A system for simulating the spread of radioactive consequences in an environment

Abstract

The present application relates to the field of environmental pollution simulation and control, and discloses a system for simulating the diffusion of radioactive consequences in the 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 environmental parameters, and initializing the calculation conditions and the concentration field; a multi-medium coupling module for realizing the coupling of the material flux between the atmosphere, water body and soil through the interface conditions; an adaptive grid module for dynamically adjusting the grid density according to the concentration gradient and error estimation in the diffusion results, so as to optimize the calculation accuracy of the hot spot area; a solving and control module for solving the high-dimensional nonlinear partial differential equation set of the multi-medium coupling model and dynamically adjusting the time step. By processing the diffusion behaviors in the atmosphere, water body and soil in a modular manner and realizing the dynamic flux coupling across the media through the interface conditions, the technical effect of accurately predicting the diffusion path of the pollutants in the complex environment is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental pollution simulation and control, in particular to a system for simulating the diffusion of radioactive consequences in the environment. BACKGROUND

[0002] The simulation of radioactive consequence diffusion in the environment is a core link in the field of environmental pollution simulation and control. It is mainly used to predict the environmental impact that radioactive pollutants may cause 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 atmosphere, water body and soil by establishing mathematical models and combining numerical calculations. The results of diffusion simulation not only provide scientific basis for pollution control and management measures, but also have important reference value in accident emergency response, long-term environmental risk assessment and policy decision-making.

[0003] However, existing diffusion simulation techniques mostly model single media independently, lacking 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 body and then penetrate into soil. However, existing techniques usually handle the exchange of substances between media through simple static boundary conditions. This approach fails to fully consider the dynamics and complexity of media interaction, resulting in ineffective description of the continuity of flux and affecting the accurate prediction of diffusion paths. This defect is particularly prominent in complex multi-media scenarios, making it difficult for existing simulation techniques to meet actual needs.

[0004] At the same time, traditional diffusion simulation generally adopts fixed grid division method, which has obvious limitations when dealing with local concentration of pollutants. In the diffusion hotspot area, the resolution of fixed grid is often insufficient to capture the details of high concentration gradient, resulting in a decrease in the accuracy of simulation results. In low concentration change areas, fixed grid wastes a lot of computing resources, leading to a decrease in overall computing efficiency. This unreasonable allocation of resources greatly limits the application ability of the simulation system in complex and variable scenarios.

[0005] In addition, the current diffusion simulation technology usually adopts fixed time step to solve partial differential equations in time discretization. Although this method can achieve stable calculation in simple diffusion scenarios, it is not sufficient in scenarios with significant multi-media interaction and dynamic changes. When the diffusion hotspot area changes rapidly, the fixed time step strategy may cause numerical oscillation, leading to instability of the calculation results. At the same time, since the time step cannot adapt to the local changes of diffusion speed, the allocation of computing resources is often not efficient, and some areas may have excessive calculation, further increasing the computational burden of the system. SUMMARY

[0006] The simulation system for radioactive consequence diffusion in environment provided by the application solves the problem that the prior art cannot accurately simulate the diffusion path of radioactive pollutants in the atmosphere, water body and soil medium.

[0007] To achieve the above object, the application is implemented by the following technical scheme: a simulation system for radioactive consequence diffusion in environment, comprising: an input and preprocessing module for receiving simulation input data, including the position of the pollution source, the release intensity, the terrain and the environmental parameters, and initializing the calculation conditions and the concentration field;

[0008] A multi-medium coupling module, which realizes the coupling of the material flux among the atmosphere, water body and soil through the interface conditions;

[0009] An adaptive grid module for dynamically adjusting the grid density according to the concentration gradient and error estimation in the diffusion result, so as to optimize the calculation accuracy of the hot spot area;

[0010] A solving and control module for solving the high-dimensional nonlinear partial differential equation set of the multi-medium coupling model and dynamically adjusting the time step to ensure the calculation stability;

[0011] A result visualization and analysis module for outputting the dynamic visualization results of the diffusion path, the pollutant concentration distribution and the affected area, and generating the pollution assessment report.

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

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

[0014] Preferably, the water body diffusion unit describes the pollutant migration process through the fluid dynamics model, wherein the adsorption behavior is described by the distribution coefficient, and the distribution coefficient is dynamically adjusted by the function relationship between the water body particle surface area and the pollutant concentration.

