Ice pool simulation method and device

Abstract

The invention discloses an ice water pool simulation method and device, belongs to the technical field of ice water pool tests, and particularly relates to seawater icing process simulation in an ice water pool. The problem that in the prior art, in the multi-phase interface tracking process, multivariable coupling solving is conducted, and salinity (concentration) influence understanding is not deep is solved. The method comprises the following steps: an input step for obtaining simulation parameters of an ice water pool; the solving step is used for solving a momentum equation, an energy equation, a concentration equation and a continuity equation according to the ice water pool simulation parameters to obtain output parameters; the output step is used for obtaining an output result according to the output parameters; the output result comprises the ice layer growth speed, the ice layer distribution and the air flowing direction. The ice water pool simulation method and device are suitable for simulating the freezing process of seawater in the ice water pool.

Description

Technical Field

[0001] The invention relates to the technical field of ice water pool testing, and in particular to simulation of seawater freezing process in an ice water pool. Background Art

[0002] In recent years, as more and more energy and resources are discovered in the Arctic region, and the traffic volume of the Northeast Passage and the Northwest Passage increases, the demand for polar ships (such as ice transport ships and icebreakers) in countries around the world is also increasing, which will have a significant impact on the development and research of high-tech ships and the demand for the ship market. With the improvement of oil and gas resources and mineral resource mining technology, the Arctic, known as the "second Middle East", is gradually becoming a base for human beings to obtain resources. In addition to the circumpolar countries, non-circumpolar powers have also made full preparations for the exploration and exploitation of Arctic resources and the research and development of ice ships.

[0003] However, polar regions are characterized by low temperatures, high ice content, and harsh climate. In the complex polar environment, ships conducting scientific expeditions and polar support platforms are prone to ice entrapment or ice damage accidents. In this case, accurately grasping the ice-ship coupling mechanism is an important prerequisite for avoiding related accidents. For example, the problem of ice-ship resistance prediction, as one of the key issues in the design process of ice-ships, has a very important impact on the design of ice-ships. Ice-ships sailing in continuous ice layers encounter great resistance, so ice-ships require good lines to reduce icebreaking resistance. Based on this fact, ice-breaking ships must be able to break the ice into small ice blocks and then remove the small ice blocks from the ship's navigation route. Accurately grasping the ice-ship coupling mechanism can better design ice-ships and thus solve the problem of ice-ship resistance prediction.

[0004] The best way to accurately understand the ice-ship coupling mechanism is to conduct field tests in the Arctic. However, due to geographical restrictions, it is difficult for existing equipment to enter the Arctic to test related performance, and the conditions for field tests are limited, which restricts the transformation and upgrading of existing marine equipment.

[0005] In order to solve the above problems, technicians in this field have established ice-water tank laboratories to replace field tests and explore relevant mechanical scenarios such as ship-ice collisions to help understand ice-ship interactions. For example, ice-water tank ship model test methods have been widely used in the prediction of ship resistance in ice areas.

[0006] However, the design requirements of the ice water pool laboratory are relatively high, involving factors such as temperature control, air flow rate, opening size, etc. 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] In order to design an ice water pool laboratory that can accurately simulate the seawater freezing process in the Arctic region, the best way is to first conduct a numerical simulation of the seawater freezing process in the ice water pool, systematically analyze the influence of various parameters, and then design an ice water pool (seawater freezing process simulation) laboratory based on the analysis results.

[0008] However, there are few studies on the simulation of ice-water pool freezing processes, and most of them are studies on freshwater freezing processes rather than seawater freezing processes, which are not suitable for simulating seawater freezing processes in the Arctic region.

[0009] At present, the method for simulating the freezing process of fresh water in an ice-water pool is generally to use the commercial software FLUENT to simulate the freezing process of fresh water in an ice-water pool. It explores the water freezing process under the conditions of different numbers and positions of cold air inlets, but it can only rely on the software's own general modules (such as solidification and melting modules and VOF models) to simulate limited scenarios, and the material parameter settings are relatively simple.

[0010] In summary, the existing research on the simulation of ice pool freezing process has the following shortcomings: (1) The parameter settings are relatively simple and the simulation scenarios are limited, which makes it difficult to guide the design of the ice water pool laboratory.

[0011] (2) Most of them are simulations of the freezing process of freshwater pools, which are not suitable for the simulation of the freezing process of seawater in the Arctic region. It should be noted that seawater can be regarded as a saltwater pool. The existing simulation research on the freezing process of freshwater pools cannot simulate the salinity segregation phenomenon during the freezing process of the water surface in the saltwater pool, that is, it cannot show the brine channel during the freezing process of seawater in the natural environment, resulting in a large error between the simulation results of ice thickness and mechanical properties and the actual values, making it difficult to guide the design of ice pool (seawater freezing process simulation) laboratories.

[0012] (3) The problem of gas-liquid-solid three-phase coupling tracking cannot be solved. It should be noted that in the actual seawater freezing process, there are two interface changes, namely the gas-liquid two-phase flow interface of cold air flow and water surface fluctuations, and the solid-liquid two-phase phase change interface of water surface freezing (i.e., water surface and ice surface). Parameters such as velocity, pressure, and temperature will have a cross-effect on the changes of these two interfaces, that is, changes in one interface will change the parameters such as velocity, pressure, and temperature, which will in turn affect the changes of the other interface, and the changes of the other interface will in turn affect the changes of the previous interface. Existing research on the simulation of ice-water pool freezing process focuses on a single interface, such as only simulating the solid-liquid two-phase phase change interface of water surface freezing, without considering the influence of air flow and water surface fluctuations (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 change interface has become a technical problem that plagues technicians in this field. Summary of the invention

[0013] The present invention proposes an ice-water pool simulation method and device, which solves the problems of multivariable coupling solution in the multiphase interface tracking process and the lack of in-depth understanding of the influence of salinity (concentration) in the prior art.

