Saline water mixed deicing method and device based on microscopic back diffusion model

Abstract

The invention discloses a salt-water mixed deicing method and device based on a microscopic back-diffusion model, belongs to salt particle deicing, and particularly relates to macroscopic eutectic solidification simulation considering a microscopic back-diffusion phenomenon. The problems that in the prior art, the influence of dynamic changes of a brine channel in the brine freezing or melting process cannot be reflected, or only the microscopic or macroscopic single scale is considered, the influence of microscopic solute back diffusion on the macroscopic brine freezing or melting process is ignored, and then the salt distribution ratio in the road deicing operation cannot be given are solved. The method comprises the following steps: a solving step: solving a momentum equation, an energy equation, a concentration equation and a continuity equation according to input parameters of a brine solidification or melting model to obtain output parameters; the salt-water mixed deicing method and device based on the microscopic back-diffusion model are suitable for efficient deicing for accurately mastering the salt distribution ratio.

Description

Technical Field

[0001] The present invention relates to salt particle deicing, and in particular to macroscopic eutectic solidification simulation considering microscopic back-diffusion phenomenon. Background Art

[0002] In winter, under low temperature conditions, precipitation or snow melts and easily freezes on the road surface. Ice on slippery roads can easily cause vehicle damage, people slipping and other accidents. In order to minimize the hazards of road icing, workers will spread salt particles on the road surface and mix them with ice and snow, thereby lowering the freezing point of ice on the road surface, making the road surface less likely to freeze. In actual operation, the amount of salt spread needs to be strictly controlled. If the salt content is too little, the effect of lowering the freezing point of ice accumulation cannot be achieved. If the salt content is too much, if the salt water mixture freezes again, the salt will be easily corroded by the road surface after being separated out, resulting in more serious consequences. Therefore, it is urgent to develop an efficient deicing method that can accurately control the salt distribution ratio.

[0003] In the process of mixing salt particles with ice and snow, there is a phenomenon of brine eutectic solidification. Brine eutectic solidification is a typical non-equilibrium solidification phenomenon, which includes macroscopic phenomena such as heat-solute natural convection and two-phase relative motion, and local (microscopic) phenomena such as solute back diffusion. Therefore, coupling analysis of macroscopic and local segregation processes can accurately study the entire solidification process. The adhesion strength of salt ice is smaller than that of freshwater ice, which shows that the influence of salinity cannot be ignored.

[0004] However, the existing technical means for analyzing the solidification of brine eutectic have the following shortcomings: On the one hand, most of them simplify the influence of salinity to the temperature value when freezing occurs, that is, the freezing temperature of pure water is 0℃. After considering the salinity, the corresponding freezing temperature is lower than 0℃, which affects the freezing process. This setting can only reflect the static influence of salinity, and cannot reflect the influence of dynamic changes in brine channels during the freezing or melting of salt water. On the other hand, the few studies that explored the impact of brine channels only considered a single scale, microscopic or macroscopic, and ignored the effect of microscopic solute back diffusion on the macroscopic brine solidification or melting process. As a result, they were unable to accurately simulate the brine channel problem when brine freezes, and were unable to provide the salt distribution ratio during road deicing operations. Summary of the invention

[0005] The present invention proposes a brine mixing deicing method and device based on a microscopic back-diffusion model, which solves the problem that the prior art cannot reflect the influence of dynamic changes in brine channels during the freezing or melting of brine, or only considers a single microscopic or macroscopic scale, ignoring the influence of microscopic solute back-diffusion on the macroscopic brine solidification or melting process, and thus cannot provide the salt distribution ratio during road deicing operations.

[0006] The brine mixing deicing method based on the microscopic back-diffusion model of the present invention has the following technical solutions: The method comprises the following steps: Input step: used to obtain input parameters of the brine solidification or melting model according to ice condition information of the road section to be de-iced; The ice condition information of the road section to be de-iced includes ambient temperature, ice thickness, ice type, and ice mechanical properties; The ambient temperature includes the ice surface temperature of the road section to be de-iced and the real-time temperature of the air in the road section to be de-iced; Solving step: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the input parameters of the brine solidification or melting model to obtain output parameters; The momentum equation is a momentum conservation equation, which complies with the fluid equation established by Newton's second law; The energy equation is an energy conservation equation, which complies with the first law of thermodynamics and takes temperature as the variable to be determined; The concentration equation is a component conservation equation that complies with the component mass conservation law and is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to achieve a closed loop when solving the algebraic equation system in a discrete solution of the momentum equation; The output parameters are temperature, salinity, velocity, pressure and liquid fraction; The solving step includes a solid-liquid interface tracking step, a microscopic anti-diffusion model selection step and a general component step; The solid-liquid interface tracking step is used to solve the energy equation and obtain the temperature and liquid fraction values ​​of each grid node in the calculation area; wherein the source term of the energy equation includes a transient term of the liquid fraction; and is also used to iteratively solve the liquid fraction value and latent heat value to obtain the liquid fraction value and latent heat value consistent with the temperature solution at the current moment; The microscopic anti-diffusion model selection step is used to solve the concentration equation using a fully implicit time integration scheme; and is also used to select an anti-diffusion model to characterize the concentration distribution; The general component steps include algebraic equation group steps, control equation discrete coefficient steps, grid coordinate steps, and coefficient zeroing steps, wherein: The algebraic equations step is used to be called and executed by the solid-liquid interface tracking step and the microscopic anti-diffusion model selection step to solve the discretized momentum, temperature and concentration equations; The control equation discrete coefficient step is used to solve the coefficients of the velocity and pressure equations in the momentum equation and provide input for the algebraic equations 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 reset the coefficients of the control equation to zero before the discrete coefficient step of the control equation is executed, so as to repeatedly execute the discrete coefficient step of the control equation; Output step / module: used to obtain output results according to output parameters; The output results are the preferred paths of salt analysis, salt distribution, accumulation patterns along the road surface, ice melting time, and reasonable salt concentration values ​​for de-icing.

