Bidirectional coupling simulation method for thermal field and flow field of hybrid gearbox

Abstract

The invention provides a hybrid gearbox thermal field and flow field bidirectional coupling simulation method, which comprises the following steps: calling a Phonlab initial calculation example to carry out initial flow field calculation to obtain a flow field heat exchange result; calling Fluent to carry out initial temperature field calculation to obtain a temperature field result; adding a temperature field result in the Phonlab, updating a thermal field boundary and carrying out iterative calculation, and adding a flow field heat exchange result in the Fluent, updating a heat exchange boundary and carrying out iterative calculation; and calculating the residual error of the iterative calculation, judging whether the result of the residual error calculation is convergent or not, and if not, repeating the calculation until the result of the residual error calculation is convergent. According to the thermal field and flow field bidirectional coupling simulation method for the hybrid gearbox, a tool chain is integrated; the algorithm is optimized, and efficient calculation is achieved under the limited hardware condition; the automatic setting of the boundary condition is realized, the error risk is reduced, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of hybrid transmission thermal simulation, and in particular to a hybrid transmission thermal field and flow field bidirectional coupling simulation method. Background Art

[0002] There are three common simulation methods in the field of hybrid transmission thermal simulation: The first one is the pure grid method, which divides the grids of each component and the flow field, and sets the dynamic grid method for gear rotation. The advantage is high calculation accuracy, but the disadvantage is huge computing resource overhead, the dynamic grid algorithm is unstable, and it is very easy to have non-convergence during the calculation process. It is difficult to implement and difficult to calculate to a stable state.

[0003] The second method is the pure particle method, which particle-izes both the liquid and the solid. This method requires more material parameters. Its advantage is that the calculation speed is faster when the particle diameter is set larger. Its disadvantage is that the accuracy is not high, and it has high requirements for software. It needs to support the particleization function of solid components and integrate the heat transfer model, and the software resources are difficult to obtain.

[0004] The third method combines the particle method with the grid method. The particle method is used to calculate the flow field, and the heat exchange boundary is derived and then time-averaged. The processing results are mapped to the grid to perform steady-state thermal calculations. The advantage is that it ensures a certain degree of accuracy while having a fast calculation speed. The disadvantage is that it is difficult to achieve transient calculations and software resources are difficult to obtain.

[0005] The third method is currently more commonly used, but this method has higher requirements on hardware. When the array dimension is too large, the memory is prone to overflow, and it cannot realize the calculation of bidirectional flow-thermal coupling, and cannot identify possible thermal risks. Preonlab and Fluent are commonly used CFD simulation analysis software, and Fluent. Summary of the Invention

[0006] The purpose of the present disclosure is to provide a hybrid transmission thermal field and flow field bidirectional coupling simulation method, which can solve one or more of the above-mentioned problems in the prior art.

[0007] According to one aspect of the present disclosure, a hybrid transmission thermal field and flow field bidirectional coupling simulation method is provided, comprising the following steps: Set the initial calculation example of Preonlab according to the initial conditions, call the initial calculation example of Preonlab to perform the initial flow field calculation, and obtain the flow field heat transfer results; Set the initial calculation example of Fluent according to the initial conditions, call Fluent to perform the initial temperature field calculation, and obtain the temperature field results; Add temperature field results in Preonlab, update thermal field boundaries, and perform iterative calculations. Add flow field heat transfer results in Fluent, update heat transfer boundaries, and perform iterative calculations. Calculate the residual of the iterative calculation, Determine whether the result of the residual calculation converges. If so, end the calculation. If not, repeat the steps of adding the temperature field results in Preonlab, updating the thermal field boundary, performing iterative calculation, adding the flow field heat transfer results in Fluent, updating the heat transfer boundary, performing iterative calculation, and calculating the residual of the iterative calculation until the residual calculation results converge.

