Train dynamic characteristic detection method and system under wind tunnel experiment

Abstract

The invention discloses a train dynamic characteristic detection method and system under a wind tunnel experiment. The train dynamic characteristic detection method under the wind tunnel experiment comprises the following steps: S110, constructing a wind tunnel experiment environment; s120, designing a train scaling model; s130, configuring a dynamic characteristic detection system through a wind tunnel experiment environment and a train scaling model; s140, carrying out reliability verification on the dynamic characteristic value; s150, wind tunnel experiment parameters are adjusted, and analysis and optimization are carried out according to the result of the dynamic characteristic detection system; according to the invention, a multi-working-condition detection method is provided for train characteristic detection under a wind tunnel experiment, and train designers can comprehensively evaluate the aerodynamic performance of the train and optimize the design scheme.

Description

Technical Field

[0001] The present invention relates to aerodynamics technology, and particularly to a method for detecting the dynamic characteristics of a train under wind tunnel experiments. Background Art

[0002] With the development of high-speed trains, aerodynamic performance has become an important factor affecting the operation efficiency, safety and economy of trains. During high-speed operation, the air resistance, lift and lateral force acting on the train increase significantly, which not only affects energy consumption, but may also lead to a decrease in operation stability or an increase in noise. Therefore, studying the dynamic characteristics of trains and optimizing the shape design to reduce air resistance and improve operation stability are key links in the design of high-speed trains. At present, wind tunnel experiments are an important method for analyzing the aerodynamic performance of trains, which can simulate the flow field characteristics and aerodynamic forces acting on the train under different working conditions. However, traditional wind tunnel experiments mainly focus on single aerodynamic parameters, lack a systematic evaluation of the comprehensive performance under multiple working conditions, and there are certain limitations in model scale reduction design, sensor arrangement and data analysis methods. Therefore, there is an urgent need for a high-precision and multi-dimensional method for detecting the dynamic characteristics of trains to comprehensively evaluate the aerodynamic performance of trains and provide a scientific basis for optimization design. Summary of the Invention

[0003] The present invention provides a method for detecting the quality of 3D printed engineering materials, including:

[0004] S110. Construct a wind tunnel experimental environment;

[0005] S120. Design a scaled-down model of the train;

[0006] S130. Configure a dynamic characteristic detection system through the wind tunnel experimental environment and the scaled-down model of the train;

[0007] S140. Verify the reliability of the dynamic characteristic eigenvalues;

[0008] S150. Adjust the wind tunnel experimental parameters and analyze and optimize according to the results of the dynamic characteristic detection system.

[0009] A method for detecting the quality of 3D printed engineering materials as described above, wherein the construction of the wind tunnel experimental environment includes the following sub-steps:

[0010] Wind tunnel device;

[0011] Installation of the train model;

[0012] Sensor arrangement;

[0013] Calibration of the wind tunnel environment flow field.

[0014] A method for quality inspection of 3D printing engineering materials as described above, wherein the train scale model needs to meet dynamic similarity and aerodynamic characteristic consistency. By ensuring the same Reynolds number, the flow attachment, separation, and wake characteristics of the train model are simulated. Through the similarity of pressure coefficients, the local pressure distribution of the scale model is ensured to be consistent with that of the actual train.

[0015] A method for quality inspection of 3D printing engineering materials as described above, wherein the method for configuring the dynamic characteristic detection system through the wind tunnel experiment environment and the train scale model includes the following sub-steps:

[0016] Aerodynamic force measurement;

[0017] Surface pressure distribution measurement;

[0018] Calculate the dynamic characteristic eigenvalue through the aerodynamic force and the surface pressure.

[0019] A method for quality inspection of 3D printing engineering materials as described above, wherein the drag coefficient reflects the overall air resistance characteristics of the train, the lift coefficient and the side force coefficient reflect the vertical stability and lateral wind resistance of the train, and the pressure coefficient reflects the surface pressure concentration area of the train. Combining the three aerodynamic coefficients and the surface pressure distribution describes the overall aerodynamic performance of the train.

