A multi-field coupling dynamics calculation method for superconducting maglev trains and related products

Abstract

This application discloses a multi-field coupled dynamics calculation method and related products for superconducting maglev trains. The method includes: determining the operating conditions of the superconducting maglev train simulation model based on its operating state information; predicting the aerodynamic forces of the superconducting maglev train simulation model by calling a response surface fitting model corresponding to the operating conditions, and predicting the electromagnetic forces of the superconducting maglev train simulation model by calling an electromagnetic force model corresponding to the operating conditions; superimposing the corresponding components of the aerodynamic forces and electromagnetic forces to obtain the target load, which includes the target magnetic drag, target levitation force, target guiding force, target magnetic drag torque, target levitation torque, and target guiding torque; and inputting the target load as an external load into the multibody dynamics model to obtain the dynamic response results. Thus, the dynamic response results can effectively reflect the actual motion of the superconducting maglev train under complex operating conditions.

Description

Technical Field

[0001] This application relates to the field of dynamics simulation technology, and in particular to a multi-field coupled dynamics calculation method for superconducting maglev trains and related products. Background Technology

[0002] Superconducting maglev trains have attracted widespread attention due to their high speed, low noise, and high comfort. To evaluate the dynamic performance of superconducting maglev trains during operation, multibody dynamics methods are typically used for modeling and simulation. Analyzing the results of multibody dynamics simulations is of great significance for evaluating the smoothness, comfort, and safety of superconducting maglev train operation.

[0003] However, existing dynamic calculation methods typically only consider the magnetic levitation force generated by the electromagnetic field as the external load input. When a superconducting maglev train operates at high speed, aerodynamic forces play a major role in the external forces acting on it. Because current dynamic calculations only use electromagnetic force as the external load, they neglect the role of aerodynamic forces during the train's operation. Therefore, it is difficult to accurately reflect the actual dynamic response of the superconducting maglev train under complex operating conditions, and it is also difficult to fully assess its operational safety, stability, and comfort under high dynamic response conditions. Summary of the Invention

[0004] This application provides a multi-field coupled dynamics calculation method and related products for superconducting maglev trains, which can more accurately evaluate the running stability, comfort and safety of superconducting maglev trains.

[0005] In a first aspect, embodiments of this application provide a method for calculating the multi-field coupled dynamics of a superconducting maglev train, the method comprising: Based on the operating status information of the superconducting maglev train simulation model, the operating conditions of the superconducting maglev train simulation model are determined. Based on the operating conditions, the aerodynamic forces of the superconducting maglev train simulation model are predicted by calling the response surface fitting model corresponding to the operating conditions, and the electromagnetic forces of the superconducting maglev train simulation model are predicted by calling the electromagnetic force model corresponding to the operating conditions. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment and yaw moment, and the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag moment, levitation moment and guiding moment. The corresponding components of the aerodynamic force and the electromagnetic force are superimposed to obtain the target load, which includes the target magnetic drag, the target levitation force, the target guiding force, the target magnetic drag torque, the target levitation torque, and the target guiding torque. The target load is input as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity, and acceleration of the superconducting maglev train simulation model.

[0006] One feasible implementation method involves constructing the response surface fitting model through the following steps: Obtain the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions, wherein the aerodynamic data set is the aerodynamic force of the superconducting maglev train simulation model under different operating conditions; Based on the aerodynamic dataset, a response surface model is constructed, which is used to predict the aerodynamic forces of the superconducting maglev train simulation model under given operating conditions.

[0007] One feasible implementation method, wherein determining the operating conditions of the superconducting maglev train simulation model based on its operating status information includes: The operating status information of the superconducting maglev train simulation model is obtained, including the travel speed, vertical height and track coordinate information; Based on the vertical height, the motion state of the superconducting maglev train simulation model is determined, including the suspension state and the running state. Based on the line coordinate information, the crosswind speed and line conditions of the superconducting maglev train simulation model are determined, including open line conditions and tunnel conditions. Based on the travel speed, the crosswind speed, the motion state, and the track conditions, the operating conditions of the superconducting maglev train simulation model are determined.

[0008] One feasible implementation method, wherein determining the motion state of the superconducting maglev train simulation model based on the vertical height, includes: If the vertical height is greater than the target height threshold, the simulation model of the superconducting maglev train is determined to be in the suspended state. If the vertical height is less than or equal to the target height threshold, the superconducting maglev train simulation model is determined to be in the running state.

[0009] One feasible implementation, wherein the superposition of the corresponding components of the aerodynamic force and the electromagnetic force to obtain the target load, includes: The target load is obtained by vector superposition of the corresponding components of the aerodynamic force and the electromagnetic force in the same direction of action.

[0010] In one feasible implementation, before obtaining the aerodynamic data sets of the superconducting maglev train simulation model under different operating conditions, the method further includes: Based on the actual structural dimensions of the superconducting maglev train, a simulation model of the superconducting maglev train is constructed. Aerodynamic simulation calculations were performed on the superconducting maglev train simulation model to obtain aerodynamic data sets of the superconducting maglev train simulation model under different operating conditions.

