A direct drive motor simulation method and device based on multi-coupling effect

Abstract

The present invention discloses a direct-drive motor simulation method and device based on multi-coupling effect, which relates to the technical field of direct-drive motor simulation; finite element meshing is performed through a pre-trained machine learning model; material property values ​​are calculated according to temperature; electromagnetic field calculation is performed according to the material property values ​​to obtain electromagnetic loss; windage loss and friction loss are calculated as mechanical loss according to simulation input data; various types of losses are used as heat sources to perform temperature field calculation to obtain temperature distribution; material property values ​​are updated according to temperature distribution, and iterative calculation of magnetothermal coupling is performed; iterative difference is calculated in the process to determine whether to stop iteration. Incorporating friction loss into the calculation process of machine loss makes the heat source data during temperature field calculation more comprehensive and rich, and iterative calculation of magnetothermal bidirectional coupling is performed, which can more accurately simulate the actual operation of the direct-drive motor and improve the accuracy of the simulation results.

Description

Technical Field

[0001] The present invention relates to the technical field of direct drive motor simulation, and in particular to a direct drive motor simulation method and device based on multi-coupling effect. Background Art

[0002] A direct-drive motor is a device that converts electrical energy directly into mechanical energy without going through an intermediate transmission mechanism such as a gearbox or belt. This type of motor has the advantages of high efficiency, high power density, high precision, and fast response, so it has been widely used in wind power, new energy vehicles, industrial automation, and other fields. In the research and development of direct-drive motors, simulation methods play a vital role. Through simulation, the actual operating environment can be simulated, the performance of the motor can be predicted and evaluated, and a theoretical basis can be provided for the design and optimization of the motor. However, the direct-drive motor involves the coupling of multiple physical fields such as electromagnetic fields and temperature fields during operation. These coupling effects make the performance prediction and optimization design of the motor complex and difficult.

[0003] Traditional temperature field simulations often do not have high requirements for accuracy. In order to simplify calculations and improve calculation efficiency, the windage loss inside the motor is used as a representation of mechanical loss during temperature calculations, ignoring the temperature rise caused by bearing friction, resulting in low accuracy of simulation results. However, this method can meet the accuracy requirements of a single temperature field, but cannot meet the accuracy requirements of the coupled calculations of the temperature field and other physical fields. As a result, when performing magnetothermal coupling, the influence of the temperature field on the magnetic field cannot be accurately reflected, thereby reducing the accuracy of the simulation. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of low simulation accuracy mentioned in the above background technology, and to propose a direct-drive motor simulation method and device based on multi-coupling effect.

[0005] A first aspect of the present invention provides a direct drive motor simulation method based on multi-coupling effect, the method comprising:

[0006] S101, obtaining simulation input data of a target motor; the simulation input data includes a geometric model, material properties and operating conditions; the operating conditions include an initial temperature;

[0007] S102, generating a first finite element mesh and a second finite element mesh using a pre-trained machine learning model according to the simulation input data;

[0008] S103, calculating a material property value according to the initial temperature and the material property;

[0009] S104, performing electromagnetic field calculation according to the first finite element mesh and the material property value to obtain magnetic field distribution and electromagnetic loss;

[0010] S105, calculating mechanical loss according to the geometric model and the operating conditions; the mechanical loss includes windage loss and friction loss;

[0011] S106, using the electromagnetic loss and the mechanical loss as heat sources, and performing temperature field calculation according to the second finite element grid to obtain temperature distribution;

[0012] S107, updating the material property value according to the temperature distribution, and calculating the iteration difference;

[0013] S108, if the iteration difference is less than the first preset threshold, output the simulation result; otherwise, return to step S104.

[0014] Optionally, generating a first finite element mesh and a second finite element mesh according to the simulation input data includes:

[0015] Extracting first characteristic data according to the simulation input data of the target motor; the first characteristic data includes geometric characteristics, material characteristics and operating condition characteristics;

[0016] Standardizing the first characteristic data to obtain second characteristic data;

[0017] Using the second feature data as an input of a pre-trained first machine learning model to obtain a first initial partitioning strategy, and generating a first initial grid according to the first initial partitioning strategy;

[0018] Performing electromagnetic field calculation according to the first initial grid, and iteratively adjusting the first initial grid according to an error estimation method to obtain a first finite element grid;

[0019] Using the second feature data as an input of a pre-trained second machine learning model to obtain a second initial partitioning strategy, and generating a second initial grid according to the second initial partitioning strategy;

[0020] The temperature field is calculated according to the second initial grid, and the second initial grid is iteratively adjusted according to an error estimation method to obtain a second finite element grid.

[0021] Optionally, calculating the mechanical loss according to the geometric model and the operating conditions includes:

[0022] Calculate windage loss:

[0023] Among them, P m is the required windage loss; A c is the corrected drag coefficient; is the air density; L is the rotor length; ω is the angular velocity of the rotor rotation; D r is the outer diameter of the rotor; rf is the roughness of the rotor surface; k is a constant; is the drag coefficient when the rotor surface is smooth; RE1 is the radial Reynolds number; RE2 is the axial Reynolds number; and is a constant; is the air gap length; μ is the air kinematic viscosity; v is the air axial flow velocity;

[0024] Calculate friction losses:

[0025] Among them, P f is the required friction loss; f is the friction coefficient of the bearing; m is the rotor mass; D r is the outer diameter of the rotor; ω is the angular velocity of the rotor rotation.

