Multi-working-condition simulation automatic processing method, device, equipment and medium

Abstract

The invention relates to the technical field of vehicle simulation, and discloses a multi-working-condition simulation automatic processing method and device, equipment and a medium, and the method comprises the steps: obtaining input parameters needed by multi-working-condition simulation; according to the input parameters, determining a modeling mode corresponding to simulation of a three-dimensional simulation model and executing volume mesh generation operation; and circularly traversing each working condition, selecting a corresponding parameter combination according to the modeling mode to perform simulation calculation on the three-dimensional simulation model, and obtaining a simulation result corresponding to each working condition. According to the technical scheme provided by the invention, the efficiency and accuracy of the multi-working-condition simulation task can be improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle simulation, and particularly to a multi-condition simulation automatic processing method, device, equipment and medium. Background Art

[0002] With the development of computer science and computational fluid dynamics (CFD) technology, the automotive industry increasingly relies on CFD for cabin flow field analysis and thermal management optimization in design and research and development.

[0003] However, in the performance calibration stage, due to large variations in parameters such as airflow, temperature, and pressure under different conditions, engineers need to perform multiple simulation calculations separately, resulting in high consumption of computing resources, low efficiency, and increased workload. Although modern CFD software has mature solutions in modeling, solving, and post-processing, the method of processing one by one is still inefficient for a large number of computing tasks under different conditions, limiting the overall efficiency of the simulation work.

[0004] Therefore, how to efficiently use automated and parallel computing methods to process multi-condition simulation tasks is an urgent problem to be solved currently. Summary of the Invention

[0005] This application provides a multi-condition simulation automatic processing method, device, equipment and medium, achieving the technical effect of improving the efficiency and accuracy of multi-condition simulation tasks.

[0006] To achieve the above object, the main technical solutions adopted in this application include: In a first aspect, an embodiment of this application provides a multi-condition simulation automatic processing method, the method includes: Obtain input parameters required for multi-condition simulation; Determine the modeling method corresponding to the three-dimensional simulation model for simulation and perform the execution body mesh generation operation according to the input parameters; loop through each condition, and select the corresponding parameter combination according to the modeling method to perform simulation calculations on the three-dimensional simulation model to obtain simulation results corresponding to each condition.

[0007] The multi-condition simulation automatic processing method provided in this embodiment can provide accurate basic data for each condition by automatically obtaining the input parameters required for multi-condition simulation, reducing manual intervention and errors. Then, using the parallel computing ability, perform simulation calculations for multiple conditions simultaneously. Through parallel processing, the computing tasks of each condition can be carried out simultaneously, greatly improving the computing efficiency and simulation speed. Generally speaking, the method combining automation and parallel computing not only improves the efficiency of multi-condition simulation tasks, but also ensures the accuracy and wide applicability of the simulation results.

[0008] In one embodiment, the method for determining the modeling method corresponding to the simulation of the three-dimensional simulation model according to the input parameters includes: If the input parameter selects to run the MRF fan modeling function, the modeling method is the MRF method; If the input parameter selects to run the PQ fan modeling function, the modeling method is the PQ method.

[0009] In this embodiment, during the fan modeling process, by selecting different modeling methods, the corresponding modeling methods can be automatically executed, reducing manual intervention. Through parallel computing, multiple simulation tasks can be simultaneously carried out on independent computing units, significantly improving the computing efficiency and shortening the simulation time. This efficient, automated, and scalable processing method meets the requirements of large-scale multi-condition simulation tasks, greatly improving the accuracy and efficiency of engineering analysis.

[0010] In one embodiment, the method for determining whether to perform the volume mesh generation operation includes: Traverse all mesh elements in the three-dimensional simulation model to determine whether there is a volume mesh; If there is no such volume mesh, perform the mesh generation operation until the volume mesh is obtained.

[0011] In this embodiment, by traversing all mesh elements to determine whether there is a volume mesh, it is ensured that the model contains the basic mesh structure required for numerical simulation. This not only helps to distinguish different types of meshes, avoiding unnecessary computational complexity, but also enables automated detection, reducing the risk of human omission. If it is found that there is no volume mesh in the model, the mesh generation operation will be automatically performed until the required volume mesh is generated. This process ensures the smooth progress of subsequent simulation calculations, improves the simulation accuracy and stability, and at the same time enhances the efficiency through automated operations, avoiding errors or omissions that may be caused by manual operations. Therefore, by automatically checking and generating the volume mesh, the integrity and accuracy of the three-dimensional simulation model are ensured.

[0012] In one embodiment, the method for determining whether to perform the volume mesh generation operation includes: Traverse all mesh elements in the three-dimensional simulation model to determine whether there is a volume mesh; If there is such a volume mesh, traverse all mesh differentiation units in the three-dimensional simulation model to determine the number of volume meshes in each mesh differentiation unit; If the number of volume meshes in any mesh differentiation unit exceeds a preset number threshold, it is determined that there is such a volume mesh in the three-dimensional simulation model, and there is no need to perform the volume mesh generation operation.

[0013] In this embodiment, by determining whether there are sufficient volume meshes in the 3D simulation model, if the requirements are met, the generation of volume meshes will be skipped, thus saving computing resources and time and avoiding unnecessary repeated calculations. At the same time, the volume mesh quantity statistics and judgment ensure that the generation is only performed for areas lacking volume meshes, improving the accuracy and efficiency of mesh generation. The preset quantity threshold also provides a flexible adjustment space, enabling dynamic optimization of the judgment criteria according to different simulation tasks. Overall, this process avoids redundant operations, improves the accuracy and stability of the simulation model, and enables the simulation system to smoothly perform efficient mesh generation and simulation tasks without increasing the computing burden.

[0014] In one embodiment, the input parameters include a custom element list, a fan speed list, and a PQ curve data list; the method loops through each working condition, selects the corresponding parameter combination according to the modeling method, and performs simulation calculations on the 3D simulation model to obtain the simulation results corresponding to each working condition, including: In the case where the modeling method is the MRF method, nested loops are used to loop through the parameter combinations including the custom element list and the fan speed list to perform simulation calculations on the 3D simulation model to obtain the simulation results corresponding to each working condition; in the case where the modeling method is the PQ method, nested loops are used to loop through the parameter combinations including the custom element list and the PQ curve data list to perform simulation calculations on the 3D simulation model to obtain the simulation results corresponding to each working condition; wherein, the custom element list includes vehicle speed and core heat exchange amount.

