Heat source position positioning system based on CFD model

Abstract

The invention discloses a CFD (computational fluid dynamics) model-based heat source position positioning system, and relates to the technical field of building energy conservation. The simulation analysis platform is in communication connection with a data input and processing module, a geometric model construction module, a parameter setting module, a CFD simulation execution module, a polymorphism analysis module, a flow state prediction module and a result display module, and all the modules are in electric signal connection; the data input and processing module is used for collecting and processing input data. The CFD model-based heat source position positioning system simulates and predicts the flowing states under different heat source positions, evaluates the influence of different heat source positions on the flowing states and predicts the possible hot-pressing ventilation effect, so that designers are guided to optimize the heat source layout and improve the ventilation effect, the comfort level of a building is improved, and the heat source position positioning system is suitable for popularization and application. And energy consumption can be effectively reduced, and the purposes of energy conservation and emission reduction are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of building energy conservation, and in particular to a heat source location positioning system based on a CFD model. Background Art

[0002] The CFD model is a computational fluid dynamics model. In underground buildings, the location of the heat source directly affects the temperature distribution and the flow field of thermal pressure ventilation. Thermal pressure ventilation in underground buildings often exhibits polymorphic phenomena, that is, under the same boundary conditions and geometric structures, due to the influence of factors such as initial conditions, heat source location and intensity, there will be a variety of stable ventilation states. The existence of this phenomenon increases the difficulty of ventilation system design and also provides the possibility of optimized design. By utilizing the simulation capabilities of the CFD model, the ventilation state under different heat source locations can be simulated to analyze the influence of different heat source locations inside underground buildings on thermal pressure ventilation, and further optimize the design and performance of the ventilation system.

[0003] In the existing technology, underground building thermal pressure ventilation has polymorphism or multiple solutions, that is, there are multiple stable flow states under the same conditions. It is difficult for the CFD model to accurately predict the final flow state during simulation. Therefore, how to analyze the stability and conversion conditions of different flow states and predict the final flow state is the problem we need to solve. For this purpose, a heat source location positioning system based on the CFD model is proposed. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a heat source location positioning system based on a CFD model, comprising a simulation analysis platform, wherein the simulation analysis platform is communicatively connected with a data input and processing module, a geometric model building module, a parameter setting module, a CFD simulation execution module, a polymorphism analysis module, a flow state prediction module and a result display module, wherein the modules are connected by electrical signals;

[0005] The data input and processing module is used to collect and process input data, including building geometry, heat source characteristics, and boundary conditions, to ensure the accuracy and effectiveness of the simulation and provide necessary basic data for subsequent modules;

[0006] The geometric model building module builds a geometric model of the underground building according to the input data, performs meshing, and performs mesh convergence analysis and time step independence verification to generate a computational domain for CFD simulation, thereby ensuring the accuracy and computational efficiency of CFD simulation and reducing truncation errors and computational convergence problems;

[0007] The parameter setting module is used to set the location, intensity, and distribution parameters of the heat source, simulate the heat pressure ventilation effect under different heat source configurations, analyze its impact on the flow state, and set the initial temperature and pressure conditions of the simulation as well as the wall, inlet, and outlet boundary conditions to ensure that the simulation process conforms to the actual situation and improve the reliability of the simulation results;

[0008] The CFD simulation execution module runs the CFD model to simulate the heat pressure ventilation process in the underground building, and generates detailed temperature and velocity distribution data to provide data support for analyzing the flow state;

[0009] The polymorphism analysis module is used to analyze CFD simulation results, identify different flow states and their stability, determine the conditions for the existence of polymorphism, and evaluate the stability and conversion conditions of each flow state;

[0010] The flow state prediction module, in combination with a machine learning algorithm, uses historical data and CFD simulation results to train a model to predict the final flow state at different heat source locations, thereby improving prediction accuracy and efficiency and solving the problem that the CFD model is difficult to directly predict the final flow state.

[0011] The result display module provides a user interface to assist users in inputting heat source locations, viewing simulation results and prediction results, enhancing the usability of the system and enabling users to intuitively understand the complex process and polymorphic phenomena of thermal pressure ventilation in underground buildings.

[0012] Preferably, in the data input and processing module, the process of collecting and processing input data includes:

[0013] Based on architectural design drawings, field measurement data, and historical records, input data on building geometry, heat source characteristics, and boundary conditions are collected. Building geometry includes building size, shape, internal layout, and construction information. Heat source characteristics include location, intensity, type (such as equipment heat dissipation, personnel heat dissipation, etc.) of heat sources and their working hours. Boundary conditions include outdoor temperature, humidity, wind speed, and wind direction information.

[0014] Conduct preliminary verification on the collected data to check whether the data is complete, accurate, and meets the simulation requirements. Check whether there are logical errors in the building geometry data and whether the heat source characteristic data is within a reasonable range. Clean the data and handle the problematic data found during the verification process. At the same time, standardize the data to ensure the consistency of data format and units.

