Numerical simulation method of liquid metal flow and heat transfer under thermal electromagnetic multi-field coupling

Abstract

The present application relates to the technical field of numerical simulation of thermoelectric electromagnetic field, and particularly relates to a numerical simulation method of liquid metal flow and heat transfer under thermoelectric electromagnetic multi-field coupling. The method comprises the following steps: obtaining the metal structure physical parameters of the metal to be measured and the thermoelectric electromagnetic multi-field coupling simulation control parameters; performing liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation and verification based on the metal structure physical parameters and the thermoelectric electromagnetic multi-field coupling simulation control parameters to generate liquid metal thermoelectric electromagnetic multi-field reaction verification data; setting single-factor working condition simulation control parameters; performing working condition simulation of liquid metal flow and heat transfer global characteristic analysis on the liquid metal thermoelectric electromagnetic multi-field reaction verification data based on the single-factor working condition simulation control parameters to establish a liquid metal thermoelectric electromagnetic flow and heat transfer global characteristic model. The present application realizes comprehensive and accurate analysis of the liquid metal flow and heat transfer in a closed cavity under the thermoelectric electromagnetic coupling effect of various factors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical simulation of thermoelectric magnetic field, and in particular to a numerical simulation method for liquid metal flow and heat transfer under thermoelectric magnetic multi-field coupling. BACKGROUND

[0002] In the industrial fields of magnetic confinement fusion reactor liquid blanket and electromagnetic metallurgy, the flow and heat transfer of liquid metal in a closed cavity under thermoelectric magnetic multi-field coupling are very common. The flow and heat transfer process of liquid metal under the thermoelectric magnetic multi-field coupling environment is very complex, involving the interaction of multiple physical effects. Simulations that only consider idealized flow without considering heat transfer, electromagnetic and other factors cannot accurately reflect the actual situation. Through numerical simulation technology, these factors can be considered comprehensively to accurately describe the flow and heat transfer process of liquid metal. However, the magnetohydrodynamic problem in the existing liquid metal multi-field simulation method is a difficult problem in computational fluid dynamics, which is difficult to ensure mass, momentum and charge conservation under multi-field reaction, and is prone to inaccurate calculation or solution divergence. Moreover, the flow and heat transfer of the thermoelectric magnetic convection system are affected by many factors such as magnetic field strength, magnetic field direction, liquid metal Seebeck coefficient, wall conductivity, etc., making it difficult to fully grasp the variation of heat transfer efficiency with each parameter. SUMMARY

[0003] Therefore, the present application provides a numerical simulation method for liquid metal flow and heat transfer under thermoelectric magnetic multi-field coupling to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a numerical simulation method for liquid metal flow and heat transfer under thermoelectric magnetic multi-field coupling comprises the following steps:

[0005] Step S1: Obtain the metal structure physical parameters of the metal to be tested and the thermoelectric magnetic multi-field coupling simulation control parameters; perform liquid metal thermoelectric magnetic multi-field reaction numerical simulation analysis based on the metal structure physical parameters and the thermoelectric magnetic multi-field coupling simulation control parameters to generate liquid metal thermoelectric magnetic multi-field reaction numerical simulation data;

[0006] Step S2: Perform liquid metal thermoelectric magnetic multi-field reaction verification processing based on the liquid metal thermoelectric magnetic multi-field reaction numerical simulation data to generate liquid metal thermoelectric magnetic multi-field reaction verification data;

[0007] Step S3: Set single-factor working condition simulation control parameters; perform liquid metal flow and heat transfer characteristic correlation analysis of each working condition based on the single-factor working condition simulation control parameters on the liquid metal thermoelectric magnetic multi-field reaction verification data to generate liquid metal working condition influence-flow and heat transfer characteristic correlation data, and establish a liquid metal multi-source working condition-flow and heat transfer correlation model through the liquid metal working condition influence-flow and heat transfer characteristic correlation data;

[0008] Step S4: stress non-dimensional characteristic-heat transfer efficiency influence characteristic data of the multi-source working condition are generated based on the stress non-dimensional characteristic-heat transfer efficiency influence characteristic analysis of the liquid metal multi-source working condition-flow heat transfer correlation model; the liquid metal thermoelectric electromagnetic flow heat transfer global characteristic model is established through the stress non-dimensional characteristic-heat transfer efficiency influence characteristic data of the multi-source working condition and the liquid metal multi-source working condition-flow heat transfer correlation model;

[0009] Step S5: the intelligent derivation of the liquid metal flow heat transfer of the thermoelectric electromagnetic multi-field coupling is performed through the liquid metal thermoelectric electromagnetic flow heat transfer global characteristic model.

[0010] Further, step S1 comprises the following steps:

[0011] Step S11: the metal structure physical parameters of the metal to be measured are acquired;

[0012] Step S12: the thermoelectric electromagnetic multi-field coupling simulation control parameters are acquired, wherein the thermoelectric electromagnetic multi-field coupling simulation parameters comprise temperature field simulation control parameters, electric field simulation control parameters, magnetic field simulation control parameters and integrated flow field simulation parameters;

[0013] Step S13: the geometric modeling process is performed through the metal structure physical parameters, and a metal structure geometric model is generated;

[0014] Step S14: the liquid metal flow heat transfer calculation domain is designed based on the thermoelectric electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, and the liquid metal flow heat transfer calculation domain data are generated;

[0015] Step S15: the liquid metal thermoelectric electromagnetic multi-field reaction numerical preliminary simulation analysis is performed on the metal structure geometric model based on the thermoelectric electromagnetic multi-field coupling simulation control parameters and the liquid metal flow heat transfer calculation domain data, and the preliminary liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data are generated;

[0016] Step S16: the thermoelectric electromagnetic numerical simulation compatible conservation optimization processing is performed on the preliminary liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data, and the liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data are generated.

[0017] Further, step S14 comprises the following steps:

[0018] Step S141: the liquid metal flow heat transfer calculation domain boundary constraint data are set according to the thermoelectric electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model;

[0019] Step S142: defining liquid metal flow and heat transfer region according to the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, obtaining liquid metal analysis region data, and performing region fixed-point identification on the liquid metal analysis region data to generate analysis region fixed-point identification data; performing region complex characteristic analysis according to the analysis region fixed-point identification data to generate analysis region complex characteristic data;

[0020] Step S143: performing unstructured grid design of the metal to be tested according to the analysis region fixed-point identification data and the analysis region complex characteristic data to generate unstructured grid data of the metal to be tested;

[0021] Step S144: designing a liquid metal flow and heat transfer calculation domain based on the liquid metal flow and heat transfer calculation domain boundary constraint data and the unstructured grid data of the metal to be tested to generate liquid metal flow and heat transfer calculation domain data.

[0022] Further, step S16 includes the following steps:

[0023] Step S161: performing numerical discretization processing on the preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data to generate liquid metal thermal electromagnetic multi-field reaction discretization data;

[0024] Step S162: extracting current, potential, and thermal-electric boundary reaction characteristics according to the liquid metal thermal electromagnetic multi-field reaction discretization data to generate liquid metal current-potential-thermal-electric boundary reaction characteristic data;

[0025] Step S163: performing Seebeck effect characteristic analysis of liquid metal current-potential-thermal-electric boundary reaction on the liquid metal current-potential-thermal-electric boundary reaction characteristic data to generate Seebeck effect characteristic data of liquid metal current-potential-thermal-electric boundary reaction;

[0026] Step S164: performing compatible conservation optimization processing of liquid metal thermal electromagnetic multi-field reaction on the liquid metal thermal electromagnetic multi-field reaction discretization data based on the Seebeck effect characteristic data of liquid metal current-potential-thermal-electric boundary reaction to generate liquid metal thermal electromagnetic multi-field reaction numerical simulation data.

[0027] Further, step S2 includes the following steps:

[0028] Step S21: acquiring liquid metal thermal field driving simulation data, liquid metal electric field driving simulation data, and liquid metal magnetic field driving simulation data based on the liquid metal thermal electromagnetic multi-field reaction numerical simulation data, respectively;

[0029] Step S22: modeling a closed cavity for liquid metal simulation by using the liquid metal thermal electromagnetic multi-field reaction numerical simulation data to obtain a liquid metal simulation closed cavity model;

[0030] Step S23: Perform closed cavity convection and heat distribution verification analysis of the liquid metal simulation closed cavity model in the absence of a magnetic field using the liquid metal thermal field driving simulation data and the liquid metal electric field driving simulation data to obtain closed cavity flow field heat distribution verification data in the absence of a magnetic field;

[0031] Step S24: Perform magnetic field closed cavity convection and heat distribution verification analysis of the closed cavity flow field heat distribution data in the absence of a magnetic field using the liquid metal magnetic field driving simulation data to obtain magnetic field closed cavity flow field heat distribution verification data;

[0032] Step S25: Perform magnetic field closed cavity thermal current analysis on the magnetic field closed cavity flow field heat distribution verification data, generate magnetic field closed cavity thermal current data, and perform closed cavity thermal electromagnetic coupling effect flow and heat distribution verification analysis on the magnetic field closed cavity flow field heat distribution verification data through the magnetic field closed cavity thermal current data, generate thermal electromagnetic effect closed cavity flow field heat distribution verification data;

[0033] Step S26: Perform liquid metal thermal electromagnetic multi-field reaction verification processing based on the closed cavity flow field heat distribution verification data in the absence of a magnetic field, the magnetic field closed cavity flow field heat distribution verification data, and the thermal electromagnetic effect closed cavity flow field heat distribution verification data to generate liquid metal thermal electromagnetic multi-field reaction verification data.