[0008] In some embodiments, setting an initial calculation example of Preonlab according to initial conditions includes: Divide the computational domain into surface meshes, divide the surface meshes into different parts, export the surface meshes as the input meshes for Preonlab, generate the volume meshes from the surface meshes, divide the volume meshes into different parts and export them as the input meshes for Fluent. Set up the initial calculation example of Preonlab by setting the speed, liquid level, flow rate of each flow inlet, inlet oil temperature, particle diameter and initial temperature field; In some embodiments, calling the Preonlab initial calculation example to perform initial flow field calculation and obtain the flow field heat transfer results includes: Call Preonlab to perform initial flow field calculations, Export the names of all surface mesh components, export the convective heat transfer results of the convective heat transfer wall and interface surfaces, export the sampling point coordinates, and export the oil temperature results of the oil pan; Downsample the exported sampling points to obtain index information corresponding to the sampling points; According to the index information, the flow field results corresponding to the sampling points are extracted.

[0009] In some implementations, downsampling the derived sampling points includes: Get the minimum values ​​of the x-axis coordinate, y-axis coordinate, and z-axis coordinate in the point cloud data respectively, where the point cloud data is composed of all sampling points before downsampling; Set the spatial grid size; Traverse point cloud data; The number of the calculation point in the spatial grid; The spatial grid number and the index of the point in the point cloud are saved in the form of a dictionary, where the key value is the spatial grid number and the value value is the index of the point in the point cloud. Determine whether the traversal is completed. If the point cloud data traversal is completed, traverse the key values ​​in the dictionary and select one from the values ​​corresponding to each key value to obtain the downsampled point cloud data. If the point cloud data traversal is not completed, continue to traverse the point cloud data, calculate the point number in the spatial grid, and save the spatial grid number and the index of the point in the point cloud in the form of a dictionary until the point cloud data traversal is completed.

[0010] In some embodiments, setting an initial calculation example of Fluent according to initial conditions includes: Perform surface meshing on the computational domain, divide the surface mesh into different parts, generate a volume mesh from the surface mesh, and then divide the volume mesh into different parts as the input mesh for Fluent; Export the center coordinates of the surface mesh on the wall domain; Set up the initial Fluent calculation example by setting the physical parameters, heat source boundary and coupling surface, and derive the coordinates of each wall.

[0011] In some embodiments, calling Fluent to perform initial temperature field calculation to obtain temperature field results includes: Time-averaged the flow field heat transfer results to obtain the flow field heat transfer results after time-averaged processing; Calculate the distance matrix and weight matrix between the downsampled sampling points and the wall coordinates; Interpolate the time-averaged flow field heat transfer results onto the surface mesh, and calculate the results of mapping the time-averaged flow field heat transfer results onto the surface mesh; Add heat transfer boundary data to the surface mesh; The oil temperature in the oil pan after passing through the heat exchanger is calculated based on the fitted heat exchanger heat transfer function. The calculated temperature is the oil inlet temperature. The oil inlet temperature is used as the temperature boundary wall to add the temperature condition, and the temperature field results are exported after the calculation is completed after initialization.

[0012] In some embodiments, calculating the distance matrix and the weight matrix between the downsampled sampling points and the wall coordinates includes: Traverse the wall coordinate data; Calculate the sum of the inverse squares of the distances between the wall coordinates and each sampling point in the downsampled point cloud, denoted as dsum; Calculate the weight value of each sampling point, recorded as weight, and the weight values ​​corresponding to all sampling points form a weight value matrix; Multiply the weight value matrix and the field value to obtain the field value of the surface grid point. The field value is the flow field heat transfer result after the average processing. Determine whether the traversal is completed. If it is completed, the calculation ends. If not, repeat the traversal of the wall coordinate data, calculate the sum of the inverse squares of the distances between the wall coordinates and each sampling point in the point cloud, calculate the weight value of each point based on the distance value, multiply the weight value matrix by the field value to obtain the field value of the center point of the surface grid until the traversal is completed.

[0013] In some embodiments, adding heat exchange boundary data to a surface mesh includes: Read boundary data; Traverse the surface mesh on the wall domain; Get the face grid pointer number; Select boundary data according to the surface grid pointer number; Add boundary data to the corresponding grid; Determine whether the traversal is completed. If the traversal is completed, the calculation ends. If the traversal is not completed, repeat the traversal of the surface mesh on the wall domain, obtain the surface mesh pointer number, select the boundary data according to the surface mesh pointer number, and add the boundary data to the corresponding mesh until the traversal is completed.