[0015] Convert the collected input data into different formats and integrate data from different sources into a unified data set to ensure the logical relationship and time consistency between the data;

[0016] Convert the integrated data into a format acceptable to the CFD model, including generating geometry files for meshing and case files for simulation, store the processed data in the database of the simulation analysis platform for subsequent modules to call and analyze, establish a data management system, classify, index and back up the stored data to ensure data security and accessibility.

[0017] Preferably, in the geometric model building module, the process of generating a computational domain for CFD simulation includes:

[0018] Based on the collected building geometry input data (including the building's size, shape, internal layout and construction information), the Geometry module (CFD pre-processing software) provided by ANSYS Fluent was used to create a preliminary geometric model of the underground building.

[0019] Refine the preliminary geometric model of the underground building to depict the complex structure and boundary conditions inside the building, including the main structure of the building, the location of local heat sources, and the entrance and exit passages, ensure that all building features and internal structures are accurately reflected, and verify whether the geometric model is consistent with the actual building and design drawings, and check whether the model has defects, such as overlapping surfaces, missing walls or other structural problems;

[0020] After the geometric model is constructed, the geometric model is meshed to generate a mesh system for CFD simulation;

[0021] By gradually refining the grid and comparing the simulation results under different grid densities, it can be determined whether the simulation results tend to be stable with the increase of grid density;

[0022] Set different time steps for simulation and observe how the simulation results change with the time step;

[0023] After completing meshing and verification, generate the computational domain for CFD simulation based on meshing and boundary condition settings to ensure that the computational domain completely covers the entire geometric model. The computational domain includes all geometric structures and mesh information of underground buildings, providing a basis for subsequent boundary condition settings, initial condition settings, and simulation solution steps.

[0024] A pre-simulation is performed before the formal simulation to check the physical rationality of the model and calculate the stability, adjust the simulation parameters to solve any convergence problems, and optimize the geometric model based on the results of the pre-simulation. The optimization includes adjusting the grid density, modifying the boundary conditions, and outputting the final geometric model and grid system to the CFD simulation execution module.

[0025] Preferably, in the parameter setting module, the process of setting the heat source position, intensity and distribution parameters includes:

[0026] According to the actual building conditions, the exact location of the heat source is set, and the location of the heat source is marked in the Geometry module of ANSYS Fluent. The selected location is marked as the heat source area. Based on the type of heat source, the heat flux of the heat source is defined in the material properties, and the distribution mode of the heat source is set, which are point heat source, line heat source, surface heat source, and the corresponding distribution density. The heat pressure ventilation effect under different heat source configurations is simulated, and the influence of heat source configuration on flow rate, flow direction and temperature distribution is analyzed;

[0027] Based on the temperature of the surrounding environment of the underground building, set the initial temperature of the entire simulation area, and set the initial pressure of the building's internal and external environments at the beginning of the simulation. For natural ventilation simulation, set the static pressure. For open systems (such as underground buildings with inlet and outlet passages), set the static pressure of the inlet and outlet to the ambient pressure, and perform iterative calculations in the internal area;

[0028] The walls of underground buildings are set as non-slip and insulating surfaces so that the walls do not participate in heat exchange and the fluid does not slide on the walls. For the inlet and outlet channels, they are set as pressure inlets based on the simulation requirements. If natural ventilation is simulated, the static pressure of the inlet is set to the ambient pressure. The boundary conditions when the air leaves the building are set, the outlet channel is set to the pressure outlet, and the static pressure is set to the ambient pressure.

[0029] Preferably, in the CFD simulation execution module, the process of generating temperature and velocity distribution data includes:

[0030] Load the verified geometric model and mesh system into the CFD software, and import the heat source position, intensity, distribution parameters, boundary conditions and initial conditions defined in the parameter setting module;

[0031] Select the pressure-based solver and set the fluid properties, define the physical properties of air density, specific heat capacity, and thermal conductivity, and select a fluid dynamics model to simulate fluid behavior. Simultaneously perform mesh independence verification to ensure that the generated mesh meets the mesh independence requirements to ensure the accuracy of the simulation results.

[0032] Configure the time step, relaxation factor and convergence criterion parameters of the pressure-based solver, start the pressure-based solver in the CFD software, perform iterative calculations, monitor the residual curve during the solution process, monitor the progress of the simulation and the use of computing resources, calculate the metric value for convergence judgment, and detect whether there are numerical instability and non-convergence problems until the convergence criterion is met;

[0033] Collect the distribution data of temperature and velocity physical quantities generated during the simulation process, and use the post-processing tools of the CFD software to view the temperature distribution map in the underground building, view the velocity vector map, and analyze the flow state and direction of the fluid in the underground building;

[0034] Conduct a preliminary analysis of the simulation results to check for abnormalities or data that do not conform to physical reality, and post-process the simulation results to extract key data of the flow state and visualize them.

[0035] Preferably, the calculation expression of the convergence determination metric value is:

[0036]

[0037] Among them, C v It is a measure of convergence, that is, the square root of the sum of squares of the residual changes, R i is the current value of the residual at the i-th iteration, R b，i is the baseline value of the residual of the ith iteration step, is the residual value of the initial iteration step, ∈ is the pre-set convergence standard, a small positive number, usually less than 1, n is the number of residuals involved in the calculation, C v The value range is [0, +∞), when C v The smaller the value, the smaller the residual change, the closer the simulation is to convergence, and R i and R b，i The value range is [0, +∞), where R i It decreases as the number of iterations increases.

