Radioactive consequence diffusion simulation system in environment

Abstract

The invention relates to the field of environmental pollution simulation and control, and discloses a radioactive consequence diffusion simulation system in an environment, which comprises an input and preprocessing module used for receiving simulation input data, including pollution source position, release intensity, terrain and environmental parameters, and initializing calculation conditions and a concentration field; the multi-medium coupling module is used for realizing material flux coupling among atmosphere, water and soil through interface conditions; the self-adaptive grid module is used for dynamically adjusting the grid density according to the concentration gradient and error estimation in the diffusion result so as to optimize the calculation precision of the hot spot region; and the solving and control module is used for solving the high-dimensional nonlinear partial differential equation set of the multi-medium coupling model and dynamically adjusting the time step. Diffusion behaviors in atmosphere, water and soil are processed by modules, and cross-medium dynamic flux coupling is realized through interface conditions, so that the technical effect of accurately predicting diffusion paths of pollutants in a complex environment is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of environmental pollution simulation and control, in particular to a system for simulating the diffusion of radioactive consequences in an environment. Background Art

[0002] The simulation of the diffusion of radioactive consequences in the environment is the core link in environmental pollution simulation and control technology. It is mainly used to predict the environmental impacts that may be caused by radioactive pollutants in nuclear accidents, waste disposal or other radioactive release events. This technology accurately reproduces the diffusion, migration and interaction processes of pollutants in various media such as the atmosphere, water bodies, and soil by establishing mathematical models and combining numerical calculations. The results of diffusion simulation can not only provide a scientific basis for pollution control and governance measures, but also have important reference value in accident emergency response, long-term environmental risk assessment and policy decision-making.

[0003] However, most existing diffusion simulation technologies independently model a single medium and lack a systematic description of multi-media interaction processes. For example, after a nuclear accident, radioactive pollutants may undergo a multi-stage diffusion process from atmospheric deposition to water bodies and then infiltrate into the soil, but existing technologies usually deal with material exchanges between media through simple static boundary conditions. This treatment method fails to fully consider the dynamics and complexity of medium interactions, resulting in the inability to effectively describe the continuity of flux, thereby affecting the accurate prediction of diffusion paths. This defect is particularly prominent in complex multi-media scenarios, making it difficult for existing simulation technologies to meet actual needs.

[0004] At the same time, fixed grid division is commonly used in traditional diffusion simulations. This method shows obvious limitations when dealing with drastic changes in local pollutant concentrations. In diffusion hotspots, the resolution of fixed grids is often insufficient to capture the details of high concentration gradients, resulting in reduced accuracy of simulation results. In areas of low concentration changes, fixed grids waste a lot of computing resources, resulting in reduced overall computing efficiency. This irrationality in resource allocation greatly limits the application capabilities of simulation systems in complex and changing scenarios.

[0005] In addition, current diffusion simulation technology usually uses a fixed time step to solve partial differential equations in time discretization. Although this method can achieve stable calculations in simple diffusion scenarios, it is not feasible in scenarios with significant multi-media interactions and dynamic changes. When the diffusion hotspot area changes rapidly, the fixed time step strategy may cause numerical oscillations, resulting in unstable calculation results. At the same time, because the time step cannot adapt to local changes in the diffusion speed, the allocation of computing resources is often not efficient enough, and over-computation may occur in some areas, further increasing the computational burden of the system. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a system for simulating the diffusion of radioactive consequences in an environment, which solves the problem that the prior art cannot accurately simulate the diffusion path of radioactive pollutants in multiple media such as the atmosphere, water and soil.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A system for simulating the diffusion of radioactive consequences in an environment, comprising: an input and preprocessing module, for receiving simulation input data, including the location of the pollution source, the release intensity, the terrain and the environmental parameters, and initializing the calculation conditions and the concentration field;

[0008] Multi-media coupling module, including coupling of material fluxes between atmosphere, water and soil through interface conditions;

[0009] Adaptive grid module, which dynamically adjusts the grid density based on the concentration gradient and error estimate in the diffusion results to optimize the calculation accuracy of hot spots;

[0010] The solution and control module is used to solve the high-dimensional nonlinear partial differential equations of the multi-medium coupling model and dynamically adjust the time step to ensure the calculation stability;

[0011] The result visualization and analysis module is used to output dynamic visualization results of diffusion paths, pollutant concentration distribution and affected areas, and generate pollution assessment reports.