[0007] Further, a preferred embodiment is provided, wherein the output parameters include temperature, salinity, velocity, pressure and liquid fraction: The temperature 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; The salinity 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 analysis during the freezing process; The velocity is used to characterize how fast the fluid flows; The pressure is used to characterize the force acting on the contact surfaces of two objects; The liquid fraction is a basic parameter for distinguishing solid and liquid phases in the enthalpy-porosity method.

[0008] Further, a preferred embodiment is provided, in which the latent heat is iteratively solved as follows: The iterative expression of latent heat value is: ; In the formula, and are the discrete equation coefficients, is the inverse function of the latent heat update expression; is the latent heat value; In the isothermal phase change problem, the updated expression of the latent heat value is simplified to: ; ; ; In the formula, the superscript Represents the variable value of the current iteration step, superscript Represents the variable value of the previous iteration.

[0009] Furthermore, a preferred embodiment is provided, in which only one layer of iteration is required in the same layer during the iterative solution of the liquid fraction value: 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; Then call the algebraic equation group module (execute the algebraic equation group step), solve the energy equation to obtain the temperature value, determine the source term and the discrete equation coefficient, and solve the algebraic equation group to obtain the current temperature; The above process is iterated many times until the temperature and liquid fraction of the current time layer reach consistent convergence conditions.

[0010] Furthermore, a preferred embodiment is provided, wherein the anti-diffusion model includes: Scheil-Gulliver model, lever principle model, Brody-Flemings model and Ohnaka model.

[0011] Furthermore, a preferred embodiment is provided in which the grid coordinate module uses a finite volume method to establish a structured staggered grid system to make the velocity solution more stable.

[0012] The present invention also proposes a brine mixing deicing device based on a microscopic reverse diffusion model, and its technical solution is as follows: The device comprises the following modules: Input module: used to obtain input parameters of the brine solidification or melting model according to the ice condition information of the road section to be de-iced; The ice condition information of the road section to be de-iced includes ambient temperature, ice thickness, ice type, and ice mechanical properties; The ambient temperature includes the ice surface temperature of the road section to be de-iced and the real-time temperature of the air in the road section to be de-iced; Solution module: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the input parameters of the brine solidification or melting model to obtain output parameters; The momentum equation is a momentum conservation equation, which complies with the fluid equation established by Newton's second law; The energy equation is an energy conservation equation, which complies with the first law of thermodynamics and takes temperature as the variable to be determined; The concentration equation is a component conservation equation that complies with the component mass conservation law and is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to achieve a closed loop when solving the algebraic equation system in a discrete solution of the momentum equation; The output parameters are temperature, salinity, velocity, pressure and liquid fraction; The solution module includes a solid-liquid interface tracking component, a microscopic anti-diffusion model selection component and a general component; The solid-liquid interface tracking component is used to solve the energy equation and obtain the temperature and liquid fraction values ​​of each grid node in the calculation area; wherein the source term of the energy equation includes the transient term of the liquid fraction; and is also used to iteratively solve the liquid fraction value and latent heat value to obtain the liquid fraction value and latent heat value consistent with the temperature solution at the current moment; The microscopic anti-diffusion model selection component is used to solve the concentration equation using a fully implicit time integration scheme; it is also used to select an anti-diffusion model to characterize the concentration distribution; The general components include an algebraic equation group module, a control equation discrete coefficient module, a grid coordinate module, and a coefficient zeroing module, wherein: The algebraic equations module is used to be called by the solid-liquid interface tracking component and the microscopic anti-diffusion model selection component to solve the discrete momentum, temperature and concentration equations; The control equation discrete coefficient module is used to solve the coefficients of the velocity and pressure equations in the momentum equation and provide input for the algebraic equation group module; The grid coordinate module is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing module is used to reset the coefficients of the control equation to zero before the discrete coefficient module of the control equation is called, so as to reuse the discrete coefficient module of the control equation; Output module: used to obtain output results according to output parameters; The output results are the preferred paths of salt analysis, salt distribution, accumulation patterns along the road surface, ice melting time, and reasonable salt concentration values ​​for de-icing.

