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; acquiring a control equation, a pre-created wall condensation function, a turbulence model, a discrete phase model, a thermophoresis force model and a pre-created diffusion phoresis force function; based on the basic parameters and the grid unit data set, solving a control equation, a pre-established wall surface condensation function, a turbulence model, a discrete phase model, a thermophoresis force model and a pre-established diffusion phoresis force function through a solver, and obtaining working condition distribution data of the to-be-analyzed fluid in a computational domain; 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, severe accident analysis programs based on lumped parameter methods are usually used for safety analysis and risk assessment, such as MELCOR, MAAP, ASTEC, etc. These programs are mainly based on macroscopic control equations and mean field models to simulate thermal and radiation transfer processes in severe accidents. However, in the actual containment, the distribution of thermal parameters such as temperature and humidity may be very uneven, especially in the case of non-condensable gases and water vapor, the thermal parameters of the gas may vary in space, and the lumped parameter method cannot capture these local and fine changes.

[0004] Therefore, existing technologies have certain limitations in dealing with some microscopic and local phenomena, such as the fine movement behavior of aerosol particles. 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 movement behavior of aerosol particles 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] Obtain the basic parameters of the fluid to be analyzed;

[0008] Constructing a geometric model of a computational domain of a fluid to be analyzed, wherein the fluid to be analyzed is a mixture of non-condensable gas, water vapor, and aerosol particles;

[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, discrete phase models, thermophoretic models, and pre-created diffusiophoretic functions.

[0011] Wherein, the control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and 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, and the diffusion-phoretic force function is used to determine the diffusion-phoretic force on the 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function 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, used to acquire basic parameters of the fluid to be analyzed;

[0019] A model building module, used to build a geometric model of a computational domain of a fluid to be analyzed, wherein the fluid to be analyzed is a mixture of non-condensable gas, water vapor and aerosol particles;

[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] The second acquisition module 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function.

[0022] Wherein, the control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and 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, and the diffusion-phoretic force function is used to determine the diffusion-phoretic force on the 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function 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 detailed operating condition distribution data by acquiring the basic parameters of the fluid to be analyzed, constructing and gridding a geometric model, and combining the solution of the control equation, wall condensation function, turbulence model, discrete phase model, thermophoretic force model and diffusion-phoretic force function. It can accurately capture the local dynamic behavior of fluids and aerosol particles, significantly improve the simulation accuracy and refinement level, and provide a reliable basis for risk assessment and nuclear power plant safety design. 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 : is a motion trajectory diagram of aerosol particles with different particle sizes according to Example 1 of the present invention;

[0035] Fig.10 : This is a structural diagram of an aerosol particle behavior analysis device according to Example 3 of the present invention.

[0036] The meanings of the marks in the figure are:

[0037] 1. Steam generator; 2. Two-position valve; 3. Critical flow limiting orifice; 4. Nitrogen supply;

[0038] 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

[0039] 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.

[0040] 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.

[0041] Computational fluid dynamics (CFD) is a combination of numerical calculation and computing technology, and is a method of numerically solving computational fluid problems.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] After the accident, the aerosols in the containment will be naturally removed, and the deposition mechanisms involved include gravity sedimentation, diffusion deposition, thermophoresis deposition and diffusophore deposition. For passive nuclear power plants such as AP1000, diffusophore and thermophoresis deposition play a dominant role, followed by gravity sedimentation. Thermophoresis is caused by the asymmetric impact of gas molecules on aerosols in a gas environment with a temperature gradient, while diffusophore is caused by the condensation of water vapor in the aerosol particle carrier gas on the wall. Both types of swimming are related to the non-uniform distribution of the carrier gas. Therefore, to accurately analyze the swimming phenomenon of aerosol particles, it is necessary to obtain a precise distribution of carrier gas thermal parameters.