[0011] One feasible implementation method includes obtaining the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions, comprising: Obtain the initial aerodynamic data set of the superconducting maglev train simulation model under different operating conditions; Based on a preset interpolation model, the initial aerodynamic data set is interpolated in the direction of the travel speed to obtain the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions.

[0012] Secondly, embodiments of this application provide a multi-field coupled dynamics calculation device for a superconducting maglev train, characterized in that it includes: The operating condition determination module is used to determine the operating conditions of the superconducting maglev train simulation model based on the operating status information of the superconducting maglev train simulation model. The dynamic determination module is used to predict the aerodynamic forces of the superconducting maglev train simulation model by calling the response surface fitting model corresponding to the operating conditions, and to predict the electromagnetic forces of the superconducting maglev train simulation model by calling the electromagnetic force model corresponding to the operating conditions. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment and yaw moment, and the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag moment, levitation moment and guiding moment. The dynamic superposition module is used to superimpose the corresponding components of the aerodynamic force and the electromagnetic force to obtain the target load, which includes the target magnetic drag, the target levitation force, the target guiding force, the target magnetic drag torque, the target levitation torque, and the target guiding torque. The dynamic response module is used to input the target load as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity and acceleration of the superconducting maglev train simulation model.

[0013] Thirdly, embodiments of this application provide an electronic device, the device including: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store a program, which includes instructions that, when executed by the processor, cause the processor to perform any of the implementation steps of the above-described method for calculating the multi-field coupled dynamics of superconducting maglev trains.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a computer program, which, when executed by a terminal device, performs any of the implementation steps of the above-described method for calculating the multi-field coupled dynamics of a superconducting maglev train.

[0015] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: In this embodiment, firstly, the operating conditions of the superconducting maglev train simulation model are determined based on its operational status information. Then, based on these operating conditions, a response surface fitting model corresponding to the operating conditions is invoked to predict the aerodynamic forces of the superconducting maglev train simulation model, and an electromagnetic force model corresponding to the operating conditions is invoked to predict the electromagnetic forces of the superconducting maglev train simulation model. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment, and yaw moment; the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag torque, levitation torque, and guiding torque. Next, the corresponding components of the aerodynamic forces and electromagnetic forces are superimposed to obtain the target load, which includes the target magnetic drag, target levitation force, target guiding force, target magnetic drag torque, target levitation torque, and target guiding torque. Finally, the target load is input as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity, and acceleration of the superconducting maglev train simulation model.

[0016] As can be seen, this application, based on the determined operating conditions, calls the corresponding response surface fitting model to predict the aerodynamic forces of the superconducting maglev train simulation model, and calls the corresponding electromagnetic force model to predict the electromagnetic forces of the superconducting maglev train simulation model. The corresponding components of the aerodynamic and electromagnetic forces are superimposed to obtain the target load. This target load is then input as an external load into the multibody dynamics model to obtain the dynamic response results, thereby achieving multi-field coupled simulation. Compared with existing technologies, this scheme can simultaneously consider the influence of aerodynamic and electromagnetic forces on the motion state of the superconducting maglev train during the dynamic calculation process. This allows the dynamic response results to effectively reflect the actual motion of the superconducting maglev train under complex operating conditions, thus enabling a more accurate assessment of the superconducting maglev train's operational stability, comfort, and safety. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a multi-field coupled dynamics calculation method for a superconducting maglev train, provided as an embodiment of this application; Figure 2 This is a schematic diagram of an interpolation result provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the fitting effect of a response surface fitting model provided in an embodiment of this application; Figure 4 A framework diagram for multi-field coupled dynamics co-simulation of a superconducting maglev train provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of a multi-field coupled dynamics calculation device for a superconducting maglev train, provided in an embodiment of this application. Detailed Implementation

[0018] Before starting the embodiments of this application, in order to facilitate understanding of the technical solutions of this application, the technical terms involved in the embodiments of this application will be described in detail first.

[0019] Superconducting maglev trains are maglev transportation systems that utilize superconducting electric maglev principles to achieve high-speed operation. These trains employ an articulated coupling structure, relying on running wheels for support and guide wheels for guidance at low speeds. When the operating speed reaches 150 km / h, the train achieves stable levitation and guidance through the electromagnetic force generated by the relative motion between the onboard superconducting magnets and the track coils.

[0020] Real-Time Dynamics Simulation (RTMS) refers to a computational technique that uses a computational rate synchronized with actual physical time to numerically solve and dynamically simulate the motion state and force behavior of objects, systems, or multibody dynamic systems.

[0021] Model Order Reduction (MOR) refers to a class of mathematical modeling and engineering analysis methods that transform high-dimensional complex models into low-dimensional approximate models while preserving the main dynamic characteristics and input-output response features of the system.