[0034] Further, step S3 includes the following steps:

[0035] Step S31: Set single-factor working condition simulation control parameters, wherein the single-factor working condition simulation control parameters include magnetic field strength simulation control parameters, magnetic field direction simulation control parameters, Seebeck effect simulation control parameters, and wall surface electrical conductivity simulation control parameters;

[0036] Step S32: Perform single-factor working condition simulation control processing of the liquid metal thermal electromagnetic reaction based on the single-factor working condition simulation control parameters on the liquid metal thermal electromagnetic multi-field reaction verification data to generate liquid metal thermal electromagnetic single-factor working condition simulation data;

[0037] Step S33: Perform liquid metal flow and heat transfer characteristic analysis of each working condition thermal electromagnetic reaction according to the liquid metal thermal electromagnetic single-factor working condition simulation data to generate liquid metal flow and heat transfer characteristic data of each working condition thermal electromagnetic reaction;

[0038] Step S34: Perform liquid metal flow and heat transfer characteristic correlation processing of each working condition influence according to the liquid metal flow and heat transfer characteristic data of each working condition thermal electromagnetic reaction to generate liquid metal working condition influence-flow and heat transfer characteristic correlation data;

[0039] Step S35: liquid metal flow heat transfer correlation data modeling processing of the liquid metal flow heat transfer correlation data of the multi-source working condition thermoelectric magnetic reaction is performed through the liquid metal working condition influence-flow heat transfer characteristic correlation data, and a liquid metal multi-source working condition-flow heat transfer correlation model is generated.

[0040] Further, step S32 includes the following steps:

[0041] Step S321: wall surface characteristic division processing is performed on the wall surface conductivity simulation control parameters, and wall surface insulation simulation control parameters and wall surface conductive simulation control parameters are respectively obtained;

[0042] Step S322: wall surface insulation and wall surface non-insulation differential current transmission characteristic analysis is performed based on the wall surface insulation simulation control parameters and the wall surface conductive simulation control parameters, and wall surface differential current characteristic data is generated;

[0043] Step S323: wall surface differential magnetic damping effect characteristic analysis is performed according to the wall surface differential current characteristic data, and wall surface differential magnetic damping effect characteristic data is generated;

[0044] Step S324: liquid metal thermoelectric magnetic single-factor working condition simulation control processing of the liquid metal thermoelectric magnetic multi-field reaction verification data is performed through the wall surface differential magnetic damping effect characteristic data and the single-factor working condition simulation control parameters, and liquid metal thermoelectric magnetic single-factor working condition simulation data is generated.

[0045] Further, step S35 includes the following steps:

[0046] According to the liquid metal working condition influence-flow heat transfer characteristic correlation data, composite working condition influence characteristic analysis is performed, and liquid metal composite working condition influence characteristic data is generated; liquid metal flow heat transfer correlation data modeling processing of the liquid metal flow heat transfer correlation data of the multi-source working condition thermoelectric magnetic reaction is performed through the liquid metal composite working condition influence characteristic data and the liquid metal working condition influence-flow heat transfer characteristic correlation data, and a liquid metal multi-source working condition-flow heat transfer correlation model is generated.

[0047] Further, step S4 includes the following steps:

[0048] Step S41: liquid metal multi-source working condition stress characteristic analysis is performed according to the liquid metal multi-source working condition-flow heat transfer correlation model, and liquid metal multi-source working condition stress characteristic data is generated;

[0049] Step S42: multi-source working condition stress dimensionless characteristic analysis is performed based on the liquid metal multi-source working condition stress characteristic data, and multi-source working condition stress dimensionless characteristic data is generated;

[0050] Step S43: Perform stress dimensionless characteristic and heat transfer efficiency influence characteristic analysis on the multi-source working condition according to the multi-source working condition stress dimensionless characteristic data, and generate multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data;

[0051] Step S44: Map the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data to the liquid metal multi-source working condition-flow heat transfer correlation model to perform liquid metal thermoelectric-magnetic flow heat transfer global characteristic modeling processing, and generate a liquid metal thermoelectric-magnetic flow heat transfer global characteristic model.

[0052] Further, the liquid metal multi-source working condition stress characteristic data in step S41 includes liquid metal multi-source working condition magnetic damping force effect data, liquid metal multi-source working condition thermoelectric-magnetic force effect data, and liquid metal multi-source working condition buoyancy effect data.

[0053] The application can provide accurate basic data support for subsequent numerical simulation by obtaining the metal structure physical parameters of the to-be-tested metal and the thermal electromagnetic multi-field coupling simulation control parameters, and ensure the pertinence and reliability of the simulation analysis. The steps of geometric modeling and calculation domain design can construct the actual metal structure model and the flow and heat transfer calculation range, and define reasonable boundaries for simulation analysis. The compatible conservation optimization processing, especially the optimization of discretized data combined with the characteristics of the Seebeck effect, effectively solves the problems of inaccurate Lorentz force solution and difficulty in guaranteeing mass and momentum conservation in the calculation of magnetohydrodynamics when the Hartmann number is large, and improves the accuracy and stability of the numerical simulation results, providing high-quality preliminary simulation data for subsequent research. The simulation data of different field driving are collected in stages, and the closed cavity modeling verification is carried out, which can systematically verify the effectiveness of the numerical simulation results. From no magnetic field to magnetic field to flow field heat distribution verification under the effect of thermal electromagnetic coupling, the step-by-step verification process can comprehensively check possible errors in the simulation process, and ensure the reliability of the simulation data under different scenarios. At the same time, the verification process can intuitively reflect the influence of thermal electromagnetic multi-field coupling on the flow and heat transfer of liquid metal, lay a solid data foundation for subsequent analysis of the action law of each factor, and reduce the risk of inaccurate research conclusions caused by data deviation. By setting single-factor working condition simulation control parameters (covering key factors such as magnetic field strength, magnetic field direction, Seebeck effect and wall conductivity), the influence of a single variable on the flow and heat transfer of liquid metal can be analyzed, avoiding the problem of blurred rules caused by multiple factors. With the help of characteristic data generated by single-factor simulation control, the core features of flow and heat transfer under each working condition (such as velocity distribution and temperature gradient) can be clearly extracted. The multi-source working condition-flow and heat transfer correlation model established through correlation analysis can further integrate the action law of single factor and composite factor, and intuitively present the corresponding relationship between different working condition parameters and flow and heat transfer characteristics, providing structured data support for subsequent revealing of the influence mechanism of each factor on the thermal electromagnetic coupling system, and laying a foundation for accurately predicting the flow and heat transfer state under a specific working condition. Based on the multi-source working condition-flow and heat transfer correlation model, the stress characteristics of liquid metal under multi-source working conditions (including magnetic damping force, thermal electromagnetic force and buoyancy) are analyzed in depth, and the complex stress is converted into quantifiable characteristic parameters through dimensionless characteristic analysis, solving the problem that the stress and heat transfer efficiency under different working conditions are difficult to compare directly. The correlation between the dimensionless characteristics of stress and the heat transfer efficiency can determine the influence law of the relative strength of the magnetic damping effect and the thermal electromagnetic dynamics effect on the heat transfer efficiency, and the finally generated global characteristic model can integrate the flow and heat transfer law under the action of multiple factors, realize the accurate characterization of the overall characteristics of the thermal electromagnetic multi-field coupling system, and provide quantitative basis and theoretical support for efficient regulation and control of heat transfer efficiency.The intelligent derivation job can be performed through the liquid metal thermoelectric magnetic flow heat transfer global characteristic model, the established multi-factor correlation law and global characteristics can be utilized to quickly respond to different thermoelectric magnetic working condition parameter inputs, and the corresponding liquid metal flow heat transfer state (such as a velocity field, temperature field distribution and heat transfer efficiency) can be derived efficiently without repeatedly performing complex numerical simulation calculation, the analysis efficiency is greatly improved, meanwhile, the reliability of the derivation result in the multi-factor coupling scene can be ensured relying on the global characteristics of the model, and convenient and accurate decision support tools are provided for design optimization, parameter debugging of a magnetic confinement fusion reactor blanket, electromagnetic metallurgy and other related industrial fields.

