Equipment thermal fluid simulation method and device, equipment and medium

Abstract

The invention discloses an equipment thermal fluid simulation method and device, equipment and a medium, and the method comprises the steps that an open document submitted by a user is analyzed, the open document comprises a plurality of page units, and equipment configuration data and simulation task data are provided through the page units; updating a three-dimensional body mesh model in a simulation framework into an equipment entity model according to the equipment configuration data; calling a solver in a simulation framework according to the simulation task data to determine fluid simulation result data of the equipment entity model; and displaying corresponding simulation effect information on a graphical user interface according to the simulation result data. According to the method and the device, the open document submitted by the user is automatically analyzed, so that the dependence on professionals is reduced, and the simulation efficiency is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of computational fluid dynamics technology, and in particular to a method, apparatus, and medium for simulating thermal fluid in equipment. Background Technology

[0002] As a refrigeration device operating in a special environment, car refrigerators are placed between vehicle seats, in the trunk, or other confined spaces with poor air circulation, easily leading to heat buildup. Furthermore, continuous high temperatures inside the vehicle can trigger compressor overheat protection, causing frequent start-stop cycles, which may damage the compressor over time. Therefore, it is necessary to design an efficient and reliable heat dissipation system for car refrigerators under extreme conditions to optimize their design.

[0003] Traditional vehicle refrigerator design optimization relies on measured data from physical prototypes. While this method directly captures the product's performance under real-world conditions, each design change necessitates rebuilding and retesting the prototype, which is not only time-consuming and costly but also severely slows down product development. Although existing technologies utilize computational fluid dynamics simulation to assist in vehicle refrigerator design optimization, each step requires manual intervention by engineers, resulting in significant time wasted on repetitive manual operations. Furthermore, due to differing operating habits and judgment criteria among engineers, analysis processes and results vary considerably, making standardization and reusability difficult. Summary of the Invention

[0004] The primary objective of this application is to solve at least one of the above-mentioned problems by providing a device, apparatus, or medium for simulating thermal fluid in equipment.

[0005] To achieve the various objectives of this application, the following technical solution is adopted: A device thermal fluid simulation method provided for one of the purposes of this application includes the following steps: Parse the open document submitted by the user, which includes multiple page units and provides device configuration data and simulation task data through the multiple page units; The 3D volumetric mesh model in the simulation framework is updated to a physical model of the equipment based on the equipment configuration data. The fluid simulation results data of the equipment entity model are determined by calling the solver in the simulation framework based on the simulation task data; The simulation results data are used to display the corresponding simulation effect information in the graphical user interface.

[0006] A device thermal fluid simulation apparatus provided for one of the purposes of this application includes: The document parsing module is configured to parse open documents submitted by users. The open documents include multiple page units, through which device configuration data and simulation task data are provided. The model update module is configured to update the three-dimensional volume mesh model in the simulation framework to the device entity model based on the device configuration data. The simulation calculation module is configured to call the solver in the simulation framework based on the simulation task data to determine the fluid simulation result data of the equipment entity model; The simulation effect display module is configured to display the corresponding simulation effect information on the graphical user interface based on the simulation result data.

[0007] A computer device provided for one of the purposes of this application includes a central processing unit and a memory, the central processing unit being configured to invoke and run a computer program stored in the memory to perform the steps of the device thermal fluid simulation method described in this application.

[0008] A computer-readable storage medium is provided for another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the described device thermal fluid simulation method, which, when invoked by a computer, performs the steps included in the method.

[0009] Compared with existing technologies, the advantages of this application are as follows: First, this application achieves automated extraction of device configuration data and simulation task data by parsing open documents submitted by users. Users only need to provide an open document containing the corresponding parameters before simulation, and the simulation platform can automatically complete the data identification and extraction. This avoids the tedious operation of users repeatedly inputting and adjusting parameters at different simulation stages, significantly simplifies the data preparation process, and improves the startup efficiency of simulation tasks.

[0010] Secondly, by updating the three-dimensional volumetric mesh model in the simulation framework to a physical device model, this application ensures that the simulation model is highly consistent with the actual device. It can dynamically adjust various parameters of the physical device model according to the device configuration data input by the user, thereby providing a more reliable and accurate simulation basis for the optimized design of device performance.

[0011] Furthermore, this application provides users with an intuitive and easy-to-understand way of displaying simulation results by showing simulation effect information through a graphical user interface. Users can clearly observe the simulation effect information through the graphical interface, thereby quickly understanding and evaluating the performance of the equipment. This visualization method not only improves the user experience, but also makes the application of simulation results more flexible and efficient. When this application is applied to the field of heat dissipation of vehicle refrigerators, it can better guide the design optimization of the heat dissipation system and ensure the efficient and reliable operation of vehicle refrigerators under extreme conditions such as high temperature. Attached Figure Description

[0012] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of a typical embodiment of the device thermal fluid simulation method of this application; Figure 2 This is a schematic diagram of the process of calling a large model to extract data in an embodiment of this application; Figure 3 This is a flowchart illustrating the process of generating the three-dimensional volume mesh model corresponding to the simulation model in the embodiments of this application; Figure 4 This is a schematic diagram of the process for generating the surface mesh model corresponding to the simulation model in the embodiments of this application; Figure 5 This is a flowchart illustrating the process of determining the radius of curvature and corresponding mesh control parameters of a fillet surface in an embodiment of this application. Figure 6 This is a schematic diagram of the process for updating the device entity model in an embodiment of this application; Figure 7 This application embodiment is a flowchart illustrating the display of simulation result data in a graphical user interface; Figure 8 This is a schematic block diagram of the thermal fluid simulation device for the equipment in this application; Figure 9 This is a schematic diagram of the structure of a computer device used in this application. Detailed Implementation

[0013] This application discloses a device thermal fluid simulation method that can be programmed into a computer program and deployed on a computational fluid dynamics (CFD) simulation platform. For example, in an exemplary application scenario, it can be further developed and deployed within professional simulation software. These platforms provide engineers and designers with fully automated capabilities for simulating the thermal fluid of an onboard refrigerator. Users can configure simulation parameters and generate open documentation through the platform, where simulation parameters include device configuration data and simulation task data. The platform then builds the thermal fluid simulation model of the onboard refrigerator based on the user's instructions and provides users with efficient and accurate simulation result display services.

[0014] In a typical embodiment of this application, the user uploads an open document to the simulation platform. This open document can be in various formats, such as XML, JSON, or a spreadsheet-based Excel file. The simulation platform parses the user-submitted open document to obtain the user-input equipment configuration data and simulation task data. The equipment configuration data includes information such as the component geometric parameters, material properties, and boundary conditions of the equipment, while the simulation task data contains information such as the simulation task and its required operating parameters. Based on this data, the three-dimensional volume mesh model in the simulation framework is updated to a solid equipment model, ensuring that the geometric features and physical properties of the model are consistent with the actual equipment. Then, based on the simulation task data, the solver in the simulation framework is called to determine the fluid simulation results data of the solid equipment model. This process includes complex numerical calculations, such as solving for the flow field and temperature field, as well as calculating possible turbulence models and energy equations. Finally, based on the simulation results data, the corresponding simulation effect information is displayed on the graphical user interface, presenting key simulation results such as fluid flow and temperature distribution in an intuitive way, helping users quickly understand and evaluate the thermal fluid performance of the equipment.