[0012] Preferably, the multi-media coupling module includes an atmospheric diffusion unit, a water diffusion unit and a soil migration unit;

[0013] The atmospheric diffusion unit calculates the concentration distribution of pollutants in the air through a convection-diffusion model, wherein the convection term is provided by the wind speed vector field, and the sedimentation behavior calculates the sedimentation rate through the relationship between the particle size, air density and gravity factor.

[0014] Preferably, the water diffusion unit describes the pollutant migration process by a fluid dynamics model, wherein the adsorption behavior is described by a distribution coefficient, and the distribution coefficient is dynamically adjusted by a functional relationship between the surface area of ​​water particles and the pollutant concentration.

[0015] Preferably, the soil migration unit is combined with a soil unsaturated permeability model to describe the permeation and diffusion behavior of pollutants in the soil, and dynamically updates the concentration field through the decay factor of the radioactive material.

[0016] Preferably, the multi-media coupling module realizes the material transfer of pollutants between the atmosphere, water bodies and soil through dynamic matching of interface fluxes, wherein the interface flux is calculated by the concentration difference on both sides of the interface and the interface mass transfer coefficient.

[0017] Preferably, the adaptive grid module dynamically refines the grid units through local residual estimation, wherein the residual takes the change of pollutant concentration gradient as the main reference indicator, and prioritizes the refinement of the grid density in the area of ​​high concentration change.

[0018] Preferably, the adaptive grid module generates a refined grid by a high-order interpolation method, and the interpolation function is a high-order approximation model based on a polynomial.

[0019] Preferably, the solution and control module controls the calculation accuracy and efficiency by dynamically adjusting the time step, and the time step is determined by the functional relationship between the grid unit size, the diffusion coefficient and the convection rate.

[0020] Preferably, the result visualization and analysis module generates a visualization diagram of the pollutant diffusion path through dynamic three-dimensional rendering technology, and marks the pollution hot spots and affected areas.

[0021] A method for simulating the spread of radioactive consequences in an environment comprises the following steps:

[0022] Input the pollution source location, release intensity, terrain and environmental parameters, and initialize the simulation conditions, including the calculation grid and initial concentration field;

[0023] Use the multi-media coupling module to calculate the diffusion behavior of pollutants in the atmosphere, water and soil respectively;

[0024] The coupling of material fluxes between atmosphere and water, and between water and soil is achieved through interface conditions;

[0025] Use the adaptive grid module to dynamically adjust the grid density based on the gradient changes in the concentration field and the residual estimation, and give priority to refining the hot spot areas;

[0026] Use the solver and control module to solve the high-dimensional nonlinear equations of the multi-media coupling model and dynamically adjust the time step according to the changes in the diffusion area;

[0027] Output the visualization results of diffusion path, pollutant concentration distribution and affected area, and generate simulation report.

[0028] The present invention provides a system for simulating the diffusion of radioactive consequences in an environment. It has the following beneficial effects:

[0029] 1. The present invention processes the diffusion behaviors in the atmosphere, water bodies and soil in modules, and realizes dynamic flux coupling across media through interface conditions, thereby achieving the technical effect of accurately predicting the diffusion path of pollutants in a complex environment. Compared with the technical solutions in the prior art that simply process the interaction of media, the present invention solves the problem of insufficient accuracy of the coupling model and the inability to accurately describe the material transfer between multiple media.

[0030] 2. The present invention adopts adaptive grid dynamic adjustment technology to optimize the grid distribution in real time according to the gradient change of the concentration field, so as to significantly improve the simulation accuracy of the high-concentration area and reduce the waste of computing resources in the low-concentration area, thus achieving the effect of giving equal importance to resources and computing efficiency.

