Aerosol particle behavior analysis method and device

Abstract

The invention discloses an aerosol particle behavior analysis method and device, and the method comprises the steps: obtaining basic parameters of a fluid to be analyzed, the fluid to be analyzed being a mixture of non-condensable gas, water vapor and aerosol particles; constructing a geometric model of a computational domain of the fluid to be analyzed; meshing the geometric model to obtain a grid unit data set of the geometric model; obtaining a control equation, a pre-created wall surface condensation function, a turbulence model, a second component transport equation and a deposition flux equation; based on the basic parameters and the grid unit data set, a control equation, a pre-created wall surface condensation function, a turbulence model, a second component transport equation and a deposition flux equation are obtained through solving by a solver, and working condition distribution data of the to-be-analyzed fluid in a computational domain are obtained; and analyzing the behavior of the aerosol particles based on the working condition distribution data. Therefore, the motion behavior of the aerosol particles can be accurately simulated.

Description

Technical Field

[0001] The invention belongs to the field of nuclear power technology, and specifically relates to an aerosol particle behavior analysis method and device. Background Art

[0002] When a serious accident occurs in a nuclear power plant, a large amount of radioactive fission products will be released into the safe atmosphere. Except for rare gases and some free iodine and ruthenium, most of the fission products exist in the form of aerosols. Therefore, the migration and evolution behavior of aerosols has always been one of the hot topics in nuclear safety research.

[0003] In the prior art, there are two methods for analyzing aerosol transport and deposition behavior based on CFD methods, namely the Euler-Lagrangian system and the Euler-Euler system. The Euler-Lagrangian system method is used to analyze the behavior of aerosol particles, which is computationally intensive and inefficient. Although the Euler-Euler system method is used to analyze the behavior of aerosol particles, the computational efficiency is high, but the environment inside the containment is usually highly complex after an accident, including a variety of non-condensable gases, variable temperature and pressure conditions, and complex flow and heat conduction phenomena. As a result, the simulation accuracy of the transport and deposition behavior of aerosol particles in the accident scenario inside the containment is low.

[0004] Therefore, the existing Euler-Euler system method has low simulation accuracy for aerosol particle behavior. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an aerosol particle behavior analysis method and device in view of the above-mentioned deficiencies in the prior art, by which the motion behavior of aerosols can be accurately simulated.

[0006] In a first aspect, an embodiment of the present invention provides a method for analyzing the behavior of aerosol particles, comprising:

[0007] Obtaining basic parameters of a fluid to be analyzed, wherein the fluid to be analyzed is a mixture of non-condensable gas, water vapor and aerosol particles;

[0008] Construct a geometric model of the computational domain of the fluid to be analyzed;

[0009] Meshing the geometric model to obtain a grid unit data set of the geometric model;

[0010] Get the governing equations, pre-created wall condensation functions, turbulence models, second component transport equations, deposition flux equations,

[0011] Wherein, the control equations include: a mass conservation equation, a momentum conservation equation, an energy conservation equation and a first component transport equation, the wall condensation function is used to calculate the mass, momentum, energy and component loss source terms of the fluid medium in the fluid to be analyzed, the second component transport equation is an improved aerosol component transport equation, which is used to simulate the transport behavior of aerosol particles affected by mechanisms such as gravity sedimentation, Brownian diffusion, thermophoresis and diffusophore, and the deposition flux equation is used to calculate the deposition rate of aerosol particles;

[0012] Based on the basic parameters and the grid unit data set, the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation are solved by a solver to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain;

[0013] The behavior of the aerosol particles is analyzed based on the operating condition distribution data.

[0014] In a second aspect, an embodiment of the present invention further provides a safety assessment method for a nuclear power plant, characterized in that it is applied to a containment, and the method comprises:

[0015] According to the aerosol particle behavior analysis method described in the first aspect, the aerosol particle behavior in the containment is analyzed to obtain an analysis result;

[0016] Based on the analysis results, a safety assessment is performed on the containment vessel.

[0017] In a third aspect, an embodiment of the present invention further provides an aerosol particle behavior analysis device, comprising:

[0018] A first acquisition module is used to acquire basic parameters of a fluid to be analyzed, wherein the fluid to be analyzed is a mixture of non-condensable gas, water vapor and aerosol particles;

[0019] A model building module, used to build a geometric model of the computational domain of the fluid to be analyzed;

[0020] A meshing module, connected to the model building module, for meshing the geometric model to obtain a mesh unit data set of the geometric model;

[0021] A second acquisition module, connected to the grid division module, is used to obtain the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation, and the deposition flux equation.

[0022] Wherein, the control equations include: a mass conservation equation, a momentum conservation equation, an energy conservation equation and a first component transport equation; the wall condensation function is used to calculate the mass, momentum, energy and component loss source terms of the fluid medium in the fluid to be analyzed; the second component transport equation is an improved aerosol component transport equation, which is used to simulate the transport behavior of aerosol particles affected by mechanisms such as gravity sedimentation, Brownian diffusion, thermophoresis and diffusophore; the deposition flux equation is used to calculate the deposition rate of aerosol particles;

[0023] A solution module connected to the second acquisition module, for solving the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation through a solver based on the basic parameters and the grid unit data set, to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain;

[0024] An analysis module, connected to the solution module, is used to analyze the behavior of the aerosol particles based on the operating condition distribution data.

[0025] The aerosol particle behavior analysis method of the present invention obtains the basic parameters of the fluid to be analyzed, constructs and meshes the geometric model, and combines the control equation, wall condensation function, turbulence model, and improved aerosol transport equation (second component transport equation) and deposition flux equation, and solves them using a solver to obtain the operating condition distribution data, and analyzes the movement and deposition behavior of aerosol particles accordingly. It can comprehensively and accurately simulate the behavior of a mixture of non-condensable gas, water vapor and aerosol particles in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 : is a flow chart of an aerosol particle behavior analysis method according to Example 1 of the present invention;

[0027] Figure 2 : A flow chart of another aerosol particle behavior analysis method according to Example 1 of the present invention

[0028] Figure 3 : It is the aerosol deposition experimental stand in the vertical heat transfer tube of Example 1 of the present invention;

[0029] Figure 4 : A two-dimensional axisymmetric model diagram of a deposition tube according to Example 1 of the present invention;

[0030] Figure 5 : is a structured grid graph of embodiment 1 of the present invention;

[0031] Figure 6 : A temperature cloud diagram of a gas mixture in Example 1 of the present invention;

[0032] Figure 7 : is a water vapor mass fraction cloud diagram of Example 1 of the present invention;

[0033] Figure 8 : is a schematic diagram of the change of radial velocity of aerosol particles with different particle sizes over time in Example 1 of the present invention;

[0034] Fig. 9 : This is a structural diagram of an aerosol particle behavior analysis device according to Example 2 of the present invention.

[0035] The meanings of the marks in the figure are:

[0036] 1. Steam generator; 2. Two-position valve; 3. Critical flow limiting orifice; 4. Nitrogen supply;

[0037] 5. Regulating valve; 6. Pressure gauge; 7. Ultrasonic atomizer; 8. Furnace; 9. Deposition tube; 10. Deposition tube cooling; 11. Diluent; 12. TEOM; 13. Pump; 14. BLPI; 15. Filter; 16. Condensate collection tank; 17. Overpressure valve; 18. Thermocouple DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0040] Computational fluid dynamics (CFD) is a combination of numerical calculation and computing technology, and is a method of numerically solving computational fluid problems.

[0041] FLUENT is the most widely used CFD software, mainly used for computer applications to simulate fluid flow and heat transfer. It has a variety of different physical models that can be used to distinguish and judge fluids.