[0026] Optionally, taking the electromagnetic loss and the mechanical loss as heat sources and performing temperature field calculation according to the second finite element mesh to obtain the temperature distribution includes:

[0027] Calculating the thermal conductivity of each component of the target motor according to the material property value;

[0028] Calculate the radial Reynolds number of the air in the air gap;

[0029] If the radial Reynolds number is greater than the second preset threshold, the equivalent thermal conductivity of the air in the air gap during flow is calculated:

[0030]

[0031] Among them, C tc is the required equivalent thermal conductivity; RE1 is the radial Reynolds number; S hc is the specific heat capacity of air; μ is the kinematic viscosity of air; C0 is the thermal conductivity of air at rest;

[0032] The thermal conductivity of each component and the equivalent thermal conductivity of the air gap are incorporated into the temperature field boundary conditions, and the temperature field is calculated to obtain the temperature distribution.

[0033] Optionally, updating the material property value according to the temperature distribution and calculating the iterative difference includes:

[0034] Establish multiple thermal-magnetic coupling functions to obtain material property values ​​from temperature mapping;

[0035] Calculate and update the material property values ​​of each component based on the thermal-magnetic coupling function and temperature distribution;

[0036] According to the temperature data obtained in this round and the temperature data obtained in the previous round, the iterative difference value is calculated:

[0037] Where diff is the iterative difference value required; is the temperature difference between the current round and the previous round of the i-th grid; T i,cur is the temperature value of the i-th grid in this round; T i,pre is the temperature value of the previous round of the i-th grid; n is the number of grids.

[0038] A second aspect of the present invention provides a direct drive motor simulation device based on multi-coupling effect, the device comprising:

[0039] A data acquisition module, used to acquire simulation input data of a target motor; the simulation input data includes a geometric model, material properties and operating conditions; the operating conditions include an initial temperature;

[0040] A mesh generation module, configured to generate a first finite element mesh and a second finite element mesh using a pre-trained machine learning model according to the simulation input data;

[0041] A physical property initialization module, used for calculating a material property value according to the initial temperature and the material property;

[0042] A magnetic field calculation module, used to perform electromagnetic field calculation according to the first finite element grid and the material property value to obtain magnetic field distribution and electromagnetic loss;

[0043] A mechanical loss calculation module, used to calculate mechanical loss according to the geometric model and the operating conditions; the mechanical loss includes windage loss and friction loss;

[0044] a temperature field calculation module, configured to use the electromagnetic loss and the mechanical loss as heat sources, perform temperature field calculation according to the second finite element grid, and obtain temperature distribution;

[0045] A physical property updating module, used for updating the material property value according to the temperature distribution and calculating the iterative difference degree;

[0046] The judgment output module is used to output the simulation result if the iteration difference is less than a first preset threshold; otherwise, return to the magnetic field calculation module.

[0047] Optionally, the grid division module includes:

[0048] A feature extraction module, used to extract first feature data according to the simulation input data of the target motor; the first feature data includes geometric features, material features and operating condition features;

[0049] A preprocessing module, used for performing standardization processing on the first characteristic data to obtain second characteristic data;

[0050] A magnetic field grid generation module, used to use the second characteristic data as an input of a pre-trained first machine learning model to obtain a first initial partitioning strategy, and generate a first initial grid according to the first initial partitioning strategy;

[0051] A first finite element mesh generation module, used for performing electromagnetic field calculation according to the first initial mesh, and iteratively adjusting the first initial mesh according to an error estimation method to obtain a first finite element mesh;

[0052] a temperature field grid generation module, configured to use the second feature data as an input of a pre-trained second machine learning model to obtain a second initial partitioning strategy, and generate a second initial grid according to the second initial partitioning strategy;

[0053] The second finite element mesh generation module is used to perform temperature field calculation according to the second initial mesh and iteratively adjust the second initial mesh according to an error estimation method to obtain a second finite element mesh.

[0054] Optionally, the mechanical loss calculation module includes:

[0055] Windage loss calculation module, used to calculate windage loss:

[0056] Among them, P m is the required windage loss; A c is the corrected drag coefficient; is the air density; L is the rotor length; ω is the angular velocity of the rotor rotation; D r is the outer diameter of the rotor; rf is the roughness of the rotor surface; k is a constant; is the drag coefficient when the rotor surface is smooth; RE1 is the radial Reynolds number; RE2 is the axial Reynolds number; and is a constant; is the air gap length; μ is the air kinematic viscosity; v is the air axial flow velocity;

[0057] Friction loss calculation module, used to calculate friction loss:

[0058] Among them, P f is the required friction loss; f is the friction coefficient of the bearing; m is the rotor mass; D r is the outer diameter of the rotor; ω is the angular velocity of the rotor rotation.