[0038] Preferably, in the polymorphism analysis module, the process of determining the polymorphism existence condition includes:

[0039] Extract the distribution data of temperature, velocity, and pressure from the CFD simulation results, and import the extracted data into the polymorphism analysis module to ensure the integrity and accuracy of the data;

[0040] Analyze simulation data and automatically identify flow states in simulation results, including laminar flow, turbulent flow, and transitional flow. For each flow state, extract characteristic parameters of velocity distribution, vortex structure, and turbulence intensity.

[0041] For each identified flow state, by analyzing the change trend of the flow state over time, evaluate its stability, determine whether it is a stable state, analyze the transition conditions between different flow states, and determine the critical parameters of the heat source intensity and inlet flow velocity of the state transition;

[0042] Search for areas that meet the polymorphism conditions in the simulation results, identify the critical values ​​of key parameters such as velocity, pressure, and temperature that affect polymorphism, conduct sensitivity analysis on key parameters, evaluate the impact of parameter changes on flow conditions, and verify whether the determined polymorphism conditions are accurate by comparing simulation results under different conditions.

[0043] Preferably, in the flow state prediction module, the process of predicting the final flow state at different heat source positions includes:

[0044] Collect historical parameter data and generated CFD simulation data, and clean and standardize the collected historical parameter data and CFD simulation data. Data cleaning removes noise and outliers. Data standardization enables data of different dimensions to be compared and calculated on the same scale. The historical parameter data are flow state data at different heat source positions, including key parameters of temperature distribution, velocity distribution, and pressure distribution, which are derived from actual experimental measurements and existing CFD simulation results. The generated CFD simulation data are obtained by simulating different heat source positions using CFD software to obtain corresponding flow state data.

[0045] The collected data is divided into a training set and a test set, a physical information neural network model is selected for model training, the physical information neural network model is trained using the training set data, a flow state prediction model is constructed, and the model is verified using the test set data to evaluate the prediction accuracy and generalization ability of the model;

[0046] Based on the flow state prediction model, historical parameter data and generated CFD simulation data, the flow state evaluation coefficient is obtained through comprehensive analysis to predict the flow state and trend under different heat source positions;

[0047] Deploy the trained flow state prediction model to the CFD simulation platform, input the new heat source position parameters into the flow state prediction model, and the flow state prediction model makes predictions based on the rules obtained by training the historical data and CFD simulation results, and outputs the prediction results, including the key parameters of temperature distribution, velocity distribution, and pressure distribution;

[0048] The prediction results are analyzed and verified to evaluate their accuracy and practicality, and the prediction results are fed back to relevant researchers or engineers to provide strong support for solving practical problems.

[0049] Preferably, the calculation expression of the flow state evaluation coefficient is:

[0050]

[0051] Where FE is the flow state evaluation coefficient, which is used to measure the overall change of flow state, T j is the temperature of the jth measurement point at a specific heat source location, T b is the reference temperature, is the initial temperature at the heat source position, ΔP j is the pressure change at the jth measurement point, P b is the base pressure, which is the initial pressure of the system, V jis the velocity of the jth measurement point at a specific heat source position, V b is the reference velocity, is the initial velocity at the heat source position, m is the total number of measurement points, and the value range of FE is [0, +∞), where FE=0 means no flow state change, and the larger the FE value, the more drastic the flow state change.

[0052] Preferably, in the result display module, the process of assisting the user to input the heat source location, view the simulation results and the prediction results includes:

[0053] Provide a user interface that allows users to easily input parameters, such as the location of the heat source, provide clear instructions and prompts, and guide users to operate, integrating the fluid dynamics data obtained from CFD simulation and the prediction results of the machine learning model into the result display module;

[0054] Visualize the physical quantities of temperature distribution, velocity vector and pressure using graphs and animations, and provide views at different perspectives and zoom levels to fully understand the data;

[0055] Present simulation and prediction results, including stability of flow regimes and flow trends, and interpret simulation results, including description of flow regimes and explanation of polymorphic phenomena;

[0056] Output simulation reports and display prediction results, provide comparative views, and show the differences between different prediction results. During the result display process, provide interactive annotation functions, allowing users to add comments, arrows, text boxes and other elements to the graph to record and share their understanding and analysis of the results.

[0057] The present invention provides a heat source location positioning system based on a CFD model. It has the following beneficial effects:

[0058] 1. The heat source location positioning system based on the CFD model simulates and predicts the flow state under different heat source positions, evaluates the impact of different heat source positions on the flow state, and predicts the possible thermal pressure ventilation effect, thereby guiding designers to optimize the heat source layout and improve the ventilation effect. It not only helps to improve the comfort of the building, but also effectively reduces energy consumption and achieves the goal of energy conservation and emission reduction. By integrating the physical information neural network machine learning algorithm and making full use of historical data and CFD simulation results, it can realize accurate prediction of the flow state under different heat source positions, significantly improve the accuracy of the prediction, and at the same time, make the prediction process more efficient, and be able to complete the processing and analysis of a large amount of data in a short time, providing timely decision support for engineering design.