[0042] Source term: A source term is a term in a fluid dynamics equation that represents the production or consumption of additional mass, momentum, or energy. In this application, the source term represents the loss of mass, momentum, and energy due to the condensation of water vapor.

[0043] In computational fluid dynamics (CFD), in order to generate flow field data and consider multiple physical phenomena (such as turbulence, thermophoresis, and diffusiophoresis), it is usually necessary to integrate various models (such as turbulence model, thermophoresis model, diffusiophoresis model, etc.) into the overall solution framework. SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm is one of the commonly used solution frameworks, which is usually used to deal with pressure-velocity coupling problems.

[0044] When a serious accident occurs in a nuclear power plant, a large amount of radioactive fission products will be released into the safe atmosphere. Except for rare gases and some free iodine and ruthenium, most of the fission products exist in the form of aerosols. Therefore, the migration and evolution behavior of aerosols has always been one of the hot topics in nuclear safety research.

[0045] After the accident, the containment vessel is in a high-humidity, high-pressure, and high-temperature environment, and there is an important phenomenon of wall condensation of water vapor containing non-condensable gases. This makes the aerosols in the containment vessel after the accident have deposition mechanisms such as thermophoresis and diffusion-phoresis, compared with the aerosol migration and evolution behavior in general industrial fields.

[0046] Generally, computational fluid dynamics (CFD) software can be used to analyze the aerosol transport and deposition behavior in the containment environment containing non-condensable gas and water vapor. There are two methods for analyzing aerosol transport and deposition behavior based on CFD methods, namely the Euler-Lagrangian system and the Euler-Euler system. Both systems use the Euler method to solve the flow and heat transfer behavior of the fluid, but for the behavior analysis of aerosol particles, the former uses the Lagrangian method to track the detailed movement of each particle in the flow field, thereby obtaining the movement trajectory of each particle; the latter treats the aerosol particles as continuous medium components and obtains the concentration distribution of the particles by solving the component transport equation. Due to the high computational efficiency of the latter, it is widely used in indoor (or closed space) air quality analysis, but for the aerosol transport and deposition in the containment environment containing non-condensable gas and water vapor after the accident, there is currently no mature analysis method.

[0047] Embodiment 1:

[0048] Based on the above research, in order to solve the existing technical problems, such as Figure 1 As shown, this embodiment provides an aerosol particle behavior analysis method including the following steps 101 to 106.

[0049] Step 101 : obtaining basic parameters of a fluid to be analyzed, where the fluid to be analyzed is a mixture of non-condensable gas, water vapor and aerosol particles.

[0050] Specifically, the basic parameters of the fluid to be analyzed can be obtained from the relevant data and standards of the nuclear power plant. These parameters include but are not limited to: fluid components: the proportion of non-condensable gases (water vapor, air, etc.) and the characteristics of aerosol particles; physical property parameters: density, viscosity, specific heat, thermal conductivity, etc. of each component; environmental condition parameters: temperature, pressure and humidity, etc., which can be obtained according to actual conditions.

[0051] Provide accurate initial conditions and physical properties for subsequent simulation and analysis to ensure the accuracy and reliability of the analysis.

[0052] Step 102: construct a geometric model of the computational domain of the fluid to be analyzed.

[0053] Specifically, according to the specific research objectives and conditions, the computational domain of the fluid to be analyzed is clarified, including the flow path and boundaries of the fluid in the required analysis area; the detailed geometric information of the computational domain is obtained, including the boundary shape, size, internal features, etc.; then computational fluid dynamics (CFD) software (such as ANSYS Fluent, OpenFOAM, etc.) or 3D modeling software (such as SolidWorks, AutoCAD, etc.) can be used to create a geometric model of the analysis area (computational domain).

[0054] Step 103, meshing the geometric model to obtain a mesh unit data set of the geometric model.

[0055] Specifically, use the meshing tools provided by computational fluid dynamics (CFD) software (such as ANSYS Meshing, ICEM CFD, OpenFOAM, etc.) or dedicated meshing software (such as Gambit, Gmsh, etc.), and set the relevant parameters of meshing according to the complexity of the geometric model and the requirements of analysis accuracy, including the type of mesh (such as structured mesh or unstructured mesh), unit size, growth rate, etc. Considering the fluid flow characteristics and the behavior of aerosol particles, mesh encryption is performed in key areas (such as boundary layers and complex geometric areas) to improve the accuracy of calculations.

[0056] Run the meshing tool to generate the initial mesh cells. During this process, you can select different mesh generation algorithms (such as quadrilateral, hexahedral, triangular or tetrahedral cells) to adapt to different geometric structures. After generating the initial mesh, check the quality of the mesh cells to ensure that no distorted, overlapping or deformed cells are formed. Use the software's built-in mesh quality check function to check the orthogonality, uniformity and cell volume change of the mesh cells; based on the quality check results, optimize and adjust the low-quality mesh areas, including manual correction or re-meshing, and perform local mesh encryption or smoothing on necessary areas to improve the overall mesh quality and calculation stability; after completing the mesh optimization, save the generated mesh cell data set in the format required for numerical calculations (such as ANSYS Fluent's .cas file, OpenFOAM's mesh file, etc.).

[0057] The grid cell data set includes the grid information of the computational domain, such as node coordinates, cell volume, cell surface area, and their connectivity, ensuring that the numerical solution has sufficient spatial resolution.

[0058] Through reasonable mesh division and optimization, we ensure that the mesh unit data set can accurately reflect the details of the geometric model and the fluid characteristics, providing a reliable basis for subsequent numerical solutions, thereby ensuring the accuracy and reliability of the simulation results.

[0059] Step 104, obtaining the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation, and the deposition flux equation.

[0060] Among them, the control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and first component transport equation. The wall condensation function is used to calculate the loss source terms of mass, momentum, energy and components of the fluid medium in the fluid to be analyzed. The second component transport equation is an improved aerosol component transport equation, which is used to simulate the transport behavior of aerosol particles affected by mechanisms such as gravitational sedimentation, Brownian diffusion, thermophoresis and diffusophore. The deposition flux equation is used to calculate the deposition rate of aerosol particles.

[0061] Here, the mass conservation equation (continuity equation) describes the accumulation and transport of mass in the fluid. It ensures that the total rate of change of mass in any control volume is equal to the net inflow rate of the fluid. The momentum conservation equation (Navier-Stokes equation) describes the change of momentum in the fluid, taking into account the effects of pressure, viscosity and external forces. The energy conservation equation describes the change of energy including heat conduction, convection and thermal radiation. The first component transport equation describes the transfer process of different fluid medium components in the fluid, including convection and diffusion.

[0062] The pre-created wall condensation function is used to calculate the condensation of the fluid medium on the wall, including the mass, momentum, energy and component losses caused by condensation.

[0063] Turbulence model is an important research direction in fluid mechanics, which aims to describe and predict the behavior of turbulence. Turbulence is a complex and irregular state in fluid motion, which is manifested by the rapid changes of fluid parameters such as velocity and pressure in space and time. There are many types of turbulence models. Each turbulence model has its own characteristics and scope of application. The selection of a suitable model mainly depends on the specific flow problem and computing resources.

[0064] The second component transport equation is an improvement of the existing component transport equation, targeting the behavior of aerosols and taking into account the specific transport mechanisms of aerosol particles (gravity, Brownian diffusion, thermophoresis, and diffusophores, etc.).

[0065] Deposition flux equation: used to calculate the deposition rate of aerosol particles on different surfaces.

[0066] Obtain and set the control equations and physical models, create special transport equations for aerosol particles to describe the aerosol particle transport mechanism, provide a comprehensive and accurate physical description for subsequent calculations, and consider the impact of water vapor condensation to ensure that the simulation fully considers various important phenomena of fluids and particles, thereby ensuring the accuracy and reliability of the simulation results.