[0031] 3. The present invention uses a dynamic time step adjustment strategy, combined with a multi-media model and grid update, to ensure numerical stability and efficiency during the solution process. This solution achieves the technical effect of stable calculation in different diffusion scenarios. Compared with the technical solution of using a fixed time step for solution in the prior art, the present invention overcomes the problems of numerical oscillation and low solution efficiency in large-scale calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0033] Figure 2 It is a schematic diagram of the architecture of the multi-media coupling module of the present invention;

[0034] Figure 3 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Please see attached Figure 1 , an embodiment of the present invention provides a system for simulating the diffusion of radioactive consequences in an environment, comprising: an input and preprocessing module for receiving simulation input data, including the location of the pollution source, the release intensity, the terrain and the environmental parameters, and initializing the calculation conditions and the concentration field;

[0037] Specifically, this module is the basic module in the system of the present invention, and its function is to receive the initial conditions of the simulation, including pollution source related parameters and environmental conditions, and provide the necessary initial concentration field and boundary conditions for the calculation of the subsequent multi-media coupling module. The input and preprocessing module is closely related to the multi-media coupling module, and its output directly affects the initial state of the atmosphere, water body and soil diffusion simulation.

[0038] Receive user input of the pollution source location, release intensity and release duration. The pollution source can be a point source, a line source or a surface source. Specifically, the input parameters of the point source include precise geographic coordinates (e.g., x, y, z) and the release rate; the line source needs to define the path coordinates and the intensity change along the path; the surface source requires the boundary polygon of the area and the release mean. In general, the release intensity of the pollution source is expressed as S (x, y, z, t), where S is a function that changes with time. For example, in some nuclear leakage accidents, the release intensity may decay exponentially, which can be described as:

[0039] S(t)=S0e -λt

[0040] Where: S0 is the initial release intensity (unit: Bq / s); λ is the release attenuation coefficient (unit: s-1); t is the time variable (unit: second).

[0041] As an option, the module also supports the input of dynamic release sources, for example, scenarios where pollutants are dispersed by wind and the source intensity is affected by meteorological conditions. In this case, the release intensity can be calculated in combination with the wind field model as:

[0042] S(x,y,z,t)=S0·f(u(t))

[0043] Where: u(t) is the vector field of wind speed changing with time (unit: m / s); f(u(t)) is the relationship function between wind field and release intensity.

[0044] In a specific implementation, this module also completes the initialization of the grid. In general, the grid division is generated according to the accuracy requirements defined by the user. Grid initialization needs to be combined with terrain characteristics to ensure the accuracy of the boundary area. Specifically, adaptive grid division technology is used to dynamically adjust the grid density according to the steepness of the terrain. For example: in areas with flat terrain, the grid unit can be larger in size (such as 10 meters × 10 meters × 10 meters); in areas with complex terrain, the grid unit size is automatically refined (such as 1 meter x 1 meter x 1 meter); in order to adapt to subsequent dynamic adaptive grid adjustments, the initial grid density can also be optimized in combination with the location of the pollution source.

[0045] For example, the mesh near the leak point will be refined in advance based on the release rate and concentration gradient.

[0046] Specifically, this module constructs the initial concentration field of pollutants C0(x,y,z). For point source release, the initial concentration field can be calculated using the Gaussian distribution function:

[0047]

[0048] Where: x ,σ y ,σz are the standard deviations in the diffusion direction, in meters.

[0049] In the case of extended sources, the concentration field is calculated in an area-weighted manner. For example, for a surface source, the concentration value is uniformly distributed according to the release intensity per unit area.

[0050] Please see attached Figure 2 , the multi-media coupling module realizes the material flux coupling between the atmosphere, water and soil through interface conditions; the multi-media coupling module includes an atmospheric diffusion unit, a water diffusion unit and a soil migration unit;

[0051] Specifically, the multi-medium coupling module is the core module of the system of the present invention, which is used to simulate the diffusion behavior of radioactive substances in the atmosphere, water and soil media, and realize the dynamic flux coupling between media through interface conditions. This module receives the initial concentration field, grid division information and boundary conditions provided by the input and preprocessing module, and completes the dynamic calculation of the cross-medium diffusion process. In general, the diffusion behavior of radioactive substances in different media is subject to the dual constraints of the physical properties of the media and environmental conditions, while its migration between media depends on the continuity of the interface flux. This module realizes the simulation of material transfer between the atmosphere, water and soil through independent modeling of multiple media and unified processing of interface conditions.

