Multifunctional application system for pathogenic airborne transmission experiment based on computational fluid dynamics

Abstract

The application discloses a pathogenic airborne transmission experiment multifunctional application system based on computational fluid dynamics, and belongs to the technical fields of pathogenic airborne transmission experiment and computational fluid dynamics. The system comprises the following modules: a geometric construction module, which acquires three-dimensional geometric information of an experiment cabin and generates a calculation domain model; a flow field simulation module, which adopts a finite volume method to solve Navier-Stokes equations to obtain flow field results; a release source setting module, which arranges pathogen release sources; a forward propagation simulation module, which adopts a Lagrangian particle tracking method to simulate pathogen particle movement to obtain concentration field results; a reverse tracing module, which carries out reverse particle tracking from monitoring point concentration data to obtain a source probability distribution; and an experiment evaluation module, which spatially correlates the concentration field results and the source probability distribution and generates an evaluation report. The system can deduce pathogen source probability from monitoring data and calibrate key exposure areas in combination with concentration distribution.

Description

Technical Field

[0001] This invention relates to the fields of pathogen airborne transmission experiments and computational fluid dynamics, specifically a multifunctional application system for pathogen airborne transmission experiments based on computational fluid dynamics. Background Technology

[0002] In pathogen airborne transmission simulations, computational fluid dynamics (CFD) methods are often used to obtain the airflow organization within the experimental chamber, followed by simulation of pathogen particle trajectories. Current techniques involve meshing the experimental chamber's geometric model, solving the Navier-Stokes equations to obtain the flow field, and then performing Lagrange particle tracking based on pre-defined release source parameters to obtain the pathogen concentration distribution. The release source setting depends on the experimental scheme or assumptions, and the forward simulation outputs the concentration field results propagating from a known source to the surrounding area. When the actual release source location is unknown or only monitoring point concentration data is available, this forward process cannot infer the possible source range from the monitoring data. The particle backpropagation information contained in the flow field data is not utilized, and source tracing analysis requires additional assumptions or manual inference. The lack of an automatic correlation mechanism between monitoring point concentration data and the source makes it difficult to directly determine which areas of pollutants contribute the most to the monitoring points during experimental evaluation. Furthermore, the concentration field obtained from the forward simulation only expresses the spatial concentration after pathogen diffusion, while the source probability distribution is obtained through inverse analysis; these two are typically processed and displayed separately. Because of the lack of a joint spatial correlation between concentration values ​​and source probability values ​​under a unified grid coordinate system, the assessment report cannot directly distinguish areas with both high concentrations and high source probabilities, making it difficult to locate and prioritize key exposure areas in pathogen transmission experiments. These shortcomings limit the analytical capabilities of pathogen airborne transmission experiment assessments in scenarios where the source is unknown. Summary of the Invention

[0003] To address the limitations of existing technologies in pathogen airborne transmission experiments, which struggle to deduce the source from monitoring data when the release source is unknown and to pinpoint key exposure areas by combining concentration distribution data, this invention provides a multifunctional application system for pathogen airborne transmission experiments based on computational fluid dynamics. The system generates a computational domain model including internal obstacles through a geometry construction module, obtains flow field results through a flow field simulation module, configures pathogen release source parameters through a release source setting module, obtains concentration field results through a forward propagation simulation module, deduces the pathogen source probability distribution from monitoring point concentration data through a reverse tracing module, and spatially correlates the concentration field results with the source probability distribution through an experimental evaluation module to generate an evaluation report for the pathogen transmission experiment.

[0004] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a multifunctional application system for pathogen airborne transmission experiments based on computational fluid dynamics, including a geometry construction module, a flow field simulation module, a release source setting module, a forward propagation simulation module, a reverse tracing module, and an experiment evaluation module. The geometry construction module is used to acquire the three-dimensional geometric information of the experimental chamber and generate a computational domain model including internal obstacles. The flow field simulation module is used to solve the Navier-Stokes equations for airflow using the finite volume method based on the computational domain model to obtain the flow field results. The release source setting module is used to arrange pathogen release sources within the computational domain model according to the experimental scheme and generate release source parameters. The forward propagation simulation module is used to simulate the motion of pathogen particles in the flow field using the Lagrange particle tracing method based on the flow field results and the release source parameters to obtain the concentration field results. The reverse tracing module is used to perform reverse particle tracing based on the flow field results, starting from the concentration data of preset monitoring points, to obtain the pathogen source probability distribution. The experiment evaluation module is used to spatially correlate the concentration field results with the source probability distribution to generate an evaluation report of the pathogen transmission experiment. The modules work together to form a complete process from geometric modeling, flow field calculation, forward propagation simulation to reverse tracing and evaluation analysis.

[0005] As a technical solution of the present invention, when the geometry construction module generates the computational domain model, it imports the three-dimensional geometric information of the experimental chamber into a preset mesh generator, recursively subdivides the bounding box using an octree method in space, and locally refines adjacent cells according to the curvature radius of the internal obstacle surface, adds multiple boundary layer meshes to the wall cells, and numbers and stores all mesh cells as the computational domain model. This module, through local refinement and boundary layer mesh settings, can accurately characterize the flow boundary near obstacles, which is beneficial to improving the accuracy of subsequent flow field simulations.

[0006] As a technical solution of this invention, when the flow field simulation module solves the Navier-Stokes equations for airflow, it establishes conserved discrete equations for pressure, velocity, turbulent kinetic energy, and turbulent dissipation rate on each grid cell of the computational domain model. The SIMPLEC algorithm is used for pressure-velocity coupled iteration, and the k-ωSST turbulence model is used to calculate the turbulent viscosity coefficient. Iteration stops when the global residual shows a continuous decreasing trend with the number of iterations, and the flow field results are output. This module balances computational stability and turbulence simulation accuracy, and can provide a reliable background flow field for pathogen particle propagation simulation.