[0014] The ice water pool simulation method of the present invention has the following technical solutions: The method comprises the following steps: Input steps: used to obtain ice water pool simulation parameters; The ice water pool simulation parameters include physical property parameters and calculation parameters; The physical property parameters include material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity and pressure of water, ice and air above the ice water pool; The calculation parameters include calculation area, number of grids, time step, initial conditions, boundary conditions, inlet position and number, outlet position and number; Solution steps: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the ice water pool simulation parameters to obtain output parameters; The momentum equation is a momentum conservation equation, which is used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, which is used to obtain the temperature parameter and liquid fraction parameter values ​​in the calculation domain; The concentration equation is a component conservation equation, which is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to solve the closed loop of the algebraic equation system when the momentum equation is discretely solved; The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, signed distance function, and mixing fraction: The solving step includes a solid-liquid interface tracking step, a gas-liquid interface tracking step and a general component step; The solid-liquid interface tracking step is used to solve the energy equation and the concentration equation to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the calculation area; The gas-liquid interface tracking step is used to solve the volume fraction and signed distance function that characterize the gas-liquid interface, and select the VOSET gas-liquid interface tracking method to process the gas-liquid interface changes during the motion of the water droplet; 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, the fluidity is maintained when the liquid phase ratio is high, and the flow is suppressed 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 component step includes an algebraic equation group step, a control equation discrete coefficient step, a grid coordinate step, and a coefficient zeroing step, wherein: The algebraic equation group step is used to solve the discrete momentum, temperature and concentration control equations, and is used to be called by the solid-liquid interface tracking step and the gas-liquid interface tracking step; The control equation discrete coefficient step is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equation system step; The grid coordinate step is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing step is used to zero the coefficients of the control equation before the control equation discrete coefficient step, so that the control equation discrete coefficient step can be repeatedly executed; Output step: used to obtain output results according to output parameters; the output results include ice layer growth rate, ice layer distribution and air flow direction, wherein: The ice layer growth rate refers to the ice layer growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, which is used to characterize the speed of ice layer growth; The ice layer distribution refers to the ice thickness at various locations on the ice surface during the freezing process, which is used to reflect the degree of ice formation ease at various locations on the ice water pool, and is also used to analyze conditions conducive to freezing; The air flow direction refers to the flow direction of the air above the ice surface during the freezing process, which is used to reflect the direction of the cold air and is also used to analyze the reasons why the ice layer grows at different speeds in different places.

[0015] Further, a preferred embodiment is provided, wherein the liquid phase fraction is a basic parameter in the enthalpy-porosity model, which is used to characterize the distribution of the solid-liquid two phases; The volume fraction is a basic parameter in the VOSET method and is used to characterize the gas-liquid two-phase distribution; The signed distance function is a basic parameter in the VOSET method, which is used to coordinate with the volume fraction to improve the solution of the physical quantity at the gas-liquid interface, and is also used to directly represent the distribution of the gas-liquid interface; The mixing fraction is used to solve the problem that the liquid phase fraction or the volume fraction alone cannot simultaneously reflect and distinguish the three phases of solid, liquid and gas, and its value is half of the sum of the liquid phase fraction and the volume fraction.

[0016] Further, a preferred embodiment is provided, wherein the liquid phase fraction is used to characterize the solid-liquid two-phase distribution as follows: The liquid phase fraction is used to directly reflect the solid-liquid mixing state in the mushy region defined in the enthalpy-porosity model and affects the two-phase flow during the phase change process: If the liquid fraction value in a grid node is 1, the fluid in the grid node is liquid. If the liquid fraction value is 0, the fluid in the grid node is solid; If the liquid fraction value is between 0 and 1, the fluid in the grid node is a solid-liquid mixture, where 0.5 corresponds to the solid-liquid interface.

[0017] Further, a preferred embodiment is provided, wherein the volume fraction used to characterize the gas-liquid two-phase distribution is: If the volume fraction value of a grid node is 1, the fluid in the grid node is liquid water; If the volume fraction value is 0, the fluid in the grid node is gas; If the volume fraction value is between 0 and 1, the fluid in the grid node is a gas-liquid mixture, where 0.5 corresponds to the gas-liquid interface.

[0018] Further, a preferred embodiment is provided, wherein the coefficient of the source term is set to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low: When the droplet region is entirely liquid, the source term is 0, and the original two-phase flow momentum equation is not affected; When the droplet begins to freeze, the interior of the droplet becomes an ice-water mixed region. At this time, the liquid phase fraction is between (0,1) and gradually decreases. As a result, the source term accounts for an increasingly larger proportion in the momentum equation and gradually exceeds the transient term, convection term, and diffusion term. Until the droplet is completely transformed into the solid phase, the source term reaches a maximum value, so the velocity of the solid phase grid node is basically 0, realizing the transition from liquid phase to solid phase.

[0019] Further, a preferred embodiment is provided, in the grid coordinate step: The finite volume method is used to establish a structured staggered grid system to define the coordinate values ​​of each grid in the calculation area.