[0052] This module designs multiple independent units, including atmospheric diffusion unit, water diffusion unit, soil migration unit and coupling unit. Each unit is responsible for processing the diffusion behavior in different media and performing cross-media calculations through coupling units.

[0053] In this embodiment, the atmospheric diffusion unit is used to simulate the diffusion and deposition behavior of radioactive substances in the air. Specifically, the unit is based on the convection-diffusion model and describes the diffusion process by solving the following control equation:

[0054]

[0055] Where: C a (x, y, z, t) is the concentration of pollutants in the atmosphere, in Bq / m 3 ;u a =(u x ,u y ,u z ) is the wind speed vector field, in m / s; D a is the atmospheric turbulence diffusion tensor, in m 2 / s;S a is the source term of pollutant release in the atmosphere, in Bq / s; R(C a )=v s C a , represents the sedimentation rate, the unit is Bq / m3 / s, where v s is the particle settling velocity, which is determined by the particle size d and air density ρ a Calculation, the formula is:

[0056]

[0057] Where g is the acceleration due to gravity, in m / s 2 ρ p is the particle density, in kg / m 3 ; μ is the air viscosity.

[0058] As an option, the unit supports dynamic wind speed field input, which can adapt to the complex situation where wind speed varies with time and space.

[0059] In some embodiments, the water diffusion unit is used to describe the migration process of pollutants in water bodies such as rivers and lakes. Specifically, the unit is modeled by the following convection-diffusion equation:

[0060]

[0061] Where: C w (x, y, z, t) is the concentration of pollutants in the water, in Bq / m 3 ;u w is the water velocity field, in m / s; D w is the water diffusion coefficient tensor, in m 2 / s;S w is the pollution source term in the water body; K d is the adsorption distribution coefficient of pollutants on the surface of water particles, in m 3 / kg.

[0062] As a possible implementation, the adsorption distribution coefficient K d It can be adjusted dynamically through empirical formulas, for example:

[0063] K d =K d0 ·e -αT

[0064] Where: K d0 is the initial distribution coefficient; α is the temperature correlation coefficient; T is the water temperature in degrees Celsius.

[0065] The soil transport unit is used to simulate the penetration, diffusion and radioactive decay behavior of pollutants in the soil. Generally, the unit uses the following convection-diffusion equation for modeling:

[0066]

[0067] Where: C s (x, y, z, t) is the concentration of pollutants in the soil, in Bq / m 3 ;D s is the soil diffusion coefficient tensor, in m 2 / s; κ is soil permeability, unit is m / s; u s is the soil water velocity vector field; λ is the decay constant of the radioactive substance, in s -1 .

[0068] As an option, the unit supports dynamic adjustment of the soil diffusion coefficient D s , to accommodate changes in soil saturation, e.g.

[0069] D s =D s0 (1-θ)

[0070] Where θ is the soil moisture content.

[0071] In one possible implementation, the coupling unit is used to establish a cross-media dynamic flux exchange model between the atmosphere, water and soil. Specifically, the interface flux is calculated using the Robin boundary condition, and the formula is:

[0072] J interface =k interface (C1-C2)

[0073] Among them: J interface is the interface flux; k interface is the interfacial mass transfer coefficient; C1 and C2 are the concentration values ​​on both sides of the interface respectively.

[0074] In general, the coupling unit inputs the interface flux into the adjacent medium unit as a boundary condition to ensure that the calculation results of each medium model at the interface are consistent.

[0075] Through the above design, the multi-media coupling module can effectively simulate the dynamic diffusion and migration behavior of radioactive substances in the atmosphere, water and soil media, and ensure the continuity of the material flux between the media.

[0076] The adaptive grid module is used to dynamically adjust the grid density according to the concentration gradient and error estimation in the diffusion results to optimize the calculation accuracy of the hot spot area; the grid unit is dynamically refined through local residual estimation, where the residual takes the change of pollutant concentration gradient as the main reference indicator, and the grid density of the high concentration change area is refined first. The refined grid is generated by a high-order interpolation method, and the interpolation function is a high-order approximation model based on polynomials.

