A high-speed maglev train cross-wind risk grading method, device and medium

Abstract

The application discloses a high-speed maglev train crosswind risk grading method, equipment and medium. The method first constructs an aerodynamic calculation model of the high-speed maglev train, and calculates the surface pressure distribution of the train body according to the train running speed and the crosswind speed applied to the train through the aerodynamic calculation model. Then, the surface pressure of the train is calculated by integration according to the pressure distribution, and corresponding lift data and lateral force data are obtained. The target excitation load is constructed according to the lift data and the lateral force data. After the dynamic model of the high-speed maglev train is constructed, the target excitation load is input into the dynamic model for calculation, and the suspension gap response data of the train body is obtained. Then, the crosswind risk grade of the train can be determined according to the suspension gap response data. The application can combine aerodynamic simulation and dynamic simulation to calculate the suspension gap, and realize the grading judgment of the crosswind risk of the high-speed maglev train through the dynamic change of the suspension gap.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of rail transit technology, and in particular to a method, device, and medium for classifying crosswind risk of high-speed maglev trains. Background Technology

[0002] High-speed maglev trains, as an important development direction for next-generation high-speed rail transit equipment, possess advantages such as high operating speed, strong climbing ability, low vibration and noise, and good operational stability. Unlike traditional wheel-rail trains, maglev trains rely on an electromagnetic levitation system to maintain a non-contact operating state between the train body and the track. The levitation gap is typically kept within a small range. When external aerodynamic loads change, the levitation system needs to dynamically adjust in a timely manner to ensure train operational stability. Therefore, in the design and safety assessment of high-speed maglev trains, in addition to considering conventional aerodynamic indicators such as aerodynamic drag, lift, and lateral force, it is also necessary to further consider the impact of aerodynamic disturbances on the operating state of the levitation system. Related research shows that crosswinds affect the aerodynamic performance of high-speed trains, and the bottom structure of high-speed maglev trains also has a significant impact on aerodynamic lift, indicating a close relationship between crosswind aerodynamic loads and the operational stability of maglev trains. In addition to open-track crosswind environments, high-speed maglev trains may also face complex situations such as low-vacuum environments, tunnel operating environments, changes in track wind environments, and random disturbances under higher speed conditions. Under these conditions, air resistance, aerodynamic loads, pressure fluctuations, and vehicle motion response may all change, further affecting train operation safety. Existing studies have analyzed the air resistance characteristics of high-speed maglev trains under low vacuum conditions and the impact of aerodynamic loads on conventional high-speed maglev systems when passing through tunnels, demonstrating that aerodynamic load and safety performance assessments are necessary for high-speed maglev trains under different operating environments. However, most current research still focuses primarily on indicators such as air resistance, lift, lateral force, pressure fluctuations, or operational stability, paying insufficient attention to the unique dynamic fluctuation characteristics of the suspension gap in maglev trains.

[0003] Currently, crosswind safety assessment methods for high-speed maglev trains mainly rely on computational fluid dynamics analysis of aerodynamic parameters such as lift, lateral force, and rolling moment generated by the train under crosswind conditions, or on vehicle dynamics models to study train stability and dynamic response characteristics. While these methods can reflect the train's operating status from different perspectives, they still have certain limitations. On the one hand, aerodynamic analysis and vehicle dynamics analysis are independent of each other, failing to consider the dynamic coupling between aerodynamic forces and the suspension system. On the other hand, existing evaluation indicators do not take into account the core parameter of suspension clearance, and there is a lack of risk grading assessment methods that can combine crosswind speed, aerodynamic changes, and the dynamic response of suspension clearance. Consequently, existing evaluation results cannot accurately reflect the actual operating status of the train under crosswind conditions, making it difficult to achieve precise quantitative assessment and safety warning of train operation risks. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0005] This application provides a method, device, and medium for classifying crosswind risk of high-speed maglev trains. It can combine aerodynamic simulation and dynamic simulation to calculate the suspension gap and realize the classification and assessment of crosswind risk of high-speed maglev trains through the dynamic changes of the suspension gap.

[0006] This application provides a method for classifying the crosswind risk of a high-speed maglev train, comprising: constructing a three-dimensional aerodynamic calculation model of the high-speed maglev train; performing simulation calculations using the three-dimensional aerodynamic calculation model based on the train's operating speed and a preset crosswind speed applied to the train to obtain the pressure distribution on the train body surface; performing integral calculations on the train surface pressure based on the pressure distribution to obtain lift data and lateral force data at the corresponding crosswind speed; constructing a target excitation load based on the lift data and the lateral force data; constructing a dynamic model of the high-speed maglev train, inputting the target excitation load into the dynamic model for simulation calculations to obtain suspension gap response data of the train body at the corresponding crosswind speed; and determining the crosswind risk level of the high-speed maglev train based on the suspension gap response data.

[0007] In one embodiment of this application, constructing a target excitation load based on the lift data and the lateral force data includes: determining the direction of action of the lift data as the vertical direction of the vehicle body, and determining the direction of action of the lateral force data as the lateral direction of the vehicle body; smoothing the numerical fluctuation data in the lift data and the lateral force data to obtain standard lift data and standard lateral force data; and constructing a target excitation load based on the standard lift data and the standard lateral force data, wherein the target excitation load is an external load of the dynamic model.

