Small-scale airborne radionuclide diffusion simulation method
By combining real terrain and airflow characteristic parameters with a three-dimensional CFD method, a small-scale airborne radionuclide diffusion model is constructed, which solves the problem of insufficient simulation accuracy in complex environments at small scales, and realizes refined simulation of nuclide diffusion and rapid emergency response.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies lack sufficient accuracy in simulating the diffusion of airborne radionuclides in complex environments on a small scale, and the simulation process lacks integrated consolidation, making it difficult to balance accuracy with the timeliness of emergency response.
Using a three-dimensional CFD method, combined with real terrain data, airflow characteristics and nuclide physical parameters, a geometric discrete model is constructed through grid discretization and turbulence physics model, boundary conditions are configured, and a three-dimensional computational fluid dynamics solution for nuclide diffusion is performed. The solution is then verified with sensor data and visualized.
It enables refined and visualized simulation of nuclide diffusion in complex environments at small scales, improving simulation accuracy and result reliability, and supporting rapid emergency decision-making.
Smart Images
Figure CN121835328A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radionuclide diffusion analysis technology, and more specifically, to a small-scale airborne radionuclide diffusion simulation method. Background Technology
[0002] In the operation of nuclear facilities and the transportation of nuclear materials, in the event of an airborne radionuclide leak, it is crucial to quickly and accurately determine the diffusion patterns and impact range of the radionuclide in small-scale areas. This provides key technical support for the development of emergency response plans and personnel evacuation decisions. Currently, commonly used models for simulating the diffusion of airborne pollutants include Gaussian models, Lagrange models, Eulerian models, and computational fluid dynamics models.
[0003] Among them, the Gaussian model, due to its simplified assumptions, has limited simulation accuracy in complex terrain or non-uniform flow fields, making it difficult to meet the needs of refined analysis in small-scale complex scenarios. Although the Lagrange and Eulerian models are suitable for diffusion simulation in large-scale regions, their spatiotemporal resolution for airflow fields in small-scale regions is insufficient, making it difficult to accurately capture the migration and distribution details of nuclides in local complex environments (such as building complexes, valleys, etc.).
[0004] Computational fluid dynamics (CFD) models solve the fluid motion control equations numerically, enabling the discretization of the flow field over continuous regions. This provides significant advantages in simulating small-scale and complex environments, making it a crucial technique for ensuring the accuracy of such simulations. However, when simulating the diffusion of airborne radionuclides, CFD methods require coupling multiple physicochemical processes, resulting in complex implementation. Furthermore, existing research often focuses on specific cases, lacking a universally applicable and rapidly deployable integrated simulation workflow. This makes it difficult to balance simulation accuracy in small-scale complex environments with the timeliness requirements of emergency response, thus limiting its application in practical assessments. Summary of the Invention
[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention aims to provide a small-scale airborne radionuclide diffusion simulation method to solve the problems of insufficient accuracy in simulating small-scale complex environments and lack of integrated simulation process in the existing technology, so as to realize refined, visualized simulation and quantitative analysis of the nuclide diffusion process.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a small-scale airborne radionuclide diffusion simulation method, which includes the following steps: S1: Obtain the airflow characteristic parameters of the small-scale airborne environment, the physical property parameters of the target airborne radionuclides, and the topographic elevation model of the simulated area; S2: Convert the terrain elevation model into a geometric model, and perform mesh generation and discretization to construct a geometric discretization model of the simulation area; S3: Based on the airflow characteristic parameters and the properties of the target airborne radionuclides, set the initial field, turbulence physics model and corresponding control equations of the geometric discrete model, and configure the airflow input and output conditions and nuclide leakage boundary conditions of the simulation region boundary. S4: Select the target airborne radionuclide, set the calculation parameters, perform three-dimensional computational fluid dynamics solution on the nuclide diffusion process, obtain nuclide concentration distribution data, and integrate them to form a dynamic dataset describing the diffusion process; S5: The dynamic dataset is transformed into a three-dimensional visualization result, feature data is extracted to generate analysis curves, and the results are compared and verified with sensor measurement data. Finally, a small-scale airborne radionuclide diffusion path is constructed.
[0007] Furthermore, the geometric discrete model is composed of multiple discrete grid cells, each cell corresponding to the airborne space or physical boundary within the simulation area.