[0077] Specifically, this module receives the concentration field and interface flux information output by the multi-media coupling module, evaluates the local concentration change characteristics in real time, and dynamically refines or coarsens the grid structure based on the residual estimate. In general, the goal of this module is to prioritize refining the grid in the diffusion hotspot area to improve the simulation accuracy of the high concentration change area, while reducing the waste of computing resources by coarsening the grid in the low-risk area.

[0078] In one possible implementation, this module is linked to the solver and control module to generate a computational domain that adapts to the diffusion behavior through a dynamically updated grid. The grid division adapts to the global characteristics of the initial conditions and also adjusts in real time according to the local dynamic changes during the pollutant diffusion process.

[0079] The adaptive mesh module includes local error estimation, dynamic mesh refinement and coarsening, and consistency maintenance of mesh nodes. First, local error estimation is based on residuals to identify areas that need to be refined.

[0080] In some embodiments, the mesh is refined using a dynamic refinement method based on high-order interpolation. Specifically, the initial mesh generates new refinement nodes through a high-order interpolation function to enhance the simulation accuracy of the concentration gradient change area. The interpolation function can use a polynomial approximation model, for example:

[0081] P n (x) = a0 + a1x + a2x 2 +…+a n x n

[0082] Where: P n (x) is a polynomial interpolation function; a0, a1, …, a n is the interpolation coefficient, which is determined by the concentration value of the existing grid nodes. In areas where the pollutant concentration changes dramatically, the grid refinement size h is adjusted according to the inverse of the concentration gradient:

[0083]

[0084] in: is the mode length of the concentration gradient.

[0085] In another possible implementation, the adaptive grid module is also responsible for the dynamic coarsening of grid cells. In general, the size of grid cells in low concentration change areas can be appropriately increased to reduce redundant calculations. Specifically, coarsening is achieved by merging adjacent grid cells and using the grid density threshold h max As a reference standard:

[0086] h new =max(h1,h2,…,h n )

[0087] Where: h new is the size of the merged grid; h1,h2,…,h n is the original size of the merged cell.

[0088] Specifically, in the multi-media coupling scenario, the adaptive grid module needs to maintain the grid consistency between different media, especially at the atmosphere-water interface and the water-soil interface. As a possible implementation method, the module ensures the alignment of grid nodes between different media through node projection and coordinate mapping. For example, the coordinate mapping relationship of the interface grid node is:

[0089]

[0090] Where: x ncw ,y new ,z new are the coordinates of the interface projection nodes; x1, y1, z1 and x2, y2, z2 are the grid node coordinates of the adjacent media respectively.

[0091] The solution and control module is used to solve the high-dimensional nonlinear partial differential equations of the multi-media coupling model and dynamically adjust the time step to ensure the calculation stability; the calculation accuracy and efficiency are controlled by dynamically adjusting the time step, and the time step is determined by the functional relationship between the grid unit size, diffusion coefficient and convection rate.

[0092] Specifically, the solution and control module dynamically solves the high-dimensional nonlinear partial differential equations in the multi-media coupling model, and combines the grid update of the adaptive grid module to adjust the time step and numerical solution process in the computational domain in real time. In general, this module works closely with the multi-media coupling module and the adaptive grid module to ensure that the dynamically adjusted grid and initial conditions can be accurately solved, while ensuring the stability and convergence of the numerical solution.

[0093] In one possible implementation, this module is not only responsible for solving complex partial differential equations, but also optimizing the model in accordance with physical constraints. For example, in the calculation of cross-medium transport, the module ensures material conservation based on the dynamic update of interface flux.

[0094] In this embodiment, the solution and control module first discretizes and solves the partial differential equations in the multi-medium coupling module. In general, the diffusion behaviors in the atmosphere, water and soil media are described by the convection-diffusion equations, which are in the unified form:

[0095]

[0096] Where: C(x,y,z,t) is the pollutant concentration in Bq / m 3; u is the velocity vector field, in m / s; D is the diffusion coefficient tensor, in m 2 / s; S is the source term; R(C) is the sedimentation or adsorption term.

[0097] Specifically, the module uses the finite element method to discretize the partial differential equations. In some embodiments, the discretized weak form equation is:

[0098]

[0099] Where: w is the test function; Ω is the computational domain.