[0047] The severe accident analysis programs currently widely used in the field of nuclear safety analysis, which are developed based on the lumped parameter method (such as MELCOR, MAAP, ASTEC, etc.), cannot obtain the precise distribution of carrier gas thermal parameters, and therefore cannot perform mechanism analysis and simulation of the aerosol particle migration phenomenon in the non-condensable gas water vapor environment in the containment after the accident.

[0048] Embodiment 1:

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Provide accurate initial conditions and physical properties for subsequent simulation and analysis to ensure the accuracy and reliability of the analysis.

[0053] Step 102: construct a geometric model of the computational domain of the fluid to be analyzed.

[0054] 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).

[0055] Step 103, meshing the geometric model to obtain a mesh unit data set of the geometric model.

[0056] 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.

[0057] 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.).

[0058] 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.

[0059] 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.

[0060] Step 104, obtaining the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusivitation force function.

[0061] The control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and 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 diffusion-phoretic force function is used to determine the diffusion-phoretic force on aerosol particles.

[0062] 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 component transport equation describes the transfer process of different components in the fluid, including convection and diffusion.

[0063] 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.

[0064] 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.

[0065] Discrete Phase Model (DPM) is a method in computational fluid dynamics (CFD) used to simulate the motion of discrete phases (such as particles, droplets or bubbles) in continuous phases (such as gas or liquid). The discrete phase model (DPM) used in this application is based on the Euler-Lagrangian method to perform computational simulations on the fluid phase and the particle phase.

[0066] Thermophoretic models are used to describe the motion of aerosol particles under pressure gradients.

[0067] Pre-created diffusiophoretic force functions are used to describe the diffusiophoretic phenomenon.

[0068] Obtain and set the control equations and physical models to provide a comprehensive and accurate physical description for subsequent calculations. At the same time, the impact of water vapor condensation is considered to ensure that the simulation fully considers various important phenomena of fluids and particles, thereby ensuring the accuracy and reliability of the simulation results.

[0069] 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function are solved by the solver to obtain the operating condition distribution data of the fluid to be analyzed in the calculation domain.

[0070] 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. Through the solver, the control equations are solved, which include the continuity equation, momentum conservation equation, energy conservation equation and component transport equation. In the solution process, the pre-created wall condensation function, turbulence model, discrete phase model, thermophoretic force model and diffusion-phoretic force function 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 concentration 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.

[0071] Step 106 , analyzing the behavior of aerosol particles based on the operating condition distribution data.

[0072] 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.

[0073] For example, using velocity field data, we can calculate and visualize the motion trajectory of particles and describe the migration path of particles in the fluid; particle velocity: analyzing the changes in the velocity of particles at different positions can understand the effect of fluid flow on particle acceleration or deceleration.

[0074] 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 trajectory diagrams, concentration distribution diagrams, etc.).

[0075] In this embodiment, by obtaining the basic parameters of the fluid to be analyzed, constructing and meshing the geometric model, and combining the solution of the control equation, wall condensation function, turbulence model, discrete phase model, thermophoretic force model and diffusion-phoretic force function, detailed operating condition distribution data is obtained, which can accurately capture the local dynamic behavior of fluids and aerosol particles, significantly improve the simulation accuracy and refinement, and provide a reliable basis for risk assessment and nuclear power plant safety design.

[0076] Optionally, the pre-created wall condensation function includes formula (1):

[0077] The condensation mass flow density m" of water vapor on the wall is calculated by the following formula (1): s :

[0078]

[0079] 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;

[0080] The mass conservation equation includes formula (2),

[0081] 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 :

[0082]

[0083] Among them, Acell is the condensation area of ​​the first layer of grid cells near the wall, V cell is the unit volume;

[0084] The momentum conservation equation includes formula (3)

[0085] The momentum loss source term S of the momentum conservation equation is calculated by the following formula (3): mom :

[0086]

[0087] Where u is the local velocity component of the fluid in the grid cell;

[0088] The energy conservation equation includes formula (4)

[0089] The energy loss source term S of the energy conservation equation is calculated by the following formula (4): h :

[0090]

[0091] Among them, H lg is the latent heat of vaporization.

