A CFD Simulation-Based Method for Indoor Pollutant Diffusion Analysis

Abstract

This invention discloses an indoor pollutant diffusion analysis method based on CFD simulation. The invention relates to the field of pollution diffusion analysis technology and includes the following steps: setting up a real-time monitoring unit to collect indoor environmental data to be monitored and inputting it into a CFD model; setting boundary conditions; constructing a background flow field including velocity, pressure, and temperature fields through iterative calculations; dividing an initial grid according to the indoor spatial layout; constructing a pollutant diffusion equation based on the background flow field; obtaining estimated pollutant concentration values ​​for a historical reference time period; outputting actual concentration values ​​through a pollutant concentration detection unit; determining whether to split or merge the grid based on the difference between the estimated and actual concentrations and the concentration gradient; correcting the parameters of the pollutant diffusion equation using the updated actual concentration values ​​of the grid to obtain an accurate diffusion equation for real-time monitoring of pollutant diffusion, while significantly improving the accuracy of the model.

Description

Technical Field

[0001] This invention relates to the field of pollution diffusion analysis technology, specifically to an indoor pollutant diffusion analysis method based on CFD simulation. Background Technology

[0002] In recent years, with the continuous development of urbanization and industrialization, air pollution has brought increasing attention to public environmental issues. Pollutants affecting public environmental safety mainly include industrial waste gas and medical waste gas. Improper emission and treatment of these pollutants not only cause environmental pollution problems but can also directly threaten people's physical and mental health.

[0003] Environmental analysis based on CFD (Computational Fluid Dynamics) can quantitatively analyze pollutant diffusion, providing a reference for building planning and the rational layout of ventilation systems, avoiding long-term pollutant retention, and minimizing the adverse effects of pollutant emissions on the surrounding environment and people. However, traditional CFD environmental analysis suffers from unclear assumptions about atmospheric boundary conditions and pollutant source boundaries, and is difficult to model. Existing technologies introduce multiple refined models to further analyze pollutant diffusion and improve the accuracy.

[0004] In the prior art, CN113191096A discloses a multi-fine-level fusion pollutant diffusion analysis method based on WRF and XFlow coupling. This method adopts a multi-fine-level coupling mechanism, that is, in the city-level three-dimensional pollutant diffusion analysis model, the representative values ​​of pollutant concentration at the microscale of buildings are upscaled to the pollution source inlet boundary conditions of the city-level model, and the WRF mesoscale three-dimensional meteorological driving field is downscaled to the initial conditions and atmospheric boundary conditions of the city-level model. Finally, multi-fine-level fusion calculation of urban pollutant diffusion is performed, and the pollutants are quantitatively assessed based on the calculation results. However, this scheme mainly uses fixed boundary conditions and initial conditions, which may lead to the inability to adapt to changing conditions in dynamic environments, affecting the accuracy of the model. At the same time, the lack of optimization strategies at the level of detail in the simulation of pollutant concentration may lead to insufficient resolution in some key areas, thus reducing the adaptability of the analysis results and limiting accuracy.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for analyzing the diffusion of indoor pollutants based on CFD simulation, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for analyzing the diffusion of indoor pollutants based on CFD simulation, comprising the following steps:

[0009] Set up a real-time monitoring unit to collect environmental data of the indoor space of the target to be monitored, input the environmental data into the CFD model, set boundary conditions, and perform iterative calculations based on the input environmental data to construct a background flow field including velocity field, pressure field and temperature field;

[0010] The spatial layout of the indoor space of the target to be monitored is determined. Based on the spatial layout of the indoor space of the target to be monitored, the indoor space of the target is divided into several initial grids. With the background flow field as the initial condition, and based on the state of pollution source terms in the historical reference time period, the estimated concentration value of pollutants in each initial grid is determined by the pollutant diffusion equation.

[0011] By setting up pollutant concentration detection units in each initial grid, the actual pollutant concentration values ​​of each initial grid within the historical reference time period are output. Based on the difference between the estimated and actual pollutant concentration values ​​and the pollutant concentration gradient within the grid, it is determined whether to perform grid splitting and grid merging operations to update the initial grid.

[0012] The variable parameters of the pollutant diffusion equation are corrected based on the updated actual pollutant concentration values ​​within the grid to obtain an accurate pollutant diffusion equation for monitoring pollutant diffusion.

[0013] Furthermore, the environmental data includes indoor temperature, indoor air velocity, and indoor atmospheric pressure. A historical reference time period is set, and environmental data of the target indoor space is collected within the historical reference time period. Missing values ​​are preprocessed on the collected environmental data, and the processed environmental data is used as input to the CFD model.

[0014] The boundary conditions are specifically defined as the properties of the solid wall surface, including thermal, isothermal, sliding, and non-slip boundary properties.

[0015] The specific logic for iterative calculation based on the input environmental data is as follows: indoor temperature data is used as the initial value of the temperature field; the initial density distribution is calculated based on the temperature field and the reference density as the initial density field; indoor air velocity data is used as the initial value of the velocity field; and indoor atmospheric pressure data is used as the initial value of the pressure field.

[0016] Using the continuity equation, combined with the density field in the corresponding time and space, and the initial value of the velocity field, the mass conservation relationship of the fluid is calculated, and the density field is updated according to the velocity field and spatial flow trend of the fluid.

[0017] Using the momentum equation, combined with the density field, pressure field, viscosity coefficient, and buoyancy effect, the change in the velocity field is calculated and the velocity field is updated. At the same time, the distribution of the pressure field is calculated through the pressure gradient.