[0100] As an option, the module introduces the Petrov-Galerkin method, which effectively suppresses numerical oscillations in the convection-dominated region by choosing an asymmetric test function w.

[0101] In some embodiments, the solution and control module is linked with the adaptive grid module to calculate the time step of the dynamically adjusted grid. In general, the time step Δt needs to meet the CFL condition, and the specific formula is:

[0102]

[0103] Where: h is the minimum size of the grid cell; D is the diffusion coefficient; |u| is the modulus of the velocity. As a possible implementation, the module will calculate the time step of different media separately to adapt to the diffusion characteristics between multiple media. For example, the atmospheric diffusion area usually has a large convection rate, while soil migration is mainly based on diffusion and infiltration, so the time step of the two may be significantly different.

[0104] Specifically, when dealing with interface fluxes between the atmosphere, water, and soil, the solver and control module combines the flux boundary conditions provided by the multi-medium coupling module to ensure conservation of material flux. In general, the module inputs this flux as a boundary condition into the partial differential equations of the adjacent media, thereby achieving a fully coupled solution of the multi-medium model.

[0105] In some embodiments, the solution and control module also introduces parallel computing strategies, especially for complex computing scenarios of large-scale grids. For example, in a hardware environment supported by a GPU, the module can divide the entire computing domain into multiple subdomains, each of which is processed by an independent computing unit, and synchronize the computing between subdomains through boundary conditions.

[0106] Through the above design, the solution and control module can efficiently solve complex multi-media partial differential equations based on the dynamic adjustment of the grid and time step. The output of this module includes updated concentration field, interface flux and convergence evaluation data, which provide direct input for the result visualization and analysis module.

[0107] The result visualization and analysis module is used to output dynamic visualization results of diffusion paths, pollutant concentration distribution and affected areas, and generate pollution assessment reports. Dynamic 3D rendering technology is used to generate visualization maps of pollutant diffusion paths, and pollution hot spots and affected areas are marked.

[0108] Specifically, the module receives pollutant concentration fields, interface fluxes and dynamic grid information, and generates three-dimensional diffusion path maps, hotspot area annotations and dynamic displays of pollution ranges through graphics rendering and data processing technology.

[0109] In this embodiment, the result visualization and analysis module first performs grid rendering on the concentration field data. Generally, the pollutant concentration field C (x, y, z, t) is obtained by solving the multi-media coupling model. In order to perform a three-dimensional display, the module maps the concentration field to a three-dimensional grid, and the color or transparency of each grid cell reflects the concentration value in the cell.

[0110] Specifically, the module uses color mapping technology to visualize the concentration field. The concentration value C is converted into a color value according to the following linear mapping formula:

[0111]

[0112] Among them: BaseColor is the basic color corresponding to low concentration (such as blue); Gradient is the color gradient vector; C min and C max As an option, the module can adjust the gradient range to highlight high concentration areas, making pollution hot spots more visible.

[0113] Through the above technical design, the result visualization and analysis module realizes the intuitive display and scientific analysis of complex pollution diffusion results, providing strong support for environmental pollution assessment and emergency decision-making.

[0114] Please see attached Figure 3 , a method for simulating the spread of radioactive consequences in an environment, comprising the following steps:

[0115] Input the pollution source location, release intensity, terrain and environmental parameters, and initialize the simulation conditions, including the calculation grid and initial concentration field;

[0116] Use the multi-media coupling module to calculate the diffusion behavior of pollutants in the atmosphere, water and soil respectively;

[0117] The coupling of material fluxes between atmosphere and water, and between water and soil is achieved through interface conditions;

[0118] Use the adaptive grid module to dynamically adjust the grid density based on the gradient changes in the concentration field and the residual estimation, and give priority to refining the hot spot areas;

[0119] Use the solver and control module to solve the high-dimensional nonlinear equations of the multi-media coupling model and dynamically adjust the time step according to the changes in the diffusion area;

[0120] Output the visualization results of diffusion path, pollutant concentration distribution and affected area, and generate simulation report.