[0008] In one embodiment of this application, constructing the target excitation load based on the standard lift data and the standard lateral force data includes: applying the standard lift data and the standard lateral force data equivalently to the center of mass of the train body, or applying them to a reference point of the car body representing the aerodynamic effect, to obtain the target excitation load.

[0009] In one embodiment of this application, determining the crosswind risk level of the high-speed maglev train based on the suspension gap response data includes: extracting features from the suspension gap response data to obtain suspension gap feature parameters; and determining the crosswind risk level of the high-speed maglev train based on the suspension gap feature parameters.

[0010] In one embodiment of this application, the suspension gap characteristic parameters include at least one of the following: maximum gap deviation, peak-to-peak value, and root mean square value.

[0011] In one embodiment of this application, the suspension gap characteristic parameters include the maximum gap deviation, the peak-to-peak value, and the root mean square value; determining the crosswind risk level of the high-speed maglev train based on the suspension gap characteristic parameters includes: determining the suspension gap fluctuation trend based on the maximum gap deviation, the peak-to-peak value, and the root mean square value; and determining the crosswind risk level of the high-speed maglev train based on the suspension gap fluctuation trend.

[0012] In one embodiment of this application, the aerodynamic calculation domain of the three-dimensional aerodynamic calculation model includes the air region around the train, and the boundary conditions of the air region around the train include at least one of the following: inflow inlet, pressure outlet, crosswind inlet, ground, and vehicle body wall.

[0013] In one embodiment of this application, the dynamic model includes at least one of the following: a rigid body model of the vehicle body, a suspension support system model, a track constraint model, and an external load input interface.

[0014] On the other hand, embodiments of this application provide an electronic device, including: at least one processor; at least one memory for storing at least one program; and when at least one of the programs is executed by at least one of the processors, implementing the crosswind risk classification method for high-speed maglev trains as described above.

[0015] On the other hand, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for executing the crosswind risk classification method for high-speed maglev trains as described above.

[0016] This application provides a method for classifying crosswind risk of high-speed maglev trains, an electronic device, and a computer-readable storage medium. The method first constructs a three-dimensional aerodynamic calculation model of the high-speed maglev train. Then, it uses this model to perform simulation calculations based on the train's operating speed and a preset crosswind speed applied to the train, obtaining the pressure distribution on the train body surface. Next, it integrates the pressure distribution on the train body surface to obtain lift and lateral force data at the corresponding crosswind speed. Based on this lift and lateral force data, a target excitation load is constructed. After constructing the dynamic model of the high-speed maglev train, the target excitation load is input into the dynamic model for simulation calculations to obtain the suspension gap response data of the train body at the corresponding crosswind speed. The crosswind risk level of the high-speed maglev train can then be determined based on the suspension gap response data. In this application embodiment, lift and lateral force data are first obtained through three-dimensional aerodynamic model simulation, and a target excitation load is constructed accordingly. The target excitation load is then input into the dynamic model to solve for the suspension gap response data, and the crosswind risk level is determined based on the suspension gap response data. This application combines aerodynamic and dynamic analysis to effectively consider the coupling relationship between aerodynamic forces and the suspension system. At the same time, it uses the suspension gap as an evaluation parameter to achieve a graded judgment of the risk of train crosswind operation based on the fluctuation state of the suspension gap. Attached Figure Description

[0017] Figure 1 This is a flowchart of a crosswind risk classification method for high-speed maglev trains provided in one embodiment of this application; Figure 2 This is a lateral pressure cloud map provided in one embodiment of this application; Figure 3 This is a longitudinal pressure cloud map provided in one embodiment of this application; Figure 4 This is a dynamic model diagram provided in one embodiment of this application; Figure 5 This is a diagram showing the fluctuation curve of the suspension gap under a crosswind condition of 10 m / s, provided in one embodiment of this application. Figure 6 This is a diagram showing the fluctuation curve of the suspension gap under a crosswind condition of 20 m / s, provided in one embodiment of this application. Figure 7 This is a structural diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] It should be noted that although the flowchart shows a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the flowchart. The terms "first," "second," etc., used in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the structures, proportions, sizes, etc., depicted in the drawings are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the implementation conditions of this application. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effects and purposes achieved by this application, should still fall within the scope of the technical content disclosed in this application. Similarly, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are only for clarity of description and are not used to limit the scope of implementation of this application. Changes or adjustments in their relative relationships, without substantially altering the technical content, should also be considered within the scope of implementation of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] High-speed maglev trains, as an important development direction for next-generation high-speed rail transit equipment, possess advantages such as high operating speed, strong climbing ability, low vibration and noise, and good operational stability. With the continuous advancement of research and development of 600 km / h-class high-speed maglev transportation systems, the aerodynamic problems, levitation stability issues, and operational safety evaluation issues faced by trains under ultra-high-speed operating conditions are becoming increasingly prominent. Unlike traditional wheel-rail trains, maglev trains rely on an electromagnetic levitation system to maintain a non-contact operating state between the train body and the track. The levitation gap is typically kept within a small range. When external aerodynamic loads change, the levitation system needs to dynamically adjust in a timely manner to ensure train operational stability. Therefore, in the design and safety assessment of high-speed maglev trains, in addition to considering conventional aerodynamic indicators such as aerodynamic drag, lift, and lateral force, it is also necessary to further consider the impact of aerodynamic disturbances on the operating state of the levitation system.