[0059] Optionally, the temperature field calculation module includes:

[0060] A static thermal conductivity calculation module, used to calculate the thermal conductivity of each component of the target motor according to the material property value;

[0061] Airflow state calculation module, used to calculate the radial Reynolds number of the air in the air gap;

[0062] The equivalent thermal conductivity calculation module is used to calculate the equivalent thermal conductivity of the air in the air gap when it is flowing if the radial Reynolds number is greater than a second preset threshold value:

[0063]

[0064] Among them, C tc is the required equivalent thermal conductivity; RE1 is the radial Reynolds number; S hc is the specific heat capacity of air; μ is the kinematic viscosity of air; C0 is the thermal conductivity of air at rest;

[0065] The thermal calculation module is used to incorporate the thermal conductivity of each component and the equivalent thermal conductivity of the air gap into the temperature field boundary conditions, perform temperature field calculations, and obtain temperature distribution.

[0066] Optionally, the physical property updating module includes:

[0067] A temperature characteristic determination module is used to establish multiple thermal-magnetic coupling functions that obtain material property values ​​from temperature mapping;

[0068] A material property value updating module is used to calculate and update the material property value of each component according to the thermal magnetic coupling function and the temperature distribution;

[0069] The difference value calculation module is used to calculate the iterative difference value based on the temperature data obtained in this round and the temperature data obtained in the previous round:

[0070] Where diff is the iterative difference value required; is the temperature difference between the current round and the previous round of the i-th grid; T i,cur is the temperature value of the i-th grid in this round; T i,pre is the temperature value of the previous round of the i-th grid; n is the number of grids.

[0071] Beneficial effects of the present invention:

[0072] The present invention proposes a direct-drive motor simulation method based on multi-coupling effect, the method comprising: S101, obtaining simulation input data of a target motor; the simulation input data comprises a geometric model, material properties and operating conditions; the operating conditions comprise an initial temperature; S102, generating a first finite element mesh and a second finite element mesh according to the simulation input data using a pre-trained machine learning model; S103, calculating material property values ​​according to the initial temperature and material properties; S104, performing electromagnetic field calculation according to the first finite element mesh and the material property values ​​to obtain magnetic field distribution and electromagnetic loss; S105, calculating mechanical loss according to the geometric model and operating conditions; mechanical loss comprises windage loss and friction loss; S106, taking electromagnetic loss and mechanical loss as heat sources, performing temperature field calculation according to the second finite element mesh to obtain temperature distribution; S107, updating material property values ​​according to the temperature distribution, and calculating iteration difference; S108, outputting the simulation result if the iteration difference is less than a first preset threshold; otherwise, returning to step S104.

[0073] Taking into account the coupling effects between multiple physical fields such as electromagnetic fields and temperature fields, by incorporating friction loss into the calculation process of machine loss, the heat source data during temperature field calculation is more comprehensive and rich, and performing iterative calculation of magnetic-thermal bidirectional coupling, the actual operation of the direct-drive motor can be simulated more accurately, thereby improving the accuracy of the simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention will be further described below in conjunction with the accompanying drawings.

[0075] Figure 1 A flowchart of a direct-drive motor simulation method based on multi-coupling effects is provided for an embodiment of the present invention;

[0076] Figure 2 A structural diagram of a direct-drive motor simulation device based on multi-coupling effect is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] The embodiment of the present invention provides a direct drive motor simulation method based on multi-coupling effect. Figure 1 , Figure 1 A flowchart of a direct drive motor simulation method based on multi-coupling effect is provided for an embodiment of the present invention. The method comprises the following steps:

[0079] S101, obtaining simulation input data of a target motor.

[0080] S102, generating a first finite element mesh and a second finite element mesh using a pre-trained machine learning model according to the simulation input data.

[0081] S103, calculating and obtaining material property values ​​according to the initial temperature and material properties.

[0082] S104, performing electromagnetic field calculation according to the first finite element mesh and material property values ​​to obtain magnetic field distribution and electromagnetic loss.

[0083] S105, calculating mechanical losses according to the geometric model and operating conditions.

[0084] S106, taking electromagnetic loss and mechanical loss as heat sources, performing temperature field calculation according to the second finite element grid to obtain temperature distribution.

[0085] S107, updating the material property value according to the temperature distribution, and calculating the iteration difference.

[0086] S108, if the iteration difference is less than the first preset threshold, output the simulation result; otherwise, return to step S104.

[0087] The simulation input data include geometric model, material properties and operating conditions; the operating conditions include initial temperature; and the mechanical losses include windage loss and friction loss.

[0088] A direct-drive motor simulation method based on multi-coupling effects is provided based on an embodiment of the present invention. The coupling effects between multiple physical fields such as electromagnetic fields and temperature fields are taken into account, and friction losses are incorporated into the calculation process of machine losses. This makes the heat source data during temperature field calculation more comprehensive and rich, and performs iterative calculation of magnetic-thermal bidirectional coupling. This can more accurately simulate the actual operation of the direct-drive motor and improve the accuracy of the simulation results.

[0089] In one implementation, by generating a finite element mesh through a machine learning model, a better partitioning strategy can be quickly found, thereby reducing the number of iterations and calculation time during mesh adaptive optimization, thereby improving the overall simulation efficiency.