[0022] As mentioned earlier, existing superconducting maglev train dynamics simulations employ a multibody dynamics simulation framework, using SIMPACK dynamics simulation software as the simulation platform. This platform constructs dynamic models including moving components such as the car body and suspension frame, as well as response constraint relationships. Simultaneously, to achieve coupling with electromagnetic field calculation results, a lookup table method is typically used for interaction. This involves organizing the electromagnetic force database obtained from external magnetic field calculations using MATLAB or Simulink to construct a lookup table module. Subsequently, the overall simulation process is built within the Simulink environment, achieving coupled calculation between electromagnetic forces and the dynamic model. Specifically, by using the electromagnetic forces acting on the superconducting maglev train as input loads, the dynamics solver obtains the suspension displacement and guidance displacement responses of the superconducting maglev train during operation, and based on this, assessments are conducted on vehicle running stability, ride comfort, and structural safety.

[0023] Therefore, existing dynamic calculation methods typically only consider the magnetic levitation force generated by the electromagnetic field as an external load input. However, when a superconducting maglev train operates at high speeds, aerodynamic forces play a major role in the external forces acting on it. Specifically, the aerodynamic effects generated during the operation of a superconducting maglev train are one of the key factors in evaluating its aerodynamic performance. Especially for high-temperature superconducting maglev trains operating at speeds up to 600 km / h, aerodynamic forces become the dominant component of the external forces acting on the train. If the aerodynamic effects are ignored in the dynamic modeling process, it will be difficult to accurately characterize the true dynamic response characteristics of the superconducting train during operation. When conducting high-precision dynamic analysis of the operating state of a superconducting maglev train, aerodynamic forces must be incorporated into the modeling system, constructing a multi-field coupled dynamic calculation method that includes electromagnetic fields and aerodynamic effects. Simultaneously, the dynamic evolution law of the train under real-time high-dynamic response conditions and its safety redundancy characteristics should also be comprehensively considered to improve the engineering reliability of the simulation analysis results and the adequacy of the safety assessment.

[0024] Therefore, existing dynamic calculations only use electromagnetic force as the external load, neglecting the role of aerodynamic forces in the operation of superconducting maglev trains. Consequently, it is difficult to accurately reflect the actual dynamic response of superconducting maglev trains under complex operating conditions, and it is also difficult to fully assess their operational safety, stability, and comfort under high dynamic response conditions.

[0025] To address the aforementioned problems, this application provides a multi-field coupled dynamics calculation method for superconducting maglev trains. First, based on the operating state information of the superconducting maglev train simulation model, the operating conditions are determined. Then, based on the determined operating conditions, a response surface fitting model is used to predict the aerodynamic forces of the superconducting maglev train simulation model, and an electromagnetic force model is used to predict the electromagnetic forces of the superconducting maglev train simulation model. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment, and yaw moment; the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag torque, levitation torque, and guiding torque. Next, the corresponding components of the aerodynamic forces and electromagnetic forces are superimposed to obtain the target load, which includes the target magnetic drag, target levitation force, target guiding force, target magnetic drag torque, target levitation torque, and target guiding torque. Finally, the target load is input as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity, and acceleration of the superconducting maglev train simulation model.

[0026] As can be seen, this application, based on the determined operating conditions, calls a response surface model to predict the aerodynamic forces of the superconducting maglev train simulation model and an electromagnetic force model to predict the electromagnetic forces of the superconducting maglev train simulation model. The corresponding components of the aerodynamic and electromagnetic forces are superimposed to obtain the target load. This target load is then input as an external load into the multibody dynamics model to obtain the dynamic response results, thereby achieving multi-field coupled simulation. Compared with existing technologies, this scheme can simultaneously consider the influence of aerodynamic and electromagnetic forces on the motion state of the superconducting maglev train during the dynamic calculation process. This allows the dynamic response results to effectively reflect the actual motion of the superconducting maglev train under complex operating conditions, thus enabling a more accurate assessment of the superconducting maglev train's operational stability, comfort, and safety.

[0027] It should be noted that the implementation subject of the multi-field coupled dynamics calculation method for superconducting maglev trains in this application embodiment is not limited. For example, the multi-field coupled dynamics calculation method for superconducting maglev trains in this application embodiment can be applied to information processing devices such as servers or terminal devices. The server can be a standalone server, a cluster server, or a cloud server. The terminal device can be an electronic device such as a smartphone, computer, personal digital assistant (PDA), or tablet computer.

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0029] Figure 1 This is a flowchart illustrating a multi-field coupled dynamics calculation method for a superconducting maglev train, provided as an embodiment of this application. (Combined with...) Figure 1 As shown, it may include steps S101-S104.

[0030] S101: Based on the operating status information of the superconducting maglev train simulation model, determine the operating conditions of the superconducting maglev train simulation model.