[0067] Step 105, based on the basic parameters and the grid unit data set, the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation are solved by the solver to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain.

[0068] Specifically, in the numerical simulation process, it is first necessary to prepare the basic parameters and grid unit data sets of the computational domain. The basic parameters may include the physical properties of the fluid (such as density, viscosity, thermal conductivity, etc.), boundary conditions (such as inlet velocity, temperature and pressure), as well as phase change parameters (such as saturation temperature and latent heat of vaporization), and the characteristics of discrete phase particles (such as particle density and diameter), etc. The control equations are solved by the solver. In the solution process, the pre-created wall condensation function, turbulence model, two-component transport equation and deposition flux equation are respectively applied to accurately describe the fluid flow and phase change. By integrating the above models and running the solver, the working condition distribution data of the fluid can be obtained in the computational domain. These data usually include velocity field, pressure field, temperature field, spatial distribution of concentrations of each phase, motion trajectory of aerosol particles, etc. Finally, these distribution data can be used for subsequent analysis and engineering applications to optimize the design and verify the flow phenomenon.

[0069] Through comprehensive numerical simulation technology, combining computational fluid dynamics (CFD) and specific aerosol behavior models, multi-scale and high-precision simulations are achieved by introducing wall condensation functions and improved transport equations.

[0070] Step 106 , analyzing the behavior of aerosol particles based on the operating condition distribution data.

[0071] By obtaining the operating distribution data of the fluid in the calculation domain (including velocity field, pressure field, temperature field, and concentration distribution of each phase) through numerical simulation, the behavior of aerosol particles can be analyzed in detail.

[0072] For example, using aerosol particle concentration, the transport behavior of particles can be calculated and visualized. The deposition rate of aerosol particles on different surfaces can be used to analyze the effects of flow field characteristics and surface properties on particle deposition behavior.

[0073] Finally, post-processing software, such as ANSYS CFD-Post, ParaView, etc., is used to visualize the previous calculation results and conduct detailed analysis (for example, generate velocity vector diagrams, temperature distribution diagrams, pressure distribution diagrams, particle concentration distribution diagrams, etc.).

[0074] In this embodiment, by obtaining basic parameters and constructing a geometric model, after gridding, multiple equations (including mass, momentum, energy conservation, etc.) and improved aerosol transport equations and deposition flux equations are combined and solved using a solver to obtain operating condition distribution data, and the movement and deposition behavior of aerosol particles are analyzed accordingly. The behavior of a mixture of non-condensable gas, water vapor and aerosol particles in a complex environment can be fully and accurately simulated.

[0075] Optionally, the pre-created wall condensation function includes formula (1):

[0076] The condensation mass flow density m" of water vapor on the wall is calculated by the following formula (1): s :

[0077]

[0078] Among them, m″ s is the water vapor condensation mass flow density, Y s is the water vapor mass fraction, D is the water vapor diffusion coefficient, and n is the wall normal vector;

[0079] The mass conservation equation includes formula (2)

[0080] The mass loss source term S of the mass conservation equation is calculated by the following formula (2): m and the component loss source term S in the component transport equation i :

[0081]

[0082] Among them, A cell is the condensation area of ​​the first layer of grid cells near the wall, V cell is the unit volume;

[0083] The momentum conservation equation includes formula (3),

[0084] The momentum loss source term S of the momentum conservation equation is calculated by the following formula (3): mom :

[0085]

[0086] Where u is the local velocity component of the fluid in the grid cell;

[0087] The energy conservation equation includes formula (4),

[0088] The energy loss source term S of the energy conservation equation is calculated by the following formula (4): h :

[0089]

[0090] Among them, H lg is the latent heat of vaporization.

[0091] The theoretical basis for compiling the water vapor wall condensation function containing non-condensable gas is: when the wall of the computational domain is low temperature, on the one hand, a temperature gradient will be formed inside the computational domain, causing the aerosol particles in the computational domain to undergo thermophoresis, and on the other hand, water vapor will condense on the wall, causing the aerosol particles in the computational domain to undergo diffusiophoresis. The mass, momentum and energy losses caused by condensation can be obtained by using empirical relationships, heat and mass transfer analogy theory or local diffusion method, and then directly deducted in the first layer of grid cells near the wall.

[0092] The wall condensation function provided in this application is the mass, momentum and energy loss source terms obtained based on the local diffusion method.

[0093] In this embodiment, the above formula of the pre-created wall condensation function can be used to describe the condensation process using the basic parameters of the fluid (such as density, diffusion coefficient, local velocity), the geometric characteristics of the wall (grid unit condensation area and unit volume) and physical properties (such as latent heat of vaporization), so that the condensation process is accurately described in the calculation, and then the behavior of aerosol particles in the fluid can be comprehensively analyzed based on the operating condition distribution data.

[0094] Optionally, the above-mentioned second component transport equation includes the following formulas (5) to (8):

[0095]

[0096] Where C is the mass concentration or number concentration of aerosol particles, x i is the spatial coordinate, u i is the velocity of the fluid in the i direction, V s is the gravity settling velocity of aerosol particles, V th is the thermophoretic velocity of aerosol particles, V diff is the diffusion velocity of aerosol particles, D p is the Brownian diffusion coefficient of aerosol particles, ε p is the turbulent diffusion coefficient of aerosol particles, φ is a user-defined scalar, S C is the source term of aerosol particles;

[0097]

[0098] Among them, ρ p is the particle density, d p is the particle size, μ is the fluid viscosity, g is the gravitational acceleration, Kn is the Knudsen number, k is the fluid thermal conductivity, k p is the particle thermal conductivity, τ p is the particle relaxation time, C s is the thermal slip coefficient, taken as 1.17, C t is the temperature jump coefficient, take 2.18, C m is the momentum exchange coefficient, m p is the particle mass, M is the molar mass of the non-condensable gas and water vapor, γ is the molar ratio of the gas components, p is the partial pressure of the gas components, and the subscripts nc and s represent the non-condensable gas and water vapor, respectively.

[0099] In this embodiment, the above-mentioned second component transport equation integrates complex processes such as fluid dynamics, thermodynamics and mass transfer into a unified framework through multi-physics field coupling, and comprehensively considers multiple mechanisms such as fluid movement, gravity sedimentation, Brownian diffusion, thermophoresis and diffusiophoresis, so that the simulation results of aerosol particle movement are closer to the actual situation. The microscopic mechanism is bridged with the macroscopic behavior, and the macroscopic aerosol behavior is predicted by accurately simulating microscopic processes such as Brownian motion and thermophoresis effect.

[0100] Optionally, the above deposition flux equation includes the following formula (9):

[0101]

[0102] Where J is the aerosol wall deposition flux, D p is the Brownian diffusion coefficient of aerosol particles, ε p is the turbulent diffusion coefficient of aerosol particles, is the gradient of aerosol concentration in the direction perpendicular to the wall, V th is the thermophoresis velocity of aerosol particles, C is the mass concentration or number concentration of aerosol particles, V diff is the diffusion velocity of aerosol particles.

[0103] The deposition flux equation in this embodiment fully and accurately describes the deposition process of aerosol particles on the wall. It integrates multiple physical mechanisms, including Brownian diffusion, turbulent diffusion, concentration gradient drive, thermophoresis and diffusiophoresis. This equation not only takes into account the diffusion movement of particles to the wall, but also includes the thermophoresis effect caused by the temperature gradient and the diffusiophoresis effect caused by the concentration gradient, making the analysis of aerosol deposition behavior more accurate and comprehensive.