[0090] In one implementation, by calculating the iteration difference, the iteration is stopped when a certain accuracy requirement is met, thereby avoiding unnecessary waste of computing resources.

[0091] In one implementation, the steady-state temperature distribution can be output as a simulation result. The transient magnetic field distribution at different times can also be output as a simulation result, so that R&D personnel can perform design optimization.

[0092] In one embodiment, step S102 includes:

[0093] Step 1: extracting first characteristic data according to simulation input data of the target motor.

[0094] Step 2: Standardize the first feature data to obtain second feature data.

[0095] Step three, using the second feature data as input of the pre-trained first machine learning model to obtain a first initial partitioning strategy, and generating a first initial grid according to the first initial partitioning strategy.

[0096] Step 4: Perform electromagnetic field calculation based on the first initial grid, and iteratively adjust the first initial grid according to an error estimation method to obtain a first finite element grid.

[0097] Step five: Use the second feature data as input of a pre-trained second machine learning model to obtain a second initial partitioning strategy, and generate a second initial grid according to the second initial partitioning strategy.

[0098] Step six, performing temperature field calculation based on the second initial mesh, and iteratively adjusting the second initial mesh according to an error estimation method to obtain a second finite element mesh.

[0099] The first characteristic data includes geometric characteristics, material characteristics and operating condition characteristics.

[0100] In one implementation, a machine learning model can be used to quickly find a better partitioning strategy and obtain an initial grid. Compared with the traditional initial coarse grid, the number of iterations and calculation time during grid adaptive optimization can be reduced, thereby improving the overall simulation efficiency.

[0101] In one implementation, different meshing methods are used to consider the significant differences in the physical phenomena and analytical methods of magnetic field and temperature field. Two different machine learning models are trained for meshing of different physical fields.

[0102] In one implementation, the machine learning model may be a random forest model. Taking the first machine learning model as an example, the grid division for magnetic field calculation, its training process may include the following:

[0103] Get training data:

[0104] Collect data on the geometry, material properties, and operating conditions of direct-drive motors of the same type but with different structures;

[0105] Each direct-drive motor is modeled and simulated to obtain simulation results under different mesh division strategies, which are then compared with actual data to obtain calculation accuracy and calculation time.

[0106] For each simulation, corresponding labels are generated, such as mesh refinement strategy (refinement location and degree), calculation accuracy, and calculation time. For the same motor model, the scores of different mesh refinement strategies are calculated based on the accuracy of magnetic field results and calculation time, and the mesh refinement strategy with the highest score is used as the label of the motor model.

[0107] Feature extraction:

[0108] Extract direct drive motor geometric features such as stator and rotor shape, size, number of slots, etc.

[0109] Extract material characteristics such as magnetic permeability, electrical conductivity, density, etc.

[0110] Extract operating condition characteristics, such as speed, current, etc.

[0111] Create a dataset:

[0112] The extracted feature data is standardized to obtain a data set, which is then divided into a training set and a test set according to a certain ratio.

[0113] Model training:

[0114] Use the training set to train the model and perform hyperparameter tuning during the training process.

[0115] Use a test machine to evaluate the performance of the model to ensure that the model performs well on unseen data.

[0116] Among them, the following formula can be used to standardize the extracted feature data:

[0117] Among them, x is the original data; u is the mean of the data; σ is the standard deviation of the data.

[0118] In this way, the data can have a uniform scale and distribution, which can make the gradient descent algorithm converge faster; standardization reduces the numerical differences between different features and helps improve the stability of the model when processing different features.

[0119] During the model usage phase, the standardization method for the first feature data is consistent with the standardization method during the model training process, and the mean and standard deviation calculated during the training phase are used to ensure that the second feature data meets the input requirements of the model.

[0120] The training process of the second machine learning model for temperature field meshing is similar to that of the first machine learning model. During the data annotation process, for the same motor model, the scores of different mesh refinement strategies are calculated based on the accuracy of the temperature results and the calculation time, and the mesh refinement strategy with the highest score is used as the annotation of the motor model.

[0121] In one implementation, iteratively adjusting the first initial grid according to the error estimation method includes:

[0122] A preliminary simulation is performed based on the first initial grid, and the error of each grid cell is evaluated based on the simulation results. The error evaluation can be based on a variety of criteria, such as the change of key physical quantities such as electromagnetic field strength and magnetic flux density gradient.

[0123] For areas with large errors, the mesh can be refined, that is, the density of the mesh can be increased to make it have a higher resolution. For areas with small errors or little change, the mesh can be coarsened to reduce the amount of calculation.

[0124] Rerun the simulation using the new mesh and evaluate the error again. Repeat the above steps until the error in all regions is within acceptable limits or the preset number of iterations is reached.

[0125] Similarly, the second initial grid can be iteratively adjusted according to the error estimation method.

[0126] There is a certain probability that the grid division strategy given by the machine learning model is not the optimal solution. Through this method, after using the machine learning model to make grid division, the traditional error estimation method is used for adaptive division, thereby ensuring the calculation accuracy of subsequent simulation calculations.