[0018] Using the energy equation, the change in temperature field is calculated based on the convection effect of the velocity field, the heat conduction term, and the heat source term. The effect of the temperature field change on the density field and buoyancy effect is coupled with the momentum equation to complete one iterative calculation.

[0019] After each iteration, environmental data in a randomly selected area is calculated, and the calculation results are compared with the monitoring data. The residual between the two is calculated. If the residual drops to a preset threshold, the iteration stops and the final background flow field is output. If the residual does not meet the requirements, the density field, velocity field, pressure field and temperature field after the previous iteration are used as the initial density field, velocity field, pressure field and temperature field, and the iteration calculation continues until the residual calculated after the iteration meets the requirements.

[0020] Furthermore, based on the current time, a monitoring period of the same length as the historical reference period is set, environmental data is collected within the monitoring period, and the changes in environmental data between the monitoring period and the historical reference period are analyzed. If the change in environmental data is greater than or equal to the set mutation threshold, the background flow field data is dynamically adjusted. Specifically, the environmental data within the monitoring period is used as the input of the CFD model, and the missing value preprocessing and iterative calculation process are performed again to output the real-time background flow field, and the monitoring period is used as the new historical reference period.

[0021] If the change in environmental data is less than the set mutation threshold, the CFD model will not be updated.

[0022] Furthermore, based on the spatial layout of the indoor space of the target to be monitored, the spatial layout specifically refers to the vacant space inside the target to be monitored, and the vacant space specifically refers to the area not occupied by furniture, equipment or other items;

[0023] The logic behind dividing the target indoor space into several initial grids is as follows: set the initial size of the grid, and based on the set initial grid size, divide the target indoor space into several initial grid areas;

[0024] Using the background flow field as the initial condition, the pollution source term status within the historical reference time period is input into the constructed pollutant diffusion equation. The finite volume method is used to discretize the equation in order to solve for the estimated pollutant concentration value of each grid cell within the historical reference time period. The pollution source term status specifically refers to the pollutant concentration of the pollution source, and the pollutant concentration of the pollution source is specifically set as a constant concentration data that does not change with time.

[0025] Furthermore, based on the difference between the estimated and actual concentrations of pollutants and the concentration gradient within the grid, the logic for determining the specific grid splitting and merging areas is as follows: by calculating the concentration deviation index and concentration gradient index within the same grid, the concentration deviation index is used to characterize the difference between the estimated and actual concentrations, and the concentration gradient index is used to characterize the spatial distribution changes of pollutants. The logic for calculating the concentration deviation index is as follows: determining the absolute difference between the estimated and actual concentrations of pollutants in the grid at different times during the period from the start to the end of the historical reference time period, and calculating the average absolute deviation between the estimated and actual concentrations during the historical reference time period through integration, and using this average absolute deviation as the concentration deviation index.

[0026] The logic behind the specific calculation of the concentration gradient index is as follows: the boundary surface of the grid is divided into several micro-element units, and the average concentration gradient of all micro-element units is used as the concentration gradient index of the grid.

[0027] A comprehensive optimization index is generated using the concentration deviation index and the concentration gradient index. The comprehensive optimization index is then used to determine how to optimize the initial grid. The specific logic for calculating the comprehensive optimization index is as follows: weight coefficients are set for the concentration deviation index and the concentration gradient index. The concentration deviation index and the concentration gradient index of different grids are normalized by using the maximum values ​​of the concentration deviation index and the concentration gradient index of all grids. The comprehensive optimization index is then calculated using a weighted method based on the corresponding weight coefficients and the normalized concentration deviation index and concentration gradient index.

[0028] Furthermore, the specific logic for determining whether to perform mesh splitting and merging operations is based on the comprehensive optimization index of the i-th mesh. Make a judgment and determine the comprehensive optimization index. Whether the grid falls within a preset grid classification range, and based on the analysis results, further determine whether to perform grid splitting and merging operations, specifically including:

[0029] like The grid is determined to be free from splitting and merging operations.

[0030] like If the grid is determined to be split, a grid splitting operation is performed and recorded as a split grid.

[0031] like The grid is determined to be a candidate grid for merging; where... For the preset grid classification interval, The minimum threshold for the grid classification interval. The maximum threshold for grid classification intervals. This represents the overall optimization index of the i-th grid.

[0032] For a grid that has undergone a mesh splitting operation, the overall optimization metric based on the grid is... The splitting range is determined by the following logic: the volume of the initial mesh is determined and adjusted according to the comprehensive optimization index. The optimization index is used to measure the degree of mesh optimization. By comparing the optimization index of different meshes, the adjustment range is determined, and a volume adjustment factor is calculated to adjust the final volume of the mesh.

[0033] For a candidate grid to be merged, its 8 neighboring grids are determined, the comprehensive optimization index of each neighboring grid is obtained, the grid with the smallest comprehensive optimization index among the neighboring grids and the current candidate grid to be merged is determined, and the pollutant concentration similarity between the candidate grid to be merged and the grid with the smallest comprehensive optimization index is calculated. If the similarity requirement is met, the current candidate grid to be merged is merged into the grid with the smallest comprehensive optimization index to obtain the merged spatial grid; otherwise, no merging is performed.

[0034] Furthermore, pollutant diffusion analysis was performed again based on the re-divided spatial grid. The actual pollutant concentration values ​​corresponding to the updated grids within the monitoring period were collected, and the variable parameters of the pollutant diffusion equation were corrected. Specifically, the effective diffusion coefficient was corrected using a data assimilation method. The logic behind this correction was as follows: by using the actual pollutant concentration values ​​corresponding to the updated grids within the monitoring period, the difference between the estimated and actual pollutant concentration values ​​at different time points for each grid was calculated. This difference was accumulated by summing the squares to form the main part of the loss function, thereby reflecting the deviation between the model prediction and the actual observation. To prevent overfitting, a regularization term was added, and the regularization parameter was used to control the impact of this term on the loss function.