[0008] Furthermore, step S2 specifically includes: The terrain elevation data is preprocessed and converted into a geometric model file; The geometric model is meshed to fit the mesh resolution required for subsequent calculations; Use mesh generation tools to generate structured or unstructured mesh files; Discretize the mesh file to construct a geometric discrete model for numerical computation.
[0009] Furthermore, step S3 specifically includes: Based on the airflow characteristic parameters and the properties of the target nuclide, a turbulence physics model was selected, and a set of governing equations including mass, momentum, energy conservation equations and nuclide diffusion and transport equations was established. Determine the parameter dimensions and numerical range that match the geometric discrete model, and complete the parameter analysis; Based on the analytical results, initial field values are assigned to the pressure, flow velocity, initial nuclide concentration, turbulent kinetic energy and dissipation rate of each discrete grid cell. Configure the airflow inlet and outlet conditions, nuclide leakage source conditions, and solid wall conditions at the boundary of the simulated region.
[0010] Furthermore, the boundary conditions specifically include: airflow inlet boundary conditions, airflow outlet boundary conditions, nuclide concentration boundary conditions, and solid wall no-slip boundary conditions.
[0011] Further, the target gaseous radionuclide includes at least one of I-131, Cs-137, Xe-135, and Kr-88. Step S4 specifically includes: Select the target nuclide according to the simulation requirements, and set the calculation time step, total simulation duration and numerical discretization format; Based on the PISO algorithm, the three-dimensional CFD solution of the governing equations is performed to obtain the nuclide concentration distribution of each grid cell at different times. Data from all time steps are integrated to form a nuclide diffusion dynamic dataset.
[0012] Furthermore, step S5 specifically includes: The dynamic dataset is imported into the post-processing system to generate nuclide concentration cloud maps and flow field distribution maps at different times; Extract data on the change of nuclide concentration over time at a specified location and the spatial distribution data of concentration at a specified time, and plot concentration-time curves and concentration-distance curves; The simulation results were verified and corrected by combining the actual sensor measurement data; Based on the verified results, the main diffusion pathways of the target nuclide in small-scale environments were analyzed and determined.
[0013] By employing the above technical solution, the present invention has at least the following beneficial effects: 1. High accuracy and realistic fit: By integrating real terrain data, environmental airflow characteristics and nuclide physical property parameters, and adopting an appropriate geometric discretization model and parameter analysis method, this invention significantly improves the physical realism and accuracy of nuclide diffusion simulation in small-scale complex environments, providing a reliable numerical basis for accident consequence assessment.
[0014] 2. Detailed process and dynamic reproduction: This invention relies on OpenFOAM for mesh discretization and combines it with three-dimensional CFD calculation to accurately capture the concentration distribution of nuclides in three-dimensional space over time. Compared with simplified models, it can more meticulously reproduce the dynamic evolution of diffusion, providing quantitative support for understanding the migration path of nuclides. It retains small-scale terrain or structural details and avoids distortion in the simulation of processes such as airflow around and nuclide deposition due to coarse mesh.
[0015] 3. Integrated process and intuitive and reliable results: This invention transforms abstract simulation data into intuitive and reliable diffusion path results through VTK visualization, Gnuplot quantification curves and sensor data verification. It can not only intuitively present the diffusion range and trend of nuclides, but also ensure the reliability of the results through verification. It can directly serve emergency decision-making, risk assessment and optimization of prevention and control measures for small-scale nuclide leaks. Attached Figure Description
[0016] The accompanying drawings, which are provided to further illustrate the invention and constitute a part of this invention, are illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention.
[0017] Figure 1 A flowchart of a small-scale airborne radionuclide diffusion simulation method provided in an embodiment of the present invention. Detailed Implementation
[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.