[0127] In one embodiment, step S105 includes:

[0128] Step 1: Calculate windage loss:

[0129] Among them, P m is the required windage loss; A c is the corrected drag coefficient; is the air density; L is the rotor length; ω is the angular velocity of the rotor rotation; D r is the outer diameter of the rotor; rf is the roughness of the rotor surface; k is a constant; is the drag coefficient when the rotor surface is smooth; RE1 is the radial Reynolds number; RE2 is the axial Reynolds number; and is a constant; is the air gap length; μ is the kinematic viscosity of air; v is the axial flow velocity of air.

[0130] Step 2: Calculate friction loss:

[0131]

[0132] Among them, P f is the required friction loss; f is the friction coefficient of the bearing; m is the rotor mass; D r is the outer diameter of the rotor; ω is the angular velocity of the rotor rotation.

[0133] The air gap length refers to the gap distance between the rotor outer diameter and the stator inner diameter, which can be calculated by the following formula:

[0134] Among them, D s is the stator inner diameter, D r is the rotor outer diameter.

[0135] In one implementation, the formula for solving windage wear is a modified empirical formula that takes into account the effect of the roughness of the rotor's circumferential surface on windage. By correcting the drag coefficient under smooth conditions, a drag coefficient that is more in line with actual conditions is obtained, thereby obtaining a more accurate windage loss.

[0136] In one implementation, considering that the kinematic viscosity of air at different temperatures is different, the temperature field and the fluid field can be coupled for calculation to obtain and update the material property values ​​of the air in each iteration process, thereby ensuring the accuracy of the heat source calculation.

[0137] In one implementation, some variables in the formula can be extracted from simulation input data, such as rotor length, rotor rotation angular velocity, rotor outer diameter, rotor shaft surface roughness, and air gap length; some variables can be obtained from fluid field calculations, such as air density, air kinematic viscosity, and air axial flow velocity.

[0138] In one implementation, the calculation formula of the drag coefficient under smooth conditions is an empirical formula, and the constant values ​​α, β, γ1 and γ2 can be 0.1, 0.05, 0.2, and 0.2. The constant k of the correction formula is determined based on experimental data.

[0139] In one embodiment, step S106 includes:

[0140] Step 1: Calculate the thermal conductivity of each component of the target motor based on the material property value.

[0141] Step 2: Calculate the radial Reynolds number of the air in the air gap.

[0142] Step 3: If the radial Reynolds number is greater than the second preset threshold, the equivalent thermal conductivity of the air in the air gap during flow is calculated:

[0143]

[0144] Among them, C tc is the required equivalent thermal conductivity; RE1 is the radial Reynolds number; S hc is the specific heat of air; μ is the kinematic viscosity of air; C0 is the thermal conductivity of air when it is still.

[0145] Step 4: Incorporate the thermal conductivity of each component and the equivalent thermal conductivity of the air gap into the temperature field boundary conditions, perform temperature field calculations, and obtain the temperature distribution.

[0146] In one implementation, the thermal conductivity of the solid component can be calculated using a general empirical formula, which will not be described in detail here.

[0147] In one implementation, the derivation process of the solution formula of the equivalent thermal conductivity is as follows:

[0148] Calculate the Prandtl number Pr:

[0149] Calculate the Nusselt number Nu:

[0150] Calculate the convection heat transfer coefficient h:

[0151] Calculate the equivalent thermal conductivity C of the air gap tc :

[0152] The characters in the above derivation process have been explained in the embodiments and will not be repeated here.

[0153] In one implementation, considering that the motor will cause continuous flow of air when it is running, a heat transfer method of heat convection is added inside the motor, and the heat exchange capacity of the air gap when the air is flowing is characterized by the equivalent thermal conductivity; considering the flow state of the airflow, by calculating the Reynolds number, it is determined whether the equivalent thermal conductivity of the air gap needs to be corrected. By correcting the equivalent thermal conductivity, a more accurate heat transfer process can be obtained, thereby improving the accuracy of the simulation results.

[0154] In one embodiment, step S107 includes:

[0155] Step 1: Establish multiple thermal-magnetic coupling functions that obtain material property values ​​from temperature mapping.

[0156] Step 2: Calculate and update the material property values ​​of each component based on the thermal magnetic coupling function and temperature distribution.

[0157] Step 3: Calculate the iterative difference value based on the temperature data obtained in this round and the temperature data obtained in the previous round:

[0158] Where diff is the iterative difference value required; is the temperature difference between the current round and the previous round of the i-th grid; T i,cur is the temperature value of the i-th grid in this round; T i,pre is the temperature value of the previous round of the i-th grid; n is the number of grids.

[0159] In one implementation, the thermal magnetic coupling function is obtained by the change characteristics of the physical parameters of each component material under temperature change, reflecting the influence of temperature on the physical characteristics, thereby affecting the magnetic field. The temperature field and the magnetic field are coupled through the thermal magnetic coupling function, making the simulation calculation results more accurate.

[0160] In one implementation, the plurality of thermal magnetic coupling functions include:

[0161] Remanence update function of permanent magnet:

[0162] Among them, B r0 is the remanence at reference temperature T0, α B is the temperature coefficient of remanence.

[0163] The material's magnetic permeability update function:

[0164] Where φ0 is the magnetic permeability at reference temperature T0, α φ is the temperature coefficient of magnetic permeability.