[0014] In some embodiments, adding temperature field results in Preonlab, updating thermal field boundaries, and performing iterative calculations, and adding flow field heat transfer results in Fluent, updating heat transfer boundaries, and performing iterative calculations include: Add temperature field results in Preonlab, correct the oil inlet temperature results, and after the calculation is completed, export the convective heat transfer results of the convective heat transfer wall and interface, export the sampling point coordinates, and export the oil sump oil temperature results; Call the distance matrix results, weight matrix results and index results to time-average the flow field heat transfer results, and calculate the results of mapping the time-averaged flow field heat transfer results on the surface grid; Call Fluent to add heat transfer boundary conditions, and export the temperature field results after initialization and calculation completion.

[0015] The hybrid transmission thermal field and flow field bidirectional coupling simulation method provided by the present disclosure has the following beneficial effects: it integrates the tool chain and can realize the linkage between Preonlab software and Fluent software; it optimizes the algorithm, reasonably allocates memory resources, and realizes efficient calculation under limited hardware conditions; it realizes the automation and batch setting of boundary conditions, reduces the risk of human setting errors, and improves calculation efficiency; it realizes the calculation of bidirectional flow-thermal coupling of the complex system of the hybrid transmission assembly, provides a basis for the thermal management design scheme of the hybrid transmission, and can identify possible thermal risks.

[0016] In addition, in the technical solutions disclosed herein, anything not specifically stated can be implemented by conventional means in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, a brief introduction will be given below to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 This is a flow chart of a hybrid transmission thermal field and flow field bidirectional coupling simulation method provided by one embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0020] Example: Reference Manual Figure 1 , shows a hybrid transmission thermal field and flow field bidirectional coupling simulation method provided by an embodiment of the present disclosure, including the following steps: Step 1: Set the initial calculation example of Preonlab according to the initial conditions, call the initial calculation example of Preonlab to perform the initial flow field calculation, and obtain the flow field heat transfer results; Step 2: Set the initial calculation case of Fluent according to the initial conditions, call Fluent to perform the initial temperature field calculation, and obtain the temperature field results; Step 3: Add the temperature field results in Preonlab, update the thermal field boundary, and perform iterative calculations. Add the flow field heat transfer results in Fluent, update the heat transfer boundary, and perform iterative calculations. Step 4: Calculate the residual of the iterative calculation, Step 5: Determine whether the residual calculation result converges. If so, end the calculation. If not, repeat steps 3 and 4 until the residual calculation result converges.

[0021] In an optional embodiment, in step 1, setting an initial calculation example of Preonlab according to initial conditions includes: Step 1.1: Perform surface meshing on the computational domain, divide the surface mesh into different components, export the surface mesh as the input mesh for Preonlab, generate a volume mesh from the surface mesh, divide the volume mesh into different components, and export it as the input mesh for Fluent. The computational domain represents the computational area in the simulation example and can include both surface meshes and volume meshes.

[0022] Step 1.2: Set up the initial calculation example of Preonlab by setting the speed, liquid level, flow rate of each flow inlet, inlet oil temperature, physical properties, particle diameter and initial temperature field.

[0023] Among them, step 1.1 can be performed through HyperMesh software.

[0024] In step 1.1, when naming surface mesh components, you should distinguish between interfaces and walls according to the established rules. When naming, you should also distinguish between the heat transfer types, which are divided into three categories: convection heat transfer boundaries, thermal radiation boundaries, and fixed temperature boundaries. This will facilitate subsequent automated settings.

[0025] For example, the surface mesh area of ​​the generator stator outer surface that is only in contact with the air can be named wall-gmstator-cht. In this case, wall-gmstator-cht is the surface mesh component name. In the component name, wall indicates that the wall type is wall, and cht indicates the convection heat transfer type boundary. The surface mesh area of ​​the generator stator outer surface that is only in contact with the shell can be named interface-gmstator-house. Interface-gmstator-house is the surface mesh component name. In the component name, interface indicates that the wall type is interface. The contact area between the shell and the generator stator outer surface can also be named interface-house-gmstator. Generally speaking, interface-type walls are paired.

[0026] In an optional embodiment, in step 1, the initial flow field calculation is performed by calling the Preonlab initial calculation example to obtain the flow field heat transfer results including: Step 1.3: Call Preonlab to perform initial flow field calculation. Specifically, you can use the secondary developed Python script to call the Preonlab software.