[0059] 2. The heat source location positioning system based on the CFD model simulates the heat pressure ventilation effect under different heat source positions through detailed CFD simulation of underground buildings, so as to analyze the influence of heat source configuration on flow rate, flow direction and temperature distribution. The polymorphism analysis module is used to identify and evaluate the stability of different flow states and determine the critical parameters of state transition. By adjusting the heat source position, the heat distribution inside the building can be effectively controlled, energy waste can be reduced, and comfort can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a module structure diagram of the heat source position positioning system based on the CFD model of the present invention;

[0061] Figure 2 A flow chart for generating temperature and velocity distribution data of the present invention;

[0062] Figure 3 The flowchart of the present invention for predicting the final flow state under different heat source positions. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0064] The first embodiment, as Figure 1 , Figure 2 As shown, the present invention provides a technical solution: a heat source location positioning system based on a CFD model, comprising a simulation analysis platform, wherein the simulation analysis platform is communicatively connected with a data input and processing module, a geometric model building module, a parameter setting module, a CFD simulation execution module, a polymorphism analysis module, a flow state prediction module and a result display module, wherein the modules are connected by electrical signals;

[0065] The data input and processing module is used to collect and process input data, including building geometry, heat source characteristics, and boundary conditions to ensure the accuracy and effectiveness of the simulation and provide necessary basic data for subsequent modules. The input data of building geometry, heat source characteristics, and boundary conditions are collected based on architectural design drawings, field measurement data, and historical record data. The building geometry includes the size, shape, internal layout, and construction information of the building. The heat source characteristics include the location, intensity, type (such as equipment heat dissipation, personnel heat dissipation, etc.) of the heat source and its working time information. The boundary conditions include outdoor temperature, humidity, wind speed, and wind direction information. The collected data is preliminarily verified to check whether the data is complete and accurate and meets the simulation requirements. Check whether the building geometry data has logical Editing errors, whether the heat source characteristic data is within a reasonable range, and clean the data, handle the problematic data found during the verification process, and standardize the data to ensure the consistency of data format and unit, convert the collected input data into data format, and integrate data from different sources into a unified data set to ensure the logical relationship and time consistency between the data, convert the integrated data into a format acceptable to the CFD model, including generating geometry files for meshing and case files for simulation, and store the processed data in the database of the simulation analysis platform for subsequent modules to call and analyze, establish a data management system, classify, index and back up the stored data to ensure data security and accessibility;

[0066] The geometric model construction module constructs the geometric model of the underground building according to the input data, performs meshing, and performs mesh convergence analysis and time step independence verification, generates the calculation domain for CFD simulation, ensures the accuracy and calculation efficiency of CFD simulation, reduces truncation errors and calculation convergence problems, and uses the Geometry module (CFD pre-processing software) provided by ANSYS Fluent to create a preliminary geometric model of the underground building based on the collected building geometry input data (including the building's size, shape, internal layout and construction information). The preliminary geometric model of the underground building is refined to depict the complex structure and boundary conditions inside the building, including the main structure of the building, the location of local heat sources, and import and export channels, to ensure that all building features and internal structures are accurately reflected, and to verify whether the geometric model is consistent with the actual building and design drawings. Check whether the model has defects, such as overlapping surfaces, missing walls or other structural problems. After the geometric model is built, the geometric model is meshed to generate a grid system for CFD simulation. The quality of meshing directly affects the accuracy and computational efficiency of CFD simulation. Complex geometric structures in underground buildings and areas with drastic temperature changes require finer meshes to ensure that the detailed features of the flow field can be accurately captured. For key areas such as around heat sources, vents, and connecting channels, local encryption processing should be performed. In the process of meshing, pay attention to the shape, size, and direction of the mesh. In order to avoid large truncation errors in the calculation, use high-quality meshes (such as structured meshes, hexahedral meshes), and control the distortion and aspect ratio of the mesh. By gradually refining the mesh and comparing the simulation results under different mesh densities, the accuracy of the mesh can be judged. Whether the simulation results tend to be stable with the increase of grid density. If the changes of simulation results are within an acceptable range after the grid is encrypted to a certain extent, it is considered that the current grid density has reached convergence. Determine a grid density that can ensure simulation accuracy and has a reasonable calculation cost, set different time steps for simulation, and observe the changes of simulation results with time steps. If the changes of simulation results are very small after the time step is reduced to a certain extent, it is considered that the current time step setting is reasonable. Ensure that the selected time step can accurately capture the dynamic changes of physical phenomena and avoid numerical instability. After completing mesh division and verification , based on the grid division and boundary condition settings, generate the computational domain for CFD simulation, ensure that the computational domain completely covers the entire geometric model, including all the geometric structures and grid information of the underground building in the computational domain, and provide a basis for the subsequent boundary condition settings, initial condition settings, and simulation solution steps. Perform a pre-simulation before the formal simulation to check the physical rationality of the model and calculate the stability, adjust the simulation parameters to solve any convergence problems, and optimize the geometric model based on the results of the pre-simulation. The optimization includes adjusting the grid density and modifying the boundary conditions. The final geometric model and grid system are output to the CFD simulation execution module;