[0092] 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.

[0093] The wall condensation function provided in this application is the mass, momentum and energy loss source terms obtained based on the local diffusion method.

[0094] 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.

[0095] Optionally, the pre-created diffusion phoresis function includes formula (5) and formula (6):

[0096] The diffusive phoretic force on aerosol particles is calculated by using formula (5) and formula (6):

[0097]

[0098] Where M is the molar mass of the noncondensable 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 noncondensable gas and water vapor, respectively.

[0099]

[0100] Among them, τ r is the relaxation time of the particle.

[0101] In this embodiment, the above-mentioned diffusiophoretic force function uses the molar mass, molar ratio, diffusion coefficient, partial pressure and its gradient of the gas component to describe the motion behavior of aerosol particles in the concentration gradient field. These formulas are integrated into the numerical simulation solver, and the diffusiophoretic force is accurately described in the calculation through the user-defined function (UDF) or the direct definition of the source term.

[0102] Optionally, the above step 105 may specifically include the following steps:

[0103] 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.

[0104] Set the solver's solution parameters, which are key variables used to control the discretization method, iteration strategy, and convergence criteria in numerical simulation;

[0105] Based on the iterative solution of the control equations, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic model and the pre-created diffusion-phoretic function according to the basic parameters, boundary conditions and solution parameters, the converged velocity field, pressure field, temperature field, species concentration field and the position and velocity of aerosol particles are obtained.

[0106] 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).

[0107] 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:

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] First, boundary conditions are set. 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.

[0115] 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.

[0116] Then, set the solution parameters of the solver. The solution parameters control the discretization method, iteration strategy and convergence criterion of the numerical simulation. Among them, discretization method: select a suitable discretization method, such as ordered difference, finite volume method, etc.; iteration strategy: configure the iteration 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.), and relative error or absolute error is often used as the standard. For example: reduce the residual to 10 -6 the following.

[0117] Finally, the iterative solution of each module is integrated. Based on the above boundary conditions and solution parameters, some basic parameters are substituted to iteratively solve the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoresis model and the diffusion-phoresis function.

[0118] In this embodiment, the iterative solution process is as follows:

[0119] (1) Initial condition setting: Set initial values ​​for the velocity field, pressure field, temperature field, species concentration field, and the position and velocity of aerosol particles.

[0120] (2) Governing equations: Discretize and solve the Navier-Stokes equations of fluid motion, including the continuity equation, momentum equation, energy equation, and species transport equation.

[0121] (3) Wall condensation function: Calculate the condensation mass flux density and subtract the condensation source term from the mass, momentum, and energy equations accordingly.

[0122] (4) Turbulence model: Select an appropriate turbulence model (such as the k-ε model, k-ω model, or large eddy simulation (LES)) and wall function, calculate the turbulent viscosity coefficient and energy dissipation rate, and update the turbulent kinetic energy and dissipation rate.

[0123] Turbulence model: Select an appropriate turbulence model (such as k-ε model, k-ω model or large eddy simulation (LES)), calculate the turbulent viscosity coefficient and energy dissipation rate, and update the turbulent kinetic energy and dissipation rate.

[0124] (5) Discrete phase model: Track the trajectory of aerosol particles and calculate the forces acting on the particles (including drag force and gravity).

[0125] (6) Thermophoretic and diffusive swimming forces can also be calculated by using the thermophoretic model and diffusive swimming function.

[0126] (7) Coupled solution: All models and functions are coupled together for solution, including fluid equations, particle trajectory equations, and source term corrections. An iterative method is used to gradually approach a convergent solution.

[0127] (8) Convergence judgment: Check whether the residuals of each variable meet the set convergence criteria. If not, continue iterating.

[0128] (9) Obtaining results

[0129] The results obtained through iterative solution include:

[0130] Velocity field: velocity distribution of the fluid in the entire domain.

[0131] Pressure field: The pressure distribution in the whole domain.