[0035] The effective diffusion coefficient corresponding to the minimum loss function value is taken as the optimal effective diffusion coefficient, and it is used to replace the effective diffusion coefficient in the pollutant diffusion equation to obtain the accurate pollutant diffusion equation.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This solution addresses the shortcomings of traditional indoor pollutant monitoring methods by setting up a real-time monitoring unit and combining it with a CFD model. It greatly improves the accuracy and real-time performance of indoor pollutant diffusion analysis. The real-time monitoring unit collects environmental data of the indoor space to be monitored. After inputting this real-time data into the CFD model, the model can perform iterative calculations based on the latest environmental conditions to construct a background flow field including velocity, pressure, and temperature fields, thereby improving the accuracy of the model's initial conditions.

[0038] Secondly, the solution involves a detailed analysis of the spatial layout of the indoor space to be monitored, dividing it into multiple initial grids. This ensures that each grid fully reflects the pollutant concentration in its area, especially important given the complex indoor environment where airflow and pollutant distribution are significantly affected by obstacles such as objects and walls. During actual monitoring, pollutant concentration detection units are set up to output the actual pollutant concentration values ​​in each initial grid in real time. Based on the differences between these values ​​and the concentration gradient, the grid division is dynamically adjusted, either splitting or merging grids. This grid adjustment strategy not only improves the model's adaptability but also enhances the simulation accuracy in key areas, thereby reducing errors caused by model settings. Finally, the variable parameters in the pollutant diffusion equation are corrected to ensure that the model reflects the latest pollutant diffusion situation at each monitoring stage. This correction mechanism significantly improves the model's accuracy, enabling it to more effectively predict pollutant diffusion trends under different environmental conditions. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0040] Figure 2 Distribution of comprehensive optimization indices for different initial grids;

[0041] Figure 3 The fitted curve of concentration deviation index versus concentration gradient index;

[0042] Figure 4 A bar chart showing the statistical data of concentration deviation index and concentration gradient index. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0044] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] Example:

[0046] Please see Figures 1-4 The present invention provides a technical solution:

[0047] A method for analyzing the diffusion of indoor pollutants based on CFD simulation, comprising the following steps:

[0048] Step 1: Set up a real-time monitoring unit to collect environmental data of the indoor space to be monitored, input the environmental data into the CFD model, set boundary conditions, and perform iterative calculations based on the input environmental data to construct a background flow field including velocity field, pressure field, and temperature field.

[0049] The environmental data includes indoor temperature, indoor air velocity, and indoor atmospheric pressure. A historical reference time period is set, and environmental data of the target indoor space is collected within the historical reference time period. Missing values ​​are preprocessed on the collected environmental data, and the processed environmental data is used as input to the CFD model.

[0050] The specific method for collecting environmental data of the target indoor space is as follows: Several environmental monitoring units are randomly set up. The environmental monitoring units include temperature sensors, wind speed sensors and pressure sensors. Ensure that the sensors are not blocked by furniture or other items to avoid local data distortion. More sensors are arranged in areas where there may be large temperature gradients or strong changes in air flow, such as air conditioning vents and near windows. Set the sampling frequency of the sensors to achieve real-time monitoring and ensure the timeliness of the data.

[0051] Within a historical reference period, the variation patterns of indoor environmental parameters are analyzed through data collection, which serve as the basic input for subsequent CFD models.

[0052] During data acquisition, data loss may occur due to equipment failure, network interruption, etc. Missing value handling methods include mean imputation: filling missing values ​​with the mean of the same position within a historical time period; linear interpolation: filling missing values ​​with linear interpolation based on data before and after the missing value; and time window imputation: filling missing values ​​with the moving average or median within a certain time window, such as within 5 minutes before and after.

[0053] Sensors may be affected by external interference, such as instantaneous environmental fluctuations or equipment vibrations, leading to outliers in the data. Noise reduction methods include: moving average method: taking a moving average of the time series data to smooth out data fluctuations; median filtering method: applying median filtering to outliers within a short period to remove significant noise.

[0054] The setting of boundary conditions specifically refers to setting the properties of the solid wall surface, including adiabatic, isothermal, sliding, and no-slip boundary properties. By correctly setting the properties of the solid wall surface, including adiabatic, isothermal, sliding, and no-slip boundary conditions, the flow and heat transfer characteristics of the indoor environment can be effectively simulated. The setting of these boundary conditions not only affects the accuracy of CFD simulation, but also provides a reliable basis for subsequent flow analysis and environmental assessment.

[0055] The specific logic for iterative calculation based on the input environmental data is as follows: indoor temperature data is used as the initial value of the temperature field; an initial density distribution is calculated based on the temperature field and the reference density as the initial density field; indoor air velocity data is used as the initial value of the velocity field; and indoor atmospheric pressure data is used as the initial value of the pressure field; wherein the reference density is specifically the air reference density at normal temperature.

[0056] Using the continuity equation, combined with the density field in the current time and space, and the initial value of the velocity field, the mass conservation relationship of the fluid is calculated, and the density field is updated according to the velocity field and spatial flow trend of the fluid.

[0057] Using the momentum equation, combined with the density field, pressure field, viscosity coefficient, and buoyancy effect, the change in the velocity field is calculated and the velocity field is updated. At the same time, the distribution of the pressure field is calculated through the pressure gradient.