[0067] The parameter setting module is used to set the location, intensity, and distribution parameters of the heat source, simulate the heat pressure ventilation effect under different heat source configurations, analyze its impact on the flow state, and set the initial temperature, pressure conditions, and wall, inlet, and outlet boundary conditions of the simulation to ensure that the simulation process conforms to the actual situation and improve the reliability of the simulation results. According to the actual building situation, the exact location of the heat source is set and the simulation results are analyzed in ANSYS. In the Geometry module that comes with Fluent, mark the location of the heat source and mark the selected location as the heat source area. Based on the type of heat source, define the heat flux of the heat source in the material properties and set the distribution mode of the heat source, which are point heat source, line heat source, surface heat source, and the corresponding distribution density. Simulate the heat pressure ventilation effect under different heat source configurations, analyze the influence of heat source configuration on flow rate, flow direction and temperature distribution, set the initial temperature of the entire simulation area based on the temperature of the surrounding environment of the underground building, and set the initial pressure of the internal and external environments of the building at the beginning of the simulation. For natural ventilation simulation, set the static pressure. For open systems (such as underground buildings with inlet and outlet channels), set the static pressure of the inlet and outlet to the ambient pressure, and perform iterative calculations in the internal area. Set the wall of the underground building as a non-slip and adiabatic surface so that the wall does not participate in heat exchange and the fluid does not slide on the wall. For the inlet and outlet channels, set them as pressure inlets based on the simulation requirements. If natural ventilation is simulated, set the static pressure of the inlet to the ambient pressure. Set the boundary conditions when the air leaves the building, set the outlet channel to the pressure outlet, and set the static pressure to the ambient pressure.

[0068] CFD simulation execution module runs CFD models, simulates the heat pressure ventilation process in underground buildings, generates detailed temperature and velocity distribution data, provides data support for analyzing flow states, loads verified geometric models and grid systems in CFD software, imports heat source positions, strengths, distribution parameters, boundary conditions and initial conditions defined in the parameter setting module, selects pressure-based solver and sets fluid properties, defines air density, specific heat capacity, thermal conductivity physical properties, selects fluid dynamics model to simulate fluid behavior, performs grid independence verification simultaneously, ensures that the generated grid meets grid independence requirements, and ensures the accuracy of simulation results, configures time step, relaxation factor and convergence of pressure-based solver Standard parameters, start the pressure-based solver in the CFD software, perform iterative calculations, monitor the residual curve during the solution process, monitor the progress of the simulation and the use of computing resources, calculate the metric value for convergence judgment, detect whether there are numerical instability and non-convergence problems, until the convergence standard is reached, collect the distribution data of temperature and velocity physical quantities generated during the simulation, and use the post-processing tools of the CFD software to view the temperature distribution map in the underground building, view the velocity vector map, analyze the flow state and direction of the fluid in the underground building, conduct a preliminary analysis of the simulation results, check whether there are abnormal or non-physical data, and post-process the simulation results to extract key data of the flow state and visualize them;

[0069] Furthermore, the calculation expression of the convergence determination metric value is:

[0070]

[0071] Among them, C v It is a measure of convergence, that is, the square root of the sum of squares of the residual changes, R i is the current value of the residual at the i-th iteration, R b，i is the baseline value of the residual of the ith iteration step, is the residual value of the initial iteration step, ∈ is the pre-set convergence standard, a small positive number, usually less than 1, n is the number of residuals involved in the calculation, C v The value range is [0, +∞), when C v The smaller the value, the smaller the residual change, the closer the simulation is to convergence, and R i and R b，i The value range is [0, +∞), where R i It decreases with the increase of the number of iterations. The value range of ∈ is (0,1], which is a pre-set threshold used to control the strictness of convergence. v The square root of the sum of squares of the deviations of all monitoring residuals from their respective benchmark values ​​is calculated, and a comprehensive convergence judgment index is given. vWhen the value of is lower than the set threshold ∈, it is considered that the solver has reached the convergence standard. v If the value of does not decrease or increases after several consecutive iterations, it indicates that numerical instability has occurred in the solution process. During the iteration process, it is necessary to monitor the residual curve in real time to see whether it decreases with the increase in the number of iterations. If the residual curve shows an oscillating or increasing trend, it may be necessary to adjust the time step, relaxation factor, or recheck the physical model and boundary conditions.