[0022] When a train operates at speeds of 600 km / h or higher, the flow field around the train becomes increasingly complex, with significant changes in the pressure distribution on the train's surface, the flow structure at the bottom, and the wake characteristics. In crosswind conditions, the train's forward speed and the lateral flow interact, easily creating a pressure difference between the windward and leeward sides, resulting in additional lift, lateral force, and rolling torque. These aerodynamic forces not only alter the overall stress state of the train but may also further cause changes in the train's attitude and the response of the suspension system. For maglev trains, changes in lift affect the vertical suspension state, while changes in lateral force affect the lateral stability of the train. When both act together, they may lead to amplified fluctuations in the suspension gap. Related research indicates that crosswind motion affects the aerodynamic performance of high-speed trains, and the bottom structure of high-speed maglev trains also significantly impacts aerodynamic lift, demonstrating a close relationship between crosswind aerodynamic loads and the operational stability of maglev trains. Besides open-track crosswind environments, high-speed maglev trains may also face complex situations such as low-vacuum environments, tunnel operating environments, changes in track wind conditions, and random disturbances at higher speeds. Under these conditions, air resistance, aerodynamic loads, pressure fluctuations, and vehicle motion response may all change, further affecting train operation safety. Existing studies have analyzed the air resistance characteristics of high-speed maglev trains under low vacuum conditions and the impact of aerodynamic loads on conventional high-speed maglev systems when passing through tunnels, demonstrating that aerodynamic load and safety performance assessments are necessary for high-speed maglev trains under different operating environments. However, most current research still focuses primarily on indicators such as air resistance, lift, lateral force, pressure fluctuations, or operational stability, paying insufficient attention to the unique dynamic fluctuation characteristics of the suspension gap in maglev trains.

[0023] Currently, crosswind safety assessment methods for high-speed maglev trains mainly rely on computational fluid dynamics analysis of aerodynamic parameters such as lift, lateral force, and roll moment generated by the train under crosswind conditions, or on vehicle dynamics models to study train stability and dynamic response characteristics. While these methods can reflect the train's operating status from different perspectives, they still have certain limitations. On the one hand, current research mostly conducts aerodynamic analysis and vehicle dynamics analysis independently, lacking a comprehensive consideration of the coupling relationship between aerodynamic forces and the dynamic response of the suspension system, making it difficult to accurately reflect the actual operating status of the train under crosswind conditions. On the other hand, current safety assessments typically use parameters such as lift coefficient, lateral force coefficient, roll moment coefficient, and car body acceleration as evaluation indicators. These indicators can reflect the train's stress conditions and motion characteristics, but cannot directly characterize the most critical operating parameter of the maglev train's suspension system—the variation law of the suspension gap. Furthermore, current research on the fluctuation characteristics of the suspension gap under crosswind conditions is relatively insufficient, lacking a risk assessment and classification method that can comprehensively consider the correlation between crosswind speed, aerodynamic changes, and the dynamic response of the suspension gap, making it difficult to achieve quantitative assessment and safety warning of train operation risks.

[0024] In summary, current methods for assessing the safety of high-speed maglev trains in crosswind conditions still have significant shortcomings. On the one hand, aerodynamic analysis and vehicle dynamics analysis are independent of each other, failing to consider the dynamic coupling effect between aerodynamic forces and the suspension system. On the other hand, existing evaluation indicators do not take into account the core parameter of suspension clearance, and there is a lack of risk grading evaluation methods that can combine crosswind speed, aerodynamic changes, and the dynamic response of suspension clearance. As a result, existing evaluation results cannot truly reflect the actual operating status of the train under crosswind conditions, making it difficult to achieve accurate quantitative assessment and safety warning of train operation risks.

[0025] In view of this, embodiments of this application provide a method for classifying crosswind risk of high-speed maglev trains, an electronic device, and a computer-readable storage medium. The method first constructs a three-dimensional aerodynamic calculation model of the high-speed maglev train, and then uses this model to perform simulation calculations based on the train's operating speed and a preset crosswind speed applied to the train, obtaining the pressure distribution on the train body surface. Next, it integrates the pressure distribution on the train body surface to obtain lift and lateral force data at the corresponding crosswind speed, and then constructs a target excitation load based on the lift and lateral force data. After constructing the dynamic model of the high-speed maglev train, the target excitation load is input into the dynamic model for simulation calculation to obtain the suspension gap response data of the train body at the corresponding crosswind speed. Subsequently, the crosswind risk level of the high-speed maglev train can be determined based on the suspension gap response data. In this embodiment, lift and lateral force data are first obtained through three-dimensional aerodynamic model simulation, and a target excitation load is constructed accordingly. The target excitation load is then input into the dynamic model to solve for the suspension gap response data, and the crosswind risk level is determined based on the suspension gap response data. This application combines aerodynamic and dynamic analysis to effectively consider the coupling relationship between aerodynamic forces and the suspension system. At the same time, it uses the suspension gap as an evaluation parameter to achieve a graded judgment of the risk of train crosswind operation based on the fluctuation state of the suspension gap.