[0132] Temperature field: temperature distribution in the entire domain.

[0133] Species concentration field: the mass fraction distribution of each species in the entire domain.

[0134] Aerosol particle position and velocity: The position and velocity trajectories of aerosol particles in the flow field.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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 non-steady-state CFD simulation, 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 simulation, a series of calculations are completed in each time step.

[0140] 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.

[0141] 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.

[0142] Optionally, the solver is a pressure-based solver,

[0143] The above iterative solution of the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function obtains the converged velocity field, pressure field, temperature field, species concentration field and the position and velocity of aerosol particles, which may specifically include:

[0144] S1. The second-order upwind scheme is used to perform spatial discretization for the control equations, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model, and the pre-created diffusive-phoretic force function.

[0145] S2. For the pressure-velocity coupling equation, the volume force weight format is used for spatial discretization;

[0146] For each time step, the following steps S3 to S17 are performed:

[0147] S3, obtaining the initial velocity field, initial pressure field, initial temperature field and initial species concentration field of each grid cell;

[0148] 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;

[0149] S5. For each first-layer grid cell near the wall, the control equations of the first-layer grid cells near the wall are updated according to the mass loss source term, the component loss source term, the momentum loss source term, and the energy loss source term.

[0150] S6. Solve the momentum equation according to the initial velocity field and the initial pressure field to obtain a predicted velocity field;

[0151] 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, updating the initial pressure field and the initial velocity field, and obtaining an updated pressure field and an updated velocity field;

[0152] S8. Solve the turbulence equations in the turbulence model according to the updated velocity field to obtain turbulence parameters, and update the initial turbulence parameters to obtain updated turbulence parameters;

[0153] S9, solving the component transport equation to obtain the species concentration field, updating the initial species concentration field to obtain an updated species concentration field;

[0154] S10, solving the energy equation to obtain the temperature field, updating the initial temperature field to obtain an updated temperature field;

[0155] S11. Solve the discrete phase model according to the updated velocity field, the updated pressure field, the updated temperature field, the species concentration field and the initial parameters of the aerosol particles to obtain the initial force of the aerosol particles in the flow field;

[0156] S12. Obtain the thermophoretic force on aerosol particles through a thermophoretic force model;

[0157] S13, solving the diffusion-phoretic force on the aerosol particles by using the diffusion-phoretic force function created in advance;

[0158] S14, updating the initial force based on the thermophoretic force and the diffusivitation force to obtain the total force on the aerosol particles;

[0159] S15. Based on the total force on the aerosol particles, the velocity and position of the aerosol particles are updated through the particle motion equation;

[0160] S16, calculating the residual of each equation;

[0161] S17, when the residuals of each equation do not meet the preset convergence criteria, repeat the above S3 to S16 until the residuals of each equation meet the preset convergence criteria, and output the updated velocity field, pressure field, temperature field, species concentration field, and velocity and position of aerosol particles;

[0162] S18, recording the updated velocity field, pressure field, temperature field, species concentration field, and velocity and position of aerosol particles at each time step;

[0163] S19. Generate flow field visualization data and aerosol particle trajectories based on the time series.

[0164] 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, the discrete phase model, the thermophoretic model and the pre-created diffusion-phoretic function, 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 good 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.

[0165] 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.

[0166] Specifically, in each time step, the initial velocity, pressure, temperature and species concentration fields of all grid cells are first obtained, and then the wall condensation function is solved to obtain the mass, component, momentum and energy loss source terms near the wall, and the influence of these loss source terms on the near-wall control equations is updated. Next, the momentum equation is solved according to the initial velocity and pressure field to obtain the predicted velocity field, and the velocity and pressure fields are corrected by the SIMPLE coupling algorithm to update their initial values. Subsequently, the turbulence equation is solved according to the updated velocity field to update the turbulence parameters, and the component transport equation and energy equation are solved to update the species concentration field and temperature field. Then, the discrete phase model is solved in combination with the updated flow field parameters to obtain the initial force on the aerosol particles, and the thermophoretic force and diffusive phoretic force of the aerosol particles are obtained respectively through the thermophoretic force model and the diffusive phoretic force function, and the total force is updated and the particle velocity and position are updated based on this. The residuals of all equations are calculated and checked in each iteration step until the preset convergence criteria are met. Finally, the updated flow field parameters and aerosol particle positions and velocities at each time step are output and recorded, and flow field visualization data and aerosol particle trajectories are generated, thereby ensuring that convergent and realistic flow field simulation results are obtained through precise numerical methods.

