintervals, In the present invention, the range of equal sub-intervals The number can be based on the maximum value of the test data and minimum value Confirm. Then count fall into The number of , then the probability density of the test data can be expressed as: ; in, For interval The midpoint of Perform fitting and obtain is the probability density distribution function of the radial static stiffness of the swing arm node.

[0040] In the present invention, the Pearson statistic is calculated, ,in , and find the chi-square critical value by looking up the chi-square table value, so that ,in, is the probability density function The number of unknowns in the equation indicates that the radial static stiffness of the swing arm node is within the chi-square critical value. Level of acceptance distributed.

[0041] In one embodiment, the radial static stiffness test data of 64 swing arm nodes is used for illustration. The maximum value in this set of test data is MN / m, minimum value MN / m, then the variable distribution range of the radial static stiffness of the swing arm node is MN / m. insert points (41.01, 43.72, 46.43, 49.14), so The data is divided into five equal range subintervals on average, and the number of test data falling into the equal range subintervals is counted, the probability density is calculated, and the probability density function is fitted as follows: , then calculate the Pearson statistic and look up the chi-square table. The results can be found in Table 1: Table 1 Statistical results of radial static stiffness of the swing arm node

[0042] Test data of axial static stiffness of the swing arm node The maximum and minimum values ​​of and , then the variable distribution range of the axial static stiffness of the swing arm node is .exist and insert Points , thus The average is equal subintervals, In the present invention, the number of equal sub-intervals is According to the maximum value of the test data and minimum value Determine, accordingly, the number of equal range subintervals in the subsequent steps can be determined according to the maximum and minimum values ​​of the corresponding test data. Then, statistics fall into The number of , then the probability density of the test data can be expressed as: ; in, For interval The midpoint of Perform fitting and obtain is the probability density distribution function of the lateral static stiffness of the swing arm node.

[0043] In the present invention, the Pearson statistic is calculated, ,in , and find the chi-square critical value by looking up the chi-square table Value makes ,in, is the probability density function The number of unknowns in the equation indicates that the lateral static stiffness of the swing arm node is within the chi-square critical value. Level of acceptance distributed.

[0044] In one embodiment, the test data of the axial static stiffness of the swing arm node is processed in the above manner, and its distribution range is [11.7, 14.61] MN / m, and the probability density distribution function of the fitting is , the calculation results can be found in Table 2: Table 2 Statistical results of axial static stiffness of the swing arm node

[0045] Anti-snaking shock absorber Test data of the damping force (the first speed damping force of the anti-snaking damper or the second speed damping force of the anti-snaking damper) at a key speed (such as the key speed is 0.01m / s, 0.02m / s) The maximum and minimum values ​​of and , then the variable distribution range of the first speed damping force of the anti-snaking shock absorber or the second speed damping force of the anti-snaking shock absorber is Then, in and Insert between Points ,Will The average is equal subintervals, . And then statistics fall into The number of , then the probability density of the test data can be expressed as: ; in, For interval The midpoint of Perform fitting and obtain Anti-snaking shock absorber Probability density distribution function of the damping force at a critical speed.

[0046] In the present invention, the Pearson statistic is calculated, , , and find the chi-square critical value by looking up the chi-square table Value makes ,in, is the probability density function The number of unknowns in the anti-snaking damper indicates The damping force at the critical speed is at the chi-square critical value Level of acceptance distributed.

[0047] In one embodiment, the test data of the damping force of the first critical speed (0.01 m / s) of the anti-snaking shock absorber is processed in the above manner, and its distribution range is [7,11] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 3: Table 3 Statistical results of damping force of anti-snaking shock absorber at the first critical speed (0.01m / s)

[0048] In another embodiment, the test data of the damping force of the second critical speed (0.02 m / s) of the anti-snaking shock absorber is processed in the above manner, and its distribution range is [9.5, 16] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 4: Table 4 Statistical results of damping force of anti-snaking shock absorber at the second critical speed (0.02m / s)

[0049] A series of vertical shock absorbers Test data of the damping force (the first speed damping force of the primary vertical shock absorber or the second speed damping force of the primary vertical shock absorber) at a key speed (such as the key speed is 0.1m / s, 0.2m / s) The maximum and minimum values ​​of and , then the variable distribution range of the first velocity damping force of the primary vertical shock absorber or the second velocity damping force of the primary vertical shock absorber is Then, in and Insert between Points ,Will The average is equal subintervals, . And then statistics fall into The number of , then the probability density of the test data can be expressed as: ; in, For interval The midpoint of Perform fitting and obtain A series of vertical shock absorbers Probability density distribution function of the damping force at a critical speed.

