Method and apparatus for simulating ice water pool

Abstract

This invention relates to a method and apparatus for simulating seawater freezing in an ice-water pool, belonging to the field of ice-water pool experimental technology, and particularly to the simulation of seawater freezing in an ice-water pool. It solves the problems of multivariate coupling in the multiphase interface tracking process and insufficient understanding of the influence of salinity (concentration) in existing technologies. The method includes the following steps: an input step for obtaining ice-water pool simulation parameters; a solution step for solving the momentum equation, energy equation, concentration equation, and continuity equation based on the ice-water pool simulation parameters to obtain output parameters; and an output step for obtaining output results based on the output parameters. The output results include ice growth rate, ice distribution, and airflow direction. This ice-water pool simulation method and apparatus are suitable for simulating the seawater freezing process in an ice-water pool.

Description

Technical Field

[0001] This invention relates to the field of ice-water pool testing technology, and more particularly to the simulation of the seawater freezing process in an ice-water pool. Background Technology

[0002] In recent years, with the discovery of more and more energy and resources in the Arctic region, and the increase in traffic flow in the Northeast Passage and Northwest Passage, the demand for polar vessels (such as ice transport ships and icebreakers) from countries around the world has been increasing. This will have a significant impact on the development and research of high-tech ships and the demand in the shipbuilding market. With the improvement of oil and gas and mineral resource extraction technologies, the Arctic, known as the "second Middle East," is gradually becoming a base for human beings to extract resources. In addition to the Arctic circumpolar countries, major non-Arctic powers have also made ample preparations for the exploration and exploitation of Arctic resources and the development of ice-carrying vessels.

[0003] However, the polar regions are characterized by low temperatures, abundant ice, and harsh climates. In these complex environments, scientific research vessels and polar support platforms are prone to ice entrapment or ice damage. Therefore, accurately understanding the ice-ship coupling mechanism is crucial for preventing such accidents. For example, ice-ship resistance prediction is a key issue in ice-ship design and has a significant impact on its overall design. Ice-ships navigating through continuous ice layers encounter substantial resistance; therefore, they require favorable hull lines to reduce icebreaking drag. Based on this, icebreaking vessels must be able to break the ice into smaller chunks and then remove them from the vessel's path. Accurately understanding the ice-ship coupling mechanism allows for better ice-ship design and addresses the ice-ship resistance prediction problem.

[0004] The best way to accurately understand the ice-ship coupling mechanism is to conduct field tests in the Arctic region. However, due to geographical limitations, existing equipment cannot easily enter the Arctic region to test its performance, and the conditions for conducting field tests are limited, which restricts the transformation and upgrading of existing ship and marine equipment.

[0005] To address these issues, those skilled in the art have established ice-water tank laboratories to replace field experiments, exploring related mechanical scenarios such as ship-ice collisions to aid in understanding ice-ship interactions. For example, ice-water tank ship model testing has been widely used in predicting ship resistance in ice-covered areas.

[0006] However, the design requirements for the ice-water pool laboratory are quite high. It involves factors such as temperature control, air flow rate, and opening size. It is necessary to accurately grasp the influence of various factors and carefully design many parameters such as the cold air inlet in order to accurately simulate the seawater freezing process in the Arctic region.

[0007] To design an ice-water pool laboratory that can accurately simulate the seawater freezing process in the Arctic region, the best approach is to first conduct a numerical simulation of the seawater freezing process in the ice-water pool, systematically analyze the influence of each parameter, and then design the ice-water pool (seawater freezing process simulation) laboratory based on the analysis results.

[0008] However, there are currently few studies simulating the freezing process of ice-water pools, and they generally focus on freshwater freezing processes rather than seawater freezing processes, making them unsuitable for simulating seawater freezing processes in the Arctic region.

[0009] Currently, the method for simulating the freshwater freezing process in ice-water pools generally uses the commercial software FLUENT to simulate the freshwater freezing process in ice-water pools. It has explored the water freezing process under different conditions of the number and location of cold air inlets. However, it can only rely on the software's own general modules (such as the solidification and melting module and VOF model) to simulate limited scenarios, and the material parameter settings are relatively simple.

[0010] In summary, existing studies simulating the freezing process in ice water pools have the following drawbacks:

[0011] (1) The parameter settings are relatively simple, the simulated scenarios are limited, and it is difficult to guide the design of the ice water pool laboratory.

[0012] (2) Most of the simulations are of the freezing process in freshwater pools and are not applicable to the simulation of the freezing process of seawater in the Arctic region. It should be noted that seawater can be regarded as a brine pool. Existing simulation studies of the freezing process in freshwater pools cannot simulate the salinity segregation phenomenon during the freezing process of the water surface in brine pools. That is, they cannot show the brine channels during the freezing process of seawater in natural environment. This results in the simulation results of ice thickness and mechanical properties having too large an error from the actual values, making it difficult to guide the design of ice-water pool (seawater freezing process simulation) laboratories.

[0013] (3) The problem of tracking the coupling of gas, liquid, and solid phases cannot be solved. It should be noted that in the actual seawater freezing process, there are two interface changes: the gas-liquid two-phase flow interface of cold air flow and water surface undulation, and the solid-liquid two-phase phase transition interface of water surface freezing (i.e., water surface and ice surface). Parameters such as velocity, pressure, and temperature will have cross-effects on the changes of these two interfaces. That is, a change in one interface will change parameters such as velocity, pressure, and temperature, which in turn will affect the change of the other interface, and the change of the other interface will in turn affect the change of the first interface. Existing studies on the simulation of the freezing process of ice-water pools mostly focus on a single interface, such as only simulating the solid-liquid two-phase phase transition interface of water surface freezing, without considering the influence of air flow and water surface undulation (i.e., gas-liquid two-phase flow interface), which leads to inaccurate simulation results. The coupling tracking of the gas-liquid two-phase flow interface and the solid-liquid two-phase phase transition interface has become a technical problem that has troubled those skilled in the art. Summary of the Invention

[0014] This invention proposes a method and apparatus for simulating ice-water pools, which solves the problems of multivariate coupling solution and insufficient understanding of the influence of salinity (concentration) in the multiphase interface tracking process of existing technologies.

[0015] The technical solution of the ice-water pool simulation method of the present invention is as follows:

[0016] The method includes the following steps:

[0017] Input steps: Used to obtain simulation parameters for the ice water pool;

[0018] The simulation parameters of the ice water pool include physical property parameters and calculation parameters;

[0019] The physical properties include the material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity, and pressure of the water, ice, and air above the ice water pool.