[0031] In this embodiment, the operational status information of the superconducting maglev train simulation model is first obtained, including the travel speed, vertical height, and track coordinates. Next, based on the vertical height, the motion state of the superconducting maglev train simulation model can be determined, including both levitation and running states. Based on the track coordinates, the crosswind speed and track conditions of the superconducting maglev train simulation model can be determined, including both open-track and tunnel conditions. Finally, based on the travel speed, crosswind speed, motion state, and track conditions, the operational status of the superconducting maglev train simulation model is determined.

[0032] It should be noted that the operational status information in this embodiment is obtained by constructing an input interface for an external calculation function. For example, in the SIMPACK-to-external calculation function interface SIMAT, a real-time updated "state vector" is constructed as the input to the external calculation function. That is, an environment variable interface is added to the SIMAT output list. By reading the line coordinate information, it is determined whether the current location is on an open track or in a tunnel, and the current crosswind speed (m / s) is transmitted to the external calculation function in real time. Simultaneously, the original real-time output of the superconducting maglev train's speed (km / h) is maintained, and the vertical height of the train body relative to the track plane is added as a physical basis for judging the motion state. Specifically, if the vertical height is greater than a target height threshold, the superconducting maglev train simulation model can be determined to be in a suspended state; if the vertical height is less than or equal to the target height threshold, the superconducting maglev train simulation model is determined to be in a running state.

[0033] Therefore, this application demonstrates that it can obtain the real-time operating status of the superconducting maglev train simulation model through external calculation functions, thereby determining the travel speed, crosswind speed, motion state, and track conditions. Based on this, a judgment process for the superconducting maglev train in both running and levitation states is added to the real-time simulation architecture of superconducting maglev train dynamics, solving the problem that the dynamic response of the superconducting maglev train cannot be accurately reflected in real time under high-speed dynamic changes. Simultaneously, this method overcomes the limitation of traditional methods that cannot simultaneously consider the resistance forms in both running and levitation states during multi-field coupled dynamic simulation calculations, thus realizing multi-field coupled dynamic calculations.

[0034] S102: Based on the operating conditions, the response surface fitting model corresponding to the operating conditions is called to predict the aerodynamic forces of the superconducting maglev train simulation model, and the electromagnetic force model corresponding to the operating conditions is called to predict the electromagnetic forces of the superconducting maglev train simulation model. Among them, the aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment and yaw moment, and the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag moment, levitation moment and guiding moment.

[0035] In this embodiment, the response surface fitting model is constructed through the following steps: A simulation model of the superconducting maglev train is built based on its actual structural dimensions. Alternatively, a 1:20 coarse simulation model of the superconducting maglev train is constructed based on its actual structural dimensions.

[0036] Next, aerodynamic simulation calculations are performed on the superconducting maglev train simulation model. By setting the driving speed, crosswind speed, motion state, and track conditions of the superconducting maglev train simulation model, aerodynamic data sets of the superconducting maglev train simulation model under different operating conditions can be obtained.

[0037] Specifically, the simulation model of the superconducting maglev train can be set to eight speed levels: 50km / h, 150km / h, 250km / h, 350km / h, 450km / h, 550km / h, 600km / h, and 650km / h; six crosswind speed levels: 0m / s, 5m / s, 10m / s, 15m / s, 20m / s, and 25m / s; two motion states: running or suspended; and two track conditions: open track or tunnel.

[0038] It should be noted that, due to the different levels of each factor, this is a mixed-level experimental design. To achieve sufficient coverage of multi-factor combined operating conditions while ensuring controllable computational costs, a mixed-level factor table is constructed, and a corresponding mixed-type orthogonal array L is selected or constructed. k (8 1 6 1 2 2 ), where k represents the number of operating condition combinations in the orthogonal array. To ensure sample coverage and statistical balance, at least 32 or 48 operating conditions are usually selected for combination design, so as to maintain the representativeness and orthogonality of factor combinations while reducing the computational scale.

[0039] In addition, before conducting aerodynamic calculations, it is necessary to convert the coded values ​​in the orthogonal table into actual physical parameters (including specific driving speed, crosswind speed, motion state and line conditions) based on the pre-established factor-level correspondence, thereby forming a combination of operating conditions with clear physical meaning.

[0040] Therefore, the embodiments of this application perform aerodynamic simulation calculations on the superconducting maglev train simulation model under different operating conditions, which can effectively overcome the problem that existing simulation calculations only perform dynamic calculations for a single speed level or a single operating condition, thereby obtaining an aerodynamic dataset covering multiple speeds and multiple operating conditions.

[0041] Based on this, aerodynamic calculations were performed on the simulation model of the superconducting maglev train under different operating conditions, obtaining the aerodynamic coefficients of the simulation model under different operating conditions. These aerodynamic coefficients include the drag coefficient. Lift coefficient Lateral force coefficient Rolling moment coefficient Pitch moment coefficient and yaw moment coefficient .