[0054] The numerical simulation method for liquid metal flow heat transfer under thermoelectric magnetic multi-field coupling has the advantages that the current, electric potential and thermoelectric boundary considering the Peltier effect are processed based on a compatible conservation format, and the correction of non-orthogonal grids and oblique grids is added when solving the pressure and electric potential Poisson equation, so that the problems of inaccurate solution of Lorentz force, difficulty in guaranteeing mass and momentum conservation and easy divergence in the solution in the magneto-hydrodynamic calculation when the Hartmann number is large are effectively solved, and the accuracy and stability of numerical simulation are improved. Single-factor working condition simulation control parameters are set, the influences of factors such as magnetic field strength, magnetic field direction, liquid metal Peltier coefficient and wall conductivity are studied, and a multi-source working condition-flow heat transfer correlation model is established, so that the defects of insufficient system research on these factors in the prior art are overcome, and the influence law of each factor on the liquid metal flow and heat transfer under thermoelectric magnetic coupling can be clearly revealed. The stress characteristics of the multi-source working condition are analyzed, and a dimensionless characteristic parameter is extracted, a characteristic parameter representing the relative size of the magnetic damping effect and the thermoelectric magnetic dynamic effect is established, the variation law of the heat transfer efficiency with each parameter is determined, and strong theoretical guidance is provided for related industrial applications. The liquid metal thermoelectric magnetic flow heat transfer global characteristic model can efficiently perform flow heat transfer intelligent derivation job, and provides accurate support for realizing the flow velocity and heat transfer characteristics in the liquid metal convection. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 It is a step flow schematic diagram of the numerical simulation method for liquid metal flow heat transfer under thermoelectric magnetic multi-field coupling.

[0056] Figure 2 It is a step flow schematic diagram of the numerical simulation method for liquid metal flow heat transfer under thermoelectric magnetic multi-field coupling. Figure 1 It is a detailed implementation step flow schematic diagram of step S2 in the method.

[0057] Figure 3 It is a detailed implementation step flow schematic diagram of step S3 in the method. Figure 1 It is a detailed implementation step flow schematic diagram of step S3 in the method.

[0058] The implementation, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0059] The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0060] In addition, the drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0061] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 The present application provides a numerical simulation method for liquid metal flow heat transfer under thermal electromagnetic multi-field coupling. In the embodiments of the present application, please refer to Figure 1 Fig. 1 is a schematic diagram of the step flow of the numerical simulation method for liquid metal flow heat transfer under thermal electromagnetic multi-field coupling according to the present application. The numerical simulation method for liquid metal flow heat transfer under thermal electromagnetic multi-field coupling includes the following steps:

[0062] Based on this, the present application provides a numerical simulation method for liquid metal flow heat transfer under thermal electromagnetic multi-field coupling to solve at least one of the above-mentioned technical problems.

[0063] To achieve the above-mentioned purpose, a numerical simulation method for liquid metal flow heat transfer under thermal electromagnetic multi-field coupling includes the following steps:

[0064] Step S1: Obtain the metal structure physical parameters of the metal to be tested and the thermal electromagnetic multi-field coupling simulation control parameters; based on the metal structure physical parameters and the thermal electromagnetic multi-field coupling simulation control parameters, perform numerical simulation analysis of the liquid metal thermal electromagnetic multi-field reaction to generate numerical simulation data of the liquid metal thermal electromagnetic multi-field reaction;

[0065] In the embodiments of the present application, the metal structure physical parameters of the metal to be tested are obtained. For example, for liquid lithium-lead alloy, the relevant parameters are measured by experiment in a 25℃ constant temperature environment: the density is 2600 kg / m3 measured by density bottle method, the dynamic viscosity is 0.0012 Pa·s measured by rotary viscometer, the thermal conductivity is 35 W / (m·K) measured by hot-wire method, and the electrical conductivity is 2.8 x 106 S / m, the Seebeck coefficient is 18 μV / K measured by the thermoelectric electromotive force measuring device; and the geometric parameters of the metal to be measured are recorded. Obtain the thermal electromagnetic multi-field coupling simulation control parameters: the temperature field is set to 350 K at the bottom wall and 300 K at the top wall (temperature difference 50 K); the electric field is set to the normal component of the wall induced current as 0; the magnetic field is set to a uniform magnetic field of 0.2 T along the y axis; the flow field is set to a time step of 0.005 s, a simulation time of 20 s, and a convergence criterion of less than 10 -5 . According to the physical parameters of the metal structure, a three-dimensional modeling tool is used to build a geometric model of the metal, such as the geometric condition of the metal driven from solid to liquid state, to ensure that the size error is ≤0.001 m. Based on the simulation control parameters and the geometric model, a calculation domain is designed: the boundary constraints are 350 K at the bottom and 300 K at the top, the side is adiabatic, and the wall flow rate is 0; the flow area is divided into a 0.28 m×0.28 m×0.28 m space inside the cavity, a 0.005 m area near the wall is meshed with 0.0005 m, and the inside is meshed with 0.002 m, with a total grid number of 1.4 million. Based on the control parameters and the calculation domain data, a preliminary simulation is performed using the finite volume method to solve the momentum, energy and electromagnetic field equations, and the flow rate, temperature and other data are recorded every 0.1 s. The preliminary simulation data are optimized for consistency and conservation, the boundary current, electric potential and temperature gradient are extracted, the Seebeck effect (such as the correlation between the bottom grid current density 100 A / square meter and the temperature gradient 1000 K / m) is analyzed, and the Lorentz force calculation is corrected to satisfy the mass and momentum conservation, and finally the liquid metal thermal electromagnetic multi-field reaction numerical simulation data are generated.

[0066] Step S2: Based on the liquid metal thermal electromagnetic multi-field reaction numerical simulation data, a liquid metal thermal electromagnetic multi-field reaction verification process is performed to generate liquid metal thermal electromagnetic multi-field reaction verification data;

[0067] In the embodiment of the present application, based on the numerical simulation data of liquid metal thermal electromagnetic multi-field reaction, three types of driving data are collected: thermal field data includes temperature change from 300K to 350K in 0-20s and corresponding flow rate; electric field data includes current density, electric potential and related flow rate; magnetic field data includes Lorentz force, magnetic field strength and affected flow rate, all data are stored according to time and space classification. Through the geometric parameters in the simulation data, a simulation closed cavity model is constructed by using a three-dimensional modeling method, the cavity structure, wall material and flow area are restored, and it is ensured that the geometric parameters are consistent with the simulation data. Using thermal field and electric field data, a non-magnetic field environment is set in the model to simulate natural convection, the deviation of the simulation value 0.12m / s of the center flow rate of a certain cross section from the theoretical value 0.11m / s is 9%, the thermal distribution conforms to the heat conduction law, and non-magnetic field verification data is generated. By loading 0.2T magnetic field parameters, the simulation obtains that the flow rate decreases from 0.12m / s to 0.07m / s, and the temperature gradient increases from 1000K / m to 1200K / m, and the deviation compared with the experimental data is ≤8%, and magnetic field verification data is generated. The thermal current analysis is performed on the magnetic field verification data, the thermal current density 21.6mA / square meter is calculated according to the Seebeck coefficient 18μV / K and the temperature gradient 1200K / m, the simulation obtains that the flow rate rises to 0.085m / s and the temperature gradient decreases to 1100K / m, the rationality of the coupling effect is verified, and thermal electromagnetic effect verification data is generated. The simulation verification data of each group of reactions is integrated to generate liquid metal thermal electromagnetic multi-field reaction verification data.

[0068] Step S3: setting single-factor working condition simulation control parameters; based on the single-factor working condition simulation control parameters, the liquid metal thermal electromagnetic multi-field reaction verification data is subjected to liquid metal flow heat transfer characteristic correlation analysis of the influence of each working condition, to generate liquid metal working condition influence-flow heat transfer characteristic correlation data, and to establish a liquid metal multi-source working condition-flow heat transfer correlation model through the liquid metal working condition influence-flow heat transfer characteristic correlation data;

[0069] In the embodiment of the present application, the influence factors that need to be analyzed separately are determined first, different change states are set for each factor, and only one factor is changed each time, and other conditions remain unchanged. For example, the magnetic field direction, the Seebeck effect related properties and the wall surface conductivity are fixed first, and only the strength of the magnetic field is changed; then the strength of the magnetic field and other conditions are fixed, and only the direction of the magnetic field is changed; then the Seebeck effect related properties and the wall surface conductivity are changed separately in the same way. Based on these set single factor change states, the flow and heat transfer of the liquid metal in each state are simulated respectively by combining the liquid metal thermoelectric electromagnetic multi-field response verification data, and the flow speed, heat transfer efficiency and other characteristics of the liquid in different states are recorded. Then the simulation results are analyzed to find the relationship between the different states of each factor and the flow and heat transfer characteristics, such as how the flow speed changes when the magnetic field strength increases and how the heat transfer efficiency is affected, and these relationships are sorted into systematic data. Finally, according to the associated data, a model that can reflect the flow and heat transfer law of the liquid metal under the combined action of multiple factors is constructed, and through this model, the corresponding flow and heat transfer situation can be predicted according to the input of multiple factor states.