[0020] The calculation parameters include the calculation region, number of grids, time step, initial conditions, boundary conditions, inlet location and number, and outlet location and number;

[0021] Solution steps: Based on the simulation parameters of the ice-water pool, solve the momentum equation, energy equation, concentration equation and continuity equation to obtain the output parameters;

[0022] The momentum equation is the momentum conservation equation, used to obtain the velocity and pressure in the flow field;

[0023] The energy equation is an energy conservation equation, used to obtain the values ​​of temperature parameters and liquid phase fraction parameters in the computational domain;

[0024] The concentration equation is a component conservation equation, used to obtain the concentration parameter values ​​in the computational domain;

[0025] The continuity equation is the mass conservation equation, used to solve the closed loop of the algebraic equation system when discretely solving the momentum equation.

[0026] The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, sign distance function, and mixture fraction.

[0027] The solution steps include solid-liquid interface tracing steps, gas-liquid interface tracing steps, and general component steps.

[0028] The solid-liquid interface tracking step is used to solve the energy equation and concentration equation, and to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the computational region.

[0029] The gas-liquid interface tracking step is used to solve for the volume fraction and sign distance function characterizing the gas-liquid interface, and the VOSET gas-liquid interface tracking method is used to handle the gas-liquid interface changes during the water droplet motion; it is also used to add a source term with respect to the liquid phase fraction in the momentum equation, and by setting the coefficient of the source term, to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low; it is also used to solve the momentum equation based on the IDEAL algorithm of the velocity-pressure correction theory.

[0030] The general component steps include the algebraic equation system step, the governing equation discrete coefficient step, the grid coordinate step, and the coefficient zeroing step, wherein:

[0031] The algebraic equation set step is used to solve the discrete momentum, temperature and concentration control equations, and is called by the solid-liquid interface tracking step and the gas-liquid interface tracking step.

[0032] The discrete coefficient step of the governing equation is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equations step.

[0033] The grid coordinate step is used to divide the calculation region and define the grid coordinate values ​​in the calculation region;

[0034] The coefficient zeroing step is used to zero out the coefficients of the control equation before the discrete coefficient step of the control equation, so that the discrete coefficient step of the control equation can be repeated.

[0035] Output steps: used to obtain output results based on output parameters; the output results include ice growth rate, ice distribution, and air flow direction, wherein:

[0036] The ice growth rate refers to the ice growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, and is used to characterize the speed of ice growth.

[0037] The ice layer distribution refers to the ice thickness at various points on the ice surface during the freezing process. It is used to reflect the ease with which the ice surface in the ice water pool freezes and to analyze conditions conducive to freezing.

[0038] The airflow direction refers to the direction of airflow above the ice surface during the freezing process. It is used to reflect the direction of cold air and to analyze the reasons for the different growth rates of ice layers in different locations.

[0039] Furthermore, a preferred embodiment is provided, wherein the liquid phase fraction is a basic parameter in the enthalpy-porosity model, used to characterize the distribution of the solid and liquid phases;

[0040] The volume fraction is a fundamental parameter in the VOSET method, used to characterize the gas-liquid two-phase distribution.

[0041] The symbolic distance function is a fundamental parameter in the VOSET method. It is used in conjunction with the volume fraction to improve the solution of physical quantities at the gas-liquid interface and also to directly represent the gas-liquid interface distribution.

[0042] The mixing fraction is used to address the problem that the liquid phase fraction or volume fraction alone cannot simultaneously reflect and distinguish the solid, liquid, and gas phases. Its value is half of the sum of the liquid phase fraction and the volume fraction.

[0043] Furthermore, a preferred embodiment is provided, wherein the liquid phase fraction is used to characterize the solid-liquid two-phase distribution as follows:

[0044] The liquid phase fraction is used to directly reflect the solid-liquid mixing state of the paste-like region defined in the enthalpy-porosity model, and affects the two-phase flow during phase change:

[0045] If the liquid fraction value within a grid node is 1, then the fluid within that grid node is in a liquid state.

[0046] If the liquid fraction is 0, the fluid within the grid node is solid;

[0047] If the liquid phase fraction value is between 0 and 1, the fluid within the grid node is in a solid-liquid mixed state, where 0.5 corresponds to the solid-liquid interface.

[0048] Furthermore, a preferred embodiment is provided, wherein the volume fraction is used to characterize the gas-liquid two-phase distribution as follows:

[0049] If the volume fraction of a grid node is 1, then the fluid within that grid node is liquid water.

[0050] If the volume fraction is 0, the fluid within the grid node is gas;

[0051] If the volume fraction value is between 0 and 1, the fluid within the grid node is in a gas-liquid mixed state, where 0.5 corresponds to the gas-liquid interface.

[0052] Furthermore, a preferred embodiment is provided, wherein the step of setting a coefficient for the source term to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low is as follows:

[0053] When the droplet region is entirely in the liquid phase, the source term is 0, and the original two-phase flow momentum equation is unaffected.

[0054] When the droplet begins to freeze, the interior of the droplet becomes a mixed region of ice and water. At this time, the liquid phase fraction is between (0,1) and gradually decreases. As a result, the proportion of the source term in the momentum equation becomes larger and larger, and gradually surpasses the transient term, convection term and diffusion term.

[0055] The source term reaches its maximum value when the droplet is completely transformed into the solid phase, thus the velocity of the solid phase mesh nodes is essentially zero, achieving the transformation from liquid to solid phase.

[0056] Furthermore, a preferred embodiment is provided in which, in the grid coordinate step:

[0057] A structured staggered mesh system is established using the finite volume method, and then the coordinate values ​​of each mesh in the computational domain are defined.

[0058] This invention also proposes an ice-water pool simulation device, the technical solution of which is as follows:

[0059] The device includes the following modules:

[0060] Input module: Used to obtain simulation parameters of the ice water pool;

[0061] The simulation parameters of the ice water pool include physical property parameters and calculation parameters;

[0062] The physical properties include the material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity, and pressure of the water, ice, and air above the ice water pool.

[0063] The calculation parameters include the calculation region, number of grids, time step, initial conditions, boundary conditions, inlet location and number, and outlet location and number;

[0064] Solver module: Used to solve the momentum equation, energy equation, concentration equation and continuity equation based on the simulation parameters of the ice water pool, and obtain the output parameters;

[0065] The momentum equation is the momentum conservation equation, used to obtain the velocity and pressure in the flow field;

[0066] The energy equation is an energy conservation equation, used to obtain the values ​​of temperature parameters and liquid phase fraction parameters in the computational domain;

[0067] The concentration equation is a component conservation equation, used to obtain the concentration parameter values ​​in the computational domain;

[0068] The continuity equation is the mass conservation equation, used to solve the closed loop of the algebraic equation system when discretely solving the momentum equation.

[0069] The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, sign distance function, and mixing fraction;

[0070] The solver module includes a solid-liquid interface tracking component, a gas-liquid interface tracking component, and a general component.