[0026] The embodiments of this application can be applied to the operational safety assessment and design optimization of high-speed maglev train systems of 600 km / h and above. They are applicable to the fields of aerodynamic analysis, vehicle dynamics simulation and operational safety monitoring of high-speed maglev trains. They can be integrated into maglev train digital simulation platforms, operation control systems and engineering design software. They are used to conduct safety assessment and risk classification of the dynamic response of the train's suspension gap in crosswind environments, and provide technical support for train aerodynamic shape optimization, suspension control parameter design and line wind environment adaptability analysis.

[0027] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0028] Reference Figure 1 , Figure 1 This is a flowchart of the crosswind risk classification method for high-speed maglev trains provided in this application embodiment. The process may specifically include, but is not limited to, steps 110 to 160.

[0029] Step 110: Construct a three-dimensional aerodynamic calculation model of the high-speed maglev train; Step 120: Using a three-dimensional aerodynamic calculation model, simulation calculations are performed based on the train's running speed and the preset crosswind speed applied to the train to obtain the pressure distribution on the train's body surface. Step 130: Integrate the pressure on the train surface based on the pressure distribution on the train body to obtain the lift and lateral force data at the corresponding crosswind speed; Step 140: Construct the target excitation load based on the lift data and lateral force data; Step 150: Construct a dynamic model of the high-speed maglev train, input the target excitation load into the dynamic model for simulation calculation, and obtain the suspension gap response data of the train body under the corresponding crosswind speed; Step 160: Determine the crosswind risk level of the high-speed maglev train based on the suspension gap response data.

[0030] In one embodiment, the three-dimensional aerodynamic calculation model of the high-speed maglev train is a computational fluid dynamics (CFD) geometric simulation model adapted for crosswind aerodynamic solutions. During modeling, the geometric model of the train body can be reasonably simplified according to the actual shape and structure of the high-speed maglev train, fully retaining the main outline of the train body and key structural features that contribute significantly to aerodynamic loads, while removing small auxiliary parts that have almost no impact on the overall aerodynamic results and can effectively increase the difficulty of mesh generation and computational volume. This effectively reduces the overall computational load of fluid simulation and improves the stability and convergence of the iterative calculation process.

[0031] In one embodiment, the three-dimensional aerodynamic calculation model defines an aerodynamic calculation domain, which mainly includes the airflow area around the train. This airflow area is equipped with multiple sets of boundary conditions, specifically including at least one of the following: incoming flow inlet, pressure outlet, crosswind inlet, ground, and vehicle body wall. By setting the aerodynamic calculation domain and its corresponding boundaries, the real working condition of crosswind airflow being stably applied from the side of the train can be simulated, ensuring that the flow field simulation closely approximates the actual crosswind conditions.

[0032] In one embodiment, after the three-dimensional aerodynamic calculation model, computational domain, and boundary conditions are set, the external flow field simulation calculation of the train under crosswind conditions can be completed using Fluent software. Fluent is a mainstream computational fluid dynamics simulation software. Its core principle is to use numerical algorithms to solve the fluid motion control equations, and then output simulation results such as flow field pressure distribution, airflow velocity, and aerodynamic forces on the vehicle body, providing a basis for subsequent integration to obtain lift and lateral force data.

[0033] In one embodiment, during actual simulation, three different levels of crosswind speeds—10 m / s, 20 m / s, and 30 m / s—can be pre-selected as simulation input conditions. In the process of simulating and solving the pressure distribution on the vehicle surface using a three-dimensional aerodynamic calculation model combined with the train's operating speed and the applied crosswind speed, the high-speed maglev train's operating speed can be set to 600 km / h, and then crosswind speeds of 10 m / s, 20 m / s, and 30 m / s can be applied sequentially for multi-condition comparative calculations. By numerically solving the external flow field around the train, the pressure distribution on the vehicle surface, the velocity distribution of the spatial flow field, and various aerodynamic parameters borne by the entire vehicle can be obtained. The lateral pressure cloud map output by the Fluent simulation calculation corresponds to... Figure 2 The vertical pressure cloud map corresponds to Figure 3 The two cloud maps illustrate the pressure distribution across the entire surface of the train under crosswind disturbance. The cloud maps reflect the pressure differences on the windward and leeward sides of the train, as well as at different local locations on the train body, and can be used to explain the causes of lift and lateral forces on the train body.

