High and cold motor train unit roadbed foreign matter adsorption critical condition prediction method and system based on aerodynamics
By establishing a three-dimensional geometric model and external flow field calculation domain for the EMU, the characteristic value of adsorption negative pressure was obtained. The critical take-off conditions were established in combination with the gravity of foreign objects, which solved the problem of quantitative prediction of foreign object adsorption in EMUs in high-altitude and cold regions and improved the accuracy of train operation safety assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CRRC CHANGCHUN RAILWAY VEHICLES CO LTD
- Filing Date
- 2026-01-30
- Publication Date
- 2026-05-08
AI Technical Summary
The lack of quantitative methods to predict the adhesion of foreign objects under high-speed trains in cold regions makes it impossible to provide accurate data support for winter driving speed limits and snow removal standards, thus affecting driving safety.
Based on aerodynamics, a three-dimensional geometric model of the EMU is established, an external flow field calculation domain is constructed and pressure monitoring points are set up to obtain the adsorption negative pressure characteristic value. Combined with the gravity of foreign objects, the critical take-off conditions for foreign object adsorption are established, and the critical size and mass of foreign objects are predicted.
It enables quantitative prediction of critical conditions for foreign objects at a given train speed, providing a basis for safety assessment and risk management of high-speed train operation in cold and high-altitude conditions.
Smart Images
Figure CN121997835A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of rail transit safety technology and aerodynamics, specifically to a method and system for predicting critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics. Background Technology
[0002] High-speed trains operate in harsh environments in cold regions, where snow, ice, and ice often cover the ballast on the tracks during winter. When a train passes at high speed, complex flow field changes occur between the train's undercarriage and the ground, especially at the bottom of the train body where strong aerodynamic negative pressure is generated, producing aerodynamic suction force.
[0003] This pneumatic suction force is sufficient to overcome the gravity of foreign objects on the roadbed, lifting them up and causing them to strike the train bogies and undercarriage equipment, resulting in equipment damage and seriously threatening driving safety. Current countermeasures mostly rely on experience-based speed limits or post-accident inspections, lacking a predictive method that can quantitatively calculate and predict the size and mass of foreign objects that will be lifted by different train models at different speeds. Therefore, it is impossible to provide accurate data support for winter driving speed limits and track snow removal standards. Summary of the Invention
[0004] The technical problem to be solved by this application is the lack of prediction of foreign object adsorption under the train car in high-altitude and cold regions. This application provides a method and system for predicting the critical conditions for foreign object adsorption on the roadbed of high-altitude and cold regions based on aerodynamics.
[0005] To achieve the above objectives, the first aspect of this application provides a method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, including:
[0006] S1, Obtain the geometric parameters of the train to be predicted, establish a three-dimensional geometric model based on the geometric parameters, and establish a car body coordinate system corresponding to the three-dimensional geometric model; S2, In the vehicle coordinate system, a computational domain of the external flow field around the EMU is constructed based on the three-dimensional geometric model, and multiple pressure monitoring points are set at the positions corresponding to the roadbed surface in the computational domain of the external flow field; boundary conditions are set for the computational domain of the flow field, and the target train running speed is used as the incoming flow speed for simulation to obtain the pressure fluctuation curve of each pressure monitoring point, and the absolute value of the minimum adsorption negative pressure value of each pressure monitoring point is extracted from the pressure fluctuation curve as the adsorption negative pressure characteristic value. S3, construct the physical model of the foreign object, and set the density parameters and geometry of the foreign object; S4. Based on the mechanical equilibrium relationship between the adsorption negative pressure characteristic value and the gravity of the foreign object, establish the critical take-off condition for foreign object adsorption, which is used to solve based on the density parameter and geometry of the foreign object. S5 outputs the prediction results of the critical conditions for foreign object adsorption corresponding to the target train speed and the roadbed surface. The prediction results include at least the critical size and critical mass of the foreign object.
[0007] In embodiments of this application, the foreign object geometry includes at least spherical and cubic shapes, with spherical foreign objects having a radius of... Characterization, cubic foreign objects are characterized by their side length Characterization.