[0020] The present invention also proposes an ice water pool simulation device, and its technical solution is as follows: The device comprises the following modules: Input module: used to obtain ice water pool simulation parameters; The ice water pool simulation parameters include physical property parameters and calculation parameters; The physical property parameters include material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity and pressure of water, ice and air above the ice water pool; The calculation parameters include calculation area, number of grids, time step, initial conditions, boundary conditions, inlet position and number, outlet position and number; Solver module: used to solve momentum equation, energy equation, concentration equation and continuity equation according to ice water pool simulation parameters to obtain output parameters; The momentum equation is a momentum conservation equation, which is used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, which is used to obtain the temperature parameter and liquid fraction parameter values ​​in the calculation domain; The concentration equation is a component conservation equation, which is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to solve the closed loop of the algebraic equation system when the momentum equation is discretely solved; The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, signed 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 the concentration equation to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the calculation area; The gas-liquid interface tracking component is used to solve the volume fraction and signed distance function that characterize the gas-liquid interface, and selects the VOSET gas-liquid interface tracking method to process the gas-liquid interface changes during the motion of the water droplet; 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, the fluidity is maintained when the liquid phase ratio is high, and the flow is suppressed 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 component includes an algebraic equation group submodule, a control equation discrete coefficient submodule, a grid coordinate submodule and a coefficient zeroing submodule, wherein: The algebraic equation group submodule is used to solve the discrete momentum, temperature and concentration control equations, and is used to be called by the solid-liquid interface tracking component and the gas-liquid interface tracking component; The control equation discrete coefficient submodule is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equation group 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 the coefficients of the control equation before calling the control equation discrete coefficient submodule, so that the control equation discrete coefficient submodule can be called repeatedly; Output module: used to obtain output results according to output parameters; the output results include ice growth rate, ice distribution and air flow direction, where: The ice layer growth rate refers to the ice layer growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, which is used to characterize the speed of ice layer growth; The ice layer distribution refers to the ice thickness at various locations on the ice surface during the freezing process, which is used to reflect the degree of ice formation ease at various locations on the ice water pool, and is also used to analyze conditions conducive to freezing; The air flow direction refers to the flow direction of the air above the ice surface during the freezing process, which is used to reflect the direction of the cold air and is also used to analyze the reasons why the ice layer grows at different speeds in different places.

[0021] The present invention also proposes a computer device, and its technical solution is as follows: A computer device comprises: a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to perform the above-mentioned ice water pool simulation method by executing the executable instructions.

[0022] The present invention also proposes a computer storage medium, and its technical solution is as follows: A computer storage medium stores a computer program, and when the computer program is run, the ice water pool simulation method is executed.

[0023] The present invention also proposes a computer program product, and its technical solution is as follows: A computer program product includes a computer program / instruction, which implements the steps of the ice water pool simulation method when executed by a processor.

[0024] The present invention has the following beneficial effects: 1. The ice water pool simulation method described in the present invention can adjust the simulation parameters to simulate the ice pool surface freezing process under different inlet temperatures, flow rates, inlet and outlet numbers and other parameters, and can simulate the seawater freezing process.

[0025] 2. The ice-water pool simulation method described in the present invention can simulate the brine channel formation process caused by salinity segregation during the freezing of salt water, thereby simulating a more realistic seawater freezing scene.

[0026] 3. The ice-water pool simulation method described in the present invention uses the more computationally efficient IDEAL algorithm to calculate the velocity and pressure fields in the domain, and accurately captures the solid-liquid interface during the freezing process through the enthalpy-porosity method.

[0027] 4. The ice-water pool simulation method described in the present invention is applicable to the field of polar ice-water pool tests, and realizes the numerical simulation of seawater freezing in ice-water pools, which is helpful for technical personnel in this field to grasp the freezing principle of ice-water pools and can provide strong support for the design of ice-water pool tests.

[0028] The ice water pool simulation method and device of the present invention are suitable for simulating the freezing process of seawater in an ice water pool. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A schematic diagram of a flow chart of an ice water pool simulation method in one embodiment of the present invention; Figure 2 A schematic flow chart of the solution steps of the ice water pool simulation method in one embodiment of the present invention; Figure 3 A logic block diagram of an ice water pool simulation device in one embodiment of the present invention; Figure 4 It is a schematic diagram of a partial improvement of the ice water pool simulation method relative to the pure water freezing model in one embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the technical solutions and advantages of the present invention more clearly described, the specific implementation methods of the present invention will be further described in detail and completely in conjunction with the accompanying drawings. The various implementation methods described below are only part of the preferred solutions of the present invention, rather than all implementation methods; the various implementation methods described below are intended to explain the present invention and cannot be understood as limitations on the present invention; the reasonable combination of technical features defined in the various implementation methods of the present invention, and all other implementation methods obtained by ordinary technicians in the field without creative work based on the implementation methods of the present invention, are within the scope of protection of the present invention.

[0032] One embodiment provides an ice water pool simulation method: The method comprises the following steps: Input steps: used to obtain ice water pool simulation parameters; The ice water pool simulation parameters include physical property parameters and calculation parameters; The physical property parameters include material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity and pressure of water, ice and air above the ice water pool; The calculation parameters include calculation area, number of grids, time step, initial conditions, boundary conditions, inlet position and number, outlet position and number; Solution steps: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the ice water pool simulation parameters to obtain output parameters; The momentum equation is a momentum conservation equation, which is used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, which is used to obtain the temperature parameter and liquid fraction parameter values ​​in the calculation domain; The concentration equation is a component conservation equation, which is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to solve the closed loop of the algebraic equation system when the momentum equation is discretely solved; The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, signed distance function, and mixing fraction: The solving step includes a solid-liquid interface tracking step, a gas-liquid interface tracking step and a general component step; The solid-liquid interface tracking step is used to solve the energy equation and the concentration equation to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the calculation area; The gas-liquid interface tracking step is used to solve the volume fraction and signed distance function that characterize the gas-liquid interface, and select the VOSET gas-liquid interface tracking method to process the gas-liquid interface changes during the motion of the water droplet; 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, the fluidity is maintained when the liquid phase ratio is high, and the flow is suppressed 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 component step includes an algebraic equation group step, a control equation discrete coefficient step, a grid coordinate step, and a coefficient zeroing step, wherein: The algebraic equation group step is used to solve the discrete momentum, temperature and concentration control equations, and is used to be called by the solid-liquid interface tracking step and the gas-liquid interface tracking step; The control equation discrete coefficient step is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equation system step; The grid coordinate step is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing step is used to zero the coefficients of the control equation before the control equation discrete coefficient step, so that the control equation discrete coefficient step can be repeatedly executed; Output step: used to obtain output results according to output parameters; the output results include ice layer growth rate, ice layer distribution and air flow direction, wherein: The ice layer growth rate refers to the ice layer growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, which is used to characterize the speed of ice layer growth; The ice layer distribution refers to the ice thickness at various locations on the ice surface during the freezing process, which is used to reflect the degree of ice formation ease at various locations on the ice water pool, and is also used to analyze conditions conducive to freezing; The air flow direction refers to the flow direction of the air above the ice surface during the freezing process, which is used to reflect the direction of the cold air and is also used to analyze the reasons why the ice layer grows at different speeds in different places.