[0015] Preferably, the soil migration unit combines the unsaturated infiltration model of the soil to describe the infiltration and diffusion behavior of the pollutants in the soil, and dynamically updates the concentration field by the decay factor of the radioactive material.

[0016] Preferably, the multi-medium coupling module realizes the material transfer among the atmosphere, water body and soil through the dynamic matching of the interface flux, 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 mesh module dynamically refines the mesh elements by local residual estimation, wherein the residual is mainly referenced to the change of the pollutant concentration gradient, and the mesh density in the high concentration change area is preferentially refined.

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

[0019] Preferably, the solving and control module controls the calculation accuracy and efficiency by dynamically adjusting the time step, and the time step is determined by the function relationship of the mesh element size, the diffusion coefficient and the convection rate.

[0020] Preferably, the result visualization and analysis module generates a visual diagram of the pollutant diffusion path by a dynamic three-dimensional rendering technology, and labels the pollution hot spot area and the affected range.

[0021] A simulation method of radioactive consequence diffusion in an environment, comprising the following steps:

[0022] Inputting the pollution source position, release intensity, terrain and environmental parameters, initializing the simulation conditions, including calculating the mesh and the initial concentration field;

[0023] Using a multi-medium coupling module to calculate the diffusion behavior of pollutants in the atmosphere, water body and soil respectively;

[0024] The material flux coupling between the atmosphere-water body and the water body-soil is realized through the interface condition;

[0025] Using an adaptive mesh module to dynamically adjust the mesh density according to the gradient change and residual estimation in the concentration field, and preferentially refining the hot spot area;

[0026] Using a solving and control module to solve the high-dimensional nonlinear equation set of the multi-medium coupling model, and dynamically adjusting the time step according to the diffusion area change;

[0027] Outputting the visualization results of the diffusion path, the pollutant concentration distribution and the affected area, and generating a simulation report.

[0028] The present application provides a radioactive consequence diffusion simulation system in an environment. It has the following beneficial effects:

[0029] 1、The present application processes the diffusion behavior in the atmosphere, water body and soil in a module, and realizes the dynamic flux coupling across media through the interface condition, so as to achieve the technical effect of accurately predicting the diffusion path of pollutants in a complex environment, compared with the technical scheme of simply processing the medium interaction in the prior art, the present application solves the problems of insufficient coupling model precision and inability to accurately describe the material transfer between multi-media.

[0030] 2、The application adopts adaptive grid dynamic adjustment technology, optimizes grid distribution in real time according to the gradient change of the concentration field, significantly improves the simulation accuracy of the high concentration area, reduces the waste of computing resources in the low concentration area, and achieves the effect of balancing resources and computing efficiency.

[0031] 3、The application adopts time step dynamic adjustment strategy, combines the multi-medium model and grid updating, and ensures the stability and efficiency of the numerical value in the solution process. BRIEF DESCRIPTION OF DRAWINGS

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

[0033] Figure 2 It is a multi-medium coupling module architecture schematic diagram of the application;

[0034] Figure 3 It is a method step schematic diagram of the application. DETAILED DESCRIPTION

[0035] The technical solutions of the application will be described clearly and completely below with reference to the drawings of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0036] Please refer to the drawings of the application Figure 1 The embodiment of the application provides a radioactive consequence diffusion simulation system in an environment, which comprises an input and preprocessing module, which is used for receiving simulation input data, including pollution source position, release intensity, terrain and environmental parameters, and initializing calculation conditions and concentration field.

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

[0038] The user inputs the location, release intensity and release duration of the pollution source. The pollution source can be a point source, a line source or an area source. Specifically, the input parameters of a point source include precise geographic coordinates (e.g., x, y, z) and release rate; a line source requires definition of path coordinates and its intensity variation along the path; an area source requires provision of the boundary polygon of the area and the release mean value. Generally, the release intensity of the pollution source is denoted as S(x, y, z, t), where S is a function of time. For example, in some nuclear leakage accidents, the release intensity can be exponentially decaying, 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 decay coefficient (unit: s-1); and t is the time variable (unit: second).