[0071] The solid-liquid interface tracking component is used to solve the energy equation and concentration equation, and to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the computational region.

[0072] The gas-liquid interface tracking component is used to solve for the volume fraction and sign distance function characterizing the gas-liquid interface, and uses the VOSET gas-liquid interface tracking method to handle the gas-liquid interface changes during the water droplet motion process; it is also used to add a source term with respect to the liquid phase fraction in the momentum equation, and by setting the coefficient of the source term, it can maintain fluidity when the liquid phase ratio is high and suppress flow when the liquid phase ratio is low; it is also used to solve the momentum equation based on the IDEAL algorithm of velocity-pressure correction theory.

[0073] The general components include a submodule for algebraic equations, a submodule for discrete coefficients of governing equations, a submodule for grid coordinates, and a submodule for zeroing coefficients, wherein:

[0074] The algebraic equations submodule is used to solve the discrete momentum, temperature and concentration control equations, and is called by the solid-liquid interface tracking component and the gas-liquid interface tracking component.

[0075] The discrete coefficient submodule of the control equation is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input to the algebraic equation system submodule;

[0076] The grid coordinate submodule is used to divide the calculation area and define the grid coordinate values ​​in the calculation area;

[0077] The coefficient zeroing submodule is used to zero out the coefficients of the control equation before calling the discrete coefficients submodule of the control equation, so that the discrete coefficients submodule of the control equation can be called repeatedly.

[0078] Output module: used to obtain output results based on output parameters; the output results include ice growth rate, ice distribution, and air flow direction, wherein:

[0079] The ice growth rate refers to the ice growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, and is used to characterize the speed of ice growth.

[0080] The ice layer distribution refers to the ice thickness at various points on the ice surface during the freezing process. It is used to reflect the ease with which the ice surface in the ice water pool freezes and to analyze conditions conducive to freezing.

[0081] The airflow direction refers to the direction of airflow above the ice surface during the freezing process. It is used to reflect the direction of cold air and to analyze the reasons for the different growth rates of ice layers in different locations.

[0082] The present invention also proposes a computer device, the technical solution of which is as follows:

[0083] A computer device includes a processor and a memory, the memory storing executable instructions of the processor, the processor being configured to perform the above-described ice water pool simulation method by executing the executable instructions.

[0084] The present invention also proposes a computer storage medium, the technical solution of which is as follows:

[0085] A computer storage medium storing a computer program, wherein when the computer program is executed, the above-described ice-water pool simulation method is performed.

[0086] This invention also proposes a computer program product, the technical solution of which is as follows:

[0087] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described ice water pool simulation method.

[0088] The present invention has the following beneficial effects:

[0089] 1. The ice-water pool simulation method of the present invention can adjust the simulation parameters to simulate the ice-water pool surface freezing process under different parameters such as inlet temperature, flow rate, and inlet / outlet quantity, and can simulate the seawater freezing process.

[0090] 2. The ice-water pool simulation method described in this invention can simulate the brine channel formation process caused by salinity segregation during the freezing of brine, thereby simulating a more realistic seawater freezing scenario.

[0091] 3. The ice-water pool simulation method described in this invention uses the more computationally efficient IDEAL algorithm to calculate the velocity and pressure fields within the domain, and accurately captures the solid-liquid interface during the freezing process using the enthalpy-porosity method.

[0092] 4. The ice-water pool simulation method described in this invention is applicable to the field of polar ice-water pool experiments. It realizes numerical simulation of seawater freezing in ice-water pools, which helps those skilled in the art to understand the freezing principle of ice-water pools and can provide strong support for ice-water pool experiment design.

[0093] The ice-water pool simulation method and apparatus described in this invention are applicable to simulating the seawater freezing process in an ice-water pool. Attached Figure Description

[0094] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0095] Figure 1 This is a schematic flowchart of an ice-water pool simulation method in one embodiment of the present invention;

[0096] Figure 2This is a flowchart illustrating the solution steps of the ice-water pool simulation method in one embodiment of the present invention.

[0097] Figure 3 A logic block diagram of an ice water pool simulation device in one embodiment of the present invention;

[0098] Figure 4 This is a schematic diagram illustrating a partial improvement of the ice-water pool simulation method compared to a pure water freezing model, as described in one embodiment of the present invention. Detailed Implementation

[0099] To make the technical solutions and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail and completely below with reference to the accompanying drawings. The various embodiments described below are only some preferred embodiments of the present invention, and not all of them; the various embodiments described below are intended to explain the present invention and should not be construed as limiting the present invention; reasonable combinations of the technical features defined in the various embodiments of the present invention, as well as all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort, are all within the scope of protection of the present invention.

[0100] One implementation method provides a method for simulating an ice-water pool:

[0101] The method includes the following steps:

[0102] Input steps: Used to obtain simulation parameters for the ice water pool;

[0103] The simulation parameters of the ice water pool include physical property parameters and calculation parameters;

[0104] The physical properties include the material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity, and pressure of the water, ice, and air above the ice water pool.

[0105] The calculation parameters include the calculation region, number of grids, time step, initial conditions, boundary conditions, inlet location and number, and outlet location and number;

[0106] Solution steps: Based on the simulation parameters of the ice-water pool, solve the momentum equation, energy equation, concentration equation and continuity equation to obtain the output parameters;

[0107] The momentum equation is the momentum conservation equation, used to obtain the velocity and pressure in the flow field;

[0108] The energy equation is an energy conservation equation, used to obtain the values ​​of temperature parameters and liquid phase fraction parameters in the computational domain;

[0109] The concentration equation is a component conservation equation, used to obtain the concentration parameter values ​​in the computational domain;

[0110] The continuity equation is the mass conservation equation, used to solve the closed loop of the algebraic equation system when discretely solving the momentum equation.

[0111] The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, sign distance function, and mixture fraction.

[0112] The solution steps include solid-liquid interface tracing steps, gas-liquid interface tracing steps, and general component steps.

[0113] The solid-liquid interface tracking step is used to solve the energy equation and concentration equation, and to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the computational region.

[0114] The gas-liquid interface tracking step is used to solve for the volume fraction and sign distance function characterizing the gas-liquid interface, and the VOSET gas-liquid interface tracking method is used to handle the gas-liquid interface changes during the water droplet motion; it is also used to add a source term with respect to the liquid phase fraction in the momentum equation, and by setting the coefficient of the source term, to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low; it is also used to solve the momentum equation based on the IDEAL algorithm of the velocity-pressure correction theory.

[0115] The general component steps include the algebraic equation system step, the governing equation discrete coefficient step, the grid coordinate step, and the coefficient zeroing step, wherein:

[0116] The algebraic equation set step is used to solve the discrete momentum, temperature and concentration control equations, and is called by the solid-liquid interface tracking step and the gas-liquid interface tracking step.