[0167] 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 particle motion equations, the SIMPLE algorithm is used for pressure-velocity coupling, and the wall condensation, turbulence, thermophoresis and diffusion-phoresis models are combined to gradually update each physical quantity, calculate the residual to determine the convergence, and finally generate flow field data and particle trajectories, which can ensure the accuracy of the simulation and the accuracy of the numerical results.

[0168] 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:

[0169] The following steps are based on the computational fluid dynamics software Fluent, as follows:

[0170] 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.

[0171] right Figure 3 The experimental bench shown is set up as Figure 4 (x-direction ratio 4:1, y-direction ratio 1:2) and generate the geometric model shown in Figure 5 The grid shown.

[0172] 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.

[0173] Step 2: Import the generated mesh file into Fluent software, select the turbulence model and appropriate wall function in the software, and activate the energy equation and component equation.

[0174] 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.

[0175] 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;

[0176] 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;

[0177] 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;

[0178] 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 gases, compile the UDF in Fluent software and specify the action locations for these UDFs.

[0179] 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.

[0180] The mass, momentum and energy loss source terms obtained based on the local diffusion method can be calculated using the following relations:

[0181]

[0182] 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.

[0183] The loss source term of the near-wall element in the mass conservation equation and the component transport equation is:

[0184]

[0185] 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.

[0186] The loss source term of the near-wall element in the momentum conservation equation is:

[0187]

[0188] The loss source term of the near-wall element in the energy conservation equation is:

[0189]

[0190] Where: H lg is the latent heat of vaporization.

[0191] 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.

[0192] Specifically in this application example, the macros involved are:

[0193] DEFINE_ON_DEMAND(), DEFINE_PROFILE(), DEFINE_ADJUST(), DEFINE_EXECUTE_AT_END(), DEFINE_SOURCE().

[0194] Step 5: Activate the discrete phase model in the Fluent software, set the particle properties, injection method and other information, and activate the thermophoretic model.

[0195] Specifically, the discrete phase model was activated in the Fluent software, and the particle properties were set to those of silver (density and thermal conductivity). The position, particle size, initial velocity, initial temperature and flow rate information of the silver particles at the inlet were specified through an external file, and the thermophoretic model was activated.

[0196] Step 6:

[0197] The aerosol particle diffusion phoretic force UDF is compiled through the macro provided by Fluent software, and the UDF is compiled in Fluent software and the action position of the UDF is specified. The diffusion phoretic force can be calculated using the following formula:

[0198]

[0199] Where: 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.

[0200]

[0201] Where: This is the diffusion-phoretic force.

[0202] Specifically in this application example, the macros involved are:

[0203] DFFINE_DPM_BODY_FORCE()

[0204] Step 7: Set the boundary conditions of the computational domain in Fluent. These boundary conditions can be inlets, outlets, walls, or symmetry surfaces, etc.

[0205] Specifically, the inlet is a mass inlet boundary condition, specifying the mass flow rate, temperature and mass fraction of water vapor of the mixed gas; the outlet is a pressure outlet boundary condition, the wall is a constant temperature, and the water vapor mass fraction at the wall is the saturated water vapor mass fraction at the corresponding wall temperature;

[0206] Step 8: Set the solution parameters in the Fluent software, including time step (if it is a transient calculation), solution method, relaxation factor, residual, etc.