[0007] As a technical solution of the present invention, when the forward propagation simulation module simulates the movement of pathogen particles, it generates multiple particles according to particle size distribution from the position set by the release source setting module. At each step of time progression, the fluid velocity and turbulent kinetic energy of the current particle's cell are read, and turbulent pulsating velocity is generated using a probability distribution function. The sum of the fluid velocity and the turbulent pulsating velocity is substituted into the particle motion equation to update the particle's position and velocity, and the cell number traversed by the particle is recorded. The particle residence time within each cell is statistically analyzed to obtain the concentration field result. This scheme, by introducing turbulent random perturbation, can reflect the random diffusion characteristics of pathogen particles in the air, making the concentration field result closer to the actual experimental process.

[0008] As a technical solution of the present invention, when the reverse tracing module performs reverse particle tracking, it generates multiple reverse particles at the monitoring point location. The initial velocity of the reverse particles is set as the negative direction of the velocity of the flow field at that point. During the time stepping process, the fluid velocity of the current cell where the reverse particle is located is inverted and used as the average velocity, and a random diffusion displacement related to turbulent kinetic energy is superimposed to update the position of the reverse particles. At the same time, the residence time of each reverse particle in each grid cell is accumulated, and the residence time of all reverse particles is normalized to obtain the source probability distribution. This module uses flow field information consistent with forward propagation for reverse analysis, so that the source probability distribution has a good correlation with the actual diffusion path of the pathogen.

[0009] As a technical solution of the present invention, when the experimental evaluation module performs spatial correlation, it interpolates the concentration field results and the source probability distribution into a unified grid coordinate system, calculates the product of the concentration value and the source probability value for each grid cell, sorts the product results from largest to smallest, and selects the grid cells corresponding to the first few sorted values ​​as key exposure areas to generate an evaluation report. This module spatially fuses forward propagation information and reverse source tracing information, which can effectively identify areas where the risk of pathogen transmission is relatively concentrated.

[0010] As a technical solution of the present invention, when the flow field simulation module solves the Navier-Stokes equations for airflow, it establishes an energy conservation discrete equation in the computational domain model and sets the boundary heat flux according to the location and intensity of the heat source in the experimental chamber. After each pressure-velocity coupling iteration, the temperature value of each unit is calculated, and the buoyancy term of the air is calculated based on the temperature value. The buoyancy term is added to the source term of the momentum equation, and the iteration continues until the temperature field and flow field converge simultaneously, outputting the flow field result including the temperature field. This scheme enables the flow field simulation to consider the natural convection caused by the temperature difference in the experimental chamber, improving the realism and completeness of the indoor airflow simulation.

[0011] As a technical solution of the present invention, when updating the position and velocity of particles, the forward propagation simulation module determines whether the new position of the particle exceeds the boundary of the computational domain model or intersects with the wall of an obstacle. If it exceeds or intersects, the deposition probability is determined based on the roughness parameter of the wall and the particle size. A random number generator is used to determine whether the particle is deposited. If it is deposited, the particle is deleted from the simulation; if it is not deposited, it is bounced back into the computational domain and the velocity direction is updated. By introducing a particle deposition and bounce mechanism, it is possible to distinguish between the deposition loss and non-deposition bounce of pathogen particles near the wall, thereby improving the rationality of the concentration field results.

[0012] Preferably, the deposition probability is determined by looking up a preset deposition probability table based on the wall roughness parameter and the particle size. This preset deposition probability table stores deposition probability values ​​corresponding to different wall roughness ranges and different particle size ranges. This method makes the determination of the wall deposition probability more consistent with actual physical conditions and reduces the impact of simplification on the simulation of particle deposition behavior.

[0013] Preferably, when the geometry construction module recursively subdivides the bounding box using the octree method, it stops subdividing when the current cell size is less than or equal to the target mesh size. The target mesh size is determined based on the radius of curvature of the internal obstacle surface and a preset minimum number of mesh layers. This approach achieves a balance between local geometric resolution and overall mesh size, ensuring computational efficiency while improving the mesh's adaptability to complex obstacles.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: The reverse source tracing module generates multiple reverse particles at the monitoring point location, setting the initial velocity of the reverse particles to the negative direction of the flow field velocity at that point. During time stepping, the fluid velocity of the current cell where the reverse particle resides is inverted and used as the average velocity, and random diffusion displacement related to turbulent kinetic energy is superimposed to update the reverse particle position. The residence time of each reverse particle in each grid cell is accumulated, and the residence time of all reverse particles is normalized to form the source probability distribution. The movement path of the reverse particles is affected by both the average transport of the flow field and turbulent random diffusion, using the same set of flow field results as the forward particle tracking, eliminating the need to construct an additional reverse flow field during source tracing. The source probability distribution is directly formed by the residence time of the reverse particle swarm originating from the monitoring point, providing the spatial range and relative elevation of possible source areas, avoiding the artificial limitations imposed by pre-specifying the release source location or release intensity. Compared to inferences based solely on concentration gradients or single meteorological data, this source probability distribution reflects the ease or difficulty of pathogen particles reaching the monitoring point from the candidate source along the actual simulated flow field, ensuring spatial consistency between the source probability results and the obstacles and airflow organization inside the experimental chamber.