[0117] The discrete coefficient step of the governing equation is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equations step.

[0118] The grid coordinate step is used to divide the calculation region and define the grid coordinate values ​​in the calculation region;

[0119] The coefficient zeroing step is used to zero out the coefficients of the control equation before the discrete coefficient step of the control equation, so that the discrete coefficient step of the control equation can be repeated.

[0120] Output steps: used to obtain output results based on output parameters; the output results include ice growth rate, ice distribution, and air flow direction, wherein:

[0121] The ice growth rate refers to the ice growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, and is used to characterize the speed of ice growth.

[0122] The ice layer distribution refers to the ice thickness at various points on the ice surface during the freezing process. It is used to reflect the ease with which the ice surface in the ice water pool freezes and to analyze conditions conducive to freezing.

[0123] The airflow direction refers to the direction of airflow above the ice surface during the freezing process. It is used to reflect the direction of cold air and to analyze the reasons for the different growth rates of ice layers in different locations.

[0124] In this embodiment, the material density in the physical property parameters is the ratio of the material's mass to its volume, used to distinguish between the three materials: water, ice, and air.

[0125] In this embodiment, the latent heat in the physical property parameters is the heat released when water turns into ice, which is used to represent the change in heat during the freezing process in the ice-water pool.

[0126] In this embodiment, the specific heat capacity among the physical property parameters is the heat absorbed or released when a unit mass of an object changes a unit temperature. It is used to distinguish between the three materials of water, ice, and air, and to determine the freezing rate of water, etc.

[0127] In this embodiment, the thermal conductivity coefficient among the physical property parameters characterizes the thermal conductivity of a material and is used to distinguish between the three materials of water, ice, and air, and to determine the heat transfer process in the three phases.

[0128] In this embodiment, the diffusion coefficient in the physical property parameters is a physical quantity that characterizes the degree of diffusion of gas or solid. It is used to distinguish between water, ice and air, and is used to solve the diffusion term in the momentum equation (also known as the Navier-Stokes equation, NS equation, used to obtain the velocity and pressure in the flow field).

[0129] In this embodiment, the coefficient of thermal expansion in the physical property parameters is a physical quantity that characterizes the thermal expansion capacity of an object. It is used to reflect the degree of expansion of the ice after freezing and is used to solve the body force term in the momentum equation.

[0130] In this embodiment, viscosity, one of the physical property parameters, characterizes the resistance exhibited by the fluid to flow, is used to distinguish between water, ice, and air, and is used to solve the diffusion term in the momentum equation.

[0131] In this embodiment, the temperature in the physical property parameters is the temperature value of each grid node in the calculation area, which is used to reflect the temperature gradient of the calculation domain, thereby determining whether each grid node has reached the solidification temperature condition.

[0132] In this embodiment, the salinity in the physical property parameters is the salinity value of each grid node in the calculation area, which is used to reflect the concentration (content) of sodium chloride in the ice water pool and to reflect the salt precipitation during the freezing process.

[0133] In this embodiment, the velocity among the physical property parameters characterizes the speed of fluid flow and is used to distinguish between the three materials: water, ice, and air.

[0134] In this embodiment, the pressure in the physical property parameters represents the force between the contact surfaces of two objects, and is used to reflect the perpendicular interaction between the surfaces of the gas phase, liquid phase or solid phase.

[0135] In this embodiment, the calculation region in the calculation parameters is the size of the ice water pool in the numerical calculation, including the air portion above the ice water pool.

[0136] In this embodiment, the number of grids in the calculation parameters is the number of grids subdivided within the calculation region, which is used as the control equation and main variable in the discrete numerical calculation process.

[0137] In this embodiment, the time step in the calculation parameters is the time interval between each time layer in the numerical calculation process, which is used to discretely solve the transient terms (also known as unsteady terms) in each control equation.

[0138] In this embodiment, the initial conditions in the calculation parameters are the conditions at the initial moment of numerical calculation, including temperature, velocity, volume fraction, and liquid phase fraction.

[0139] In this embodiment, the boundary conditions in the calculation parameters are conditions at the boundary of the computational domain, used to specify variables such as temperature, velocity, and volume fraction.

[0140] In this embodiment, the inlet position and quantity in the calculation parameters refer to the position and quantity of cold air inlets above or to the left and right sides of the ice water pool, which are used to control the amount of cold air entering the ice water pool.

[0141] In this embodiment, the outlet position and quantity in the calculation parameters refer to the air outlet position and quantity above the water surface on the left and right sides of the ice water pool, which are used to control the amount of cold air emitted from the ice water pool.

[0142] In this embodiment, the momentum conservation equation is also called the Navier-Stokes equation (NS equation).

[0143] In this embodiment, the momentum equation is solved mainly using the IDEAL algorithm based on the velocity-pressure correction theory, which can improve the solution rate of velocity-pressure.

[0144] In this embodiment, based on the IDEAL algorithm, an energy equation solution is added, and a source term concerning the liquid phase fraction is added to the energy equation, thereby establishing a connection with the momentum equation.

[0145] In this embodiment, the convection and diffusion terms of the control discrete equations are both expressed in power form.

[0146] In this embodiment, the physical scenario is an ice-covered pool with air. The second and third types of boundary conditions are handled using the additional source term method. Temperature corresponds to an adiabatic boundary, and velocity is handled using a no-slip boundary.

[0147] In another embodiment, during the solid-liquid interface tracking step, the temperature and liquid fraction of each grid node within the computational region are obtained using the following method:

[0148] The energy equation source terms include transient terms involving the liquid phase fraction;

[0149] The liquid fraction is iteratively solved to obtain a liquid fraction value consistent with the solution at the current temperature.

[0150] During iterative solving, only one iteration is needed within the same time layer:

[0151] After the current layer velocity and pressure are solved, the liquid phase fraction is updated and its upper and lower limits are specified, thereby obtaining the source terms of the energy equation and the coefficients of the discrete equation;

[0152] The temperature is obtained by solving the energy equation using the algebraic equation system steps, identifying the source terms and coefficients of the discrete equations, and then solving the algebraic equation system to obtain the temperature at the current moment.

[0153] The above steps are repeated iteratively multiple times until the temperature and liquid fraction of the current time layer reach a consistent convergence condition.

[0154] It should be noted that the energy equation source term includes a transient term for the liquid phase fraction, which means that the energy equation source term contains both the liquid phase fraction value at the previous time step and the liquid phase fraction value at the current time step. In other words, the energy equation of the current time step layer has two unknowns (temperature and liquid phase fraction). If the piecewise relationship between the liquid phase fraction and temperature is used directly to update the liquid phase fraction, it is easy to cause calculation divergence.