[0034] In one embodiment, after obtaining the pressure distribution on the train body surface, the surface pressure is integrated based on this distribution to obtain the lift and lateral force data at the corresponding crosswind speeds. Lift refers to the aerodynamic force acting on the train body in the vertical direction, and lateral force refers to the aerodynamic force acting on the train body in the transverse direction. Specifically, using all outer surfaces of the high-speed maglev train body as the integration solution surface, the surface static pressure values ​​of each mesh element node output by Fluent simulation are retrieved. Area integration is performed on all pressure loads along the vertical direction of the vehicle coordinate system to obtain complete vertical lift data under the crosswind condition. Area integration is performed on the surface pressure along the transverse direction of the vehicle coordinate system to obtain lateral force data under the same crosswind condition. For three different crosswind speeds of 10 m / s, 20 m / s, and 30 m / s, the above surface integration operation is repeated to obtain independent lift and time-history lateral force data for each wind speed condition. In a specific example, the lift and lateral force results calculated under crosswind conditions of 10 m / s, 20 m / s, and 30 m / s are shown in Table 1. Table 1 illustrates the changes in aerodynamic forces under different crosswind speeds and can also serve as the external load input basis for subsequent dynamic models. This data table establishes the correspondence between crosswind speed and aerodynamic forces, providing a foundation for the coupled analysis of aerodynamic and dynamic simulations.

[0035] Table 1. Magnitude of lift load under different crosswind conditions

[0036] It should be understood that, because aerodynamic and dynamic calculations may use different time steps, output formats, and coordinate orientations, directly inputting unprocessed lift and lateral force data into the dynamic model of a high-speed maglev train may result in unclear load directions, inconsistent time correspondences, or excessive local numerical fluctuations, directly affecting the accuracy of the dynamic solution results. Therefore, before formally importing lift and lateral force data into the dynamic model, the original aerodynamic data can be standardized to obtain target excitation loads that are compatible with the dynamic solution environment and meet the simulation requirements.

[0037] In one embodiment, when constructing the target excitation load based on lift data and lateral force data, the direction of action of the lift data can first be determined as the vertical direction of the vehicle body, and the direction of action of the lateral force data can be determined as the lateral direction of the vehicle body. Then, the numerical fluctuation data in the lift data and lateral force data are smoothed to obtain standard lift data and standard lateral force data. Subsequently, the target excitation load is constructed based on the standard lift data and standard lateral force data, and the target excitation load can be used as the external load of the dynamic model.

[0038] In one embodiment, when constructing the target excitation load based on standard lift data and standard lateral force data, the standard lift data and standard lateral force data can be applied equivalently to the center of mass of the train body, or applied to a reference point of the car body representing the aerodynamic effect, to obtain the target excitation load.

[0039] In one embodiment, the complete process of constructing the target excitation load based on lift and lateral force data is as follows: First, the aerodynamic data (i.e., lift and lateral force data) undergoes multi-dimensional normalization, including at least aerodynamic data screening, simulation time step matching, load direction verification, and determination of equivalent loading positions. For lift data, its direction of action is defined as vertical; for lateral force data, its direction of action is defined as lateral. For data with numerical fluctuations, smoothing can be performed without changing the overall trend, thus obtaining standard lift and standard lateral force data. After data processing, the lift and lateral force data are converted into external excitation loads that the dynamic model can recognize. Specifically, the two sets of standardized force values ​​are applied equivalently to the center of mass of the high-speed maglev train body, or to a reference node of the body that can equivalently represent the overall aerodynamic resultant force effect, ultimately generating a target excitation load that can be directly accessed and called by the dynamic model, used as an external input load for dynamic simulation. This data processing workflow standardizes the aerodynamic inputs for various crosswind conditions, ensuring consistent load input rules across all conditions and making the train dynamic response results comparable at different crosswind speeds. Simultaneously, through standardized data processing, the raw aerodynamic data obtained from Fluent simulations can be effectively transformed into external excitation loads that the dynamic model can recognize and utilize, thereby achieving the transfer of aerodynamic loads to the vehicle's dynamic response and completing the effective coupling of aerodynamic and dynamic simulations.

[0040] In one embodiment, a dynamic model of the high-speed maglev train can be established using UM software. UM is a multibody dynamics simulation software that can be used to establish the dynamic relationship between the car body, the suspension support system, and the track constraints. In this embodiment, the dynamic model includes at least one of the following: a rigid body model of the car body, a suspension support system model, a track constraint model, and an external load input interface. These dynamic models can effectively characterize the variation law of the suspension gap under the action of crosswind aerodynamic forces. Among them, the rigid body model of the car body can be used to describe the overall motion of the train body, the suspension support system model can be used to describe the support relationship between the car body and the track, the track constraint model can be used to provide the reference benchmark required for the calculation of the suspension gap, and the external load input interface can be used to apply the lift and lateral forces calculated by Fluent and import the target excitation load obtained by data construction, which can provide load input support for the subsequent simulation calculation of the car suspension gap response under crosswind conditions.