[0015] The experimental evaluation module interpolates the concentration field results and source probability distribution into a unified grid coordinate system. It calculates the product of the concentration value and the source probability value for each grid cell, sorts the products from largest to smallest, and selects the top few grid cells as key exposure areas. The product of the concentration value and the source probability value forms a joint index; only cells with both high concentration and high source probability obtain a large product, while cells with only high concentration or only high source probability are ranked relatively low. The ranking results are used as key exposure areas in the evaluation report, ensuring that the area labeling directly corresponds to the spatial location jointly pointed to by pathogen spread and reverse source inference. Compared to displaying concentration and source probability cloud maps separately and relying on manual overlay judgment, the ranking results after joint spatial correlation reduce the uncertainty of choosing between the two indicators, enhancing the spatial positioning specificity of the evaluation report in scenarios with unknown sources. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a schematic diagram of a multi-functional application system for experimental airborne transmission of pathogens based on computational fluid dynamics. Figure 2 This is the solution flowchart for the flow field simulation module; Figure 3 It is the effect of the preset minimum number of grid layers on the relationship between the target grid size and the radius of curvature of the internal obstacles; Figure 4 It is the convergence curve of the iterative residual of the flow field simulation module; Figure 5 It is the probability density curve of pathogen particle size distribution; Figure 6 It is the probability distribution curve of reverse tracing of monitoring points. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1This invention provides a multifunctional application system for pathogen airborne transmission experiments based on computational fluid dynamics, including a geometry construction module, a flow field simulation module, a release source setting module, a forward propagation simulation module, a reverse source tracing module, and an experiment evaluation module. The geometry construction module acquires the three-dimensional geometric information of the experimental chamber and generates a computational domain model including internal obstacles. The flow field simulation module solves the Navier-Stokes equations for airflow using the finite volume method based on the computational domain model to obtain the flow field results. The release source setting module arranges pathogen release sources within the computational domain model according to the experimental plan and generates release source parameters. The forward propagation simulation module simulates the motion of pathogen particles in the flow field using the Lagrange particle tracing method based on the flow field results and release source parameters to obtain the concentration field results. The reverse source tracing module starts from the concentration data of preset monitoring points and performs reverse particle tracing based on the flow field results to obtain the pathogen source probability distribution. The experiment evaluation module spatially correlates the concentration field results with the source probability distribution to generate an evaluation report of the pathogen transmission experiment. The computational domain model generated by the geometry construction module is input into the flow field simulation module. The flow field results output by the flow field simulation module are input into the forward propagation simulation module and the reverse source tracing module, respectively. The release source parameters generated by the release source setting module are input into the forward propagation simulation module. The concentration field results output by the forward propagation simulation module and the source probability distribution output by the reverse source tracing module are input into the experimental evaluation module.

[0020] In practical implementation, the geometry construction module imports the 3D geometric information of the experimental chamber into a preset mesh generator. The 3D geometric information is acquired through methods including: directly reading the 3D computer-aided design drawings from the experimental chamber's design phase, or receiving geometric files obtained by surface reconstruction from point cloud data output after scanning the physical experimental chamber by a 3D laser scanner. The 3D geometric information of the experimental chamber includes the chamber walls, the surfaces of internal obstacles, the locations of ventilation openings, and the installation locations of release sources. After receiving the 3D geometric information, the preset mesh generator establishes an outer bounding box for the experimental chamber, which serves as the root node of the octree method. The preset mesh generator recursively subdivides the root node into an octree, uniformly dividing the current node into eight child nodes. During the recursive subdivision process, the preset mesh generator determines the relative position of the child node to the experimental chamber walls and the surfaces of internal obstacles. If the child node is entirely inside the internal obstacles, or entirely outside the fluid domain and does not contain the experimental chamber walls or the surfaces of internal obstacles, the subdivision of the child node stops; if the child node contains the experimental chamber walls or the surfaces of internal obstacles, the subdivision continues. Subdivision stops when the cell size of a child node is less than or equal to the target mesh size. The target mesh size is determined based on the radius of curvature of the internal obstacle surface and the preset minimum number of mesh layers. The formula for calculating the target mesh size is:

[0021] in, This indicates the target mesh size, and the unit is consistent with the length unit of the experimental module's three-dimensional geometric information. The radius of curvature representing the current position of the internal obstacle surface is obtained by extracting the average value of the circumcircle radius of the triangular facets on the internal obstacle surface; This represents the preset minimum number of grid layers, ranging from 2 to 10. The value is determined by the number of grid layers required to cover the wall boundary layer. When the preset minimum number of boundary layer grid layers is 5, ... Take 5.

[0022] When locally refining neighboring elements based on the radius of curvature of the internal obstacle surface, for neighboring elements with smaller radii of curvature, the target mesh size is reduced, resulting in a corresponding reduction in the size of the neighboring elements. When adding multiple boundary layer meshes to wall elements, multiple prismatic elements are generated in the wall normal direction, and the number of boundary layer meshes is determined according to the preset minimum mesh layer number. All mesh elements are numbered and stored as a computational domain model. Mesh elements include hexahedral elements generated by octree subdivision and prismatic layer elements near the walls. The numbering method is spatially ordered, and the stored content includes the vertex coordinates of the mesh elements, the index of adjacent elements, and the boundary condition markers.

[0023] See Figure 3 In the graph, the vertical axis represents the target mesh size in millimeters, and the horizontal axis represents the radius of curvature of the current position of the internal obstacle surface in millimeters. The curves in the graph correspond to different preset minimum mesh layer numbers, including five values: 2, 4, 6, 8, and 10. The numerical trend of the curves indicates that as the radius of curvature of the internal obstacle surface increases, the target mesh size increases linearly, meaning that the target mesh size is directly proportional to the radius of curvature.