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

[0042] S(x, y, z, t) = S0·f(u(t))

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

[0044] In a specific implementation, the module also completes the initialization of the grid. Generally, the grid division is generated according to the accuracy requirement defined by the user. The grid initialization needs to be combined with the terrain features to ensure the accuracy of the boundary area. Specifically, an adaptive grid division technique is adopted to dynamically adjust the grid density according to the steepness of the terrain. For example: in a flat terrain area, the grid unit can take a larger size (such as 10 m x 10 m x 10 m); in a complex terrain area, the grid unit size is automatically refined (such as 1 m x 1 m x 1 m); in order to adapt to the subsequent dynamic adaptive grid adjustment, the initial grid density can also be optimized in combination with the pollution source location.

[0045] For example, the grid near the leakage point will be refined in advance according to the release rate and the concentration gradient.

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

[0047]

[0048] where σ x , σ y , σz Standard deviation in the direction of dispersion, unit: meter.

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

[0050] Please refer to the accompanying Figure 2 The multi-medium coupling module realizes the coupling of material flux among the atmosphere, water body and soil through the interface condition; the multi-medium coupling module comprises an atmospheric diffusion unit, a water body diffusion unit and a soil migration unit.

[0051] Specifically, the multi-medium coupling module is the core module of the system, used for simulating the diffusion behavior of radioactive substances in the atmosphere, water body and soil medium, and realizing the dynamic flux coupling among the media through the interface condition. The module receives the initial concentration field, grid division information and boundary condition provided by the input and preprocessing module, and completes the dynamic calculation of the cross-medium diffusion process. Generally, the diffusion behavior of radioactive substances in different media is subject to the dual constraints of medium physical properties and environmental conditions, and the migration among the media depends on the continuity of the interface flux. Through the independent modeling of multiple media and the unified processing of the interface condition, the module realizes the simulation of the material transfer among the atmosphere, water body and soil.

[0052] The module designs multiple independent units, including an atmospheric diffusion unit, a water body diffusion unit, a soil migration unit and a coupling unit, each unit being responsible for processing the diffusion behavior in different media, and performing the cross-medium calculation through the coupling unit.

[0053] In the embodiment, the atmospheric diffusion unit is used for simulating 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] Wherein: C a (x,y,z,t) is the concentration of pollutants in the atmosphere, unit: Bq / m 3 ; u a =(u x ,u y ,u z ) is the wind speed vector field, unit: m / s; D a is the atmospheric turbulent diffusion tensor, unit: m 2 / s; S a is the pollutant release source term in the atmosphere, unit: Bq / s; R(C a ) = v s C a , indicating the deposition rate, unit: Bq / m3 v, where v s is the settling velocity of the particle, specifically given by the particle diameter d and the air density p a The calculation is given by:

[0056]

[0057] where g is the gravitational acceleration, in m / s 2 ; p p is the particle density, in kg / m 3 ; and m is the viscosity of air.

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

[0059] In some embodiments, the water body diffusion unit is used to describe the migration process of pollutants in rivers, lakes, and other water bodies. 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 body, in Bq / m 3 ; u w is the water flow velocity field, in m / s; D w is the water body diffusion coefficient tensor, in m 2 / s; S w is the pollution source term in the water body; and K d is the adsorption partition coefficient of the pollutant on the surface of the water body particles, in m 3 / kg.

[0062] As a possible implementation, the adsorption partition coefficient K d can be dynamically adjusted by an empirical formula, for example:

[0063] K d = K d0 · e -αT

[0064] where K d0 is the initial partition coefficient; a is the temperature-dependent coefficient; and T is the water temperature, in degrees Celsius.

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

[0066]

[0067] Where: C s (x,y,z,t) represents the concentration of pollutants in the soil, in units of Bq / m³. 3 ;D s This is the soil diffusion coefficient tensor, in meters. 2 / s; κ is soil permeability, in m / s; u s λ represents the soil water velocity vector field; λ is the decay constant of the radioactive material, in seconds. -1 .

[0068] As an option, this unit supports dynamic adjustment of the soil diffusion coefficient D. s To adapt to changes in soil saturation, for example:

[0069] D s =D s0 ·(1-θ)

[0070] Where θ represents soil moisture content.

[0071] In one possible implementation, coupling elements are used to establish a dynamic flux exchange model across media, including the atmosphere, water, and soil. Specifically, the interface flux is calculated using Robin boundary conditions, as shown in the following formula:

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

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

[0074] In general, the coupling element uses the interface flux as a boundary condition input to the adjacent medium element 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 materials in atmospheric, water and soil media, and ensure the continuity of material flux between media.

