Automatic simulation and prediction system and method for aerodynamic characteristics of standard airflow temperature sensor
By constructing an automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor, the problems of high simulation cost and low efficiency in existing technologies have been solved, and an efficient and accurate simulation process has been achieved, which is suitable for metrology testing of aero-engines and gas turbines.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING CHANGCHENG INST OF METROLOGY & MEASUREMENT AVIATION IND CORP OF CHINA
- Filing Date
- 2025-11-28
- Publication Date
- 2026-05-05
AI Technical Summary
Existing standard airflow temperature sensor simulation methods rely on experimental wind tunnels, which are costly and time-consuming. Existing PyFluent applications are cumbersome and inefficient under complex working conditions, lack systematic design, have high learning costs, and are not sufficiently automated.
An automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor is constructed, including a user input module, an input checking module, an automated mesh generation module, an automated boundary loading module, an automated solution module, and an automated result analysis module. Through the PyFluent API, seamless data transfer and intelligent process coupling are achieved to form a closed-loop automated simulation pipeline.
It significantly improves simulation efficiency and accuracy, reduces human intervention, lowers the error rate, and enables accurate simulation of complex operating conditions, making it suitable for metrological testing of aero engines and gas turbines.
Smart Images
Figure CN121980665A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an automated simulation and prediction system and method for the aerodynamic characteristics of a standard airflow temperature sensor, which is applicable to the measurement of high-temperature gases in aircraft engines, gas turbines, etc., and belongs to the field of metrology and testing technology. Background Technology
[0002] Standard airflow temperature sensors are critical components used in aero-engine testing to measure the total airflow temperature. Their performance directly affects the accuracy of engine performance evaluation. Traditional testing methods for standard airflow temperature sensors rely on experimental wind tunnels, which are costly and time-consuming. In recent years, with the development of computational fluid dynamics (CFD) technology, numerical simulation has become a feasible alternative. However, existing simulation processes are mainly based on graphical user interfaces (GUIs), which are not only cumbersome and inefficient when simulating complex operating conditions and optimizing parameters, but also prone to introducing human error, making it difficult to meet the needs of efficient simulation under complex operating conditions.
[0003] PyFluent is the Python API for ANSYS Fluent, allowing users to interact with Fluent via Python scripts, thereby automating the simulation process. PyFluent has brought greater flexibility and scalability to the CFD simulation field, especially in scenarios requiring repetitive operations or batch processing of complex tasks. However, existing PyFluent applications still have the following limitations:
[0004] 1. Functional limitations: Existing PyFluent scripts are usually limited to simple operations, such as model import, mesh generation, and solver settings, and lack targeted support for complex working conditions.
[0005] 2. Insufficient automation: Although PyFluent can automate some operations, the overall simulation process still requires a lot of manual intervention, especially in boundary condition setting, motion simulation and result analysis.
[0006] 3. Lack of systematic solutions: Existing PyFluent applications are mostly fragmented scripts, lacking a systematic framework and modular design, making it difficult to meet the needs of complex simulation tasks.
[0007] 4. High learning cost: Using PyFluent requires a deep understanding of Fluent software and Python programming, which makes it difficult for non-professional users to learn and apply. Summary of the Invention
[0008] To address the problems existing in traditional simulation methods for standard airflow temperature sensors, the present invention aims to provide an automated simulation and prediction system and method for the aerodynamic characteristics of standard airflow temperature sensors. Through a systematic framework design and modular functional implementation, the system improves the simulation efficiency and accuracy of standard airflow temperature sensors, and contributes to the design and optimization of standard airflow temperature sensors.
[0009] The objective of this invention is achieved through the following technical solution:
[0010] The standard airflow temperature sensor aerodynamic characteristics automated simulation and prediction system disclosed in this invention includes a user input module, an input checking module, an automated mesh generation module, an automated boundary loading module, an automated solution module, and an automated result analysis module. The modules are connected through the PyFluent API to achieve seamless data transfer and intelligent process coupling, forming a closed-loop automated simulation pipeline.
[0011] The user input module provides a graphical user interface (GUI) and a command-line interface (CLI), supports parameterized input and batch task configuration, and is suitable for multi-condition simulation and optimization design.
[0012] The input checking module automatically verifies the geometric model, material properties, and operating parameters input by the user based on a preset reasonableness threshold, and triggers a feedback mechanism when the verification fails to prompt the user to make corrections.