[0058] Using the energy equation, the change in temperature field is calculated based on the convection effect of the velocity field, the heat conduction term, and the heat source term. The effect of the temperature field change on the density field and buoyancy effect is coupled with the momentum equation to complete one iterative calculation.

[0059] After each iteration, environmental data in a randomly selected area is calculated, and the calculation results are compared with the monitoring data. The residual between the two is calculated. If the residual drops to a preset threshold, the iteration stops and the final background flow field is output. If the residual does not meet the requirements, the density field, velocity field, pressure field and temperature field after the previous iteration are used as the initial density field, velocity field, pressure field and temperature field, and the iterative calculation continues until the residual calculated after the iteration meets the requirements.

[0060] The specific formulas upon which the continuity equation, momentum equation, and energy equation are based are:

[0061] ;

[0062] In the formula, For the density of the fluid, For velocity vector field, For divergence operators, For time variables within a historical reference period, The convection term describes the change in momentum of a fluid during flow. For effective dynamic viscosity, Represents the pressure gradient term. For pressure field, For the temperature field, It is the acceleration due to gravity. The coefficient of thermal expansion is... For reference temperature, For specific heat capacity, This is the thermal conductivity term, describing the heat diffusion caused by the temperature gradient. This is a heat source term.

[0063] It should be noted that in CFD (Computational Fluid Dynamics), especially in monitoring pollutant diffusion and heat transfer, establishing the background flow field using continuity, momentum, and energy equations is a systematic process. These equations incorporate the physical properties of the fluid and can effectively simulate complex airflow and heat transfer phenomena.

[0064] The continuity equation describes the mass conservation of a fluid. It states that the rate of change of fluid mass within a control volume is equal to the mass flow rate of the fluid entering and leaving the control volume. The fluid density represents the mass per unit volume. : Velocity vector field, describing the motion of a fluid at various points in space. The divergence of fluid mass in space reflects the inflow and outflow of fluid.

[0065] The momentum equation describes the conservation of momentum in a fluid, taking into account the effects of inertia, pressure, viscosity, and gravity, and includes the convection term. It describes the change in momentum caused by a change in fluid velocity; : Pressure gradient term, representing the flow caused by pressure differences; Viscosity term, representing the shear force of a fluid, describing the internal friction of the fluid; Gravity term: The gravitational effect on a fluid as a result of temperature changes.

[0066] The energy equation describes the conservation of energy and characterizes the changes in temperature and the diffusion of heat; Specific heat capacity of a fluid indicates its ability to absorb or release heat per unit mass. Thermal conductivity describes the transfer of heat through a thermally conductive medium. The heat source term represents the influence of external heat sources, such as heaters and mechanical equipment, on the temperature field.

[0067] In the process of pollutant diffusion, fluid flow is the main driving force, and the momentum equation describes the flow characteristics of the fluid. Changes in temperature affect the density of the airflow, thus influencing pollutant diffusion. By introducing effective viscosity... and thermal conductivity It can effectively take into account the physical properties of fluids and external influences, making the simulation results more consistent with reality, including the effective viscosity. and thermal conductivity The specific determination method is as follows: Effective viscosity is the viscosity exhibited by a fluid under different flow conditions, which can be affected by fluid composition, temperature, pressure and flow state. The viscosity of the fluid is measured at a specific temperature using equipment such as a rotational viscometer or capillary viscometer. Thermal conductivity is a parameter describing the heat conduction ability of a material. It is determined by measuring the heat flow rate per unit temperature gradient using a heat flow meter.

[0068] Temperature changes leading to density changes (thermal expansion) affect the stability and flow patterns of fluids. Therefore, by... The item considers the effect of thermal expansion on fluid motion; among which... The general setting is 25. .

[0069] The coefficient of thermal expansion indicates how sensitive a material's volume is to changes in temperature. The coefficient of thermal expansion can be calculated by experimentally measuring the volume change of a material at different temperatures, or by using thermogravimetric analysis equipment to monitor the mass and volume changes of a material at different temperatures. For common fluids, such as water and air, typical values ​​of their coefficients of thermal expansion can be obtained by consulting relevant literature.

[0070] Specifically, based on the current time, a monitoring period of the same length as the historical reference period is set. Environmental data is collected within the monitoring period, and the changes in environmental data between the monitoring period and the historical reference period are analyzed. If the change in environmental data is greater than or equal to the set mutation threshold, the background flow field data is dynamically adjusted. Specifically, the environmental data within the monitoring period is used as the input of the CFD model, and the missing value preprocessing and iterative calculation process are performed again to output the real-time background flow field. The monitoring period is then used as the new historical reference period. The mutation threshold can be set according to the actual situation and expert experience.

[0071] If the change in environmental data is less than the set mutation threshold, the CFD model will not be updated.

[0072] The environmental data changes mentioned above are specifically compared by comparing the differences in environmental data at different times. Specifically, the differences are compared with the mutation threshold by comparing characteristic data of environmental data such as maximum wind speed and average wind speed, as well as environmental data randomly selected at specific times, and the corresponding operation is determined based on the comparison results.

[0073] By monitoring changes in current environmental data and comparing them with historical data, significant changes in the indoor environment, such as sudden changes in wind speed and temperature fluctuations, can be quickly identified. This allows the CFD model to proactively adjust its input parameters. If the environmental data changes exceed the mutation threshold, the model will dynamically update the background flow field to make the simulation results more closely match the actual indoor environment. When the environmental data changes are small and below the mutation threshold, the CFD model will not be updated to avoid redundant calculations, thereby saving computational resources and maintaining model stability. This dynamic adjustment makes the output background flow field closer to the actual indoor environment and avoids deviations in simulation results caused by environmental fluctuations.