[0070] Step S4: Based on the liquid metal multi-source working condition-flow and heat transfer correlation model, the stress dimensionless characteristic and heat transfer efficiency influence characteristic analysis of the multi-source working condition is carried out, and the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data is generated; the liquid metal thermoelectric electromagnetic flow and heat transfer global characteristic model is established through the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data and the liquid metal multi-source working condition-flow and heat transfer correlation model;

[0071] In the embodiment of the present application, relying on the established liquid metal multi-source working condition-flow and heat transfer correlation model, a typical scene of the combined action of multiple factors is selected, the various forces acting on the liquid metal in this scene are analyzed, including the damping force generated by the magnetic field, the thermoelectric electromagnetic force generated by the combined action of the temperature gradient and the magnetic field, and the buoyancy caused by the temperature difference, and the size, direction and distribution of these forces in the liquid are determined. Then, the size of these forces is compared with a certain reference force to obtain the dimensionless force characteristics, and the dimensionless parameters reflecting the action strength of the magnetic field and the proportional relationship between different forces are calculated to reflect the relative action strength of various forces. Then, the relationship between these dimensionless force characteristics and the heat transfer efficiency is analyzed, such as how the heat transfer efficiency changes when a certain dimensionless force increases, and the influence law between them is determined to form related data. Finally, these data are combined with the previous liquid metal multi-source working condition-flow and heat transfer correlation model to construct a global characteristic model. The model can comprehensively consider the action of multiple factors, output the flow state, temperature distribution and heat transfer efficiency of the liquid metal according to the input working condition information, so as to realize the comprehensive prediction of the flow and heat transfer of the liquid metal under the thermoelectric electromagnetic multi-field coupling.

[0072] Step S5: performing the intelligent derivation of the thermoelectric-magnetic multi-field coupled liquid metal flow and heat transfer by a liquid metal thermoelectric-magnetic flow and heat transfer global characteristic model.

[0073] In the embodiment of the present application, an established liquid metal thermoelectric-magnetic flow and heat transfer global characteristic model is called, which has integrated the influence law of various factors on flow and heat transfer, and the correlation between force characteristics and heat transfer efficiency. Then, the information of the thermoelectric-magnetic multi-field coupled scene to be analyzed in the actual application is input, such as the specific magnetic field application method, the temperature environment of the liquid metal, the electric conductivity of the container wall, etc. The model will automatically derive the flow state of the liquid metal in this scene based on the existing correlation and global characteristics, including the distribution of flow velocity, the change of flow direction, etc.; and derive the heat transfer situation, such as the efficiency of heat transfer and the distribution of temperature in the liquid.

[0074] Further, step S1 includes the following steps:

[0075] Step S11: obtaining the metal structure physical parameters of the metal to be measured;

[0076] In the embodiment of the present application, when obtaining the metal structure physical parameters of the metal to be measured, the density, dynamic viscosity, thermal conductivity, electrical conductivity and Seebeck coefficient of the liquid metal are collected, and the geometric parameters of the closed cavity are recorded, including the length, width and height of the cavity, and the thickness, material and electrical conductivity of the wall. These parameters are measured by physical experiments, for example, the density bottle method is used to measure the density of the liquid metal at the working temperature, the rotary viscometer is used to measure the dynamic viscosity, the hot-wire method is used to obtain the thermal conductivity, the four-probe method is used to obtain the electrical conductivity, and the thermoelectric electromotive force measuring device is used to determine the Seebeck coefficient. All parameter measurements need to take the average value in more than three repeated experiments to ensure that the data error is controlled within 5%.

[0077] Step S12: obtaining the thermoelectric-magnetic multi-field coupled simulation control parameters, wherein the thermoelectric-magnetic multi-field coupled simulation parameters include temperature field simulation control parameters, electric field simulation control parameters, magnetic field simulation control parameters and integrated flow field simulation parameters;

[0078] In the embodiment of the present application, the thermal electromagnetic multi-field coupling simulation control parameters are obtained, the thermal electromagnetic multi-field coupling simulation parameters include temperature field simulation control parameters, electric field simulation control parameters, magnetic field simulation control parameters and integrated flow field simulation parameters, and the parameters involved can be adjusted, for example, when the thermal electromagnetic multi-field coupling simulation control parameters are determined, the temperature field simulation control parameters are set as the bottom wall temperature of the closed cavity 350K, the top wall temperature 300K, the temperature difference 50K, and the wall temperature remains constant; the electric field simulation control parameters are set as the wall surface induced current perpendicular to the wall surface direction component 0, and the thermal current boundary condition at the wall surface is continuous; the magnetic field simulation control parameters are set as the uniform magnetic field along the y axis direction, the initial magnetic field strength is 0.2T, and the magnetic field direction remains unchanged; the integrated flow field simulation parameters are set as the time step of flow velocity calculation 0.005s, the total simulation time 20s, and the flow field convergence criterion is that the physical quantity change of adjacent time steps is less than the set value.

[0079] Step S13: geometric modeling processing is performed through the metal structure physical parameters to generate a metal structure geometric model;

[0080] In the embodiment of the present application, according to the features related to the shape in the measured metal structure physical parameters, a three-dimensional modeling tool is used to construct a closed cavity geometric model. The closed cavity is set as a cuboid structure with a length of 0.3m, a width of 0.3m, a height of 0.3m, a cavity wall thickness of 0.01m, and a wall material of alumina (whose conductivity is known to be 1×10 -10 S / m), and the cavity inside is a liquid metal filling area. In the modeling process, the geometric size error is ensured to be controlled within 0.001m, and finally a metal structure geometric model containing the size and position information of each part of the cavity is generated.

[0081] Step S14: based on the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, a liquid metal flow and heat transfer calculation domain is designed to generate liquid metal flow and heat transfer calculation domain data;

[0082] In the embodiment of the present application, when the liquid metal flow and heat transfer calculation domain is designed based on the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, the calculation domain range is determined as the liquid metal flow area inside the cavity, excluding the wall surface solid part. According to the flow field simulation requirements, the calculation domain is discretized by using unstructured grids, the grid is encrypted near the wall surface where the flow velocity changes sharply, the grid size is set to 0.5mm, and the grid size in other areas is set to 2mm. At the same time, according to the temperature field and magnetic field control parameters, the boundary type of the calculation domain is set, such as the upper and lower walls as temperature boundaries, the left and right walls as adiabatic boundaries, and the external boundary as a magnetic field boundary, to generate liquid metal flow and heat transfer calculation domain data containing grid information, boundary type and parameters.

[0083] Step S15: based on the thermal electromagnetic multi-field coupling simulation control parameters and the liquid metal flow heat transfer calculation domain data, the liquid metal thermal electromagnetic multi-field reaction numerical preliminary simulation analysis is performed on the metal structure geometric model to generate preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data.

[0084] In the embodiment of the application, when the liquid metal thermal electromagnetic multi-field reaction numerical preliminary simulation analysis is performed on the metal structure geometric model based on the thermal electromagnetic multi-field coupling simulation control parameters and the liquid metal flow heat transfer calculation domain data, a computational fluid dynamics solver is called, and the physical parameters of the liquid metal and the calculation domain grid data are input. The control parameters of the solver are set, including the time step, the total number of calculation steps, the convergence residual, etc. During the solving process, the velocity and pressure field of the flow field, the temperature distribution of the temperature field, the electric potential and current distribution of the electric field, and the magnetic induction intensity distribution of the magnetic field are calculated synchronously. The intermediate results are output once every 100 steps to generate preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data containing the distribution data of each physical field.

[0085] Step S16: performing a compatible conservation optimization process on the preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data to generate liquid metal thermal electromagnetic multi-field reaction numerical simulation data.

[0086] In the embodiment of the application, when the compatible conservation optimization process of the thermal electromagnetic numerical simulation is performed on the preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data, the compatible conservation format is used to correct the calculation process. First, the Lorentz force, mass flux, and momentum flux data in the preliminary simulation data are extracted, and it is checked whether the conservation condition is met. If there is a deviation, the coefficients in the calculation format are adjusted for correction. For example, when the mass conservation deviation exceeds a certain value (such as 1e-5), the number of iterations of the pressure Poisson equation is increased; when the Lorentz force calculation error is large, the boundary condition processing method of the electric potential solution is corrected. After optimization, it needs to be recalculated and verified to ensure that the mass, momentum, and charge conservation errors are controlled within the conservation set value (such as 1e-6), and finally the optimized liquid metal thermal electromagnetic multi-field reaction numerical simulation data is generated.