[0015] This embodiment can achieve accurate performance prediction and optimization by combining custom elements (such as vehicle speed and core heat exchange amount) with the combination of fan speed or PQ curve data. This method can simulate the behaviors under multiple different working conditions, provide detailed performance data for designers, and help optimize parameters such as fan design, vehicle speed control, and core heat exchange amount.

[0016] In one embodiment, the method further includes setting the boundary conditions of the current working condition, and the setting method of the boundary conditions includes: Extract the current vehicle speed of the current working condition from the list of custom elements, and determine the current vehicle speed as the boundary condition for the air inlet of the wind tunnel in the three-dimensional simulation model; or when the temperature field is selected in the input parameters, extract the current heat exchange amount of the core from the list of custom elements, and determine the current heat exchange amount of the core as the boundary condition for the porous medium domain in the three-dimensional simulation model; or when the MRF fan modeling function is selected in the input parameters, extract the fan speed from the list of fan speeds, and determine the fan speed as the boundary condition for the fan domain in the three-dimensional simulation model; or when the PQ fan modeling function is selected in the input parameters, extract the PQ curve data from the list of PQ curve data, and determine the PQ curve data as the boundary condition for the fan domain in the three-dimensional simulation model.

[0017] In this embodiment, by dynamically setting different boundary conditions, the behaviors such as fluid flow, heat exchange, and fan performance under different working conditions are accurately simulated. In the simulation model, working condition parameters such as vehicle speed, core heat exchange amount, fan speed, and PQ curve data are applied in real time to ensure that the simulation results are more in line with the actual situation. The method provides flexible boundary condition settings, and can flexibly adjust the model settings according to different input parameters, such as dynamically adjusting the core heat exchange amount as the boundary condition, or setting the boundary condition of the fan domain according to the fan speed and PQ curve data. This flexibility improves the applicability and accuracy of the simulation model. Especially in heat exchange and fan performance analysis, it can optimize the simulation results. Through automatic boundary condition setting, not only the calculation efficiency is improved, the cumbersome operation of manual adjustment is avoided, but also the human error is reduced, thus ensuring the accuracy and reliability of the simulation process.

[0018] In one implementation, the input parameters include the total number of calculation steps, the save step, and the maximum number of removals; the method further includes: When the current iteration step exceeds the preset step threshold, obtain multiple groups of corresponding maximum speed values; Determine the average speed corresponding to the multiple maximum speed values, and determine the current speed limit based on the average speed and the preset safety threshold; In the case where the current maximum speed exceeds the speed limit, determine that there are abnormal grids in the current three-dimensional simulation model; where the abnormal grids represent the volume grids where the current speed is greater than the speed limit; Remove the abnormal grids from the three-dimensional simulation model, and update the number of removals; When the number of removals exceeds the maximum number of removals or the current iteration step reaches the total number of calculation steps, stop the simulation calculation.

[0019] In this embodiment, by introducing the calculation of multiple groups of maximum speeds and setting a speed limit, this method effectively reduces the influence of occasional fluctuations on the simulation results and improves the stability of simulation calculations. At the same time, by comparing the maximum speed with the speed limit, abnormal grids can be automatically detected and removed, thus preventing the interference of unreasonable grids on the simulation results. This automated abnormal detection and correction mechanism greatly improves the simulation accuracy and reliability. In addition, the set stop condition ensures that the simulation automatically stops when there are too many abnormal grids or the number of calculation steps is too long, thereby optimizing the utilization of computing resources, avoiding unnecessary calculation delays, and further ensuring the efficiency and accuracy of simulation calculations.

[0020] In one embodiment, the method further includes: Based on the input parameters, Java scripts corresponding to each working condition are generated in a loop, as well as bat scripts corresponding to each Java script; multiple bat scripts are simultaneously called through the multi-threaded function, so that the multiple bat scripts start the simulation software to run the Java scripts corresponding to each working condition respectively.

[0021] In a second aspect, an embodiment of the present application provides a multi-condition simulation automation processing device, and the device includes: A parameter acquisition unit for acquiring input parameters required for multi-condition simulation; A modeling and meshing unit for determining the modeling method corresponding to the three-dimensional simulation model for simulation and performing the execution body meshing generation operation according to the input parameters; A simulation calculation unit for looping through each working condition, selecting a corresponding parameter combination according to the modeling method to perform simulation calculations on the three-dimensional simulation model, and obtaining simulation results corresponding to each working condition.

[0022] In a third aspect, an embodiment of the present application provides a computer device, including: A memory and a processor, which are communicatively connected to each other, a computer instruction is stored in the memory, and the processor executes the computer instruction to execute the above-mentioned multi-condition simulation automation processing method.

[0023] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer instruction is stored, and the computer instruction is used to cause a computer to execute the above-mentioned multi-condition simulation automation processing method. Description of the Drawings

[0024] To more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of a multi-condition simulation automation processing method provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the front-end window provided by an embodiment of the present application; Figure 3 It is a flowchart of step S3 provided by an embodiment of the present application; Figure 4 It is a flowchart of a judgment method for performing a body mesh generation operation provided by an embodiment of the present application; Figure 5 It is a flowchart of another judgment method for performing a body mesh generation operation provided by an embodiment of the present application; Figure 6 It is a flowchart of step S5 provided by an embodiment of the present application; Figure 7 It is a schematic diagram of the parameter combination for nested loop traversal of the cycles list and the rpms list provided by an embodiment of the present application; Figure 8 It is a flowchart of another multi-condition simulation automation processing method provided by an embodiment of the present application; Figure 9 It is a block diagram of a multi-condition simulation automation processing device provided by an embodiment of the present application; Figure 10 It is a schematic diagram of the structure of a computer device provided by an embodiment of the present application. Specific Embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0027] With the continuous development of computer science and technology, especially the continuous progress in the field of computational fluid dynamics (CFD), the automotive industry increasingly relies on CFD technology for cabin flow field analysis and heat exchanger performance evaluation during the design and R & D process. CFD technology can simulate and analyze the fluid flow, temperature distribution, and heat transfer process inside the cabin, thereby helping engineers deeply understand the thermal management and air flow characteristics under different working conditions, and then optimizing the cabin design scheme to improve the comfort and performance of the vehicle.