[0074] Step 2: Determine the spatial layout of the indoor space to be monitored. Based on the spatial layout of the indoor space to be monitored, divide the indoor space into several initial grids. Using the background flow field as the initial condition and the state of pollution source terms during the historical reference time period, determine the estimated concentration of pollutants in each initial grid through the pollutant diffusion equation.

[0075] Based on the spatial layout of the indoor space of the target to be monitored, the spatial layout specifically refers to the vacant space inside the target to be monitored, and the vacant space specifically refers to the area not occupied by furniture, equipment or other items;

[0076] The logic behind dividing the target indoor space into several initial grids is as follows: set the initial size of the grid, and based on the set initial grid size, divide the target indoor space into several initial grid areas;

[0077] Using the background flow field as the initial condition, the pollution source term status within the historical reference time period is input into the constructed pollutant diffusion equation. The finite volume method is used to discretize the equation in order to solve for the estimated pollutant concentration value of each grid cell within the historical reference time period. The pollution source term status specifically refers to the pollutant concentration of the pollution source, and the pollutant concentration of the pollution source is specifically set as a constant concentration data that does not change with time.

[0078] The specific formula upon which the pollutant diffusion equation is based is:

[0079] ;

[0080] In the formula, For pollutant concentration, For the effective diffusion coefficient, The status of pollution source items, Indicates the diffusion term. For pollutant concentration gradient;

[0081] It should be noted that the diffusion equation follows the law of conservation of mass, considering both the generation and consumption of pollutants, as well as the transport of pollutants due to fluid motion. Pollutant transport is typically caused by both convection and diffusion. Convection describes the displacement of pollutants due to fluid motion, while diffusion describes the random movement of pollutants due to concentration differences. Combining these two factors provides a more comprehensive description of the spatial migration process of pollutants. Based on the pollutant diffusion equation, the effective diffusion coefficient is used. In actual flow, the diffusion of pollutants may be affected by fluid properties and topographical factors such as obstacles and room shape.

[0082] The equation is discretized using the finite volume method to solve for the estimated pollutant concentration for each grid cell within the historical reference time period. Specific steps include: time discretization using explicit or implicit time integration methods, and convection terms. Discretization can be performed using a convection center difference scheme or an upwind scheme, with the diffusion term... The discretization can be performed using the central difference scheme, and the pollution source term state Discretization can be performed at the center point of the control volume, and all discretization results can be combined into the time update equation and calculated in each grid cell to obtain the pollutant concentration of all grid cells.

[0083] Step 3: Using the pollutant concentration detection units set in each initial grid, output the actual pollutant concentration values ​​of each initial grid within the historical reference time period. Based on the difference between the estimated and actual pollutant concentration values ​​and the pollutant concentration gradient within the grid, determine whether to perform grid splitting and grid merging operations to update the initial grid.

[0084] Based on the difference between the estimated and actual concentrations of pollutants and the concentration gradient within the grid, the logic for determining the specific areas for grid splitting and merging is as follows: by calculating the concentration deviation index and concentration gradient index within the same grid, the concentration deviation index is used to characterize the difference between the estimated and actual concentrations, and the concentration gradient index is used to characterize the spatial distribution changes of pollutants. The specific logic for calculating the concentration deviation index is as follows: by determining the absolute difference between the estimated and actual concentrations of pollutants in the grid at different times during the period from the start to the end of the historical reference time period, and by calculating the average absolute deviation between the estimated and actual concentrations during the historical reference time period through integration, the average absolute deviation is used as the concentration deviation index.

[0085] The logic behind the specific calculation of the concentration gradient index is as follows: the boundary surface of the grid is divided into several micro-element units, and the average concentration gradient of all micro-element units is used as the concentration gradient index of the grid.

[0086] The specific formula used to calculate the concentration deviation index is as follows:

[0087] ;

[0088] In the formula, Let i be the concentration deviation index for the i-th grid. for The estimated pollutant concentration value for the i-th grid at time i. for The actual pollutant concentration value of the i-th grid at time i. This is the starting point of the historical reference period. This represents the end time of the historical reference period, where i is the index of the initial grid.

[0089] It should be noted that the concentration deviation index of the i-th grid... This reflects the average absolute deviation between the estimated and actual concentrations over a historical reference period, where the concentration deviation index for the i-th grid is... The larger the value, the greater the difference between the pollutant concentration predicted by the model and the actual pollutant concentration, and the more it should be broken down for further refinement.

[0090] By integrating the sum of concentration deviations, the model can reflect the deviation over the entire time range. Integrating over a time period allows for consideration of the dynamic characteristics of pollutant concentration changes over time. This makes the deviation not just an instantaneous value, but a reflection of the overall performance over a period of time. The time-weighted approach provides a more comprehensive evaluation of the model's performance, even when using absolute values. This ensures the non-negativity of the deviation, allowing deviations at multiple time points to accumulate without canceling each other out.

[0091] The specific formula used to calculate the concentration gradient index is as follows:

[0092] ;

[0093] In the formula, Let i be the concentration gradient index for the i-th grid. Let i be the volume of the i-th grid. For the boundary surface of the mesh, Let be the gradient of the actual pollutant concentration within the i-th grid. Represents the area of ​​a infinitesimal element on the boundary surface;

[0094] It should be noted that the concentration gradient index of the i-th grid... This reflects the rate of change of pollutant concentration within the grid. The larger the concentration gradient, the more uneven the distribution of pollutants within the grid and the more drastic the changes.