[0013] The present invention also proposes a computer device, and its technical solution is as follows: A computer device includes: 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 brine mixing deicing method based on a microscopic reverse diffusion model by executing the executable instructions.

[0014] 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 brine mixing deicing method based on the microscopic back-diffusion model is executed.

[0015] 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, when executed by a processor, implements the steps of the brine mixing deicing method based on a microscopic counter-diffusion model.

[0016] The present invention has the following beneficial effects: The brine mixing deicing method based on the microscopic back-diffusion model described in the present invention innovatively proposes to simultaneously consider the microscopic solute back-diffusion phenomenon and the macroscopic double-diffusion convection phenomenon, and can simulate the eutectic solidification process of a binary mixture composed of salt (sodium chloride and other substances) and pure water, thereby solving the problem of unclear salt distribution ratio in the road deicing process.

[0017] The brine mixing deicing method and device based on the microscopic back-diffusion model described in the present invention are suitable for efficient deicing with accurate control of the salt distribution ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 A flow chart of a brine mixing deicing method based on a microscopic reverse diffusion model in one embodiment of the present invention; Figure 2 The flowchart of the macroscopic eutectic solidification simulation method considering the microscopic back-diffusion phenomenon in one embodiment of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] One embodiment provides a brine mixing deicing method based on a microscopic reverse diffusion model: The method comprises the following steps: Input step: used to obtain input parameters of the brine solidification or melting model according to ice condition information of the road section to be de-iced; The ice condition information of the road section to be de-iced includes ambient temperature, ice thickness, ice type, and ice mechanical properties; The ambient temperature includes the ice surface temperature of the road section to be de-iced and the real-time temperature of the air in the road section to be de-iced; Solving step: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the input parameters of the brine solidification or melting model to obtain output parameters; The momentum equation is a momentum conservation equation, which complies with the fluid equation established by Newton's second law; The energy equation is an energy conservation equation, which complies with the first law of thermodynamics and takes temperature as the variable to be determined; The concentration equation is a component conservation equation that complies with the component mass conservation law and is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to achieve a closed loop when solving the algebraic equation system in a discrete solution of the momentum equation; The output parameters are temperature, salinity, velocity, pressure and liquid fraction; The solving step includes a solid-liquid interface tracking step, a microscopic anti-diffusion model selection step and a general component step; The solid-liquid interface tracking step is used to solve the energy equation and obtain the temperature and liquid fraction values ​​of each grid node in the calculation area; wherein the source term of the energy equation includes a transient term of the liquid fraction; and is also used to iteratively solve the liquid fraction value and latent heat value to obtain the liquid fraction value and latent heat value consistent with the temperature solution at the current moment; The microscopic anti-diffusion model selection step is used to solve the concentration equation using a fully implicit time integration scheme; and is also used to select an anti-diffusion model to characterize the concentration distribution; The general component steps include algebraic equation group steps, control equation discrete coefficient steps, grid coordinate steps, and coefficient zeroing steps, wherein: The algebraic equations step is used to be called and executed by the solid-liquid interface tracking step and the microscopic anti-diffusion model selection step to solve the discretized momentum, temperature and concentration equations; The control equation discrete coefficient step is used to solve the coefficients of the velocity and pressure equations in the momentum equation and provide input for the algebraic equations 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 reset the coefficients of the control equation to zero before the discrete coefficient step of the control equation is executed, so as to repeatedly execute the discrete coefficient step of the control equation; Output step / module: used to obtain output results according to output parameters; The output results are the preferred paths of salt analysis, salt distribution, accumulation patterns along the road surface, ice melting time, and reasonable salt concentration values ​​for de-icing.

[0022] In this embodiment, the ambient temperature is obtained by a high-precision thermometer; to avoid errors, each temperature value is the average of three measurement results; The ice accumulation thickness includes the average thickness and the maximum thickness of the road section to be de-iced; In this embodiment, the ice thickness is sampled and measured; to avoid errors, each measurement value is the average of three measurement results.

[0023] The types of ice accumulation include dense ice and porous ice; In this embodiment, the dense ice has few impurities or bubbles in the ice body and is smooth and transparent.

[0024] In this embodiment, the porous ice is ice with a large number of bubbles distributed in the ice body, and appears milky white.

[0025] The ice accretion mechanical properties include ice adhesion strength and density.

[0026] In this embodiment, the ice adhesion strength represents the adhesion strength between the road surface and the accumulated ice, and is usually measured by a shear method.

[0027] In this embodiment, the density in the mechanical properties of ice accumulation is the ratio of the mass to the volume of ice.