[0027] Step 1.4: Export the names of all surface mesh components. Based on the wall type in the surface mesh component name, divide the surface mesh components into two text files, wall and interface. Based on the wall type and heat transfer type in the surface mesh component name, export the convective heat transfer results of the wall surface and interface surface respectively, export the sampling point coordinates, and export the oil sump oil temperature results. Step 1.5: Downsample the derived sampling points to obtain index information corresponding to the downsampled sampling points; Step 1.6: Extract the flow field results corresponding to the sampling points based on the index information.

[0028] Among them, the names of each wall surface can be exported using the secondary developed python script and saved as a text file.

[0029] In an optional embodiment, in step 1.5, downsampling the derived sampling points includes: Step 1.5.1: Obtain the minimum x-axis coordinate, y-axis coordinate, and z-axis coordinate of the point cloud data, where the point cloud data consists of all the sampling points derived in step 1.4. Step 1.5.2: Set the spatial grid size. Specifically, assume the computational domain consists of a large cube that can be divided into several smaller cubes. The side length of each smaller cube is the spatial grid size. The spatial grid size can range from 1 to 3 mm, depending on the computational accuracy and computer hardware requirements. In this example, it can be set to 1 mm. Step 1.5.3: Traverse the point cloud data; Step 1.5.4: Calculate the number of the point in the spatial grid. Taking the x-axis coordinate as an example, the number of the point in the x-direction of the spatial grid is floor(x-x_min); Step 1.5.5: Save the spatial grid number and the index of the point in the point cloud in the form of a dictionary. The spatial grid number is a 1*3 tuple, corresponding to the number of the point in the x, y, and z directions respectively. The key value is the spatial grid number, and the value value is the index of the point in the point cloud. Step 1.5.6: Determine whether the traversal is complete. If the point cloud data traversal is complete, execute step 1.5.7. If the point cloud data traversal is not complete, continue to execute steps 1.5.3 to 1.5.5 until the point cloud data traversal is complete. Step 1.5.7: Traverse the key values ​​in the dictionary and select one value from the value corresponding to each key value to obtain the downsampled point cloud data.

[0030] In step 1.5, the index information corresponding to the downsampled sampling points can be saved in the hard disk space, thereby reducing the computing pressure of the memory and improving the computing power under limited hardware conditions.

[0031] Therefore, by downsampling, the dimension of point cloud data can be reduced from tens of millions to millions, which increases the computing speed and reduces the dependence on computing resources.

[0032] In an optional embodiment, in step 2, setting an initial calculation example of Fluent according to the initial conditions includes: Step 2.1: Perform surface meshing on the computational domain, divide the surface mesh into different parts, generate a volume mesh from the surface mesh, and then divide the volume mesh into different parts as the input mesh for Fluent. Step 2.2: Export the center coordinates of the surface mesh on the wall domain. This can be achieved by generating a UDF. UDF is the abbreviation of "User-Defined Function" in Fluent. Step 2.3: Set up the initial Fluent case by setting the physical parameters, heat source boundary, and coupling surface. Load the UDF generated in step 2.2 and export the coordinates of each wall.

[0033] Heat sources can include the motor stator, motor windings, motor rotor, magnets, motor mechanical losses, gear meshing transmission losses, and bearing transmission losses. Motor mechanical losses can be calculated using the motor efficiency map by interpolating the speed and torque, while gear meshing transmission losses can be calculated using Romax software.

[0034] Specifically, in step 2.2, deriving the center coordinates of the surface mesh on the wall domain includes: Step 2.2.1: Traverse the surface mesh on the wall domain; Step 2.2.2: Get the center coordinates of the surface grid; Step 2.2.3: Export the center coordinates of the surface grid; Step 2.2.4: Determine whether the traversal is completed. If the traversal is completed, end here. If the traversal is not completed, repeat steps 2.2.1 to 2.2.3 until the traversal is completed.

[0035] In step 2.2, the center coordinates of the surface mesh on the wall domain can be exported by batch generating UDF using Python scripts.

[0036] In step 2.3, the coordinates of each wall can be derived using UDF.