[0024] Specifically, when the preset minimum grid layer number is 2, the target grid size is the largest, and the corresponding curve is located at the highest position in the graph. As the preset minimum grid layer number increases, the target grid size gradually decreases, and the curves descend sequentially, with the target grid size being the smallest when the preset minimum grid layer number is 10. This pattern demonstrates that the target grid size is inversely proportional to the preset minimum grid layer number in the target grid size calculation formula. The target grid size ranges from approximately 0 to 100 mm in the graph, and the radius of curvature ranges from 0 to 200 mm. When the radius of curvature is small, the target grid size is close to zero, reflecting the principle of local grid refinement based on the radius of curvature. When the radius of curvature is greater than 100 mm, the target grid size varies more significantly, and the curves for different minimum grid layer numbers are significantly spaced, indicating that the radius of curvature and the minimum grid layer number have a significant effect on controlling the target grid size.

[0025] In specific implementation, please refer to Figure 2The flow field simulation module obtains the computational domain model from the geometry construction module. Each grid cell in the computational domain model stores the cell number, vertex coordinates, and adjacent cell indices. The flow field simulation module establishes discrete equations for pressure conservation, velocity conservation, turbulent kinetic energy conservation, and turbulent dissipation rate conservation on each grid cell of the computational domain model. The pressure conservation equation converts the pressure gradient term into pressure flux on the cell surface using Green's formula. The velocity conservation equation discretizes the velocity convection and diffusion terms using the finite volume method. The turbulent kinetic energy and turbulent dissipation rate conservation equations use the same finite volume scheme for their discretization. The convection term in the discrete equations uses a second-order upwind scheme, and the diffusion term uses a central difference scheme.

[0026] The flow field simulation module also establishes energy conservation discrete equations in the computational domain model. These equations use the total enthalpy of air as the solution variable, and their discrete form is the same as the velocity conservation discrete equations. Furthermore, the inlet and wall boundary conditions of the flow field simulation module can interface with a physical sensor network deployed within the experimental chamber via a data interface. This physical sensor network includes at least an anemometer, temperature and humidity sensors, and pressure sensors, each outputting measured values ​​according to a preset sampling frequency. The system uses the measured wind speed at the inlet boundary as the velocity inlet boundary condition and the measured temperature as the temperature boundary condition for the energy equation. It also calculates the inlet profile values ​​of turbulent kinetic energy and turbulent dissipation rate based on the measured inlet turbulence intensity. Boundary heat flux is set according to the location and intensity of the heat source within the experimental chamber. The heat source location is determined by the position of the heat source equipment within the experimental chamber, and the heat source intensity is determined by the power density of the heat source equipment. The boundary heat flux is applied to the wall grid cell surface corresponding to the heat source location. The heat source intensity is converted into a wall heat flux value by dividing the power of the heat source equipment by the heat dissipation area.

[0027] During the solution process, the flow field simulation module employs the SIMPLEC (SIMPLEC-Consistent, Pressure-Velocity Coupling Consistency Correction Algorithm) algorithm for pressure-velocity coupling iteration. In the iterative process, the SIMPLEC algorithm first solves the momentum equation based on the current pressure field to obtain the intermediate velocity field, then solves the pressure correction equation to obtain the pressure correction value, and finally corrects the pressure and velocity fields based on this value. The pressure correction term coefficients in the pressure correction equation are corrected using the main diagonal elements of the velocity correction equation, thereby improving the convergence speed of the pressure-velocity coupling.

[0028] The flow field simulation module uses the k-ωSST (k-omega Shear Stress Transport) turbulence model to calculate the turbulent viscosity coefficient. The k-ωSST turbulence model comprises two transport equations: one with turbulent kinetic energy as the variable, and the other with specific dissipation rate as the variable. In the near-wall region, the k-ωSST turbulence model uses wall function boundary conditions to handle the viscous sublayer; in the far-wall region, the k-ωSST turbulence model uses a mixing function to transform the specific dissipation rate equation into an equation based on turbulent dissipation rate. The turbulent viscosity coefficient is calculated as the ratio of turbulent kinetic energy to specific dissipation rate, calculated by dividing the turbulent kinetic energy by the specific dissipation rate and then multiplying by the air density and a constant.

[0029] After each pressure-velocity coupling iteration, the flow field simulation module solves the discrete energy conservation equations to obtain the temperature values ​​of each element. When calculating the buoyancy term based on the temperature values, the Businesko approximation is used. The specific value of the buoyancy term is equal to the air density multiplied by the coefficient of thermal expansion, the difference between the temperature value and the reference temperature, and then multiplied by the gravitational acceleration. The coefficient of thermal expansion is taken as the volumetric expansion coefficient of air at the reference temperature, obtained by consulting an air property table. The reference temperature is taken as the air temperature at the air inlet of the experimental chamber. The air density is taken as the air density at the reference temperature. The buoyancy term is added to the source term of the momentum equation, which includes both gravity and buoyancy terms. The gravity term is the air density multiplied by the gravitational acceleration.

[0030] The iteration stopping condition is that the global residual shows a continuous decreasing trend with the number of iterations and the temperature and flow fields converge simultaneously. A continuous decreasing trend is determined by comparing the global residuals of two adjacent iterations; if the current global residual is less than the previous global residual, and this condition is met consecutively, the decreasing trend is confirmed. Simultaneous convergence of the temperature and flow fields is determined by the temperature residual and velocity residual falling below the temperature convergence threshold and velocity convergence threshold, respectively. The temperature convergence threshold is set to 10. -6 The velocity convergence threshold is set to 10. -5 When the iteration stopping condition is met, the flow field simulation module stops iterating and outputs flow field results including the temperature field. The flow field results include the pressure value, velocity vector, turbulent kinetic energy value, turbulent dissipation rate value, and temperature value of each grid cell.