[0207] The solution parameters were set in the Fluent software, where the pressure-velocity coupling adopted the SIMPLE format, the spatial discretization of density, momentum, turbulent kinetic energy, turbulent dissipation rate, components and energy was the second-order upwind format, and the spatial discretization of pressure was the volume force weight.

[0208] Step 9: Initialize and start calculation;

[0209] Step 10: Use CFD-Post to post-process the calculation results. The calculation results can obtain detailed thermal parameter distribution, such as temperature, water vapor mass fraction, etc., and the motion trajectory of particles under the electrophoretic force.

[0210] Use CFD-Post to post-process the calculation results. Figure 6 (x direction 4:1, y direction 1:2) gives the simulated mixed gas temperature cloud map, Figure 7 (x direction 4:1, y direction 1:2) gives the simulated water vapor mass fraction distribution cloud map, Figure 8 The time-dependent changes of the speed of particles with two different particle sizes moving toward the wall are given. Fig. 9 The motion trajectories of particles of these two different sizes are given.

[0211] The simulation method for the swimming phenomenon of aerosol particles in an environment containing non-condensable gas and water vapor based on Fluent software provided in this embodiment can obtain a fine three-dimensional distribution of the carrier gas medium, set the thermophoretic force, diffusion swimming force and passive drag force applied to the aerosol particles in the calculation domain based on the three-dimensional thermal parameter distribution, and solve the motion equation of the particles by the Lagrangian method to obtain the motion trajectory of the particles under the action of the swimming mechanism, which can be used to analyze the swimming law of particles in an environment containing non-condensable gas and water vapor.

[0212] Embodiment 2:

[0213] This embodiment provides a safety assessment method for a nuclear power plant, which is applied to a containment, and specifically includes the following steps:

[0214] 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;

[0215] Based on the analysis results, a safety assessment of the containment is conducted.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] Embodiment 3:

[0220] like Fig.10 As shown, this embodiment provides an aerosol particle behavior analysis device 1000, comprising:

[0221] The first acquisition module 1001 is used to acquire basic parameters of the fluid to be analyzed;

[0222] A model building module 1002 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;

[0223] A meshing module 1003, connected to the model building module, is used to mesh the geometric model to obtain a mesh unit data set of the geometric model;

[0224] The second acquisition module 1004 is connected to the grid generation module and is used to acquire the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function.

[0225] Among them, the control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and 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 diffusion-phoretic force function is used to determine the diffusion-phoretic force on the aerosol particles.

[0226] The solving module 1005 is connected to the second acquisition module and is used to solve the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function through the solver based on the basic parameters and the grid unit data set to obtain the working condition distribution data of the fluid to be analyzed in the calculation domain;

[0227] The analysis module 1006 is connected to the solution module and is used to analyze the behavior of aerosol particles based on the operating condition distribution data.

[0228] Optionally, the solution module 1005 includes:

[0229] 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;

[0230] 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;

[0231] The iterative calculation module is used to iteratively solve the control equations, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic model and the pre-created diffusion-phoretic function to obtain the converged velocity field, pressure field, temperature field, species concentration field and the position and velocity of aerosol particles.

[0232] The pre-created wall condensation function includes:

[0233] The condensation mass flow density m" of water vapor on the wall is calculated by the following formula (1): s :

[0234]

[0235] 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;

[0236] 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 :

[0237]

[0238] Among them, A cell is the condensation area of ​​the first layer of grid cells near the wall, V cell is the unit volume;

[0239] The momentum loss source term S of the momentum conservation equation is calculated by the following formula (3): mom :

[0240]

[0241] Where u is the local velocity component of the fluid in the grid cell;

[0242] The energy loss source term S of the energy conservation equation is calculated by the following formula (4): h :

[0243]

[0244] Among them, H lg is the latent heat of vaporization.

[0245] The pre-created diffusion phoresis function includes formula (5) and formula (6):

[0246] The diffusive phoretic force on aerosol particles is calculated by using formula (5) and formula (6):

[0247]

[0248] Where M is the molar mass of the noncondensable 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 noncondensable gas and water vapor, respectively.