[0050] In the present invention, the Pearson statistic is calculated, ,in , and find the chi-square critical value by looking up the chi-square table Value makes ,in, is the probability density function The number of unknowns in the vertical shock absorber indicates that The damping force at the critical speed is at the chi-square critical value Level of acceptance distributed.

[0051] In one embodiment, the above method is used to process the test data of the damping force of a series of vertical shock absorbers at the first critical speed (0.1m / s), and the distribution range is [0.747, 1.477] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 5: Table 5 Statistical results of damping force of the first critical speed (0.1 m / s) of the primary vertical shock absorber

[0052] In another embodiment, the above method is also used to process the test data of the damping force of the second critical speed (0.2 m / s) of a series of vertical shock absorbers, and its distribution range is [1.593, 2.416] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 6: Table 6 Statistical results of damping force of the primary vertical shock absorber at the second critical speed (0.2 m / s)

[0053] Secondary vertical shock absorber Test data of the damping force (first speed damping force of the secondary vertical shock absorber or second speed damping force of the secondary vertical shock absorber) at a key speed (such as the key speed is 0.1m / s, 0.3m / s) The maximum and minimum values ​​of and , then the variable distribution range of the first velocity damping force of the secondary vertical shock absorber or the second velocity damping force of the secondary vertical shock absorber is Then, in and Insert between Points , thus The average is equal subintervals, . And then statistics fall into The number of , then the probability density of the test data can be expressed as: ; in, For interval The midpoint of Perform fitting and obtain The second vertical shock absorber Probability density distribution function of the damping force at a critical speed.

[0054] In the present invention, the Pearson statistic is calculated, , , and find the chi-square critical value by looking up the chi-square table Value makes ,in, is the probability density function The number of unknowns in the vertical shock absorber indicates that the The damping force at the critical speed is at the chi-square critical value Level of acceptance distributed.

[0055] In one embodiment, the test data of the damping force of the second vertical shock absorber at the first critical speed (0.1 m / s) is processed in the above manner, and its distribution range is [0.819, 1.217] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 7: Table 7 Statistical results of damping force of the second-order vertical shock absorber at the first critical speed (0.1 m / s)

[0056] In another embodiment, the test data of the damping force of the second critical speed (0.3 m / s) of the secondary vertical shock absorber is processed in the same manner as above, and its distribution range is [2.584, 3.349] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 8: Table 8 Statistical results of damping force of the second critical speed (0.3 m / s) of the secondary vertical shock absorber

[0057] Secondary lateral shock absorber Test data of the damping force (first speed damping force of the secondary lateral shock absorber or second speed damping force of the secondary lateral shock absorber) at a key speed (such as the key speed is 0.1m / s, 0.3m / s) The maximum and minimum values ​​are and , then the variable distribution range of the first velocity damping force of the secondary lateral shock absorber or the second velocity damping force of the secondary lateral shock absorber is Then, in and Insert between Points ,Will The average is equal subintervals, . And then statistics fall into The number of , then the probability density of the test data can be expressed as: ; in, For interval The midpoint of Perform fitting and obtain Secondary lateral shock absorber Probability density distribution function of the damping force at a critical speed.

[0058] In the present invention, the Pearson statistic is calculated, ,in , and find the chi-square critical value by looking up the chi-square table Value makes ,in, is the probability density function The number of unknowns in the second series lateral shock absorber indicates that The damping force at the critical speed is at the chi-square critical value Level of acceptance distributed.