[0019] Currently, small-scale airborne radionuclide diffusion is typically simulated using CFD methods. However, compared to traditional air pollutants, the diffusion process of airborne radionuclides is influenced by a combination of multiple physicochemical factors, and existing CFD research often focuses on specific scenarios. This necessitates the introduction of targeted correction mechanisms in CFD simulations, increasing the technical difficulty and limiting the applicability of small-scale airborne radionuclide diffusion simulation methods, making rapid deployment challenging. To achieve accurate three-dimensional simulation of radionuclide diffusion in complex small-scale environments, providing reliable numerical calculations for accident assessment, and simplifying the simulation process while improving computational efficiency to meet the need for rapid simulation initiation under emergency conditions, this invention proposes a small-scale airborne radionuclide diffusion simulation method, such as... Figure 1 As shown, the method includes the following steps: First, we obtain the airflow characteristic parameters of the small-scale airborne environment, the basic parameters of the target airborne radionuclide, and the terrain elevation model of the simulation area. This provides accurate and suitable basic inputs for the preprocessing construction and core calculation of the small-scale airborne radionuclide diffusion simulation, ensuring that the simulation process closely matches the actual scenario and the results are reliable. The terrain elevation model of the simulation area in TIF format can be converted into an STL format geometric file, and then meshed using tools such as snappyHexMesh, providing a realistic geometric discretization basis for subsequent calculations. The airflow characteristic parameters of the small-scale airborne environment include ambient wind speed, air pressure, and temperature, which are key data for setting the initial field and boundary conditions, providing a realistic airflow field background for the nuclide diffusion calculation. The basic parameters of the target airborne radionuclide include nuclide type, initial leakage concentration, and leakage location coordinates. These are prerequisites for selecting the simulated nuclide and configuring the solution parameters for the nuclide diffusion calculation, ensuring that the simulation can accurately reflect the diffusion law of a specific nuclide.
[0020] Converting a TIF-format terrain elevation model to an STL-format geometric model and meshing it generates an OpenFOAM-format mesh file. This file is then discretized to construct a geometrically discrete model of the simulation area. This discrete model contains multiple discrete mesh elements, corresponding to the airborne space and boundary regions within the simulation area. This provides the small-scale nuclide diffusion calculation module with realistic 3D discrete geometric elements. The process involves converting TIF to STL to restore terrain features, using snappyHexMesh for mesh generation to ensure geometric precision, and adapting the OpenFOAM format to meet solver requirements. It also provides the mesh element foundation for initial field (pressure, velocity, etc.) and boundary condition settings, ensuring that subsequent 3D CFD solutions based on the PISO algorithm can be performed on a real geometric scene. The specific steps include the following: The terrain elevation data in TIF format is converted into geometric model files in STL format to restore the terrain undulation features (such as slope and ridge) of the simulated area, turning the abstract elevation data into concrete geometric units that can be recognized by meshing tools. The geometric model files are then converted into OpenFOAM format mesh files. Since the OpenFOAM solver only supports its own proprietary mesh format (such as polyMesh), the general STL geometric model needs to be converted into a basic mesh file in this format to achieve compatibility between the geometric data and the OpenFOAM toolchain, providing an operable input file for subsequent refinement processing by snappyHexMesh. Mesh generation tools, such as snappyHexMesh, are used to discretize the OpenFOAM format mesh file, ensuring that the base mesh closely fits the terrain surface. The mesh density is optimized for key areas to preserve small-scale terrain or structural details and avoid distortion in the simulation of processes such as airflow and nuclide deposition due to coarse mesh. This is how a geometric discretized model of the simulation area is constructed.
[0021] Based on airflow characteristic parameters and the target airborne radionuclides, a turbulence model and governing equations are established, employing a turbulence physics model, such as Standard. k - e The turbulence model provides accurate, efficient, and adaptable turbulence field calculation support for the three-dimensional CFD solution of small-scale airborne radionuclide diffusion, ensuring that the nuclide diffusion process closely matches the airflow characteristics of small-scale scenarios; Standard k - e Turbulent physical models can close the mathematical equations of the Reynolds-averaged simulation method (RANS) by solving for turbulent kinetic energy (TGI). k ) and turbulent dissipation rate ( eThe transport equation transforms the Reynolds stress term, which cannot be directly solved in the RANS equation, into a calculable turbulent viscosity coefficient, providing a mathematical basis for the coupled solution of the airflow field and the nuclide diffusion field.