[0072] The polymorphism analysis module is used to analyze the CFD simulation results, identify different flow states and their stability, determine the conditions for the existence of polymorphism, evaluate the stability and conversion conditions of each flow state, extract the distribution data of temperature, velocity, and pressure physical quantities from the CFD simulation results, and import the extracted data into the polymorphism analysis module to ensure the integrity and accuracy of the data, analyze the simulation data, and automatically identify the flow state in the simulation results, including laminar flow, turbulent flow, and transitional flow. For each flow state, extract the characteristic parameters of its velocity distribution, vortex structure, and turbulence intensity. For each identified flow state, analyze the change trend of the flow state over time, evaluate its stability, and determine whether it is a stable state. Analyze the conversion conditions between different flow states, determine the critical parameters of the heat source intensity and inlet flow velocity of the state conversion, search for areas that meet the polymorphism conditions in the simulation results, and identify the critical values ​​of the key parameters of velocity, pressure, and temperature that affect polymorphism. Perform sensitivity analysis on key parameters, evaluate the impact of parameter changes on the flow state, and verify whether the determined polymorphism conditions are accurate by comparing the simulation results under different conditions.

[0073] The flow state prediction module combines machine learning algorithms and uses historical data and CFD simulation results to train models to predict the final flow state at different heat source locations, improve prediction accuracy and efficiency, and solve the problem that CFD models are difficult to directly predict the final flow state;

[0074] The result display module provides a user interface to assist users in inputting heat source locations, viewing simulation results and prediction results, enhancing the usability of the system and enabling users to intuitively understand the complex process and polymorphic phenomena of thermal pressure ventilation in underground buildings.

[0075] The second embodiment is based on the first embodiment. Figure 3 As shown in the figure, in the flow state prediction module, the process of predicting the final flow state at different heat source positions includes:

[0076] Collect historical parameter data and generated CFD simulation data, clean and standardize the collected historical parameter data and CFD simulation data, remove noise and outliers, and standardize data so that data of different dimensions can be compared and calculated on the same scale. The historical parameter data are flow state data at different heat source positions, including key parameters of temperature distribution, velocity distribution, and pressure distribution, which are derived from actual experimental measurements and existing CFD simulation results. The generated CFD simulation data are simulated at different heat source positions using CFD software to obtain the corresponding flow state data. The collected data are divided into training sets and test sets, and a physical information neural network model is selected for model training. The physical information neural network model is trained using the training set data to construct a flow Dynamic state prediction model, and use test set data to verify the model, evaluate the prediction accuracy and generalization ability of the model, based on the flow state prediction model, historical parameter data and generated CFD simulation data, comprehensive analysis to obtain the flow state evaluation coefficient, predict the flow state and trend under different heat source positions, deploy the trained flow state prediction model to the CFD simulation platform, input new heat source position parameters into the flow state prediction model, the flow state prediction model predicts according to the rules obtained by training with historical data and CFD simulation results, outputs the prediction results, including key parameters of temperature distribution, velocity distribution, and pressure distribution, analyzes and verifies the prediction results, evaluates their accuracy and practicality, and feeds back the prediction results to relevant researchers or engineers to provide strong support for solving practical problems;

[0077] Furthermore, the calculation expression of the flow state evaluation coefficient is:

[0078]

[0079] Where FE is the flow state evaluation coefficient, which is used to measure the overall change of flow state, T j is the temperature of the jth measurement point at a specific heat source location, T b is the reference temperature, is the initial temperature at the heat source position, ΔP j is the pressure change at the jth measurement point, P b is the base pressure, which is the initial pressure of the system, V j is the velocity of the jth measurement point at a specific heat source position, V b is the reference velocity, is the initial velocity at the heat source position, m is the total number of measurement points, and the value range of FE is [0, +∞), where FE = 0 means no flow state change, and the larger the FE value, the more drastic the flow state change, T j , ΔP j , and V jThe range of FE depends on the actual measured data. As the heat source position changes, the value of FE will change according to the flow state. The exponential function is very effective in measuring temperature changes and capturing the nonlinear characteristics of temperature changes. When the temperature changes slightly, the exponential function value is close to 1, and the change is not obvious. When the temperature changes significantly, the exponential function value increases significantly, indicating that the flow state changes significantly. Measures the effect of pressure changes on flow conditions, using squares to emphasize the effect of larger pressure changes, through Measure the effect of velocity changes on flow conditions, use squares to emphasize the effect of larger velocity changes, combine changes in temperature, pressure, and velocity through a summation function to form a flow state assessment coefficient, and take the root form to convert the flow state assessment coefficient into a unitless quantity for easy comparison and analysis;

[0080] In the result display module, the process of assisting users to input heat source locations, view simulation results, and predict results includes:

[0081] Provide a user interface that allows users to easily input parameters, such as the location of the heat source, provide clear instructions and prompts, and guide users to perform operations. Integrate the fluid dynamics data obtained from CFD simulation and the prediction results of the machine learning model into the result display module, use graphics and animations to visualize the physical quantities of temperature distribution, velocity vector and pressure, and provide views with different perspectives and zoom levels so that users can fully understand the data, display simulation and prediction results, including the stability of flow states and flow trends, and interpret the simulation results, including the description of flow states and the explanation of polymorphic phenomena, output simulation reports and display prediction results, provide comparative views to show the differences between different prediction results, and provide interactive annotation functions during the result display process, allowing users to add notes, arrows, text boxes and other elements to the graphics to record and share their understanding and analysis of the results.