[0095] No. Each grid This is used to normalize the concentration gradient index, so that the results can reflect the concentration change per unit volume. Indicates the first Each grid's intrinsic moment The actual pollutant concentration gradient, which refers to how the concentration changes with spatial location, is an important parameter for assessing the diffusion and distribution of pollutants;

[0096] This formula, by calculating the integral of the concentration gradient, can capture the spatial distribution changes of pollutants, and by calculating the concentration gradient index... This allows us to identify which grids exhibit significant pollutant concentration variations. If the concentration gradient index of a grid exceeds a set threshold, it indicates that the pollutant distribution in that area is uneven, and grid splitting may be necessary to improve resolution. Conversely, if the concentration gradient index of a grid is small, it suggests that the concentration changes within that grid are stable, and grid merging can be considered to reduce computational resource consumption.

[0097] A comprehensive optimization index is generated using the concentration deviation index and the concentration gradient index. The comprehensive optimization index is then used to determine how to optimize the initial grid. The specific logic for calculating the comprehensive optimization index is as follows: weight coefficients are set for the concentration deviation index and the concentration gradient index. The concentration deviation index and the concentration gradient index of different grids are normalized by using the maximum values ​​of the concentration deviation index and the concentration gradient index of all grids. The comprehensive optimization index is calculated by weighting the corresponding weight coefficients and the normalized concentration deviation index and concentration gradient index.

[0098] The specific formula used to calculate the comprehensive optimization index is as follows:

[0099] ;

[0100] In the formula, Let i be the comprehensive optimization index for the i-th grid. The maximum value of all grid concentration deviation indices. The maximum value of the concentration gradient index across all grids. and These are the weighting coefficients for the deviation index and the gradient index, respectively. and and All are greater than 0.

[0101] It should be noted that, Indicates the first The comprehensive optimization index for each grid integrates the concentration bias and concentration gradient of that grid, aiming to provide a single quantitative standard for grid optimization. The larger the value, the greater the problem in terms of concentration bias or concentration gradient of the grid, which may require grid splitting to improve resolution.

[0102] Since the above explanation relates to the relationship between concentration deviation, gradient, and mesh generation, it will not be repeated here.

[0103] The maximum value of the concentration deviation index across all grids is used for standardization, allowing for comparison of deviations between different grids. Similarly.

[0104] Concentration gradient indices reflect the degree of variation of pollutants across different grids. In environmental monitoring and assessment, spatial variations in concentration can reveal potential pollution diffusion problems. By assigning higher weights to concentration gradient indices, areas requiring segmentation can be quickly identified, ensuring the model can respond promptly to rapidly changing environments and improving model accuracy. Therefore, setting higher weights is crucial. Weighting ensures that areas with dramatic changes in concentration distribution are prioritized when conducting grid assessments.

[0105] The logic for determining whether to perform mesh splitting and merging operations is based on the comprehensive optimization index of the i-th mesh. Make a judgment and determine the comprehensive optimization index. Whether the grid falls within a preset grid classification range, and based on the analysis results, further determine whether to perform grid splitting and merging operations, specifically including:

[0106] like The grid is determined to be free from splitting and merging operations.

[0107] like If the grid is determined to be split, a grid splitting operation is performed and recorded as a split grid.

[0108] like The grid is determined to be a candidate grid for merging; where... For the preset grid classification interval, The minimum threshold for the grid classification interval. The maximum threshold for the grid classification interval;

[0109] For a grid that has undergone a mesh splitting operation, the overall optimization metric based on the grid is... The splitting range is determined by the following logic: the volume of the initial mesh is determined and adjusted according to the comprehensive optimization index. The optimization index is used to measure the degree of mesh optimization. By comparing the optimization index of different meshes, the adjustment range is determined, and a volume adjustment factor is calculated to adjust the final volume of the mesh.

[0110] The specific formula used for splitting is as follows:

[0111] ;

[0112] In the formula, Let q be the volume of the sub-mesh after the q-th split mesh is divided. Let q be the initial volume of the q-th split mesh. Let q be the comprehensive optimization index for the q-th split grid. The maximum comprehensive optimization index for all grids, is a scaling constant, where q is the index of the split grid;

[0113] It should be noted that the comprehensive optimization index of the q-th split grid It is a key factor in assessing whether the split grid needs further subdivision, and a higher [percentage] A value indicating poor mesh performance may affect model accuracy. Therefore, the volume of sub-mesh after splitting should be inversely proportional to this metric. When it is large, The value is large, resulting in The scale is significantly reduced, which prompts the mesh to be broken down into more detailed parts, ensuring that the model can better capture the changes in the region. The scale constant is generally 1 or 2.

[0114] For a candidate grid to be merged, its 8 neighboring grids are determined, the comprehensive optimization index of each neighboring grid is obtained, the grid with the smallest comprehensive optimization index among the neighboring grids and the current candidate grid to be merged is determined, and the pollutant concentration similarity between the candidate grid to be merged and the grid with the smallest comprehensive optimization index is calculated. If the similarity requirement is met, the current candidate grid to be merged is merged into the grid with the smallest comprehensive optimization index to obtain the merged spatial grid; otherwise, no merging is performed.

[0115] Step 4: Based on the updated actual pollutant concentration values ​​within the grid, correct the variable parameters of the pollutant diffusion equation to obtain an accurate pollutant diffusion equation for monitoring pollutant diffusion.