[0020] The present invention also provides a train dynamic characteristic detection system under wind tunnel experiments, including: a wind tunnel experiment environment module, a train scale model module, a dynamic characteristic detection system module, and an analysis and optimization module.

[0021] Wind tunnel experiment environment module: Construct a wind tunnel experiment environment;

[0022] Train scale model module: Design a train scale model;

[0023] Dynamic characteristic detection system module: Configure a dynamic characteristic detection system through the wind tunnel experiment environment and the train scale model;

[0024] Reliability verification module: Conduct reliability verification on the dynamic characteristic eigenvalue;

[0025] Analysis and optimization module: Adjust the wind tunnel experiment parameters and perform analysis and optimization according to the results of the dynamic characteristic detection system.

[0026] A train dynamic characteristic detection system under wind tunnel experiments as described above, wherein the construction of the wind tunnel experiment environment includes the following sub-steps:

[0027] Wind tunnel device;

[0028] Train model installation;

[0029] Sensor arrangement;

[0030] Wind tunnel environmental flow field calibration.

[0031] A train dynamic characteristic detection system under wind tunnel experiments as described above, in which the train scaled model needs to meet dynamic similarity and aerodynamic characteristic consistency. By ensuring the same Reynolds number, the flow attachment, separation and wake characteristics of the train model are simulated. Through the similarity of pressure coefficients, the local pressure distribution of the scaled model is ensured to be consistent with that of the actual train.

[0032] A train dynamic characteristic detection system under wind tunnel experiments as described above, in which the method of configuring the dynamic characteristic detection system through the wind tunnel experimental environment and the train scaled model includes the following sub-steps:

[0033] Aerodynamic force measurement;

[0034] Surface pressure distribution measurement;

[0035] Calculate the dynamic characteristic eigenvalue through the aerodynamic force and the surface pressure.

[0036] A train dynamic characteristic detection system under wind tunnel experiments as described above, in which the drag coefficient reflects the overall air resistance characteristics of the train, the lift coefficient and the side force coefficient reflect the vertical stability and lateral wind resistance of the train, and the pressure coefficient reflects the surface pressure concentration area of the train. Combining the three aerodynamic coefficients and the surface pressure distribution describes the overall aerodynamic performance of the train.

[0037] The beneficial effects achieved by the present invention are as follows: It provides a detection method with multiple working conditions for the train characteristic detection under wind tunnel experiments, and train designers can comprehensively evaluate the aerodynamic performance of the train and optimize the design scheme. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a method flow chart of a train dynamic characteristic detection under wind tunnel experiments provided by Embodiment 1 of the present application;

[0040] Figure 2 It is a system schematic diagram of a train dynamic characteristic detection under wind tunnel experiments provided by Embodiment 2 of the present application. Detailed Embodiments

[0041] Combined with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0042] Embodiment 1

[0043] As Figure 1 shown, Embodiment 1 of the present application provides a method and system for detecting the dynamic characteristics of a train under a wind tunnel experiment

[0044] Step S110: Construct a wind tunnel experiment environment;

[0045] The construction of the wind tunnel experiment environment is the basis for detecting the dynamic characteristics of the train. Its purpose is to simulate the aerodynamic effects and flow field characteristics during the train operation, including air resistance, lift, lateral force, and air flow distribution. The method for constructing the wind tunnel experiment environment specifically includes the following sub-steps:

[0046] Step S111: Wind tunnel device;

[0047] Adopt a closed-circuit return wind tunnel device with low turbulence and high stability to ensure the uniformity of the flow field in the wind tunnel and the repeatability of experimental data; be equipped with a fan system with adjustable wind speed, and the wind speed range covers 0 - 80 m / s to meet the simulation requirements of different train operation speeds; install a constant temperature control system to keep the temperature in the wind tunnel stable (20 ± 1 °C) to avoid the interference of the environmental temperature on the aerodynamic characteristics; set a test area with a long enough experimental section length to accommodate the train model and ensure the full development of the flow field; install smooth guide plates on both sides of the test area to reduce the influence of the boundary layer effect on the experimental results.