[0082] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A heat source location positioning system based on a CFD model, including a simulation analysis platform, characterized in that: The simulation analysis platform is communicatively connected with a data input and processing module, a geometric model building module, a parameter setting module, a CFD simulation execution module, a polymorphism analysis module, a flow state prediction module and a result display module, wherein the modules are connected by electrical signals; The data input and processing module is used to collect and process input data, including building geometry, heat source characteristics, and boundary conditions; The geometric model building module builds a geometric model of the underground building according to the input data, performs meshing, and performs mesh convergence analysis and time step independence verification to generate a calculation domain for CFD simulation; The parameter setting module is used to set the location, intensity, and distribution parameters of the heat source, simulate the heat pressure ventilation effect under different heat source configurations, analyze its impact on the flow state, and set the initial temperature and pressure conditions of the simulation as well as the wall, inlet, and outlet boundary conditions; The CFD simulation execution module runs the CFD model to simulate the heat pressure ventilation process in the underground building and generate temperature and velocity distribution data; The polymorphism analysis module is used to analyze CFD simulation results, identify different flow states and their stability, determine the conditions for the existence of polymorphism, and evaluate the stability and conversion conditions of each flow state; The flow state prediction module, in combination with a machine learning algorithm, uses historical data and CFD simulation results to train a model to predict the final flow state at different heat source locations; The result display module provides a user interface to assist users in inputting heat source locations, viewing simulation results and prediction results.

2. The heat source location positioning system based on the CFD model according to claim 1, characterized in that: In the data input and processing module, the process of collecting and processing input data includes: Collect input data of building geometry, heat source characteristics, and boundary conditions based on building design drawings, field measurement data, and historical record data, wherein the building geometry includes the size, shape, internal layout, and construction information of the building, the heat source characteristics include the location, intensity, type, and working time information of the heat source, and the boundary conditions include outdoor temperature, humidity, wind speed, and wind direction information; Conduct preliminary verification on the collected data to check whether the data is complete, accurate, and meets the simulation requirements. Check whether there are logical errors in the building geometry data and whether the heat source characteristic data is within a reasonable range. Clean the data, handle the problematic data found during the verification process, and standardize the data. Convert the collected input data into different formats and integrate data from different sources into a unified data set; Convert the integrated data into a format acceptable to the CFD model, including generating geometry files for meshing and case files for simulation, store the processed data in the database of the simulation analysis platform, establish a data management system, and classify, index and back up the stored data.

3. The heat source location positioning system based on the CFD model according to claim 2, characterized in that: In the geometric model building module, the process of generating a computational domain for CFD simulation includes: Based on the collected building geometry input data, the Geometry module provided by ANSYS Fluent was used to create a preliminary geometric model of the underground building; Refine the preliminary geometric model of the underground building to depict the complex structure and boundary conditions inside the building, including the main structure of the building, the location of local heat sources, and the entrance and exit passages. Verify whether the geometric model is consistent with the actual building and design drawings, and check whether the model has any defects. After the geometric model is constructed, the geometric model is meshed to generate a mesh system for CFD simulation; By gradually refining the grid and comparing the simulation results under different grid densities, it can be determined whether the simulation results tend to be stable with the increase of grid density; Set different time steps for simulation and observe how the simulation results change with the time step; After the meshing and verification are completed, the computational domain for CFD simulation is generated according to the meshing and boundary condition settings. The computational domain includes all the geometric structures and mesh information of the underground building. A pre-simulation is performed before the formal simulation to check the physical rationality of the model and calculate the stability. Based on the results of the pre-simulation, the geometric model is optimized. The optimization includes adjusting the grid density, modifying the boundary conditions, and outputting the final geometric model and grid system to the CFD simulation execution module.

4. The heat source location positioning system based on the CFD model according to claim 3, characterized in that: In the parameter setting module, the process of setting the heat source position, intensity, and distribution parameters includes: According to the actual building conditions, the exact location of the heat source is set, and the location of the heat source is marked in the Geometry module of ANSYS Fluent. The selected location is marked as the heat source area. Based on the type of heat source, the heat flux of the heat source is defined in the material properties, and the distribution mode of the heat source is set, which are point heat source, line heat source, surface heat source, and the corresponding distribution density. The heat pressure ventilation effect under different heat source configurations is simulated, and the influence of heat source configuration on flow rate, flow direction and temperature distribution is analyzed; Based on the temperature of the surrounding environment of the underground building, set the initial temperature of the entire simulation area, and set the initial pressure of the building's internal and external environment at the beginning of the simulation. For natural ventilation simulation, set the static pressure. For open systems, set the static pressure of the inlet and outlet to the ambient pressure, and perform iterative calculations in the internal area. Set the wall surface of the underground building as a non-slip and insulating surface. For the inlet and outlet channels, set them as pressure inlets based on the simulation requirements. If natural ventilation is simulated, set the static pressure of the inlet to ambient pressure. Set the boundary conditions when the air leaves the building, set the outlet channel to a pressure outlet, and set the static pressure to ambient pressure.