[0041] In one embodiment, in modeling the suspension support system, the magnetic levitation support can be equivalent to a support unit with stiffness and damping characteristics. Stiffness reflects the suspension system's ability to resist displacement changes, while damping reflects the system's ability to dissipate vibration energy. This simplification reflects the dynamic response characteristics of the suspension system under aerodynamic disturbances while avoiding overly complex models that make parameter determination difficult. The dynamic model established in UM in this embodiment is as follows: Figure 4 As shown, this illustrates the basic connection between the vehicle body, the suspension support system, and the track.

[0042] In one embodiment, after the dynamic model is established, the suspension gap output can be set in the model. The suspension gap can be defined as the instantaneous vertical distance between a specified measuring point on the vehicle body and the track reference plane, or it can be defined as the change of this instantaneous distance relative to the initial equilibrium gap. To achieve comparison between different crosswind conditions, the 10m / s and 20m / s conditions can use the same measuring point location, the same initial conditions, and the same output method. For the 30m / s condition, subsequent calculations can also be performed according to the same measuring points and the same output rules to ensure the consistency of the suspension gap index used for risk classification.

[0043] In one embodiment, the target excitation load is a standardized aerodynamic load that can be directly imported into the dynamic model as an external input. It can be processed and input into the model in the form of a data table or by importing a data file.

[0044] In one embodiment, the target excitation load includes standardized lift and lateral force data under various crosswind conditions. This target excitation load is used as an external excitation and input into the dynamic model to solve for the suspension gap response data of the train body under corresponding crosswind speeds. The simulation employs a unidirectional aerodynamic-dynamic coupling analysis method, which involves first solving for the aerodynamic forces of the train body using Fluent software, and then importing the pre-processed aerodynamic data into the dynamic model to solve for the vehicle's dynamic response. This method can intuitively demonstrate the disturbance effect of crosswind aerodynamic loads on the suspension gap. The overall calculation steps are simple and clear, facilitating engineering application and repeated verification.

[0045] In one embodiment, the obtained suspension gap response data can be represented as a response curve of the suspension gap dynamically changing over time. The function of the suspension gap changing over time is denoted as Δg(t), where t represents the simulation time, and Δg(t) is the amount of shift of the suspension gap relative to the initial equilibrium state at any given time. In a specific example, the calculated suspension gap fluctuation curves under crosswind conditions of 10 m / s and 20 m / s are shown below. Figure 5 and Figure 6 As shown, this illustrates the differences in the suspension gap response under different crosswind velocities. Figure 5Corresponding to a crosswind condition of 10 m / s, it mainly characterizes the response of the suspension system under relatively weak crosswind disturbance; Figure 6 For the 20 m / s crosswind condition, the main characteristic is the increased fluctuation of the suspension gap after the crosswind speed increases. The gap fluctuation results shown in the figure indicate that the degree of suspension gap fluctuation increases with the increase of crosswind speed. This phenomenon demonstrates that crosswinds not only change the magnitude of the aerodynamic forces acting on the train but also further affect the dynamic stability of the suspension system through aerodynamic force input. For the 30 m / s crosswind condition, the lift and lateral force data under this condition can be normalized using the aforementioned data processing procedure. The processed aerodynamic data can then be used as the input condition for the strong crosswind load, and the simulation calculation can be performed according to the same load import procedure to obtain the corresponding suspension gap response results at this wind speed.

[0046] In one embodiment, when determining the crosswind risk level of a high-speed maglev train based on suspension gap response data, feature extraction can be performed on the suspension gap response data to obtain suspension gap characteristic parameters. Then, the crosswind risk level of the high-speed maglev train can be determined based on these suspension gap characteristic parameters. Specifically, relying solely on visually observing the suspension gap fluctuation curve is insufficient to establish a stable and reproducible quantitative risk assessment standard. Therefore, after obtaining the suspension gap response data, i.e., the history curve of the suspension gap changing over time, feature value extraction is first performed on the curve to transform the visualized curve into quantitative values ​​that can be directly used for risk assessment, i.e., suspension gap characteristic parameters. Then, the risk level of the high-speed maglev train under crosswind conditions is determined based on the extracted suspension gap characteristic parameters.

[0047] In one embodiment, the suspension gap characteristic parameters include at least one of the following: maximum gap deviation, peak-to-peak value, and root mean square (RMS) value. The maximum gap deviation can be obtained from the maximum absolute change in the suspension gap over time, reflecting the maximum deviation of the suspension gap relative to its initial state. The peak-to-peak value can be obtained from the difference between the maximum and minimum values ​​of the suspension gap, reflecting the maximum range of gap fluctuations over the entire calculation period. The RMS value can be obtained by taking the square root of the average of the squared changes in the suspension gap over the entire time period, reflecting the overall intensity of gap fluctuations. These indicators can characterize the system state from different perspectives: the maximum gap deviation reflects extreme responses, the peak-to-peak value reflects the fluctuation range, and the RMS value reflects the overall fluctuation level. Through these indicators, even with a small number of graphs, a relatively complete risk assessment basis can be formed.