[0165] Other update functions, such as electrical conductivity update function, thermal conductivity update function, specific heat capacity update function and other physical property update functions of materials, can be established based on the temperature coefficient determined by the numerical value at the reference temperature and the experimental data.

[0166] In one implementation, by selecting the maximum temperature difference as the iteration difference, it can be ensured that even if the temperature change of a single grid exceeds the threshold, it can be captured, thereby ensuring the accuracy and stability of the entire temperature field.

[0167] The embodiment of the present invention provides a direct drive motor simulation device based on multi-coupling effect. Figure 2 , Figure 2 The present invention provides a structural diagram of a direct drive motor simulation device based on multi-coupling effect. The device includes:

[0168] The data acquisition module is used to acquire simulation input data of the target motor.

[0169] A meshing module is used to generate a first finite element mesh and a second finite element mesh according to simulation input data using a pre-trained machine learning model.

[0170] The physical property initialization module is used to calculate the material property value according to the initial temperature and material properties.

[0171] The magnetic field calculation module is used to perform electromagnetic field calculation according to the first finite element grid and material property values ​​to obtain magnetic field distribution and electromagnetic loss.

[0172] The mechanical loss calculation module is used to calculate the mechanical loss according to the geometric model and operating conditions.

[0173] The temperature field calculation module is used to use electromagnetic loss and mechanical loss as heat sources, perform temperature field calculation according to the second finite element grid, and obtain temperature distribution.

[0174] The physical property update module is used to update the material property values ​​according to the temperature distribution and calculate the iterative difference.

[0175] The judgment output module is used to output the simulation result if the iteration difference is less than the first preset threshold; otherwise, return to the magnetic field calculation module.

[0176] The simulation input data include geometric model, material properties and operating conditions; the operating conditions include initial temperature; and the mechanical losses include windage loss and friction loss.

[0177] A direct-drive motor simulation method based on multi-coupling effects is provided based on an embodiment of the present invention. The coupling effects between multiple physical fields such as electromagnetic fields and temperature fields are taken into account, and friction losses are incorporated into the calculation process of machine losses. This makes the heat source data during temperature field calculation more comprehensive and rich, and performs iterative calculation of magnetic-thermal bidirectional coupling. This can more accurately simulate the actual operation of the direct-drive motor and improve the accuracy of the simulation results.

[0178] In one embodiment, the grid partitioning module includes:

[0179] The feature extraction module is used to extract first feature data according to simulation input data of the target motor.

[0180] The preprocessing module is used to perform standardization processing on the first characteristic data to obtain the second characteristic data.

[0181] The magnetic field grid generation module is used to use the second characteristic data as the input of the pre-trained first machine learning model to obtain a first initial partitioning strategy, and generate a first initial grid according to the first initial partitioning strategy.

[0182] The first finite element mesh generation module is used to perform electromagnetic field calculation according to the first initial mesh and iteratively adjust the first initial mesh according to an error estimation method to obtain a first finite element mesh.

[0183] The temperature field grid generation module is used to use the second feature data as the input of the pre-trained second machine learning model to obtain a second initial partitioning strategy and generate a second initial grid according to the second initial partitioning strategy.

[0184] The second finite element mesh generation module is used to perform temperature field calculation according to the second initial mesh and iteratively adjust the second initial mesh according to the error estimation method to obtain the second finite element mesh.

[0185] The first characteristic data includes geometric characteristics, material characteristics and operating condition characteristics.

[0186] In one embodiment, the mechanical loss calculation module includes:

[0187] Windage loss calculation module, used to calculate windage loss:

[0188] Among them, P m is the required windage loss; A c is the corrected drag coefficient; is the air density; L is the rotor length; ω is the angular velocity of the rotor rotation; D r is the outer diameter of the rotor; rf is the roughness of the rotor surface; k is a constant; is the drag coefficient when the rotor surface is smooth; RE1 is the radial Reynolds number; RE2 is the axial Reynolds number; and is a constant; is the air gap length; μ is the kinematic viscosity of air; v is the axial flow velocity of air.

[0189] Friction loss calculation module, used to calculate friction loss:

[0190] Among them, P f is the required friction loss; f is the friction coefficient of the bearing; m is the rotor mass; D r is the outer diameter of the rotor; ω is the angular velocity of the rotor rotation.

[0191] In one embodiment, the temperature field calculation module includes:

[0192] The static thermal conductivity calculation module is used to calculate the thermal conductivity of each component of the target motor according to the material property value.

[0193] The airflow state calculation module is used to calculate the radial Reynolds number of the air in the air gap.

[0194] The equivalent thermal conductivity calculation module is used to calculate the equivalent thermal conductivity of the air in the air gap when it is flowing if the radial Reynolds number is greater than the second preset threshold value:

[0195] Among them, C tc is the required equivalent thermal conductivity; RE1 is the radial Reynolds number; S hc is the specific heat of air; μ is the kinematic viscosity of air; C0 is the thermal conductivity of air when it is still.

[0196] The thermal calculation module is used to incorporate the thermal conductivity of each component and the equivalent thermal conductivity of the air gap into the temperature field boundary conditions, perform temperature field calculations, and obtain temperature distribution.