[0015] The device thermal fluid simulation method of this application can be applied to the optimization of heat dissipation systems in the field of vehicle refrigerators. This method can significantly improve simulation efficiency, reduce reliance on professionals, and ensure the accuracy and consistency of simulation results.

[0016] Please see Figure 1 The device thermal fluid simulation method of this application, in its typical embodiment, includes the following steps: Step S5100: Parse the open document submitted by the user. The open document includes multiple page units, through which device configuration data and simulation task data are provided. Open documentation can be generated in various ways. In one embodiment, the simulation platform provides users with standardized template files, guiding them to fill in information such as the component geometry parameters, material properties, boundary conditions, simulation tasks, and required operating parameters of the equipment. The template file is designed to contain multiple page units, each corresponding to the input of different parameters. For example, the first page unit is used to input the component geometry parameters of the equipment, such as the length, diameter, and radius of curvature of the refrigerant pipes; the second page unit is used to input material properties, such as the thermal conductivity and density of the pipe material; the third page unit is used to define boundary conditions, such as the refrigerant inlet velocity and outlet pressure; and the fourth page unit is used to set the simulation task and its simulation operating parameters, such as ambient temperature, initial refrigerant temperature, running time, and compressor speed. Users can input specific values ​​or select predefined options in the various page units of the template file according to their actual needs. After completing the equipment configuration data and simulation task data, the completed template file is submitted as an open documentation.

[0017] In another embodiment, users can directly describe the equipment configuration data and simulation task data required for the simulation using natural language, generating open documentation. It should be noted that if the user's natural language description does not include the necessary parameters, the simulation platform can prompt the user to configure the required parameters via a pop-up prompt window component.

[0018] The simulation platform uses different parsing methods to parse open documents based on their format. In one embodiment, if the open document is a standardized template file format, the simulation platform will call a preset parsing program to read the data from each page cell according to predefined format rules. For example, for an Excel template, the simulation platform will identify specific worksheets and cell locations to extract information such as equipment geometric parameters, material properties, boundary conditions, simulation tasks, and their required operating parameters; for XML or JSON formats, the simulation platform will parse the document's structured tags or key-value pairs to accurately extract the required data.

[0019] In another embodiment, if the open document is a natural language description text, the simulation platform will invoke Natural Language Processing (NLP) technology for parsing. Specifically, it will utilize pre-trained language models and semantic analysis techniques to identify key information in the document, such as equipment names, parameter values, and units, and convert them into structured data formats. For example, when a user describes "the pipe is 2 meters long, 0.1 meters in diameter, and made of carbon steel," NLP technology can accurately extract the pipe's length, diameter, and material properties, and store them as structured data that can be used for subsequent simulations.

[0020] This step allows users to simply fill out an open document, and the simulation platform can automatically parse and extract the required data, eliminating the need for frequent manual parameter configuration and significantly improving the efficiency of simulation processing.

[0021] Step S5200: Update the three-dimensional volume mesh model in the simulation framework to the device solid model according to the device configuration data; The 3D volumetric mesh model is a discretized geometric model used to approximate the internal and external spaces of a vehicle-mounted refrigerator. It consists of multiple volumetric elements (such as tetrahedrons, hexahedrons, etc.) interconnected by nodes and edges, forming a mesh structure that covers the entire geometry of the vehicle-mounted refrigerator. Before being updated to a solid device model, the 3D volumetric mesh model has not been assigned specific physical properties (such as material properties, boundary conditions, etc.); it is generated through mesh generation techniques.

[0022] This step updates the 3D volumetric mesh model in the simulation framework to a device solid model based on the device configuration data input by the user. In one embodiment, the device configuration data package contains material property parameters, which are used to assign corresponding material types and their corresponding physical properties to different component regions corresponding to the 3D volumetric mesh model, so as to update the 3D volumetric mesh model to a device solid model. Physical characteristics include thermal conductivity, density, specific heat capacity, etc. For example, for the inner liner of a refrigerator, the user can specify the material as polyurethane foam and assign the corresponding thermal conductivity and density, etc. The device configuration data also includes boundary condition parameters, which create corresponding boundary conditions for the target boundary surfaces of the device solid model. The boundary conditions define the interaction mode between the simulation model and the external environment, such as temperature boundary conditions, pressure boundary conditions, or velocity boundary conditions, etc. According to the boundary condition parameters, the corresponding boundary conditions are set on the corresponding boundaries of the device solid model, thereby ensuring that the simulation calculation can accurately reflect the fluid behavior of the equipment under actual operating conditions. The device configuration data also includes component motion parameters, which determine the rotating machinery region in the device solid model based on the component motion parameters in the device configuration data. For example, if the refrigeration system of a vehicle refrigerator includes a rotating compressor impeller, the user needs to provide information such as the impeller's rotational speed and the position of its axis in the open documentation. Based on these parameters, the simulation platform defines the rotating region in the device's solid model and sets the corresponding equations of motion and boundary conditions for that region, thereby ensuring accurate simulation of the dynamic behavior of rotating machinery during the simulation process.

[0023] This step successfully updates the 3D volumetric mesh model in the simulation framework into a physical model of the equipment, providing an accurate physical basis for subsequent fluid simulation calculations.

[0024] Step S5300: Based on the simulation task data, call the solver in the simulation framework to determine the fluid simulation result data of the equipment entity model; Users configure simulation task data in the open documentation. This data includes the simulation task and its corresponding operating parameters. The simulation platform selects an appropriate solver based on the analysis type specified in the simulation task. For example, for steady-state thermal analysis, the platform calls the steady-state solver to solve the Navier-Stokes equations and energy equations using an iterative method to obtain the temperature distribution and fluid flow characteristics of the equipment under stable operating conditions. For transient analysis, the platform calls the transient solver and uses a time-stepping method to handle time-dependent terms, thereby simulating the dynamic behavior of the equipment during startup, shutdown, or changes in operating conditions. Furthermore, for simulation tasks involving turbulence, the platform selects an appropriate turbulence model (such as the k-ε model, k-ω model, or LES) based on the operating parameters to accurately simulate the complex characteristics of turbulent flow.

[0025] After the solver is configured, the simulation platform invokes the solver to begin calculations. In some embodiments, the solver uses numerical methods to solve fluid dynamics equations (such as the Navier-Stokes equations) and energy equations, calculating the flow and temperature field distributions in the device's physical model. During the calculation process, the simulation platform sets up integral detectors based on preset monitoring points in the simulation task data to extract fluid simulation results data from key locations. For example, if the user specifies in the open documentation that parameters such as flow velocity, pressure, and temperature need to be monitored at the device's inlet and outlet, the simulation platform will set up integral detectors at the corresponding locations in the device's physical model, obtaining statistical information such as the average, maximum, or minimum values ​​of these monitoring points through integration calculations, providing detailed data support for subsequent result analysis.

[0026] In some embodiments, the simulation organizes the extracted simulation result data into a user-readable format and stores it in the data structure of the simulation framework so that the simulation effect information can be displayed in the graphical user interface later.

[0027] Step S5400: Display the corresponding simulation effect information on the graphical user interface based on the simulation result data.

[0028] This step includes the visualization of simulation results data, the interactive design of the graphical interface, and the implementation of user-defined display functions. Specifically, the simulation results data is first extracted from the simulation framework. The simulation structure data includes key physical quantities such as the fluid's velocity field, pressure field, temperature field, and turbulent kinetic energy field. This data is stored in the form of grid cells, with each grid cell containing the physical quantity value at that location. This step calls the graphics rendering engine and uses graphics processing technology to map the numerical data into visualization elements such as colors, contour lines, and vector fields, thereby converting the simulation structure data into a visualized image.