5. The heat source location positioning system based on the CFD model according to claim 4, characterized in that: In the CFD simulation execution module, the process of generating temperature and velocity distribution data includes: Load the verified geometric model and mesh system into the CFD software, and import the heat source position, intensity, distribution parameters, boundary conditions and initial conditions defined in the parameter setting module; Select the pressure-based solver and set the fluid properties, define the physical properties of air density, specific heat capacity, and thermal conductivity, and select the fluid dynamics model to simulate the fluid behavior, and perform grid independence verification simultaneously; Configure the time step, relaxation factor and convergence criterion parameters of the pressure-based solver, start the pressure-based solver in the CFD software, perform iterative calculations, monitor the residual curve during the solution process, monitor the progress of the simulation and the use of computing resources, calculate the metric value for convergence judgment, and detect whether there are numerical instability and non-convergence problems until the convergence criterion is met; Collect the distribution data of temperature and velocity physical quantities generated during the simulation process, and use the post-processing tools of the CFD software to view the temperature distribution map in the underground building, view the velocity vector map, and analyze the flow state and direction of the fluid in the underground building; Conduct a preliminary analysis of the simulation results to check for abnormalities or data that do not conform to physical reality, and post-process the simulation results to extract key data of the flow state and visualize them.

6. The heat source location positioning system based on the CFD model according to claim 5, characterized in that: The calculation expression of the convergence determination metric value is: Among them, C v is the measure of convergence, R i is the current value of the residual at the i-th iteration, R b，i is the baseline value of the residual of the i-th iteration step, ∈ is the pre-set convergence standard, n is the number of residuals involved in the calculation, when C v The smaller the value, the smaller the residual change, the closer the simulation is to convergence, and R i It decreases as the number of iterations increases.

7. The heat source location positioning system based on CFD model according to claim 6, characterized in that: In the polymorphism analysis module, the process of determining the polymorphism existence condition includes: Extract the distribution data of temperature, velocity and pressure from the CFD simulation results, and import the extracted data into the polymorphism analysis module; Analyze simulation data and automatically identify flow states in simulation results, including laminar flow, turbulent flow, and transitional flow. For each flow state, extract characteristic parameters of velocity distribution, vortex structure, and turbulence intensity. For each identified flow state, by analyzing the change trend of the flow state over time, evaluate its stability, determine whether it is a stable state, analyze the transition conditions between different flow states, and determine the critical parameters of the heat source intensity and inlet flow velocity of the state transition; Search for areas that meet the polymorphism conditions in the simulation results, identify the critical values ​​of key parameters such as velocity, pressure, and temperature that affect polymorphism, conduct sensitivity analysis on key parameters, evaluate the impact of parameter changes on flow conditions, and verify whether the determined polymorphism conditions are accurate by comparing simulation results under different conditions.

8. The heat source location positioning system based on CFD model according to claim 7, characterized in that: In the flow state prediction module, the process of predicting the final flow state at different heat source positions includes: Collect historical parameter data and generated CFD simulation data, and clean and standardize the collected historical parameter data and CFD simulation data. The historical parameter data is the flow state data at different heat source positions, including key parameters of temperature distribution, velocity distribution, and pressure distribution, which are derived from actual experimental measurements and existing CFD simulation results. The generated CFD simulation data is obtained by simulating different heat source positions using CFD software to obtain corresponding flow state data. The collected data is divided into a training set and a test set, a physical information neural network model is selected for model training, the physical information neural network model is trained using the training set data, a flow state prediction model is constructed, and the model is verified using the test set data; Based on the flow state prediction model, historical parameter data and generated CFD simulation data, the flow state evaluation coefficient is obtained through comprehensive analysis to predict the flow state and trend under different heat source positions; Deploy the trained flow state prediction model to the CFD simulation platform, input the new heat source position parameters into the flow state prediction model, and the flow state prediction model makes predictions based on the rules obtained by training the historical data and CFD simulation results, and outputs the prediction results, including the key parameters of temperature distribution, velocity distribution, and pressure distribution; Analyze and verify the forecast results, evaluate their accuracy and practicality, and provide feedback to relevant personnel.

9. The heat source location positioning system based on CFD model according to claim 8, characterized in that: The calculation expression of the flow state evaluation coefficient is: Where FE is the flow state evaluation factor, T j is the temperature of the jth measurement point, T b is the reference temperature, ΔP j is the pressure change at the jth measurement point, P b is the reference pressure, V j is the velocity of the jth measurement point, V b is the reference velocity, m is the total number of measurement points, and the larger the FE value is, the more drastic the flow state changes.

10. The heat source location positioning system based on CFD model according to claim 9, characterized in that: In the result display module, the process of assisting the user to input the heat source location, view the simulation results and the prediction results includes: Provide a user interface to allow users to input parameters and guide users to perform operations, integrating the fluid dynamics data obtained from CFD simulation and the prediction results of the machine learning model into the result display module; Visualize temperature distribution, velocity vectors, and pressure using graphics and animations, and provide views at different perspectives and zoom levels; Present simulation and prediction results, including stability of flow regimes and flow trends, and interpret simulation results, including description of flow regimes and explanation of polymorphic phenomena; Output simulation reports and display prediction results, provide comparison views, show the differences between different prediction results, and provide interactive annotation functions during the result display process.

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