[0028] In the current automotive development process, especially during the performance calibration stage, engineers often need to perform simulation calculations on the cabin flow field under different working conditions. Since the parameters such as air flow, temperature, and pressure vary greatly under different working conditions, multiple simulation calculations must be carried out, with each calculation focusing on a specific working condition for detailed analysis. This not only requires a large amount of computing resources but also requires engineers to perform separate simulation settings and calculations for each working condition, resulting in low computing efficiency and increased workload. In addition, since these simulation calculations involve a large number of complex physical fields (such as fluid flow, heat transfer, multiphase flow, etc.), each calculation and subsequent data processing require engineers to invest a large amount of time and effort.

[0029] Against this background, although modern CFD simulation software has provided relatively mature technical solutions in aspects such as model construction, solution, and post-processing, due to the large number of working conditions and simulation tasks involved in the simulation process, the traditional method of calculating and processing one by one is particularly inefficient. Especially when a large number of different working conditions need to be calculated, engineers have to manually adjust various parameters and settings and gradually push forward the simulation process for each working condition, which greatly limits the efficiency of the simulation work and the shortening of the entire development cycle.

[0030] To solve the above technical problems, according to the embodiments of the present application, an embodiment of a multi-condition simulation automatic processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0031] In this embodiment, a multi-condition simulation automatic processing method is provided. Figure 1 The flowchart of a multi-condition simulation automatic processing method provided by the embodiments of the present application is as follows Figure 1 As shown, this process includes the following steps: Step S1, obtain the input parameters required for multi-condition simulation.

[0032] Specifically, input parameters from the front-end window of the calculation script generator. The input parameters include the following parameters for controlling the script operation: a. cycles list: Type: custom element.

[0033] Content: stores vehicle speed and core heat exchange quantity.

[0034] Purpose: used to simulate vehicle operating conditions or heat exchange processes.

[0035] b. rpms list: Type: list collection.

[0036] Content: stores fan rotation speed.

[0037] Purpose: used to control the operating state of the fan, related to vehicle speed or heat exchange quantity calculation.

[0038] c. countNumber collection: Type: list collection.

[0039] Content: includes total calculation steps, save steps, and maximum removal times.

[0040] Purpose: controls the iteration of the simulation calculation process, data saving frequency, and limits of removal operations.

[0041] d. temperatureField: Type: boolean value.

[0042] Content: records whether the model has a temperature field.

[0043] Purpose: determines whether to perform temperature-related calculations or simulations.

[0044] e. axle_direction: Type: boolean value.

[0045] Content: records the axial direction of fan rotation.

[0046] Purpose: affects the rotation direction of the fan, related to the air flow direction or heat exchange efficiency.

[0047] f. rpmOrPq: Type: boolean value.

[0048] Content: records the fan modeling method (MRF method or PQ method).

[0049] Purpose: determines which method to use to simulate the operation of the fan.

[0050] g. fan_PQ list: Type: list collection.

[0051] Content: stores PQ curve data.

[0052] Usage: For parameter input during PQ method modeling.

[0053] h. Specified path: Type: String set.

[0054] Content: Store the path of the simulation software and the path of the simulation model.

[0055] Usage: The bat script can call the simulation software through the simulation software path and call the simulation model through the simulation model path.

[0056] Please refer to Figure 2 , which shows a schematic diagram of the front-end window and supports the simultaneous input of multiple parameter sets. This design enables users to set multiple working conditions or multiple parameter combinations within one interface, thereby improving operation efficiency, reducing cumbersome repeated input, and facilitating the automation and parallel computing of multi-working-condition simulation tasks. In this way, users can more conveniently configure the input parameters required for parallel computing and further enhance the processing efficiency of simulation tasks.

[0057] Step S3, determine the modeling method corresponding to the simulation of the three-dimensional simulation model according to the input parameters and perform the operation of generating volume meshes.

[0058] Specifically, in multi-working-condition simulation, determining the modeling method for each working condition according to the input parameters and performing the operation of generating volume meshes ensures that the simulation model for each working condition can accurately reflect the actual physical phenomena, while avoiding unnecessary waste of computing resources. According to the rpmOrPq boolean value in the input parameters, select the appropriate modeling method. After determining the modeling method, it is necessary to check and generate volume meshes to ensure the integrity of the simulation model. Specifically, traverse all grid elements in the three-dimensional simulation model to check whether there are volume meshes (volume type). If there are volume meshes, further check whether the number of volume meshes in each grid division unit (region) exceeds the preset number threshold (for example, 1000). If the number of volume meshes in any region exceeds the preset number threshold, it is considered that there are already sufficient volume meshes in the model and no generation is required. If there are no volume meshes in the model, or the number of volume meshes in all regions does not exceed the preset number threshold, then call the generateVolumeMesh() function to generate volume meshes. The generated volume meshes will be used for subsequent simulation calculations to ensure that the model can correctly simulate the behavior of fluids or solids in three-dimensional space.

[0059] Step S5, loop through each working condition, select the corresponding parameter combination according to the modeling method, and perform simulation calculations on the three-dimensional simulation model to obtain the simulation results corresponding to each working condition.

[0060] Specifically, by selecting different parameter combinations according to the modeling method for simulation calculation, it can be ensured that the simulation model for each working condition can accurately reflect the actual physical phenomenon. Specifically, the MRF method or the PQ method is selected according to the rpmOrPq boolean value. Use nested loops to iterate through all combinations of the cycles list and the rpms list (MRF method) or the fan_PQ list (PQ method). Call the simulation calculation function for each parameter combination to obtain the corresponding simulation results. During the iteration process, check the validity of the vehicle speed and the fan speed, and skip invalid working conditions.

[0061] A multi-condition simulation automation processing method provided in this embodiment can provide accurate basic data for each working condition by automatically obtaining the input parameters required for multi-condition simulation, reducing manual intervention and errors. Then, using the parallel computing ability, the simulation calculations for multiple working conditions are carried out simultaneously. Through parallel processing, the calculation tasks for each working condition can be carried out simultaneously, greatly improving the calculation efficiency and simulation speed. Generally speaking, the method combining automation and parallel computing not only improves the efficiency of the multi-condition simulation task, but also ensures the accuracy and wide applicability of the simulation results.