[0031] See Figure 4 In the figure, the horizontal axis represents the number of iterations in the pressure-velocity coupling iteration in the flow field simulation module, ranging from 0 to 1000; the vertical axis represents the global residual, expressed on a logarithmic scale, with the residual ranging from 10... -2 Up to 10 -6 The graph shows five curves: velocity residual, pressure correction equation residual, temperature residual, turbulent kinetic energy residual, and turbulent dissipation rate residual. They are labeled with different colors and line types, as detailed in the legend.

[0032] As the number of iterations increases, all residuals show a clear monotonically decreasing trend, indicating that the SIMPLEC algorithm and finite volume method discretization scheme used in the flow field simulation module can effectively improve the solution accuracy of pressure, velocity, temperature, and turbulence parameters. Specifically, the residual of the pressure correction equation decreases the fastest, from approximately 10 initially. -2 The value rapidly drops below 10 at the end of the iteration. -6 This indicates that the pressure-velocity coupling converges relatively quickly. The decreasing trends of the velocity residual and the temperature residual are basically the same, both decreasing to 10 after 1000 iterations. -5 Up to 10 -6 Within this range, ensure synchronous convergence of the flow field and temperature field. The turbulent kinetic energy residual and turbulent dissipation rate residual decrease relatively slowly, and the final residual remains at 10. -4 Up to 10 -3 The range is relatively small, but the overall trend is continuously decreasing, which is consistent with the numerical solution characteristics of the k-ωSST turbulence model.

[0033] In practice, the forward propagation simulation module obtains release source parameters from the release source setting module. These parameters include the release source location, release rate, pathogen particle size distribution type, and particle size distribution parameters. The forward propagation simulation module generates multiple pathogen particles at the release source location based on these parameters. The number of generated particles is determined by the release rate and the simulation time step, and the particle size is sampled according to the particle size distribution type. When the particle size distribution type is log-normal, the particle size distribution parameters include the geometric mean particle size and geometric standard deviation. When the particle size distribution type is Rosing-Lammler distribution, the particle size distribution parameters include the mean particle size and distribution index.

[0034] At each step of time progression, the forward propagation simulation module reads the fluid velocity and turbulent kinetic energy of the current particle's grid cell. The fluid velocity and turbulent kinetic energy are derived from the flow field results output by the flow field simulation module. The forward propagation simulation module generates turbulent fluctuating velocities using a probability distribution function. This probability distribution function is a Gaussian distribution with a mean of zero, and its variance is determined by the turbulent kinetic energy. The three components of the turbulent fluctuating velocity are generated independently, each following a Gaussian distribution with a mean of zero, and the variance of this Gaussian distribution is equal to two-thirds of the turbulent kinetic energy divided by the air density.

[0035] The forward propagation simulation module substitutes the sum of fluid velocity and turbulent fluctuation velocity into the particle motion equations. The position update in the particle motion equations uses the following formula:

[0036] in, Indicates pathogen particles at time step position vector, Indicates pathogen particles at time step position vector, Indicates pathogen particles at time step velocity vector Indicates the time step. Time step The value is the ratio of the smallest grid cell size to the maximum fluid velocity in the flow field simulation module multiplied by 0.1, based on the premise that the distance a particle travels in one time step does not exceed one-tenth of the smallest grid cell size. Pathogen particles in time step... velocity vector The update method is to use the sum of fluid velocity and turbulent pulsation velocity as the instantaneous wind speed of pathogen particles, and to relax and update the pathogen particle velocity according to the pathogen particle relaxation time.

[0037] When updating the position and velocity of pathogen particles, the forward propagation simulation module determines whether the new position of the pathogen particle exceeds the boundary of the computational domain model or intersects with the wall of an obstacle. When determining whether it exceeds the boundary of the computational domain model, it checks whether the coordinates of the new position of the pathogen particle are within the outer boundary of the computational domain model. When determining whether it intersects with the wall of an obstacle, it checks whether the line segment from the old position to the new position of the pathogen particle passes through the triangular element of the wall.

[0038] If the new position of a pathogen particle exceeds the boundary of the computational domain model or intersects with the wall of an obstacle, the forward propagation simulation module determines the deposition probability based on the wall roughness parameter and the particle size. The wall roughness parameter is determined by the material roughness level of the experimental chamber wall and the internal obstacle wall, and the particle size is taken as the current pathogen particle size. The forward propagation simulation module determines the deposition probability by looking up a preset deposition probability table. The preset deposition probability table stores the deposition probability values ​​corresponding to different wall roughness intervals and different particle size intervals. The preset deposition probability table is generated before the forward propagation simulation by performing a series of collision deposition experiments on different wall roughness levels and different particle sizes, statistically analyzing the particle deposition ratio in each wall roughness interval and each particle size interval, and writing the deposition ratio as the deposition probability value into the preset deposition probability table. When looking up the preset deposition probability table, first determine the wall roughness interval into which the wall roughness parameter falls, then determine the particle size interval into which the pathogen particle size falls, and read the deposition probability value at the intersection of the corresponding row and column.

[0039] The forward propagation simulation module uses a random number generator to determine whether pathogen particles have been deposited. The random number generator produces random numbers uniformly distributed in the interval between 0 and 1. If the random number is less than the deposition probability value, the pathogen particle is deposited, and the forward propagation simulation module removes the pathogen particle from the simulation. If the random number is greater than or equal to the deposition probability value, the pathogen particle is not deposited, and the forward propagation simulation module bounces the pathogen particle back into the computational domain. When bouncing back into the computational domain, the component of the pathogen particle velocity vector along the obstacle wall normal is inverted, while the component of the pathogen particle velocity vector along the obstacle wall tangential remains unchanged. The new position of the pathogen particle is taken as the position offset by a small distance along the wall normal at the wall intersection point. The small distance is one ten-thousandth of the smallest mesh cell size near the wall.