[0048] Step S112: Installation of the train model;

[0049] Fix the train model on a multi-degree-of-freedom adjustment platform. The adjustment platform has the following adjustment modes: angle of attack adjustment: can accurately adjust the angle of attack of the train model, with a range of -10° to 10° and a step accuracy of 0.1°; sideslip angle adjustment: can adjust the sideslip of the train model, with a range of -15° to 15°; height adjustment: the platform can adjust the height according to the size of the train model to make it located at the center position of the wind tunnel flow field, so as to simulate different operating conditions.

[0050] Step S113: Sensor arrangement;

[0051] Install a three-component force sensor at the bottom of the train model to measure the drag, lift, and lateral forces acting on the model, with a sensor accuracy better than 0.1 N. Arrange a high-density pressure sensor array on the train surface. The sensor arrangement positions include the front of the train, the middle of the car body, the rear of the train, and the connection part of the car body. Each sensor records the local pressure value for calculating the surface pressure distribution and pressure coefficient. Arrange PIV equipment in the wake region of the train to capture the flow field velocity distribution and vortex structure. Using fluorescent particles and laser illumination technology, obtain the position of the wake separation point and vortex characteristics. Synchronously record the force, pressure, and flow field data through a high-speed data acquisition device, with a sampling frequency not less than 1 kHz to ensure the timeliness and integrity of the experimental data.

[0052] Step S114: Calibrate the flow field in the wind tunnel environment;

[0053] Use high-precision flow field detection equipment to calibrate the flow field in the wind tunnel experimental area, detect the uniformity of the wind speed distribution, ensure that the wind speed deviation in the experimental section does not exceed ±0.5 m / s, check whether the flow field turbulence meets the expectations (such as the turbulence is lower than 1%), ensure the stability of the experimental environment, adjust the guide vanes and the structure of the test area to eliminate factors that may cause flow field distortion, and record the reference flow field data as a reference for experimental data correction.

[0054] Step S120: Design a scaled-down train model;

[0055] Use lightweight and high-strength materials (such as carbon fiber) to ensure the stability of the model and match the real shape. The surface is coated with a micro-roughness coating to simulate the surface characteristics of an actual train. The front of the train needs to be designed with an optimized streamline shape to reduce flow separation, and the transition area at the connection part of the car body ensures the similarity of the wake.

[0056] The scaled-down train model needs to meet dynamic similarity and aerodynamic characteristic consistency. By ensuring the same Reynolds number, simulate the flow attachment, separation, and wake characteristics of the train model. Through the similarity of the pressure coefficient, ensure that the local pressure distribution of the scaled-down model is consistent with that of the actual train. Design the scaled-down model according to Reynolds number similarity and pressure coefficient similarity. Specifically, use the formula: Design a scaled-down train model, where Re m represents Reynolds number similarity, ρ represents air density, υ represents the characteristic wind speed, L represents the characteristic length, representing the geometric size of the train, μ represents the air dynamic viscosity, and the higher the viscosity, the more significant the tangential force of the fluid on the train surface. Among them, C p represents pressure coefficient similarity, p represents the pressure at a certain point on the train surface, and p0 represents the reference static pressure, that is, the static air pressure in the experimental environment.