[0008] In the embodiments of this application, the adsorption critical takeoff condition of S4 is as follows: The critical equilibrium equation established for the spherical foreign body is:
[0009] The critical equilibrium equation for the cubic foreign body is:
[0010] In the formula, The characteristic value of adsorption negative pressure, It is the acceleration due to gravity; The density of the corresponding foreign object; The critical size and critical mass of the foreign object are obtained by solving the critical equilibrium equation based on the geometry and density parameters of the foreign object and the corresponding geometry.
[0011] In embodiments of this application, the types of foreign matter include at least: ballast, ice, and a mixture of iced ballast; the density of the iced ballast mixture is determined by the density of the ballast, the density of the ice, and the ballast-to-ice volume ratio. Weighted confirmation, satisfying the calculation formula:
[0012] In the formula, The density of the ballast; The density of ice, The density of the icing ballast mixture and the volume ratio of ballast to ice. The range of values is .
[0013] In the embodiments of this application, the geometric parameters of the EMU include at least: body geometric parameters, head shape geometric parameters, simplified bogie geometric parameters, and overall length, overall width, and overall height; the overall height is selected as the train characteristic dimension, and the size of the flow field calculation domain is determined based on the train characteristic dimension.
[0014] It should also be noted that the actual parameters of the car body geometry, head shape geometry, and simplified bogie geometry can all be obtained by consulting the EMU model data, and will not be elaborated in detail in this application.
[0015] In the embodiments of this application, the boundary conditions include: the roadbed surface at the bottom of the flow field calculation domain is a sliding wall with the sliding velocity being the same as the incoming flow velocity, and the vehicle body and bogie surfaces are non-slip walls; the remaining boundaries are symmetrical planes.
[0016] In the embodiments of this application, in S2, the simulation uses a time step to advance the time, and performs pressure sampling at the pressure monitoring point within a preset calculation time to form the pressure fluctuation curve.
[0017] The second aspect of this application provides a critical condition prediction system for foreign object adsorption on the roadbed of high-speed trains in cold regions, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described method.
[0018] The above technical solution achieves the following technical effects: This application establishes a vehicle coordinate system corresponding to a three-dimensional geometric model and constructs a flow field calculation domain between the vehicle bottom and the roadbed surface. Pressure monitoring points are set up on the roadbed surface boundary to obtain pressure fluctuation curves and extract the adsorption negative pressure characteristic value corresponding to the minimum adsorption negative pressure value. Then, the adsorption critical take-off condition is established by combining the adsorption negative pressure characteristic value with the mechanical balance relationship between the gravity of the foreign object. Thus, under a given train operating speed, the critical size and critical mass of the foreign object can be given, realizing the prediction of the critical condition for adsorption of foreign objects on the roadbed, and providing a basis for the safety assessment of EMU operation and the risk management of foreign objects under high-altitude and cold conditions. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 A schematic flowchart of a method according to an embodiment of this application is shown. Figure 2 This illustration schematically shows a three-dimensional geometric model of the train set in an embodiment of this application; Figure 3 This illustration shows a schematic diagram of the mesh division of the vehicle body in an embodiment of this application; Figure 4 The diagram illustrates the monitoring curve of roadbed surface pressure fluctuation during operation in an embodiment of this application. Detailed Implementation
[0021] To facilitate understanding of this application, the following description will be more comprehensive and detailed in conjunction with the accompanying drawings and preferred embodiments, but the scope of protection of this application is not limited to the following specific embodiments.
[0022] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of this application.
[0023] Figure 1 The schematic diagram illustrates a flowchart of an embodiment of this application. In one embodiment of this application, a method for predicting critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions, based on aerodynamics, is provided, comprising the following steps: S1. Obtain the geometric parameters of the train to be predicted, establish a three-dimensional geometric model of the train based on the geometric parameters, and establish a car body coordinate system corresponding to the three-dimensional geometric model. Specifically, CATIA (Computer Aided Three-dimensional Interactive Application) can be used to create a three-dimensional geometric model of the EMU (Electric Multiple Unit). S2. In the vehicle coordinate system, a computational domain of the external flow field around the EMU is constructed based on a three-dimensional geometric model. The computational domain of the flow field is meshed, and multiple pressure monitoring points are set on the roadbed surface (specifically, the ground boundary surface of the computational domain). Boundary conditions are set for the computational domain of the flow field, and transient CFD simulation is performed with the target train speed as the incoming flow speed to obtain the pressure fluctuation curves of each pressure monitoring point. The absolute value of the minimum adsorption negative pressure value among each pressure monitoring point is extracted from the pressure fluctuation curves as the adsorption negative pressure characteristic value. In this step, a Realizable k-ε turbulence model is used to perform flow field simulation calculations. The simulation is performed using a time step and pressure sampling is performed at the pressure monitoring points within a preset calculation time to obtain the pressure sequence of each pressure monitoring point over time. The pressure sequence constitutes a pressure fluctuation curve, and the adsorption negative pressure characteristic value is extracted from the pressure fluctuation curve.