[0037] In an optional embodiment, in step 2, calling Fluent to perform initial temperature field calculation, and obtaining the temperature field result includes: Step 2.4: Average the heat transfer results of the flow field derived from the Preonlab calculation, and calculate the distance matrix and weight matrix between the downsampled sampling points and the wall coordinates of the corresponding components of the fluent. Step 2.5: Use the IDW interpolation algorithm to interpolate the time-averaged flow field heat transfer results onto the fluent surface grid to generate the corresponding data file; Step 2.6: Add heat transfer boundary data to the surface mesh, which can be achieved through UDF; Step 2.7: Calculate the oil temperature after the oil pan passes through the heat exchanger based on the fitted heat exchanger heat transfer function. This temperature is the oil inlet temperature and also the fixed temperature boundary of the flow channel wall in Fluent. The heat exchanger heat transfer function is T_out = 0.624 * T_in + 25.52, where T_out is the heat exchanger oil outlet temperature (°C), which is also the oil inlet temperature of the DHT assembly, and T_in is the heat exchanger oil inlet temperature (°C). Step 2.8: Add the oil inlet temperature obtained in step 2.7 as the temperature boundary wall temperature condition. After initialization and calculation, export the temperature field results.

[0038] Among them, calling Fluent can be achieved through pyfluent.

[0039] In an optional embodiment, in step 2.4, the distance matrix and weight matrix between the downsampled sampling points and the wall coordinates of the corresponding fluent components are calculated by an IDW interpolation algorithm. Specifically, step 2.4 may include: Step 2.4.1: Traverse the wall coordinates; Step 2.4.2: Calculate the sum of the inverse squares of the distances between the wall coordinates and each point in the downsampled point cloud, denoted as dsum. Specifically, after dividing the point cloud of the sampling points into blocks, multi-threadedly calculate the distance between each point in the point cloud and the wall coordinates and save the data to the hard disk. This data is the distance matrix. The distance values ​​are inversely squared and summed, and the sum is returned to free up memory. Step 2.4.3: Calculate the weight of each sampling point in the downsampled point cloud based on the distance value, denoted as weight. The closer the distance (the smaller the absolute value of the distance value), the greater the weight. In multi-threaded calculations, the distance matrix can be read and the weights can be calculated separately before assembling and saving the weight matrix. Step 2.4.4: Multiply the weight matrix by the field value to obtain the field value at the center of the surface grid, where the field value is the flow field heat transfer result after averaging the flow field heat transfer result derived from Preonlab calculation; Step 2.4.5: Determine whether the traversal is completed. If it is completed, the calculation ends. If not, repeat steps 2.4.1 to 2.4.4 until the traversal is completed.

[0040] Therefore, the distance matrix calculation and the weight matrix calculation are separated before iteration, which greatly reduces the amount of calculation.

[0041] In an optional embodiment, in step 2.6, adding heat exchange boundary data to the surface mesh can be achieved by batch generating a Fluent UDF for adding heat exchange boundaries to the wall surface. Specifically, adding heat exchange boundaries to the surface mesh may include: Step 2.6.1: Read boundary data, which is the data file generated in step 2.5; Step 2.6.2: Traverse the surface mesh on the wall domain; Step 2.6.3: Get the face grid pointer number. The pointer is a built-in function of Fluent UDF. Step 2.6.4: Select boundary data according to the surface grid pointer number; Step 2.6.5: Add boundary data to the corresponding grid; Step 2.6.6: Determine whether the traversal is completed. If the traversal is completed, the calculation ends. If the traversal is not completed, repeat steps 2.6.2 to 2.6.5 until the traversal is completed.

[0042] In an optional embodiment, step 2.8 can call Fluent to batch add heat exchange boundary conditions through pyfluent.

[0043] In an optional embodiment, in step 3, adding the temperature field results in Preonlab, updating the thermal field boundary, and performing iterative calculations, and adding the flow field heat transfer results in Fluent, updating the heat transfer boundary, and performing iterative calculations may include: Step 3.1: Add the temperature field results in Preonlab and modify the oil inlet temperature based on the calculation results of Fluent. After the calculation is completed, export the convective heat transfer results of the convective heat transfer wall and interface, export the sampling point coordinates, and export the oil sump oil temperature results. Step 3.2: Call the distance matrix results, weight matrix results, and index results to time-average the flow field heat transfer results and calculate the result of mapping the time-averaged flow field heat transfer results onto the grid; Step 3.3: Call Fluent to add heat transfer boundary conditions. After initialization and calculation, export the temperature field results.