[0104] Optionally, the above step 105 may specifically include the following steps:

[0105] Set boundary conditions based on basic parameters and grid cell data sets. Boundary conditions are specific physical constraints imposed on the boundaries of the computational domain in numerical simulations.

[0106] Set the solver's solution parameters, which are key variables used to control the discretization method, iteration strategy, and convergence criteria in numerical simulation;

[0107] Based on the iterative solution of the control equations, the pre-created wall condensation function, the turbulence model and the deposition flux equation according to the basic parameters, boundary conditions and solution parameters, the converged velocity field, pressure field, temperature field, fluid medium concentration field and aerosol particle concentration are obtained.

[0108] Here, the solver refers to the core algorithm or program that executes the entire numerical simulation process, which can solve various conservation equations including mass, momentum, energy and component transport, as well as model equations used to describe various complex phenomena (such as turbulence and interaction forces).

[0109] In practical applications, there are a variety of commercial and open source CFD (computational fluid dynamics) based software that contain the above solver functions and can be used for complex numerical simulations of aerosol particle behavior. For example:

[0110] ANSYS Fluent: is a widely used computational fluid dynamics (CFD) software that supports a variety of solution algorithms, including the SIMPLE and SIMPLEC algorithms for pressure-velocity coupling, and can handle turbulence, mixing, multiphase flow and reactive flow problems. Fluent has many built-in models that can be used to simulate turbulence, heat transfer, chemical reactions, particle motion, etc.

[0111] COMSOL Multiphysics: is a multi-physics modeling software that allows users to perform joint simulations of fluid dynamics, heat conduction, structural mechanics, and electromagnetic fields, and supports simulations of fluid flows under complex geometries and multiple boundary conditions.

[0112] OpenFOAM: is an open source CFD software package widely used in academic research and industrial applications. It provides a flexible solver system through a C++-based underlying structure, which can be customized to meet specific needs. OpenFOAM supports a variety of turbulence models, phase change models, discrete phase models, etc.

[0113] CFX: Also a CFD solver under ANSYS, CFX performs well in dealing with rotating machinery, turbulence and multiphase flow. It can simulate full three-dimensional flow fields and thermal fields well.

[0114] There are also TAR-CCM+, NVIDIA SimNet, etc. These software and programs can provide accurate numerical simulation and solve complex flow problems in many fields such as industrial design, environmental simulation, electronic cooling, biomedicine, etc. through advanced numerical methods and algorithms. Which one to choose depends on the specific application requirements, budget, user familiarity and customization requirements.

[0115] This embodiment is actually a numerical simulation process, including boundary conditions, solution parameter settings and iterative solutions. Specifically, in numerical simulation, in order to accurately describe the behavior of fluids and particles in the computational domain, appropriate boundary conditions and solution parameters must be configured, and comprehensive solutions must be performed at the same time. This process covers the detailed steps of setting boundary conditions, solution parameters, and iterative solutions.

[0116] Set boundary conditions. Boundary conditions are used to impose specific physical constraints on the boundaries of the computational domain to ensure the physical authenticity of the simulation results. Specific physical constraints refer to physical conditions imposed on the boundaries of the computational domain. They are used to define the boundary conditions of the problem and reflect the characteristics and limitations of the actual physical system. Specific physical constraints can be determined based on the physical characteristics of the actual problem, experimental conditions, or theoretical assumptions to ensure that the numerical simulation can accurately reflect the behavior of the actual system.

[0117] Common boundary conditions include: Inlet boundary conditions: specify the velocity, pressure, temperature and concentration of the fluid entering the computational domain. For example, set the inlet velocity, temperature, pressure and species mass fraction; Outlet boundary conditions: specify the conditions for the fluid to leave the computational domain, such as setting the outlet pressure or free outflow conditions; Wall boundary conditions: impose no-slip conditions and thermal boundary conditions, for example, set the wall temperature or heat flux. For particles, the wall condensation function can be set; Symmetric boundary conditions: used to simulate symmetry. The normal velocity component of the symmetry plane is specified to be zero, and the normal gradient is zero. Therefore, specific boundary conditions can be set based on basic parameters and grid unit data sets.

[0118] Set the solution parameters of the solver. Based on the above boundary conditions and solution parameters, substitute some basic parameters to control the discretization method, iteration strategy and convergence criterion of the numerical simulation. Discretization method: select a suitable discretization method, such as ordered difference, finite volume method, etc.; Iteration strategy: configure the iterative method of the solver, such as alternating direction implicit method (ADI), conjugate gradient method (CG), SIMPLE algorithm, etc.; Convergence criterion: set the convergence standard of each variable (velocity, pressure, temperature, concentration, etc.), usually relative error or absolute error is used as the standard, for example: reduce the residual to 10 -6 the following.

[0119] In this embodiment, first, based on the basic parameters and grid unit data set, a solver is used to solve a series of control equations, including the fluid mechanics control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation. In order to ensure the accuracy and stability of the numerical simulation, it is necessary to set boundary conditions, that is, to impose specific physical constraints on the boundaries of the computational domain. In addition, it is necessary to set the solution parameters of the solver, which are used to control the discretization method, iteration strategy and convergence criterion. Subsequently, iterative solutions are performed for the control equations and related models until converged results are obtained. Finally, the operating condition distribution data provided by the numerical simulation include velocity field, pressure field, temperature field, fluid medium concentration field and aerosol particle concentration.

[0120] Optionally, the boundary conditions include an inlet boundary and an outlet boundary, and the solution parameters include a discretization method, a relaxation factor, a coupling method, a time step, and a residual convergence criterion.

[0121] Among them, the time step is the length of the time step. Specifically, it refers to the length of the discrete intervals that continuous time is divided into during the numerical simulation process. It determines the frequency of updating physical quantities (such as velocity, temperature, pressure, etc.) during the simulation process. A smaller time step can usually improve the accuracy of the simulation because it can better capture rapidly changing physical processes. In practical applications, choosing an appropriate time step requires considering multiple factors, including the characteristic time of the physical process, the requirements of the numerical method, and the available computing resources.

[0122] Specifically, when numerically simulating complex flows, it is first necessary to set reasonable boundary conditions, including inlet boundaries and outlet boundaries. The inlet boundary conditions determine the state of the fluid entering the computational domain. Common ones include velocity inlet (setting inlet velocity), pressure inlet (setting inlet pressure) and mass flow inlet (setting mass flow rate). The outlet boundary conditions determine the state of the fluid leaving the computational domain, such as pressure outlet (setting outlet pressure) and free outflow (setting velocity gradient to zero).

[0123] In the solution process, choosing an appropriate discretization method, such as the finite volume method (FVM), is an important step in converting the flow equation from a continuous form to a discrete form. In order to ensure the stability of the iterative process, the relaxation factor also needs to be set reasonably. Usually, the momentum equation, pressure equation, and energy equation have their own relaxation factor ranges.

[0124] The coupling method is used to coordinate the coupling relationship between velocity and pressure. Common ones include SIMPLE (for steady-state flow), PISO (for transient flow) and SIMPLEC (an improved version of SIMPLE with faster convergence). The time step is also a key parameter. It can be a fixed time step or dynamically adjusted according to the flow field changes. In unsteady CFD simulations, the time step determines the time accuracy of the simulation. It affects the stability, accuracy and efficiency of the calculation. By setting the time step and using the time step as a basic unit of a cycle, the entire solution process is clearer and more structured. In actual CFD simulations, a series of calculations are completed in each time step.

[0125] Finally, in order to determine whether the iterative process has converged, it is necessary to set the residual convergence criterion, such as relative residual or absolute residual. The setting of these solution parameters and boundary conditions can ensure the accuracy and efficiency of the numerical simulation, and ultimately accurately simulate the turbulent characteristics and other physical phenomena in the flow field.