[0024] S3, construct the physical model of the foreign object, and set the density and geometry of the foreign object in the physical model; wherein, the physical model of the foreign object is used to parameterize the foreign object and use it for force analysis, and the density and geometry are used to characterize the response differences of different types of foreign objects under the same aerodynamic action; S4. The critical take-off condition for foreign object adsorption is established based on the mechanical equilibrium relationship between the adsorption negative pressure characteristic value and the gravity of the foreign object. Based on the density parameter and geometry of the foreign object, the critical size and critical mass of the foreign object can be obtained. Among them, the mechanical equilibrium relationship is used to link the aerodynamic adsorption effect with the gravitational constraint. S5 outputs the prediction results of the critical conditions for foreign matter adsorption corresponding to the target train speed and roadbed location. The prediction results include at least the characteristic value of adsorption negative pressure, the critical size of the foreign matter, and the critical mass of the foreign matter.
[0025] This method establishes a vehicle coordinate system corresponding to a three-dimensional geometric model and constructs a flow field calculation domain between the vehicle bottom and the roadbed surface. Pressure monitoring points are set up on the roadbed surface boundary to obtain pressure fluctuation curves and extract adsorption negative pressure characteristic values. Then, the adsorption critical take-off conditions are established by combining the adsorption negative pressure characteristic values with the mechanical balance relationship between the gravity of foreign objects. Thus, under given train speed and roadbed location conditions, the critical size and critical mass of foreign objects can be quantitatively given, enabling the prediction of the critical conditions for foreign object adsorption on the roadbed. This provides a basis for the safety assessment of EMU operation and the risk management of foreign objects under high-altitude and cold conditions.
[0026] In one embodiment, the foreign object geometry includes at least spherical and cubic foreign objects, wherein the spherical foreign object has a radius of... Characterization, cubic foreign objects are characterized by their side length Characterization. In the adsorption critical takeoff condition of S4: Adsorption force according to Calculation, where A represents the characteristic value of negative pressure adsorption, and A is the windward projected area of the foreign object.
[0027] The critical equilibrium equation established for the spherical foreign body is:
[0028] The critical equilibrium equation for the cubic foreign body is:
[0029] The critical size of the foreign object is calculated using a formula derived from the equilibrium equation. Formula for the critical radius of a spherical foreign object:
[0030] Formula for the critical side length of a cubic foreign object:
[0031] In the formula, The characteristic value of adsorption negative pressure, The set gravitational acceleration; The density of the corresponding foreign object; The adsorption negative pressure characteristic value obtained from the simulation Substituting the above formula for calculating critical size, the critical size of the foreign object at the target train speed can be directly calculated. Furthermore, the critical mass of the foreign object can be calculated using its critical size and density.
[0032] In specific embodiments, the types of foreign matter include at least: ballast, ice, and a mixture of iced ballast; the density of the iced ballast mixture is determined by the density of the ballast, the density of the ice, and the ballast-to-ice volume ratio. Weighted confirmation, satisfying the calculation formula:
[0033] In the formula, The density of the ballast; The density of ice, The density of the icing ballast mixture and the volume ratio of ballast to ice. The range of values is .
[0034] In one embodiment, S1 specifically refers to: The geometric parameters of the train to be predicted were obtained and organized using CATIA software, and a system was established as follows: Figure 2 The three-dimensional geometric model shown. The three-dimensional geometric model includes at least the outer shape boundaries of the vehicle body, the front shape, and the simplified bogie (e.g., directly simplified into a block shape), which are used as the source of geometric boundary conditions for flow field simulation; This embodiment uses a three-car train model. Its main geometric parameters, excluding the car body, head shape, and simplified bogies, include: total length L1 = 76.525m, total width Wt = 3.265m, and total height H = 3.890m. The total height H is selected as the characteristic length for subsequent flow field computation domain scale definition and dimensionless reference for mesh generation. Specific mesh generation details can be found in [reference needed]. Figure 3 This ensures that the modeling scale is consistent and comparable under different working conditions or different vehicle parameters.