[0028] The input parameters of the brine solidification or melting model include ice density, dynamic viscosity, initial temperature, eutectic concentration, eutectic temperature, partition coefficient, liquid phase latent heat, specific heat capacity, thermal diffusion coefficient, component diffusion coefficient, expansion coefficient, back diffusion coefficient, mushy coefficient, latent heat renewal relaxation factor, liquid phase fraction and calculation parameters; In this embodiment, the ice density is the ice density on the road surface.

[0029] In this embodiment, the dynamic viscosity includes ice phase dynamic viscosity and liquid phase dynamic viscosity, which characterizes the proportionality coefficient between the friction resistance in the fluid and the relative velocity between two layers of fluid per unit distance.

[0030] In this embodiment, the initial temperature includes the temperature in the ice phase (simulating the melting process example) or the liquid phase (simulating the solidification process example) at the initial state and the temperature of the heat source or cold source wall.

[0031] In this embodiment, the initial temperature is the minimum concentration at which the salt solution can simultaneously crystallize two solid phases with different compositions and different crystal structures at the eutectic temperature. This is applicable to the road surface salting and de-icing scenario. The salt specifically refers to sodium chloride, and the eutectic concentration of sodium chloride aqueous solution is 23.7%.

[0032] In this embodiment, the eutectic temperature is the lowest temperature at which the solid phases of all components in a multi-component system reach equilibrium at the same time.

[0033] In this embodiment, the distribution coefficient represents the solid-liquid concentration ratio in the binary solidification / melting system, and can also be regarded as the ratio of the slope of the liquidus line or the solidus line in the binary solidification / melting phase diagram.

[0034] In this embodiment, the liquid phase latent heat is the heat released when water turns into ice.

[0035] In this embodiment, the specific heat capacity is the amount of heat absorbed or released when a unit mass of an object changes unit temperature.

[0036] In this embodiment, the thermal diffusion coefficient is a physical quantity that represents the degree of heat diffusion in the liquid during solidification or melting.

[0037] In this embodiment, the component diffusion coefficient is a physical quantity that represents the degree of salt diffusion in the liquid during solidification or melting.

[0038] In this embodiment, the expansion coefficient includes the thermal expansion coefficient and the solute expansion coefficient, which are physical quantities that characterize the expansion capacity of the liquid due to heat or salinity during solidification or melting.

[0039] In this embodiment, the back diffusion coefficient characterizes the degree of diffusion (back diffusion) of salt in the solid phase during solidification or melting, and its value can determine the type of the back diffusion model in this embodiment.

[0040] In this embodiment, the mushy coefficient characterizes the coefficient before the term characterizing the speed of liquid phase movement in the momentum equation in the enthalpy-porosity method. It is one of the key parameters linking the energy equation and the momentum equation. Whether its value is reasonable or not is directly related to the accuracy of fluid motion simulation in the calculation area. A reasonable mushy coefficient can maintain the fluidity of the liquid when the liquid phase ratio in the calculation area is high, and hinder viscous flow when the liquid phase ratio is low.

[0041] In this embodiment, the latent heat update relaxation factor characterizes the degree of slowdown in the update iteration from the old value of the latent heat to the new value in the latent heat update expression in the enthalpy-porosity method.

[0042] In this 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, directly reflects the solid-liquid mixing state of the pasty region defined in the enthalpy-porosity model, and affects the two-phase flow during the phase change process. If the liquid phase fraction value in the grid node is 1, the fluid in the grid node is liquid; if the liquid phase fraction value is 0, the fluid in the grid node is solid; if the liquid phase fraction value is between 0 and 1, the fluid in the grid node is in a solid-liquid mixing state, where 0.5 corresponds to the solid-liquid interface.

[0043] The calculation parameters include calculation area, number of grids, time step, initial conditions and boundary conditions; In this embodiment, the calculation region refers to the size of the ice phase or the water phase.

[0044] In this implementation manner, the number of grids refers to the number of subdivided grids in the calculation area, which is used to discretize the control equations and main variables in the numerical calculation process.

[0045] In this implementation, the time step is the time interval between each time layer in the numerical calculation process.

[0046] In this embodiment, the initial conditions are the conditions at the initial moment of numerical calculation, including temperature, velocity, liquid fraction, etc.

[0047] In this embodiment, the boundary conditions are conditions at the boundaries of the computational domain and are used to specify variables such as temperature, velocity, and liquid fraction.

[0048] In this embodiment, the fluid under study is a Newtonian fluid with the characteristics of incompressibility, laminar flow, and transient state, and the density is considered to be constant over time, so the mass conservation equation is: ; In the formula and Both represent divergence.

[0049] It should be noted that the continuity equation (i.e., the mass conservation equation) is not solved separately, but is reflected in the process of solving the momentum equation. It is not solved separately, but is reflected in the process of solving the momentum equation. It is solved by executing the steps of solving the algebraic equation system to close the loop of solving the momentum equation.