[0013] The automated mesh partitioning module adopts an adaptive network partitioning strategy based on feature size, combined with a network quality feedback mechanism to achieve dynamic encryption and reconstruction.
[0014] The automated boundary loading module dynamically generates boundary condition expressions based on the operating parameters input by the user, and realizes real-time loading and updating of boundary conditions through the PyFluent API.
[0015] The automated solution module has a built-in adaptive solution strategy that can automatically adjust the solution parameters and convergence criteria according to the flow field characteristics, thereby improving computational efficiency and stability.
[0016] The automated results analysis module integrates the functions of automatically extracting key performance indicators and generating visualization reports.
[0017] The key performance indicators include flow field and temperature field data near the standard airflow temperature sensor.
[0018] Furthermore, the user input module provides a graphical user interface (GUI) and a command-line interface (CLI) for users to input the geometric model of a standard airflow temperature sensor, material properties, incoming flow velocity, total incoming flow temperature, and simulation settings, which are then saved as files for simulation use. It also supports batch task input, suitable for multi-condition simulation requirements. Material properties include density, specific heat ratio, thermal conductivity, and surface emissivity. Simulation settings include mesh density and solver type.
[0019] Furthermore, the input checking module performs a rationality check on each parameter input by the user input module, including the geometric model, material, material properties, incoming flow velocity, total incoming flow temperature, and simulation settings. The geometric model check is implemented using Python's secondary development of SpaceClaim, mainly to check for small faces, overlapping faces, closures, and loopholes in the model. When the check fails, a feedback mechanism is triggered to prompt the user to make corrections.
[0020] Furthermore, the automated mesh generation module works in conjunction with the user interface module and the PyFluent control module. By calling the automated functions of ANSYS Fluent Meshing, the automated mesh generation module intelligently meshes the geometric model of the standard airflow temperature sensor, ensuring the generation of high-quality, high-precision simulation meshes and providing a reliable foundation for subsequent automated simulations of the standard airflow temperature sensor.
[0021] Furthermore, the automated boundary loading module interacts with Fluent software via the PyFluent API to achieve geometry import, mesh generation, boundary condition setting, solver configuration, and solution calculation.
[0022] Furthermore, the automated solver module calls the Pyfluent library to automate the operations of ANSYS Fluent, ensuring the smooth progress of the simulation process. Automated operations include starting the simulation flow, controlling the number of calculation steps, and saving intermediate results.
[0023] Furthermore, the automated results analysis module reads and processes the output data from ANSYS Fluent, utilizing Python's computational and visualization tools to analyze and visualize the data, generating various charts and animations to help users intuitively understand the simulation results. Visualization tools include Pandas and Matplotlib.
[0024] The automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this invention is implemented based on the automated simulation and prediction system for the aerodynamic characteristics of the standard airflow temperature sensor disclosed in this invention. The automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this invention includes the following steps:
[0025] Step S1. Input information through the user input module. This input includes the geometric model of the standard airflow temperature sensor, material, material properties, incoming flow velocity, and total incoming flow temperature. The user input module allows users to input the geometric model, material properties, incoming flow velocity, total incoming flow temperature, and simulation settings for the standard airflow temperature sensor. The geometric model is created using 3D modeling software, including the ball welds, coupling wires, shielding cover, uprights, and cooling pipe models. The completed geometric model is then exported to an ANSYS Spaceclaim compatible format and saved for simulation use. ANSYS Spaceclaim compatible formats include x_t or .stp, etc.
[0026] Step S2. The input check module automatically verifies the parameters input by the user based on a preset reasonableness threshold, specifically including geometric model check, material property check, working condition parameter check, and feedback mechanism.
[0027] The geometric model check is used to call ANSYS SpaceClaim to perform geometric integrity checks and identify defects such as small faces, overlapping faces, and non-closed bodies.
[0028] Material property checks are used to verify, based on material databases (such as NIST), whether density, specific heat capacity, thermal conductivity, etc., are within reasonable ranges.
[0029] Operating parameters include checking whether the incoming Mach number is within the compressible flow range and whether the total temperature is below the material's tolerance temperature.
[0030] The feedback mechanism is as follows: if the check fails, the system will automatically prompt the user to correct the non-compliant items and provide a suggested value range.