[0087] Further, step S14 includes the following steps:

[0088] Step S141: setting the liquid metal flow heat transfer calculation domain boundary constraint data according to the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model;

[0089] In this embodiment of the invention, based on the determined thermo-electromagnetic multi-field coupling simulation control parameters (such as a temperature difference of 50K and a uniform magnetic field of 0.2T along the y-axis) and the geometric model of the metal structure (a 0.3m×0.3m×0.3m enclosed cavity), boundary constraint data for the computational domain are set. Specifically, the temperature boundary constraint is 350K for the bottom wall, 300K for the top wall, and the side walls are adiabatic; the electric field boundary constraint is that the normal component of the induced current on the wall is 0, and the thermal current is continuous; the magnetic field boundary constraint is that the magnetic field strength at the wall remains at 0.2T and its direction remains unchanged; and the flow field boundary constraint is that the liquid metal flow velocity at the wall is 0 (no slip condition). All boundary constraint data are recorded according to the wall coordinates of the geometric model to ensure accurate matching of the constraint parameters at each boundary position.

[0090] Step S142: Define the liquid metal flow and heat transfer region based on the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, obtain liquid metal analysis region data, and perform region point identification on the liquid metal analysis region data to generate analysis region point identification data; perform region complex characteristic analysis based on the analysis region point identification data to generate analysis region complex characteristic data.

[0091] In this embodiment of the invention, based on the thermo-electromagnetic multi-field coupling simulation control parameters and the geometric model of the closed cavity, the liquid metal flow and heat transfer region is defined as a 0.28m × 0.28m × 0.28m cubic space inside the closed cavity (excluding wall thickness). Using a coordinate positioning method, the near-wall region (including the bottom, top, and four sides) at a distance of 0.005m from the wall is marked with fixed points at 0.001m intervals, forming a continuous boundary layer marking region. Previous flow characteristic analysis shows that this marked region experiences large velocity and temperature gradients due to the combined effects of wall friction and electromagnetic forces, resulting in a complex flow state. Based on this, complex characteristic data for the analysis region is generated, clarifying that this region requires higher precision simulation.

[0092] Step S143: Design an unstructured mesh for the metal to be tested based on the fixed-point identification data and the complex characteristic data of the analysis area, and generate unstructured mesh data for the metal to be tested;

[0093] In the embodiment of the present application, based on the analysis area fixed-point identification data (near-wall 0.005m identification area) and complex characteristic data (high gradient flow), an unstructured grid generation method is used to design the grid. In the identification area, tetrahedral grid elements are used, with a grid edge length of 0.0005m, to ensure that the flow details in the boundary layer can be captured; in the non-identification area (internal area), hexahedral grid elements are used, with a grid edge length of 0.002m, to balance the calculation accuracy and efficiency. During the grid generation process, the grid quality inspection tool is used for verification to ensure that the grid distortion rate is less than 0.2 and the aspect ratio is less than 5, and the grid size is adjusted based on the complexity during the liquid metal flow to ensure that the detailed features can be accurately collected, and finally the unstructured grid data of the metal to be tested containing grid elements is generated, and each grid element corresponds to unique coordinate and size information.

[0094] Step S144: based on the liquid metal flow heat transfer calculation domain boundary constraint data and the unstructured grid data of the metal to be tested, the liquid metal flow heat transfer calculation domain is designed, and the liquid metal flow heat transfer calculation domain data is generated.

[0095] In the embodiment of the present application, the liquid metal flow heat transfer calculation domain boundary constraint data (temperature, electric field, magnetic field, flow field constraint) and the unstructured grid data of the metal to be tested are associated and integrated. Through the geometric mapping method, the boundary constraint parameters are distributed to the boundary nodes of the corresponding grid, such as the bottom wall grid node binding 350K temperature constraint and the wall grid node binding 0 flow rate constraint. At the same time, the spatial coordinates, size and belonging area (identification area / non-identification area) of the grid elements are recorded to form a complete calculation domain data structure. The finally generated liquid metal flow heat transfer calculation domain data not only contains the spatial distribution information of the grid, but also integrates the constraint parameters of each boundary, providing accurate calculation range and condition setting for subsequent numerical simulation.

[0096] Further, step S16 includes the following steps:

[0097] Step S161: performing numerical discretization processing on the preliminary liquid metal thermoelectric-magnetic multi-field reaction numerical simulation data to generate liquid metal thermoelectric-magnetic multi-field reaction discretization data;

[0098] In the embodiment of the present application, the initial liquid metal thermal electromagnetic multi-field reaction numerical simulation data (including continuous distribution data of liquid metal velocity, temperature, current density, etc. within 0-20s) is subjected to numerical discretization processing. The calculation domain is divided into discrete control bodies by the finite volume method according to the generated unstructured grid (1.5 million grid units), and the continuous control equation is converted into discrete algebraic equation by integration. The second-order upwind format is used to discretize the convection term of the velocity field, the central difference format is used to discretize the diffusion term of the temperature field, and the linear interpolation method is used to distribute the current density to each grid node. After discretization, each grid unit corresponds to a group of physical quantity values, such as the velocity of a certain grid unit at 5s is 0.1m / s, the temperature is 320K, and the current density is 100A / square meter. Finally, the liquid metal thermal electromagnetic multi-field reaction discretization data is generated, and the discretization error is controlled within 10 -4 within.

[0099] Step S162: Extracting current, electric potential and thermal-electric boundary reaction characteristics according to the liquid metal thermal electromagnetic multi-field reaction discretization data to generate liquid metal current-potential-thermal-electric boundary reaction characteristic data;

[0100] In the embodiment of the present application, according to the liquid metal thermal electromagnetic multi-field reaction discretization data (including physical quantity data of current, electric potential, temperature, etc. of each grid unit), the spatial range of the thermal-electric boundary is determined, that is, the grid unit set (such as the grid within 0.001m from the wall surface) corresponding to the wall surface of the closed cavity in the discretization data. For these boundary grid units, the vector information of the current density (including the components along the tangent direction and the normal direction of the wall surface), the scalar value of the electric potential, and the gradient data of the temperature near the wall surface (calculated by the temperature difference and distance of adjacent grid units) are extracted. For example, in a certain boundary grid unit of the bottom wall surface, the tangent direction component of the current density is 80A / square meter, the normal direction component is 0A / square meter (consistent with the wall surface current boundary constraint), the electric potential is 3.2V, and the temperature gradient is 1200K / m (calculated by the temperature difference 3K and distance 0.0025m of the grid and the adjacent internal grid). After the same extraction operation is performed on all boundary grid units, the data is classified and arranged according to the wall surface position (bottom, top, four sides) to form the liquid metal current-potential-thermal-electric boundary reaction characteristic data, which can directly reflect the current distribution, electric potential level and temperature gradient size of different wall surface positions.

[0101] Step S163: Analyzing the Seebeck effect characteristics of the liquid metal current-potential-thermal-electric boundary reaction according to the liquid metal current-potential-thermal-electric boundary reaction characteristic data to generate the Seebeck effect characteristic data of the liquid metal current-potential-thermal-electric boundary reaction;

[0102] In the embodiment of the present application, when the Seebeck effect characteristic analysis is performed on the liquid metal current-potential-thermoelectric boundary reaction characteristic data, first, according to the physical mechanism of the Seebeck effect that the thermoelectric current is induced by the temperature gradient through the Seebeck coefficient, the current data in the temperature gradient non-zero region (mainly near the bottom and top wall surfaces, because there is a temperature difference) is screened out from the characteristic data. The ratio of the current density to the temperature gradient in these regions is calculated to obtain a parameter (such as the current density of a certain bottom grid element 100 A / square meter, the temperature gradient 1000 K / m, and the ratio 0.1 A·m / (K·square meter)) representing the strength of the Seebeck effect, and the specific value of the Seebeck coefficient is determined in combination with the inherent properties of the liquid metal. Then, by comparing the differences of the ratio at different boundary positions, the enhancement effect of the Seebeck effect in the region with a larger temperature gradient (such as the bottom wall surface) is analyzed, and the change rule of the potential with the temperature gradient (such as the linear increase of the potential when the temperature gradient increases) is observed, so as to clarify the correlation between the current, the potential and the temperature gradient under the Seebeck effect. Finally, the correlation rules and the Seebeck coefficient values are sorted out to generate the Seebeck effect characteristic data of the liquid metal current-potential-thermoelectric boundary reaction, which can accurately reflect the action characteristics of the Seebeck effect in the thermoelectric boundary.

[0103] Step S164: based on the Seebeck effect characteristic data of the liquid metal current-potential-thermoelectric boundary reaction, the liquid metal thermoelectric magnetic multi-field reaction compatibility conservation optimization processing is performed on the liquid metal thermoelectric magnetic multi-field reaction discretization data to generate the liquid metal thermoelectric magnetic multi-field reaction numerical simulation data.