[0126] In this embodiment, through reasonable settings and parameter selection, numerical simulation can efficiently and stably approximate the real flow behavior and provide reliable flow field analysis results for engineering and scientific research.

[0127] Optionally, the solver is a pressure-based solver,

[0128] The above solver is a pressure-based solver.

[0129] The above iterative solution of the control equations, the pre-created wall condensation function, the turbulence model and the deposition flux equation obtains the converged velocity field, pressure field, temperature field and fluid medium concentration field and aerosol particle concentration, including:

[0130] S1. A second-order upwind scheme is used to spatially discretize the governing equations, the pre-created wall condensation function, the turbulence model, and the deposition flux equation.

[0131] S2. For the pressure-velocity coupling equation, the volume force weight format is used for spatial discretization;

[0132] For each time step, the following steps S3 to S14 are performed:

[0133] S3, obtaining the initial velocity field, initial pressure field, initial temperature field, initial fluid medium concentration field and initial aerosol particle concentration of all grid cells;

[0134] S4, solving the pre-created wall condensation function to obtain the mass loss source term, component loss source term, momentum loss source term, and energy loss source term of the first layer of grid cells near the wall;

[0135] S5. For each first-layer grid unit near the wall, update the control equation of the first-layer grid unit near the wall according to the mass loss source term, component loss source term, momentum loss source term, and energy loss source term.

[0136] S6. Solve the momentum equation according to the initial velocity field and the initial pressure field to obtain a predicted velocity field;

[0137] S7, according to the predicted velocity field, adopting the pressure-velocity coupling method, using the SIMPLE algorithm to obtain a corrected pressure field and a corrected velocity field, and updating the pressure field and the velocity field;

[0138] S8, solving the turbulence equation in the turbulence model according to the updated velocity field, obtaining turbulence parameters, and updating the turbulence parameters;

[0139] S9, solving the first component transport equation according to the updated pressure field and the updated velocity field to obtain the fluid medium concentration, and updating the fluid medium concentration;

[0140] S10, solving the second component transport equation according to the updated turbulence parameter and the updated fluid medium concentration to obtain the aerosol particle concentration, and updating the aerosol particle concentration;

[0141] S11, solving the energy equation according to the updated velocity field to obtain the temperature field, updating the initial temperature field to obtain an updated temperature field;

[0142] S12, solving the deposition flux equation according to the updated fluid medium concentration and the updated aerosol particle concentration, and updating the aerosol particle deposition velocity;

[0143] S13, calculating the residual of each equation;

[0144] S14, when the residuals of each equation do not meet the preset convergence criteria, repeat the above S3 to S13 until the residuals of each equation meet the preset convergence criteria, and output the updated velocity field, pressure field, temperature field, and species concentration field;

[0145] S14, recording the updated velocity field, pressure field, temperature field, fluid medium concentration field and aerosol particle concentration at each time step;

[0146] S15. Generate flow field visualization data and aerosol particle concentration distribution data according to the time series.

[0147] In the numerical simulation using the pressure-based solver, the control equations and related models are spatially discretized by the second-order upwind format and the body force weighted format, and then iteratively solved. The control equations (including mass conservation, momentum conservation, energy conservation and component transport equations), the pre-created wall condensation function, the turbulence model and the deposition flux equation, in these equations, density, momentum, energy and components are the main calculation objects, so the application of the second-order upwind format on these terms can achieve better numerical accuracy. The pressure-velocity coupling equation is subsequently used to solve the pressure field. In most CFD solvers, the pressure field is processed by the pressure-velocity coupling equation. The use of the body force weighted format can better calculate the pressure gradient and ensure the stability and accuracy of the calculation.

[0148] In the embodiments of the present application, the time step (usually denoted as Δt) is a parameter used to discretize the time domain. In each time step, the numerical simulation calculates the transient behavior of the flow field, so that the numerical calculation can be gradually evolved to approach the dynamic evolution process of the fluid.

[0149] Specifically, in the numerical simulation using the pressure-based solver, the control equations and related models are spatially discretized by the second-order upwind format and the body force weight format, and then iteratively solved. In each time step, all physical fields are initialized first, then the wall condensation effect is processed, and then the fluid dynamics calculation is performed (including pressure-velocity coupling using the SIMPLE algorithm), followed by turbulence calculation, multi-component transport calculation, heat transfer calculation and aerosol deposition calculation. This method iteratively solves multiple tightly coupled physical equations, including the momentum equation, continuity equation, turbulence model equation, component transport equation, energy equation and deposition flux equation until strict convergence criteria are met, thereby achieving high-precision simulation of fluid dynamics, heat transfer, material transport and aerosol behavior. In each iteration, each physical field (such as velocity field, pressure field, temperature field, concentration field, etc.) will be continuously updated. Finally, through the result output and post-processing, detailed flow field and aerosol distribution data are generated, thereby ensuring that converged and realistic flow field simulation results are obtained through accurate numerical methods.

[0150] In this embodiment, the flow field is discretized into a large number of small grid units, and numerical calculations are performed in each unit. By iteratively solving the pressure, velocity, temperature, species concentration and aerosol component transport equations, the SIMPLE algorithm is used for pressure-velocity coupling, and combined with the wall condensation, turbulence, and deposition flux equations, each physical quantity is gradually updated, the residual is calculated to determine the convergence, and finally the flow field data and aerosol distribution data are generated, which can ensure the accuracy of the simulation and the accuracy of the numerical results.

[0151] In order to facilitate the understanding of the aerosol particle behavior analysis method provided in this embodiment, a practical application description of the above method is provided here, such as Figure 2 For details, see the following example:

[0152] The following steps are implemented based on the computational fluid dynamics software Fluent, as follows:

[0153] Step 1: Conduct geometric modeling for the computational domain involved in the study of the aerosol particle migration phenomenon in an environment containing non-condensable gas and water vapor, and divide the geometry into grids.

[0154] right Figure 3 The experimental bench shown is set up as Figure 4 The geometric model shown in the figure is generated Figure 5 The grid shown.

[0155] This experiment is an experiment on aerosol transport and deposition in the heat transfer tube of the passive containment cooling system of an economical simplified boiling water reactor. The aerosol deposition tube in the test bench simulates the heat transfer tube of the passive containment cooling system of the ESBWR. The tube is 0.85m long and 0.022m in inner diameter. There is a cooling water interlayer on the outside of the tube to control the low temperature of the heat transfer tube wall during the experiment. The gases used in the experiment are nitrogen and water vapor. Under the condition of low wall temperature, the temperature of the gas in the tube decreases due to convection heat transfer and condensation, and the aerosol particles entrained by the gas are deposited on the tube wall under the swimming mechanism of thermophoresis and diffusion swimming. The aerosol particles used in the experiment are silver.

[0156] Step 2: Import the generated grid file into Fluent software, select the turbulence model and appropriate wall function in the software, and activate the energy equation and component equation (equivalent to the first component transport equation mentioned above).

[0157] The energy equation describes the change of internal energy of the fluid, including the conversion of various energies such as thermal energy, kinetic energy and potential energy, and can describe the temperature field distribution and energy balance in the system. The component equation describes the distribution and change of each component in space and time, that is, the conservation of each chemical composition or phase component.

[0158] Specifically, the generated mesh file is imported into the Fluent software, and the realizable k-ε model and enhanced wall function are selected in the software; and the energy equation and component equation are activated;

[0159] Step 3: Create a gas mixture containing non-condensable gas and water vapor in Fluent software according to the material library provided by the software, and set the fluid medium in the calculation domain to be the gas mixture;

[0160] In Fluent software, a gas mixture containing nitrogen and water vapor is created according to the material library provided by the software, and the fluid medium is set as the gas mixture for the calculation domain;

[0161] Step 4: Use the built-in macros of Fluent software to compile the water vapor wall condensation UDF (User Defined Functions, UDF) containing non-condensable gas, compile the UDF in Fluent software and specify the action positions for these UDFs.