[0022] Using the three fundamental equations of physics—mass conservation, momentum conservation, and energy conservation—and the diffusion and transport equations of airborne radionuclides, the following RANS equations can be established: The equation for the conservation of mass is as follows: In the above equation, This represents the velocity components after Reynolds average. This represents the component of fluid velocity in the Cartesian coordinate system; The equation for the conservation of momentum is as follows:
[0023] In the above equation, This represents the component of fluid velocity in the Cartesian coordinate system. This represents the time during the simulation process. Represents the coordinate components of the Cartesian coordinate system. Represents the buoyancy term. Indicates the average temperature of the fluid. Indicates reference temperature. Indicates the average pressure of the fluid. Indicates fluid density, - Represents Reynolds stress, Indicates the dynamic viscosity of a fluid. Indicates turbulent dynamic viscosity. Represents turbulent kinetic energy. Represents the Kronecker function; The equation for the conservation of energy is as follows: In the above equation, This represents the fluid temperature after Reynolds average. Indicates the thermal conductivity of a fluid. Indicates specific heat capacity. Indicates the heat source term; The equation for the diffusion and transport of airborne radionuclides is as follows:
[0024] In the above equation, Represents the Reynolds average concentration of airborne radionuclides. Indicates the effective diffusion coefficient. Indicates the nuclide source term. Indicates the coefficient of atmospheric wet deposition term, Indicates the dry settling rate of the nuclide. Indicates the height of the atmospheric boundary layer. This represents the radioactive decay constant of a nuclide.
[0025] RANS represents the instantaneous fluctuations of the fluid velocity field through Reynolds stress terms and uses a turbulence physics model to close the additional equations. The choice of the turbulence physics model is a key factor determining the accuracy of the simulation. Based on the number of transport equations introduced, these models can be categorized into several types, including zero-equation models, single-equation models, and two-equation models. k-e The model is mainly used to solve turbulent kinetic energy. k and turbulent dissipation rate e The transport equation is used, and this model is particularly common in urban atmospheric wind field simulations. Among them, Standard... k-e The model is suitable for simulating fully turbulent flow processes with high Reynolds numbers, and features low computational cost and high operational stability. Standard k-e The equations of the model are as follows: In the above equation, Represents turbulent kinetic energy. This represents the generation of turbulent kinetic energy due to the average velocity gradient. This represents the increase in turbulent kinetic energy attributable to buoyancy. The Prandtl number, representing turbulent kinetic energy, Indicates turbulent kinematic viscosity. Indicates the turbulent dissipation rate. The Prandtl number, representing the dissipation rate, is a turbulent number. All of these represent empirical constants.
[0026] A mathematical framework for fluid motion is constructed by combining governing equations such as mass conservation, momentum conservation, and energy conservation. Simultaneously, the nuclide diffusion and transport equations are incorporated into this framework, enabling precise mapping of airflow characteristic parameters and nuclide parameters to each discrete grid cell. This provides physical constraints and numerical solution basis for subsequent initial field settings and boundary condition configurations. Next, initial field settings are performed on each discrete grid cell in the geometric discrete model, and airflow input / output conditions and nuclide leakage boundary conditions are configured at the boundary of the simulation region. By configuring nuclide leakage boundary conditions to correspond to actual nuclide release scenarios, these boundary constraints limit the physical behavior of the computational domain. Specifically, the steps include: By extracting the airflow characteristic parameters of small-scale airborne environments and the basic parameters of target airborne radionuclides, a precise and suitable input parameter system is provided for small-scale airborne nuclide diffusion simulation. Based on the turbulence physics model and governing equations, the parameter dimensions and numerical ranges that are compatible with the geometric discrete model are determined to achieve parameter analysis. This ensures that the physical parameters of airflow and nuclides are completely matched with the geometric discrete model in terms of dimensions and numerical ranges, avoiding the disconnect between the physical parameters of airflow and nuclides and the geometric discrete model, and ensuring the physical rationality of the simulation from the source. Based on the analyzed parameters, the initial field settings are performed for the pressure, airflow velocity, initial nuclide concentration, turbulent kinetic energy and dissipation rate of each discrete grid cell in the geometric discrete model. This provides an initial state benchmark that is close to reality for CFD numerical solution, allowing the physical quantities of each discrete grid cell to evolve from the initial state of the real scene, reducing the iteration convergence time and improving the accuracy of the results. For the boundary regions of the simulation area, airflow input and output conditions and nuclide leakage boundary conditions are configured respectively to construct realistic physical boundary constraints for the simulation domain, clarify the input and output of airflow, accurately define the nuclide leakage boundary, and ensure the reliability of subsequent calculations of concentration peak and diffusion range.