[0076] An adaptive mesh module dynamically adjusts the mesh density based on the concentration gradient and error estimation in the diffusion results to optimize the computational accuracy of hotspot regions. It dynamically refines the mesh cells through local residual estimation, where the residual uses the change in pollutant concentration gradient as the primary reference indicator, prioritizing the refinement of mesh density in areas of high concentration variation. A refined mesh is generated using a high-order interpolation method, with the interpolation function being a polynomial-based high-order approximation model.

[0077] In particular, the module receives the concentration field and interface flux information outputted by the multi-medium coupling module, evaluates the local concentration variation characteristics in real time, and refines or coarsens the grid structure according to the residual error. Generally, the module aims to refine the grid in the diffusion hotspot area preferentially, so as to improve the simulation accuracy of the high-concentration variation area, and coarsen the grid in the low-risk area to reduce the waste of computing resources.

[0078] In a possible implementation, the module is associated with the solving and control module to generate a calculation domain that adapts to the diffusion behavior through the dynamically updated grid. The grid division adapts to the global characteristics of the initial condition and is adjusted in real time according to the local dynamic change in the pollutant diffusion process.

[0079] The adaptive grid module includes local error estimation, dynamic grid refinement and coarsening, and consistency maintenance of the grid nodes. First, the local error estimation is based on the residual error and is used to identify the area that needs to be refined.

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

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

[0082] wherein P n (x) is a polynomial interpolation function; a0, a1, …, a n are interpolation coefficients determined by the concentration values of the existing grid nodes. In the area with a sharp change in the concentration of the pollutant, the grid refinement size h is adjusted according to the reciprocal of the concentration gradient:

[0083]

[0084] wherein: is the modulus of the concentration gradient.

[0085] In another possible implementation, the adaptive grid module is also responsible for the dynamic coarsening of the grid cells. Generally, the size of the grid cells in the low-concentration variation area can be appropriately increased to reduce redundant calculation. Specifically, the coarsening is realized by merging adjacent grid cells, and the grid density threshold h max is taken as the reference standard:

[0086] h new =max(h1,h2,…,h n )

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

[0088] Specifically, in the multi-medium 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, the module ensures the alignment of the 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] wherein: x ncw ,y new ,z new are the coordinates of the interface projection node; x1, y1, z1 and x2, y2, z2 are the grid node coordinates of the adjacent media, respectively.

[0091] The solving and control module is used to solve the high-dimensional nonlinear partial differential equation set of the multi-medium coupling model and dynamically adjust the time step to ensure the calculation stability; the calculation precision and efficiency are controlled by dynamically adjusting the time step, and the time step is determined by the functional relationship of the grid cell size, the diffusion coefficient and the convection rate.

[0092] Specifically, the solving and control module dynamically solves the high-dimensional nonlinear partial differential equation set in the multi-medium coupling model, and combines the grid update of the adaptive grid module to adjust the time step and the numerical solution process in the calculation domain in real time. Generally, this module highly cooperates with the multi-medium coupling module and the adaptive grid module to ensure that the dynamically adjusted grid and the initial conditions can be accurately solved, while ensuring the stability and convergence of the numerical solution.

[0093] In a possible implementation, this module is not only responsible for solving complex partial differential equation sets, but also optimizes the model combined with physical constraint conditions. For example, in the calculation of cross-medium transfer, the module ensures the conservation of mass according to the dynamic update of the interface flux.

[0094] In this embodiment, the solving and control module first discretizes and solves the partial differential equation set in the multi-medium coupling module. Generally, the diffusion behavior in the atmosphere, water body and soil medium is described by the convection-diffusion equation, and its unified form is:

[0095]

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

[0097] Specifically, the module discretizes the partial differential equations by finite element method. In some embodiments, the discretized weak form equations are:

[0098]

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

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

[0101] In some embodiments, the solution and control module works with the adaptive mesh module to perform time step calculation on the dynamically adjusted mesh. Generally, the time step Δt needs to satisfy the CFL condition, which is specifically formulated as:

[0102]

[0103] where h is the minimum size of the mesh element; D is the diffusion coefficient; |u| is the modulus of the flow velocity. As a possible implementation, the module can perform separate calculation of the time step for different media to adapt to the diffusion characteristics between multiple media. For example, the atmospheric diffusion region usually has a large convection rate, while the soil migration is mainly dominated by diffusion and infiltration, so there may be significant differences in the time step between the two.