[0162] The theoretical basis for compiling the UDF for water vapor wall condensation containing non-condensable gases is: when the wall of the computational domain is low temperature, on the one hand, a temperature gradient will be formed inside the computational domain, causing the aerosol particles in the computational domain to undergo thermophoresis, and on the other hand, water vapor will condense on the wall, causing the aerosol particles in the computational domain to undergo diffusiophoresis. Fluent software does not include a model for water vapor wall condensation. The mass, momentum and energy losses caused by condensation can be obtained using empirical relationships, heat and mass transfer analogy theory or local diffusion methods, and then directly deducted in the first layer of grid cells near the wall, that is, the mass, momentum and energy losses are added as source terms to the mass, momentum and energy conservation equations to simulate the various loss rates when a specific physical process (such as water vapor condensation) occurs.

[0163] The mass, momentum and energy loss source terms obtained based on the local diffusion method can be calculated using the following relations:

[0164]

[0165] Where: m″ s is the water vapor condensation mass flow density, Y s is the water vapor mass fraction, D is the water vapor diffusion coefficient, and n is the wall normal vector.

[0166] The loss source term of the near-wall element in the mass conservation equation and the component transport equation is:

[0167]

[0168] Where: A cell is the condensation area of ​​the unit near the wall, V cell is the unit volume, S i is the component source term, here it is the source term of water vapor, and the total mass source term S m are equal because in this simplified model, only the condensation of water vapor is considered.

[0169] The loss source term of the near-wall element in the momentum conservation equation is:

[0170]

[0171] The loss source term of the near-wall element in the energy conservation equation is:

[0172]

[0173] Where: H lg is the latent heat of vaporization.

[0174] By adding source terms in the near-wall unit to simulate the condensation process, the effect of water vapor condensation on the flow field and the motion of aerosol particles can be effectively captured. When actually writing UDF, it is necessary to implement the corresponding calculations and source term additions based on these equations and Fluent's API functions.

[0175] Specifically in this application example, the macros involved are:

[0176] DEFINE_ON_DEMAND(), DEFINE_PROFILE(), DEFINE_ADJUST(), DEFINE_EXECUTE_AT_END(), DEFINE_SOURCE().

[0177] Step 5: Use the macros provided by Fluent software to compile the convection UDF and diffusion UDF in the aerosol component transport equation based on the particle properties (particle size, density, etc.) and fluid properties (pressure, temperature, density, etc.), and compile these UDFs in Fluent software; activate the UDS equation, and specify the action positions of the convection UDF and diffusion UDF in the UDS equation interface.

[0178] The theoretical basis for compiling the convection UDF and diffusion UDF is briefly described as follows:

[0179] The basic form of the default UDS equation in Fluent software is:

[0180]

[0181] Where: φ is a user-defined scalar, u i is the velocity of the fluid in the i direction, Γ is the diffusion coefficient, S φ is the source term of φ. The first term on the left side of the equation is the non-steady-state term, and the ρu in the second term i φ is the convection term of φ, is the diffusion term of φ.

[0182] In the Euler-Euler system, the aerosol component transport equation (i.e., the above-mentioned second component transport equation) considering the gravity sedimentation, Brownian diffusion, thermophoresis, and diffusiophoresis mechanisms in an environment containing non-condensable gas water vapor is:

[0183]

[0184] Where: C is the mass concentration or number concentration of aerosol particles; V s is the gravity settling velocity of aerosol particles; V th is the thermophoresis velocity; V diff D is the diffusion velocity; p is the Brownian diffusion coefficient of aerosol particles; εp is the turbulent diffusion coefficient of aerosol particles.

[0185] Comparing equations (5) and (6), it can be seen that the convection term and diffusion term in the default UDS equation of Fluent software need to be modified. Use the DEFINE_UDS_FLUX() macro and DEFINE_DISSUSIVITY() of Fluent software to modify the convection term and diffusion term respectively, where:

[0186]

[0187]

[0188] Where ρ p : is the particle density; d p is the particle size; μ is the fluid viscosity; g is the gravitational acceleration; Kn is the Knudsen number; k is the fluid thermal conductivity; k p is the particle thermal conductivity; τ p is the particle relaxation time; C s is the thermal slip coefficient, which is taken as 1.17; C t is the temperature jump coefficient, take 2.18; C m is the momentum exchange coefficient, which is taken as 1.14; m p is the particle mass, M is the molar mass of the non-condensable gas and water vapor; γ is the molar ratio of the gas components; p is the partial pressure of the gas components; the subscripts nc and s represent the non-condensable gas and water vapor, respectively.

[0189] Therefore, when the aerosol particles are silver, the convection UDF and diffusion UDF in the aerosol component transport equation are compiled based on the physical properties of Ag particles (particle size, density, etc.) and fluid properties (pressure, temperature, density, etc.) through the macro provided by the Fluent software, and these UDFs are compiled in the Fluent software; the UDS equation is activated, and the action positions of the convection UDF and diffusion UDF are specified in the UDS equation interface.

[0190] Step 6: Use the macro DEFINE_PROFILE() provided by Fluent software to compile the aerosol wall deposition flux UDF and compile it in Fluent software. The calculation formula of the deposition flux is as follows:

[0191]

[0192] Step 7: Set the boundary conditions of the computational domain in Fluent. These boundary conditions can be inlets, outlets, walls, or symmetry surfaces. When setting these boundary conditions, you need to pay attention to the settings of the UDF compiled by the user.

[0193] In practical applications, the boundary conditions of the computational domain can be set in Fluent, where the inlet is a mass inlet boundary condition, specifying the mass flow rate, temperature, mass fraction of water vapor of the mixed gas, and the inlet parameters of the UDS equation (i.e., the aerosol mass concentration at the inlet), the outlet is a pressure outlet boundary condition, the wall is a constant temperature, the water vapor mass fraction at the wall is the saturated water vapor mass fraction at the corresponding wall temperature, and the UDS boundary at the wall is specified as SpecifiedValue, and the wall deposition flux UDF compiled by the user is selected;

[0194] Step 8: Set the solution parameters in Fluent software, including time step (if it is a transient calculation), solution method, relaxation factor, residual, etc.;

[0195] Specifically, the solution parameters are set in the Fluent software, in which the pressure-velocity coupling adopts the SIMPLE format, the spatial discretization method of density, momentum, turbulent kinetic energy, turbulent dissipation rate, component and energy is the second-order upwind format, and the spatial discretization method of pressure is the volume force weight.

[0196] Step 9: Initialize and start calculation;

[0197] Step 10: Use CFD-Post to post-process the calculation results. The calculation results can obtain detailed thermal parameter distributions, such as temperature and water vapor mass fraction, and detailed aerosol mass fraction distributions.

[0198] Use CFD-Post to post-process the calculation results. Figure 5 The simulated mixed gas temperature cloud diagram is given. Figure 6 The simulated water vapor mass fraction distribution cloud diagram is given. Figure 7 The simulated aerosol mass fraction distribution cloud diagram is given. Figure 8 The variation of radial velocity of aerosol particles with different particle sizes over time is given.