[0031] Step S3. Using a Python script, call the Pyfluent library to start ANSYS Fluent Meshing and load the standard airflow temperature sensor geometric model. Simultaneously, perform surface mesh generation on the standard airflow temperature sensor model. The mesh size is set according to the characteristic dimensions of the standard airflow temperature sensor model, which are taken as the ball weld diameter of the standard airflow temperature sensor. After surface mesh generation, check the mesh quality. Only after the mesh quality meets the built-in standard should volume mesh generation be performed. If the quality of the generated surface mesh does not meet the standard, the program reduces the mesh size and re-generates the surface mesh until the mesh quality meets the requirements.
[0032] Step S4. Boundary Condition Generation and Loading: The boundary condition generation module constructs dynamic boundary condition functions based on user-input working condition parameters, specifically including:
[0033] Total pressure inlet conditions:
[0034]
[0035] Where P0 is the total inlet pressure, P ∞ γ is the incoming static pressure, γ is the specific heat ratio, and Ma is the incoming Mach number.
[0036] Radiation boundary conditions:
[0037] q r =εσ(T) 4 -T ∞ 4 )
[0038] Where, q r ε is the radiative heat flux, σ is the surface emissivity, and σ is the Stefan-Boltzmann constant.
[0039] The dynamic loading mechanism uses PyFluent's Named Expression feature to load the above expressions into Fluent in real time, enabling dynamic updates of boundary conditions.
[0040] Step S5. Use a Python script to call the Pyfluent library, start ANSYS Fluent, load the standard airflow temperature sensor mesh model, and set the physical parameters required for the simulation, including fluid density, viscosity, material of the standard airflow temperature sensor, and emissivity. Set the parameters of the solver, including the number of iterations and convergence criteria. Simultaneously, set the initial conditions, including the initial velocity and pressure fields. Run the Python script to automate the operation of ANSYS Fluent through Pyfluent, performing the simulation calculation of the standard airflow temperature sensor. During the calculation, monitor the simulation progress in real time and output intermediate results as needed.
[0041] Step S6. The results analysis module automatically extracts key performance indicators, including the flow field, pressure field, and velocity field of the standard airflow temperature sensor. Integrating NumPy, Matplotlib, and Pandas, it performs post-processing and analysis on the results data, generating a multi-dimensional simulation report containing key indicators, contour plots, and curves, showcasing the flow field and temperature field characteristics of the standard airflow temperature sensor.
[0042] Step S7. Based on automated simulation methods, perform multi-condition simulations by changing simulation parameters. Compare the simulation results under different conditions and analyze the impact of each parameter on the temperature measurement characteristics of the standard airflow temperature sensor, which helps to optimize the parameters of the standard airflow temperature sensor. Simulation parameters include inflow conditions, cooling water conditions, and structural parameters.
[0043] Beneficial effects:
[0044] 1. The automated simulation and prediction system and method for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this invention are based on the deep integration of PyFluent API and ANSYS Fluent, constructing a fully automated simulation framework from parameter input to result analysis. The automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor includes six core modules: user input module, input checking module, automated mesh generation module, automated boundary condition loading module, automated solution module, and automated result analysis module. It realizes integrated closed-loop simulation from geometry import, intelligent mesh generation, dynamic boundary condition loading, adaptive solution, and automatic extraction of key performance indicators, significantly shortening simulation time and improving work efficiency.
[0045] 2. The automated simulation and prediction system and method for the aerodynamic characteristics of the standard airflow temperature sensor disclosed in this invention adopts a systematic framework design and modular function implementation. It decomposes the simulation process into multiple independent functional modules, realizes the "one-click" operation of the simulation of the aerodynamic characteristics of the standard airflow temperature sensor, significantly improves the simulation efficiency and result consistency, and can reduce manual intervention, reduce the error rate, and improve the reliability of the simulation results.
[0046] 3. The automated simulation and prediction system and method for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this invention includes an input checking module that intelligently verifies parameters based on preset thresholds; boundary conditions that support dynamic boundary modeling based on physical formulas; a solution module with an adaptive strategy to improve computational stability; and a result analysis module that automatically extracts key indicators such as flow field and temperature field data near the standard airflow temperature sensor and generates a visual report. This invention can accurately simulate complex operating conditions such as the aerodynamic characteristics of a standard airflow temperature sensor, meeting the needs of various application scenarios.