[0062] Figure 3 It is a flowchart of step S3 provided in an embodiment of this application, and this process may include the following steps: Step S31, if the input parameter selects to run the MRF fan modeling function, the modeling method is the MRF method.

[0063] Step S33, if the input parameter selects to run the PQ fan modeling function, the modeling method is the PQ method.

[0064] Specifically, according to rpmOrPq in the input parameter, if the rpmOrPq boolean value is True, then select to run the doEverything() function, and if the rpmOrPq boolean value is False, then select to run the doEverything2() function. Among them, the doEverything() function is a fan modeling function based on the MRF method; the doEverything2() function is a fan modeling function based on the PQ method.

[0065] The MRF method is a steady-state simulation technology used to simulate rotating components (such as fans). In the MRF method, the fan area is regarded as a rotating reference frame. The motion of the fluid in this area is described as relative motion, that is, the velocity of the fluid relative to the rotating coordinate system. Based on the MRF method, the doEverything() function defines the rotation behavior of the fan by setting the rotational speed (rpm) of the fan. The boundary in modeling by the MRF method needs to set the fan rotation axis direction.

[0066] The PQ method simulates the behavior of a fan through the fan's performance curve (flow - pressure curve). Based on the PQ method, the doEverything2() function defines the flow rate and pressure characteristics of the fan through the PQ curve data. When modeling using the PQ method, the boundary of the fan's inlet surface needs to be set as a sector interface.

[0067] Therefore, by selectively running the doEverything() function or the doEverything2() function according to the rpmOrPq boolean value in the input parameters, it is possible to flexibly switch between the MRF method and the PQ method to meet different simulation requirements. The MRF method has high calculation accuracy and clear physical meaning, but high calculation costs. The PQ method has advantages in calculation efficiency. Both modeling methods have application scenarios in engineering practice.

[0068] In the process of fan modeling in this embodiment, by selecting different modeling methods, the corresponding modeling methods can be automatically executed, reducing manual intervention. Through parallel computing, multiple simulation tasks can be carried out simultaneously on independent computing units, significantly improving the calculation efficiency and shortening the simulation time. This efficient, automated, and scalable processing method meets the requirements of large - scale multi - condition simulation tasks, greatly improving the accuracy and efficiency of engineering analysis.

[0069] Figure 4 It is a flowchart of a judgment method for performing a volume mesh generation operation provided by an embodiment of this application. This process may include the following steps: Step S321, traverse all mesh elements in the three - dimensional simulation model to determine whether there are volume meshes.

[0070] Step S323, if there are no volume meshes, perform the mesh generation operation until volume meshes are obtained.

[0071] Specifically, in a 3D simulation model, mesh elements are the basic units that make up the model, including geometric shapes, imported models (import), and volume meshes. The purpose of traversing these mesh elements is to check for the existence of volume meshes. A volume mesh is a mesh form used to simulate the behavior of fluids or solids in 3D space. If the type of any mesh element is found to be "volume" during the traversal, it is considered that there is a volume mesh in the model. Specifically, hasVo() is a function used to determine the existence of a volume mesh. By default, hasVo() returns true, indicating the absence of a volume mesh. By traversing all mesh elements, hasVo() checks for the existence of mesh elements of the "volume" type. If a mesh element of the "volume" type is found, it returns false, indicating the existence of a volume mesh. If hasVo() returns true (i.e., there is no volume mesh), a mesh generation operation needs to be performed. The mesh generation operation is completed by calling the API provided by the simulation software, generateVolumeMesh(). The mesh generation operation includes generating surface meshes of the model, which is the basis for volume mesh generation because surface meshes describe the boundaries of objects. Then, based on the generated surface meshes, 3D space is segmented to generate volume meshes. Volume meshes are usually composed of small units (such as tetrahedrons, hexahedrons, pyramids, etc.), and these units fill the entire 3D space of the object. The generated volume meshes will be used for subsequent simulation calculations to ensure that the 3D simulation model can correctly simulate the behavior of fluids or solids in 3D space.

[0072] In this embodiment, by traversing all mesh elements to determine the existence of volume meshes, it is ensured that the model contains the basic mesh structure required for numerical simulation. This not only helps to distinguish different types of meshes, avoiding unnecessary computational complexity, but also enables automated detection, reducing the risk of human omission. If it is found that there is no volume mesh in the model, the mesh generation operation will be automatically executed until the required volume mesh is generated. This process guarantees the smooth progress of subsequent simulation calculations, improves the simulation accuracy and stability, and at the same time enhances the efficiency through automated operations, avoiding errors or omissions that may be caused by manual operations. Therefore, by automatically checking and generating volume meshes, the integrity and accuracy of the 3D simulation model are ensured.

[0073] Figure 5 The following is a flowchart of another method for determining the execution of the volume mesh generation operation provided by the embodiment of the present application. The process may include the following steps: Step S341: Traverse all mesh elements in the 3D simulation model to determine the existence of volume meshes.

[0074] Step S343: If there is a volume mesh, traverse all mesh differentiation units in the 3D simulation model to determine the number of volume meshes in each mesh differentiation unit.

[0075] Step S345, if the number of volume meshes in any grid division unit exceeds a preset number threshold, it is determined that there are volume meshes in the 3D simulation model, and there is no need to perform the volume mesh generation operation.

[0076] Specifically, in a 3D simulation model, grid elements are the basic units that make up the model, including geometric shapes, imported models (import), and volume meshes, etc. The purpose of traversing these grid elements is to check whether there are volume meshes. A volume mesh is a grid form used to simulate the behavior of fluids or solids in 3D space. If the type of any grid element is found to be volume during the traversal process, it is considered that there are volume meshes in the model. Specifically, hasVo() is a function used to determine whether there are volume meshes. By traversing all grid elements, hasVo() checks whether there are grid elements of the volume type. If a grid element of the volume type is found, it returns false, indicating that there are volume meshes.