[0040] The forward propagation simulation module records the cell numbers traversed by pathogen particles and calculates the residence time of pathogen particles within each grid cell. The residence time of pathogen particles in each grid cell is accumulated by one time step. When all pathogen particles have deposited, escaped the computational domain, or are counted until the end of the total simulation duration, the forward propagation simulation module divides the residence time of pathogen particles in each grid cell by the volume of that grid cell and the total simulation duration to obtain the concentration field results. The concentration field results are expressed as the residence time of pathogen particles within each grid cell.

[0041] See Figure 5 In the figure, the horizontal axis represents the particle size of pathogens, in micrometers (μm), ranging from 0 to 20 μm; the vertical axis represents the probability distribution function value of the corresponding particle size, ranging from 0 to 0.6.

[0042] Curve 1 follows a log-normal distribution with a geometric mean particle size of 2 μm and a geometric standard deviation of 1.5 (blue solid line with dots). This curve peaks between approximately 1.5 and 2.5 μm in particle size, with a peak probability density of approximately 0.53, exhibiting a distinct unimodal shape. The peak position is concentrated, and the probability rapidly decreases to near zero when the particle size is less than 1 μm or greater than 4 μm. Curve 2 follows a log-normal distribution with a geometric mean particle size of 5 μm and a geometric standard deviation of 2.0 (orange dashed line with squares). This curve has a lower peak than Curve 1, with a maximum probability density of approximately 0.15. It has a wider peak width and a relatively flat distribution, covering a particle size range of 3 to 12 μm, showing a wider particle size dispersion. Curve 3 follows a Rosing-Lammler distribution with an average particle size of 3 μm and a distribution index of 2.5 (green dotted line with triangles). This curve exhibits a unimodal distribution skewed to the right, with a peak probability density of approximately 0.3. The peak is located at approximately 3 μm in particle size, and the probability density is relatively concentrated in the 1 to 5 μm particle size range, corresponding to the medium particle size range. Curve 4 shows the Rosing-Lammler distribution with an average particle size of 7 μm and a distribution index of 3.5 (red dotted line with diamond shape). The peak probability density of this curve is approximately 0.17, with a peak particle size of approximately 7 μm. The distribution is relatively wide, covering a particle size range from 4 to 12 μm, indicating that larger particles are more dispersed.

[0043] In practice, the reverse tracing module acquires the concentration data of preset monitoring points and the flow field results output by the flow field simulation module. The concentration data of the monitoring points are the pathogen concentration observation values ​​set at each monitoring location in the experimental chamber, and the flow field results include the pressure value, velocity vector, turbulent kinetic energy value, turbulent dissipation rate value, and temperature value of each grid cell.

[0044] The reverse tracing module generates multiple reverse particles at the monitoring point location. The number of reverse particles is set to 10,000, corresponding to the time sampling interval of the concentration data at the monitoring point. The initial position of the reverse particles is located at the center of the grid cell containing the monitoring point. The reverse tracing module sets the initial velocity of the reverse particles to the negative direction of the flow field velocity at the monitoring point. Specifically, it reads the fluid velocity vector of the grid cell containing the monitoring point, multiplies each component of the fluid velocity vector by -1, and obtains the corresponding component of the initial velocity vector of the reverse particles. During time stepping, the reverse tracing module inverts the fluid velocity of the current grid cell containing the reverse particles and uses it as the average velocity. Each component of the average velocity vector is equal to the negative of the corresponding component of the fluid velocity vector of the current grid cell containing the reverse particles.

[0045] The reverse tracing module superimposes random diffusion displacements related to turbulent kinetic energy. The random diffusion displacement vector consists of three independent components, each following a Gaussian distribution with a mean of zero. The variance of the Gaussian distribution is determined by the turbulent kinetic energy value, air density, and reverse tracing time step of the current grid cell containing the reverse particle. The three components of the random diffusion displacement vector are generated separately: first, a random number generator produces three independent standard normal distribution random numbers, and then each is multiplied by the positive square root of its variance.

[0046] The reverse tracing module updates the position of the reversed particle using the following formula:

[0047] in, Indicates the first A reverse particle in the reverse time step position vector, Indicates the first A reverse particle in the reverse time step position vector, Indicates the first A reverse particle in the reverse time step The average velocity vector of motion, Indicates the time step for reverse tracing. Indicates the first A reverse particle in the reverse time step The random diffusion displacement vector. (Subscript) The value ranges from 1 to the total number of reversed particles, with the subscript... The value ranges from 0 to the maximum number of reverse tracking steps. Reverse tracking time step. The value is the ratio of the smallest grid cell size to the maximum fluid velocity in the flow field simulation module multiplied by 0.1, based on the premise that the distance traveled by the reverse particle in one reverse time step does not exceed one-tenth of the smallest grid cell size. Average velocity vector. The method of obtaining is to read the first The fluid velocity vector of the current grid cell where the inverse particle resides, with each component of the fluid velocity vector inverted. Random diffusion displacement vector. The method of obtaining the data is to generate three independent Gaussian random numbers. The mean of each Gaussian random number is zero, and the variance is determined by the turbulent kinetic energy value, air density and reverse tracking time step of the current grid cell. The three Gaussian random numbers are used as the three components of the random diffusion displacement vector.