[0047] 4. The automated simulation and prediction system and method for the aerodynamic characteristics of the standard airflow temperature sensor disclosed in this invention can significantly improve the efficiency and accuracy of the simulation of the aerodynamic characteristics of the standard airflow temperature sensor based on the above three beneficial effects. It is applicable to the simulation of complex working conditions in fields such as metrology and testing, and helps in the design, optimization and performance evaluation of the standard airflow temperature sensor, as well as the design, optimization and evaluation of the total temperature probe. Attached Figure Description
[0048] Figure 1This is a flowchart of an automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor, as described in an embodiment of this application.
[0049] Figure 2 This is a standard airflow temperature sensor model diagram input by the user in an embodiment of this application.
[0050] Figure 3 This is a standard airflow temperature sensor computational domain mesh diagram showing the output results of an embodiment of this application.
[0051] Figure 4 The Mach number cloud map of the flow field output results of the embodiments of this application is shown.
[0052] Figure 5 The temperature distribution cloud map near the probe tip is the output result of the embodiment of this application.
[0053] Figure 6 The output results of this application's embodiments are temperature cloud diagrams of the coupler wire and ball bonding. Detailed Implementation
[0054] To better illustrate the purpose and advantages of the present invention, the invention will be further described below in conjunction with the accompanying drawings and examples.
[0055] Example 1:
[0056] The standard airflow temperature sensor aerodynamic characteristic automated simulation prediction system disclosed in this embodiment includes a user input module, an input checking module, an automated mesh generation module, an automated boundary loading module, an automated solution module, and an automated result analysis module. The modules are connected through the PyFluent API to achieve seamless data transfer and intelligent process coupling, forming a closed-loop automated simulation pipeline.
[0057] The user input module provides a graphical user interface (GUI) and a command-line interface (CLI) for users to input standard airflow temperature sensor geometric models, material properties (density, specific heat ratio, thermal conductivity, surface emissivity), inflow velocity, total inflow temperature, and simulation settings (such as mesh density, solver type, etc.), and save them to a file for simulation use. It also supports batch task input, suitable for multi-condition simulation needs. Material properties include density, specific heat ratio, thermal conductivity, and surface emissivity. Simulation settings include mesh density and solver type.
[0058] The input checking module is used to check the rationality of parameters such as geometric model, material, material properties, inflow velocity, inflow total temperature, and simulation settings input by the user input module. The geometric model check is implemented by secondary development of SpaceClaim in Python. It is mainly used to check whether the model has small faces, overlapping faces, closures, and loopholes. When the verification fails, a feedback mechanism is triggered to prompt the user to make corrections.
[0059] The automated mesh generation module works in conjunction with the user interface module and the PyFluent control module. By calling the automated functions of ANSYS Fluent Meshing, the automated mesh generation module intelligently meshes the geometric model of the standard airflow temperature sensor, ensuring the generation of high-quality, high-precision simulation meshes and providing a reliable foundation for subsequent automated simulations of the standard airflow temperature sensor.
[0060] The automated boundary loading module interacts with Fluent software via the PyFluent API to achieve geometry import, mesh generation, boundary condition setting, solver configuration, and solution calculation.
[0061] The automated solver module calls the Pyfluent library to automate the operations of ANSYS Fluent, ensuring the smooth progress of the simulation process. Automated operations include starting the simulation flow, controlling the number of calculation steps, and saving intermediate results.
[0062] The automated results analysis module is used to read and process the results data output by ANSYS Fluent. It uses Python's calculation and visualization tools to analyze and visualize the data, generating various charts and animations to help users intuitively understand the simulation results. Visualization tools include Pandas and Matplotlib.
[0063] The key performance indicators include flow field and temperature field data near the standard airflow temperature sensor.
[0064] The automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this embodiment is implemented based on the automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this invention. The automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor disclosed in this embodiment includes the following steps:
[0065] Step S1. User Input: Input the user information, which mainly includes: the geometric model of the standard airflow temperature sensor, the material (air, high-temperature alloy, iridium-rhodium alloy, ceramic), the material properties (density, specific heat ratio, thermal conductivity, surface emissivity), the incoming flow velocity, and the total incoming flow temperature. The geometric model needs to be built using 3D modeling software, including detailed components such as ball welds, coupling wires, shielding covers, uprights, and cooling pipes. The completed geometric model should be exported to an ANSYS Spaceclaim compatible format, such as .x_t or .stp.