[0033] In this embodiment, the material density in the physical property parameters is the ratio of the mass to the volume of the material, and is used to distinguish between the three materials of water, ice and air.

[0034] In this embodiment, the latent heat in the physical property parameters is the heat released when water turns into ice, and is used to indicate the change in heat during the freezing process in the ice water pool.

[0035] In this embodiment, the specific heat capacity in the physical property parameters is the amount of heat absorbed or released when a unit mass of an object changes unit temperature, and is used to distinguish between water, ice, and air, and to determine the freezing rate of water.

[0036] In this embodiment, the thermal conductivity coefficient in the physical property parameters characterizes the thermal conductivity of a substance, and is used to distinguish between water, ice, and air, and to determine the heat transfer process in the three phases.

[0037] 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, and is used to distinguish between the three materials of 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).

[0038] In this embodiment, the thermal expansion coefficient in the physical property parameters is a physical quantity that characterizes the thermal expansion capacity of an object, and is used to reflect the expansion degree of the ice body after freezing, and is used to solve the body force term in the momentum equation.

[0039] In this embodiment, the viscosity in the physical property parameters characterizes the resistance of the fluid to flow, is used to distinguish between three materials: water, ice, and air, and is used to solve the diffusion term in the momentum equation.

[0040] In this embodiment, the temperature in the physical property parameter is the temperature value of each grid node in the calculation area, which is used to reflect the temperature gradient of the calculation domain, so as to determine whether each grid node reaches the solidification temperature condition.

[0041] In this embodiment, the salinity in the physical property parameter 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 water in the ice water pool and reflect the salt precipitation during the freezing process.

[0042] In this embodiment, the velocity in the physical property parameter represents the speed of fluid flow and is used to distinguish between three materials: water, ice, and air.

[0043] In this embodiment, the pressure in the physical property parameter represents the force acting on the contact surfaces of two objects, and is used to reflect the vertical effect of the surfaces between gas phases, liquid phases or solid phases.

[0044] In this embodiment, the calculation area 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.

[0045] In this implementation manner, the number of grids in the calculation parameters is the number of subdivided grids in the calculation area, which is used for control equations and main variables in the discrete numerical calculation process.

[0046] In this implementation, the time step in the calculation parameter is the time interval between each time layer in the numerical calculation process, and is used to discretely solve the transient terms (also called non-steady-state terms) in each control equation.

[0047] In this embodiment, the initial conditions in the calculation parameters are the conditions at the initial moment of the numerical calculation, including temperature, velocity, volume fraction and liquid fraction.

[0048] In this implementation manner, the boundary conditions in the calculation parameters are conditions at the boundary of the calculation domain, which are used to specify variables such as temperature, velocity and volume fraction.

[0049] In this embodiment, the inlet positions and numbers in the calculation parameters are the positions and numbers of the cold air inlets above or on 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.

[0050] In this embodiment, the outlet positions and numbers in the calculation parameters are the air outlet positions and numbers above the water surface on the left and right sides of the ice water pool, which are used to control the air outlet volume of the cold air in the ice water pool.

[0051] In this implementation manner, the momentum conservation equation is also referred to as the Navier-Stokes equation (NS equation).

[0052] In this implementation, the momentum equation is solved mainly by using the IDEAL algorithm based on the velocity-pressure correction theory, which can improve the speed of solving the velocity-pressure.

[0053] In this implementation, based on the IDEAL algorithm, an energy equation solution is added, and a source term related to the liquid phase fraction is added to the energy equation, thereby establishing a connection with the momentum equation.

[0054] In this implementation, both the convection term and the diffusion term of the control discrete equation are in a power format.

[0055] In this implementation, the physical scene is the freezing of the water surface in the pool considering the air, the second and third boundary conditions are processed by the additional source term method, the temperature corresponds to the adiabatic boundary, and the velocity adopts the no-slip boundary.

[0056] In addition, in one embodiment, in the solid-liquid interface tracking step, the temperature and liquid fraction of each grid node in the calculation area are obtained by the following method: The source term of the energy equation contains a transient term of liquid fraction; Iteratively solve the liquid fraction to obtain a liquid fraction value consistent with the temperature solution at the current moment; When solving iteratively, only one iteration is required at a time: After the solution of the layer velocity and pressure at the current time is completed, the liquid phase fraction is updated and its upper and lower limits are specified, thereby obtaining the source term of the energy equation and the coefficient of the discrete equation; Using the steps of algebraic equations, solve the energy equation to obtain the temperature value, determine the source term and the discrete equation coefficient, and solve the algebraic equations to obtain the current temperature; The above steps are iterated several times until the temperature and liquid fraction of the current time layer reach consistent convergence conditions.

[0057] It should be noted that the source term of the energy equation contains the transient term of the liquid fraction, which means that the source term of the energy equation contains both the liquid fraction value at the previous moment and the liquid fraction value at the current moment, that is, there are two unknown quantities (temperature and liquid fraction) in the energy equation of the current time layer. If the liquid fraction is updated directly using the piecewise relationship between the liquid fraction and the temperature, it is easy to cause calculation divergence.

[0058] In this implementation, in order to improve convergence and avoid computational divergence, the liquid fraction is iteratively solved to obtain a liquid fraction value consistent with the temperature solution at the current moment.

[0059] It should be noted that the existing liquid fraction iteration method performs two iterations in the same time layer, which has low computational efficiency.

[0060] In this implementation, only one iteration is required in the same layer, and the calculation efficiency is high.