[0050] In this embodiment, the output parameters include temperature, salinity, velocity, pressure and liquid fraction: The temperature 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; The salinity 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 analysis during the freezing process; The velocity is used to characterize how fast the fluid flows; The pressure is used to characterize the force acting on the contact surfaces of two objects; The liquid fraction is a basic parameter for distinguishing solid and liquid phases in the enthalpy-porosity method.

[0051] In this embodiment, the liquid fraction value and the latent heat value are iteratively solved, that is, the latent heat is updated.

[0052] In this embodiment, the liquid fraction value is used Indicates the phase state in the solid-liquid interface region; Liquid fraction value The relationship with temperature satisfies a certain functional relationship, and the linear relationship expression is:

[0053] In this embodiment, the liquid fraction value The expression of the relationship with temperature ( The relationship is not unique, and other relationships can be used according to specific physical problems.

[0054] In this embodiment, based on the liquid fraction value The relationship between the liquid fraction and the temperature shows that Temperature The range and value determine its size, and at the same time act on the source term of the energy equation, thus affecting the temperature The solution of , and the source term Contains variables The non-steady-state term 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, the energy equation contains two variables to be determined and .

[0055] It should be noted that directly adopting the above Relationship (i.e. liquid fraction value The linear relationship expression with temperature) is updated , which may easily lead to calculation divergence. In order to improve convergence, in this embodiment, the liquid fraction value (or latent heat value ) to iteratively solve to obtain the liquid fraction value consistent with the temperature solution at the current moment (or latent heat value ).

[0056] In addition, in one embodiment, the latent heat is iteratively solved as: The iterative expression of latent heat value is: ; In the formula, and are the discrete equation coefficients, is the inverse function of the latent heat update expression; is the latent heat value; In the isothermal phase change problem, the updated expression of the latent heat value is simplified to: ; ; ; In the formula, the superscript Represents the variable value of the current iteration step, superscript Represents the variable value of the previous iteration.

[0057] In addition, in one embodiment, when iteratively solving the liquid fraction value, only one iteration is required in the same layer: 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; Then call the algebraic equation group module (execute the algebraic equation group step), solve the energy equation to obtain the temperature value, determine the source term and the discrete equation coefficient, and solve the algebraic equation group to obtain the current temperature; The above process is iterated many times until the temperature and liquid fraction of the current time layer reach consistent convergence conditions.

[0058] It should be noted that, considering that the existing liquid fraction iteration method performs two iterations in the same time layer, the calculation efficiency is low; in this embodiment, only one iteration is required in the same time layer, which improves the calculation efficiency.

[0059] 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 calculation accuracy, the time step must still be taken as a smaller value when solving the specific discrete solution.

[0060] 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 area, it will hinder some solutes from entering the liquid phase area. 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 area. The diffusion of solutes in the liquid phase occurs evenly and does not require special attention.

[0061] It should be noted that the solutes in the liquid phase will move rapidly and then remain fixed. In order to study the specific process of solute redistribution at the microscopic scale, this embodiment uses a microscopic back-diffusion model selection component to select a suitable back-diffusion model to characterize the concentration distribution according to the characteristics of different materials, so as to better reflect the brine solidification or melting process.

[0062] More specifically, the microscopic anti-diffusion model selection component is used to select a corresponding anti-diffusion model according to ice condition information of the road section to be de-iced, with respect to solute redistribution and anti-diffusion phenomena at a microscopic scale during the solidification process of the solution.

[0063] The anti-diffusion model includes the Scheil-Gulliver model, the lever principle model, the Brody-Flemings model and the Ohnaka model.