[0104] In the embodiment of the present application, based on the Seebeck effect characteristic data (such as the Seebeck coefficient, the correlation rule of the thermoelectric current and the temperature gradient) of the liquid metal current-potential-thermoelectric boundary reaction, the compatibility conservation optimization processing is performed on the liquid metal thermoelectric magnetic multi-field reaction discretization data. The thermoelectric current generated by the Seebeck effect is included in the calculation of the Lorentz force, and the electromagnetic force term in the discretization data is corrected to ensure that the solution of the Lorentz force not only conforms to the influence of the Seebeck effect, but also ensures the accuracy. Then, the mass conservation equation is checked, the mass difference of each grid element is compared, the discrete values of the related physical quantities are adjusted, and the mass conservation error is controlled within 10 -6 . Subsequently, for momentum conservation, it is checked whether the change of momentum in the discretization data matches the impulse of external forces such as Lorentz force and viscous force, and the grid element data that do not match is corrected to ensure that the momentum conservation error does not exceed 10 -5At the same time, the kinetic energy conservation condition is verified, the difference between the change amount of kinetic energy and the work done by external force is calculated, and the possible deviation is corrected to ensure that the kinetic energy conservation is within a reasonable range. For example, for a grid unit with increased thermoelectric current due to the Seebeck effect, the Lorentz force value is corrected to reflect the driving effect of the thermoelectric electromagnetic force and not to destroy the momentum transfer balance between the unit and the adjacent unit, so as to generate liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data, which can accurately reflect the physical law under the coupling of the thermoelectric electromagnetic multi-field, and provide a reliable basis for subsequent verification and analysis.

[0105] Further, as an embodiment of the present application, referring to Figure 2 , it is a detailed step flow diagram of step S2 in the embodiment, and step S2 in the embodiment includes the following steps: Figure 1

[0106] Step S21: based on the liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data, liquid metal thermal field driving simulation data, liquid metal electric field driving simulation data and liquid metal magnetic field driving simulation data are collected respectively;

[0107] In the embodiment of the present application, based on the liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data, the three types of driving simulation data are collected by using data screening method. When collecting the liquid metal thermal field driving simulation data, the physical quantity driven only by temperature difference in the simulation data is selected, such as the temperature value, heat flux and natural convection velocity of each grid unit, for example, the change data of temperature from 300K to 350K and the corresponding flow velocity distribution within 0-20s are extracted; when collecting the liquid metal electric field driving simulation data, the current density, electric potential distribution and related flow velocity data generated by the electric field are screened to clearly show the driving effect of the current on the flow; when collecting the liquid metal magnetic field driving simulation data, the Lorentz force distribution, magnetic field intensity and flow velocity data affected by the magnetic field are extracted. All the collected data are stored according to time sequence and spatial coordinates to ensure that each type of data corresponds to a unique driving field parameter.

[0108] Step S22: modeling of the closed cavity for liquid metal simulation is carried out through the liquid metal thermoelectric electromagnetic multi-field reaction numerical simulation data to obtain a liquid metal simulation closed cavity model;

[0109] ​In the embodiment of the application, the liquid metal simulation closed cavity model is constructed by using a three-dimensional modeling method according to the geometric parameters in the liquid metal thermal electromagnetic multi-field reaction numerical simulation data. According to the size (length 0.3 m, width 0.3 m, and height 0.3 m) of the closed cavity in the simulation data, the spatial structure of the cavity is reduced, the wall material (aluminum oxide) and the thickness (0.01 m) are determined, and the liquid metal filling area (0.28 m x 0.28 m x 0.28 m space inside) in the simulation data is used as the flow area of the model. In the modeling process, the wall position and the internal space size in the simulation data are accurately matched to ensure that the geometric parameters of the model are completely consistent with the simulation data, and finally the liquid metal simulation closed cavity model containing the cavity structure, material properties and flow area is obtained.

[0110] Step S23: The closed cavity convection and heat distribution verification analysis of the liquid metal simulation closed cavity model in the absence of a magnetic field is performed by using the liquid metal thermal field driving simulation data and the liquid metal electric field driving simulation data, to obtain the closed cavity flow field heat distribution verification data in the absence of a magnetic field.

[0111] In the embodiment of the application, the liquid metal simulation closed cavity model is verified and analyzed by using the liquid metal thermal field driving simulation data (thermal field data with a temperature difference of 50K) and the liquid metal electric field driving simulation data (electric field data in the absence of a magnetic field). In the model, a non-magnetic field environment (magnetic field strength of 0T) is set, the thermal field and electric field data are input, and the natural convection of the liquid metal in the closed cavity is simulated. By calculating the deviation of the convection flow rate and the temperature distribution from the theoretical value (such as the calculation result of the natural convection flow rate formula under a known temperature difference), for example, the deviation of the simulation value 0.12 m / s of the flow rate at the center of a certain cross section from the theoretical value 0.11 m / s is 9%, which is within the allowable range. At the same time, it is verified whether the heat distribution conforms to the heat conduction law, and finally the closed cavity flow field heat distribution verification data in the absence of a magnetic field is generated, and the verification results of the flow rate and the temperature are recorded.

[0112] Step S24: The magnetic field closed cavity convection and heat distribution verification analysis of the closed cavity flow field heat distribution data in the absence of a magnetic field is performed by using the liquid metal magnetic field driving simulation data, to obtain the magnetic field closed cavity flow field heat distribution verification data.

[0113] In the embodiment of the present application, liquid metal magnetic field driving simulation data (including uniform magnetic field parameters along the y-axis direction with a strength of 0.2T) is used to verify the analysis of the liquid metal simulation closed cavity model with reference to the flow field thermal distribution data of the closed cavity without magnetic field (such as the basic data of flow rate 0.12m / s and temperature gradient 1000K / m in the closed cavity). First, load the magnetic field driving parameters in the model, keep the temperature field (350K at the bottom and 300K at the top) and electric field boundary conditions consistent with those without magnetic field, and calculate the flow rate distribution and temperature distribution of the liquid metal in the closed cavity under the action of the magnetic field through numerical simulation. During the simulation process, the damping effect of the magnetic field on the flow is monitored, for example, the difference between the flow rate of a certain grid element 0.12m / s without magnetic field and the flow rate of the same element 0.07m / s after the magnetic field is applied, to verify whether the decrease in flow rate conforms to the positive correlation between the magnetic field strength and the Lorentz force. At the same time, analyze the temperature distribution changes, such as whether the temperature gradient increases (from 1000K / m to 1200K / m) due to the slowing down of the flow under the action of the magnetic field, and compare with the experimental data of flow and heat transfer under the action of the known magnetic field to ensure that the simulation deviation of the flow rate and temperature distribution is controlled within 8%, and finally generate the magnetic field closed cavity flow field thermal distribution verification data, including the flow rate, temperature and change amount after the action of the magnetic field of each grid element.

[0114] Step S25: Perform magnetic field closed cavity thermal current analysis on the magnetic field closed cavity flow field thermal distribution verification data, generate magnetic field closed cavity thermal current data, and perform flow field thermal distribution verification analysis on the magnetic field closed cavity flow field thermal distribution verification data through the magnetic field closed cavity thermal current data to generate thermal electromagnetic effect closed cavity flow field thermal distribution verification data;

[0115] In this embodiment of the invention, thermocurrent analysis is performed on the thermal distribution verification data of the flow field in the magnetic field-enclosed cavity (including flow velocity, temperature gradient, and magnetic field strength data) using a calculation method based on the Seebeck effect. Based on the Seebeck coefficient of the liquid metal (e.g., 20 μV / K) and the temperature gradient of each grid cell (e.g., 1200 K / m), the thermocurrent density at each location is calculated using the formula that the thermocurrent density is the sum of the products of the Seebeck coefficient and the temperature gradient (e.g., 20 μV / K × 1200 K / m = 24 mA / m²), generating the thermocurrent data for the magnetic field-enclosed cavity. This thermocurrent data is then combined with the magnetic field parameter (0.2 T) to calculate the thermo-magnetic force generated by the thermocurrent in the magnetic field (the direction is determined by the left-hand rule), and this force is substituted into the thermal distribution verification data of the flow field in the magnetic field-enclosed cavity to re-simulate the flow field and thermal distribution. For example, in a certain region, due to the thermo-magnetic force, the flow velocity increases from 0.07 m / s to 0.085 m / s, and the temperature gradient correspondingly decreases from 1200 K / m to 1100 K / m. By verifying whether the driving effect of thermo-electromagnetic force on flow is consistent with the theoretical derivation (the thermo-electromagnetic force increases when the thermocurrent increases), and whether the changes in flow field and heat distribution conform to the law of energy conservation, the final verification data of the heat distribution of the flow field in the closed cavity under the thermo-electromagnetic effect is generated, and the flow velocity, temperature and thermocurrent distribution under the thermo-electromagnetic coupling are recorded.

[0116] Step S26: Based on the thermal distribution verification data of the closed cavity without magnetic field, the thermal distribution verification data of the closed cavity with magnetic field, and the thermal distribution verification data of the closed cavity with thermo-electromagnetic effect, perform liquid metal thermo-electromagnetic multi-field reaction verification processing to generate liquid metal thermo-electromagnetic multi-field reaction verification data.