[0048] In one embodiment, fluctuation frequency or fluctuation period characteristics can be further introduced as needed to determine whether there is significant periodic oscillation in the suspension gap. If, under a certain crosswind condition, the suspension gap not only increases in amplitude but also exhibits continuous periodic fluctuations, it indicates a more significant dynamic coupling relationship between external aerodynamic disturbances and the suspension system response, which should be given greater attention in risk assessment. This part can be obtained through data processing of the suspension gap's time-varying history curve, suitable for situations where the number of current maps is limited but the calculation chain is complete.

[0049] In one embodiment, the process of determining the crosswind risk level of a high-speed maglev train based on the characteristic parameters of the suspension gap is essentially a comprehensive crosswind risk assessment that combines the aerodynamic changes caused by crosswinds with the dynamic fluctuations of the suspension gap under aerodynamic loads. This evaluation method avoids the one-sidedness of relying solely on crosswind speed or a single aerodynamic amplitude to determine risk, making the final risk level more closely reflect the actual operating conditions of the maglev vehicle.

[0050] In one embodiment, when the suspension gap characteristic parameters include the maximum gap deviation, peak-to-peak value, and root mean square value, in the process of determining the crosswind risk level of the high-speed maglev train based on the suspension gap characteristic parameters, the suspension gap fluctuation trend can be determined first based on the maximum gap deviation, peak-to-peak value, and root mean square value, and then the crosswind risk level of the high-speed maglev train can be determined based on the suspension gap fluctuation trend.

[0051] In one embodiment, regarding the classification of crosswind risk levels, the train's operating status can be divided into a safe state, a warning state, and a dangerous state. A safe state indicates that the suspension gap fluctuation is small, and the system can maintain stable operation; a warning state indicates that the suspension gap fluctuation has significantly increased, but has not yet reached the danger boundary, requiring attention to the operating status; a dangerous state indicates that the suspension gap fluctuation is large, the system stability has significantly decreased, and there is a risk of approaching or exceeding the safety threshold or permissible range. It should be noted that the specific threshold can be determined based on the design-permissible suspension gap range, the control system's adjustment capability, and simulation statistical results under different crosswind conditions; this embodiment does not impose such limitations.

[0052] It should be understood that when a train travels at a high speed of 600 km / h, the surrounding external flow field is simultaneously affected by the coupling effect of the vehicle's speed and the crosswind. The lateral flow directly changes the pressure distribution on the surface of the car body, thereby generating additional aerodynamic lift and lateral force. Traditional wheel-rail trains generally assess the risk of crosswind operation using parameters such as wheel-rail interaction force, derailment coefficient, and car body vibration acceleration. However, high-speed maglev trains rely on electromagnetic levitation force to maintain a tiny levitation gap between the car body and the track. The dynamic change of this levitation gap is itself a core physical quantity for measuring the operational stability of the levitation control system.

[0053] The crosswind risk classification method proposed in this application is applicable to the safety assessment of crosswind operation of 600km / h high-speed maglev trains, and can also provide a reference for the wind environment adaptability verification of rail transit lines, the tuning of suspension control algorithm parameters, and the optimization of train body aerodynamic performance. The technical logic of the entire evaluation system includes the following core steps: aerodynamic load input, dynamic simulation response, dynamic fluctuation extraction of suspension gap, and quantitative risk level determination. Even with a limited number of attached figures, the overall architecture of this application can be fully and clearly illustrated by the progressive data transmission logic and feature index extraction path. The final crosswind risk classification can be used as a safety evaluation basis in the operation control system or digital simulation platform, providing support for the safe operation of high-speed maglev trains in crosswind environments.

[0054] The beneficial effects of the crosswind risk classification method for high-speed maglev trains proposed in this application include at least the following: (1) This paper combines aerodynamic load analysis with the dynamic response of the magnetic levitation system, and takes the suspension gap as the final evaluation object. Current technologies usually conduct aerodynamic analysis or vehicle dynamics analysis separately. Aerodynamic analysis focuses on external loads such as lift, lateral force, and rolling moment, while dynamic analysis focuses on vehicle displacement, acceleration, or attitude changes. However, it pays insufficient attention to the dynamic changes of the suspension gap, which is the most critical factor for magnetic levitation trains. This application uses Fluent to calculate the lift and lateral force under crosswind conditions and further inputs them into the UM dynamic model. This allows for a more direct analysis of the impact of crosswind aerodynamic forces on the stability of the suspension system, thereby improving the pertinence of crosswind operation safety evaluation.

[0055] (2) The risk assessment indicators in this application are more in line with the operating characteristics of maglev trains. Maglev trains rely on an electromagnetic levitation system to maintain a stable gap between the train body and the track. Once the levitation gap fluctuates significantly, it will directly affect the smoothness and safety margin of the train operation. Therefore, compared with the method of judging risk solely based on the magnitude of aerodynamic forces, this application extracts parameters such as the maximum gap deviation, peak-to-peak value, and root mean square value of the levitation gap. These parameters can more intuitively reflect the actual response state of the levitation system under crosswinds. Especially in the comparative analysis of different crosswind speeds, the fluctuation characteristics of the levitation gap can reflect the change process of the system from stable to warning and then to dangerous state, which has stronger engineering application significance.