[0057] Step S130: Configure a dynamic characteristic detection system through the wind tunnel experimental environment and the scaled-down train model;

[0058] Through the wind tunnel experimental environment and the scaled-down train model, measure the aerodynamic forces acting on the train, including drag, lift, and lateral force, capture the surface pressure distribution, and analyze the pressure coefficient, so as to provide a basis for optimizing the design. Specifically, it includes the following sub-steps:

[0059] Step S131, Aerodynamic force measurement;

[0060] Measure the three major aerodynamic forces of the train model in the wind tunnel flow field through the three-component force sensors installed at the bottom: drag, lift, and lateral force. Specifically, use the formula:

[0061] Calculate the three major aerodynamic forces, where C D represents the drag aerodynamic coefficient, C L represents the lift aerodynamic coefficient, represents the lateral force aerodynamic coefficient, D represents drag, L represents lift, F y represents the lateral force, ρ represents the air density, υ represents the wind speed, and A represents the frontal area of the train.

[0062] Step S132, Surface pressure distribution measurement;

[0063] Record the pressure values at each point in real time through the high-density pressure sensor array arranged on the train surface, so as to calculate the pressure coefficient. Specifically, use the formula: Calculate the pressure coefficient, where C p (x,y) represents the pressure coefficient, p(x,y) represents the air pressure at a certain point on the train surface, p0 represents the reference static pressure of the static air, ρ represents the air density, and υ represents the wind speed.

[0064] Step S133, Calculate the dynamic characteristic eigenvalues through the aerodynamic force and the surface pressure;

[0065] The drag coefficient reflects the overall air resistance characteristics of the train. The lift coefficient and the lateral force coefficient reflect the vertical stability and the lateral wind resistance ability of the train. The pressure coefficient reflects the pressure concentration area on the train surface, providing a basis for optimizing the shape design. The above measurement and analysis results provide key data support for the train shape design and the optimization of the running performance. In order to comprehensively evaluate the aerodynamic performance of the train, define the dynamic characteristic eigenvalues, combine the three major aerodynamic coefficients and the surface pressure distribution, and describe the overall aerodynamic performance of the train. Specifically, use the formula: Calculate the dynamic

[0066] characteristic eigenvalues, where C composite represents the dynamic characteristic eigenvalue, ω D 、ω L 、 ω p respectively represent the weights of the drag, lift, and lateral force aerodynamic coefficients, CD represents the drag aerodynamic coefficient, C L represents the lift aerodynamic coefficient, represents the side force aerodynamic coefficient, represents the standard deviation of the surface pressure distribution, reflecting the uniformity of the surface pressure distribution. The smaller the standard deviation, the more uniform the pressure distribution, which is beneficial to reducing the aerodynamic instability, represents the average value of the pressure coefficient.

[0067] Step S140: Conduct reliability verification on the dynamic characteristic eigenvalues;

[0068] The dynamic characteristic eigenvalues calculated through aerodynamic forces and surface pressure. To ensure their reliability, reliability verification is conducted on the dynamic characteristic eigenvalues by combining theoretical calculations and experimental data, which specifically includes the following sub-steps:

[0069] Step S141: Calculate the theoretical values;

[0070] According to the aerodynamics theory, combined with the input parameters of the wind tunnel experiment (such as wind speed, angle of attack, train geometric parameters), calculate the theoretical values of the dynamic characteristic eigenvalues as the benchmark for experimental value verification. Specifically, use the formula: Calculate the theoretical values of the dynamic characteristic eigenvalues, where C theory represents the theoretical value, ω D , ω L , ω p respectively represent the weights of the drag, lift, and side force aerodynamic coefficients, C D,theory represents the drag aerodynamic coefficient, C L,theory represents the lift aerodynamic coefficient, represents the side force aerodynamic coefficient, represents the standard deviation of the surface pressure distribution. The theoretical values of each aerodynamic force are represented by the formula: is represented, where C X,theory represents the theoretical value of each aerodynamic force, X theory represents the aerodynamic force calculated by theoretical calculation (such as D theory , L theory , Y theory ), ρ represents the air density, V represents the wind speed, and A represents the frontal area.