[0197] In one embodiment, the physical property update module includes:

[0198] The temperature characteristic determination module is used to establish multiple thermal magnetic coupling functions that obtain material property values ​​through temperature mapping.

[0199] The material property value updating module is used to calculate and update the material property value of each component according to the thermal magnetic coupling function and temperature distribution.

[0200] The difference value calculation module is used to calculate the iterative difference value based on the temperature data obtained in this round and the temperature data obtained in the previous round:

[0201] Where diff is the iterative difference value required; is the temperature difference between the current round and the previous round of the i-th grid; T i,cur is the temperature value of the i-th grid in this round; T i,pre is the temperature value of the previous round of the i-th grid; n is the number of grids.

[0202] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A direct drive motor simulation method based on multi-coupling effect, characterized in that: The method comprises: S101, obtaining simulation input data of a target motor; the simulation input data includes a geometric model, material properties and operating conditions; the operating conditions include an initial temperature; S102, generating a first finite element mesh and a second finite element mesh using a pre-trained machine learning model according to the simulation input data; the first finite element mesh is a mesh used for magnetic field calculation; the second finite element mesh is a mesh used for temperature field calculation; S103, calculating a material property value according to the initial temperature and the material property; S104, performing electromagnetic field calculation according to the first finite element mesh and the material property value to obtain magnetic field distribution and electromagnetic loss; S105, calculating mechanical loss according to the geometric model and the operating conditions; the mechanical loss includes windage loss and friction loss; S106, using the electromagnetic loss and the mechanical loss as heat sources, and performing temperature field calculation according to the second finite element grid to obtain temperature distribution; S107, updating the material property value according to the temperature distribution, and calculating the iteration difference; S108, if the iteration difference is less than the first preset threshold, output the simulation result; otherwise, return to step S104.

2. A direct drive motor simulation method based on multi-coupling effect according to claim 1, characterized in that: Generating the first finite element mesh and the second finite element mesh according to the simulation input data comprises: Extracting first characteristic data according to the simulation input data of the target motor; the first characteristic data includes geometric characteristics, material characteristics and operating condition characteristics; Standardizing the first characteristic data to obtain second characteristic data; Using the second characteristic data as an input of a pre-trained first machine learning model to obtain a first initial partitioning strategy, and generating a first initial grid according to the first initial partitioning strategy; the first initial partitioning strategy is used to determine the degree of grid refinement at different positions of the magnetic field; Performing electromagnetic field calculation according to the first initial grid, and iteratively adjusting the first initial grid according to an error estimation method to obtain a first finite element grid; Using the second feature data as an input of a pre-trained second machine learning model to obtain a second initial partitioning strategy, and generating a second initial grid according to the second initial partitioning strategy; the second initial partitioning strategy is used to determine the degree of grid refinement at different positions of the temperature field; The temperature field is calculated according to the second initial grid, and the second initial grid is iteratively adjusted according to an error estimation method to obtain a second finite element grid.

3. A direct drive motor simulation method based on multi-coupling effect according to claim 1, characterized in that: The calculating of mechanical losses according to the geometric model and the operating conditions comprises: Calculate windage loss: Among them, P m is the required windage loss; A c is the corrected drag coefficient; is the air density; L is the rotor length; ω is the angular velocity of the rotor rotation; D r is the outer diameter of the rotor; rf is the roughness of the rotor surface; k is a constant; is the drag coefficient when the rotor surface is smooth; RE1 is the radial Reynolds number; RE2 is the axial Reynolds number; and is a constant; g is the air gap length; μ is the air kinematic viscosity; v is the air axial flow velocity; Calculate friction losses: Among them, P f is the required friction loss; f is the friction coefficient of the bearing; m is the rotor mass; D r is the outer diameter of the rotor; ω is the angular velocity of the rotor rotation.

4. The direct drive motor simulation method based on multi-coupling effect according to claim 1, characterized in that: The step of taking the electromagnetic loss and the mechanical loss as heat sources and performing temperature field calculation according to the second finite element grid to obtain the temperature distribution comprises: Calculating the thermal conductivity of each component of the target motor according to the material property value; Calculate the radial Reynolds number of the air in the air gap; If the radial Reynolds number is greater than the second preset threshold, the equivalent thermal conductivity of the air in the air gap during flow is calculated: Among them, C tc is the required equivalent thermal conductivity; RE1 is the radial Reynolds number; S hc is the specific heat capacity of air; μ is the kinematic viscosity of air; C0 is the thermal conductivity of air at rest; The thermal conductivity of each component and the equivalent thermal conductivity of the air gap are incorporated into the temperature field boundary conditions, and the temperature field is calculated to obtain the temperature distribution.

5. The direct drive motor simulation method based on multi-coupling effect according to claim 1, characterized in that: The updating of the material property value according to the temperature distribution and calculating the iteration difference comprises: Establish multiple thermal-magnetic coupling functions to obtain material property values ​​from temperature mapping; Calculate and update the material property values ​​of each component based on the thermal-magnetic coupling function and temperature distribution; According to the temperature data obtained in this round and the temperature data obtained in the previous round, the iterative difference value is calculated: Where diff is the iterative difference value required; is the temperature difference between the current round and the previous round of the i-th grid; T i,cur is the temperature value of the i-th grid in this round; T i,pre is the temperature value of the previous round of the i-th grid; n is the number of grids.