[0059] In one embodiment, the test data of the damping force of the second-order lateral shock absorber at the first critical speed (0.1 m / s) is processed in the above manner, and its distribution range is [1.378, 1.98] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 9: Table 9 Statistical results of damping force of the second-order lateral shock absorber at the first critical speed (0.1 m / s)

[0060] In another embodiment, the test data of the damping force of the second critical speed (0.3 m / s) of the secondary lateral damper is processed, and its distribution range is [2.916, 3.565] kN. The probability density distribution function of the fitting is , the calculation results can be found in Table 10: Table 10 Statistical results of damping force of the second critical speed (0.3 m / s) of the secondary lateral shock absorber

[0061] Furthermore, according to the variable distribution range and probability density function of the bogie system random variables obtained in the above embodiment, the probability distribution range is probability-averaged and stratified.

[0062] Specifically, in the present invention, according to the variable distribution range of the radial static stiffness of the axle box swing arm node and the probability density function ,calculate ,exist insert Points ,form Sub-interval ,in, For the convenience of expression, replace ,use replace , so that .

[0063] According to the variable distribution range of the axial static stiffness of the axle box swing arm node and the probability density function ,calculate ,exist insert Points ,form Sub-interval ,in, For the convenience of expression, replace ,use replace , so that .

[0064] According to the anti-snaking shock absorber The variable distribution range of the damping force at the key speed and the probability density function ,calculate ,exist insert Points ,form Sub-interval ,in, For the convenience of expression, replace ,use replace , so that .

[0065] According to the first series of vertical shock absorbers The variable distribution range of the damping force at the key speed and the probability density function ,calculate ,exist insert Points ,form Sub-interval ,in, For the convenience of expression, replace ,use replace , so that .

[0066] According to the secondary vertical shock absorber The variable distribution range of the damping force at the key speed and the probability density function ,calculate ,exist insert Points ,form Sub-interval ,in, For the convenience of expression, replace ,use replace , so that .

[0067] According to the secondary lateral shock absorber The variable distribution range of the damping force at the key speed and the probability density function ,calculate ;exist insert Points ,form Sub-interval ,in, For the convenience of expression, replace ,use replace , so that .

[0068] In one embodiment, according to the variable distribution range of the radial static stiffness of the axle box swing arm node and the probability density function ,calculate , then Insert 10 points (the number of subinterval divisions corresponds to the number of dynamic parameter types, which can be determined according to the number of Latin hypercube sampling samples of the variable) 40.213, 41.279, 42.104, 42.824, 43.496, 44.158, 44.843, 45.594, 46.487, 47.735, to form 11 intervals so that the probability of the bogie system random variable falling into each subinterval is equal to .

[0069] The same processing is performed on the axial static stiffness results of the axle box swing arm node, the damping force of the first critical speed (0.01m / s) of the anti-snaking damper, the damping force of the second critical speed (0.02m / s) of the anti-snaking damper, the damping force of the first critical speed (0.1m / s) of the primary vertical damper, the damping force of the second critical speed (0.2m / s) of the primary vertical damper, the damping force of the first critical speed (0.1m / s) of the secondary vertical damper, the damping force of the second critical speed (0.3m / s) of the secondary vertical damper, the damping force of the first critical speed (0.1m / s) of the secondary lateral damper, and the damping force of the second critical speed (0.3m / s) of the secondary lateral damper. The resulting probability homogenization stratified interval boundary point list can be referred to as shown in Table 11: Table 11 Results of probability averaging stratified interval cutoff points

[0070] Furthermore, based on the probability averaged stratified interval boundary point results, a point is randomly selected in each subinterval of the bogie system random variable to construct the initial sampling matrix. Specifically: Radial static stiffness of the axle box arm node Sub-interval ( ), randomly pick a point ; Axial static stiffness of the axle box swing arm node Sub-interval ( ), randomly pick a point ; Anti-snaking shock absorber Damping force at a critical speed Sub-interval ( ), randomly pick a point ; A series of vertical shock absorbers Damping force at a critical speed Sub-interval ( ), randomly pick a point ; Secondary vertical shock absorber Damping force at a critical speed Sub-interval ( ), randomly pick a point ; Secondary lateral shock absorber Damping force at a critical speed Sub-interval ( ), randomly pick a point ; Furthermore, construct the initial sampling matrix: ; ; in, are the numbers of critical speeds corresponding to the anti-snaking damper, the first vertical damper, the second vertical damper, and the second lateral damper. Then the matrix The number of rows and columns are and .