[0035] A vehicle coordinate system corresponding to the 3D geometric model is established. The origin of the vehicle coordinate system is set at a designated reference point at the tip of the train's nose (e.g., the foremost point of the nose or a preset nose reference point in the model). The X-axis runs longitudinally from the rear to the front of the train; the Y-axis runs laterally from the centerline to one side of the train; and the Z-axis runs vertically downwards. The vehicle coordinate system is used to determine the spatial positions of the undercarriage area, the roadbed surface, and the pressure monitoring points within the same reference frame, ensuring that the positions of the pressure monitoring points are consistent with the positions of the subsequent output results.
[0036] S2 specifically refers to: The 3D geometric model established in S1 was imported into the STAR-CCM+ software, and a flow field computational domain including the vehicle body surface and the roadbed surface was constructed in the vehicle body coordinate system. The roadbed surface was defined as the region located below the bottom of the train and coinciding with the ground boundary. This region was used to characterize the influence of the aerodynamic action of the train under the vehicle on the pressure of the roadbed surface during train operation.
[0037] Multiple pressure monitoring points are deployed on the boundary surface of the roadbed. The location of each pressure monitoring point is defined using the vehicle coordinate system. The number and placement of the pressure monitoring points cover the roadbed surface within the influence range of the train's underside to ensure that the minimum pressure value (negative pressure extreme value) on the roadbed surface can be captured. Each pressure monitoring point outputs a pressure sequence that changes over time, and the pressure sequences form a pressure fluctuation curve.
[0038] A Realizable k-ε turbulence model was used for flow field simulation calculations. This turbulence model was used to solve the external flow field of the train and the turbulent structure under the train, thereby obtaining the response of the roadbed surface pressure as a function of time.
[0039] Set boundary conditions, which are specified as follows: Inlet boundary: velocity inlet, inlet velocity (incoming flow velocity) is taken as 69.44 m / s, which is equivalent to the train running speed of 250 km / h; a reference frame setting is adopted in which the train is stationary and the incoming flow is moving, that is, the train model remains stationary in the computational domain, and the inlet incoming flow velocity is equal to the target train running speed.
[0040] Outlet boundary: Pressure outlet, with a reference pressure of 0 Pa. The longitudinal distance between the outlet boundary and the tip of the tail section is 30H, which is used to reduce the backflow or reflection effect of the wake on the outlet boundary.
[0041] Ground boundary (roadbed surface): sliding wall, with a sliding velocity set to 69.44 m / s and a sliding direction consistent with the inlet flow direction, used to simulate the relative motion effect of the ground.
[0042] Train body surface and bogie surface: non-slip walls, used to satisfy the viscous boundary condition that the wall velocity is zero. Other outer boundaries (side and top boundaries): symmetrical planar boundaries, used to limit the interference of the side and top boundaries on the flow field.
[0043] The dimensions of the computational domain are explicitly determined by the feature length H: the longitudinal distance from the tip of the front of the vehicle to the entrance boundary is 12H; the longitudinal distance from the tip of the rear of the vehicle to the exit boundary is 30H; the total height of the computational domain is 15H; and the total width of the computational domain is 20H.
[0044] After completing the simulation, the pressure fluctuation curves of each pressure monitoring point are read, and the minimum adsorption negative pressure value of the roadbed surface is determined from the pressure fluctuation curves of all pressure monitoring points. The absolute value is then taken to obtain the characteristic value of the adsorption negative pressure.
[0045] like Figure 4 As shown, Figure 4The vertical axis represents pressure value, and the horizontal axis represents time, in seconds. This embodiment obtains the minimum adsorption negative pressure value on the roadbed surface under the simulated operating conditions of a CRH5G high-speed train running at 250 km / h. = Therefore, the adsorption negative pressure characteristic value is taken as 1306.08 Pa, and this adsorption negative pressure characteristic value is used for the subsequent calculation of the critical take-off condition for foreign matter adsorption.