[0116] Specifically, the effective diffusion coefficient is corrected using a data assimilation method. The logic behind this correction is as follows: by monitoring the actual pollutant concentration values ​​of the updated grid within the monitoring period, the difference between the estimated and actual pollutant concentration values ​​at different time points for each grid is calculated. This difference is accumulated using a sum of squares to form the main part of the loss function, thus reflecting the deviation between the model prediction and actual observation. To prevent overfitting, a regularization term is added. The regularization parameter controls the impact of this term on the loss function. The specific explanation and correction formula are as follows:

[0117] Based on the re-divided spatial grid, pollutant diffusion analysis was performed again. The actual pollutant concentration values ​​corresponding to the updated grid within the monitoring period were collected, and the variable parameters of the pollutant diffusion equation were corrected, specifically the effective diffusion coefficient. This correction was performed using a data assimilation method, and the specific formula used for this correction is as follows:

[0118] ;

[0119] In the formula, To minimize the loss function, For the first At time n, the estimated concentration of pollutants within the nth newly divided spatial grid is... For the first At time n, the actual concentration of pollutants in the grid after the nth update. For regularization parameters, Here is the regularization term for the effective diffusion coefficient, where Here, n is the index of the target time point randomly selected in the pollutant diffusion analysis, and n is the index of the newly subdivided spatial grid. This represents the total number of grid cells after the update. The total number of randomly selected target time moments;

[0120] It should be noted that the main objective of the formula is to minimize the loss function. To optimize the effective diffusion coefficient, the loss function consists of two parts: the first part is the squared error between the actual concentration value and the estimated concentration value, and the second part is a regularization term to control the model complexity and avoid overfitting. This demonstrates the difference between the actual concentration and the model's predicted concentration at each grid and time point. Summing over all grids and time points yields the overall bias, thus reflecting the model's accuracy.

[0121] Regularization is used to balance the model's fit and complexity. It limits the range of the effective diffusion coefficient, prevents the model from being sensitive to noise, and ensures that the obtained effective diffusion coefficient has physical meaning and reliability.

[0122] The effective diffusion coefficient plays a crucial role in the pollutant diffusion equation. It directly affects the diffusion rate and range of pollutants, making its accurate correction particularly important. By minimizing the loss function, the optimal effective diffusion coefficient can be obtained, better reflecting actual environmental conditions.

[0123] It is a regularization parameter used to balance the model's fit and complexity. An initial value is set based on domain knowledge or previous experimental results, and then fine-tuned based on expert experience.

[0124] It is the regularization term of the effective diffusion coefficient, and its specific form can be set according to the actual situation. Specific methods include L2 regularization (ridge regression) and L1 regularization (LASSO).

[0125] The effective diffusion coefficient corresponding to the minimum loss function value is taken as the optimal effective diffusion coefficient, and it is used to replace the effective diffusion coefficient in the pollutant diffusion equation to obtain the accurate pollutant diffusion equation.

[0126] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0127] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0129] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method of indoor pollutant dispersion analysis based on CFD simulation, characterized in that, The specific steps include: setting a real-time monitoring unit, collecting environmental data of the indoor space to be monitored, inputting the environmental data into a CFD model, setting boundary conditions, and performing iterative calculation based on the input environmental data to construct a background flow field including a velocity field, a pressure field, and a temperature field; determining the spatial layout of the indoor space to be monitored, dividing the target indoor space into a plurality of initial grids based on the spatial layout of the indoor space to be monitored, taking the background flow field as the initial condition, determining the pollutant concentration value of each initial grid based on the state of the pollution source term in the historical reference period through the pollutant diffusion equation; outputting the actual concentration value of the pollutant in each initial grid in the historical reference period through the pollutant concentration detection unit arranged in each initial grid, and determining whether to perform grid splitting and grid merging operations according to the difference between the estimated concentration value and the actual concentration value and the concentration gradient of the pollutant in the grid to update the initial grid; correcting the variable parameters of the pollutant diffusion equation based on the actual concentration value of the pollutant in the updated grid to obtain an accurate pollutant diffusion equation for monitoring the diffusion of the pollutant. 2.The method of claim 1, wherein: The environmental data includes indoor temperature, indoor air flow rate, and indoor atmospheric pressure, a historical reference period is set, the environmental data of the target indoor space is collected in the historical reference period, and the collected environmental data is preprocessed for missing values, and the processed environmental data is taken as the input of the CFD model; The setting of the boundary condition is specifically setting the properties of the solid wall, including adiabatic, isothermal, sliding wall, and no sliding boundary properties; The logic for performing iterative calculation based on the input environmental data is: taking the indoor temperature data as the initial value of the temperature field, calculating the initial density distribution as the initial density field according to the temperature field and the reference density, taking the indoor air flow rate data as the initial value of the velocity field, and taking the indoor atmospheric pressure data as the initial value of the pressure field; using the continuity equation, combining the density field and the initial value of the velocity field corresponding to the time and space, and calculating the mass conservation relationship of the fluid, updating the density field according to the velocity field and the space flow trend of the fluid; using the momentum equation, combining the density field, the pressure field, the viscosity coefficient, and the buoyancy effect to calculate the change of the velocity field and update the velocity field, and calculating the distribution of the pressure field through the pressure gradient; using the energy equation, calculating the change of the temperature field according to the velocity field, the convection term, and the heat source term, and coupling the influence results of the change of the temperature field on the density field and the buoyancy effect with the momentum equation to complete one iteration calculation; comparing the calculation results with the monitoring data in the randomly selected area after each iteration, calculating the residual between the two, and if the residual decreases to a preset threshold, stopping the iteration, outputting the final background flow field, and if the residual does not meet the requirement, taking the density field, the velocity field, the pressure field, and the temperature field after the last iteration as the initial density field, the initial velocity field, the initial pressure field, and the initial temperature field, and continuing the iteration calculation until the residual calculated after the iteration meets the requirement.