[0061] In addition, in one embodiment, in the solid-liquid interface tracking step, the concentration parameter value of each grid node in the calculation area is obtained by the following method: The concentration parameter value is used to reflect salinity; A fully implicit time integration scheme is used to solve the concentration equation; Based on the Scheil-Gulliver non-equilibrium theory, a counter-diffusion model was established to simulate the double diffusion convection process of thermosolvents and reflect the coupling effect between the interface dynamics and the thermosolvent driving force. The concentration value in the fluid domain is used to modify the latent heat update method in the source term of the energy equation to realize the lateral solidification process of the brine solution.

[0062] In this implementation, 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 time step value. However, in order to improve the calculation accuracy, the time step should still be a small value when solving the specific discrete solution; It should be noted that the diffusion of solutes in the solid phase is usually called back-diffusion. When a solid phase is formed in the solid-liquid mixing region, it will hinder some solutes from entering the liquid phase region. Subsequently, this part of the solute is redistributed in the solid and liquid phases through mass diffusion. A part of it is discharged in the solid phase after the pure water freezes, resulting in an increase in the solute concentration in the solid-liquid mixing region. The diffusion of solutes in the liquid phase occurs uniformly and does not require special attention. A back-diffusion model was established based on the Scheil-Gulliver non-equilibrium theory to simulate the double diffusion convection process of hot solutes, which can reflect the coupling effect of interface dynamics and hot solute driving force.

[0063] In this embodiment, the ice layer growth rate is obtained by the following method: The mixing fraction data is output as a cloud map through post-processing software such as Tecplot. Based on the cloud maps of multiple time layers, an animation of the dynamic growth process of the ice layer can be obtained, and then the growth rate of the ice layer can be obtained.

[0064] In this embodiment, the ice layer distribution is obtained by the following method: The mixing fraction data is output as a cloud map through post-processing software such as Tecplot, so that the distribution of ice layers in the ice water pool at different times can be obtained.

[0065] In this embodiment, the air flow direction is obtained by the following method: The speed data is output as a cloud map through post-processing software such as Tecplot. The direction of air flow can be observed from the direction of the vector line arrows in the speed cloud map.

[0066] It should be noted that existing methods mostly focus on a single interface, such as simulating water freezing, without considering the flow of air. This will make it impossible to take into account water surface fluctuations, which will lead to inaccurate simulation results.

[0067] In addition, in one embodiment, the liquid phase fraction is a basic parameter in the enthalpy-porosity model, which is used to characterize the distribution of solid-liquid two phases; The volume fraction is a basic parameter in the VOSET method and is used to characterize the gas-liquid two-phase distribution; The signed distance function is a basic parameter in the VOSET method, which is used to coordinate with the volume fraction to improve the solution of the physical quantity at the gas-liquid interface, and is also used to directly represent the distribution of the gas-liquid interface; The mixing fraction is used to solve the problem that the liquid phase fraction or the volume fraction alone cannot simultaneously reflect and distinguish the three phases of solid, liquid and gas, and its value is half of the sum of the liquid phase fraction and the volume fraction.

[0068] In this embodiment, the mixture fraction is a basic parameter unique to this embodiment.

[0069] In this embodiment, the volume fraction is used to describe the gas-liquid interface, and the liquid phase fraction is used to describe the solid-liquid interface. Both fractions can only display the two-phase interface and cannot display the gas-liquid-solid interface at the same time. For this reason, a mixed fraction is introduced, and its value is equal to half of the sum of the volume fraction and the liquid phase fraction. In this way, the three phases of air-water-ice in the freezing process of the ice-water pool can be displayed simultaneously.

[0070] It should be noted that in order to simultaneously reflect the gas-liquid-solid three-phase interface, the introduction of the mixture fraction comprehensively considers the influence of the two fractions. To distinguish the gas, liquid and solid three phases, as shown in Table 1.

[0071] Table 1 Three volume fraction values

[0072] In addition, in one embodiment, the liquid phase fraction is used to characterize the solid-liquid two-phase distribution: The liquid phase fraction is used to directly reflect the solid-liquid mixing state in the mushy region defined in the enthalpy-porosity model and affects the two-phase flow during the phase change process: If the liquid fraction value in a grid node is 1, the fluid in the grid node is liquid. If the liquid fraction value is 0, the fluid in the grid node is solid; If the liquid fraction value is between 0 and 1, the fluid in the grid node is a solid-liquid mixture, where 0.5 corresponds to the solid-liquid interface.

[0073] In addition, in one embodiment, the volume fraction is used to characterize the gas-liquid two-phase distribution: If the volume fraction value of a grid node is 1, the fluid in the grid node is liquid water; If the volume fraction value is 0, the fluid in the grid node is gas; If the volume fraction value is between 0 and 1, the fluid in the grid node is a gas-liquid mixture, where 0.5 corresponds to the gas-liquid interface.

[0074] In this embodiment, the signed distance function is used to directly represent the distribution of the gas-liquid interface: When the value of the signed distance function is positive, the fluid in the corresponding grid is in liquid phase; When the value of the signed distance function is negative, the fluid in the corresponding grid is gas phase. When the value of the signed distance function is zero, the corresponding grid is a gas-liquid interface grid.

[0075] In addition, in one embodiment, the coefficient of the source term is set to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low: When the droplet region is entirely liquid, the source term is 0, and the original two-phase flow momentum equation is not affected; When the droplet begins to freeze, the interior of the droplet becomes an ice-water mixed region. At this time, the liquid phase fraction is between (0,1) and gradually decreases. As a result, the source term accounts for an increasingly larger proportion in the momentum equation and gradually exceeds the transient term, convection term, and diffusion term. Until the droplet is completely transformed into the solid phase, the source term reaches a maximum value, so the velocity of the solid phase grid node is basically 0, realizing the transition from liquid phase to solid phase.