[0071] In one embodiment, sampling values ​​are obtained by randomly selecting points in a subinterval determined by two adjacent rows in the same column of Table 11 to form an initial sampling matrix: ; Furthermore, construct a The matrix , whose elements , and the elements of each row and each column are not repeated, that is, a Latin hypercube matrix is ​​constructed. Then, the Latin hypercube matrix is ​​adjusted so that each column represents a sample of the bogie suspension parameters, and then the adjusted Latin hypercube matrix is ​​updated according to the sample values ​​in the initial sampling matrix. Specifically, randomly select rows, forming a new matrix ; Then, construct a new sampling matrix , its Line The elements of the columns are matrices No. Line Elements of a column ,Right now .

[0072] In one embodiment, construct a Latin hypercube , whose elements , and the elements in each row and column are not repeated. The elements in each row of the Latin hypercube matrix correspond to a subinterval, as follows: ; Then, the Latin hypercube matrix is ​​adjusted to obtain variable Latin hypercube sampling samples, wherein each column in the variable Latin hypercube sampling samples represents a sampling sample of the bogie suspension parameters. The specific implementation steps are as follows: Randomly select the 2nd, 3rd, 6th, 10th, 7th, 1st, 4th, 9th, 5th, and 8th rows from the Latin hypercube matrix to form a new matrix : ; Then, construct a new sampling matrix , its Line The elements of the column are the initial sampling matrix No. Line Elements of a column ,Right now , the variable Latin hypercube sampling samples are as follows: .

[0073] In the present invention, based on the traversal combination method, a set of variable combination samples is constructed by combining the wheel profile and the rail profile in the wheel-rail profile with sampling samples, and then all wheel profiles, rail profiles and variable Latin hypercube sampling samples are traversed to obtain all variable combination samples.

[0074] On the basis of the above embodiment, the method of assigning values ​​to corresponding elements in the dynamics simulation model according to the variable combination sample to obtain a new dynamics simulation model includes: Based on the subintervals corresponding to the random variables of the bogie system in the variable combination sample, corresponding elements in the dynamic simulation model are assigned values ​​according to the random variables of the bogie system in the variable combination sample to obtain the new dynamic simulation model.

[0075] In the present invention, for each combination in the variable combination sample, the corresponding element in the dynamic simulation model is assigned a value according to the value of each random variable in the combination. These random variables usually have uncertainty, so in actual modeling, it is necessary to define a reasonable value range for each random variable, that is, a sub-interval, which reflects the range of all values ​​that the variable may take in actual applications. The variable combination sample randomly extracts a value from the sub-interval of each random variable to form a specific variable combination. Through multiple extractions, a sample set containing multiple variable combinations can be generated, and then the values ​​of the random variables are converted into specific parameter values ​​in the dynamic simulation model, thereby simulating the dynamic behavior of the bogie system under different conditions.

[0076] On the basis of the above embodiment, the dynamics simulation is performed on the EMU trailer to be evaluated according to the new dynamics simulation model to obtain the dynamics evaluation index result corresponding to the EMU trailer to be evaluated, including: According to the new dynamic simulation model, a dynamic simulation is performed on the EMU trailer to be evaluated to obtain an initial dynamic time domain signal corresponding to the EMU trailer to be evaluated, wherein the initial dynamic time domain signal includes time domain signals of a derailment coefficient, a wheel load reduction rate, a wheel-rail vertical force, a wheel axle lateral force, a vehicle body lateral acceleration, and a vehicle body vertical acceleration; performing a first low-pass filtering process on the time domain signals corresponding to the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force respectively, performing smoothing process on the time domain signals of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force after the first low-pass filtering based on a preset window, and obtaining evaluation index results of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force respectively according to preset percentiles in the time domain signals of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force after the smoothing process; A second low-pass filtering process is performed on the time domain signal of the wheel axle lateral force, and the time domain signal of the wheel axle lateral force after the second low-pass filtering is smoothed based on a preset window, and an evaluation index result of the wheel axle lateral acceleration is obtained according to a preset percentile in the time domain signal of the wheel axle lateral force after the smoothing process.