[0066] Taking a standard airflow temperature sensor as an example, its geometry includes ball welds, ferrules, ceramic tubes, shielding covers, support rods, cooling structures, etc. It is necessary to simulate its temperature measurement deviation under the following conditions: total incoming flow temperature of 2300K, incoming flow Mach number of 0.6, and incoming flow static pressure of 1.0 MPa.
[0067] Users upload a standard airflow temperature sensor geometry model via GUI or CLI, and input the following parameters: Standard airflow temperature sensor geometry model: total_temperature_sensor.stp (e.g., Figure 2 (as shown), materials (air, high-temperature alloy, iridium-rhodium alloy, ceramics), incoming flow parameters (incoming flow Mach number: 0.6, incoming flow total temperature: 2300K, incoming flow static pressure: 1.0MPa).
[0068] Step S2. Input Check: The input check module automatically validates the parameters entered by the user based on a preset reasonableness threshold, specifically including:
[0069] Geometric model check: Call ANSYS SpaceClaim to perform a geometric integrity check to identify defects such as small faces, overlapping faces, and non-closed bodies;
[0070] Material property checks: Verify that density, specific heat capacity, thermal conductivity, etc. are within reasonable ranges according to material databases (such as NIST);
[0071] Operating parameters check: such as whether the incoming flow Mach number is within the compressible flow range, and whether the total temperature is below the material's tolerance temperature;
[0072] Feedback mechanism: If the check fails, the system will automatically prompt the user to correct the non-compliant items and provide a suggested value range;
[0073] Step S3. Mesh Generation: Using a Python script, the Pyfluent library is invoked to start ANSYS Fluent Meshing and load the standard airflow temperature sensor geometric model. Simultaneously, the surface mesh of the standard airflow temperature sensor model is generated. The mesh size is set based on the characteristic dimensions of the standard airflow temperature sensor model, which are taken as the ball weld diameter of the standard airflow temperature sensor. After the surface mesh is generated, the mesh quality is checked. If the mesh quality meets the built-in standard, the volume mesh is then generated. If the quality of the generated surface mesh is substandard, the program reduces the mesh size and re-generates the surface mesh until the mesh quality meets the requirements.
[0074] Mesh quality: Generate standard airflow temperature sensor mesh: total_temperature_sensor.msh.h5 (e.g.) Figure 3 As shown in the figure, the total number of grids is approximately 5.2 million, the boundary layer grid accounts for 15%, and the maximum aspect ratio is 4.2, which meets the simulation requirements.
[0075] parameter Example value Function Description Global grid size 1.0mm Controlling overall grid density Number of boundary layers 5 floors Ensure near-wall flow resolution Local encryption level Level 2 Improve the calculation accuracy near the shield.
[0076] Step S4. Boundary Condition Generation and Loading: The boundary condition generation module constructs dynamic boundary condition functions based on user-input working condition parameters, specifically including:
[0077] Total pressure inlet conditions:
[0078]
[0079] Where P0 is the total inlet pressure, P ∞ γ is the incoming static pressure, γ is the specific heat ratio, and Ma is the incoming Mach number;
[0080] Radiation boundary conditions:
[0081] q r =εσ(T) 4 -T ∞ 4 )
[0082] Where, q r ε is the radiative heat flux, ε is the surface emissivity, and σ is the Stefan-Boltzmann constant.
[0083] Dynamic loading mechanism: The above expression is loaded into Fluent in real time through PyFluent's Named Expression function to achieve dynamic updates of boundary conditions;
[0084] Step S5. Solving the Calculation: Using a Python script, call the Pyfluent library to launch ANSYS Fluent and load the standard airflow temperature sensor mesh model. Set the physical parameters required for the simulation, including fluid density, viscosity, the material of the standard airflow temperature sensor, and emissivity. Set the solver parameters, such as the number of iterations and convergence criteria. Simultaneously, set the initial conditions, including the initial velocity and pressure fields. Finally, run the Python script to automate the operation of ANSYS Fluent through Pyfluent, performing the simulation calculation for the standard airflow temperature sensor. During the calculation, monitor the simulation progress in real time and output intermediate results as needed.