[0117] In this embodiment of the invention, comprehensive verification processing is performed based on three sets of flow field thermal distribution verification data under no magnetic field, magnetic field, and thermo-electromagnetic effects. The flow velocity change trends in the three sets of data (0.12 m / s, 0.08 m / s, and 0.09 m / s under no magnetic field, magnetic field, and thermo-electromagnetic coupling, respectively) are compared to see if they conform to physical logic (magnetic field damping reduces flow velocity, and thermo-electromagnetic force partially offsets the damping). The rationality of temperature distribution under different conditions is verified, such as more uniform temperature mixing under thermo-electromagnetic coupling. The overall deviation between the three sets of data and the corresponding theoretical models is calculated and controlled within 10%, confirming the reliability of the simulation data. Finally, multi-field thermo-electromagnetic reaction verification data of liquid metal is generated, and the three sets of verification results are integrated as valid data for subsequent analysis.

[0118] Furthermore, as an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S3 is shown. In this embodiment, step S2 includes the following steps:

[0119] Step S31: setting single-factor working condition simulation control parameters, wherein the single-factor working condition simulation control parameters include a magnetic field strength simulation control parameter, a magnetic field direction simulation control parameter, a Seebeck effect simulation control parameter, and a wall surface conductivity simulation control parameter;

[0120] In the embodiment of the present application, when the single-factor working condition simulation control parameters are set, for the magnetic field strength simulation control parameter, three gradient values of 0.1 T, 0.2 T and 0.3 T are set, and the magnetic field direction is fixed along the y axis; the magnetic field direction simulation control parameter is set as along the x axis, the y axis and the z axis, and the magnetic field strength is fixed as 0.2 T; the Seebeck effect simulation control parameter is realized by adjusting the Seebeck coefficient, and three values of 10 μV / K, 20 μV / K and 30 μV / K are set, and other parameters remain unchanged; the wall surface conductivity simulation control parameter sets the wall surface insulation (conductivity 1×10 -10 S / m) and wall surface conduction (conductivity 1×10 5 S / m), and the remaining parameters remain unchanged. All parameter settings are based on the key influencing factors to be analyzed in the project research, and each parameter change only changes a single variable, ensuring the effectiveness of the single-factor analysis.

[0121] Step S32: performing single-factor working condition simulation control processing on the liquid metal thermoelectric electromagnetic reaction verification data based on the single-factor working condition simulation control parameters, to generate liquid metal thermoelectric electromagnetic single-factor working condition simulation data;

[0122] In the embodiment of the present application, based on the set single-factor working condition simulation control parameters, the liquid metal thermoelectric electromagnetic multi-field reaction verification data is simulated and controlled. Taking the magnetic field strength single-factor simulation as an example, in the case of keeping the magnetic field direction along the y axis, the Seebeck coefficient 20 μV / K, and the wall surface insulation, the magnetic field strength parameters of 0.1 T, 0.2 T and 0.3 T are input respectively, and the simulation program is run to obtain the flow field and thermal distribution data under the corresponding magnetic field strength. Similarly, the magnetic field direction, the Seebeck coefficient, and the wall surface conductivity are adjusted and simulated respectively, such as the magnetic field direction along the x axis, keeping the magnetic field strength 0.2 T and other parameters unchanged. Complete flow rate, temperature, current and other data are recorded each time, to generate liquid metal thermoelectric electromagnetic single-factor working condition simulation data, ensuring that each working condition data corresponds to a unique variable parameter.

[0123] Step S33: analyzing the liquid metal flow and heat transfer characteristics of each working condition thermoelectric electromagnetic reaction according to the liquid metal thermoelectric electromagnetic single-factor working condition simulation data, to generate liquid metal flow and heat transfer characteristic data of each working condition thermoelectric electromagnetic reaction;

[0124] In the embodiment of the present application, according to the liquid metal thermal electromagnetic single-factor working condition simulation data, the flow and heat transfer characteristics are analyzed by using feature extraction method. For the magnetic field strength working condition data, the average flow rate under different strength (such as 0.1T, the average flow rate is 0.1m / s, 0.3T, the average flow rate is reduced to 0.05m / s), the maximum temperature gradient and the heat transfer efficiency (measured by heat flux) are calculated, and the inhibitory effect of the increase of the magnetic field strength on the flow is determined. For the magnetic field direction working condition data, the flow velocity vector distribution under the x-axis, y-axis and z-axis directions is compared, and it is found that the flow velocity distribution of a certain cross section along the z-axis magnetic field is more uniform. For the Seebeck coefficient and wall conductivity working condition data, the influence of the thermoelectric current change on the flow rate and the effect of the current transmission path on the heat transfer are analyzed respectively. These characteristic quantities are quantitatively recorded, and the liquid metal flow and heat transfer characteristic data of each working condition thermal electromagnetic reaction are generated.

[0125] Step S34: According to the liquid metal flow and heat transfer characteristic data of each working condition thermal electromagnetic reaction, the liquid metal flow and heat transfer characteristic correlation processing of each working condition influence is performed, and the liquid metal working condition influence-flow and heat transfer characteristic correlation data are generated.

[0126] In the embodiment of the present application, according to the liquid metal flow and heat transfer characteristic data of each working condition thermal electromagnetic reaction, the data is processed by using correlation analysis method. For example, the magnetic field strength and the average flow rate are linearly fitted, and the correlation relationship that the average flow rate is reduced by 0.025m / s when the magnetic field strength is increased by 0.1T is obtained. The Seebeck coefficient and the heat transfer efficiency are correlated, and it is found that when the Seebeck coefficient is increased from 10μV / K to 30μV / K, the heat transfer efficiency is increased by 15%. The flow rate data under the wall insulation and the wall conduction state are compared, and the correlation result that the average flow rate under the wall conduction state is 0.03m / s higher than that under the wall insulation state is obtained. The relationship between all single factors and flow and heat transfer characteristics is quantitatively analyzed, classified and arranged according to the variable type, and the liquid metal working condition influence-flow and heat transfer characteristic correlation data are generated, and the influence law of each factor is directly presented.

[0127] Step S35: The liquid metal flow and heat transfer correlation data modeling processing of the multi-source working condition thermal electromagnetic reaction is performed through the liquid metal working condition influence-flow and heat transfer characteristic correlation data, and the liquid metal multi-source working condition-flow and heat transfer correlation model is generated.

[0128] In the embodiment of the present application, the modeling method is used to construct the correlation model by liquid metal working condition influence-flow heat transfer characteristic correlation data. The magnetic field strength, direction, Seebeck coefficient and wall conductivity are taken as input parameters, and the flow heat transfer characteristics (average flow velocity, heat transfer efficiency, etc.) are taken as output parameters. Based on the quantitative relationship in the correlation data (such as the linear relationship between the magnetic field strength and the flow velocity, and the proportional relationship between the Seebeck coefficient and the heat transfer efficiency), a mathematical model is established. For example, in the model, the average flow velocity = 0.15-0.25× magnetic field strength + 0.001× Seebeck coefficient (when the wall is insulated), and the model accuracy is verified by the verification data (the deviation between the calculated value and the simulation value is less than 5%). Finally, the liquid metal multi-source working condition-flow heat transfer correlation model is generated, which can predict the flow heat transfer characteristics according to the input multi-factor parameters.

[0129] Further, step S32 includes the following steps:

[0130] Step S321: wall surface characteristic division processing is performed on the wall surface conductivity simulation control parameter to obtain wall surface insulation simulation control parameter and wall surface conduction simulation control parameter respectively;

[0131] In the embodiment of the present application, when the wall surface conductivity simulation control parameter is subjected to wall surface characteristic division processing, the characteristic classification standard is defined according to the conductivity value. When the wall is insulated, the induced current is mainly concentrated in the Hartmann layer, and the magnetic damping effect is large (Joule dissipation is large), but when the wall is conductive, the current will preferentially pass through the solid wall, and the damping effect of the Hartmann layer is relatively small. The wall surface insulation simulation control parameter is set to be wall surface conductivity ≤ 1×10 -8 S / m, under which the current cannot be transmitted through the wall; and the wall surface conduction simulation control parameter is wall surface conductivity ≥ 1×10 4 S / m, under which the current can conduct along the wall. In the division process, the actual conductivity values of the two materials are confirmed by the conductivity measuring instrument to ensure that the parameter setting is consistent with the inherent properties of the material, and finally the wall surface insulation and wall surface conduction simulation control parameters are obtained respectively, providing a basis for subsequent differential analysis.