[0029] For example, for the visualization of the velocity field, the simulation platform maps the magnitude of the velocity to the shades of color, and the velocity direction is represented by vector arrows. Users can observe the direction and velocity distribution of fluid flow inside the device on the graphical user interface. For the temperature field, the simulation platform uses color gradients (such as from blue to red) to represent temperature changes, allowing users to intuitively observe the heat distribution inside the device. In addition, the simulation platform also generates contour maps to clearly show the isopleth regions of specific physical quantities (such as pressure or temperature), helping users quickly identify the distribution characteristics of key areas.

[0030] In one embodiment, the simulation platform provides multiple view modes in its graphical user interface design to meet the needs of different users. Users can choose to display a 3D view of the entire device and observe the simulation results from all angles through rotation, zoom, and dragging. The platform also provides a local zoom function, allowing users to focus on details of specific areas, such as a key component of the device or areas with complex flow. Furthermore, the platform supports 2D slice views, allowing users to view the distribution of physical quantities inside the device by selecting different planes. Further, the platform integrates various interactive tools into the graphical user interface. For example, users can hover the mouse over a grid cell to view specific numerical information at that location, such as velocity, pressure, and temperature. It also provides data filtering functions, allowing users to filter areas of interest based on specific conditions (such as velocity exceeding a certain threshold or temperature within a certain range) and highlight them on the interface. Additionally, the platform supports animation functionality. For transient simulation results, users can play animations to observe the changes in physical quantities over time, helping them understand flow and heat transfer phenomena in dynamic processes.

[0031] In some embodiments, the simulation platform can save simulation results data and corresponding visualization images in multiple formats to facilitate subsequent analysis and report writing. Users can choose to save the simulation results data as common image formats (such as PNG or JPEG) or as file formats containing detailed data (such as CSV or VTK).

[0032] From the above description of the typical embodiments of this application, it can be understood that this application has many advantages, including but not limited to the following aspects: First, this application achieves automated extraction of device configuration data and simulation task data by parsing open documents submitted by users. Users only need to provide an open document containing the corresponding parameters before simulation, and the simulation platform can automatically complete the data identification and extraction. This avoids the tedious operation of users repeatedly inputting and adjusting parameters at different simulation stages, significantly simplifies the data preparation process, and improves the startup efficiency of simulation tasks.

[0033] Secondly, by updating the three-dimensional volumetric mesh model in the simulation framework to a physical device model, this application ensures that the simulation model is highly consistent with the actual device. It can dynamically adjust various parameters of the physical device model according to the device configuration data input by the user, thereby providing a more reliable and accurate simulation basis for the optimized design of device performance.

[0034] Furthermore, this application provides users with an intuitive and easy-to-understand way of displaying simulation results by showing simulation effect information through a graphical user interface. Users can clearly observe the simulation effect information through the graphical interface, thereby quickly understanding and evaluating the performance of the equipment. This visualization method not only improves the user experience, but also makes the application of simulation results more flexible and efficient. When this application is applied to the field of heat dissipation of vehicle refrigerators, it can better guide the design optimization of the heat dissipation system and ensure the efficient and reliable operation of vehicle refrigerators under extreme conditions such as high temperature.

[0035] For further embodiments, please refer to Figure 2 The process involves parsing user-submitted open documents, which include multiple page units, and providing device configuration data and simulation task data through these multiple page units. This includes the following steps: Step S5110: Embed the open document into the text classification prompt template to obtain the text classification prompt instruction; If a user generates an open document containing device configuration data and simulation task data using natural language descriptions, this open document will contain a large amount of unstructured text information. This text information includes the device's component geometric parameters, material property parameters, boundary condition parameters, and simulation task operating condition parameters. This step embeds this open document into a text classification prompt template. This text classification prompt template includes placeholders for the open document and prompt text to guide the Large Language Model (LLM) to classify and label the natural language descriptions in the open document based on preset parameter categories. The parameter categories are the various parameters included in the device configuration data and simulation task data. This step embeds the open document into the text classification prompt template to obtain text classification prompt instructions. These text classification prompt instructions are used to guide the Large Language Model (LLM) to perform structured processing of the natural language descriptions in the open document. Specifically, the text classification prompt instruction is subsequently used to guide the large language model to classify the unstructured text in the document into various parameter categories in the equipment configuration data and simulation task data, such as component geometric parameters, material property parameters, boundary condition parameters, and simulation task operating condition parameters, according to the preset parameter categories.

[0036] Step S5120: Input the text classification prompt instruction into the large language model, and control the large language model to output the structured data text corresponding to the device configuration data and simulation task data based on the natural language description in the open document; The Large Language Model (LLM), through pre-trained deep learning algorithms, can understand the semantics of natural language descriptions and transform them into structured data formats. This step involves inputting text classification prompts into the LLM, leveraging its powerful natural language processing capabilities to convert the natural language descriptions in open documents into precise structured data text. Specifically, the text classification prompts provide the LLM with explicit guidance, enabling it to identify and extract key information from the documents. This key information corresponds to various parameters included in the equipment configuration data and simulation task data. For example, key information includes the component geometric parameters of the equipment (such as the length, diameter, and radius of curvature of refrigerant pipes), material property parameters (such as the thermal conductivity and density of pipe materials), boundary condition parameters (such as refrigerant inlet velocity and outlet pressure), and simulation task operating parameters (such as ambient temperature, initial refrigerant temperature, running time, and compressor speed).

[0037] Step S5130: Based on preset semantic mapping rules, map the structured data text to the device configuration data field and simulation task data field corresponding to the simulation framework.

[0038] This step accurately maps the structured data text extracted from open documents (such as component geometry parameters, material property parameters, boundary condition parameters, and operating condition parameters) to predefined data fields in the simulation framework. Specifically, it first identifies each parameter in the structured data text according to preset semantic mapping rules and matches it with the corresponding field in the simulation framework. The semantic mapping rules define the correspondence between parameters in the structured data and fields in the simulation framework. For example, the "refrigerant pipe length" parameter in the structured data will be mapped to the "pipe length" field under the "equipment configuration data" field in the simulation framework; the "refrigerant initial temperature" parameter will be mapped to the "initial temperature" field under the "simulation task data" field.

[0039] During the mapping process, the simulation platform performs data format and unit conversions to ensure data consistency and compatibility. For example, if the temperature unit in the structured data is Celsius, while the simulation framework requires Kelvin, the simulation platform will automatically perform unit conversion to ensure that the data format is consistent with the simulation framework's requirements.

[0040] This step enables the structured data extracted from open documents to be accurately mapped into the simulation framework, ensuring that the simulation model can realistically reflect the user-input equipment configuration and simulation task requirements, thus providing a reliable data foundation for subsequent thermal fluid simulation calculations.

[0041] In this embodiment, by combining text classification prompt templates, large language models, and semantic mapping rules, unstructured text information submitted by users can be efficiently transformed into structured data required by the simulation framework, significantly reducing the workload of users in data input and configuration, while improving the accuracy and efficiency of data processing.