[0077] A grid division unit (region) is a logical unit that divides the grid into different parts and is usually used to group grids with similar characteristics. To further confirm whether the number of volume meshes meets specific conditions, by traversing all regions, the number of volume meshes in each region is counted. A number threshold (for example, 1000) is preset to judge whether the number of volume meshes is sufficient. If the number of volume meshes in any region exceeds this threshold, it is considered that there are already sufficient volume meshes in the model, and there is no need to perform the volume mesh generation operation, thus saving computing resources and time. If there are volume meshes and the number of volume meshes in any region exceeds the threshold, there is no need to perform the volume mesh generation operation. If the number of volume meshes in all regions does not exceed the preset number threshold, the generateVolumeMesh() function needs to be called to generate volume meshes to ensure that subsequent simulation calculations can proceed smoothly.

[0078] In this embodiment, by judging whether there are already sufficient volume meshes in the 3D simulation model, if the requirements are met, the volume mesh generation will be skipped, thus saving computing resources and time and avoiding unnecessary repeated calculations. At the same time, the volume mesh number statistics and judgment ensure that the generation is only targeted at regions lacking volume meshes, improving the accuracy and efficiency of mesh generation. The preset number threshold also provides a flexible adjustment space and can dynamically optimize the judgment criteria according to different simulation tasks. Overall, this process avoids redundant operations, improves the accuracy and stability of the simulation model, and enables the simulation system to smoothly perform efficient mesh generation and simulation tasks without increasing the computing burden.

[0079] Figure 6It is the flowchart of step S5 provided by the embodiment of the present application. The input parameters include a custom element list, a fan speed list, and a PQ curve data list. The process may include the following steps: Step S51, when the modeling method is the MRF method, through nested loops, traverse the parameter combinations including the custom element list and the fan speed list to perform simulation calculations on the three-dimensional simulation model, and obtain the simulation results corresponding to each working condition.

[0080] Step S53, when the modeling method is the PQ method, through nested loops, traverse the parameter combinations including the custom element list and the PQ curve data list to perform simulation calculations on the three-dimensional simulation model, and obtain the simulation results corresponding to each working condition.

[0081] Among them, the custom element list includes vehicle speed and core heat exchange amount.

[0082] Specifically, in multi-condition simulation, according to different modeling methods (MRF method or PQ method), different parameter combinations need to be traversed to complete the simulation calculations.

[0083] For the MRF method, use nested loops to traverse the cycles list (different combinations of vehicle speed and core heat exchange amount) and the rpms list (different fan speed combinations) to form multiple different parameter combinations. Each set of parameter combinations corresponds to a specific working condition. After calling the simulation calculation function, the simulation results under this working condition are obtained. Exemplarily, please refer to Figure 7 , by using nested loops to traverse the parameter combinations of the cycles list and the rpms list, 9 combinations can be obtained. It means that each combination can be used to simulate a different working condition, and finally 9 sets of simulation results are obtained. This method can efficiently explore the combinations of different vehicle speeds, heat exchange amounts, and fan speeds, so as to obtain comprehensive simulation data.

[0084] For the PQ method, use nested loops to traverse the parameter combinations of the cycles list (custom element list) and the fan_PQ list (PQ curve data list). Each combination represents a specific working condition, and the simulation calculation function will also be called to obtain the simulation results under this working condition.

[0085] Through the nested loop traversal of these two methods, different parameter combinations can be quickly traversed, greatly improving the simulation efficiency. Whether it is the MRF method or the PQ method, various working conditions can be explored through different parameter combinations, and simulation results for each working condition can be generated. This enables a comprehensive understanding and optimization of the system's performance under different working conditions, and thus provides a scientific basis for the design.

[0086] It should be noted that in multi-condition simulation, some parameter combinations may be physically invalid or have no practical significance in engineering applications. For example, the situation where the vehicle speed is less than 0 or the fan speed is equal to -1 usually does not conform to the actual operating conditions. Therefore, when traversing parameter combinations, these invalid conditions need to be skipped to save computing resources and improve the efficiency of the simulation.

[0087] In this embodiment, by combining custom elements (such as vehicle speed and core heat exchange) with the combination of fan speed or PQ curve data, accurate performance prediction and optimization can be achieved. This method can simulate the behavior under multiple different conditions, providing detailed performance data for designers to help optimize parameters such as fan design, vehicle speed control, and core heat exchange.

[0088] In one implementation, the method further includes setting the boundary conditions of the current condition. The setting method of the boundary conditions includes: extracting the current vehicle speed of the current condition from the custom element list and determining the current vehicle speed as the boundary condition of the wind tunnel inlet in the three-dimensional simulation model.

[0089] Specifically, extract the current vehicle speed of the current condition from the custom element list, that is, the cycles list. Determine the current vehicle speed as the boundary condition of the wind tunnel inlet in the three-dimensional simulation model and input it to the wind tunnel inlet boundary.

[0090] Or When the input parameter selects the temperature field, extract the current core heat exchange of the current condition from the custom element list and determine the current core heat exchange as the boundary condition of the porous media domain in the three-dimensional simulation model.

[0091] Specifically, if the input parameter selects the temperature field and the core heat exchange needs to be considered, use it as the boundary condition of the porous media domain. Check the temperatureField boolean value in the input parameters. If temperatureField is True, extract the current core heat exchange of the current condition from the custom element list, that is, the cycles list, determine the current core heat exchange as the boundary condition of the porous media domain in the three-dimensional simulation model, and input it into the porous media domain. If temperatureField is False, skip this step, indicating that the heat exchange process is not involved. The role of the porous media domain is to simulate the heat exchange process of components such as radiators and coolers. The input of the current core heat exchange can affect the temperature distribution and heat exchange efficiency of these components.

[0092] Or When the input parameter selects to run the MRF fan modeling function, extract the fan speed from the fan speed list and determine the fan speed as the boundary condition of the fan domain in the three-dimensional simulation model.

[0093] Specifically, if the fan modeling using the MRF method is selected, the fan speed is used as the boundary condition. According to the rpmOrPq boolean value in the input parameters, determine which fan modeling method to use. If rpmOrPq is True, extract the fan speed from the fan speed list, i.e., the rpms list, and determine the fan speed as the boundary condition of the fan domain in the 3D simulation model and input it into the fan domain.

[0094] Or When the PQ fan modeling function is selected in the input parameters, extract the PQ curve data from the PQ curve data list and determine the PQ curve data as the boundary condition of the fan domain in the 3D simulation model.