[0064] In addition, in one embodiment, the reverse diffusion model is selected to characterize the concentration distribution, and the process is as follows: The representative elemental volume (REV) in the solid-liquid mixing area is selected as the research object; the liquid concentration in REV Uniform distribution; while a solute gradient will be formed in the solid phase. This solute diffusion in the solid phase is called back diffusion. The solute concentration distribution in the solid phase is uneven and needs to be obtained by integration; Define the characteristic length of the solid in a single REV as , the entire grid characteristic length is equal to half the typical secondary dendrite arm spacing length , when dendrite coarsening is not considered, the solid fraction is introduced. To interpolate, we can get the total concentration in the grid: ; In the formula, is the average solid concentration, obtained by integration; In order to consider the change of solute with time, the time partial derivative of the above formula is obtained: ; Among them, the first term on the right side of the equal sign in the above formula reflects the distribution of solutes at the solid-liquid interface, and the second term on the right side of the equal sign is the expression of the solute back diffusion rate. In the present invention, different expressions are selected for specific solutions, thereby reflecting the function of selecting different back diffusion models according to different material characteristics of the present invention. The solidification form of ice is determined according to the ice condition information of the road section to be de-iced. The solidification phenomenon in nature can be mainly divided into equilibrium solidification and non-equilibrium solidification. In different solidification types, the solute concentration in the liquid With solid fraction The relationship is different: In equilibrium solidification, the concentration of solute in the liquid and solid phases is uniformly distributed throughout the phase. The classical lever rule is used to describe the role of solute concentration in controlling phase change. ,but and The relationship is as follows: ; In the formula, is the mixed solute concentration, which is the initial value of the calculation or the converged value at the end of the previous time layer; The lever principle is concise and easy to handle the solidification process, but it assumes that the solid-liquid concentrations are uniformly distributed during the freezing process, that is, the back diffusion is infinite. In fact, the back diffusion in the eutectic solidification process such as brine freezing is limited, and it needs to be explained by the non-equilibrium theory that considers the back diffusion rate of the finite solute: Assuming that the solidification process does not consider back diffusion at all, that is, the solid phase diffusion rate is 0, it mainly represents the Scheil-Gulliver rapid solidification theory principle (Scheil-Gulliver model). and The relationship is as follows: ; Regardless of the initial concentration, the Scheil-Gulliver model can always be used to predict the eutectic solidification microstructure when The eutectic fraction can be obtained when : ; In reality, the diffusion of solutes in the mushy region is finite, neither infinite backdiffusion as assumed by the lever principle nor completely no backdiffusion as assumed by the Scheil-Gulliver principle; Therefore, it is necessary to deal with the local scale solute diffusion process in detail. A general reverse diffusion treatment method is proposed, and the rate of change of solute concentration in REV (reverse diffusion rate) is expressed as: ; In the formula, Defined as the anti-diffusion parameter, the value is generally within the scope; Pick As a constant, the liquid concentration can be further deduced With solid fraction The expression is: ; Anti-diffusion parameters Can reflect the degree of solute back diffusion: When , the solute does not diffuse at all, and the above formula becomes the expression of Scheil-Gulliver principle; When , the solute diffuses infinitely, and the above formula becomes the classical lever principle expression.

[0065] The above-mentioned reverse diffusion model can simulate the heat-solute double diffusion convection process of salt water freezing on the road surface, and reflect the coupling effect of interface dynamics and heat-solute driving force. The accurate concentration value in the fluid domain can be used to correct the latent heat update method in the source term of the energy equation, which can realize the lateral solidification process of the salt water solution.

[0066] In this embodiment, the control equation refers to the discrete form of the concentration equation.

[0067] Additionally, in one embodiment, the grid coordinate module uses a finite volume method to establish a structured staggered grid system to make the velocity solution more stable.

[0068] In this embodiment, the preferential path of salt separation refers to the preferential flow direction of salt in the gaps between ice crystals during solidification or melting, and the salt is aggregated from the solution in the gaps between ice crystals to form brine cells.

[0069] In this embodiment, the salt distribution refers to the salt distribution of ice or water in the simulated area after melting or solidification, which is displayed by a salinity cloud map.

[0070] In this embodiment, the tendency to accumulate along the road surface refers to the distribution of salinity in ice on the road surface area obtained based on the salt distribution, and the salinity in ice under a specific salt spreading amount is obtained.

[0071] In this embodiment, the ice melting time refers to the time it takes for the ice in the simulated area to completely melt into water.

[0072] In this embodiment, the reasonable value of salt concentration used for deicing is the final result to be obtained and is also an important parameter guiding the road deicing process. A reasonable amount of salt can not only make the ice solidify at a temperature lower than 0°C or even melt at ambient temperature, but also ensure that the amount of salt will not be too much and corrode the road surface; at the same time, it can also avoid the problem of refreezing after melting, and judge how to prevent the salt water from freezing according to the ambient temperature.

[0073] In this embodiment, the technical principle of the brine mixing deicing method based on the microscopic back-diffusion model is a macroscopic eutectic solidification simulation method based on consideration of the microscopic back-diffusion phenomenon.

[0074] The macroscopic eutectic solidification simulation method considering the microscopic back-diffusion phenomenon is used to simulate the phase change process of pure substances or binary solutions in a two-dimensional square cavity. When simulating the phase change of a binary solution, a suitable back-diffusion model can be selected according to the solidification characteristics of the material, so as to consider the influence of the microscopic solute redistribution phenomenon in the macroscopic solidification process of the solution; the square cavity calculation area can also be set, and a single boundary or multiple boundaries and multiple heat sources or cold sources can be set in the square cavity to complete the melting / solidification simulation.

[0075] The process of the macroscopic eutectic solidification simulation method considering the microscopic back-diffusion phenomenon is as follows: Figure 2 shown.