[0071] Step S142: Compare the experimental values with the theoretical values and calculate the error rate;

[0072] Compare and analyze the dynamic characteristic eigenvalues calculated through the wind tunnel experiment with the theoretical values, and evaluate the error range of the results. Specifically, use the formula: Calculate the error rate, where represents the error rate, C compositeRepresents the experimental value, C theory Represents the theoretical value. When the error rate is less than the preset threshold (5%), the dynamic characteristic eigenvalue is reliable, and the wind tunnel experiment parameters can be adjusted to analyze and optimize the results. If it exceeds the threshold, the experimental conditions and theoretical model need to be rechecked.

[0073] Step S150: Adjust the wind tunnel experiment parameters and analyze and optimize according to the results of the dynamic characteristic detection system;

[0074] For the train model established through wind tunnel experiments, adjust the angle of attack and sideslip angle, and study the variation law of aerodynamic forces of the train under different operating conditions. The range of the angle of attack is: α ∈ [-10°, 10°], and the range of the sideslip angle is: β ∈ [-15°, 15°]. Measure the aerodynamic forces under different working conditions and calculate the corresponding coefficient C D , C L , For the dynamic characteristic eigenvalues calculated by different wind tunnel experiment parameters, train designers can comprehensively evaluate the aerodynamic performance of the train and optimize the design scheme. When C composite < 0.2, it indicates that the aerodynamic performance of the train is excellent; when 0.2 ≤ C composite < 0.3, it indicates that the aerodynamic performance of the train is good; when C composite ≥ 0.3, it indicates that the aerodynamic performance of the train needs to be optimized.

[0075] Embodiment 2

[0076] As Figure 2 shown, Embodiment 1 of the present application provides a train dynamic characteristic detection system under wind tunnel experiments, including:

[0077] Wind tunnel experiment environment module 21: Construct a wind tunnel experiment environment;

[0078] The construction of the wind tunnel experiment environment is the basis for the detection of train dynamic characteristics. Its purpose is to simulate the aerodynamic effects and flow field characteristics during the train operation, including air resistance, lift, lateral force, and airflow distribution. The method for constructing the wind tunnel experiment environment specifically includes the following sub-steps:

[0079] Wind tunnel device module: Wind tunnel device;

[0080] Adopt a closed-loop return wind tunnel device with low turbulence intensity and high stability to ensure the uniformity of the flow field in the wind tunnel and the repeatability of experimental data; be equipped with a fan system with adjustable wind speed, and the wind speed range covers 0–80 m / s to meet the simulation requirements of different train running speeds; install a constant temperature control system to keep the temperature in the wind tunnel stable (20±1°C) to avoid the interference of environmental temperature on aerodynamic characteristics; set up a test area with a long enough experimental section length to accommodate the train model and ensure the full development of the flow field; install smooth flow deflectors on both sides of the test area to reduce the influence of boundary layer effects on experimental results.

[0081] Train model module: Installation of train model;

[0082] Fix the train model on a multi-degree-of-freedom adjustment platform. The adjustment platform has angle-of-attack adjustment: it can accurately adjust the angle of attack of the train model, with a range of -10° to 10° and a step accuracy of 0.1°; sideslip angle adjustment: it can adjust the sideslip of the train model, with a range of -15° to 15°; height adjustment: the platform can adjust the height according to the size of the train model to make it located at the center position of the wind tunnel flow field, with three adjustment modes to simulate different operating conditions.

[0083] Sensor module: Arrangement of sensors;

[0084] Install a three-component force sensor at the bottom of the train model to measure the drag, lift, and lateral forces on the model, with a sensor accuracy better than 0.1 N; arrange a high-density pressure sensor array on the train surface. The sensor arrangement positions include the front of the head, the middle of the car body, the rear of the car body, and the connection part of the car body. Each sensor records the local pressure value for calculating the surface pressure distribution and pressure coefficient; arrange PIV equipment in the wake area of the train to capture the flow field velocity distribution and vortex structure, and use fluorescent particles and laser illumination technology to obtain the position of the wake separation point and vortex characteristics; synchronously record force, pressure, and flow field data through a high-speed data acquisition device, with a sampling frequency not less than 1 kHz to ensure the timeliness and integrity of experimental data.