[0042] Based on the aerodynamic force conversion formula, the aerodynamic force coefficient is converted into an aerodynamic force. The aerodynamic force conversion formula is as follows: ; in, , , , , and These are drag, lift, lateral force, roll moment, pitch moment, and yaw moment. The density of air is usually taken as 1.225 kg / m³. 3 , The speed of the simulation model of the superconducting maglev train. This represents the cross-sectional area of ​​the simulation model of the superconducting maglev train. The height of the simulation model of the superconducting maglev train.

[0043] Furthermore, based on this aerodynamic force, an initial aerodynamic force dataset is constructed. Based on a preset interpolation model, the initial aerodynamic force dataset is interpolated in the direction of travel speed to obtain the aerodynamic force dataset of the superconducting maglev train simulation model under different operating conditions.

[0044] Specifically, in the embodiments of this application, the preset interpolation model can be a Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). By constructing piecewise cubic polynomial functions between adjacent data points, it achieves an interpolation effect with continuous function values ​​and continuous first derivatives, while maintaining the shape characteristics and monotonicity of the original data and avoiding the overshoot phenomenon that may occur in traditional cubic spline interpolation. Figure 2 This is a schematic diagram of an interpolation result provided in an embodiment of this application, combined with... Figure 2As shown, the figure contains eight sub-figures, each corresponding to one of eight different superconducting maglev train simulation models. The blue hollow circles represent the original data points for each superconducting maglev train simulation model at its corresponding speed, and the red solid lines represent the continuous curves fitted using the PCHIP conformal interpolation method. The results show that the interpolated curves accurately fit the original data points, maintaining the continuity of the first derivative and the smoothness of the curve, while strictly preserving the data's variation trend and monotonic characteristics. This verifies the applicability and computational accuracy of the interpolation method under multiple operating conditions and model scenarios.

[0045] Based on the aerodynamic dataset, a response surface model was constructed to predict the aerodynamic forces of a superconducting maglev train simulation model under given operating conditions. It should be noted that the aerodynamic dataset can be fitted and modeled using a deep learning convolutional neural network. Figure 3 This is a schematic diagram illustrating the fitting effect of a response surface fitting model provided in an embodiment of this application, combined with... Figure 3 As shown, the eight sub-plots in the figure correspond to eight different simulation models of superconducting maglev trains. The relative residuals of the corresponding response surface fitting models are labeled above each sub-plot. For example, the relative residuals of the response surface model constructed from the aerodynamic dataset corresponding to train 1 are 0.003%, train 2 is 0.002%, train 3 is 0.001%, train 4 is 0.001%, train 5 is 0.001%, train 6 is 0.001%, train 7 is 0.002%, and train 8 is 0.002%. Since the relative residuals of the fitting models of each superconducting maglev train simulation model are controlled within a very small range, and the surfaces of each sub-plot are smooth and continuous, clearly showing the nonlinear mapping relationship between travel speed, crosswind speed, and aerodynamic forces, it indicates that the constructed response surface fitting models have good smoothing effects and can accurately predict aerodynamic forces under different operating conditions.

[0046] Specifically, Table 1 shows the average relative residual of a response surface fitting model provided in the embodiments of this application. As can be seen from Table 1, for vehicles 1 to 8, the maximum average relative residual of the response surface fitting model constructed in this application is only 0.0071%.

[0047] Table 1. Mean relative residuals of the response surface fitting model

[0048] Table 2 shows the maximum relative residual of a response surface fitting model provided in the embodiments of this application. According to Table 2, for vehicles 1 to 8, the maximum value of the maximum relative residual of the response surface fitting model constructed in this application is 1.167%.

[0049] Table 2 Maximum relative residuals of the response surface fitting model

[0050] Furthermore, the electromagnetic force model in this embodiment is a lookup calculation model based on a pre-established electromagnetic force database, which can predict electromagnetic forces according to operating conditions. The electromagnetic force database is obtained through finite element simulation of electromagnetic fields under different operating conditions, and includes magnetic resistance, levitation force, guiding force, magnetic resistance torque, levitation torque, and guiding torque of the superconducting maglev train under different travel speeds, crosswind speeds, motion states, and track conditions.

[0051] Therefore, when the electromagnetic force model makes predictions, it calls the electromagnetic force database corresponding to the operating conditions, uses the real-time operating conditions as the query basis, completes the matching and interpolation calculation of the operating conditions in the electromagnetic force database, and quickly outputs the corresponding magnetic drag, levitation force, guiding force, magnetic drag torque, levitation torque and guiding torque under the real-time operating conditions, so as to achieve electromagnetic force prediction that is accurately adapted to the operating conditions.

[0052] S103: Superimpose the corresponding components of aerodynamic force and electromagnetic force to obtain the target load, which includes target magnetic drag, target levitation force, target guiding force, target magnetic drag torque, target levitation torque and target guiding torque.