[0076] In addition, in one embodiment, a brine mixing deicing device based on a microscopic reverse diffusion model is provided: The device comprises the following modules: Input module: used to obtain input parameters of the brine solidification or melting model according to the ice condition information of the road section to be de-iced; The ice condition information of the road section to be de-iced includes ambient temperature, ice thickness, ice type, and ice mechanical properties; The ambient temperature includes the ice surface temperature of the road section to be de-iced and the real-time temperature of the air in the road section to be de-iced; Solution module: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the input parameters of the brine solidification or melting model to obtain output parameters; The momentum equation is a momentum conservation equation, which complies with the fluid equation established by Newton's second law; The energy equation is an energy conservation equation, which complies with the first law of thermodynamics and takes temperature as the variable to be determined; The concentration equation is a component conservation equation that complies with the component mass conservation law and is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to achieve a closed loop when solving the algebraic equation system in a discrete solution of the momentum equation; The output parameters are temperature, salinity, velocity, pressure and liquid fraction; The solution module includes a solid-liquid interface tracking component, a microscopic anti-diffusion model selection component and a general component; The solid-liquid interface tracking component is used to solve the energy equation and obtain the temperature and liquid fraction values ​​of each grid node in the calculation area; wherein the source term of the energy equation includes the transient term of the liquid fraction; and is also used to iteratively solve the liquid fraction value and latent heat value to obtain the liquid fraction value and latent heat value consistent with the temperature solution at the current moment; The microscopic anti-diffusion model selection component is used to solve the concentration equation using a fully implicit time integration scheme; it is also used to select an anti-diffusion model to characterize the concentration distribution; The general components include an algebraic equation group module, a control equation discrete coefficient module, a grid coordinate module, and a coefficient zeroing module, wherein: The algebraic equations module is used to be called by the solid-liquid interface tracking component and the microscopic anti-diffusion model selection component to solve the discrete momentum, temperature and concentration equations; The control equation discrete coefficient module is used to solve the coefficients of the velocity and pressure equations in the momentum equation and provide input for the algebraic equation group module; The grid coordinate module is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing module is used to reset the coefficients of the control equation to zero before the discrete coefficient module of the control equation is called, so as to reuse the discrete coefficient module of the control equation; Output module: used to obtain output results according to output parameters; The output results are the preferred paths of salt analysis, salt distribution, accumulation patterns along the road surface, ice melting time, and reasonable salt concentration values ​​for de-icing.

[0077] It should be noted that the continuity equation (i.e., the mass conservation equation) is not solved separately, but is reflected in the process of solving the momentum equation. It is not solved separately, but is reflected in the process of solving the momentum equation. It is solved by calling the algebraic equation solving module to close the loop of the solution of the momentum equation.

[0078] 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. A brine mixing deicing method based on a microscopic back-diffusion model, characterized in that: The method comprises the following steps: Input step: used to obtain input parameters of the brine solidification or melting model according to ice condition information of the road section to be de-iced; The ice condition information of the road section to be de-iced includes ambient temperature, ice thickness, ice type, and ice mechanical properties; The ambient temperature includes the ice surface temperature of the road section to be de-iced and the real-time temperature of the air in the road section to be de-iced; Solving step: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the input parameters of the brine solidification or melting model to obtain output parameters; The momentum equation is a momentum conservation equation, which complies with the fluid equation established by Newton's second law; The energy equation is an energy conservation equation, which complies with the first law of thermodynamics and takes temperature as the variable to be determined; The concentration equation is a component conservation equation that complies with the component mass conservation law and is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to achieve a closed loop when solving the algebraic equation system in a discrete solution of the momentum equation; The output parameters are temperature, salinity, velocity, pressure and liquid fraction; The solving step includes a solid-liquid interface tracking step, a microscopic anti-diffusion model selection step and a general component step; The solid-liquid interface tracking step is used to solve the energy equation and obtain the temperature and liquid fraction values ​​of each grid node in the calculation area; wherein the source term of the energy equation includes a transient term of the liquid fraction; and is also used to iteratively solve the liquid fraction value and latent heat value to obtain the liquid fraction value and latent heat value consistent with the temperature solution at the current moment; The microscopic anti-diffusion model selection step is used to solve the concentration equation using a fully implicit time integration scheme; and is also used to select an anti-diffusion model to characterize the concentration distribution; The general component steps include algebraic equation group steps, control equation discrete coefficient steps, grid coordinate steps, and coefficient zeroing steps, wherein: The algebraic equations step is used to be called and executed by the solid-liquid interface tracking step and the microscopic anti-diffusion model selection step to solve the discretized momentum, temperature and concentration equations; The control equation discrete coefficient step is used to solve the coefficients of the velocity and pressure equations in the momentum equation and provide input for the algebraic equations 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 reset the coefficients of the control equation to zero before the discrete coefficient step of the control equation is executed, so as to repeatedly execute the discrete coefficient step of the control equation; Output step / module: used to obtain output results according to output parameters; The output results are the preferred paths of salt analysis, salt distribution, accumulation patterns along the road surface, ice melting time, and reasonable salt concentration values ​​for de-icing.