[0048] After updating the position of the reversed particle, the reverse tracing module determines the grid cell where the reversed particle is located and increases the cumulative residence time of the reversed particle in the corresponding grid cell by one reverse tracing time step. When the position of the reversed particle exceeds the boundary of the computational domain model or the reverse tracing time step reaches the preset maximum number of reverse tracing steps, the tracking of the corresponding reversed particle stops. After all reversed particles have been tracked, the reverse tracing module divides the cumulative residence time of the reversed particle in each grid cell by the sum of the cumulative residence times of the reversed particles in all grid cells to obtain the normalized residence time of each grid cell. The normalized residence time serves as the source probability distribution, where the probability value of each grid cell represents the probability that the pathogen originates from the corresponding grid cell.

[0049] See Figure 6 In the figure, the vertical axis represents the source probability value, and the horizontal axis represents the grid cell number. The curves represent the source probability distributions obtained by the reverse tracing module after performing reverse particle tracing on the concentration data of three preset monitoring points (monitoring point 1, monitoring point 2, and monitoring point 3) in the experimental chamber. The source probability curve corresponding to monitoring point 1 is a solid blue line, the source probability curve corresponding to monitoring point 2 is a dashed orange line, and the source probability curve corresponding to monitoring point 3 is a dotted green line.

[0050] As shown in the figure, all three source probability curves exhibit a clear unimodal distribution, with peak values ​​concentrated within specific intervals corresponding to the grid cell numbers. The peak value for monitoring point 1 appears in the grid cell region numbered 80 to 110, with a peak value of approximately 0.025, indicating that this area is the primary pathogen source region for monitoring point 1. The peak value for monitoring point 2 is distributed within the grid cell region numbered 180 to 220, with a peak value of approximately 0.017, indicating that this area is the primary pathogen source for monitoring point 2. The peak value for monitoring point 3 is located within the grid cell range numbered 350 to 390, with a peak value of approximately 0.013, indicating that this range is a potential pathogen source for monitoring point 3.

[0051] The peak positions of the three curves are significantly dispersed, and all curves rapidly decrease to near zero outside their peak ranges, indicating that the reverse tracing results have high spatial resolution and can distinguish the pathogen source locations corresponding to different monitoring points. The source probability value of monitoring point 1 is generally higher than that of monitoring points 2 and 3, indicating that the pathogen source probability in its corresponding area is relatively high. The smooth changes in the curves and the sharpness of the peaks reflect the diffusion effect introduced by turbulent kinetic energy and the influence of random diffusion displacement during the reverse particle tracing process.

[0052] In practice, the experimental evaluation module acquires the concentration field results output by the forward propagation simulation module and the source probability distribution output by the reverse source tracing module. The concentration field results are stored in the grid coordinate system corresponding to the forward propagation simulation module, and include the residence time of pathogen particles in each grid cell. The source probability distribution is stored in the grid coordinate system corresponding to the reverse source tracing module, and includes the source probability value for each grid cell.

[0053] The experimental evaluation module interpolates the concentration field results and source probability distribution into a unified grid coordinate system. This unified grid coordinate system is the same as the grid coordinate system corresponding to the forward propagation simulation module, with the grid cell numbers matching those of the computational domain model grid cells in the forward propagation simulation module. The concentration field results remain in their original grid coordinate system, while the source probability distribution is interpolated from the grid coordinate system corresponding to the reverse source tracing module to the unified grid coordinate system. The source probability distribution is interpolated linearly. In the specific implementation, for each grid cell center point in the unified grid coordinate system, the grid cell containing that center point is found in the grid coordinate system corresponding to the reverse source tracing module. Trilinear interpolation is then performed using the source probability values ​​at the eight nodes of the grid cell containing that center point to obtain the interpolated source probability value corresponding to that center point. The weights of the trilinear interpolation are determined by the local coordinates of the center point within the grid cell containing that center point. The local coordinates range from 0 to 1 and are obtained by subtracting the smallest vertex coordinate of the grid cell containing the center point from the center point coordinates and then dividing by the side lengths of each direction of the grid cell.

[0054] After interpolation, the experimental evaluation module calculates the product of the concentration value and the source probability value for each grid cell. For the first cell in a unified grid coordinate system... For each grid cell, the product value is calculated using the following formula:

[0055] in, Indicates the first The product value corresponding to each grid cell. For the numbering of grid cells in a unified grid coordinate system, The value ranges from 1 to the total number of grid cells in a unified grid coordinate system; Indicates the first Concentration values ​​corresponding to each grid cell Taken from the concentration field results Pathogen particle residence time within each grid cell; Indicates the first The source probability value corresponding to each grid cell The source probability distribution after interpolation is taken at the th The interpolation source probability value of the center point of each grid cell.

[0056] The experimental evaluation module sorts all the product values ​​corresponding to the grid cells in descending order. After sorting, the grid cells corresponding to the first few positions are selected as key exposure areas. The number of the first few positions is determined by the preset number of key exposure areas, which is one percent of the total number of grid cells in the unified grid coordinate system and rounded up. When one percent of the total number of grid cells in the unified grid coordinate system is less than 1 after rounding up, the number of key exposure areas is 1.

[0057] The experimental evaluation module generates an evaluation report for the pathogen transmission experiment. The report includes the grid cell number, grid cell center coordinates, concentration value, source probability value, and product value for key exposure areas. The evaluation report is output in text format, with key exposure areas arranged from largest to smallest product value.

[0058] In a preferred embodiment, the system further includes a simulation verification submodule. After the forward propagation simulation is completed, the simulation verification submodule extracts the simulated concentration values ​​at the grid cells where each monitoring point is located, and compares them point-by-point with the concentration data of the monitoring points actually measured by the aerosol concentration sampler and entered into the system during the experiment. It calculates the average relative error and root mean square error. When the average relative error exceeds a preset threshold, the system issues a boundary condition or releases a source parameter adjustment prompt, allowing the user to correct the error and re-perform the flow field simulation and forward propagation simulation. Only after successful verification are the concentration field results and source probability distribution sent to the experimental evaluation module for spatial correlation. This verification step creates a closed-loop feedback between the simulation results and the measured data, improving the credibility of the evaluation report.