[0042] For further embodiments, please refer to Figure 3 Before updating the 3D volume mesh model in the simulation framework to the device solid model based on the device configuration data, the following steps are included: Step S6100: Based on the surface mesh model of the simulation model in the simulation framework, construct a fluid envelope, wherein the space covered by the fluid envelope is sufficient to enclose the spatial volume of the surface mesh model; A surface mesh model is a discrete representation of the device's geometry, composed of a series of surface elements (such as triangles or quadrilaterals) that define the device's surface geometry. Based on this surface mesh model, a fluid envelope is constructed. In one embodiment, the bounding box of the surface mesh model is first calculated. This bounding box is the smallest rectangular box that can enclose the surface mesh model, with its six faces aligned with the outermost points of the surface mesh model in various directions. By calculating the maximum and minimum coordinate values ​​of the surface mesh model in the X, Y, and Z directions, the size and position of the bounding box can be determined. Based on this determined bounding box, it is further expanded to construct the fluid envelope for simulating the flow field of an in-vehicle refrigerator. The expansion distance can be set according to the device's geometry and simulation requirements, specifically determined by empirical formulas or user-specified parameters. For example, the fluid envelope may need to expand the device's dimensions by 5 times around the device; this multiple can be input by the user when configuring the device configuration data.

[0043] Step S6200: Extract the fluid computational domain corresponding to the simulation model from the fluid envelope; Based on the surface mesh model, the fluid envelope is identified as having fluid and solid regions. By analyzing the geometric features of the surface mesh model, the fluid envelope is divided into internal and external fluid regions. For example, in the refrigeration system of a vehicle refrigerator, the surface mesh model defines the inner and outer walls of the refrigerant pipes. The space within the fluid envelope can be divided into the internal fluid region (used to simulate refrigerant flow) and the external fluid region (used to simulate the flow of the external environment). Furthermore, it also includes the internal and external fluid regions of components such as the evaporator and condenser.

[0044] When extracting the fluid computational domain, the interaction boundaries between the fluid and the equipment need to be considered. These interaction boundaries are defined on the surfaces of the equipment, such as the inner walls of refrigerant pipes, the fin surfaces of evaporators, or the surfaces of condensers. The simulation platform identifies these interaction boundaries as boundary conditions of the fluid computational domain based on the geometric characteristics of the surface mesh model. Furthermore, the simulation platform also needs to consider the topological structure of the fluid computational domain. For complex equipment geometries, the fluid computational domain contains multiple discontinuous regions. For example, an evaporator with multiple baffles will have its fluid computational domain divided into multiple independent regions by the baffles. In this case, the simulation platform needs to identify these discontinuous regions through topological analysis and ensure that each region is correctly extracted.

[0045] When extracting the fluid computational domain, the direction and characteristics of fluid flow must also be considered. For example, for a refrigerant pipeline with an inlet and an outlet, the fluid computational domain needs to extend from the inlet to the outlet to ensure the continuity of fluid flow. The simulation platform adjusts the shape and size of the fluid computational domain according to the direction of fluid flow to ensure that the fluid can flow smoothly from the inlet to the outlet. Simultaneously, the boundary layer effect of fluid flow must also be considered. Near the equipment surface, fluid flow forms a boundary layer, whose flow characteristics differ from those in areas farther from the surface. In this case, sufficient boundary layer area needs to be preserved in the fluid computational domain to ensure accurate simulation of flow and heat transfer phenomena within the boundary layer.

[0046] In some embodiments, after the fluid computational domain is extracted, verification and optimization are required. The verification process includes checking whether the fluid computational domain is completely contained within the fluid envelope and whether it is correctly aligned with the surface mesh model of the device. The optimization process involves adjusting the shape and size of the fluid computational domain to improve the efficiency of the simulation. For example, unnecessary regions in the fluid computational domain may be removed, or the boundary layer region may be refined to improve computational accuracy.

[0047] Finally, the extracted fluid computational domain is stored as an independent data structure for subsequent mesh generation and simulation calculations. The data structure of the fluid computational domain includes the geometric information of the mesh elements, boundary condition information, and fluid property information. This information will be used to generate the computational mesh and serve as input data for the simulation calculations.

[0048] Step S6300: Generate a three-dimensional volume mesh model corresponding to the fluid computing domain and the target boundary surface based on the volume mesh parameters in the device configuration data and the target boundary surface and its surface type associated in the surface mesh model.

[0049] Volume mesh parameters define the generation rules and quality requirements of the volume mesh. These parameters include mesh cell type (e.g., tetrahedral, hexahedral, or hybrid mesh), mesh density, and mesh size. During the generation of the 3D volume mesh model, for the target boundary surfaces associated with the surface mesh model, which are considered key regions, the volume mesh parameters are dynamically adjusted based on their surface type (e.g., refrigerant inlet, outlet, or evaporator fin surface). For example, for refrigerant inlets and outlets, the simulation platform dynamically adjusts the mesh density and cell type according to the refrigerant flow characteristics (e.g., velocity, flow rate) to ensure accurate capture of refrigerant entry and exit details. In these regions, the mesh density is set higher to better simulate refrigerant flow details and pressure changes. For the evaporator fin surface, the mesh density and cell type are adjusted based on heat transfer efficiency and temperature gradient to better simulate the heat transfer process. The mesh density of the fin surface is adjusted according to the heat transfer coefficient and temperature gradient; the higher the heat transfer coefficient or the greater the temperature gradient, the higher the mesh density. Furthermore, the simulation platform uses smaller tetrahedral mesh cells to better capture temperature changes and heat transfer efficiency on the fin surface.

[0050] In one embodiment, the simulation platform dynamically adjusts the volume mesh parameters of the target boundary surface by combining a preset intelligent mesh adjustment algorithm with user-defined device configuration data. Specifically, the simulation platform first identifies the surface type of each target boundary surface in the surface mesh model, such as the refrigerant inlet, outlet, or evaporator fin surface. For the refrigerant inlet and outlet, the platform automatically invokes intelligent algorithms to increase the mesh density in these key areas based on flow characteristics such as flow velocity and flow rate, and selects appropriate mesh cell types, such as using smaller tetrahedral cells in high-velocity regions to capture flow details. For the evaporator fin surface, the platform dynamically adjusts the mesh density based on the heat transfer coefficient and temperature gradient, while selecting mesh cell types suitable for simulating heat transfer, such as using denser hexahedral mesh cells in high heat transfer coefficient regions. This process, automated by the simulation platform, requires no manual intervention from the user, thereby ensuring the accuracy and adaptability of the generated three-dimensional volume mesh model in key areas, providing a foundation for accurate simulation in the fluid computational domain.

[0051] In this embodiment, by combining the construction of the fluid envelope, the extraction of the fluid computational domain, and the dynamic adjustment of the volume mesh parameters, the automation of the simulation platform reduces user intervention in the model building process. At the same time, by dynamically adjusting the volume mesh parameters, the high accuracy of the simulation model in key areas is ensured, providing a more efficient and reliable solution for fluid simulation of vehicle-mounted refrigerators.

[0052] For further embodiments, please refer to Figure 4Before constructing the fluid envelope based on the surface mesh model of the simulation model in the simulation framework, the following steps are included: Step S7100: Based on the component geometric parameters in the device configuration data, call the corresponding three-dimensional geometric components to assemble the simulation model and render it into the simulation framework; First, component geometric parameters are extracted from the equipment configuration data. These parameters define the position and orientation of each component in the simulation model. In some embodiments, the component geometric parameters include the component's center point coordinates (x, y, z), rotation angles (rotation angles around the x, y, and z axes), and scaling ratio. Then, the simulation platform calls the corresponding 3D geometric components based on the component geometric parameters and assembles these components according to the parameters to obtain the simulation model. These 3D geometric components are predefined standard models, such as refrigerant pipes, evaporators, and condensers. The simulation platform selects suitable 3D geometric components from the geometric component library by determining the component type and parameters corresponding to the component geometric parameters. For example, if a refrigerant pipe component is specified in the equipment configuration data, the corresponding 3D geometric component is loaded from the component library, and its geometric features are adjusted according to the parameters (such as length, diameter, and wall thickness) of the component in the equipment configuration data. The geometry component library stores templates for various standard 3D geometry components, covering common automotive refrigerator components such as refrigerant pipes, evaporators, condensers, and compressors. Each template has adjustable geometric parameters to allow for customization based on user-provided equipment configuration data.