[0085] Flow field calibration module: Calibration of the wind tunnel environmental flow field;

[0086] Use high-precision flow field detection equipment to calibrate the flow field in the wind tunnel experimental area, detect the uniformity of the wind speed distribution, ensure that the wind speed deviation in the experimental section does not exceed ±0.5 m / s, check whether the flow field turbulence intensity meets the expectations (such as the turbulence intensity is lower than 1%), ensure the stability of the experimental environment, adjust the flow deflectors and the structure of the test area to eliminate factors that may cause flow field distortion, and record the flow field reference data as a reference for experimental data correction.

[0087] Train scale model module 22: Design of train scale model;

[0088] Use lightweight and high-strength materials (such as carbon fiber) to ensure the stability of the model and match the real shape. The surface is coated with a micro-roughness coating to simulate the surface characteristics of the actual train. The front of the train needs to be designed with an optimized streamline shape to reduce flow separation, and the transition area at the connection of the car body ensures wake similarity.

[0089] The scaled model of the train needs to meet dynamic similarity and aerodynamic characteristic consistency. By ensuring the same Reynolds number, the flow attachment, separation, and wake characteristics of the train model are simulated. Through the similarity of pressure coefficients, the local pressure distribution of the scaled model is ensured to be consistent with that of the actual train. Design the scaled model according to Reynolds number similarity and pressure coefficient similarity. Specifically, use the formula: Design the scaled model of the train, where Re m represents Reynolds number similarity, ρ represents air density, υ represents characteristic wind speed, L represents characteristic length, which represents the geometric size of the train, μ represents air dynamic viscosity. The higher the viscosity, the more significant the tangential force of the fluid on the train surface. Among them, C p represents pressure coefficient similarity, p represents the pressure at a certain point on the train surface, and p0 represents the reference static pressure, that is, the static air pressure in the experimental environment.

[0090] Dynamic characteristic detection system module 23: Configure the dynamic characteristic detection system through the wind tunnel experimental environment and the scaled model of the train;

[0091] Through the wind tunnel experimental environment and the scaled model of the train, measure the aerodynamic forces on the train, including drag, lift, and lateral force, capture the surface pressure distribution, and analyze the pressure coefficient, so as to provide a basis for optimization design. Specifically, it includes the following sub-steps:

[0092] Aerodynamic force module: Measure aerodynamic forces;

[0093] Measure the three major aerodynamic forces of the train model in the wind tunnel flow field through the three-component force sensor installed at the bottom: drag, lift, and lateral force. Specifically, use the formula:

[0094] Calculate the three major aerodynamic forces, where C D represents the drag aerodynamic force coefficient, C L represents the lift aerodynamic force coefficient, represents the lateral force aerodynamic force coefficient, D represents drag, L represents lift, F y represents lateral force, ρ represents air density, υ represents wind speed, and A represents the frontal area of the train.

[0095] Surface pressure module: Measure surface pressure distribution;

[0096] Through the high-density pressure sensor array arranged on the train surface, record the pressure values at each point in real time, and then calculate the pressure coefficient. Specifically, use the formula: Calculate the pressure coefficient, where C p (x, y) represents the pressure coefficient, p(x, y) represents the air pressure at a certain point on the train surface, p0 represents the reference static pressure of the stationary air, ρ represents the air density, and υ represents the wind speed.