[0155] In this embodiment, to improve convergence and avoid computational divergence, the liquid phase fraction is iteratively solved to obtain a liquid phase fraction value consistent with the solution at the current temperature.

[0156] It should be noted that the existing liquid phase fractional iteration method involves two iterations within the same time layer, which results in low computational efficiency.

[0157] In this embodiment, only one iteration is required within the same time layer, resulting in high computational efficiency.

[0158] In another embodiment, during the solid-liquid interface tracking step, the concentration parameter values ​​of each grid node within the computational region are obtained using the following method:

[0159] The concentration parameter value is used to reflect salinity;

[0160] The concentration equation is solved using a fully implicit time integration scheme;

[0161] An anti-diffusion model was established based on the Scheil-Gulliver nonequilibrium theory to simulate the dual-diffusion convection process of hot solutes and to reflect the coupling effect of interfacial dynamics and hot solute driving force.

[0162] The concentration values ​​in the fluid domain are used to correct the latent heat update method in the source term of the energy equation to realize the lateral solidification process of the brine solution.

[0163] In this embodiment, the advantage of using a fully implicit time integration scheme to solve the concentration equation is that it is unconditionally stable, and no oscillation of the solution will occur regardless of the value of the time step. However, in order to improve the computational accuracy, the time step should still be taken as a relatively small value in the actual discrete solution.

[0164] It should be noted that the diffusion of solute in the solid phase is usually called reverse diffusion. When a solid phase forms in the solid-liquid mixing region, it will hinder some solute from entering the liquid phase region. Subsequently, this part of the solute will be redistributed in the solid and liquid phases through mass diffusion. Some of it will be discharged in solid form after the pure water freezes, resulting in an increase in the solute concentration in the solid-liquid mixing region. The diffusion of solute in the liquid phase occurs uniformly and does not require special attention. A reverse diffusion model was established based on the Scheil-Gulliver nonequilibrium theory to simulate the dual diffusion convection process of hot solute, which can reflect the coupling effect of interfacial dynamics and hot solute driving force.

[0165] In this embodiment, the ice layer growth rate is obtained using the following method:

[0166] By using post-processing software such as Tecplot to output the mixed fractional data as a cloud map, an animation of the dynamic growth process of the ice layer can be obtained based on the cloud maps of multiple time layers, thereby obtaining the ice layer growth rate.

[0167] In this embodiment, the ice layer distribution is obtained using the following method:

[0168] By using post-processing software such as Tecplot to output the mixed fractional data as a cloud map, the distribution of ice layer in the ice-water pool at different times can be obtained.

[0169] In this embodiment, the airflow direction is obtained using the following method:

[0170] The velocity data can be output as a cloud map using post-processing software such as Tecplot. The direction of airflow can be observed in the direction of the arrows on the vector lines of the velocity cloud map.

[0171] It should be noted that existing methods often focus on a single interface, such as simulating water freezing, without considering air flow. This makes it impossible to account for water surface fluctuations, leading to inaccurate simulation results.

[0172] In another embodiment, the liquid phase fraction is a fundamental parameter in the enthalpy-porosity model, used to characterize the distribution of the solid and liquid phases.

[0173] The volume fraction is a fundamental parameter in the VOSET method, used to characterize the gas-liquid two-phase distribution.

[0174] The symbolic distance function is a fundamental parameter in the VOSET method. It is used in conjunction with the volume fraction to improve the solution of physical quantities at the gas-liquid interface and also to directly represent the gas-liquid interface distribution.

[0175] The mixing fraction is used to address the problem that the liquid phase fraction or volume fraction alone cannot simultaneously reflect and distinguish the solid, liquid, and gas phases. Its value is half of the sum of the liquid phase fraction and the volume fraction.

[0176] In this embodiment, the mixing fraction is a unique basic parameter.

[0177] In this embodiment, the gas-liquid interface is described by volume fraction and the solid-liquid interface is described by liquid phase fraction. Both of these fractions can only display two-phase interfaces and cannot display the gas-liquid-solid interface at the same time. Therefore, a mixing fraction is introduced, which is equal to half the sum of the volume fraction and the liquid phase fraction. In this way, the air-water-ice three phases in the freezing process of the ice-water pool can be displayed at the same time.

[0178] It should be noted that, in order to simultaneously represent the gas-liquid-solid three-phase interface, a mixing fraction is introduced to comprehensively consider the influence of both fractions. (Introduction of mixing fraction) To distinguish between the gas, liquid, and solid phases, as shown in Table 1.

[0179] Table 1. Volume fraction values ​​for the three types of volumes

[0180]

[0181] In another embodiment, the liquid phase fraction is used to characterize the solid-liquid two-phase distribution as follows:

[0182] The liquid phase fraction is used to directly reflect the solid-liquid mixing state of the paste-like region defined in the enthalpy-porosity model, and affects the two-phase flow during phase change:

[0183] If the liquid fraction value within a grid node is 1, then the fluid within that grid node is in a liquid state.

[0184] If the liquid fraction is 0, the fluid within the grid node is solid;

[0185] If the liquid phase fraction value is between 0 and 1, the fluid within the grid node is in a solid-liquid mixed state, where 0.5 corresponds to the solid-liquid interface.

[0186] In another embodiment, the volume fraction is used to characterize the gas-liquid two-phase distribution as follows:

[0187] If the volume fraction of a grid node is 1, then the fluid within that grid node is liquid water.

[0188] If the volume fraction is 0, the fluid within the grid node is gas;

[0189] If the volume fraction value is between 0 and 1, the fluid within the grid node is in a gas-liquid mixed state, where 0.5 corresponds to the gas-liquid interface.

[0190] In this embodiment, the symbolic distance function is used to directly represent the gas-liquid interface distribution as follows:

[0191] When the value of the symbolic distance function is positive, the fluid within the corresponding grid is in the liquid phase;

[0192] When the value of the symbolic distance function is negative, the fluid within the corresponding grid is in the gas phase.

[0193] When the value of the symbolic distance function is zero, the corresponding grid is a gas-liquid interface grid.

[0194] In another embodiment, the step of setting a coefficient for the source term to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low is as follows:

[0195] When the droplet region is entirely in the liquid phase, the source term is 0, and the original two-phase flow momentum equation is unaffected.

[0196] When the droplet begins to freeze, the interior of the droplet becomes a mixed region of ice and water. At this time, the liquid phase fraction is between (0,1) and gradually decreases. As a result, the proportion of the source term in the momentum equation becomes larger and larger, and gradually surpasses the transient term, convection term and diffusion term.

[0197] The source term reaches its maximum value when the droplet is completely transformed into the solid phase, thus the velocity of the solid phase mesh nodes is essentially zero, achieving the transformation from liquid to solid phase.