[0053] In another embodiment, the simulation platform determines the three-dimensional geometric components required for the simulation task based on the component geometric parameters in the equipment configuration data. Then, it assembles these three-dimensional geometric components according to preset component connection rules to obtain the simulation model, without requiring the user to set the component coordinates. The equipment configuration data also includes the connection methods between the components. For example, if multiple refrigerant piping components are connected by flanges, the simulation platform ensures that the components are geometrically correctly connected according to the connection methods defined in the equipment configuration data.

[0054] After component assembly is complete, the entire simulation model is rendered into the simulation framework. The rendering process includes converting the 3D geometric model into a visual image so that users can intuitively view the model's geometry. The simulation platform uses a graphics rendering engine to convert the vertices, edges, and faces of the geometric components into pixel data and display it on the screen. During the rendering process, factors such as lighting, material properties, and viewpoint can be considered to generate high-quality visual images.

[0055] In some embodiments, the simulation platform provides interactive features that allow users to view different angles and details of the simulation model by rotating, scaling, and dragging, to help users check whether the geometry of the simulation model is correct and make adjustments as necessary.

[0056] Step S7200: Based on preset geometric feature recognition rules, determine the surface type of the target boundary surface in the simulation model and its corresponding mesh control parameters; The geometric feature identification rules are pre-set by those skilled in the art based on the equipment's geometric design parameters, fluid flow characteristics, and simulation accuracy requirements. First, based on the surface mesh model of the simulation model, the surface type of the target boundary surface is identified. The target boundary surface has specific physical significance in the fluid flow and heat transfer process. For example, the inner wall of a refrigerant pipe requires velocity boundary conditions, while the surface of the evaporator fins requires heat exchange boundary conditions. In these cases, the geometric features of the surface mesh model, such as the location, shape, and topology of the boundary surfaces, need to be analyzed to determine these surface types.

[0057] After identifying the surface type, corresponding mesh control parameters are assigned to each target boundary surface. In one embodiment, a preset geometric feature recognition rule defines the mapping relationship between the surface type of the target boundary surface and the mesh control parameters. For example, for the inner wall of a refrigerant pipe, due to its crucial role in fluid flow, the geometric feature recognition rule specifies the use of a denser mesh to capture flow details; while for the surface of the evaporator fins, considering its importance in heat transfer, the geometric feature recognition rule sets a higher mesh density and a smaller mesh cell size to accurately simulate the heat exchange process. This geometric feature recognition rule sets corresponding mesh control parameters for different target boundary surfaces. Once the surface type of the target boundary surface is identified, the corresponding mesh control parameters can be directly extracted based on the geometric feature recognition rule. The mesh control parameters directly affect the quality and computational efficiency of the surface mesh model, ensuring that the generated mesh accurately reflects the geometric details and physical characteristics of the equipment.

[0058] Step S7300: Generate a corresponding surface mesh model based on the envelope size of the simulation model and the mesh control parameters, and map the surface type to the surface mesh model.

[0059] The overall extent of the surface mesh model is determined based on the envelope size of the simulation model. The envelope size defines the boundary of the simulation model, ensuring that the surface mesh model can completely cover the geometry of the device. For example, for the refrigeration system of an in-vehicle refrigerator, the envelope size determines the boundary extent of the surface mesh model, enabling it to include the surface features of all components such as refrigerant pipes, evaporators, and condensers. Then, mesh control parameters are used to refine the generation process of the surface mesh model. Furthermore, this step maps the surface types identified in the aforementioned embodiments to the surface mesh model generated in this step.

[0060] In this embodiment, by combining component assembly, surface type recognition, and dynamic adjustment of mesh parameters, intelligent algorithms ensure high accuracy of the model in key areas, especially showing greater advantages when dealing with complex geometries and multiphysics coupling problems.

[0061] For further embodiments, please refer to Figure 5 Based on preset geometric feature recognition rules, the surface type of the target boundary surface in the simulation model and its corresponding mesh control parameters are determined, including the following steps: Step S7210: Based on the geometric features of the simulation model and the preset size threshold, determine the rounded surfaces with different radii of curvature; Rounded surfaces, such as bends in refrigerant pipes, connections between evaporator fins, and transition areas between components, directly affect the characteristics of fluid flow and heat transfer efficiency. Curvature describes the degree of bending of a rounded surface. In one embodiment, it can be obtained by calculating the rate of change of the normal vector of a mesh cell. For example, for a triangular mesh cell, the curvature of its vertex can be estimated by the difference in the normal vectors of adjacent triangles. The simulation platform calculates the curvature value of each vertex or mesh cell by traversing the entire surface mesh model.

[0062] The simulation platform categorizes the curvature of rounded surfaces based on preset size thresholds. These thresholds are set according to practical engineering experience and simulation requirements to distinguish rounded surfaces with different radii of curvature. For example, three thresholds can be set: low curvature (large radius), medium curvature, and high curvature (small radius). The specific values ​​can be adjusted according to the geometric characteristics of the equipment and the fluid flow characteristics.

[0063] After identifying fillet surfaces with different radii of curvature, a unique identifier is assigned to each fillet surface, and its geometric features (such as radius of curvature, location, and extent) are stored in a data structure. This information will be used in subsequent mesh control parameter allocation and mesh generation steps. For example, for fillet surfaces with high curvature, a denser mesh is assigned to capture their complex flow characteristics; while for fillet surfaces with low curvature, the mesh can be relatively sparse to save computational resources.

[0064] Step S7220: According to the preset mesh size division rules and the radius of curvature of the rounded surface, assign corresponding mesh control parameters to the rounded surface, wherein the smaller the radius of curvature, the denser the mesh density corresponding to the assigned mesh control parameters; The mesh size division rules are preset by those skilled in the art based on the geometric features, flow characteristics, and simulation accuracy requirements of the equipment. In one embodiment, the mesh size division rules define the mapping relationship between different radii of curvature of the fillet surface and mesh control parameters. For example, multiple radius of curvature thresholds can be set, and when the radius of curvature of the fillet surface falls within the corresponding radius of curvature threshold range, the corresponding mesh control parameters are assigned. This step will iterate through the radius of curvature of all identified fillet surfaces and assign mesh control parameters to them according to the preset mesh size division rules.

[0065] Step S7230: Associate and store the mesh control parameters with the corresponding rounded surfaces for use in the subsequent generation of surface mesh models.

[0066] In one embodiment, a unique identifier (ID) is created for each fillet surface. This identifier can be a simple numeric index or a composite key containing geometric feature information. Then, the mesh control parameters of each fillet surface are associated with its corresponding identifier. These parameters can be stored in a structured data table, where each row corresponds to an identifier for a fillet surface and each column corresponds to a mesh control parameter.

[0067] In this embodiment, by using preset size thresholds and mesh size division rules, automatic classification of rounded surfaces and intelligent adjustment of mesh parameters are achieved, reducing manual intervention and improving the automation and reliability of the simulation.