[0199] The present embodiment provides a simulation method for aerosol transport and deposition in an environment containing non-condensable gas and water vapor based on the Euler-Euler system, and uses the Euler method to solve the flow and heat transfer behavior of the carrier gas medium and the transport and deposition behavior of the aerosol particles. The key content is: treating the aerosol particles as discrete media, calculating the aerosol component transport equation based on the calculation of the flow and heat transfer behavior of the carrier gas medium, modifying the convective transport term and the diffusion term in the aerosol component transport equation to reflect the difference between the aerosol behavior and the carrier gas behavior, and calculating the aerosol deposition flux at the wall to solve the aerosol deposition behavior.

[0200] Embodiment 2:

[0201] This embodiment provides a safety assessment method for a nuclear power plant, which is applied to a containment, and specifically includes the following steps:

[0202] According to the aerosol particle behavior analysis method provided in any of the above embodiments, the aerosol particle behavior in the containment is analyzed to obtain an analysis result;

[0203] Based on the analysis results, a safety assessment of the containment is conducted.

[0204] Specifically, by simulating the behavior of aerosol particles within the containment, the concentration, distribution and migration path of aerosols at different locations can be accurately predicted, the release rate and amount of radioactive materials in the containment can be precisely calculated, and the radioactive risk of the containment can be quantitatively assessed.

[0205] The safety assessment method of a nuclear power plant in this embodiment can accurately assess the risk of diffusion and deposition of radioactive materials in the containment, which helps to quickly and accurately assess the risk when an accident occurs, thereby making emergency response decisions faster and more effectively, such as evacuation plans and radiation protection measures. Through refined aerosol particle behavior analysis, the risk assessment and accident response capabilities of the nuclear power plant containment can be significantly improved.

[0206] The above analysis results can also be used to optimize the design and operation parameters of the containment, such as the location and specifications of the ventilation system, sprinkler system and filter device, so as to improve the ability to mitigate accidents. By simulating different accident scenarios, a reliable basis can be provided to improve existing operating strategies and emergency plans.

[0207] Embodiment 3:

[0208] like Fig. 9 As shown, this embodiment provides an aerosol particle behavior analysis device 900, which can execute the aerosol particle behavior analysis method provided in any of the above embodiments, and the device specifically includes:

[0209] The first acquisition module 901 is used to acquire basic parameters of the fluid to be analyzed;

[0210] A model building module 902 is used to build a geometric model of a computational domain of a fluid to be analyzed, where the fluid to be analyzed is a mixture of non-condensable gas, water vapor, and aerosol particles;

[0211] A meshing module 903, connected to the model building module, is used to mesh the geometric model to obtain a mesh unit data set of the geometric model;

[0212] The second acquisition module 904 is connected to the grid division module and is used to obtain the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation, and the deposition flux equation.

[0213] Wherein, the control equations include: a mass conservation equation, a momentum conservation equation, an energy conservation equation and a first component transport equation; the wall condensation function is used to calculate the mass, momentum, energy and component loss source terms of the fluid medium in the fluid to be analyzed; the second component transport equation is an improved aerosol component transport equation, which is used to simulate the transport behavior of aerosol particles affected by mechanisms such as gravity sedimentation, Brownian diffusion, thermophoresis and diffusophore; the deposition flux equation is used to calculate the deposition rate of aerosol particles;

[0214] A solution module 905, connected to the second acquisition module, is used to solve the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation through a solver based on the basic parameters and the grid unit data set to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain;

[0215] The analysis module 906 is connected to the solution module and is used to analyze the behavior of aerosol particles based on the operating condition distribution data.

[0216] Optionally, the solution module 905 includes:

[0217] The first setting submodule is used to set boundary conditions, which are specific physical constraints imposed on the boundaries of the computational domain in numerical simulation;

[0218] The second setting submodule is used to set the solution parameters of the solver, which are key variables used to control the discretization method, iteration strategy and convergence criterion in numerical simulation;

[0219] The iterative calculation module is used to iteratively solve the control equation, the pre-created wall condensation function, the turbulence model and the deposition flux equation to obtain converged velocity field, pressure field, temperature field and fluid medium concentration field and aerosol particle concentration.

[0220] In this embodiment, by obtaining the basic parameters of the fluid to be analyzed, constructing and meshing the geometric model, and combining the control equation, wall condensation function, turbulence model, and improved aerosol transport equation (second component transport equation) and deposition flux equation, the solver is used to solve the working condition distribution data, and the movement and deposition behavior of aerosol particles are analyzed accordingly. The behavior of a mixture of non-condensable gas, water vapor and aerosol particles in a complex environment can be fully and accurately simulated.

[0221] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0222] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0223] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0224] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable scheduling device to produce a machine so that these instructions executed by the processor of the computer or other programmable scheduling device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0225] The above are only specific implementation methods of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for analyzing aerosol particle behavior, characterized in that: include: Obtaining basic parameters of a fluid to be analyzed, wherein the fluid to be analyzed is a mixture of non-condensable gas, water vapor and aerosol particles; Construct a geometric model of the computational domain of the fluid to be analyzed; Meshing the geometric model to obtain a grid unit data set of the geometric model; Get the governing equations, pre-created wall condensation functions, turbulence models, second component transport equations, deposition flux equations, Wherein, the control equations include: a mass conservation equation, a momentum conservation equation, an energy conservation equation and a first component transport equation, the wall condensation function is used to calculate the mass, momentum, energy and component loss source terms of the fluid medium in the fluid to be analyzed, the second component transport equation is an improved aerosol component transport equation, which is used to simulate the transport behavior of aerosol particles affected by mechanisms such as gravity sedimentation, Brownian diffusion, thermophoresis and diffusophore, and the deposition flux equation is used to calculate the deposition rate of aerosol particles; Based on the basic parameters and the grid unit data set, the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation are solved by a solver to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain; The behavior of the aerosol particles is analyzed based on the operating condition distribution data.

2. The method according to claim 1, characterized in that The pre-created wall condensation function includes formula (1): The condensation mass flow density m" of water vapor on the wall is calculated by the following formula (1): s : Among them, m″ s is the water vapor condensation mass flow density, Y s is the water vapor mass fraction, D is the water vapor diffusion coefficient, and n is the wall normal vector; The mass conservation equation includes formula (2), The mass loss source term S of the mass conservation equation is calculated by the following formula (2): m and the component loss source term S in the component transport equation i : Among them, A cell is the condensation area of ​​the first layer of grid cells near the wall, V cell is the unit volume; The momentum conservation equation includes formula (3), The momentum loss source term S of the momentum conservation equation is calculated by the following formula (3): mom : Where u is the local velocity component of the fluid in the grid cell; The energy conservation equation includes formula (4), The energy loss source term S of the energy conservation equation is calculated by the following formula (4): h : Among them, H lg is the latent heat of vaporization.

3. The method according to claim 2, characterized in that: The second component transport equation includes the following formula (5) to formula (8): Where C is the mass concentration or number concentration of aerosol particles, x i is the spatial coordinate, u i is the velocity of the fluid in the i direction, V s is the gravity settling velocity of aerosol particles, V th is the thermophoretic velocity of aerosol particles, V diff is the diffusion velocity of aerosol particles, D p is the Brownian diffusion coefficient of aerosol particles, ε p is the turbulent diffusion coefficient of aerosol particles, S C is the source term of aerosol particles; Among them, ρ p is the particle density, d p is the particle size, μ is the fluid viscosity, g is the gravitational acceleration, Kn is the Knudsen number, k is the fluid thermal conductivity, k p is the particle thermal conductivity, τ p is the particle relaxation time, C s is the thermal slip coefficient, taken as 1.17, C t is the temperature jump coefficient, take 2.18, C m is the momentum exchange coefficient, m p is the particle mass, M is the molar mass of the non-condensable gas and water vapor, γ is the molar ratio of the gas components, p is the partial pressure of the gas components, and the subscripts nc and s represent the non-condensable gas and water vapor, respectively.