[0095] Specifically, if the fan modeling using the PQ method is selected, the PQ curve data is used as the boundary condition of the fan domain. According to the rpmOrPq boolean value in the input parameters, determine which fan modeling method to use. If rpmOrPq is False, extract the PQ curve data from the PQ curve data list, i.e., the fan_PQ list, and determine the PQ curve data as the boundary condition of the fan domain in the 3D simulation model and input it into the fan domain.

[0096] In this embodiment, by dynamically setting different boundary conditions, the behaviors such as fluid flow, heat exchange, and fan performance under different working conditions are accurately simulated. In the simulation model, working condition parameters such as vehicle speed, core heat exchange amount, fan speed, and PQ curve data are applied in real time to ensure that the simulation results are more in line with the actual situation. The method provides flexible boundary condition settings and can flexibly adjust the model settings according to different input parameters, such as dynamically adjusting the core heat exchange amount as the boundary condition, or setting the boundary condition of the fan domain according to the fan speed and PQ curve data. This flexibility improves the applicability and accuracy of the simulation model, especially in heat exchange and fan performance analysis, and can optimize the simulation results. Through automatic boundary condition setting, not only the calculation efficiency is improved, the cumbersome operation of manual adjustment is avoided, but also the human error is reduced, thus ensuring the accuracy and reliability of the simulation process.

[0097] Figure 8 FIG. 15 is a flowchart of another multi-condition simulation automatic processing method provided by an embodiment of the present application. The input parameters include the total calculation step, the save step, and the maximum removal times. The process may include the following steps: Step S71, when the current iteration step exceeds the preset step threshold, obtain multiple groups of corresponding maximum speed values.

[0098] Step S73, determine the average speed corresponding to the multiple maximum speed values, and determine the current speed limit based on the average speed and the preset safety threshold.

[0099] Step S75, when the current maximum speed exceeds the speed limit, determine that there are abnormal meshes in the current 3D simulation model; where the abnormal meshes represent the volume meshes with the current speed greater than the speed limit.

[0100] Step S77, remove the abnormal meshes from the 3D simulation model and update the removal times.

[0101] Step S79, stop the simulation calculation when the removal times exceed the maximum removal times or the current iteration step reaches the total number of calculation steps.

[0102] Specifically, the preset step threshold is usually set to 200 steps, aiming to ensure that the simulation calculation has run for a period of time and avoid speed monitoring at the very beginning of the simulation process. In the initial stage, there may be large fluctuations in the simulation, and premature speed monitoring may be affected by these fluctuations, resulting in misjudgment. When the current iteration step exceeds 200, the simulation enters a relatively stable stage, and at this time, the system begins to focus on the long-term change trend and stability. Therefore, obtain the maximum speed values of several time periods (the previous 5 steps, the previous 10 steps, and the previous 15 steps) before the current iteration step. This can help capture the speed fluctuations in different time periods to more comprehensively evaluate the simulation state. By calculating the average value of these maximum speed values, a comprehensive speed index can be obtained to avoid the influence of abnormal fluctuations in a single time period on the judgment. After calculating the average speed, add it to the preset safety threshold (such as 8). The purpose of this preset safety threshold is to provide a buffer for the simulation system to ensure that the speed of the system does not exceed the predetermined safety range, thereby avoiding potential risks (such as inaccurate data or calculation errors) caused by excessive speed. Therefore, the current speed limit is the average speed value after adding the safety threshold. Once the maximum speed of the current iteration step exceeds the calculated speed limit, further search for the volume meshes with the current speed exceeding the target speed limit and determine that the volume meshes are abnormal meshes. This is because overspeed may mean that something abnormal has occurred in a part of the simulation calculation (for example, calculation errors, physical model problems, etc.), resulting in abnormal speed performance of the meshes. Therefore, it is necessary to mark the volume meshes as abnormal meshes for subsequent processing.

[0103] To ensure the stability and accuracy of the simulation calculation, these abnormal meshes marked as abnormal are removed from the three-dimensional simulation model. Removing these abnormal meshes helps to correct the unreasonable results in the simulation and prevent the impact of abnormal data on the entire simulation process. Each time an abnormal mesh is removed, the removal counter is incremented, which can ensure that the number of abnormal mesh removals is controlled during the execution process to avoid overcorrection or interference with the simulation calculation. When the number of removals exceeds the preset maximum number of removals, or when the current iteration step has reached the predetermined total number of calculation steps, the simulation calculation is stopped. If the number of abnormal meshes removed is too large, it means that there are serious abnormalities in the simulation process, which may affect the accuracy and stability of the simulation. Therefore, the simulation is stopped when the number of removals exceeds the maximum number of removals to prevent untrustworthy results from being generated if it continues to run. Additionally, the simulation calculation may automatically end according to the preset total number of calculation steps. When the total number of steps is reached, the simulation automatically stops regardless of whether abnormal meshes are removed. This is to avoid endless simulation runs.

[0104] In this embodiment, by introducing the calculation of multiple sets of maximum speeds and setting a speed limit, this method effectively reduces the impact of sporadic fluctuations on the simulation results and improves the stability of the simulation calculation. At the same time, by comparing the maximum speed with the speed limit, abnormal meshes can be automatically detected and removed, thereby preventing the interference of unreasonable meshes on the simulation results. This automated abnormal detection and correction mechanism greatly improves the simulation accuracy and reliability. In addition, the set stop conditions ensure that the simulation automatically stops when there are too many abnormal meshes or the calculation steps are too long, thereby optimizing the utilization of computing resources, avoiding unnecessary calculation delays, and further ensuring the efficiency and accuracy of the simulation calculation.

[0105] In one implementation, the method further includes: based on the input parameters, cyclically generating java scripts corresponding to each working condition, and bat scripts corresponding to each java script; simultaneously calling multiple bat scripts through the multi-threaded function, so that the multiple bat scripts start the simulation software to run the java scripts corresponding to each working condition respectively.

[0106] Specifically, set the boundary conditions of the simulation model according to the input parameters, call the simulation software for calculation, and extract the required data from the simulation results. Each working condition has a corresponding java script, and these scripts are generated by cyclically traversing the input parameters. Each java script requires a corresponding bat script, which is used to call the simulation software to run the java script in the background. The role of the bat script is to automate the execution of the simulation task. It contains commands to start the simulation software, specify the path of the java script, specify the path of the simulation model, and other contents.