[0085] Step S6. Result Analysis: The result analysis module automatically extracts key performance indicators, including information such as the flow field, pressure field, and velocity field of the standard airflow temperature sensor. It integrates NumPy, Matplotlib, and Pandas to post-process and analyze the result data, generating a multi-dimensional simulation report containing key indicators, contour plots, and curves, showcasing the flow field and temperature field characteristics of the standard airflow temperature sensor.
[0086] Simulation results: The simulation took approximately 4 hours; the temperature measurement deviation of the standard airflow temperature sensor under this operating condition was 19.27K.
[0087] Mach number cloud map of the flow field: (e.g.) Figure 4 (As shown); Temperature distribution cloud map near the probe tip: (as shown) Figure 5 (As shown); Temperature contour plots of the coupler wire and ball bonding: (as shown) Figure 6 (as shown);
[0088] Simulation efficiency: During the simulation, it takes 10 minutes for technicians to operate manually using this system, compared to 8 hours using traditional methods, which is 48 times more efficient. The entire simulation process using this system (including pre- and post-processing) takes 4 hours, compared to 12 hours using traditional methods, which is 3 times more efficient.
[0089] Step S7. Multi-condition simulation: Based on automated simulation methods, multi-condition simulations are performed by changing simulation parameters, such as inflow conditions, cooling water conditions, and structural parameters. The simulation results under different conditions are compared, and the influence of each parameter on the temperature measurement characteristics of the standard airflow temperature sensor is analyzed, providing a basis for parameter optimization of the standard airflow temperature sensor.
[0090] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. An automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor, characterized in that: It includes a user input module, an input checking module, an automated mesh generation module, an automated boundary loading module, an automated solution module, and an automated result analysis module. The modules are connected through the PyFluent API to achieve seamless data transfer and intelligent process coupling, forming a closed-loop automated simulation. The user input module provides a graphical user interface (GUI) and a command-line interface (CLI), supports parameterized input and batch task configuration, and is suitable for multi-condition simulation and optimization design. The input checking module automatically verifies the geometric model, material properties, and operating parameters input by the user based on a preset reasonableness threshold, and triggers a feedback mechanism when the verification fails to prompt the user to make corrections. The automated mesh partitioning module adopts an adaptive network partitioning strategy based on feature size, combined with a network quality feedback mechanism to achieve dynamic encryption and reconstruction; The automated boundary loading module dynamically generates boundary condition expressions based on the operating parameters input by the user, and realizes real-time loading and updating of boundary conditions through the PyFluent API. The automated solution module has a built-in adaptive solution strategy that automatically adjusts the solution parameters and convergence criteria according to the flow field characteristics. The automated results analysis module integrates the functions of automatically extracting key performance indicators and generating visualization reports.
2. The automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor as described in claim 1, characterized in that: The key performance indicators include flow field and temperature field data near the standard airflow temperature sensor.
3. The automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor as described in claim 2, characterized in that: The user input module provides a graphical user interface (GUI) and a command-line interface (CLI) for users to input standard airflow temperature sensor geometric models, material properties, inflow velocity, total inflow temperature, and simulation settings, and save them to a file for simulation use. It also supports batch task input, making it suitable for multi-condition simulation needs. The input checking module checks the rationality of each parameter input by the user, including the geometric model, material, material properties, inflow velocity, inflow total temperature, and simulation settings. The geometric model check is implemented using Python's secondary development of SpaceClaim, mainly to check for small faces, overlapping faces, closures, and loopholes in the model. When the check fails, a feedback mechanism is triggered to prompt the user to make corrections. The automated mesh generation module works in conjunction with the user interface module and the PyFluent control module. The automated mesh generation module uses the automated function of ANSYS Fluent Meshing to intelligently generate a simulation mesh for the geometric model of a standard airflow temperature sensor. The automated boundary loading module interacts with Fluent software via the PyFluent API to achieve geometry import, mesh generation, boundary condition setting, solver configuration, and solution calculation. The automated solver module calls the Pyfluent library to automate the operation of ANSYS Fluent, ensuring the smooth progress of the simulation process. The automated operation includes starting the simulation process, controlling the number of calculation steps, and saving intermediate results. The automated results analysis module is used to read and process the results data output by ANSYS Fluent. It uses Python's calculation and visualization tools to analyze and visualize the data, generating various charts and animations to help users intuitively understand the simulation results.
4. The automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor as described in claim 3, characterized in that: Material properties include density, specific heat ratio, thermal conductivity, and surface emissivity; simulation settings include mesh density and solver type.