4. The method according to claim 1, characterized in that The deposition flux equation includes the following formula (9): Where J is the aerosol wall deposition flux, D p is the Brownian diffusion coefficient of aerosol particles, ε p is the turbulent diffusion coefficient of aerosol particles, is the gradient of aerosol concentration in the direction perpendicular to the wall, V th is the thermophoresis velocity of aerosol particles, C is the mass concentration or number concentration of aerosol particles, V diff C is the diffusion velocity of aerosol particles.

5. The method according to any one of claims 1 to 4, characterized in that: The method of solving the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation by a solver based on the basic parameters and the grid unit data set to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain includes: Setting boundary conditions according to the basic parameters and the grid unit data set, wherein the boundary conditions are specific physical constraints imposed on the boundary of the computational domain in the numerical simulation; Setting the solution parameters of the solver, wherein the solution parameters are key variables used to control the discretization method, iteration strategy and convergence criterion in numerical simulation; Based on iteratively solving the control equation, the pre-created wall condensation function, the turbulence model and the deposition flux equation according to the basic parameters, the boundary conditions and the solution parameters, a converged velocity field, a pressure field, a temperature field, a fluid medium concentration field and an aerosol particle concentration are obtained.

6. The method according to claim 4, characterized in that The boundary conditions include inlet boundary and outlet boundary, and the solution parameters include discretization method, relaxation factor, coupling method, time step, and residual convergence criterion. The time step is the length of the time step.

7. The method according to claim 6, characterized in that The solver is a pressure-based solver, The method of iteratively solving the control equation, the pre-created wall condensation function, the turbulence model and the deposition flux equation according to the basic parameters, the boundary conditions and the solution parameters to obtain a converged velocity field, a pressure field, a temperature field and a fluid medium concentration field and an aerosol particle concentration includes: S1. For the control equation, the pre-created wall condensation function, the turbulence model and the deposition flux equation, a second-order upwind scheme is used for spatial discretization; S2. For the pressure-velocity coupling equation, the volume force weight format is used for spatial discretization; For each time step, the following steps S3 to S14 are performed: S3, obtaining the initial velocity field, initial pressure field, initial temperature field, initial fluid medium concentration field and initial aerosol particle concentration of all grid cells; S4, solving the pre-created wall condensation function to obtain the mass loss source term, component loss source term, momentum loss source term, and energy loss source term of the first layer of grid cells near the wall; S5. For each first-layer grid unit near the wall, update the control equation of the first-layer grid unit near the wall according to the mass loss source term, component loss source term, momentum loss source term, and energy loss source term. S6. Solve the momentum equation according to the initial velocity field and the initial pressure field to obtain a predicted velocity field; S7, according to the predicted velocity field, using the pressure-velocity coupling equation, using the SIMPLE algorithm to obtain a corrected pressure field and a corrected velocity field, and updating the pressure field and the velocity field; S8, solving the turbulence equation in the turbulence model according to the updated velocity field, obtaining turbulence parameters, and updating the turbulence parameters; S9, solving the first component transport equation according to the updated pressure field and the updated velocity field to obtain the fluid medium concentration, and updating the fluid medium concentration; S10, solving the second component transport equation according to the updated turbulence parameter and the updated fluid medium concentration to obtain the aerosol particle concentration, and updating the aerosol particle concentration; S11, solving the energy equation according to the updated velocity field to obtain the temperature field, updating the initial temperature field to obtain an updated temperature field; S12, solving the deposition flux equation according to the updated fluid medium concentration and the updated aerosol particle concentration, and updating the aerosol particle deposition velocity; S13, calculating the residual of each equation; S14, when the residuals of each equation do not meet the preset convergence criteria, repeat the above S3 to S13 until the residuals of each equation meet the preset convergence criteria, and output the updated velocity field, pressure field, temperature field, and species concentration field; S14, recording the updated velocity field, pressure field, temperature field, fluid medium concentration field and aerosol particle concentration at each time step; S15. Generate flow field visualization data and aerosol particle concentration distribution data according to the time series.

8. A safety assessment method for a nuclear power plant, characterized in that: Applied to containment, the method include: According to the aerosol particle behavior analysis method according to any one of claims 1 to 7, the aerosol particle behavior in the containment is analyzed to obtain an analysis result; Based on the analysis results, a safety assessment is performed on the containment vessel.

9. An aerosol particle behavior analysis device, characterized in that: The device comprises: A first acquisition module is used to acquire basic parameters of a fluid to be analyzed, wherein the fluid to be analyzed is a mixture of non-condensable gas, water vapor and aerosol particles; A model building module, used to build a geometric model of the computational domain of the fluid to be analyzed; A meshing module, connected to the model building module, for meshing the geometric model to obtain a mesh unit data set of the geometric model; A second acquisition module, connected to the grid division module, is used to obtain the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation, and the deposition flux equation. Wherein, the control equations include: a mass conservation equation, a momentum conservation equation, an energy conservation equation and a first component transport equation; the wall condensation function is used to calculate the mass, momentum, energy and component loss source terms of the fluid medium in the fluid to be analyzed; the second component transport equation is an improved aerosol component transport equation, which is used to simulate the transport behavior of aerosol particles affected by mechanisms such as gravity sedimentation, Brownian diffusion, thermophoresis and diffusophore; the deposition flux equation is used to calculate the deposition rate of aerosol particles; A solution module connected to the second acquisition module, for solving the control equation, the pre-created wall condensation function, the turbulence model, the second component transport equation and the deposition flux equation through a solver based on the basic parameters and the grid unit data set, to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain; An analysis module, connected to the solution module, is used to analyze the behavior of the aerosol particles based on the operating condition distribution data.

10. The device according to claim 7, characterized in that The pre-created wall condensation function includes: The condensation mass flow density m" of water vapor on the wall is calculated by the following formula (1): s : Among them, m″ s is the water vapor condensation mass flow density, Y s is the water vapor mass fraction, D is the water vapor diffusion coefficient, and n is the wall normal vector; The mass loss source term S of the mass conservation equation is calculated by the following formula (2): m and the component loss source term S in the component transport equation i : Among them, A cell is the condensation area of ​​the first layer of grid cells near the wall, V cell is the unit volume; The momentum loss source term S of the momentum conservation equation is calculated by the following formula (3): mom : Where u is the local velocity component of the fluid in the grid cell; The energy loss source term S of the energy conservation equation is calculated by the following formula (4): h : Among them, H lg is the latent heat of vaporization.

11. The device according to claim 7, characterized in that The second component transport equation includes the following formula (5) to formula (8): Where C is the mass concentration or number concentration of aerosol particles, x i is the spatial coordinate, u i is the velocity of the fluid in the i direction, V s is the gravity settling velocity of aerosol particles, V th is the thermophoretic velocity of aerosol particles, V diff is the diffusion velocity of aerosol particles, D p is the Brownian diffusion coefficient of aerosol particles, ε p is the turbulent diffusion coefficient of aerosol particles, φ is a user-defined scalar, S C is the source term of aerosol particles; Among them, ρ p is the particle density, d p is the particle size, μ is the fluid viscosity, g is the gravitational acceleration, Kn is the Knudsen number, k is the fluid thermal conductivity, k p is the particle thermal conductivity, τ p is the particle relaxation time, C s is the thermal slip coefficient, taken as 1.17, C t is the temperature jump coefficient, take 2.18, C m is the momentum exchange coefficient, m p is the particle mass, M is the molar mass of the non-condensable gas and water vapor, γ is the molar ratio of the gas components, p is the partial pressure of the gas components, and the subscripts nc and s represent the non-condensable gas and water vapor, respectively.

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