[0068] For further embodiments, please refer to Figure 6 The process of updating the 3D volumetric mesh model in the simulation framework to a solid device model based on the device configuration data includes the following steps: Step S5210: Based on the material property parameters in the equipment configuration data, set corresponding material properties for different component regions in the three-dimensional volume mesh model to update the three-dimensional volume mesh model into an equipment entity model; First, the material property parameters in the equipment configuration data clearly indicate the material type used for each component in the 3D volumetric mesh model. For example, the refrigerant pipe material is copper, and the refrigerator liner material is polyurethane foam. Each material type includes various parameters, such as thermal conductivity, density, specific heat capacity, and viscosity. These parameters are stored in the material property parameters in the equipment configuration data. The simulation platform will retrieve the corresponding data from the preset material library based on these material property parameters and set it to the corresponding component in the 3D volumetric mesh model to update the 3D volumetric mesh model into the equipment solid model. The preset material library stores the material property parameters of various materials used in the vehicle refrigerator.

[0069] Specifically, the simulation platform assigns these material property parameters to the corresponding component regions in the 3D volumetric mesh model. Each mesh cell in the 3D volumetric mesh model represents a volume region in the model. The simulation platform identifies the set of mesh cells belonging to the same component based on the component's geometric boundaries and topology. For example, for a refrigerant pipe component, it identifies the mesh cells located inside the pipe and on the wall, and assigns the material property parameters of copper to these mesh cells; for a refrigerator liner component, it identifies the mesh cells in the liner region and assigns them the material property parameters of polyurethane foam.

[0070] In some embodiments, the complex geometry of the component and the presence of multiple material contact surfaces are taken into account when setting material property parameters. For example, a refrigerant pipe with a liner may contain two layers of material, with an outer layer of copper and an inner liner of a corrosion-resistant material (such as stainless steel). In this case, it is necessary to accurately identify the boundaries between the inner and outer layers and assign corresponding material properties to the mesh cells of different layers.

[0071] In other embodiments, it is also necessary to address the issues of continuity and discontinuity of material properties. Specifically, near the interface of multiple materials, material properties may change abruptly. In this case, hybrid mesh elements or transition mesh elements can be used to smooth the changes in material properties. The hybrid mesh element calculates an average material property value based on its position and the material properties of its neighboring elements, thereby avoiding numerical oscillations caused by abrupt changes in material properties.

[0072] Finally, the updated 3D volumetric mesh model is stored as a device entity model, which not only sets the material type used for each component, but also sets various parameters of the material.

[0073] Step S5220: Based on the boundary condition parameters in the device configuration data, create corresponding boundary conditions for the target boundary surface corresponding to the device entity model; Boundary conditions define the interaction between the equipment and the external environment, such as fluid inflow and outflow, heat exchange, and no-slip conditions on the walls. By accurately setting boundary conditions, the simulation model can realistically reflect the physical behavior of the equipment in actual operation.

[0074] Specifically, firstly, boundary condition parameters are extracted from the equipment configuration data. These parameters include boundary type (e.g., velocity boundary, pressure boundary, temperature boundary), boundary values ​​(e.g., flow velocity, pressure, temperature), and specific location information of the boundaries. For example, the equipment configuration data might specify that "the flow velocity at the refrigerant inlet is 5 m / s, the pressure at the outlet is 0.1 MPa, and the evaporator fin surface is an adiabatic boundary." These parameters provide the simulation platform with a concrete basis for creating boundary conditions. Then, the simulation platform identifies the target boundary surfaces in the equipment entity model based on the boundary condition parameters. Target boundary surfaces refer to the surfaces where the equipment interacts with the fluid, such as the inner wall of a refrigerant pipe, the fin surface of an evaporator, or the inlet and outlet of the equipment. The simulation platform determines the boundary surface corresponding to each boundary condition by analyzing the geometric features and topology of the equipment entity model.

[0075] After identifying the target boundary surfaces, corresponding boundary conditions are created for each boundary surface based on the boundary condition parameters. For velocity boundaries, the fluid velocity value is set at the inlet. For example, if the inlet velocity is 10 m / s, the simulation platform will apply this velocity value to all mesh cells of the inlet boundary surface. For pressure boundaries, the pressure value is set at the outlet. For example, if the outlet pressure is 0.1 MPa, this pressure value will be applied to the mesh cells of the outlet boundary surface. For temperature boundaries, the temperature value is set at the specified boundary surface. For example, if the evaporator fin surface is an adiabatic boundary, an adiabatic condition, i.e., zero heat flux, is applied to the mesh cells of the fin surface boundary surface.

[0076] Finally, the simulation platform stores the created boundary conditions in the data structure of the device entity model and associates them with the corresponding target boundary surfaces.

[0077] Step S5230: Determine the rotating mechanical region of the physical model of the equipment based on the component motion parameters in the equipment configuration data.

[0078] Rotating mechanical regions exist within certain components of a refrigeration system, such as impellers or fans in compressors. This step first extracts component motion parameters from the equipment configuration data. These parameters include the name of the rotating component, the position and direction of its rotation axis, its rotational speed (angular velocity), rotational direction (clockwise or counterclockwise), and the geometric extent of the rotating component. Then, the simulation platform identifies the specific location and extent of the rotating mechanical region within the equipment's solid model. Specifically, this is determined by analyzing the component's name and geometric features, combined with the position and direction information from the motion parameters. For example, for a compressor model with an impeller, the set of mesh elements containing the impeller is identified and marked as the rotating mechanical region. After determining the rotating mechanical region, corresponding motion boundary conditions are set for this region based on the component motion parameters. These boundary conditions describe the motion characteristics of the rotating component, including its rotational speed, rotational direction, and the position of its rotation axis.

[0079] In some embodiments, the simulation platform handles the interaction between rotating and stationary regions. For example, there is often relative motion between rotating and stationary components, such as the gap between an impeller and the compressor housing. In this case, the simulation platform needs to set appropriate slip boundary conditions in these regions to simulate fluid flow between the rotating and stationary areas. These slip boundary conditions allow relative sliding of the fluid on the surfaces of rotating components while ensuring fluid continuity. For example, in the gap region between the impeller and the compressor housing, setting slip boundary conditions allows fluid to flow freely between the impeller surface and the inner wall of the housing.

[0080] In this embodiment, by accurately allocating material properties, setting boundary conditions, and identifying and simulating rotating machinery regions, this embodiment can simultaneously handle the static physical characteristics and dynamic behavior of the equipment, making the simulation results closer to actual operating conditions. Specifically, by combining static material properties, boundary conditions, and dynamic motion characteristics, this embodiment can more accurately simulate the interaction between rotating machinery regions and stationary regions, improving the accuracy and reliability of the simulation model.

[0081] For further embodiments, please refer to Figure 7 The process of determining the fluid simulation results data of the equipment entity model by calling the solver in the simulation framework based on the simulation task data includes the following steps: Step S5310: Based on the preset operating parameters in the simulation task data, create integral detectors for the flow field and temperature field at preset monitoring points of the equipment entity model. An integral detector is used to collect and process flow and temperature field data at specific locations on the physical model of the equipment. The integral detector is designed and configured using operating parameters from the simulation data. Specifically, pre-defined operating parameters are first extracted from the simulation data. These parameters define the initial conditions and environmental settings for the simulation, such as the refrigerant inlet velocity, temperature, and pressure, as well as the equipment's operating status (e.g., compressor speed, heating power). These operating parameters provide the simulation platform with the fundamental basis for creating the integral detector, ensuring that the detector can capture the flow and heat transfer characteristics relevant to actual operating conditions.