[0249]

[0250] Among them, τ r is the relaxation time of the particle.

[0251] The solution module includes:

[0252] A first setting submodule is used to set boundary conditions, where the boundary conditions are specific physical constraints imposed on the boundary of the computational domain in numerical simulation;

[0253] A second setting submodule is used to set 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;

[0254] The iterative calculation module is used to iteratively solve the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function to obtain a converged velocity field, a pressure field, a temperature field, a species concentration field and aerosol particle positions and velocities.

[0255] 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.

[0256] The time step is the length of the time step.

[0257] Optionally, the solver is a pressure-based solver,

[0258] The iterative calculation module is specifically used for:

[0259] S1. For the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function, a second-order upwind scheme is used for spatial discretization;

[0260] S2. For the pressure of the pressure-velocity coupling equation, the volume force weight format is used for spatial discretization;

[0261] For each time step, the following steps S3 to S17 are performed:

[0262] S3, obtaining the initial velocity field, initial pressure field, initial temperature field and initial species concentration field of the grid unit;

[0263] 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;

[0264] S5. For each first-layer grid unit near the wall, the control equation of the first-layer grid unit near the wall is updated according to the mass loss source term, component loss source term, momentum loss source term and energy loss source term;.

[0265] S6. Solve the momentum equation according to the initial velocity field and the initial pressure field to obtain a predicted velocity field;

[0266] 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, updating the initial pressure field and the initial velocity field, and obtaining an updated pressure field and an updated velocity field;

[0267] S8. Solve the turbulence equations in the turbulence model according to the updated velocity field to obtain turbulence parameters, and update the initial turbulence parameters to obtain updated turbulence parameters;

[0268] S9, solving the component transport equation to obtain the species concentration field, updating the initial species concentration field to obtain an updated species concentration field;

[0269] S10, solving the energy equation to obtain the temperature field, updating the initial temperature field to obtain an updated temperature field;

[0270] S11. Solve the discrete phase model according to the updated velocity field, the updated pressure field, the updated temperature field, the species concentration field and the initial parameters of the aerosol particles to obtain the initial force of the aerosol particles in the flow field;

[0271] S12. Obtain the thermophoretic force on aerosol particles through a thermophoretic force model;

[0272] S13, solving the diffusion-phoretic force on the aerosol particles by using the diffusion-phoretic force function created in advance;

[0273] S14, updating the initial force based on the thermophoretic force and the diffusivitation force to obtain a total force on the aerosol particle;

[0274] S15. Based on the total force on the aerosol particles, updating the velocity and position of the aerosol particles through the particle motion equation;

[0275] S16, calculating the residual of each equation;

[0276] S17, when the residuals of each equation do not meet the preset convergence criteria, repeat the above S3 to S16 until the residuals of each equation meet the preset convergence criteria, and output the updated velocity field, pressure field, temperature field, species concentration field, and velocity and position of aerosol particles;

[0277] S18, recording the updated velocity field, pressure field, temperature field, species concentration field, and velocity and position of aerosol particles at each time step;

[0278] S19. Generate flow field visualization data and aerosol particle trajectories based on the time series.

[0279] In this embodiment, by obtaining the basic parameters of the fluid to be analyzed, constructing and meshing the geometric model, and combining the solution of the control equations, wall condensation function, turbulence model, discrete phase model, thermophoretic force model and diffusion-phoretic force function, detailed operating condition distribution data is obtained, which can accurately capture the local dynamic behavior of fluids and aerosol particles, significantly improve the simulation accuracy and refinement, and provide a reliable basis for risk assessment and nuclear power plant safety design.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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, discrete phase models, thermophoretic models, and pre-created diffusiophoretic functions. Wherein, the control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and 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, and the diffusion-phoretic force function is used to determine the diffusion-phoretic force on the 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function 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 pre-created diffusion phoresis function includes formula (5) and formula (6): The diffusive force on aerosol particles is calculated by the following formula (5) and formula (6): Where M is the molar mass of the noncondensable 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 noncondensable gas and water vapor, respectively. Among them, τ r is the relaxation time of the particle.