[0077] In the present invention, after establishing a parameterized dynamic simulation model of a certain type of EMU trailer, based on different wheel-rail profile combinations, the radial static stiffness and axial static stiffness of the swing arm node, as well as the damping force of the anti-snaking damper at a speed of 0.01m / s, the damping force of the anti-snaking damper at a speed of 0.02m / s, the damping force of the first vertical damper at a speed of 0.1m / s, the damping force of the first vertical damper at a speed of 0.2m / s, the damping force of the second vertical damper at a speed of 0.1m / s, the damping force of the second vertical damper at a speed of 0.3m / s, the damping force of the second lateral damper at a speed of 0.1m / s, and the damping force of the second lateral damper at a speed of 0.2m / s are set as different variables, and then the variables set in the parameterized dynamic simulation model of a certain type of EMU trailer are assigned values, and a dynamic simulation model that concretizes the parameters of a certain type of EMU trailer (specifically 66 parameters) is obtained.

[0078] Furthermore, dynamic simulation was performed on the obtained dynamic simulation model with 66 specific parameters of a certain type of EMU trailer, and the time domain signals corresponding to the derailment coefficient, wheel weight reduction rate, wheel-rail vertical force, wheel axle lateral force, car body lateral acceleration and car body vertical acceleration were obtained.

[0079] In the present invention, the time domain signals of the derailment coefficient, wheel weight reduction rate, wheel-rail vertical force and wheel axle lateral force are subjected to 40 Hz low-pass filtering (i.e., the first low-pass filtering process), and then smoothed with a 2 m (m is the size of the preset unit window) window, and the 99.85% percentile is taken as the final derailment coefficient evaluation index, wheel weight reduction rate evaluation index, wheel-rail vertical force evaluation index and wheel axle lateral force evaluation index.

[0080] The wheel axle lateral force time domain signal is subjected to a 0.4 Hz to 10 Hz band-pass filter (i.e., the second low-pass filter process), and then smoothed with a 2 m window to take the 99.85% percentile as the wheel axle lateral acceleration evaluation index.

[0081] Based on the above embodiment, the method further includes: Performing Fourier transformation on the time domain signal of the vehicle body lateral acceleration to obtain a vehicle body lateral acceleration amplitude spectrum; Obtaining an evaluation index result of the lateral stability of the vehicle body according to the vehicle body lateral acceleration amplitude spectrum and the frequency range corresponding to the vehicle body lateral acceleration amplitude spectrum; Performing Fourier transform on the time domain signal of the vehicle body vertical acceleration to obtain a vehicle body vertical acceleration amplitude spectrum; An evaluation index result of the vertical stability of the vehicle body is obtained according to the vehicle body vertical acceleration amplitude spectrum and the frequency range corresponding to the vehicle body vertical acceleration amplitude spectrum.

[0082] In the present invention, the time domain signal of the vehicle body lateral acceleration is Fourier transformed to obtain the vehicle body lateral acceleration amplitude spectrum ( ), and then calculate the vehicle body lateral stability index as follows: ; ; .

[0083] The time domain signal of the vehicle body vertical acceleration is Fourier transformed to obtain the vehicle body vertical acceleration amplitude spectrum ( ), and then calculate the vehicle body vertical stability index as follows: ; ; ; Specifically, the calculation results of the corresponding indicators of wheel axle lateral force, wheel-rail vertical force, derailment coefficient, wheel weight reduction rate, frame lateral acceleration, lateral stability, and vertical stability can be referred to as shown in Tables 12 to 18: Table 12 Calculation results of axle lateral force