[0056] It should be noted that, in terms of aerodynamic calculations, in addition to Fluent software, other computational fluid dynamics software such as STAR-CCM+ and OpenFOAM can also be used, as long as the aerodynamic load data such as lift and lateral force experienced by the vehicle under different crosswind conditions can be obtained, meeting the aerodynamic input requirements of this application. In terms of dynamic modeling, in addition to UM software, dynamics or control system simulation platforms such as SIMPACK, ADAMS, and MATLAB / Simulink can also be used, as long as the dynamic relationship between the vehicle body, suspension system, and track can be established, and the response results of the suspension gap changing over time can be output, achieving the purpose of this application.

[0057] It should be noted that the aerodynamic loading method is not limited to the methods mentioned in the above embodiments. The aerodynamic force can be applied equivalently to the center of gravity of the car body, or it can be applied to different parts such as the head car, body car, and tail car, depending on the train structure. In addition, the risk assessment indicators are not limited to the maximum clearance deviation, peak-to-peak value, and root mean square value mentioned in the above embodiments. They can also be combined with parameters such as maximum clearance deviation, standard deviation, fluctuation frequency, or over-limit duration for comprehensive evaluation.

[0058] On the other hand, embodiments of this application provide an electronic device, such as... Figure 7 As shown, the device 700 includes at least one processor 710; at least one memory 720 for storing at least one program; when the at least one program is executed by the at least one processor 710, it implements the crosswind risk classification method for high-speed maglev trains as in any of the preceding embodiments.

[0059] On the other hand, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the crosswind risk classification method for high-speed maglev trains as described in any of the preceding embodiments.

[0060] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for classifying crosswind risk of high-speed maglev trains, characterized in that, include: Construct a three-dimensional aerodynamic calculation model for a high-speed maglev train; The pressure distribution on the surface of the train body is obtained by simulating the train's running speed and the preset crosswind speed applied to the train using the three-dimensional aerodynamic calculation model. Based on the pressure distribution on the train body surface, the surface pressure of the train is integrated to obtain the lift data and lateral force data at the corresponding crosswind speed; The target excitation load is constructed based on the lift data and the lateral force data; A dynamic model of the high-speed maglev train is constructed, and the target excitation load is input into the dynamic model for simulation calculation to obtain the suspension gap response data of the train body under the corresponding crosswind speed. The crosswind risk level of the high-speed maglev train is determined based on the suspension gap response data.

2. The method for classifying crosswind risk of high-speed maglev trains according to claim 1, characterized in that, The construction of the target excitation load based on the lift data and the lateral force data includes: The direction of the lift data is determined as the vertical direction of the vehicle body, and the direction of the lateral force data is determined as the lateral direction of the vehicle body. The numerical fluctuations in the lift data and the lateral force data are smoothed to obtain standard lift data and standard lateral force data. The target excitation load is constructed based on the standard lift data and the standard lateral force data, and the target excitation load is the external load of the dynamic model.

3. The method for classifying crosswind risk of high-speed maglev trains according to claim 2, characterized in that, The construction of the target excitation load based on the standard lift data and the standard lateral force data includes: The standard lift data and the standard lateral force data are applied equivalently to the center of mass of the train body, or to a reference point on the train body representing the aerodynamic effect, to obtain the target excitation load.

4. The method for classifying crosswind risk of high-speed maglev trains according to claim 1, characterized in that, The process of determining the crosswind risk level of the high-speed maglev train based on the suspension gap response data includes: Feature extraction is performed on the suspension gap response data to obtain suspension gap feature parameters; The crosswind risk level of the high-speed maglev train is determined based on the suspension gap characteristic parameters.

5. The method for classifying crosswind risk of a high-speed maglev train according to claim 4, characterized in that, The suspension gap characteristic parameters include at least one of the following: maximum gap deviation, peak-to-peak value, and root mean square value.

6. The method for classifying crosswind risk of a high-speed maglev train according to claim 5, characterized in that, The suspension gap characteristic parameters include the maximum gap deviation, the peak-to-peak value, and the root mean square value; The step of determining the crosswind risk level of the high-speed maglev train based on the suspension gap characteristic parameters includes: The fluctuation trend of the suspension gap is determined based on the maximum gap deviation, the peak-to-peak value, and the root mean square value. The crosswind risk level of the high-speed maglev train is determined based on the fluctuation trend of the suspension gap.

7. The method for classifying crosswind risk of high-speed maglev trains according to claim 1, characterized in that, The aerodynamic calculation domain of the three-dimensional aerodynamic calculation model includes the air region around the train, and the boundary conditions of the air region around the train include at least one of the following: inflow inlet, pressure outlet, crosswind inlet, ground, and car body wall.

8. The method for classifying crosswind risk of high-speed maglev trains according to claim 1, characterized in that, The dynamic model includes at least one of the following: a rigid body model of the vehicle body, a suspension support system model, a track constraint model, and an external load input interface.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; The crosswind risk classification method for high-speed maglev trains as described in any one of claims 1 to 8 is implemented when at least one of the programs is executed by at least one of the processors.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the crosswind risk classification method for high-speed maglev trains as described in any one of claims 1 to 8.

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