[0097] Dynamic characteristic eigenvalue calculation module: Calculate the dynamic characteristic eigenvalue through aerodynamic force and surface pressure;

[0098] The drag coefficient reflects the overall air resistance characteristics of the train. The lift coefficient and the side force coefficient reflect the vertical stability and lateral wind resistance of the train. The pressure coefficient reflects the pressure concentration area on the train surface, providing a basis for optimizing the shape design. The above measurement and analysis results provide key data support for the train shape design and operation performance optimization. In order to comprehensively evaluate the aerodynamic performance of the train, the dynamic characteristic eigenvalue is defined, combining the three aerodynamic coefficients and the surface pressure distribution to describe the overall aerodynamic performance of the train. Specifically, use

[0099] Formula: Calculate the dynamic characteristic eigenvalue, where C composite represents the dynamic characteristic eigenvalue, ω D 、ω L 、 ω p respectively represent the weights of the drag, lift, and side force aerodynamic coefficients, C D represents the drag aerodynamic coefficient, C L represents the lift aerodynamic coefficient, represents the side force aerodynamic coefficient, represents the standard deviation of the surface pressure distribution, reflecting the uniformity of the surface pressure distribution. The smaller the standard deviation, the more uniform the pressure distribution, which is beneficial to reducing the instability of the aerodynamic force, represents the average value of the pressure coefficient.

[0100] Reliability verification module 24: Verify the reliability of the dynamic characteristic eigenvalue;

[0101] The dynamic characteristic eigenvalue calculated through aerodynamic force and surface pressure. To ensure its reliability, the reliability of the dynamic characteristic eigenvalue is verified by combining theoretical calculations and experimental data, specifically including the following sub-steps:

[0102] Theoretical value calculation module: Calculate the theoretical value;

[0103] According to the aerodynamic theory, combined with the input parameters of the wind tunnel experiment (such as wind speed, angle of attack, train geometric parameters), calculate the theoretical value of the dynamic characteristic eigenvalue as the benchmark for experimental value verification. Specifically, use the formula: Calculate the theoretical value of the dynamic characteristic eigenvalue, where C theory represents the theoretical value, ωD and ω L and ω p represent the weights of the aerodynamic coefficients of drag, lift, and side force respectively. C D,theory represents the aerodynamic coefficient of drag, C L,theory represents the aerodynamic coefficient of lift, represents the aerodynamic coefficient of side force, represents the standard deviation of the surface pressure distribution. The theoretical values of each aerodynamic force are represented by the formula: where C X,theory represents the theoretical value of each aerodynamic force, X theory represents the aerodynamic force calculated by theoretical calculation (such as D theory , L theory , Y theory ), ρ represents the air density, V represents the wind speed, and A represents the frontal area.

[0104] Error rate calculation module: Compare the experimental value with the theoretical value and calculate the error rate;

[0105] Compare and analyze the dynamic characteristic eigenvalues obtained by wind tunnel experiments with the theoretical values, and evaluate the error range of the results. Specifically, use the formula: to calculate the error rate, where represents the error rate, C composite represents the experimental value, C theory represents the theoretical value. When the error rate is less than the preset threshold (5%), the dynamic characteristic eigenvalues are reliable, and the wind tunnel experiment parameters can be adjusted to analyze and optimize the results; if it exceeds the threshold, the experimental conditions and theoretical model need to be rechecked.

[0106] Analysis and optimization module 25: Adjust the wind tunnel experiment parameters and analyze and optimize according to the results of the dynamic characteristic detection system;

[0107] For the train model established through wind tunnel experiments, adjust the angle of attack and sideslip angle, study the variation law of the aerodynamic force of the train under different operating conditions. The range of the angle of attack is: α ∈ [-10°, 10°], and the range of the sideslip angle is: β ∈ [-15°, 15°]. Measure the aerodynamic force under different working conditions and calculate the corresponding coefficients C D , C L , For the dynamic characteristic eigenvalues calculated by different wind tunnel experiment parameters, train designers can comprehensively evaluate the aerodynamic performance of the train and optimize the design scheme. When C composite < 0.2, it indicates that the aerodynamic performance of the train is excellent; when 0.2 ≤ C composite < 0.3, it indicates that the aerodynamic performance of the train is good; when C composite ≥ 0.3, it indicates that the aerodynamic performance of the train needs to be optimized.