[0198] It should be noted that the momentum equation used in the traditional VOSET method only includes commonly used transient, convection, diffusion, surface tension, and body force terms. However, this embodiment considers both airflow and liquid motion in a two-phase flow system, as well as the freezing of the free surface within the ice-water pool. Therefore, this embodiment adds a source term related to the liquid phase fraction to the momentum equation to reflect the effect of freezing on the motion.

[0199] It should be noted that one of the technical challenges in simulating the seawater freezing process in an ice-water pool is the coupled tracking of the gas-liquid two-phase flow interface, such as cold air flow and water surface ripples, and the solid-liquid two-phase phase transition interface, such as water surface freezing. The gas-liquid two-phase flow interface focuses on solving the momentum equation and updating the fluid volume fraction, while the water surface freezing interface focuses on solving the energy equation and updating the liquid phase fraction. How to comprehensively consider and solve the parameters such as velocity, pressure, and temperature for solving the two types of equations is a major challenge.

[0200] It should be noted that traditional velocity and pressure correction methods, including algorithms such as SIMPLE, SIMPLER, SIMPLEC, and PISO, mostly require certain assumptions. For the gas-liquid-solid three-phase coupling tracking problem, this implementation first employs the pressure correction IDEAL algorithm to improve the convergence efficiency of velocity and pressure solutions, thereby constructing a fluid computational domain. This overcomes two fundamental assumptions in traditional methods, making the calculation process more efficient and stable. Based on this, the VOSET method is used to track the gas-liquid interface.

[0201] It should be noted that the mainstream methods for gas-liquid interface tracking are VOF and Level Set methods. The former has the advantage of mass conservation but the disadvantage of discontinuity in physical quantities at the interface. The latter has the advantage of smooth and continuous physical quantities at the interface but the disadvantage of not being able to satisfy mass conservation. This implementation uses the VOSET method to track the gas-liquid interface, combining the advantages of the VOF and Level Set methods, which can both conserve mass and ensure smooth and continuous parameters such as density and surface tension at the interface.

[0202] In this embodiment, the enthalpy-porosity method is used to process the macroscopic solid-liquid interface. By establishing a single-domain continuous medium formula, explicit tracking of the interface is avoided. The solid-liquid mixing region (also known as the mushy region) is regarded as a porous medium. The porosity corresponding to the porous structure reflects the frictional resistance brought by the solid phase in the problem domain. By iteratively calculating the liquid phase fraction of each grid, the advancement change of the solid-liquid interface can be reflected, thereby accurately tracking the phase transition interface with topological complexity and macroscopic diffusion uncertainty.

[0203] It should be noted that although the VOSET method and enthalpy-porosity method have been used in existing methods for gas-liquid interface tracking and solid-liquid interface tracking problems (such as water droplet impact spreading dynamics, metal melting, welding, etc.), no literature has been found to combine the two to track the gas-liquid-solid three-phase system, let alone to apply them to the simulation of the freezing process in an ice-water pool. The difficulty lies in the fact that the two methods have different focuses, and it is difficult to establish the relationship between parameters, such as the effect of temperature on the liquid phase fraction. Speed ​​affects the change in volume fraction. It is difficult to explain how the two scores influence each other, and only one score can be displayed in the same cloud map.

[0204] In this embodiment, to reflect the effect of freezing on liquid flow, the relationship between the momentum equation and the energy equation was improved, and the liquid phase fraction was... It directly reflects the solid-liquid mixing state of the paste-like region and affects the two-phase flow during the phase transition. This effect can be achieved by adding a source term to the basic Navier-Stokes equations. (Assuming that the motion within the mushy region obeys Darcy's law for flow in porous media and conforms to the Carman-Koseny assumptions.) This can be understood as the flow resistance caused by solidification, specifically in the form of:

[0205] ;

[0206] In the formula It is a very small value, and to ensure that the denominator is not zero, it is taken as 0.001; This is a constant value for the paste-like region, which can control the rate of speed reduction. It is recommended to take a value between 10⁴ and 10⁷. A suitable value can maintain fluidity when the liquid phase ratio is high and suppress flow when the liquid phase ratio is low.

[0207] When the droplet region is entirely in the liquid phase That is, source item The original two-phase flow Navier-Stokes equations remain unaffected; when the droplet begins to freeze, the interior of the droplet becomes a mixed ice-water region, i.e., a paste-like region. It is between (0,1) and gradually decreases, thus The proportion of the liquid in the Navier-Stokes equations increases, gradually surpassing the transient, convection, and diffusion terms, until the droplet is completely converted into a solid phase, at which point the velocity is essentially zero.

[0208] It should be noted that another technical challenge in simulating the seawater freezing process in an ice-water pool is how to account for the influence of salinity. In the ice-water mixing zone, under the combined influence of concentration (salinity) and temperature gradients, solutes precipitate and drain into the liquid phase, forming brine channels. These channels are primarily caused by heat-salt dual diffusion convection. Compared to freshwater freezing, where natural convection is solely caused by temperature gradients, the heat-solute dual diffusion convection in brine freezing is more complex. Its mechanism of action on the formation and evolution of brine channels remains unclear, and no relevant literature has been found. This remains a major challenge in seawater freezing research. The key to solving this problem is to accurately track the various interfaces in the heat-solute dual diffusion convection process within the brine film and to reflect the coupling effect of interface dynamics and heat-solute driving forces.

[0209] It should be noted that, in order to address the problem that existing ice-water pool simulations cannot consider the dynamic effects of salinity, this implementation method introduces a concentration equation while solving the momentum equation and the energy equation.

[0210] Considering that the main solute component in seawater is sodium chloride, this embodiment treats seawater as a binary mixture of sodium chloride and pure water. Based on the pure water freezing model, a salinity (concentration) equation is introduced. Based on the assumption of local thermodynamic equilibrium and phase diagram, the solidus and liquidus temperatures in the energy equation are determined by salinity, thereby achieving the coupling of the temperature field and the solute field.

[0211] Meanwhile, the volume force term and source term of the momentum equation are improved to reflect the effects of thermal buoyancy, solute buoyancy and forced convection on salt diffusion and convection, and to simulate the thermal-solute dual diffusion convection process under the combined action of temperature and salinity gradient.

[0212] The freezing of brine films involves not only macroscopic phenomena such as thermal-solute dual diffusion convection and ice-water interface propulsion, but also local segregation phenomena such as salt expulsion from dendrites and back diffusion. Therefore, it is necessary to couple the analysis of macroscopic and local segregation processes. We propose to establish a back diffusion model based on the lever principle and the Scheil-Gulliver principle, modify the source term of the macroscopic salinity equation, describe the role of salinity in controlling the phase transition, and reflect the redistribution of salt through diffusion transport.