[0107] The bat script is used to call the simulation software in the background to run the Java script. Each Java script has a corresponding bat script. The statement format of the bat script is: simulation software path -bat Java script path -np number of cores simulation model path. Among them, the simulation software path is the executable file path of the specified simulation software. -bat indicates that the simulation software runs in batch mode, which means that the simulation will be executed in the form of an automated script without manual intervention. The Java script path specifies the file path of the Java script to be run, and the Java script path corresponds to the Java script for each working condition. -np number of cores specifies the number of CPU cores used during the simulation. Here, parallel computing is achieved through -np, which can be adjusted according to the hardware resources of the computer, and an appropriate number of cores can be specified to optimize the computing efficiency. The simulation model path specifies the file path of the simulation model file.

[0108] For each working condition, an independent thread is generated to execute the corresponding bat script. Each thread runs the simulation software by calling the bat script, so that multiple simulation tasks can be executed in parallel without waiting for other tasks to complete. Assuming there are multiple different working conditions in the input parameters, after generating multiple Java scripts and bat scripts, the multi-threading of Java can be used to control the parallel execution of tasks, which can speed up the simulation and reduce the calculation time.

[0109] It should be noted that in multi-condition simulation, model post-processing and result saving ensure the integrity and traceability of simulation data.

[0110] Traverse the reports generated in the simulation software and add their names or identifiers to the global variable existReports list. Determine the number of rows according to the number of simulation times to be stored, and determine the number of columns according to the number of fields, including fixed fields and dynamic fields. Create two two-dimensional arrays and initialize each position to an empty string. The first array is used to store the regular simulation results, and the fixed field titles include vehicle speed, fan parameters, air volume, wind speed, temperature of the core body, etc. The second array is used to store specific post-processing results, and the fixed field titles include vehicle speed and wind speed, and the dynamic field is the name of the existing report.

[0111] Regularly save the model to prevent data loss and facilitate subsequent analysis and recovery. In each iteration, check whether the current step number is a multiple of the save step size. If it is, extract the vehicle speed and fan parameters from the operating conditions. Generate a new suffix for the model name based on these parameters, concatenate the original model name with the suffix "_vehicle speed kph_rotation speed rpm" to generate a new model name, and save the model as a new file. Read the report names from the global variable existReports and obtain the numerical values of these reports through the interface of the simulation software. Add the obtained report values as a new row to a two-dimensional array. Save the content of the two-dimensional array to a file in CSV format. Each save operation will overwrite the old file to ensure that both the old and new simulation results are saved in the file. A CSV file is essentially a text file, where the data is stored as strings. Each line of data is separated by a newline character, and the newly generated simulation result string is appended to the existing CSV content string. Use the file write operation to write the updated CSV content string to the CSV file at the specified path. If the file already exists, overwrite the original file; if the file does not exist, create a new file. Through the above steps, the new simulation results are appended to the CSV file, achieving automatic recording and updating of the simulation results.

[0112] Accordingly, please refer to Figure 9 FIG. A block diagram of a multi-condition simulation automation processing device provided by an embodiment of the present application. The device includes: A parameter acquisition unit 101 for acquiring input parameters required for multi-condition simulation; A modeling and meshing unit 103 for determining a modeling method corresponding to the simulation of a three-dimensional simulation model according to the input parameters and performing an execution body mesh generation operation;

[0113] In some optional embodiments, the modeling and meshing unit 103 includes: If the input parameter selects to run the MRF fan modeling function, the modeling method is the MRF method; If the input parameter selects to run the PQ fan modeling function, the modeling method is the PQ method.

[0114] In some optional embodiments, the determination method of the execution body mesh generation operation includes: Traverse all mesh elements in the three-dimensional simulation model to determine whether there is a volume mesh; If there is no volume mesh, perform the mesh generation operation until a volume mesh is obtained.

[0115] In some alternative embodiments, the determination method for performing the volume mesh generation operation includes: Traverse all mesh elements in the three-dimensional simulation model to determine whether there are volume meshes; If there are volume meshes, traverse all mesh division units in the three-dimensional simulation model to determine the number of volume meshes in each mesh division unit; If the number of volume meshes in any mesh division unit exceeds a preset quantity threshold, it is determined that there are volume meshes in the three-dimensional simulation model, and the volume mesh generation operation does not need to be performed.

[0116] In some alternative embodiments, the input parameters include a custom element list, a fan speed list, and a PQ curve data list; the simulation calculation unit 105 includes: In the case where the modeling method is the MRF method, perform simulation calculations on the three-dimensional simulation model by traversing the parameter combinations including the custom element list and the fan speed list through nested loops to obtain the simulation results corresponding to each working condition; In the case where the modeling method is the PQ method, perform simulation calculations on the three-dimensional simulation model by traversing the parameter combinations including the custom element list and the PQ curve data list through nested loops to obtain the simulation results corresponding to each working condition; Among them, the custom element list includes vehicle speed and core heat exchange amount; In some alternative embodiments, the device further includes setting the boundary conditions of the current working condition, and the setting method of the boundary conditions includes: extracting the current vehicle speed of the current working condition from the custom element list and determining the current vehicle speed as the boundary condition of the wind tunnel inlet air in the three-dimensional simulation model; or when a temperature field is selected in the input parameters, extracting the current core heat exchange amount of the current working condition from the custom element list and determining the current core heat exchange amount as the boundary condition of the porous medium domain in the three-dimensional simulation model; or when the MRF fan modeling function is selected to run in the input parameters, extracting the fan speed from the fan speed list and determining the fan speed as the boundary condition of the fan domain in the three-dimensional simulation model; or when the PQ fan modeling function is selected to run in the input parameters, extracting the PQ curve data from the PQ curve data list and determining the PQ curve data as the boundary condition of the fan domain in the three-dimensional simulation model.

[0117] In some alternative embodiments, the input parameters include the total calculation step length, the save step length, and the maximum removal times; the device further includes: When the current iteration step exceeds a preset step threshold, obtain multiple groups of corresponding maximum speed values; Determine the average speed corresponding to the multiple maximum speed values, and determine the current speed upper limit based on the average speed and a preset safety threshold; in the case where the current maximum speed exceeds the speed upper limit, it is determined that there are abnormal meshes in the current three-dimensional simulation model; among them, the abnormal meshes represent the volume meshes where the current speed is greater than the speed upper limit. Remove the abnormal grid from the 3D simulation model and update the removal count. Stop the simulation calculation when the removal count exceeds the maximum removal count or the current iteration step reaches the total number of calculation steps.