[0053] In this embodiment, the aerodynamic force predicted by the response surface fitting model and the corresponding component of the electromagnetic force predicted by the electromagnetic force model are superimposed. That is, in the centroid coordinate system of the superconducting maglev train simulation model, the corresponding components of the aerodynamic force and the electromagnetic force are vector superimposed in the same direction of action to obtain the target load.

[0054] Specifically, the target magnetic drag is obtained by superimposing drag and magnetic drag; the target magnetic levitation force is obtained by superimposing lift and magnetic levitation force; the target guiding force is obtained by superimposing lateral force and guiding force; the target magnetic drag torque is obtained by superimposing roll torque and magnetic drag torque; the target levitation torque is obtained by superimposing pitch torque and levitation torque; and the target guiding torque is obtained by superimposing yaw torque and guiding torque. Using this method, a comprehensive target load can be generated in the center-of-mass coordinate system for each superconducting maglev train simulation model from car 1 to car 8, for subsequent dynamic analysis.

[0055] It should be noted that when the simulation model of the superconducting maglev train is in a suspended state, the magnetic drag is 0, and the target magnetic drag equals the drag. Furthermore, the electromagnetic force model in this embodiment can be a general electromagnetic field analytical model or a finite element-based electromagnetic calculation model, used to calculate magnetic drag, magnetic levitation force, guiding force, magnetic drag torque, levitation torque, and guiding torque.

[0056] S104: Input the target load as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity and acceleration of the superconducting maglev train simulation model.

[0057] In this embodiment, the target load is input as an external load into the multibody dynamics model to obtain dynamic response results. These results include the position, velocity, and acceleration of the superconducting maglev train simulation model. Based on these dynamic response results, the operational stability, comfort, and safety of the superconducting maglev train can be more accurately assessed.

[0058] Furthermore, by feeding back the updated motion state to an external calculation function and using it for target load judgment and indexing in the next time step, real-time simulation of multi-field coupled response under dynamic velocity changes is achieved.

[0059] Based on the relevant content of steps S101-S104 above, in this embodiment, firstly, the operating conditions of the superconducting maglev train simulation model are determined based on the operating state information of the model. Then, based on these operating conditions, the aerodynamic forces of the superconducting maglev train simulation model are predicted by calling the response surface fitting model corresponding to the operating conditions, and the electromagnetic forces of the superconducting maglev train simulation model are predicted by calling the electromagnetic force model corresponding to the operating conditions. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment, and yaw moment; the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag torque, levitation torque, and guiding torque. Next, the corresponding components of the aerodynamic forces and electromagnetic forces are superimposed to obtain the target load, which includes the target magnetic drag, target levitation force, target guiding force, target magnetic drag torque, target levitation torque, and target guiding torque. Finally, the target load is input as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity, and acceleration of the superconducting maglev train simulation model.

[0060] As can be seen, this application, based on the determined operating conditions, calls the corresponding response surface fitting model to predict the aerodynamic forces of the superconducting maglev train simulation model, and calls the corresponding electromagnetic force model to predict the electromagnetic forces of the superconducting maglev train simulation model. The corresponding components of the aerodynamic and electromagnetic forces are superimposed to obtain the target load. This target load is then input as an external load into the multibody dynamics model to obtain the dynamic response results, thereby achieving multi-field coupled simulation. Compared with existing technologies, this scheme can simultaneously consider the influence of aerodynamic and electromagnetic forces on the motion state of the superconducting maglev train during the dynamic calculation process. This allows the dynamic response results to effectively reflect the actual motion of the superconducting maglev train under complex operating conditions, thus enabling a more accurate assessment of the superconducting maglev train's operational stability, comfort, and safety.

[0061] Furthermore, for ease of understanding, the embodiments of this application can also be described from the perspective of the overall framework in conjunction with the accompanying drawings.

[0062] Figure 4 This application provides a framework diagram for a multi-field coupled dynamics co-simulation of a superconducting maglev train, combined with... Figure 4 As shown, this embodiment of the application uses a vehicle kinematics model (MATLAB / Simulink) as a foundation, connected to a traction control simulation system, to provide control and motion input information during the operation of the superconducting maglev train. Specifically, the vehicle kinematics model inputs information such as travel speed and track conditions into the SIMPACK system. The multibody dynamics model on the SIMPACK platform solves for the dynamic response of the pre-established superconducting maglev train simulation model. The dynamic response solution outputs the displacement and attitude angles of the superconducting maglev train simulation model. Specifically, the displacement includes vertical, lateral, and longitudinal displacements, and the attitude angles include roll angle, pitch angle, and yaw angle.

[0063] Next, the aforementioned travel speed, displacement, and attitude angle are transmitted to the downstream calculation module via reflected memory. Specifically, the displacement, attitude angle, and travel speed of the superconducting maglev train simulation model are used as inputs. The model is then queried through a pre-established electromagnetic force database, i.e., the magnetic drag and torque, levitation force and torque, and guiding force and torque are obtained through a lookup table method. The real-time travel speed, crosswind speed, motion state, and track conditions of the superconducting maglev train simulation model are obtained through an environmental variable interface. The information obtained from the environmental variable interface is input into a pre-established response surface fitting model to calculate the first drag and roll torque, the first lift and pitch torque, and the first lateral force and yaw torque.