[0104] Specifically, when dealing with the interface flux between the atmosphere, water body and soil, the solution and control module combines with the flux boundary condition provided by the multi-medium coupling module to ensure the conservation of mass flux. Generally, the module inputs this flux as a boundary condition into the partial differential equations of the adjacent medium, thereby realizing the 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 mesh. For example, in a hardware environment supported by GPU, the module can divide the entire computational domain into multiple sub-domains, each of which is processed by an independent computing unit, and the synchronous calculation between sub-domains is realized through boundary conditions.

[0106] Through the above design, the solution and control module can efficiently solve complex multi-medium partial differential equations on the basis of dynamically adjusting the mesh and time step. The results output by this module include the 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 path, pollutant concentration distribution and affected area, and generate a pollution assessment report. A visualization graph of the pollutant diffusion path is generated by dynamic three-dimensional rendering technology, and the pollution hot spot area and affected range are marked.

[0108] Specifically, the module receives the pollutant concentration field, interface flux and dynamic grid information, and generates a three-dimensional diffusion path graph, hot spot area marking and dynamic display of the pollution range through graphic rendering and data processing technology.

[0109] In this embodiment, the result visualization and analysis module first renders the concentration field data in a grid. Generally, the pollutant concentration field C(x, y, z, t) is obtained by solving a multi-medium coupling model. In order to display in three dimensions, 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 to a color value according to the following linear mapping formula:

[0111]

[0112] where BaseColor is the base color corresponding to low concentration (such as blue); Gradient is the color gradient vector; C min and C max are the minimum and maximum values of the concentration, respectively. As an option, the module can highlight the high concentration area by adjusting the gradient range, making the pollution hot spot area more clearly visible.

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

[0114] Please refer to the attached Figure 3 A method for simulating the diffusion of radioactive consequences in an environment, comprising the following steps:

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

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

[0117] Material flux coupling between atmosphere-water body and water body-soil is achieved through interface conditions;

[0118] The adaptive mesh module is used to dynamically adjust the grid density based on the gradient changes in the concentration field and residual estimates, with a preference for refining hot spot areas.

[0119] The high-dimensional nonlinear equations of the multi-medium coupling model are solved using the solving and control module, and the time step is dynamically adjusted according to the changes in the diffusion region.

[0120] The visualization results of the diffusion path, pollutant concentration distribution and affected area are output, and the simulation report is generated.

[0121] Specifically, first, the basic information of the pollution source needs to be input. The pollution source includes location, release intensity and leakage duration. These information can be obtained from the accident monitoring system, or provided through field investigation. The location is input 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 also needs to be provided. In addition, detailed topographic and environmental parameters must be input, including surrounding topographic features, meteorological conditions (horizontal wind speed, wind direction, temperature, humidity) and water body 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 the calculation grid, divide the entire simulation area into several grid cells. The density of the initial grid is determined according to the location of the leakage source and the complexity of the surrounding terrain. Generally, the grid near the leakage 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 completed in combination with the boundary conditions.

[0123] Next, the multi-medium coupling module is used to calculate the diffusion behavior of pollutants in the atmosphere, water body and soil respectively. The atmospheric diffusion process is mainly driven by wind speed and turbulent diffusion, while considering the settling effect of particulate matter. The module will dynamically update the wind field characteristics according to the meteorological data, and distribute the concentration change of pollutants in the entire calculation grid. The diffusion behavior in the water body is affected by the water flow velocity and turbulent diffusion, and 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 be combined with the characteristics of permeability, soil type and radioactive decay to simulate the migration law of pollutants in the saturated and unsaturated zones. The simulation of these individual media provides the basis for the entire coupling system.

[0124] To realize the exchange of substances between the atmosphere, water body and soil, the present application 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 to the soil. The system will calculate the transfer efficiency according to the concentration difference of pollutants and the mass transfer coefficient between media, while dynamically adjusting the transfer strength of the boundary conditions. The coupling of atmosphere-water body needs to consider the deposition velocity and the surface area of the water body, and the coupling of water body-soil combines the soil permeability coefficient and the water flow rate, to realize the coupling of mass flux across media.