[0118] In some alternative embodiments, the apparatus further includes: Based on the input parameters, cyclically generate Java scripts corresponding to each working condition, and a BAT script corresponding to each Java script; simultaneously call multiple BAT scripts through the multi-threading function, so that the multiple BAT scripts start the simulation software to respectively run the Java scripts corresponding to each working condition.

[0119] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0120] A multi-condition simulation automation processing apparatus in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0121] Please refer to Figure 10 , Figure 10 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 10 shown, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other through different buses and can be installed on a common main board or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 10 Taking one processor 10 as an example in

[0122] The processor 10 may be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 may further include a hardware chip. The above-mentioned hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device may be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0123] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0124] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may further include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0125] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memories.

[0126] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0127] The embodiments of the present application also provide a computer-readable storage medium. The method according to the embodiments of the present application may be implemented in hardware, firmware, or may be implemented as computer code recorded on a storage medium, or may be implemented as computer code originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component capable of storing or receiving software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0128] The methods, apparatuses or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0129] For convenience of description, when describing the above apparatuses, they are divided into various units according to functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0130] Those skilled in the art should understand that the embodiments of the present application can be provided as methods and apparatuses. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices, and apparatuses according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0132] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the process Figure 1 one process or multiple processes and / or blocks Figure 1 steps for the functions specified in one block or multiple blocks.

[0134] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the said element.

[0135] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments.

[0136] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0137] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A multi-condition simulation automatic processing method, characterized in that, The method includes: Obtaining the input parameters required for multi-condition simulation; Determining the modeling method corresponding to the simulation of the three-dimensional simulation model according to the input parameters and performing the operation of generating the execution body grid; Looping through each condition, selecting the corresponding parameter combination according to the modeling method to perform simulation calculations on the three-dimensional simulation model, and obtaining the simulation results corresponding to each condition.

2. The method according to claim 1, wherein The determining the modeling method corresponding to the simulation of the three-dimensional simulation model according to the input parameters includes: If the input parameters select to run the MRF fan modeling function, the modeling method is the MRF method; If the input parameters select to run the PQ fan modeling function, the modeling method is the PQ method.

3. The method according to claim 1, characterized in that, The determination method of the execution body grid generation operation includes: traversing all grid elements in the three-dimensional simulation model to determine whether there is a body grid; If there is no such body grid, perform the grid generation operation until the body grid is obtained.

4. The method according to claim 1, characterized in that, The determination method of the execution body grid generation operation includes: traversing all grid elements in the three-dimensional simulation model to determine whether there is a body grid; If there is such a body grid, traverse all grid division units in the three-dimensional simulation model to determine the number of body grids in each grid division unit; If the number of body grids in any grid division unit exceeds the preset quantity threshold, it is determined that there is such a body grid in the three-dimensional simulation model, and there is no need to perform the body grid generation operation.

5. The method according to claim 1, wherein The input parameters include a custom element list, a fan speed list, and a PQ curve data list; The looping through each condition, selecting the corresponding parameter combination according to the modeling method to perform simulation calculations on the three-dimensional simulation model, and obtaining the simulation results corresponding to each condition includes: In the case where the modeling method is the MRF method, perform simulation calculations on the three-dimensional simulation model by looping through the parameter combinations including the custom element list and the fan speed list through nested loops, and obtain the simulation results corresponding to each condition; In the case where the modeling method is the PQ method, perform simulation calculations on the three-dimensional simulation model by looping through the parameter combinations including the custom element list and the PQ curve data list through nested loops, and obtain the simulation results corresponding to each condition; Among them, the custom element list includes vehicle speed and core heat exchange amount.

6. The method according to claim 5, characterized in that, The method further includes setting the boundary conditions of the current condition, and the setting method of the boundary conditions includes: Extracting the current vehicle speed of the current condition from the custom element list and determining the current vehicle speed as the boundary condition of the wind tunnel inlet air in the three-dimensional simulation model; or When the input parameters select a temperature field, extracting the current core heat exchange amount of the current condition from the custom element list and determining the current core heat exchange amount as the boundary condition of the porous medium domain in the three-dimensional simulation model; or When the input parameters select to run the MRF fan modeling function, extracting the fan speed from the fan speed list and determining the fan speed as the boundary condition of the fan domain in the three-dimensional simulation model; or When running the PQ fan modeling function with the input parameters selected, extract the PQ curve data from the PQ curve data list, and determine the PQ curve data as the boundary condition of the fan domain in the 3D simulation model.

7. The method according to claim 1, characterized in that, The input parameters include the total calculation step, the save step, and the maximum removal times; the method further includes: When the current iteration step exceeds the preset step threshold, obtain multiple groups of corresponding maximum speed values; Determine the average speed corresponding to the multiple maximum speed values, and determine the current speed upper limit based on the average speed and the preset safety threshold; In the case where the current maximum speed exceeds the speed upper limit, determine that there are abnormal grids in the current 3D simulation model; wherein, the abnormal grids represent the volume grids where the current speed is greater than the speed upper limit; Remove the abnormal grids from the 3D simulation model and update the removal times; Stop the simulation calculation when the removal times exceed the maximum removal times or the current iteration step reaches the total number of calculation steps.

8. The method according to claim 1, wherein The method further includes: Based on the input parameters, cyclically generate java scripts corresponding to each working condition, and bat scripts corresponding to each java script; Simultaneously call multiple bat scripts through the multi-thread function, so that the multiple bat scripts start the simulation software to run the java scripts corresponding to each working condition respectively.

9. A multi-condition simulation automatic processing device, characterized in that, The device includes: A parameter acquisition unit for acquiring input parameters required for multi-condition simulation; A modeling and meshing unit for determining the modeling method corresponding to the simulation of the 3D simulation model according to the input parameters and performing the volume mesh generation operation; A simulation calculation unit for cyclically traversing each working condition, and performing simulation calculations on the 3D simulation model according to the corresponding parameter combination selected by the modeling method to obtain simulation results corresponding to each working condition.

10. A computer device, characterized in that Including: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the multi-condition simulation automation processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the multi-condition simulation automation processing method according to any one of claims 1 to 8.

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