[0076] It should be noted that the momentum equation used in the traditional VOSET method only contains commonly used transient terms, convection terms, diffusion terms, surface tension terms, and body terms. In this embodiment, while considering gas-liquid two-phase flow phenomena such as air flow and liquid movement, the freezing of the free liquid surface in the ice water pool is also considered. For this reason, the source term about the liquid phase fraction is added to the momentum equation in this embodiment to reflect the effect of freezing on movement.

[0077] It should be noted that one of the technical difficulties involved in simulating the seawater freezing process in the ice water pool is the coupled tracking of the gas-liquid two-phase flow interface such as cold air flow and water surface fluctuations and the solid-liquid two-phase phase change interface such as water surface ice. The gas-liquid two-phase flow interface focuses on solving the momentum equation and updating the fluid volume fraction, while the water surface ice interface focuses on solving the energy equation and updating the liquid phase fraction. How to comprehensively consider and solve the speed, pressure, temperature and other parameters for solving the two types of equations is a major difficulty.

[0078] It should be noted that traditional velocity and pressure correction methods include SIMPLE, SIMPLER, SIMPLEC, PISO and other algorithms, most of which require assumptions. In view of the gas-liquid-solid three-phase coupling tracking problem, this implementation first uses the pressure correction IDEAL algorithm to improve the convergence efficiency of velocity and pressure solutions, thereby constructing a fluid calculation domain, which can overcome the two basic assumptions in the traditional method, making the calculation process more efficient and stable, and on this basis, the VOSET method is used to achieve gas-liquid interface tracking.

[0079] It should be noted that the mainstream methods for tracking the gas-liquid interface are VOF and Level Set methods. The former has the advantage of mass conservation, but the disadvantage is that the physical quantities at the interface are discontinuous, while the latter has the advantage of smooth and continuous physical quantities at the interface, but the disadvantage is that it cannot meet the mass conservation. This implementation adopts the VOSET method to track the gas-liquid interface, combining the advantages of the VOF method and the Level Set method, which can not only conserve mass but also ensure the smooth and continuous parameters such as density and surface tension at the interface.

[0080] In this embodiment, the enthalpy-porosity method is used to deal with the macro-scale solid-liquid interface. By establishing a single-domain continuous medium formula, explicit tracking of the interface is avoided. The solid-liquid mixed region (also called 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. The propulsion change of the solid-liquid interface can be reflected by iteratively calculating the liquid phase fraction of each grid, thereby accurately tracking the phase change interface with complex topology and macroscopic diffusion uncertainty.

[0081] It should be noted that although the VOSET method and the enthalpy-porosity method are used for tracking gas-liquid interface and solid-liquid interface respectively in existing methods (such as water droplet impact spreading dynamics, metal melting, welding, etc.), there is no literature combining the two to track gas-liquid-solid three-phase, and there is no literature combining the two to simulate the freezing process of ice-water pools. The difficulty lies in that the two methods have different focuses, and the relationship between the parameters is difficult to establish. For example, the effect of temperature on the liquid fraction , while the velocity affects the volume fraction change , it is difficult to explain how the two scores affect each other, and only one score can be displayed in the same cloud chart.

[0082] In this embodiment, in order to reflect the effect of freezing on liquid flow, the relationship between the momentum equation and the energy equation is improved, and the liquid fraction Directly reflects the solid-liquid mixing state in the mushy region and affects the two-phase flow during the phase change process. This effect can be achieved by adding source terms to the basic NS equations. (Assuming that the motion in the mushy region obeys Darcy's law for flow in porous media and conforms to the Carman-Koseny hypothesis), It can be understood as the flow resistance caused by solidification, and its specific form is: ; In the formula It is a minimum value. To ensure that the denominator is not 0, it is taken as 0.001; It is a constant value in the pasty region, which can control the speed reduction rate. It is recommended to be between 104 and 107. The appropriate value can maintain fluidity when the liquid phase ratio is high and inhibit flow when the liquid phase ratio is low.

[0083] When the droplet area is entirely liquid, , i.e. the source term , the original two-phase flow NS equation is not affected; when the droplet begins to freeze, the inside of the droplet becomes an ice-water mixed area, that is, a mushy area. Between (0,1) and gradually decreases, thus It accounts for an increasingly larger proportion in the NS equations and gradually exceeds the transient term, convection term and diffusion term until the droplet is completely transformed into a solid phase and the velocity is basically zero.

[0084] It should be noted that another technical difficulty involved in simulating the freezing process of seawater in an ice-water pool is how to consider the influence of salinity. Under the combined action of concentration (salinity) and temperature gradient, solutes will precipitate and move toward the liquid phase in the fluid in the ice-water mixing area, forming brine channels in the process. That is, brine channels are mainly caused by heat-salt double diffusion convection. Compared with natural convection caused only by temperature gradient in freshwater freezing, heat-solute double diffusion convection in salt water freezing is more complicated. Its mechanism of action on the formation and evolution of brine channels is still unclear, and no relevant literature has been found. It is still a major problem that needs to be solved in the study of seawater freezing. The key to solving this problem is to accurately track the various interfaces in the heat-solute double diffusion convection process in the salt water film, and reflect the coupling effect of interface dynamics and heat-solute driving force.

[0085] It should be noted that, in order to solve the problem that the existing ice water pool simulation cannot take into account the dynamic influence of salinity, this embodiment introduces the concentration equation while solving the momentum equation and the energy equation.

[0086] Considering that the main solute component in seawater is sodium chloride, this embodiment regards seawater as a binary mixture of sodium chloride and pure water. On the basis of the pure water freezing model, the salinity (concentration) equation is introduced. Based on the local thermodynamic equilibrium assumption and phase diagram, the solidus and liquidus temperatures in the energy equation are determined by the salinity, thereby realizing the coupling of the temperature field and the solute field.

[0087] At the same time, the body and source terms 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 heat-solute double diffusion convection process under the combined action of temperature and salinity gradients.