6. A direct drive motor simulation device based on multi-coupling effect, characterized in that: The device comprises: A data acquisition module, used to acquire simulation input data of a target motor; the simulation input data includes a geometric model, material properties and operating conditions; the operating conditions include an initial temperature; A mesh generation module, configured to generate a first finite element mesh and a second finite element mesh according to the simulation input data using a pre-trained machine learning model; the first finite element mesh is a mesh for magnetic field calculation; the second finite element mesh is a mesh for temperature field calculation; A physical property initialization module, used for calculating a material property value according to the initial temperature and the material property; A magnetic field calculation module, used to perform electromagnetic field calculation according to the first finite element grid and the material property value to obtain magnetic field distribution and electromagnetic loss; A mechanical loss calculation module, used to calculate mechanical loss according to the geometric model and the operating conditions; the mechanical loss includes windage loss and friction loss; a temperature field calculation module, configured to use the electromagnetic loss and the mechanical loss as heat sources, perform temperature field calculation according to the second finite element grid, and obtain temperature distribution; A physical property updating module, used for updating the material property value according to the temperature distribution and calculating the iterative difference degree; The judgment output module is used to output the simulation result if the iteration difference is less than a first preset threshold; otherwise, return to the magnetic field calculation module.

7. A direct drive motor simulation device based on multi-coupling effect according to claim 6, characterized in that: The grid division module comprises: A feature extraction module, configured to extract first feature data according to simulation input data of a target motor; the first feature data includes geometric features, material features and operating condition features; A preprocessing module, used for performing standardization processing on the first characteristic data to obtain second characteristic data; A magnetic field grid generation module, used to use the second feature data as an input of a pre-trained first machine learning model to obtain a first initial partitioning strategy, and generate a first initial grid according to the first initial partitioning strategy; the first initial partitioning strategy is used to determine the degree of grid refinement at different positions of the magnetic field; A first finite element mesh generation module, used for performing electromagnetic field calculation according to the first initial mesh, and iteratively adjusting the first initial mesh according to an error estimation method to obtain a first finite element mesh; a temperature field grid generation module, configured to use the second feature data as an input of a pre-trained second machine learning model to obtain a second initial partitioning strategy, and generate a second initial grid according to the second initial partitioning strategy; the second initial partitioning strategy is used to determine the degree of grid refinement at different positions of the temperature field; The second finite element mesh generation module is used to perform temperature field calculation according to the second initial mesh and iteratively adjust the second initial mesh according to an error estimation method to obtain a second finite element mesh.

8. The direct drive motor simulation device based on multi-coupling effect according to claim 6, characterized in that: The mechanical loss calculation module includes: Windage loss calculation module, used to calculate windage loss: Among them, P m is the required windage loss; A c is the corrected drag coefficient; is the air density; L is the rotor length; ω is the angular velocity of the rotor rotation; D r is the outer diameter of the rotor; rf is the roughness of the rotor surface; k is a constant; is the drag coefficient when the rotor surface is smooth; RE1 is the radial Reynolds number; RE2 is the axial Reynolds number; and is a constant; g is the air gap length; μ is the air kinematic viscosity; v is the air axial flow velocity; Friction loss calculation module, used to calculate friction loss: Among them, P f is the required friction loss; f is the friction coefficient of the bearing; m is the rotor mass; D r is the outer diameter of the rotor; ω is the angular velocity of the rotor rotation.

9. The direct drive motor simulation device based on multi-coupling effect according to claim 6, characterized in that: The temperature field calculation module includes: A static thermal conductivity calculation module, used to calculate the thermal conductivity of each component of the target motor according to the material property value; Airflow state calculation module, used to calculate the radial Reynolds number of the air in the air gap; The equivalent thermal conductivity calculation module is used to calculate the equivalent thermal conductivity of the air in the air gap when it is flowing if the radial Reynolds number is greater than a second preset threshold value: Among them, C tc is the required equivalent thermal conductivity; RE1 is the radial Reynolds number; S hc is the specific heat capacity of air; μ is the kinematic viscosity of air; C0 is the thermal conductivity of air at rest; The thermal calculation module is used to incorporate the thermal conductivity of each component and the equivalent thermal conductivity of the air gap into the temperature field boundary conditions, perform temperature field calculations, and obtain temperature distribution.

10. The direct drive motor simulation device based on multi-coupling effect according to claim 6, characterized in that: The physical property update module includes: A temperature characteristic determination module is used to establish multiple thermal-magnetic coupling functions that obtain material property values ​​from temperature mapping; A material property value updating module is used to calculate and update the material property value of each component according to the thermal magnetic coupling function and the temperature distribution; The difference value calculation module is used to calculate the iterative difference value based on the temperature data obtained in this round and the temperature data obtained in the previous round: Where diff is the iterative difference value required; is the temperature difference between the current round and the previous round of the i-th grid; T i,cur is the temperature value of the i-th grid in this round; T i,pre is the temperature value of the previous round of the i-th grid; n is the number of grids.

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