[0082] Pre-defined monitoring points are determined in the physical model of the equipment based on operating parameters. These pre-defined monitoring points are areas within the equipment that require focused attention, such as the inlet and outlet of refrigerant pipes, the fin surface of the evaporator, and the impeller area of ​​the compressor. The selection of these monitoring points is based on the equipment's geometric features, flow paths, and key areas of heat transfer. For example, for the evaporator, monitoring points include the refrigerant inlet and outlet, as well as several key locations inside the evaporator, used to monitor refrigerant temperature changes and flow characteristics.

[0083] After determining the monitoring points, integral detectors for the flow and temperature fields are created at these locations based on the operating parameters. The function of the integral detector is to calculate the integral values ​​of the flow and temperature fields, such as average velocity, average temperature, flow rate, and heat flux. These integral values ​​provide global information about fluid flow and heat transfer for subsequent calculations and simulation results. In some embodiments, the type of detector and the calculation method need to be considered when creating the integral detector. For flow field integral detectors, the integral value of velocity can be calculated; for example, the average velocity or total flow rate of the refrigerant at a certain cross-section can be calculated using the integral detector. For temperature field integral detectors, the integral value of temperature can be calculated; for example, the average temperature or heat flux of the refrigerant in a certain region can be calculated using the integral detector.

[0084] In some embodiments, the settings of the integrator detector can be adjusted according to the operating parameters. For example, if the operating parameters specify a high flow rate or temperature gradient, the sampling frequency of the integrator detector will be increased to ensure that rapidly changing flow and heat transfer characteristics can be captured. For complex flow conditions, such as turbulent or unsteady flow, more advanced integration methods, such as a combination of time integration and spatial integration, are used to provide more comprehensive flow and heat transfer information.

[0085] Step S5320: According to the analysis type specified in the simulation task data, call the solver configured with the corresponding turbulence model and energy equation, and obtain the fluid simulation result data characterizing the physical model of the device in combination with the integral detector.

[0086] Analysis types define the objectives and methods of simulation calculations. For example, steady-state analysis is used to study the flow and heat transfer characteristics of equipment under stable operating conditions, transient analysis is used to study the dynamic behavior of equipment during time-varying processes, and turbulence analysis is used to simulate turbulence effects in complex flows. Depending on the analysis type, the simulation platform will select the appropriate solver configuration, including the time integration method, spatial discretization method, and the selection of the turbulence model.

[0087] For steady-state analysis, the simulation platform is configured with a steady-state solver that solves the steady-state Navier-Stokes equations and energy equations using an iterative method. For example, in steady-state analysis, the finite volume method (FVM) is used to discretize the fluid flow equations, and a pressure-velocity coupled algorithm (such as the SIMPLE algorithm) is used to solve the pressure and velocity fields. For transient analysis, a transient solver is configured that solves the transient flow and heat transfer equations using a time-stepping method. For example, time integration methods (such as the implicit Euler method or the Runge-Kutta method) are used to handle time dependencies, and the spatially discretized equations are solved in each time step.

[0088] When selecting a solver configuration, it is also necessary to choose an appropriate turbulence model based on the analysis type. Turbulence models are used to describe turbulent effects in fluid flow. Common turbulence models include the k-ε model, k-ω model, Reynolds stress model (RSM), and Large Eddy Simulation (LES). For example, the k-ε model is widely used for common engineering applications due to its high computational efficiency; while for more complex turbulent phenomena, such as separated flows or high Reynolds number flows, the more accurate k-ω model or LES is selected. The simulation platform selects the most suitable turbulence model based on the operating parameters (such as refrigerant inlet velocity, temperature, pressure, etc.) in the simulation task data and configures it in the solver.

[0089] Simultaneously, the energy equation is incorporated into the solver configuration to simulate the heat transfer process of the fluid. The energy equation describes the change in fluid temperature and is solved in conjunction with the flow equation. The solution parameters of the energy equation are configured based on the thermal characteristics of the equipment (such as thermal conductivity, specific heat capacity, etc.) and thermal boundary conditions (such as adiabatic, isothermal, etc.). For example, in evaporator simulation, the energy equation is solved by combining the thermophysical parameters of the refrigerant and the thermal boundary conditions of the evaporator to obtain the temperature distribution of the refrigerant.

[0090] After the solver is configured, the solution process is initiated. In some embodiments, the solver uses numerical methods to solve the flow and heat transfer equations, calculating the velocity, pressure, and temperature fields in the physical model of the equipment. During the solution process, the integral detector created in the aforementioned embodiments is used to acquire fluid simulation result data for key regions.

[0091] In this embodiment, by creating an integral detector at a preset monitoring point, key data of the flow field and temperature field can be accurately acquired. This data provides a direct basis for subsequent solver configuration. Then, according to the analysis type, an appropriate turbulence model and energy equation are selected, and the solution is performed in combination with the data from the integral detector. This makes the simulation results closer to the actual working conditions and can more accurately reflect the performance of the equipment under different operating conditions.

[0092] Please see Figure 8 This invention provides a device for simulating thermal fluid dynamics, which is a functional embodiment of the device thermal fluid dynamics simulation method of this application. The device includes: a document parsing module 5100, a model updating module 5200, a simulation calculation module 5300, and a simulation effect display module 5400. The document parsing module 5100 is configured to parse open documents submitted by users, the open documents including multiple page units, through which device configuration data and simulation task data are provided. The model updating module 5200 is configured to update the three-dimensional volume mesh model in the simulation framework to a device solid model based on the device configuration data. The simulation calculation module 5300 is configured to call the solver in the simulation framework based on the simulation task data to determine the fluid simulation result data of the device solid model. The simulation effect display module 5400 is configured to display the corresponding simulation effect information on a graphical user interface based on the simulation result data.

[0093] In a further embodiment, the document parsing module 5100 includes: a prompt instruction generation unit, configured to embed the open document into a text classification prompt template to obtain a text classification prompt instruction; a structured text output unit, configured to input the text classification prompt instruction into a large language model, and control the large language model to output structured data text corresponding to device configuration data and simulation task data based on the natural language description in the open document; and a data mapping unit, configured to map the structured data text to the device configuration data field and simulation task data field corresponding to the simulation framework based on preset semantic mapping rules.

[0094] In a further embodiment, before the model update module 5200, there are: a fluid envelope construction unit, configured to construct a fluid envelope based on the surface mesh model of the simulation model in the simulation framework, wherein the space covered by the fluid envelope is sufficient to enclose the spatial volume of the surface mesh model; a fluid computational domain extraction unit, configured to extract the fluid computational domain corresponding to the simulation model from the fluid envelope; and a three-dimensional volume mesh generation unit, configured to generate a three-dimensional volume mesh model corresponding to the fluid computational domain and the target boundary surface according to the volume mesh parameters in the device configuration data and the target boundary surface and its surface type associated in the surface mesh model.

[0095] In a further embodiment, before the fluid envelope construction unit, the system includes: a simulation model rendering unit, configured to call corresponding three-dimensional geometric components to assemble a simulation model and render it into the simulation framework based on the component geometric parameters in the device configuration data; a mesh control parameter determination unit, configured to determine the surface type of the target boundary surface in the simulation model and its corresponding mesh control parameters based on preset geometric feature recognition rules; and a surface mesh model generation unit, configured to generate a corresponding surface mesh model based on the envelope size of the simulation model and the mesh control parameters, and map the surface type to the surface mesh model.