[0088] The freezing of brine film includes not only macroscopic phenomena such as heat-solute double diffusion convection and ice-water interface advancement, 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. It is proposed to establish a back diffusion model based on the lever principle and the Scheil-Gulliver principle, correct the source term of the macroscopic salinity equation, describe the role of salinity in controlling phase change, and reflect the redistribution of salt through diffusion transport.

[0089] This embodiment is relative to some improvements on the pure water freezing model. Figure 4 As shown, the brine film freezing model refers to the method described in this embodiment, specifically: In the pure water freezing model: The inverse function form of latent heat is a constant:

[0090] Mush coefficient All are constant values:

[0091] The body force term of the momentum equation only considers thermal expansion:

[0092] In the salt water film freezing model: The inverse function of latent heat is expressed as the solid-liquid phase temperature:

[0093] Mush coefficient All are given by the dendrite arm length:

[0094] The body force term of the momentum equation takes into account thermal expansion and solute expansion: .

[0095] In addition, in one embodiment, in the grid coordinate step: The finite volume method is used to establish a structured staggered grid system to define the coordinate values ​​of each grid in the calculation area.

[0096] In this implementation, a structured staggered grid system is established through a finite volume method to make the velocity solution more stable.

[0097] In this embodiment, the finite volume method and staggered grids are used to discretize the basic control equations.

[0098] In this embodiment, a structured staggered grid is adopted, and three sets of grid schemes are used in the two-dimensional grid, which can effectively avoid the divergence of pressure gradient.

[0099] In one embodiment, an ice water pool simulation device is provided: The device comprises the following modules: Input module: used to obtain ice water pool simulation parameters; The ice water pool simulation parameters include physical property parameters and calculation parameters; The physical property parameters include material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity and pressure of water, ice and air above the ice water pool; The calculation parameters include calculation area, number of grids, time step, initial conditions, boundary conditions, inlet position and number, outlet position and number; Solver module: used to solve momentum equation, energy equation, concentration equation and continuity equation according to ice water pool simulation parameters to obtain output parameters; The momentum equation is a momentum conservation equation, which is used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, which is used to obtain the temperature parameter and liquid fraction parameter values ​​in the calculation domain; The concentration equation is a component conservation equation, which is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to solve the closed loop of the algebraic equation system when the momentum equation is discretely solved; The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, signed 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 the concentration equation to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the calculation area; The gas-liquid interface tracking component is used to solve the volume fraction and signed distance function that characterize the gas-liquid interface, and selects the VOSET gas-liquid interface tracking method to process the gas-liquid interface changes during the motion of the water droplet; 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, the fluidity is maintained when the liquid phase ratio is high, and the flow is suppressed 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 component includes an algebraic equation group submodule, a control equation discrete coefficient submodule, a grid coordinate submodule and a coefficient zeroing submodule, wherein: The algebraic equation group submodule is used to solve the discrete momentum, temperature and concentration control equations, and is used to be called by the solid-liquid interface tracking component and the gas-liquid interface tracking component; The control equation discrete coefficient submodule is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equation group 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 the coefficients of the control equation before calling the control equation discrete coefficient submodule, so that the control equation discrete coefficient submodule can be called repeatedly; Output module: used to obtain output results according to output parameters; the output results include ice growth rate, ice distribution and air flow direction, where: The ice layer growth rate refers to the ice layer growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, which is used to characterize the speed of ice layer growth; The ice layer distribution refers to the ice thickness at various locations on the ice surface during the freezing process, which is used to reflect the degree of ice formation ease at various locations on the ice water pool, and is also used to analyze conditions conducive to freezing; The air flow direction refers to the flow direction of the air above the ice surface during the freezing process, which is used to reflect the direction of the cold air and is also used to analyze the reasons why the ice layer grows at different speeds in different places.

[0100] The device is used to implement the above-mentioned ice water pool simulation method.

[0101] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable changes and improvements to the present invention, reasonable combinations of implementation modes and equivalent substitutions based on the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Ice water pool simulation method, characterized in that: The method comprises the following steps: Input steps: used to obtain ice water pool simulation parameters; The ice water pool simulation parameters include physical property parameters and calculation parameters; The physical property parameters include material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity and pressure of water, ice and air above the ice water pool; The calculation parameters include calculation area, number of grids, time step, initial conditions, boundary conditions, inlet position and number, outlet position and number; Solution steps: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the ice water pool simulation parameters to obtain output parameters; The momentum equation is a momentum conservation equation, which is used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, which is used to obtain the temperature parameter and liquid fraction parameter values ​​in the calculation domain; The concentration equation is a component conservation equation, which is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to solve the closed loop of the algebraic equation system when the momentum equation is discretely solved; The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, signed distance function, and mixing fraction: The solving step includes a solid-liquid interface tracking step, a gas-liquid interface tracking step and a general component step; The solid-liquid interface tracking step is used to solve the energy equation and the concentration equation to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the calculation area; The gas-liquid interface tracking step is used to solve the volume fraction and signed distance function that characterize the gas-liquid interface, and select the VOSET gas-liquid interface tracking method to process the gas-liquid interface changes during the motion of the water droplet; 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, the fluidity is maintained when the liquid phase ratio is high, and the flow is suppressed 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 component step includes an algebraic equation group step, a control equation discrete coefficient step, a grid coordinate step, and a coefficient zeroing step, wherein: The algebraic equation group step is used to solve the discrete momentum, temperature and concentration control equations, and is used to be called by the solid-liquid interface tracking step and the gas-liquid interface tracking step; The control equation discrete coefficient step is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equation system step; The grid coordinate step is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing step is used to zero the coefficients of the control equation before the control equation discrete coefficient step, so that the control equation discrete coefficient step can be repeatedly executed; Output step: used to obtain output results according to output parameters; the output results include ice layer growth rate, ice layer distribution and air flow direction, wherein: The ice layer growth rate refers to the ice layer growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, which is used to characterize the speed of ice layer growth; The ice layer distribution refers to the ice thickness at various locations on the ice surface during the freezing process, which is used to reflect the degree of ice formation ease at various locations on the ice water pool, and is also used to analyze conditions conducive to freezing; The air flow direction refers to the flow direction of the air above the ice surface during the freezing process, which is used to reflect the direction of the cold air and is also used to analyze the reasons why the ice layer grows at different speeds in different places.