[0121] Specifically, first of all, you need to input the basic information of the pollution source. The pollution source includes the location, release intensity, and leakage duration. This information can be obtained from the accident monitoring system or provided through on-site investigation. The location is entered in the form of three-dimensional coordinates, and the release intensity is expressed as a function of time. For line sources or surface sources, the leakage path or boundary area is also required. In addition, detailed terrain and environmental parameters must be entered, including surrounding terrain characteristics, meteorological conditions (horizontal wind speed, wind direction, temperature, humidity) and water properties (river flow rate, lake depth, etc.). These data are used to initialize the simulation conditions.

[0122] After completing the data input, the simulation conditions need to be initialized. First, generate a computational grid and divide the entire simulation area into several grid cells. The density of the initial grid is determined according to the location of the leak source and the complexity of the surrounding terrain. Generally, the grid near the leak source will be refined, while the area far from the pollution source will be divided into coarser grids. After the grid is initialized, the initial concentration field is generated according to the input pollution source parameters, and the initial scene is constructed in combination with the boundary conditions.

[0123] Next, the multi-media coupling module is used to calculate the diffusion behavior of pollutants in the atmosphere, water bodies, and soil respectively. The atmospheric diffusion process is mainly driven by wind speed and turbulent diffusion, while taking into account the settling effect of particulate matter. The module dynamically updates the wind field characteristics based on meteorological data and distributes the concentration changes of pollutants throughout the computational grid. The diffusion behavior in water bodies is affected by water flow velocity and turbulent diffusion. The module dynamically tracks the migration of pollutants by simulating the water flow path and the adsorption characteristics of particulate matter. The diffusion behavior in the soil needs to combine the characteristics of permeability, soil type, and radioactive decay to simulate the migration laws of pollutants in the saturated and unsaturated ranges. The simulation of these individual media provides the basis for the entire coupled system.

[0124] In order to realize the material exchange between the atmosphere, water bodies and soil, the present invention establishes a dynamic coupling model between media through interface conditions. The interface conditions deal with the amount of pollutants deposited from the atmosphere to the water body, and the flux of water body infiltration into the soil. The system will calculate the transfer efficiency based on the difference in pollutant concentration and the mass transfer coefficient between the media, and dynamically adjust the transfer intensity of the boundary conditions. The coupling between the atmosphere and the water body needs to consider the sedimentation velocity and the surface area of ​​the water body, while the coupling between the water body and the soil combines the soil permeability coefficient and the water body flow rate to achieve cross-medium material flux coupling.

[0125] As the pollutant concentration field gradually diffuses, the adaptive grid module needs to be used to dynamically adjust the grid density. The module will identify hot spots in real time based on changes in concentration gradients, and will prioritize refining the grid in areas of high concentration changes. At the same time, for low-concentration or stable areas, the module will automatically coarsen the grid to reduce the consumption of computing resources. The identification of hot spots is completed through the gradient distribution of the concentration field. The module evaluates the concentration changes of each grid cell, identifies high-gradient areas, and performs local refinement. After the grid is adjusted, the module will generate updated grid information for use by the solution module.

[0126] The solution and control module is responsible for numerically solving the high-dimensional nonlinear equations of the multi-medium coupling model. The module receives the grid structure updated by the adaptive grid module, and completes the numerical calculation in each time step in combination with the dynamically adjusted time step. For diffusion hotspot areas, the time step will be appropriately shortened to improve the accuracy of local solutions; for stable areas, the time step will be appropriately relaxed to improve computational efficiency. The module will also monitor the convergence of the solution process in real time, and avoid the accumulation of computational errors by adjusting the numerical iteration method. At the same time, the module passes the calculation results to the result visualization module to provide data support for subsequent display and analysis.

[0127] Finally, the result visualization and analysis module generates dynamic display results of the diffusion path, concentration distribution and affected areas of pollutants. The module uses three-dimensional rendering technology to display the diffusion process of pollutants in a dynamic form. Users can observe the overall picture of the diffusion through interactive methods such as rotation and zooming, and quickly locate high-concentration pollution points by combining the hot spot area annotation function. In addition, the module also provides data analysis functions, such as statistical volume change trends of polluted areas and generating risk maps of cumulative distribution of pollutants.