4. The method according to any one of claims 1 to 3, characterized in that: The method of solving the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function 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 the iterative solution of the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function according to the basic parameters, the boundary conditions and the solution parameters, a converged velocity field, pressure field, temperature field, species concentration field and aerosol particle position and velocity are obtained.

5. 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.

6. The method according to claim 5, characterized in that The solver is a pressure-based solver, The method iteratively solves the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function according to the basic parameters, the boundary conditions and the solution parameters to obtain a converged velocity field, a pressure field, a temperature field, a species concentration field and aerosol particle positions and velocities, including: S1. For the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function, 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 S17 are performed: S3, obtaining the initial velocity field, initial pressure field, initial temperature field and initial species concentration field of the grid unit; 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, the control equation of the first-layer grid unit near the wall is updated according to the mass loss source term, the component loss source term, the momentum loss source term, and the 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, updating the initial pressure field and the initial velocity field, and obtaining an updated pressure field and an updated velocity field; S8. Solve the turbulence equations in the turbulence model according to the updated velocity field to obtain turbulence parameters, and update the initial turbulence parameters to obtain updated turbulence parameters; S9, solving the component transport equation to obtain the species concentration field, updating the initial species concentration field to obtain an updated species concentration field; S10, solving the energy equation to obtain the temperature field, updating the initial temperature field to obtain an updated temperature field; S11. Solve the discrete phase model according to the updated velocity field, the updated pressure field, the updated temperature field, the species concentration field and the initial parameters of the aerosol particles to obtain the initial force of the aerosol particles in the flow field; S12. Obtain the thermophoretic force on aerosol particles through a thermophoretic force model; S13, solving the diffusion-phoretic force on the aerosol particles by using the diffusion-phoretic force function created in advance; S14, updating the initial force based on the thermophoretic force and the diffusophoretic force to obtain the total force on the aerosol particle; S15. Based on the total force on the aerosol particles, updating the velocity and position of the aerosol particles through the particle motion equation; S16, calculating the residual of each equation; S17, when the residuals of each equation do not meet the preset convergence criteria, repeat the above S3 to S16 until the residuals of each equation meet the preset convergence criteria, and output the updated velocity field, pressure field, temperature field, species concentration field, and velocity and position of aerosol particles; S18, recording the updated velocity field, pressure field, temperature field, species concentration field, and velocity and position of aerosol particles at each time step; S19. Generate flow field visualization data and aerosol particle trajectories based on the time series.

7. A safety assessment method for a nuclear power plant, characterized in that: Applied to a containment vessel, the method comprises: According to the aerosol particle behavior analysis method according to any one of claims 1 to 6, 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.

8. 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; The second acquisition module 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function. Wherein, the control equations include: mass conservation equation, momentum conservation equation, energy conservation equation and 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, and the diffusion-phoretic force function is used to determine the diffusion-phoretic force on the 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 discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function 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.

9. 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.

10. The device according to claim 8, characterized in that The pre-created diffusion phoresis function includes formula (5) and formula (6): The diffusive phoretic force on aerosol particles is calculated by using formula (5) and formula (6): Where M is the molar mass of the noncondensable 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 noncondensable gas and water vapor, respectively. Among them, τ r is the relaxation time of the particle.

11. The device according to claim 8, characterized in that The solution module includes: A first setting submodule is used to set boundary conditions, where the boundary conditions are specific physical constraints imposed on the boundary of the computational domain in numerical simulation; A second setting submodule is used to set 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; The iterative calculation module is used to iteratively solve the control equation, the pre-created wall condensation function, the turbulence model, the discrete phase model, the thermophoretic force model and the pre-created diffusion-phoretic force function to obtain a converged velocity field, a pressure field, a temperature field, a species concentration field and aerosol particle positions and velocities.

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