2. The ice water pool simulation method according to claim 1, characterized in that: The liquid phase fraction is a basic parameter in the enthalpy-porosity model and is used to characterize the distribution of solid-liquid two phases; The volume fraction is a basic parameter in the VOSET method and is used to characterize the gas-liquid two-phase distribution; The signed distance function is a basic parameter in the VOSET method, which is used to coordinate with the volume fraction to improve the solution of the physical quantity at the gas-liquid interface, and is also used to directly represent the distribution of the gas-liquid interface; The mixing fraction is used to solve the problem that the liquid phase fraction or the volume fraction alone cannot simultaneously reflect and distinguish the three phases of solid, liquid and gas, and 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-liquid two phases: The liquid phase fraction is used to directly reflect the solid-liquid mixing state in the mushy region defined in the enthalpy-porosity model and affects the two-phase flow during the phase change process: If the liquid fraction value in a grid node is 1, the fluid in the grid node is liquid. If the liquid fraction value is 0, the fluid in the grid node is solid; If the liquid fraction value is between 0 and 1, the fluid in the grid node is a solid-liquid mixture, 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: If the volume fraction value of a grid node is 1, the fluid in the grid node is liquid water; If the volume fraction value is 0, the fluid in the grid node is gas; If the volume fraction value is between 0 and 1, the fluid in the grid node is a gas-liquid mixture, where 0.5 corresponds to the gas-liquid interface.

5. The ice water pool simulation method according to claim 1, characterized in that: The coefficient of the source term is set to maintain fluidity when the liquid phase ratio is high and to suppress flow when the liquid phase ratio is low: When the droplet region is entirely liquid, the source term is 0, and the original two-phase flow momentum equation is not affected; When the droplet begins to freeze, the interior of the droplet becomes an ice-water mixed region. At this time, the liquid phase fraction is between (0,1) and gradually decreases. As a result, the source term accounts for an increasingly larger proportion in the momentum equation and gradually exceeds the transient term, convection term, and diffusion term. Until the liquid droplet is completely transformed into the solid phase, the source term reaches a maximum value, so the velocity of the solid phase grid node is basically 0, realizing the transition from liquid phase to solid phase.

6. The ice water pool simulation method according to claim 1, characterized in that: In the grid coordinate step: The finite volume method is used to establish a structured staggered grid system to define the coordinate values ​​of each grid in the calculation area.

7. Ice water pool simulation device, characterized in that: The device comprises the following modules: Input module: used to obtain ice water pool simulation parameters; The ice water pool simulation parameters include physical property parameters and calculation parameters; The physical property parameters include material density, latent heat, specific heat capacity, thermal conductivity, diffusion coefficient, thermal expansion coefficient, viscosity, temperature, salinity, velocity and pressure of water, ice and air above the ice water pool; The calculation parameters include calculation area, number of grids, time step, initial conditions, boundary conditions, inlet position and number, outlet position and number; Solver module: used to solve momentum equation, energy equation, concentration equation and continuity equation according to ice water pool simulation parameters to obtain output parameters; The momentum equation is a momentum conservation equation, which is used to obtain the velocity and pressure in the flow field; The energy equation is an energy conservation equation, which is used to obtain the temperature parameter and liquid fraction parameter values ​​in the calculation domain; The concentration equation is a component conservation equation, which is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to solve the closed loop of the algebraic equation system when the momentum equation is discretely solved; The output parameters are temperature, velocity, pressure, liquid fraction, volume fraction, signed 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 the concentration equation to obtain the temperature, liquid fraction and concentration parameter values ​​of each grid node in the calculation area; The gas-liquid interface tracking component is used to solve the volume fraction and signed distance function that characterize the gas-liquid interface, and selects the VOSET gas-liquid interface tracking method to process the gas-liquid interface changes during the motion of the water droplet; 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, the fluidity is maintained when the liquid phase ratio is high, and the flow is suppressed when the liquid phase ratio is low; It is also used to solve the momentum equation using the IDEAL algorithm based on velocity pressure correction theory; The general component includes an algebraic equation group submodule, a control equation discrete coefficient submodule, a grid coordinate submodule and a coefficient zeroing submodule, wherein: The algebraic equation group submodule is used to solve the discrete momentum, temperature and concentration control equations, and is used to be called by the solid-liquid interface tracking component and the gas-liquid interface tracking component; The control equation discrete coefficient submodule is used to solve the coefficients of the velocity and pressure equations in the momentum equation, thereby providing input for the algebraic equation group 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 the coefficients of the control equation before calling the control equation discrete coefficient submodule, so that the control equation discrete coefficient submodule can be called repeatedly; Output module: used to obtain output results according to output parameters; the output results include ice growth rate, ice distribution and air flow direction, where: The ice layer growth rate refers to the ice layer growth rate at the location of the maximum ice thickness on the water surface during the freezing process of the ice water pool, which is used to characterize the speed of ice layer growth; The ice layer distribution refers to the ice thickness at various locations on the ice surface during the freezing process, which is used to reflect the degree of ice formation ease at various locations on the ice water pool, and is also used to analyze conditions conducive to freezing; The air flow direction refers to the flow direction of the air above the ice surface during the freezing process, which is used to reflect the direction of the cold air and is also used to analyze the reasons why the ice layer grows at different speeds in different places.

8. A computer device comprising: A processor and a memory, characterized in that the memory is used to store executable instructions of the processor, and the processor is configured to execute the ice water pool simulation method described in 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, and when the computer program is run, the ice-water pool simulation method according to any one of claims 1 to 7 is executed.

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

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