[0128] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for simulating the diffusion of radioactive consequences in an environment, characterized in that: include: The input and preprocessing module is used to receive simulation input data, including pollution source location, release intensity, terrain and environmental parameters, and initialize calculation conditions and concentration fields; Multi-media coupling module, including coupling of material fluxes between atmosphere, water and soil through interface conditions; Adaptive grid module, which dynamically adjusts the grid density based on the concentration gradient and error estimate in the diffusion results to optimize the calculation accuracy of hot spots; The solution and control module is used to solve the high-dimensional nonlinear partial differential equations of the multi-medium coupling model and dynamically adjust the time step to ensure the calculation stability; The result visualization and analysis module is used to output dynamic visualization results of diffusion paths, pollutant concentration distribution and affected areas, and generate pollution assessment reports.

2. A system for simulating the spread of radioactive consequences in an environment according to claim 1, characterized in that: The multi-media coupling module includes an atmospheric diffusion unit, a water diffusion unit and a soil migration unit; The atmospheric diffusion unit calculates the concentration distribution of pollutants in the air through a convection-diffusion model, wherein the convection term is provided by the wind speed vector field, and the sedimentation behavior calculates the sedimentation rate through the relationship between the particle size, air density and gravity factor.

3. A system for simulating the spread of radioactive consequences in an environment according to claim 2, characterized in that: The water diffusion unit describes the pollutant migration process through a fluid dynamics model, wherein the adsorption behavior is described by a distribution coefficient, and the distribution coefficient is dynamically adjusted by a functional relationship between the surface area of ​​water particles and the pollutant concentration.

4. The system for simulating the diffusion of radioactive consequences in an environment according to claim 2, characterized in that: The soil migration unit combines the soil unsaturated permeability model to describe the permeation and diffusion behavior of pollutants in the soil, and dynamically updates the concentration field through the decay factor of the radioactive material.

5. The system for simulating the diffusion of radioactive consequences in an environment according to claim 1, characterized in that: The multi-media coupling module realizes the material transfer of pollutants between the atmosphere, water bodies and soil through dynamic matching of interface fluxes, wherein the interface flux is calculated by the concentration difference on both sides of the interface and the interface mass transfer coefficient.

6. The system for simulating the spread of radioactive consequences in an environment according to claim 1, characterized in that: The adaptive grid module dynamically refines the grid cells through local residual estimation, wherein the residual takes the change of pollutant concentration gradient as the main reference indicator and prioritizes the refinement of the grid density in the area of ​​high concentration change.

7. The system for simulating the spread of radioactive consequences in an environment according to claim 1, characterized in that: The adaptive grid module generates a refined grid by a high-order interpolation method, and the interpolation function is a high-order approximation model based on a polynomial.

8. The system for simulating the spread of radioactive consequences in an environment according to claim 1, characterized in that: The solution and control module controls the calculation accuracy and efficiency by dynamically adjusting the time step, and the time step is determined by the functional relationship between the grid unit size, the diffusion coefficient and the convection rate.

9. The system for simulating the spread of radioactive consequences in an environment according to claim 1, characterized in that: The result visualization and analysis module generates a visualization diagram of the pollutant diffusion path through dynamic three-dimensional rendering technology, and marks the pollution hot spots and affected areas.

10. A method for simulating the spread of radioactive consequences in an environment, according to a system for simulating the spread of radioactive consequences in an environment according to any one of claims 1 to 9, characterized in that: The following steps are involved: Input the pollution source location, release intensity, terrain and environmental parameters, and initialize the simulation conditions, including the calculation grid and initial concentration field; Use the multi-media coupling module to calculate the diffusion behavior of pollutants in the atmosphere, water and soil respectively; The coupling of material fluxes between atmosphere and water, and between water and soil is achieved through interface conditions; Use the adaptive grid module to dynamically adjust the grid density based on the gradient changes in the concentration field and the residual estimation, and give priority to refining the hot spot areas; Use the solver and control module to solve the high-dimensional nonlinear equations of the multi-media coupling model and dynamically adjust the time step according to the changes in the diffusion area; Output the visualization results of diffusion path, pollutant concentration distribution and affected area, and generate simulation report.

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