[0084] Table 13 Wheel-rail vertical force

[0085] Table 14 Derailment coefficient

[0086] Table 15 Wheel weight reduction rate

[0087] Table 16 Lateral acceleration

[0088] Table 17 Lateral stability

[0089] Table 18 Vertical stability

[0090] Finally, the distribution range and probability density function of wheel-rail vertical force, wheel-axle lateral force, derailment coefficient, wheel load reduction rate, frame lateral acceleration, lateral stability index, and vertical stability index are statistically analyzed. Figure 2 The distribution range diagram of the lateral stability index provided by the present invention can be referred to Figure 2 As shown, the range of lateral stability index is 1.2418 to 1.3454. Correspondingly, the range of wheel axle lateral force is 10.230 to 15.271 kN, the range of wheel-rail vertical force is 88.3833 to 90.2953 kN, the range of derailment coefficient is 0.0824 to 0.1258, the range of wheel weight reduction rate is 0.3865 to 0.4053, and the range of lateral acceleration is 1.0620 to 1.2561 m / s 2 , the vertical stability index ranges from 2.2455 to 2.2696. The probability density functions corresponding to each index are as follows: The probability density function of the lateral force on the wheel axle is: ; The probability density function of the wheel-rail vertical force is: ; The probability density function of the derailment coefficient is: ; The probability density function of wheel load reduction rate is: ; The probability density function of the lateral acceleration is: ; The probability density function of the horizontal stability index is: ; The probability density function of the vertical stability index is: .

[0091] The rail vehicle dynamics simulation system based on parameter change probability distribution provided by the present invention is described below. The rail vehicle dynamics simulation system based on parameter change probability distribution described below and the rail vehicle dynamics simulation method based on parameter change probability distribution described above can correspond to each other.

[0092] Figure 3 The schematic diagram of the structure of the rail vehicle dynamics simulation system based on parameter change probability distribution provided by the present invention is as follows: Figure 3 As shown, the present invention provides a rail vehicle dynamics simulation system based on parameter change probability distribution, including a variable combination sample acquisition module 301, a dynamics simulation model generation module 302 and a simulation module 303, wherein the variable combination sample acquisition module 301 is used to obtain variable combination samples by traversal combination based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, wherein the variable Latin hypercube sampling sample is constructed from test data of multiple bogie system random variables of different types; the dynamics simulation model generation module 302 is used to assign values ​​to corresponding elements in the dynamics simulation model according to the variable combination sample to obtain a new dynamics simulation model; the simulation module 303 is used to perform dynamic simulation on the EMU trailer to be evaluated according to the new dynamics simulation model to obtain the dynamics evaluation index result corresponding to the EMU trailer to be evaluated.

[0093] The rail vehicle dynamics simulation system based on parameter change probability distribution provided by the present invention performs rail vehicle dynamics simulation by analyzing the probability distribution characteristics of random changes in dynamic parameters of bogie components, and then obtains the distribution range and probability density function of railway vehicle dynamics evaluation indicators, so that the rail vehicle dynamics simulation results are more accurate, and the amount of data calculation in the simulation process is also reduced.

[0094] The system provided in the embodiment of the present invention is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for the specific process and detailed contents, which will not be repeated here.

[0095] Figure 4 A schematic diagram of the structure of an electronic device provided by the present invention, such as Figure 4As shown, the electronic device may include: a processor (Processor) 401, a communication interface (Communications Interface) 402, a memory (Memory) 403 and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404. The processor 401 may call the logic instructions in the memory 403 to execute a rail vehicle dynamics simulation method based on parameter change probability distribution, the method comprising: based on the wheel-rail profile and variable Latin hypercube sampling samples of the EMU trailer to be evaluated, obtaining variable combination samples by traversing combination mode, wherein the variable Latin hypercube sampling samples are constructed by test data of multiple bogie system random variables of different types; assigning values ​​to corresponding elements in the dynamics simulation model according to the variable combination samples to obtain a new dynamics simulation model; performing dynamics simulation on the EMU trailer to be evaluated according to the new dynamics simulation model to obtain the dynamics evaluation index result corresponding to the EMU trailer to be evaluated.