[0064] Subsequently, the magnetic drag and torque, levitation force and torque, and guiding force and torque calculated by electromagnetic force are superimposed with the stress and torque of the first drag and roll torque, first lift and pitch torque, and first lateral force and yaw torque calculated by aerodynamic force to obtain the target magnetic drag and torque, target levitation force and torque, and target guiding force and torque. Finally, the obtained target magnetic drag and torque, target levitation force and torque, and target guiding force and torque are transferred back to SIMPACK to update the dynamic response of the superconducting maglev train simulation model.

[0065] Furthermore, Figure 5 This is a schematic diagram of the structure of a multi-field coupled dynamics calculation device for a superconducting maglev train, provided as an embodiment of this application. (Combined with...) Figure 5 As shown in the embodiments of this application, the multi-field coupled dynamics calculation device 500 for superconducting maglev trains may include: The operating condition determination module 501 is used to determine the operating condition of the superconducting maglev train simulation model based on the operating status information of the superconducting maglev train simulation model. The dynamic determination module 502 is used to predict the aerodynamic forces of the superconducting maglev train simulation model by calling the response surface fitting model corresponding to the operating conditions, and to predict the electromagnetic forces of the superconducting maglev train simulation model by calling the electromagnetic force model corresponding to the operating conditions. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment and yaw moment, and the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag moment, levitation moment and guiding moment. The dynamic superposition module 503 is used to superimpose the corresponding components of the aerodynamic force and the electromagnetic force to obtain the target load, wherein the target load includes the target magnetic drag, the target levitation force, the target guiding force, the target magnetic drag torque, the target levitation torque and the target guiding torque; The dynamic response module 504 is used to input the target load as an external load into the multibody dynamics model to obtain dynamic response results, which include the position, velocity and acceleration of the superconducting maglev train simulation model.

[0066] Optionally, the multi-field coupled dynamics calculation device 500 for the superconducting maglev train may include: The dataset acquisition module is used to acquire the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions, wherein the aerodynamic data set is the aerodynamic force of the superconducting maglev train simulation model under different operating conditions. The model building module is used to build a response surface fitting model based on the aerodynamic dataset. The response surface fitting model is used to predict the aerodynamic forces of the superconducting maglev train simulation model under given operating conditions.

[0067] Optionally, the operating condition determination module 501 may include: The motion state acquisition module is used to acquire the running state information of the superconducting maglev train simulation model, including the running speed, vertical height and track coordinate information; The motion state determination module is used to determine the motion state of the superconducting maglev train simulation model based on the vertical height. The motion state includes a suspension state and a running state. The line condition determination module is used to determine the crosswind speed and line condition of the superconducting maglev train simulation model based on the line coordinate information. The line condition includes open line condition and tunnel condition. The operating condition determination module is used to determine the operating conditions of the superconducting maglev train simulation model based on the travel speed, the crosswind speed, the motion state, and the track conditions.

[0068] Optionally, the motion state determination module is specifically used for: If the vertical height is greater than the target height threshold, the simulation model of the superconducting maglev train is determined to be in the suspended state. If the vertical height is less than or equal to the target height threshold, the superconducting maglev train simulation model is determined to be in the running state.

[0069] Optionally, the power superposition module 503 is specifically used for: The target load is obtained by vector superposition of the corresponding components of the aerodynamic force and the electromagnetic force in the same direction of action.

[0070] Optionally, the multi-field coupled dynamics calculation device 500 for the superconducting maglev train may include: The model building module is used to construct a simulation model of the superconducting maglev train based on its actual structural dimensions. The simulation calculation module is used to perform aerodynamic simulation calculations on the superconducting maglev train simulation model to obtain aerodynamic data sets of the superconducting maglev train simulation model under different operating conditions.

[0071] Optionally, the dataset acquisition module is specifically used for: Obtain the initial aerodynamic data set of the superconducting maglev train simulation model under different operating conditions; Based on a preset interpolation model, the initial aerodynamic data set is interpolated in the direction of the travel speed to obtain the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions.

[0072] Furthermore, embodiments of this application also provide an electronic device, including: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store one or more programs, the one or more programs including instructions, which, when executed by the processor, cause the processor to perform any of the implementation steps of the above-described superconducting maglev train multi-field coupled dynamics calculation method.

[0073] Furthermore, embodiments of this application also provide a computer-readable storage medium for storing a computer program, which, when executed by a terminal device, implements any of the implementation steps of the above-described method for calculating the multi-field coupled dynamics of superconducting maglev trains.

[0074] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application. It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on describing the differences from other embodiments. The same or similar parts between the various embodiments can be referred to mutually.