5. The automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor as described in claim 3, characterized in that: Visualization tools include Pandas and Matplotlib.
6. An automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor, implemented based on the automated simulation and prediction system for the aerodynamic characteristics of a standard airflow temperature sensor as described in claims 1, 2, 3, 4, or 5, characterized in that: Includes the following steps: Step S1. Input information through the user input module. The input information includes the geometric model of the standard airflow temperature sensor, material, material properties, incoming flow velocity, and total incoming flow temperature. The user input module allows users to input the geometric model of the standard airflow temperature sensor, material properties, incoming flow velocity, total incoming flow temperature, and simulation settings. The geometric model is built using 3D modeling software, including the ball weld, ferrule, shield, pole, and cooling pipe model of the standard airflow temperature sensor. The built geometric model is then exported in an ANSYS Spaceclaim compatible format and saved for simulation use. Step S2. The input check module automatically verifies the parameters input by the user based on a preset reasonableness threshold, specifically including geometric model check, material property check, working condition parameter check, and feedback mechanism; Step S3. Use a Python script to call the Pyfluent library, start ANSYS Fluent Meshing, load the standard airflow temperature sensor geometric model, and simultaneously perform surface mesh generation on the standard airflow temperature sensor model. The mesh size is set according to the characteristic dimensions of the standard airflow temperature sensor model, which is the diameter of the ball weld of the standard airflow temperature sensor. After the surface mesh generation is completed, check the mesh quality first. If the mesh quality meets the built-in standard, then perform volume mesh generation. If the quality of the generated surface mesh does not meet the standard, the program reduces the mesh size and re-generates the surface mesh until the mesh quality meets the requirements. Step S4. Boundary condition generation and loading: The boundary condition generation module constructs dynamic boundary condition functions based on the user-input working condition parameters; Total pressure inlet conditions: Where P0 is the total inlet pressure, P ∞ γ is the incoming static pressure, γ is the specific heat ratio, and Ma is the incoming Mach number; Radiation boundary conditions: q r =εσ(T 4 -T ∞ 4 ) Where, q r ε is the radiative heat flux, ε is the surface emissivity, and σ is the Stefan-Boltzmann constant. The dynamic loading mechanism uses PyFluent's Named Expression feature to load the above expressions into Fluent in real time, enabling dynamic updates of boundary conditions. Step S5. Use a Python script to call the Pyfluent library, start ANSYS Fluent and load the standard airflow temperature sensor mesh model. Set the physical parameters required for the simulation, including fluid density, viscosity, material of the standard airflow temperature sensor, and emissivity. Set the parameters of the solver, including the number of iterations and convergence criteria. Simultaneously, set the initial conditions, including the initial velocity field and pressure field. Run the Python script to automate the operation of ANSYS Fluent through Pyfluent and perform the simulation calculation of the standard airflow temperature sensor. During the calculation, monitor the simulation progress in real time and output intermediate results as needed. Step S6. The results analysis module automatically extracts key performance indicators, including the flow field, pressure field, and velocity field of the standard airflow temperature sensor; it integrates NumPy, Matplotlib, and Pandas to post-process and analyze the results data, generating a multi-dimensional simulation report that includes key indicators, cloud maps, and curves, showcasing the flow field and temperature field characteristics of the standard airflow temperature sensor. Step S7. Based on the automated simulation method, perform multi-condition simulation by changing the simulation parameters; compare the simulation results under different conditions and analyze the influence of each parameter on the temperature measurement characteristics of the standard airflow temperature sensor.
7. The automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor as described in claim 6, characterized in that: In step S2, The geometric model check is used to call ANSYS SpaceClaim to perform a geometric integrity check and identify the presence of facets, overlapping faces, and non-closed bodies. Material property checks are used to verify, based on material databases, whether density, specific heat capacity, and thermal conductivity are within reasonable ranges. Operating parameters include checking whether the incoming Mach number is within the compressible flow range and whether the total temperature is below the material's tolerance temperature. The feedback mechanism is as follows: if the check fails, the system will automatically prompt the user to correct the non-compliant items and provide a suggested value range.
8. The automated simulation and prediction method for the aerodynamic characteristics of a standard airflow temperature sensor as described in claim 7, characterized in that: In step S7, Simulation parameters include inflow conditions, cooling water conditions, and structural parameters.