[0108] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting train dynamic characteristics under wind tunnel test, characterized in that: include: S110, build wind tunnel experimental environment; S120. Design a scaled model of a train; S130, configure a dynamic characteristics detection system through a wind tunnel test environment and a scaled train model; S140, verifying the reliability of the dynamic characteristic characteristic value; S150. Adjust wind tunnel test parameters and perform analysis and optimization based on the results of the dynamic characteristics detection system.

2. The method for detecting train dynamic characteristics in a wind tunnel test according to claim 1, characterized in that: The construction of the wind tunnel experimental environment includes the following sub-steps: Wind tunnel apparatus; Train model installation; Sensor placement; Wind tunnel environmental flow field calibration.

3. The method for detecting train dynamic characteristics in a wind tunnel test according to claim 1, characterized in that: The scaled-down model of the train must meet the requirements of dynamic similarity and consistency of aerodynamic characteristics. By ensuring the same Reynolds number, the flow attachment, separation and wake characteristics of the train model can be simulated. By ensuring the similarity of the pressure coefficient, the local pressure distribution of the scaled-down model is consistent with that of the actual train.

4. The method for detecting train dynamic characteristics in a wind tunnel test according to claim 1, characterized in that: The method for configuring a dynamic characteristics detection system using a wind tunnel test environment and a scaled train model includes the following sub-steps: Aerodynamic force measurements; Surface pressure distribution measurement; The dynamic characteristic characteristic values ​​are calculated from the aerodynamic force and surface pressure.

5. The method for detecting train dynamic characteristics in a wind tunnel test according to claim 4, characterized in that: The drag coefficient reflects the overall air resistance characteristics of the train, the lift coefficient and lateral force coefficient reflect the vertical stability and lateral wind resistance of the train, and the pressure coefficient reflects the pressure concentration area on the train surface. The three aerodynamic coefficients and surface pressure distribution are combined to describe the overall aerodynamic performance of the train.

6. A train dynamic characteristics detection system under wind tunnel test, characterized in that: include: Wind tunnel experiment environment module: build wind tunnel experiment environment; Train scale model module: design train scale model; Power characteristics detection system module: configure the power characteristics detection system through the wind tunnel test environment and the train scale model; Reliability verification module: reliability verification of dynamic characteristic eigenvalues; Analysis and optimization module: adjust wind tunnel test parameters and perform analysis and optimization based on the results of the dynamic characteristics detection system.

7. A train dynamic characteristics detection system under wind tunnel test as claimed in claim 6, characterized in that: The construction of the wind tunnel experimental environment includes the following sub-steps: Wind tunnel apparatus; Train model installation; Sensor placement; Wind tunnel environment flow field calibration.

8. The train dynamic characteristics detection system under wind tunnel test as claimed in claim 6, characterized in that: The scaled-down model of the train must meet the requirements of dynamic similarity and consistency of aerodynamic characteristics. By ensuring the same Reynolds number, the flow attachment, separation and wake characteristics of the train model can be simulated. By ensuring the similarity of the pressure coefficient, the local pressure distribution of the scaled-down model is consistent with that of the actual train.

9. The train dynamic characteristics detection system under wind tunnel test as claimed in claim 6, characterized in that: The method for configuring a dynamic characteristics detection system using a wind tunnel test environment and a scaled train model includes the following sub-steps: Aerodynamic force measurements; Surface pressure distribution measurement; The dynamic characteristic characteristic values ​​are calculated from the aerodynamic force and surface pressure.

10. A train dynamic characteristics detection system under wind tunnel test as claimed in claim 9, characterized in that: The drag coefficient reflects the overall air resistance characteristics of the train, the lift coefficient and lateral force coefficient reflect the vertical stability and lateral wind resistance of the train, and the pressure coefficient reflects the pressure concentration area on the train surface. The three aerodynamic coefficients and surface pressure distribution are combined to describe the overall aerodynamic performance of the train.

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