[0213] This implementation method has some improvements over the pure water freezing model, such as... Figure 4 As shown, the saline film freezing model refers to the method described in this embodiment, specifically:

[0214] In the pure water freezing model:

[0215] The inverse function of latent heat is a constant:

[0216]

[0217] Paste coefficient All are constant values:

[0218]

[0219] The momentum equation's body force term only considers thermal expansion:

[0220]

[0221] In the brine film freezing model:

[0222] The inverse function of latent heat is expressed as an expression for the solid-liquid phase line temperature:

[0223]

[0224] Paste coefficient All are determined by the dendrite arm length:

[0225]

[0226] The momentum equation's body force term takes into account thermal expansion and solute expansion:

[0227] .

[0228] In another embodiment, during the grid coordinate step:

[0229] A structured staggered mesh system is established using the finite volume method, and then the coordinate values ​​of each mesh in the computational domain are defined.

[0230] In this embodiment, a structured staggered mesh system is established using the finite volume method, which makes the speed solution more stable.

[0231] In this embodiment, the finite volume method and staggered mesh are used to discretize the basic governing equations.

[0232] In this embodiment, a structured staggered mesh is adopted, and three mesh schemes are used in the two-dimensional mesh, which can effectively avoid pressure gradient divergence.

[0233] In one embodiment, an ice water pool simulation device is provided:

[0234] The device includes the following modules:

[0235] Input module: Used to obtain simulation parameters of the ice water pool;

[0236] The simulation parameters of the ice water pool include physical property parameters and calculation parameters;

[0237] The physical properties include the material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity, and pressure of the water, ice, and air above the ice water pool.

[0238] The calculation parameters include the calculation region, number of grids, time step, initial conditions, boundary conditions, inlet location and number, and outlet location and number;

[0239] Solver module: Used to solve the momentum equation, energy equation, concentration equation and continuity equation based on the simulation parameters of the ice water pool, and obtain the output parameters;

[0240] The momentum equation is the momentum conservation equation, used to obtain the velocity and pressure in the flow field;

[0241] The energy equation is an energy conservation equation, used to obtain the values ​​of temperature parameters and liquid phase fraction parameters in the computational domain;

[0242] The concentration equation is a component conservation equation, used to obtain the concentration parameter values ​​in the computational domain;

[0243] The continuity equation is the mass conservation equation, used to solve the closed loop of the algebraic equation system when discretely solving the momentum equation.

[0244] The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, sign distance function, and mixing fraction;

[0245] The solver module includes a solid-liquid interface tracking component, a gas-liquid interface tracking component, and a general component.

[0246] The solid-liquid interface tracking component is used to solve the energy equation and concentration equation, and to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the computational region.

[0247] The gas-liquid interface tracking component is used to solve for the volume fraction and sign distance function characterizing the gas-liquid interface, and uses the VOSET gas-liquid interface tracking method to handle the gas-liquid interface changes during the water droplet motion process; it is also used to add a source term with respect to the liquid phase fraction in the momentum equation, and by setting the coefficient of the source term, it can maintain fluidity when the liquid phase ratio is high and suppress flow when the liquid phase ratio is low; it is also used to solve the momentum equation based on the IDEAL algorithm of velocity-pressure correction theory.

[0248] The general components include a submodule for algebraic equations, a submodule for discrete coefficients of governing equations, a submodule for grid coordinates, and a submodule for zeroing coefficients, wherein:

[0249] The algebraic equations submodule is used to solve the discrete momentum, temperature and concentration control equations, and is called by the solid-liquid interface tracking component and the gas-liquid interface tracking component.

[0250] The discrete coefficient submodule of the control equation is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input to the algebraic equation system submodule;

[0251] The grid coordinate submodule is used to divide the calculation area and define the grid coordinate values ​​in the calculation area;

[0252] The coefficient zeroing submodule is used to zero out the coefficients of the control equation before calling the discrete coefficients submodule of the control equation, so that the discrete coefficients submodule of the control equation can be called repeatedly.

[0253] Output module: used to obtain output results based on output parameters; the output results include ice growth rate, ice distribution, and air flow direction, wherein:

[0254] The ice growth rate refers to the ice growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, and is used to characterize the speed of ice growth.

[0255] The ice layer distribution refers to the ice thickness at various points on the ice surface during the freezing process. It is used to reflect the ease with which the ice surface in the ice water pool freezes and to analyze conditions conducive to freezing.

[0256] The airflow direction refers to the direction of airflow above the ice surface during the freezing process. It is used to reflect the direction of cold air and to analyze the reasons for the different growth rates of ice layers in different locations.

[0257] The device is used to implement the above-mentioned ice-water pool simulation method.

[0258] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, reasonable combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating an ice-water pool, characterized in that, The method includes the following steps: Input steps: Used to obtain simulation parameters for the ice water pool; The simulation parameters of the ice water pool include physical property parameters and calculation parameters; The physical properties include the material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity, and pressure of the water, ice, and air above the ice water pool. The calculation parameters include the calculation region, number of grids, time step, initial conditions, boundary conditions, inlet location and number, and outlet location and number; Solution steps: Based on the simulation parameters of the ice-water pool, solve the momentum equation, energy equation, concentration equation and continuity equation to obtain the output parameters; The momentum equation is the momentum conservation equation, used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, used to obtain the values ​​of temperature parameters and liquid phase fraction parameters in the computational domain; The concentration equation is a component conservation equation, used to obtain the concentration parameter values ​​in the computational domain; The continuity equation is the mass conservation equation, used to solve the closed loop of the algebraic equation system when discretely solving the momentum equation. The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, sign distance function, and mixture fraction. The solution steps include solid-liquid interface tracing steps, gas-liquid interface tracing steps, and general component steps. The solid-liquid interface tracking step is used to solve the energy equation and concentration equation, and to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the computational region. The gas-liquid interface tracking step is used to solve for the volume fraction and sign distance function characterizing the gas-liquid interface, and the VOSET gas-liquid interface tracking method is used to handle the gas-liquid interface changes during the water droplet motion; it is also used to add a source term with respect to the liquid phase fraction in the momentum equation, and by setting the coefficient of the source term, to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low; it is also used to solve the momentum equation based on the IDEAL algorithm of the velocity-pressure correction theory. The general component steps include the algebraic equation system step, the governing equation discrete coefficient step, the grid coordinate step, and the coefficient zeroing step, wherein: The algebraic equation set step is used to solve the discrete momentum, temperature and concentration control equations, and is called by the solid-liquid interface tracking step and the gas-liquid interface tracking step. The discrete coefficient step of the governing equation is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equations step. The grid coordinate step is used to divide the calculation region and define the grid coordinate values ​​in the calculation region; The coefficient zeroing step is used to zero out the coefficients of the control equation before the discrete coefficient step of the control equation, so that the discrete coefficient step of the control equation can be repeated. Output steps: used to obtain output results based on output parameters; the output results include ice growth rate, ice distribution, and air flow direction, wherein: The ice growth rate refers to the ice growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, and is used to characterize the speed of ice growth. The ice layer distribution refers to the ice thickness at various points on the ice surface during the freezing process. It is used to reflect the ease with which the ice surface in the ice water pool freezes and to analyze conditions conducive to freezing. The airflow direction refers to the direction of airflow above the ice surface during the freezing process. It is used to reflect the direction of cold air and to analyze the reasons for the different growth rates of ice layers in different locations.