[0046] Set gravitational acceleration Setting the material density for different types of foreign objects: ballast ,ice Set the density of the icing ballast mixture: Set the ballast-to-ice volume ratio. At that time, based on the formula:
[0047] The density of the icing ballast mixture can be calculated. .
[0048] For the CRH5G high-speed train (250km / h): For pure ballast (spherical), the critical particle size is calculated as follows ( )for ,quality For pure ice (spherical), the calculated critical particle size is... quality For cubical foreign objects, the critical side length of pure ballast is calculated. Critical side length of pure ice .
[0049] In one embodiment, this application also provides a critical condition prediction system for foreign object adsorption on roadbed of high-speed trains in cold regions, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.
[0050] The above are merely preferred embodiments of this application. It should be noted that this application is not limited to the above embodiments. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should also be considered within the scope of protection of this application.
Claims
1. A method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, characterized in that, include: S1, Obtain the geometric parameters of the train to be predicted, establish a three-dimensional geometric model based on the geometric parameters, and establish a car body coordinate system corresponding to the three-dimensional geometric model; S2, In the vehicle coordinate system, a computational domain of the external flow field around the EMU is constructed based on the three-dimensional geometric model, and multiple pressure monitoring points are set at the positions corresponding to the roadbed surface in the computational domain of the external flow field; boundary conditions are set for the computational domain of the flow field, and the target train running speed is used as the incoming flow speed for simulation to obtain the pressure fluctuation curve of each pressure monitoring point, and the absolute value of the minimum adsorption negative pressure value of each pressure monitoring point is extracted from the pressure fluctuation curve as the adsorption negative pressure characteristic value. S3, construct the physical model of the foreign object, and set the density parameters and geometry of the foreign object; S4. Based on the mechanical equilibrium relationship between the adsorption negative pressure characteristic value and the gravity of the foreign object, establish the critical take-off condition for foreign object adsorption, which is used to solve based on the density parameter and geometry of the foreign object. S5 outputs the prediction results of the critical conditions for foreign object adsorption corresponding to the target train speed and the roadbed surface. The prediction results include at least the critical size and critical mass of the foreign object.
2. The method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, as described in claim 1, is characterized in that... The foreign object includes at least spherical and cubical foreign objects, with spherical foreign objects having a radius of... Characterization, cubic foreign objects are characterized by their side length Characterization.
3. The method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, as described in claim 2, is characterized in that... In the adsorption critical takeoff condition of S4: The critical equilibrium equation established for the spherical foreign body is: The critical equilibrium equation for the cubic foreign body is: In the formula, The characteristic value of adsorption negative pressure, It is the acceleration due to gravity; The density of the corresponding foreign object; The critical size and critical mass of the foreign object are obtained by solving the critical equilibrium equation based on the geometry and density parameters of the foreign object and the corresponding geometry.
4. The method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, as described in claim 1, is characterized in that... The types of foreign matter include at least: ballast, ice, and a mixture of iced ballast; the density of the iced ballast mixture is determined by the density of the ballast, the density of the ice, and the ballast-to-ice volume ratio. Weighted confirmation, satisfying the calculation formula: In the formula, The density of the ballast; The density of ice, The density of the icing ballast mixture and the volume ratio of ballast to ice. The range of values is .
5. The method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, as described in claim 1, is characterized in that... The geometric parameters of the EMU include at least: body geometric parameters, head shape geometric parameters, simplified bogie geometric parameters, as well as overall length, overall width and overall height; the overall height is selected as the train characteristic dimension, and the size of the flow field calculation domain is determined based on the train characteristic dimension.
6. The method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, as described in claim 1, is characterized in that... The boundary conditions include: the roadbed surface at the bottom of the flow field calculation domain is a sliding wall with the sliding velocity being the same as the incoming flow velocity; the vehicle body and bogie surfaces are non-slip walls; and the remaining boundaries are symmetrical planes.
7. The method for predicting the critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions based on aerodynamics, as described in claim 1, is characterized in that... In S2, the simulation uses a time step to advance the time, and performs pressure sampling at the pressure monitoring point within a preset calculation time to form the pressure fluctuation curve.
8. A system for predicting critical conditions for foreign object adsorption on the roadbed of high-speed trains in cold regions, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.