[0075] The system disclosed in the embodiments is described simply because it corresponds to the method disclosed in the embodiments; relevant details can be found in the method section.

[0076] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0077] 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 calculating the multi-field coupled dynamics of a superconducting maglev train, characterized in that, The method includes: Based on the operating status information of the superconducting maglev train simulation model, the operating conditions of the superconducting maglev train simulation model are determined. Based on the operating conditions, the aerodynamic forces of the superconducting maglev train simulation model are predicted by calling the response surface fitting model corresponding to the operating conditions, and the electromagnetic forces of the superconducting maglev train simulation model are predicted by calling the electromagnetic force model corresponding to the operating conditions. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment and yaw moment, and the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag moment, levitation moment and guiding moment. The corresponding components of the aerodynamic force and the electromagnetic force are superimposed to obtain the target load, which includes the target magnetic drag, the target levitation force, the target guiding force, the target magnetic drag torque, the target levitation torque, and the target guiding torque. The target load is input as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity, and acceleration of the superconducting maglev train simulation model.

2. The method according to claim 1, characterized in that, The response surface fitting model is constructed through the following steps: Obtain the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions, wherein the aerodynamic data set is the aerodynamic force of the superconducting maglev train simulation model under different operating conditions; Based on the aerodynamic dataset, a response surface model is constructed, which is used to predict the aerodynamic forces of the superconducting maglev train simulation model under given operating conditions.

3. The method according to claim 1, characterized in that, The determination of the operating conditions of the superconducting maglev train simulation model based on its operating status information includes: The operating status information of the superconducting maglev train simulation model is obtained, including the travel speed, vertical height and track coordinate information; Based on the vertical height, the motion state of the superconducting maglev train simulation model is determined, including the suspension state and the running state. Based on the line coordinate information, the crosswind speed and line conditions of the superconducting maglev train simulation model are determined, including open line conditions and tunnel conditions. Based on the travel speed, the crosswind speed, the motion state, and the track conditions, the operating conditions of the superconducting maglev train simulation model are determined.

4. The method according to claim 3, characterized in that, Determining the motion state of the superconducting maglev train simulation model based on the vertical height includes: If the vertical height is greater than the target height threshold, the simulation model of the superconducting maglev train is determined to be in the suspended state. If the vertical height is less than or equal to the target height threshold, the superconducting maglev train simulation model is determined to be in the running state.

5. The method according to claim 1, characterized in that, The step of superimposing the corresponding components of the aerodynamic force and the electromagnetic force to obtain the target load includes: The target load is obtained by vector superposition of the corresponding components of the aerodynamic force and the electromagnetic force in the same direction of action.

6. The method according to claim 2, characterized in that, Before obtaining the aerodynamic data sets of the superconducting maglev train simulation model under different operating conditions, the method further includes: Based on the actual structural dimensions of the superconducting maglev train, a simulation model of the superconducting maglev train is constructed. Aerodynamic simulation calculations were performed on the superconducting maglev train simulation model to obtain aerodynamic data sets of the superconducting maglev train simulation model under different operating conditions.

7. The method according to claim 2, characterized in that, The process of obtaining the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions includes: Obtain the initial aerodynamic data set of the superconducting maglev train simulation model under different operating conditions; Based on a preset interpolation model, the initial aerodynamic data set is interpolated in the direction of the travel speed to obtain the aerodynamic data set of the superconducting maglev train simulation model under different operating conditions.

8. A multi-field coupled dynamics calculation device for a superconducting maglev train, characterized in that, include: The operating condition determination module is used to determine the operating conditions of the superconducting maglev train simulation model based on the operating status information of the superconducting maglev train simulation model. The dynamic determination module is used to predict the aerodynamic forces of the superconducting maglev train simulation model by calling the response surface fitting model corresponding to the operating conditions, and to predict the electromagnetic forces of the superconducting maglev train simulation model by calling the electromagnetic force model corresponding to the operating conditions. The aerodynamic forces include drag, lift, lateral force, roll moment, pitch moment and yaw moment, and the electromagnetic forces include magnetic drag, levitation force, guiding force, magnetic drag moment, levitation moment and guiding moment. The dynamic superposition module is used to superimpose the corresponding components of the aerodynamic force and the electromagnetic force to obtain the target load, which includes the target magnetic drag, the target levitation force, the target guiding force, the target magnetic drag torque, the target levitation torque, and the target guiding torque. The dynamic response module is used to input the target load as an external load into the multibody dynamics model to obtain the dynamic response results, which include the position, velocity and acceleration of the superconducting maglev train simulation model.

9. An electronic device, characterized in that, The device includes: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store a program, the program including instructions that, when executed by the processor, cause the processor to perform the steps of the multi-field coupled dynamics calculation method for superconducting maglev trains according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which, when executed by a terminal device, implements the steps of the multi-field coupled dynamics calculation method for superconducting maglev trains as described in any one of claims 1 to 7.

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