2. The brine mixing deicing method based on the microscopic back-diffusion model according to claim 1 is characterized in that: The output parameters include temperature, salinity, velocity, pressure and liquid fraction: The temperature 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; The salinity 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 analysis during the freezing process; The velocity is used to characterize how fast the fluid flows; The pressure is used to characterize the force acting on the contact surfaces of two objects; The liquid fraction is a basic parameter for distinguishing solid and liquid phases in the enthalpy-porosity method.

3. The brine mixing deicing method based on the microscopic back-diffusion model according to claim 1 is characterized in that: The latent heat is iteratively solved as: The iterative expression of latent heat value is: ； In the formula, and are the discrete equation coefficients, is the inverse function of the latent heat update expression; is the latent heat value; In the isothermal phase change problem, the updated expression of the latent heat value is simplified to: ； ； ； In the formula, the superscript Represents the variable value of the current iteration step, superscript Represents the variable value of the previous iteration.

4. The brine mixing deicing method based on the microscopic back-diffusion model according to claim 1 is characterized in that: When iterating the solution of the liquid fraction value, only one iteration is required in the same layer: 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; Then call the algebraic equation group module (execute the algebraic equation group step), solve the energy equation to obtain the temperature value, determine the source term and the discrete equation coefficient, and solve the algebraic equation group to obtain the current temperature; The above process is iterated many times until the temperature and liquid fraction of the current time layer reach consistent convergence conditions.

5. The brine mixing deicing method based on the microscopic back-diffusion model according to claim 1 is characterized in that: The anti-diffusion models include: Scheil-Gulliver model, lever principle model, Brody-Flemings model and Ohnaka model.

6. The brine mixing deicing method based on the microscopic back-diffusion model according to claim 1 is characterized in that: The grid coordinate module uses the finite volume method to establish a structured staggered grid system to make the velocity solution more stable.

7. A brine mixing deicing device based on a microscopic back-diffusion model, characterized in that: The device comprises the following modules: Input module: used to obtain input parameters of the brine solidification or melting model according to the ice condition information of the road section to be de-iced; The ice condition information of the road section to be de-iced includes ambient temperature, ice thickness, ice type, and ice mechanical properties; The ambient temperature includes the ice surface temperature of the road section to be de-iced and the real-time temperature of the air in the road section to be de-iced; Solution module: used to solve the momentum equation, energy equation, concentration equation and continuity equation according to the input parameters of the brine solidification or melting model to obtain output parameters; The momentum equation is a momentum conservation equation, which complies with the fluid equation established by Newton's second law; The energy equation is an energy conservation equation, which complies with the first law of thermodynamics and takes temperature as the variable to be determined; The concentration equation is a component conservation equation that complies with the component mass conservation law and is used to obtain the concentration parameter value in the calculation domain; The continuity equation is a mass conservation equation, which is used to achieve a closed loop when solving the algebraic equation system in a discrete solution of the momentum equation; The output parameters are temperature, salinity, velocity, pressure and liquid fraction; The solution module includes a solid-liquid interface tracking component, a microscopic anti-diffusion model selection component and a general component; The solid-liquid interface tracking component is used to solve the energy equation and obtain the temperature and liquid fraction values ​​of each grid node in the calculation area; wherein the source term of the energy equation includes the transient term of the liquid fraction; and is also used to iteratively solve the liquid fraction value and latent heat value to obtain the liquid fraction value and latent heat value consistent with the temperature solution at the current moment; The microscopic anti-diffusion model selection component is used to solve the concentration equation using a fully implicit time integration scheme; it is also used to select an anti-diffusion model to characterize the concentration distribution; The general components include an algebraic equation group module, a control equation discrete coefficient module, a grid coordinate module, and a coefficient zeroing module, wherein: The algebraic equations module is used to be called by the solid-liquid interface tracking component and the microscopic anti-diffusion model selection component to solve the discrete momentum, temperature and concentration equations; The control equation discrete coefficient module is used to solve the coefficients of the velocity and pressure equations in the momentum equation and provide input for the algebraic equation group module; The grid coordinate module is used to divide the calculation area and define the grid coordinate values ​​in the calculation area; The coefficient zeroing module is used to reset the coefficients of the control equation to zero before the discrete coefficient module of the control equation is called, so as to reuse the discrete coefficient module of the control equation; Output module: used to obtain output results according to output parameters; The output results are the preferred paths of salt analysis, salt distribution, accumulation patterns along the road surface, ice melting time, and reasonable salt concentration values ​​for de-icing.

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 brine mixing deicing method based on the microscopic counter-diffusion model as described in any one of claims 1 to 6 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 brine mixing deicing method based on the microscopic counter-diffusion model described in any one of claims 1 to 6 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 brine mixing deicing method based on a microscopic counter-diffusion model described in any one of claims 1 to 6 are implemented.

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