[0044] In step 3.1, the temperature field boundary in Preonlab can be updated through a Python script, and the heat transfer results can be exported after calculation.

[0045] In step 3.2, the results can be filtered according to the downsampling index information, and the weight matrix can be called to calculate the mapping result.

[0046] Step 3.3 can be implemented by adding UDF in Fluent, and exporting the temperature field results after initializing the calculation.

[0047] In an optional embodiment, in step 4, calculating the residual of the iterative calculation refers to calculating the temperature field residual.

[0048] The disclosed method for bidirectionally coupled thermal and flow field simulation of a hybrid transmission has the following beneficial effects: algorithmic improvements are made to data reading, downsampling, distance matrix calculation, weight matrix calculation, and interpolation calculation, making the algorithms applicable to multithreading and significantly improving computational speed. Data downsampling reduces the dimension of point cloud data from tens of millions to millions, improving computational speed and reducing reliance on computing resources. Furthermore, based on the property that coordinates derived from PreonLab and Fluent grids remain unchanged, the IDW algorithm is decomposed, separating the distance matrix calculation from the weight matrix calculation before iteration, significantly reducing the amount of computation. In the multi-threaded calculation of the distance matrix and weight matrix, the matrices are divided into blocks of appropriate sizes, and the calculated data is saved to hard disk storage space, freeing up memory space, resolving the problem of memory overflow in multi-threaded calculations and reducing reliance on memory resources. Automated and batch-configured boundary conditions are implemented, reducing the risk of human error and improving computational efficiency. Bidirectional flow-thermal coupling calculations are implemented for the complex hybrid transmission assembly, providing a basis for hybrid transmission thermal management design and identifying potential thermal risks.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure.

Claims

1. A two-way coupled simulation method for the thermal field and flow field of a hybrid transmission, characterized in that: The following steps are involved: Set the initial calculation example of Preonlab according to the initial conditions, call the initial calculation example of Preonlab to perform the initial flow field calculation, and obtain the flow field heat transfer results; Set the initial calculation example of Fluent according to the initial conditions, call Fluent to perform the initial temperature field calculation, and obtain the temperature field results; Add temperature field results in Preonlab, update thermal field boundaries, and perform iterative calculations. Add flow field heat transfer results in Fluent, update heat transfer boundaries, and perform iterative calculations. Calculate the residual of the iterative calculation, Determine whether the result of the residual calculation converges. If so, end the calculation. If not, repeat the steps of adding the temperature field results in Preonlab, updating the thermal field boundary, performing iterative calculation, adding the flow field heat transfer results in Fluent, updating the heat transfer boundary, performing iterative calculation, and calculating the residual of the iterative calculation until the residual calculation results converge.

2. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 1 is characterized in that: The initial calculation example of setting up Preonlab according to the initial conditions includes: Divide the computational domain into surface meshes, divide the surface meshes into different parts, export the surface meshes as the input meshes for Preonlab, generate the volume meshes from the surface meshes, divide the volume meshes into different parts and export them as the input meshes for Fluent. The initial calculation example of Preonlab is set by setting the speed, liquid level, flow rate of each flow inlet, inlet oil temperature, particle diameter and initial temperature field.

3. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 1 is characterized in that: The initial flow field calculation is performed by calling the Preonlab initial calculation example to obtain the flow field heat transfer results including: Call Preonlab to perform initial flow field calculation, Export the names of all surface mesh components, export the convective heat transfer results of the convective heat transfer wall and interface surfaces, export the sampling point coordinates, and export the oil temperature results of the oil pan; Downsample the exported sampling points to obtain index information corresponding to the sampling points; According to the index information, the flow field results corresponding to the sampling points are extracted.

4. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 3 is characterized in that: The downsampling of the derived sampling points includes: Get the minimum values ​​of the x-axis coordinate, y-axis coordinate, and z-axis coordinate in the point cloud data respectively, where the point cloud data is composed of all sampling points before downsampling; Set the spatial grid size; Traverse point cloud data; The number of the calculation point in the spatial grid; The spatial grid number and the index of the point in the point cloud are saved in the form of a dictionary, where the key value is the spatial grid number and the value value is the index of the point in the point cloud. Determine whether the traversal is completed. If the point cloud data traversal is completed, traverse the key values ​​in the dictionary and select one from the values ​​corresponding to each key value to obtain the downsampled point cloud data. If the point cloud data traversal is not completed, continue to traverse the point cloud data, calculate the point number in the spatial grid, and save the spatial grid number and the index of the point in the point cloud in the form of a dictionary until the point cloud data traversal is completed.

5. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 1, characterized in that: The initial calculation example of setting Fluent according to the initial conditions includes: Perform surface meshing on the computational domain, divide the surface mesh into different parts, generate a volume mesh from the surface mesh, and then divide the volume mesh into different parts as the input mesh for Fluent; Export the center coordinates of the surface mesh on the wall domain; Set up the initial Fluent calculation example by setting the physical parameters, heat source boundary and coupling surface, and derive the coordinates of each wall.

6. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 5, characterized in that: The calling of Fluent to perform initial temperature field calculation and obtain the temperature field results includes: Time-averaged the flow field heat transfer results to obtain the flow field heat transfer results after time-averaged processing; Calculate the distance matrix and weight matrix between the downsampled sampling points and the wall coordinates; Interpolate the time-averaged flow field heat transfer results onto the surface mesh, and calculate the results of mapping the time-averaged flow field heat transfer results onto the surface mesh; Add heat transfer boundary data to the surface mesh; The oil temperature in the oil pan after passing through the heat exchanger is calculated based on the fitted heat exchanger heat transfer function. The calculated temperature is the oil inlet temperature. The oil inlet temperature is used as the temperature boundary wall to add the temperature condition, and the temperature field results are exported after the calculation is completed after initialization.

7. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 6, characterized in that: The distance matrix and weight matrix between the downsampled sampling points and the wall coordinates are calculated as follows: Traverse the wall coordinate data; Calculate the sum of the inverse squares of the distances between the wall coordinates and each sampling point in the downsampled point cloud, denoted as dsum; Calculate the weight value of each sampling point, recorded as weight, and the weight values ​​corresponding to all sampling points form a weight value matrix; Multiply the weight value matrix and the field value to obtain the field value of the surface grid point. The field value is the flow field heat transfer result after the average processing. Determine whether the traversal is completed. If it is completed, the calculation ends. If not, repeat the traversal of the wall coordinate data, calculate the sum of the inverse squares of the distances between the wall coordinates and each sampling point in the point cloud, calculate the weight value of each point based on the distance value, multiply the weight value matrix by the field value to obtain the field value of the center point of the surface grid until the traversal is completed.

8. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 6, characterized in that: Adding heat transfer boundary data to the surface mesh includes: Read boundary data; Traverse the surface mesh on the wall domain; Get the face grid pointer number; Select boundary data according to the surface grid pointer number; Add boundary data to the corresponding grid; Determine whether the traversal is completed. If the traversal is completed, the calculation ends. If the traversal is not completed, repeat the traversal of the surface mesh on the wall domain, obtain the surface mesh pointer number, select the boundary data according to the surface mesh pointer number, and add the boundary data to the corresponding mesh until the traversal is completed.

9. The hybrid transmission thermal field and flow field bidirectional coupling simulation method according to claim 1, characterized in that: The steps of adding temperature field results in Preonlab, updating thermal field boundaries, and performing iterative calculations, and adding flow field heat transfer results in Fluent, updating heat transfer boundaries, and performing iterative calculations include: Add temperature field results in Preonlab, correct the oil inlet temperature results, and after the calculation is completed, export the convective heat transfer results of the convective heat transfer wall and interface, export the sampling point coordinates, and export the oil sump oil temperature results; Call the distance matrix results, weight matrix results and index results to time-average the flow field heat transfer results, and calculate the results of mapping the time-averaged flow field heat transfer results on the surface grid; Call Fluent to add heat transfer boundary conditions, and export the temperature field results after initialization and calculation completion.

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