[0059] 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 multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics, characterized in that, include: The geometry construction module is used to acquire the three-dimensional geometric information of the experimental chamber and generate a computational domain model that includes internal obstacles. The flow field simulation module is used to solve the Navier-Stokes equations for airflow using the finite volume method based on the computational domain model, and obtain the flow field results. The release source setting module is used to arrange pathogen release sources within the computational domain model according to the experimental protocol and generate release source parameters; The forward propagation simulation module is used to simulate the motion of pathogen particles in the flow field based on the flow field results and the release source parameters, and to obtain the concentration field results. The reverse tracing module is used to start from the concentration data of preset monitoring points and perform reverse particle tracking based on the flow field results to obtain the pathogen source probability distribution; The experimental evaluation module is used to spatially correlate the concentration field results with the source probability distribution to generate an evaluation report of the pathogen transmission experiment.

2. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics as described in claim 1, characterized in that, When the geometry construction module generates the computational domain model, it specifically includes: The three-dimensional geometric information of the experimental chamber is imported into a preset mesh generator. The bounding box is recursively subdivided in space using the octree method. The adjacent cells are locally densified according to the curvature radius of the internal obstacle surface. Then, multiple boundary layer meshes are added to the wall cells. Finally, all mesh cells are numbered and stored as a computational domain model.

3. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics as described in claim 1, characterized in that, When the flow field simulation module solves the Navier-Stokes equations for airflow, it specifically includes: Conservational discrete equations for pressure, velocity, turbulent kinetic energy, and turbulent dissipation rate are established on each grid cell of the computational domain model. The SIMPLEC algorithm is used for pressure-velocity coupled iteration, and the k-ωSST turbulence model is used to calculate the turbulent viscosity coefficient. The iteration stops when the global residual shows a continuous decreasing trend with the number of iterations, and the flow field results are output.

4. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 1, characterized in that, When the forward propagation simulation module simulates the motion of pathogen particles, it specifically includes: Multiple particles are generated from the position set by the release source setting module according to the particle size distribution. At each step of time advancement, the fluid velocity and turbulent kinetic energy of the cell where the current particle is located are read. Turbulent pulsating velocity is generated using a probability distribution function. The sum of the fluid velocity and the turbulent pulsating velocity is substituted into the particle motion equation to update the particle's position and velocity. The cell number that the particle passes through is recorded. The particle residence time in each cell is counted to obtain the concentration field result.

5. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 1, characterized in that, When the reverse tracing module performs reverse particle tracking, it specifically includes: Multiple reverse particles are generated at the monitoring point. The initial velocity of the reverse particles is set as the negative direction of the velocity of the flow field at that point. During the time step, the fluid velocity of the cell where the reverse particle is currently located is inverted and used as the average velocity. The random diffusion displacement related to turbulent kinetic energy is superimposed to update the position of the reverse particles. At the same time, the residence time of each reverse particle in each grid cell is accumulated. The residence time of all reverse particles is normalized and used as the source probability distribution.

6. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 1, characterized in that, When the experimental evaluation module performs spatial correlation, it specifically includes: After the forward propagation simulation is completed, the simulated concentration value at the grid cell where each monitoring point is located is extracted and compared point by point with the actual concentration data of the monitoring points measured by the aerosol concentration sampler and entered into the system during the experiment. The average relative error and root mean square error are calculated. When the average relative error exceeds the preset threshold, the system issues a prompt to adjust the boundary conditions or release the source parameters, allowing the user to correct the error and re-perform the flow field simulation and forward propagation simulation. After successful verification, the experimental evaluation module then performs spatial correlation, specifically including: The concentration field results and the source probability distribution are interpolated into a unified grid coordinate system. The product of the concentration value and the source probability value is calculated for each grid cell. The product results are sorted from largest to smallest. The grid cells corresponding to the first few values ​​after sorting are taken as key exposure areas, and an assessment report is generated.

7. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 3, characterized in that, When the flow field simulation module solves the Navier-Stokes equations for airflow, it specifically includes: Energy conservation discrete equations are established in the computational domain model, and boundary heat fluxes are set according to the location and intensity of heat sources in the experimental chamber. After each pressure-velocity coupling iteration, the temperature value of each unit is calculated, and the buoyancy term of the air is calculated based on the temperature value. The buoyancy term is added to the source term of the momentum equation. The iteration continues until the temperature field and flow field converge simultaneously, and the flow field result including the temperature field is output.

8. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 4, characterized in that, The forward propagation simulation module, when updating the position and velocity of particles, specifically includes: Determine whether the new position of the particle exceeds the boundary of the computational domain model or intersects with the wall of the obstacle. If it exceeds or intersects, determine the deposition probability based on the roughness parameter of the wall and the particle size. Use a random number generator to determine whether the particle is deposited. If it is deposited, delete the particle from the simulation. If it is not deposited, bounce it back into the computational domain and update the velocity direction.

9. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 8, characterized in that, The deposition probability is determined by looking up a preset deposition probability table based on the roughness parameters of the wall surface and the particle size. The preset deposition probability table stores deposition probability values ​​corresponding to different wall roughness ranges and different particle size ranges.

10. The multifunctional application system for experimental airborne transmission of pathogens based on computational fluid dynamics according to claim 2, characterized in that, When the geometry building module recursively subdivides the bounding box using the octree method, it stops subdividing when the current cell size is less than or equal to the target mesh size. The target mesh size is determined based on the radius of curvature of the internal obstacle surface and the preset minimum number of mesh layers.

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