[0096] In addition, the logic instructions in the above-mentioned memory 403 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, 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, and the computer software product is stored in a storage medium, including a number of instructions for 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 method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0097] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the rail vehicle dynamics simulation method based on the parameter change probability distribution provided by the above-mentioned methods, the method including: based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, obtaining the variable combination sample by traversal combination method, wherein the variable Latin hypercube sampling sample is constructed by test data of multiple bogie system random variables of different types; according to the variable combination sample, assigning values ​​to the corresponding elements in the dynamic simulation model to obtain a new dynamic simulation model; according to the new dynamic simulation model, performing dynamic simulation on the EMU trailer to be evaluated to obtain the dynamic evaluation index result corresponding to the EMU trailer to be evaluated.

[0098] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the rail vehicle dynamics simulation method based on the parameter change probability distribution provided in the above-mentioned embodiments, the method comprising: based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, obtaining the variable combination sample by traversal combination method, wherein the variable Latin hypercube sampling sample is constructed by test data of multiple bogie system random variables of different types; according to the variable combination sample, assigning values ​​to the corresponding elements in the dynamics simulation model to obtain a new dynamics simulation model; according to the new dynamics simulation model, performing dynamic simulation on the EMU trailer to be evaluated to obtain the dynamics evaluation index result corresponding to the EMU trailer to be evaluated.

[0099] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0100] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0101] 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 make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rail vehicle dynamics simulation method based on parameter change probability distribution, characterized in that: include: Based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, a variable combination sample is obtained by traversing the combination method, wherein the variable Latin hypercube sampling sample is constructed by test data of random variables of multiple bogie systems of different types; According to the variable combination sample, assign values ​​to corresponding elements in the dynamics simulation model to obtain a new dynamics simulation model; According to the new dynamics simulation model, dynamics simulation is performed on the EMU trailer to be evaluated to obtain dynamics evaluation index results corresponding to the EMU trailer to be evaluated.

2. The rail vehicle dynamics simulation method based on parameter change probability distribution according to claim 1 is characterized in that: The multiple bogie system random variables include test data of multiple types of dynamic parameters, and the multiple types of dynamic parameters include the radial static stiffness of the axle box arm node, the axial static stiffness of the axle box arm node, the first speed damping force of the anti-snaking damper, the second speed damping force of the anti-snaking damper, the first speed damping force of the primary vertical damper, the second speed damping force of the primary vertical damper, the first speed damping force of the secondary vertical damper, the second speed damping force of the secondary vertical damper, the first speed damping force of the secondary lateral damper, and the second speed damping force of the secondary lateral damper.

3. The rail vehicle dynamics simulation method based on parameter change probability distribution according to claim 2 is characterized in that: The variable combination samples are obtained by traversing the combination method based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, including: Determining the variable distribution range of the bogie system random variable of each of the dynamic parameter types according to the minimum value and the maximum value corresponding to the test data of the bogie system random variable of each of the dynamic parameter types; According to the variable distribution range of the bogie system random variable of each of the dynamic parameter types, a range equalization sub-interval of the bogie system random variable of each of the dynamic parameter types is constructed, and based on the number of test data corresponding to the bogie system random variable within the range equalization sub-interval, a probability density function corresponding to the bogie system random variable of each of the dynamic parameter types is calculated; wherein the range equalization sub-interval is obtained by equally dividing the variable distribution range of the bogie system random variable; According to the variable distribution range and the probability density function, the variable distribution range of the bogie system random variable of each of the dynamic parameter types is segmented according to the number of sub-interval segmentations to obtain a plurality of sub-intervals corresponding to the bogie system random variables of each of the dynamic parameter types, wherein the probability of the bogie system random variables of each of the dynamic parameter types falling into each of the sub-intervals is equal, and the number of sub-interval segmentations is determined by the number of variable Latin hypercube sampling samples; Based on a sampling value obtained by randomly sampling each subinterval of the bogie system random variable of each dynamic parameter type, an initial sampling matrix is ​​constructed, wherein each row of the initial sampling matrix corresponds to a sampling value of the bogie system random variable of a different dynamic parameter type in each subinterval; Based on the number of subinterval divisions, construct a Latin hypercube matrix of a preset specification, wherein the elements in the Latin hypercube matrix are natural numbers greater than or equal to 1 and less than or equal to the number of subinterval divisions; According to the Latin hypercube matrix, the initial sampling matrix is ​​adjusted to obtain the variable Latin hypercube sampling samples, wherein each column of the variable Latin hypercube sampling samples is a sampling sample; Based on the traversal combination method, a set of variable combination samples is constructed by combining the wheel profile and the rail profile in the wheel-rail profile with the sampling samples, and all the wheel profiles, the rail profiles and the variable Latin hypercube sampling samples are traversed to obtain all the variable combination samples.