2. The ice-water pool simulation method according to claim 1, characterized in that, The liquid phase fraction is a fundamental parameter in the enthalpy-porosity model, used to characterize the distribution of the solid and liquid phases. The volume fraction is a fundamental parameter in the VOSET method, used to characterize the gas-liquid two-phase distribution. The symbolic distance function is a fundamental parameter in the VOSET method. It is used in conjunction with the volume fraction to improve the solution of physical quantities at the gas-liquid interface and also to directly represent the gas-liquid interface distribution. The mixing fraction is used to address the problem that the liquid phase fraction or volume fraction alone cannot simultaneously reflect and distinguish the solid, liquid, and gas phases. Its value is half of the sum of the liquid phase fraction and the volume fraction.

3. The ice-water pool simulation method according to claim 2, characterized in that, The liquid phase fraction is used to characterize the distribution of the solid and liquid phases as follows: The liquid phase fraction is used to directly reflect the solid-liquid mixing state of the paste-like region defined in the enthalpy-porosity model, and affects the two-phase flow during phase change: If the liquid fraction value within a grid node is 1, then the fluid within that grid node is in a liquid state. If the liquid fraction is 0, the fluid within the grid node is solid; If the liquid phase fraction value is between 0 and 1, the fluid within the grid node is in a solid-liquid mixed state, where 0.5 corresponds to the solid-liquid interface.

4. The ice-water pool simulation method according to claim 2, characterized in that, The volume fraction is used to characterize the gas-liquid two-phase distribution as follows: If the volume fraction of a grid node is 1, then the fluid within that grid node is liquid water. If the volume fraction is 0, the fluid within the grid node is gas; If the volume fraction value is between 0 and 1, the fluid within the grid node is in a gas-liquid mixed state, where 0.5 corresponds to the gas-liquid interface.

5. The ice-water pool simulation method according to claim 1, characterized in that, The method of setting a coefficient for the source term to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low is as follows: When the droplet region is entirely in the liquid phase, the source term is 0, and the original two-phase flow momentum equation is unaffected. When the droplet begins to freeze, the interior of the droplet becomes a mixed region of ice and water. At this time, the liquid phase fraction is between (0,1) and gradually decreases. As a result, the proportion of the source term in the momentum equation becomes larger and larger, and gradually surpasses the transient term, convection term and diffusion term. The source term reaches its maximum value when the droplet is completely transformed into the solid phase, thus the velocity of the solid phase mesh nodes is essentially zero, achieving the transformation from liquid to solid phase.

6. The ice-water pool simulation method according to claim 1, characterized in that, In the grid coordinate step: A structured staggered mesh system is established using the finite volume method, and then the coordinate values ​​of each mesh in the computational domain are defined.

7. An ice-water pool simulation device, characterized in that, The device includes the following modules: Input module: Used to obtain simulation parameters of the ice water pool; The simulation parameters of the ice water pool include physical property parameters and calculation parameters; The physical properties include the material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity, and pressure of the water, ice, and air above the ice water pool. The calculation parameters include the calculation region, number of grids, time step, initial conditions, boundary conditions, inlet location and number, and outlet location and number; Solver module: Used to solve the momentum equation, energy equation, concentration equation and continuity equation based on the simulation parameters of the ice water pool, and obtain the output parameters; The momentum equation is the momentum conservation equation, used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, used to obtain the values ​​of temperature parameters and liquid phase fraction parameters in the computational domain; The concentration equation is a component conservation equation, used to obtain the concentration parameter values ​​in the computational domain; The continuity equation is the mass conservation equation, used to solve the closed loop of the algebraic equation system when discretely solving the momentum equation. The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, sign distance function, and mixing fraction; The solver module includes a solid-liquid interface tracking component, a gas-liquid interface tracking component, and a general component. The solid-liquid interface tracking component is used to solve the energy equation and concentration equation, and to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the computational region. The gas-liquid interface tracking component is used to solve the volume fraction and sign distance function characterizing the gas-liquid interface, and uses the VOSET gas-liquid interface tracking method to handle the gas-liquid interface changes during the water droplet motion process; it is also used to add a source term about the liquid phase fraction to the momentum equation, and by setting the coefficient of the source term, it can maintain fluidity when the liquid phase ratio is high and suppress flow when the liquid phase ratio is low. It is also used to solve the momentum equation using the IDEAL algorithm based on the velocity-pressure correction theory; The general components include a submodule for algebraic equations, a submodule for discrete coefficients of governing equations, a submodule for grid coordinates, and a submodule for zeroing coefficients, wherein: The algebraic equations submodule is used to solve the discrete momentum, temperature and concentration control equations, and is called by the solid-liquid interface tracking component and the gas-liquid interface tracking component. The discrete coefficient submodule of the control equation is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input to the algebraic equation system submodule; The grid coordinate submodule is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing submodule is used to zero out the coefficients of the control equation before calling the discrete coefficients submodule of the control equation, so that the discrete coefficients submodule of the control equation can be called repeatedly. Output module: used to obtain output results based on output parameters; the output results include ice growth rate, ice distribution, and air flow direction, wherein: The ice growth rate refers to the ice growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, and is used to characterize the speed of ice growth. The ice layer distribution refers to the ice thickness at various points on the ice surface during the freezing process. It is used to reflect the ease with which the ice surface in the ice water pool freezes and to analyze conditions conducive to freezing. The airflow direction refers to the direction of airflow above the ice surface during the freezing process. It is used to reflect the direction of cold air and to analyze the reasons for the different growth rates of ice layers in different locations.

8. A computer device, comprising: A processor and a memory, characterized in that the memory is used to store executable instructions of the processor, the processor being configured to perform the ice-water pool simulation method of any one of claims 1-7 by executing the executable instructions.

9. A computer storage medium, characterized in that, The storage medium stores a computer program, which, when executed, performs the ice-water pool simulation method according to any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the ice-water pool simulation method according to any one of claims 1-7.

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