[0096] In a further embodiment, the mesh control parameter determination unit includes: a rounded surface division unit, configured to determine rounded surfaces with different radii of curvature based on the geometric features of the simulation model and a preset size threshold; a control parameter allocation unit, configured to allocate corresponding mesh control parameters to the rounded surfaces according to preset mesh size division rules and the radii of curvature of the rounded surfaces, wherein the smaller the radii of curvature, the denser the mesh density corresponding to the allocated mesh control parameters; and a parameter storage unit, configured to associate and store the mesh control parameters with the corresponding rounded surfaces for subsequent generation of surface mesh models.

[0097] In a further embodiment, the model update module 5200 includes: a material property setting unit, configured to set corresponding material properties for different component regions in the three-dimensional volume mesh model according to the material property parameters in the equipment configuration data, so as to update the three-dimensional volume mesh model into an equipment entity model; a boundary condition creation unit, configured to create corresponding boundary conditions for the target boundary surface corresponding to the equipment entity model according to the boundary condition parameters in the equipment configuration data; and a rotating machinery region determination unit, configured to determine the rotating machinery region of the equipment entity model according to the component motion parameters in the equipment configuration data.

[0098] In a further embodiment, the simulation calculation module 5300 includes: an integral detector preset unit, configured to create an integral detector for flow field and temperature field at preset monitoring points of the equipment entity model based on preset operating parameters in the simulation task data; and a simulation result data acquisition unit, configured to call a solver configured with a corresponding turbulence model and energy equation according to the analysis type specified in the simulation task data, and acquire fluid simulation result data characterizing the equipment entity model in conjunction with the integral detector.

[0099] To address the aforementioned technical problems, embodiments of this application also provide computer equipment. For example... Figure 9The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, the processor can implement a device thermal fluid simulation method. The processor of the computer device provides computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the device thermal fluid simulation method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] In this embodiment, the processor is used to execute... Figure 8 The system contains the specific functions of each module and its sub-modules, and the memory stores the program code and various data required to execute these modules or sub-modules. A network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / sub-modules in the thermal fluid simulation device of this application, and the server can call the server's program code and data to execute the functions of all sub-modules.

[0101] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the device thermal fluid simulation method of any embodiment of this application.

[0102] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0103] Those skilled in the art will understand that the steps, measures, and solutions in the various operations, methods, and processes discussed in this application can be alternated, modified, combined, or deleted. Furthermore, other steps, measures, and solutions in the various operations, methods, and processes discussed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and solutions in the prior art that are similar to those disclosed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted.

[0104] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for simulating the thermal fluid of equipment, characterized in that, Includes the following steps: Parse the open document submitted by the user, which includes multiple page units and provides device configuration data and simulation task data through the multiple page units; The 3D volumetric mesh model in the simulation framework is updated to a physical model of the equipment based on the equipment configuration data. The fluid simulation results data of the equipment entity model are determined by calling the solver in the simulation framework based on the simulation task data; The simulation results data are used to display the corresponding simulation effect information in the graphical user interface.

2. The equipment thermal fluid simulation method according to claim 1, characterized in that, The system parses user-submitted open documents, which include multiple page units that provide device configuration data and simulation task data, including: The open document is embedded in the text categorization prompt template to obtain the text categorization prompt instruction; The text classification prompt instruction is input into the large language model, which controls the large language model to output structured data text corresponding to device configuration data and simulation task data based on the natural language description in the open document; Based on preset semantic mapping rules, the structured data text is mapped to the device configuration data field and simulation task data field corresponding to the simulation framework.

3. The equipment thermal fluid simulation method according to claim 1, characterized in that, Before updating the 3D volumetric mesh model in the simulation framework to the device solid model based on the device configuration data, the following steps are included: Based on the surface mesh model of the simulation model in the simulation framework, a fluid envelope is constructed, and the space covered by the fluid envelope is sufficient to enclose the spatial volume of the surface mesh model. Extract the fluid computational domain corresponding to the simulation model from the fluid envelope; Based on the volume mesh parameters in the device configuration data and the target boundary surfaces and their surface types associated in the surface mesh model, a three-dimensional volume mesh model corresponding to the fluid computing domain and the target boundary surfaces is generated.

4. The equipment thermal fluid simulation method according to claim 3, characterized in that, Before constructing the fluid envelope based on the surface mesh model of the simulation model in the simulation framework, the following steps are included: Based on the component geometric parameters in the device configuration data, the corresponding three-dimensional geometric components are called to assemble the simulation model and render it into the simulation framework; Based on preset geometric feature recognition rules, the surface type of the target boundary surface and its corresponding mesh control parameters in the simulation model are determined; Based on the envelope size of the simulation model and the mesh control parameters, a corresponding surface mesh model is generated, and the surface type is mapped to the surface mesh model.

5. The equipment thermal fluid simulation method according to claim 4, characterized in that, Based on preset geometric feature recognition rules, the surface type of the target boundary surface in the simulation model and its corresponding mesh control parameters are determined, including: Based on the geometric features of the simulation model and the preset size threshold, rounded surfaces with different radii of curvature are determined; Based on the preset mesh size division rules and the radius of curvature of the rounded surface, corresponding mesh control parameters are assigned to the rounded surface. The smaller the radius of curvature, the denser the mesh density corresponding to the assigned mesh control parameters. The mesh control parameters are associated with and stored with the corresponding rounded surfaces for use in the generation of subsequent surface mesh models.

6. The equipment thermal fluid simulation method according to claim 1, characterized in that, The simulation framework's 3D volumetric mesh model is updated to a solid device model based on the device configuration data, including: Based on the material property parameters in the equipment configuration data, corresponding material properties are set for different component regions in the three-dimensional volume mesh model to update the three-dimensional volume mesh model into an equipment entity model; Based on the boundary condition parameters in the device configuration data, create corresponding boundary conditions for the target boundary surface corresponding to the device entity model; Based on the component motion parameters in the equipment configuration data, the rotating mechanical region of the equipment entity model is determined.

7. The equipment thermal fluid simulation method according to claim 1, characterized in that, The fluid simulation results data of the equipment entity model are determined by calling the solver in the simulation framework based on the simulation task data, including: Based on the preset operating parameters in the simulation task data, an integral detector for the flow field and temperature field is created at the preset monitoring points of the equipment entity model. Based on the analysis type specified in the simulation task data, a solver configured with the corresponding turbulence model and energy equation is invoked, and the fluid simulation result data characterizing the physical model of the device is obtained in conjunction with the integral detector.

8. A device for simulating thermal fluid in equipment, characterized in that, include: The document parsing module is configured to parse open documents submitted by users. The open documents include multiple page units, through which device configuration data and simulation task data are provided. The model update module is configured to update the three-dimensional volume mesh model in the simulation framework to the device entity model based on the device configuration data. The simulation calculation module is configured to call the solver in the simulation framework based on the simulation task data to determine the fluid simulation result data of the equipment entity model; The simulation effect display module is configured to display the corresponding simulation effect information on the graphical user interface based on the simulation result data.

9. A computer device comprising a central processing unit and a memory, characterized in that, The central processing unit is used to invoke and run a computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores, in the form of computer-readable instructions, a computer program implemented according to any one of claims 1 to 7, which, when invoked by a computer, executes the steps included in the corresponding method.

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