3. The method of claim 2, wherein: wherein, Based on the current time, a monitoring time period with the same length as the historical reference time period is set, environmental data in the monitoring time period is collected, changes in the environmental data in the monitoring time period and the historical reference time period are analyzed, if the change in the environmental data is greater than or equal to a set mutation threshold, the background flow field data is dynamically adjusted, specifically, the environmental data in the monitoring time period is taken as the input of the CFD model, the missing value preprocessing and iterative operation process are performed again, the real-time background flow field is output, and the monitoring time period is taken as a new historical reference time period; If the change in the environmental data is less than the set mutation threshold, the CFD model is not updated.

4. The method of claim 3, wherein the method is characterized by: Based on the spatial layout of the target indoor space to be monitored, the spatial layout is specifically the idle space of the target indoor space, and the idle space specifically refers to an area that is not occupied by furniture, equipment or other articles; The logic for dividing the target indoor space into a plurality of initial grids is that: an initial size of the grid is set, and the target indoor space is divided into a plurality of initial grid areas based on the set initial size of the grid; The initial condition is the background flow field, the state of the pollution source term in the historical reference time period is input into the constructed pollutant diffusion equation, the finite volume method is used to discretize the equation, and the pollutant estimated concentration value of each grid cell in the historical reference time period is solved; the state of the pollution source term specifically refers to the pollutant concentration of the pollution source, and the pollutant concentration of the pollution source is specifically set as a constant concentration data that does not change with time.

5. The method of claim 4, wherein the method is characterized by: The logic for determining the grid splitting and grid merging area based on the difference between the pollutant estimated concentration value and the actual concentration value and the pollutant concentration gradient in the grid is that: the concentration deviation index and the concentration gradient index in the same grid are calculated, the concentration deviation index is used to represent the difference between the estimated concentration value and the actual concentration value, and the concentration gradient index is used to represent the distribution change of the pollutant in space, wherein the logic for calculating the concentration deviation index is that: the absolute difference between the pollutant estimated concentration value and the pollutant actual concentration value of the grid at different time points in the time period from the start time of the historical reference time period to the end time of the historical reference time period is determined, and the average absolute deviation between the estimated concentration and the actual concentration in the historical reference time period is calculated by integral form, and the average absolute deviation is taken as the concentration deviation index; The logic for calculating the concentration gradient index is that: the boundary surface of the grid is divided into a plurality of microelement units, and the average concentration gradient of all microelement units is taken as the concentration gradient index of the grid; The comprehensive optimization index is generated by the concentration deviation index and the concentration gradient index, and the initial grid is optimized according to the comprehensive optimization index, and the logic for calculating the comprehensive optimization index is that: the weight coefficients of the concentration deviation index and the concentration gradient index are set, the concentration deviation index and the concentration gradient index of different grids are normalized by the maximum value of the concentration deviation index and the maximum value of the concentration gradient index, and the comprehensive optimization index is calculated by weighting according to the corresponding weight coefficients and the normalized concentration deviation index and concentration gradient index.

6. The method of claim 5, wherein: The logic for determining whether to perform the grid splitting and grid merging operations is based on the comprehensive optimization index of the i-th grid A judgment is made to determine whether the comprehensive optimization index falls within a preset grid classification interval, and whether to perform the grid splitting and grid merging operations is further determined based on the analysis result, specifically including: If , it is determined that the grid does not need to perform splitting and merging operations; If , it is determined that the mesh performs a mesh splitting operation, denoted as split mesh; If , it is judged that the grid performs a grid merging operation, denoted as a merging candidate grid; wherein is a preset grid classification interval, is a minimum threshold of the grid classification interval, is a maximum threshold of the grid classification interval, denotes a comprehensive optimization index of the i-th grid; For the grid performing the grid splitting operation, a grid-based comprehensive optimization index A splitting range is determined, and the logic for determining the splitting range is as follows: the volume of the initial grid is determined, and adjustment is made according to the comprehensive optimization index, the optimization index is used to measure the optimization degree of the grid, the adjustment range is determined by comparing the optimization indexes of different grids, an adjustment factor of a volume is calculated, and thus the final volume of the grid is adjusted; For the merging candidate grid, its adjacent grids are determined, the comprehensive optimization indexes of each adjacent grid are obtained, the grid with the minimum comprehensive optimization index in the adjacent grids and the current merging candidate grid is determined, the pollutant concentration similarity between the merging candidate grid and the grid with the minimum comprehensive optimization index is calculated, if the similarity requirement is met, the current merging candidate grid is merged into the grid with the minimum comprehensive optimization index, and the merged spatial grid is obtained, otherwise, no merging is performed.

7. The method of claim 6, wherein the method is based on CFD simulation of indoor pollutant dispersion. According to the re-divided spatial grid, pollutant diffusion analysis is performed again, actual pollutant concentration values corresponding to the updated grid in the monitoring time period are collected, and variable parameters of the pollutant diffusion equation are corrected, specifically, the effective diffusion coefficient is corrected, the data assimilation method is used for correction, and the logic for the correction is as follows: through the actual pollutant concentration values corresponding to the updated grid in the monitoring time period, the difference between the estimated pollutant concentration values and the actual pollutant concentration values of each grid at different time points is calculated, the difference is accumulated in the form of sum of squares, forming the main part of the loss function, so as to reflect the deviation between the model prediction and the actual observation; in order to prevent overfitting, a regularization term is added, and a regularization parameter is used to control the influence of the term on the loss function; The effective diffusion coefficient corresponding to the minimum loss function value is taken as the optimal effective diffusion coefficient, and the effective diffusion coefficient in the pollutant diffusion equation is replaced, and an accurate pollutant diffusion equation is obtained.

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