4. The rail vehicle dynamics simulation method based on parameter change probability distribution according to claim 3 is characterized in that: The step of assigning values ​​to corresponding elements in the dynamics simulation model according to the variable combination sample to obtain a new dynamics simulation model includes: Based on the subintervals corresponding to the random variables of the bogie system in the variable combination sample, corresponding elements in the dynamic simulation model are assigned values ​​according to the random variables of the bogie system in the variable combination sample to obtain the new dynamic simulation model.

5. The rail vehicle dynamics simulation method based on parameter change probability distribution according to claim 1, characterized in that: The step of performing dynamic simulation on the EMU trailer to be evaluated according to the new dynamic simulation model to obtain a dynamic evaluation index result corresponding to the EMU trailer to be evaluated includes: According to the new dynamic simulation model, a dynamic simulation is performed on the EMU trailer to be evaluated to obtain an initial dynamic time domain signal corresponding to the EMU trailer to be evaluated, wherein the initial dynamic time domain signal includes time domain signals of a derailment coefficient, a wheel load reduction rate, a wheel-rail vertical force, a wheel axle lateral force, a vehicle body lateral acceleration, and a vehicle body vertical acceleration; performing a first low-pass filtering process on the time domain signals corresponding to the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force respectively, performing smoothing process on the time domain signals of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force after the first low-pass filtering based on a preset window, and obtaining evaluation index results of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force respectively according to preset percentiles in the time domain signals of the derailment coefficient, the wheel-weight reduction rate, the wheel-rail vertical force and the wheel-axle lateral force after the smoothing process; A second low-pass filtering process is performed on the time domain signal of the wheel axle lateral force, and the time domain signal of the wheel axle lateral force after the second low-pass filtering is smoothed based on a preset window, and an evaluation index result of the wheel axle lateral acceleration is obtained according to a preset percentile in the time domain signal of the wheel axle lateral force after the smoothing process.

6. The rail vehicle dynamics simulation method based on parameter change probability distribution according to claim 5, characterized in that: The method further comprises: Performing Fourier transformation on the time domain signal of the vehicle body lateral acceleration to obtain a vehicle body lateral acceleration amplitude spectrum; Obtaining an evaluation index result of the lateral stability of the vehicle body according to the vehicle body lateral acceleration amplitude spectrum and the frequency range corresponding to the vehicle body lateral acceleration amplitude spectrum; Performing Fourier transform on the time domain signal of the vehicle body vertical acceleration to obtain a vehicle body vertical acceleration amplitude spectrum; An evaluation index result of the vertical stability of the vehicle body is obtained according to the vehicle body vertical acceleration amplitude spectrum and the frequency range corresponding to the vehicle body vertical acceleration amplitude spectrum.

7. A rail vehicle dynamics simulation system based on parameter change probability distribution, characterized in that: include: A variable combination sample acquisition module is used to acquire variable combination samples by traversing the combination method based on the wheel-rail profile of the EMU trailer to be evaluated and the variable Latin hypercube sampling sample, wherein the variable Latin hypercube sampling sample is constructed from the test data of random variables of multiple bogie systems of different types; A dynamics simulation model generation module is used to assign values ​​to corresponding elements in the dynamics simulation model according to the variable combination samples to obtain a new dynamics simulation model; The simulation module is used to perform dynamic simulation on the EMU trailer to be evaluated according to the new dynamic simulation model to obtain the dynamic evaluation index results corresponding to the EMU trailer to be evaluated.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the rail vehicle dynamics simulation method based on parameter change probability distribution as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the rail vehicle dynamics simulation method based on parameter change probability distribution as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the rail vehicle dynamics simulation method based on parameter change probability distribution as claimed in any one of claims 1 to 6 is implemented.

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