[0125] When the concentration field of pollutants gradually diffuses, it is necessary to use an adaptive grid module to dynamically adjust the grid density. The module will identify hot spot areas in real time according to the change of concentration gradient, and preferentially refine the grid in the area with high concentration change. 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 spot areas is completed through the gradient distribution of the concentration field, and the module evaluates the concentration change of each grid unit, identifies the area with high gradient and performs local refinement. After grid adjustment, the module will generate updated grid information for the solving module to use.

[0126] The solving and control module is responsible for numerically solving the high-dimensional nonlinear equation set of the multi-medium coupling model. The module receives the updated grid structure from the adaptive grid module, and combines the dynamically adjusted time step to complete the numerical calculation in each time step. For diffusion hot spot areas, the time step will be appropriately shortened to improve the accuracy of local calculation; for stable areas, the time step will be appropriately relaxed to improve the calculation efficiency. The module also monitors the convergence in the solving process in real time, and avoids the accumulation of calculation errors by adjusting the numerical iteration. At the same time, the module will pass 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 area of pollutants. The module uses three-dimensional rendering technology to dynamically display the diffusion process of pollutants. Users can observe the overall diffusion by rotating, zooming and other interactive methods, and quickly locate high-concentration pollution points combined with the hot spot area labeling function. In addition, the module also provides data analysis functions, such as statistical volume change trend of contaminated areas, and generates risk maps of pollutant accumulation distribution.

[0128] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for simulating the spread of radioactive consequences in an environment, characterized in that, The method comprises the following steps: inputting the position of the pollution source, the release intensity, the terrain and the environmental parameters, and initializing the calculation conditions and the concentration field; a multi-medium coupling module for calculating the diffusion behavior of the pollutants in the atmosphere, the water body and the soil; an adaptive grid module for dynamically adjusting the grid density according to the concentration gradient and the error estimation in the diffusion result, so as to optimize the calculation accuracy of the hot spot area; a solving and control module for solving the high-dimensional nonlinear partial differential equation set of the multi-medium coupling model and dynamically adjusting the time step to ensure the calculation stability; a result visualization and analysis module for outputting the dynamic visualization result of the diffusion path, the pollutant concentration distribution and the affected area, and generating the pollution assessment report.

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

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

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

5. A system for modeling the propagation of radioactive consequences in an environment according to claim 1, wherein, The multi-medium coupling module realizes the mass transfer of the pollutants among the atmosphere, the water body and the soil through the dynamic matching of the interface flux, wherein the interface flux is calculated by the concentration difference on both sides of the interface and the interface mass transfer coefficient.

6. A system for modeling the propagation of radioactive consequences in an environment according to claim 1, wherein, The adaptive grid module dynamically refines the grid unit through local residual error estimation, wherein the residual error mainly takes the change of the pollutant concentration gradient as the reference index, and the grid density in the high concentration change area is preferentially refined.

7. A system for modeling the propagation of radioactive consequences in an environment according to claim 1, wherein, The adaptive grid module generates the refined grid through a high-order interpolation method, and the interpolation function is a high-order approximation model based on a polynomial.

8. A system for modeling the propagation of radioactive consequences in an environment according to claim 1, wherein, The solving and control module controls the calculation accuracy and efficiency by dynamically adjusting the time step, and the time step is determined by the function relationship among the grid unit size, the diffusion coefficient and the convection rate.

9. The system for modeling the consequences of radioactive releases in the environment of claim 1, wherein, The result visualization and analysis module generates the visualization graph of the pollutant diffusion path through dynamic three-dimensional rendering technology, and labels the pollution hot spot area and the affected range.

10. A method of simulating the spread of radioactive consequences in an environment, according to a simulation system of the spread of radioactive consequences in an environment according to any one of claims 1-9, characterized in that, The method comprises the following steps: inputting the position of the pollution source, the release intensity, the terrain and the environmental parameters, and initializing the calculation conditions, including the calculation grid and the initial concentration field; calculating the diffusion behavior of the pollutants in the atmosphere, the water body and the soil by using the multi-medium coupling module; realizing the mass flux coupling among the atmosphere-water body and the water body-soil through the interface conditions; dynamically adjusting the grid density according to the gradient change and the residual error estimation in the concentration field by using the adaptive grid module, and preferentially refining the hot spot area; solving the high-dimensional nonlinear equation set of the multi-medium coupling model by using the solving and control module, and dynamically adjusting the time step according to the diffusion area change; The output diffusion path, contaminant concentration distribution, and visualization of the affected area are generated, and a simulation report is generated.

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