[0027] To address the characteristics of target airborne radionuclide diffusion, target airborne radionuclides are selected, including I-131, Cs-137, Xe-135, and Kr-88. Calculation parameters are set to transform the physical laws of radionuclide diffusion into numerically solvable mathematical equations. This allows for three-dimensional CFD calculations of the airborne radionuclide diffusion process in a geometrically discrete model, obtaining radionuclide concentration distribution data for each discrete grid cell at different simulation times for radionuclide diffusion risk assessment. This forms a dynamic dataset of the radionuclide diffusion process, comprehensively depicting the spatiotemporal evolution of radionuclides after release from the leakage source, including convection, turbulent diffusion, decay, and wet / dry deposition. The specific steps include: Since the physical properties of different airborne radionuclides vary significantly, selecting a target airborne radionuclide can accurately match the type of nuclide release in the actual scenario, ensuring that the simulation object is highly consistent with the real problem. Based on the basic parameters of the nuclide, select the target airborne radionuclide and set the calculation time step, total calculation time, numerical discretization format and physical model for the nuclide diffusion simulation. The PISO algorithm is used to perform three-dimensional CFD solution on the diffusion process of airborne radionuclides in a geometrically discrete model, and the nuclide concentration distribution data of each discrete grid cell at different simulation times are obtained, forming a dynamic data set of the nuclide diffusion process.
[0028] Transforming dynamic datasets into 3D visualizations, such as using visualization tools like VTK or ParaView, can visually display key information about the spatial distribution, high-concentration accumulation areas, and diffusion direction of radionuclides at different times after they originate from the leak source. Data is extracted and combined with Gnuplot plotting tools to generate curves, which are then verified using sensor data. Ultimately, a small-scale diffusion path of airborne radionuclides is constructed, identifying hazardous areas at different times and planning evacuation routes and the deployment scope of protective equipment. The specific steps include: The dynamic dataset is output to an external data system in VTK format, and the nuclide concentration cloud map and airflow velocity field distribution at different times are visualized. The visualization results allow non-technical personnel to quickly understand the overall trend of nuclide diffusion and avoid misinterpretation of information due to data abstraction. The time series data of nuclide concentration at preset spatial locations and the spatial distribution data of nuclide concentration at preset time nodes are extracted from the dynamic dataset to generate curves of nuclide concentration versus time and curves of nuclide concentration versus diffusion distance. This can transform the dynamic change pattern of the simulation data into quantifiable trend conclusions, providing data support for subsequent analysis of sensitive factors of nuclide diffusion. Based on the visualization results and curve analysis data, and combined with the sensor observation data read by serial communication, the data can be compared and verified. Combined with the actual on-site measurement data of the sensors, the simulated concentration data can be compared with the real monitoring data to verify the accuracy of the simulation results. Finally, the diffusion path of the target airborne radionuclide in a small-scale airborne environment can be constructed.
[0029] This simulation method first acquires the airflow characteristic parameters of the small-scale airborne environment, the basic parameters of the target airborne radionuclide, and the topographic elevation model of the simulation area. Then, the topographic elevation model is converted into a geometric model and meshed, generating an OpenFOAM format mesh file and discretizing it to construct a geometric discrete model of the simulation area. Next, the initial field and boundary conditions of the geometric discrete model are set, along with the airflow input / output conditions and radionuclide leakage boundary conditions of the simulation area boundary. Then, the target airborne radionuclide is selected, and the physical model and calculation parameters are set. A three-dimensional CFD solution is performed on the diffusion process of the airborne radionuclide in the geometric discrete model to obtain the radionuclide concentration distribution data of each discrete mesh element at different simulation times, forming a dynamic dataset of the radionuclide diffusion process. Finally, the dynamic dataset is converted into a three-dimensional visualization result through VTK output, and the data is extracted and combined with Gnuplot to generate curves. This data is then validated using sensor data, ultimately constructing a small-scale airborne radionuclide diffusion path.
[0030] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0031] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are fundamentally similar to the method embodiments, they are described relatively simply; relevant parts can be referred to the descriptions of the method embodiments.
[0032] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for simulating the diffusion of small-scale airborne radionuclides, characterized in that, The method includes the following steps: Obtain airflow characteristic parameters of a small-scale airborne environment, physical property parameters of the target airborne radionuclides, and topographic elevation models of the simulated area; The terrain elevation model is converted into a geometric model, and the geometric model is then meshed and discretized to establish a geometric discretization model of the simulation area. Based on the airflow characteristic parameters and the target airborne radionuclide physical property parameters, the initial field, turbulence physical model and corresponding control equations of the geometric discrete model are set, and the airflow input and output conditions and the boundary conditions of nuclide leakage at the boundary of the simulation region are configured. By selecting a target airborne radionuclide, setting calculation parameters, and performing three-dimensional computational fluid dynamics to solve the nuclide diffusion process, nuclide concentration distribution data is obtained, forming a dynamic dataset describing the nuclide diffusion process. The dynamic dataset is transformed into a three-dimensional visualization result, the data is extracted to generate change curves, and compared and verified with sensor measurement data, thereby constructing a small-scale airborne radionuclide diffusion path.
2. The method for simulating the diffusion of small-scale airborne radionuclides according to claim 1, characterized in that, The geometric discrete model consists of multiple discrete grid cells, each of which corresponds to either the airborne space region or the physical boundary region within the simulation area.
3. The method for simulating the diffusion of small-scale airborne radionuclides according to claim 1, characterized in that, The process of converting the terrain elevation model into a geometric model and performing mesh generation and discretization to construct a geometrically discrete model of the simulated area specifically includes: The terrain elevation data is preprocessed and converted into a geometric model file that can be meshed; The geometric model file is subjected to local or global mesh refinement to match the mesh resolution required for subsequent calculations; The geometric model file is converted into a structured or unstructured mesh file using a mesh generation tool. The mesh file is discretized to generate a geometric discrete model for numerical computation.
4. The method for simulating the diffusion of small-scale airborne radionuclides according to claim 1, characterized in that, The process involves setting the initial field of the geometrically discrete model, the turbulence physics model, and the governing equations based on airflow characteristic parameters and the target airborne radionuclide, and configuring the airflow input / output conditions and radionuclide leakage boundary conditions of the simulation region boundary. Specifically, this includes: Based on the airflow characteristic parameters and the properties of the target airborne radionuclides, a turbulence physics model is selected, and a set of governing equations is established, including the mass conservation equation, momentum conservation equation, energy conservation equation, and airborne radionuclide diffusion and transport equation. Based on the selected turbulence physics model and governing equations, determine the parameter dimensions and numerical range that match the geometric discrete model, and complete the parameter analysis; Based on the analytical results, initial field values are assigned to the pressure, velocity, initial nuclide concentration, turbulent kinetic energy, and turbulent dissipation rate of each discrete grid cell in the geometric discrete model. Configure the airflow inlet conditions, airflow outlet conditions, nuclide leakage conditions, and solid wall conditions at the boundary of the simulated region.
5. The method for simulating the diffusion of small-scale airborne radionuclides according to claim 4, characterized in that, The specific boundary conditions include: airflow inlet boundary conditions, airflow outlet boundary conditions, nuclide concentration boundary conditions, and solid wall no-slip boundary conditions.
6. The method for simulating the diffusion of small-scale airborne radionuclides according to claim 1, characterized in that, The target airborne radionuclide includes at least one of I-131, Cs-137, Xe-135, and Kr-88; the selection of the target airborne radionuclide and the setting of calculation parameters to perform three-dimensional CFD calculations on the radionuclide diffusion process to obtain radionuclide concentration distribution data and form a dynamic data set of the radionuclide diffusion process specifically includes: Based on the simulation requirements and the basic physical properties of the nuclide, the target airborne radionuclide was selected; Set the calculation parameters, including the calculation time step, total simulation duration, and numerical discretization format; Based on the PISO algorithm, the three-dimensional computational fluid dynamics solution of the governing equations is obtained to obtain the nuclide concentration distribution data of each discrete grid cell at different times. By integrating the concentration distribution data from all time steps, a dynamic dataset describing the radionuclide diffusion process is formed.
7. The method for simulating the diffusion of small-scale airborne radionuclides according to claim 1, characterized in that, The process of transforming dynamic datasets into 3D visualizations, extracting data generation curves, verifying them with sensor data, and ultimately constructing small-scale airborne radionuclide diffusion pathways specifically includes: The dynamic dataset is exported to the post-processing system to generate nuclide concentration cloud maps and airflow velocity field distribution maps at different simulation times; Extract time-varying data of nuclide concentration at a specified spatial location and spatial distribution data of nuclide concentration at a specified time from a dynamic dataset, and generate concentration-time curves and concentration-distance curves accordingly. By combining the sensor measurement data obtained through serial communication, the simulated curves and distributions are compared and verified. Based on the verification results, the main diffusion pathways of the target airborne radionuclides in small-scale environments were analyzed and determined.