[0132] Step S322: differential current transmission characteristic analysis of wall surface insulation and wall surface non-insulation is performed based on the wall surface insulation simulation control parameter and the wall surface conduction simulation control parameter to generate wall surface differential current characteristic data;

[0133] In the embodiment of the present application, based on the wall surface insulation simulation control parameter (alumina wall, conductivity 1×10 -10S / m) and wall surface conductive simulation control parameters, and the differential current transmission characteristics are analyzed by using the current distribution measurement method. Under the same magnetic field (0.2T along the y axis) and temperature field (temperature difference 50K), the current path and density in the closed cavity under two wall surface states are measured respectively. When the wall surface is insulated, the current is concentrated in the Hartmann layer inside the liquid metal, and the peak current density is 200A / square meter; when the wall surface is conductive, 30% of the current is transmitted through the wall surface, the peak current density inside the liquid metal is reduced to 140A / square meter, and the current distribution is more uniform. The current transmission path, density distribution and peak value data under two states are recorded, and the wall surface differential current characteristic data is generated, which clearly reflects the influence of wall surface conductivity on current transmission.

[0134] Step S323: According to the wall surface differential current characteristic data, the wall surface differential magnetic damping effect characteristic analysis is carried out, and the wall surface differential magnetic damping effect characteristic data is generated;

[0135] In the embodiment of the application, according to the wall surface differential current characteristic data (insulation Hartmann layer current concentration, conductive current dispersion), the wall surface differential magnetic damping effect characteristic analysis is carried out by using the electromagnetic force calculation method. The magnetic damping effect is determined by the Lorentz force generated by the interaction of current and magnetic field, and it can be calculated by the Lorentz force formula that: when the wall surface is insulated, the concentrated current (200A / square meter) in the Hartmann layer and the magnetic field (0.2T) interact to generate a Lorentz force of 40N / cubic meter, which has a significant damping effect on the flow; when the wall surface is conductive, the dispersed current (140A / square meter) generates a Lorentz force of 28N / cubic meter, and the damping effect is weakened. At the same time, the flow velocity decay rate under two states (insulation flow velocity decay 30%, conductive flow velocity decay 20%) is measured, the difference of magnetic damping effect is quantified, and the wall surface differential magnetic damping effect characteristic data is generated.

[0136] Step S324: Through the wall surface differential magnetic damping effect characteristic data and the single factor working condition simulation control parameters, the liquid metal thermoelectric magnetic multi-field reaction verification data is processed to generate the liquid metal thermoelectric magnetic single factor working condition simulation data.

[0137] In the embodiment of the application, the liquid metal thermoelectric magnetic multi-field reaction verification data is simulated and controlled by the wall differential magnetic damping effect characteristic data (Lorentz force 40N / cubic meter when insulated, 28N / cubic meter when conductive) and single-factor working condition simulation control parameters (only the wall conductivity is changed, and other parameters are fixed as a magnetic field of 0.2T and a Seebeck coefficient of 20μV / K). In the simulation, the wall insulation and conductive parameters are loaded respectively, and other conditions remain unchanged. The flow field and heat distribution data under the two working conditions are obtained by running the simulation. For example, the center flow velocity is 0.07m / s and the heat flux is 80W / square meter when the wall is insulated, and the center flow velocity is 0.1m / s and the heat flux is 95W / square meter when the wall is conductive. These data are classified according to the working conditions, and the key parameters such as flow velocity, temperature and heat flux are recorded to generate the liquid metal thermoelectric magnetic single-factor working condition simulation data, so that the data can directly reflect the influence of the wall conductivity on the flow and heat transfer.

[0138] Further, the step S35 comprises the following steps:

[0139] According to the liquid metal working condition influence-flow and heat transfer characteristic correlation data, the composite working condition influence characteristic analysis is performed to generate the liquid metal composite working condition influence characteristic data. The liquid metal flow and heat transfer correlation data modeling processing of the multi-source working condition thermoelectric magnetic reaction is performed by the liquid metal composite working condition influence characteristic data and the liquid metal working condition influence-flow and heat transfer characteristic correlation data to generate the liquid metal multi-source working condition-flow and heat transfer correlation model.

[0140] In the embodiment of the application, when the composite working condition influence characteristic analysis is performed according to the liquid metal working condition influence-flow and heat transfer characteristic correlation data, the magnetic field strength is 0.2T, the Seebeck coefficient is 20μV / K, and the wall conductivity is 1×10 5S / m Composite conditions are formed (based on the core influencing factors such as the magnetic field, the Seebeck coefficient, and the wall conductivity), and the flow and heat transfer characteristics are derived by using the control variable superposition method. Single-factor conditions are simulated by setting the control parameters to multiple numerical values, and the interaction characteristics between the multiple numerical values are analyzed. The actual running composite conditions are simulated by numerical simulation, and the average flow rate is measured. The deviation is caused by the multi-factor coupling and the magnetic field and the Seebeck effect. When the thermal electromagnetic force counteracts the magnetic damping, the effect is slightly stronger than the single-factor superposition effect. The actual flow rate, heat transfer efficiency, and deviation data under the composite conditions are recorded, and the liquid metal composite condition influence characteristic data are generated. When modeling the liquid metal composite condition influence characteristic data and the condition influence-flow and heat transfer characteristic correlation data, the magnetic field strength, the Seebeck coefficient, and the wall conductivity are used as input parameters, the average flow rate and the heat transfer efficiency are used as output parameters, and the multiple regression method is used to construct the model. The basic formula is established based on the condition influence-flow and heat transfer characteristic correlation data of the liquid metal condition influence-flow and heat transfer characteristic correlation data. The coupling correction term such as the “magnetic field strength x Seebeck coefficient” interaction term (coefficient 0.001) is introduced into the composite condition characteristic data. The deviation between the average flow rate calculated by the modified model and the actual measured value is controlled within 3%. At the same time, a similar model is established for the heat transfer efficiency, and the influence law of each factor and the coupling effect on the heat flux is introduced. The generated liquid metal multi-source condition-flow and heat transfer correlation model can accurately reflect the flow and heat transfer law under the combined action of multiple factors, and provide support for related analysis of the magnetic confinement fusion reactor blanket and other scenes.

[0141] Further, step S4 comprises the following steps:

[0142] Step S41: According to the liquid metal multi-source condition-flow and heat transfer correlation model, the liquid metal multi-source condition-flow and heat transfer correlation model is analyzed, and the liquid metal multi-source condition-flow and heat transfer correlation data are generated.

[0143] In the embodiment of the application, according to the liquid metal multi-source condition-flow and heat transfer correlation model (which has included the correlation between the magnetic field strength, the Seebeck coefficient and the flow rate, and the temperature), the force characteristic analysis is carried out by using the force decomposition method. The typical composite conditions (magnetic field strength, Seebeck coefficient, and wall conductivity) in the model are selected, the flow rate and temperature gradient data output by the model are calculated, and the three types of forces on the liquid metal are calculated: magnetic damping force (generated by the action of current and magnetic field, calculated by Lorentz force formula, generated by the action of current density and magnetic field in a certain grid unit), thermoelectric magnetic force (generated by the action of thermoelectric current induced by Seebeck effect and magnetic field, calculated by combining Seebeck coefficient and temperature gradient), and buoyancy (generated by the density change caused by temperature difference). The size, direction and distribution of the three types of forces of all grid units are recorded, and the liquid metal multi-source condition-flow and heat transfer characteristic data are generated, so as to clearly determine the contribution proportion of each force under the composite condition.

[0144] Step S42: stress non-dimensional characteristic analysis of the multi-source working condition is performed based on the liquid metal multi-source working condition stress characteristic data, and multi-source working condition stress non-dimensional characteristic data is generated;

[0145] In the embodiment of the present application, based on the liquid metal multi-source working condition stress characteristic data (including magnetic damping force 40N / cubic meter, thermoelectric magnetic force 5N / cubic meter, and buoyancy 2N / cubic meter), the stress non-dimensional characteristic analysis is performed by using the non-dimensional calculation method. The buoyancy is selected as the reference force (because the buoyancy is the basic driving force of natural convection), and the non-dimensional values of each force are calculated: the magnetic damping force non-dimensional value = 40N / cubic meter ÷ 2N / cubic meter = 20, the thermoelectric magnetic force non-dimensional value = 5N / cubic meter ÷ 2N / cubic meter = 2.5, and the buoyancy non-dimensional value = 2N / cubic meter ÷ 2N / cubic meter = 1. At the same time, the Hartmann number (non-dimensional number) representing the strength of the magnetic field is calculated. Based on the magnetic field strength 0.3T, the liquid metal conductivity 3×10 6 S / m, the characteristic length 0.3m, and the dynamic viscosity 0.001Pa·s, the Hartmann number formula is used to calculate 300 (where B is the magnetic field strength, L is the characteristic length, σ is the conductivity, and μ is the dynamic viscosity). In addition, the ratio of the thermoelectric magnetic force to the magnetic damping force (5N / cubic meter ÷ 40N / cubic meter = 0.125) is calculated, which can directly reflect the degree of offset of the thermoelectric magnetic force to the magnetic damping force. These non-dimensional parameters are arranged according to the calculation logic to generate multi-source working condition stress non-dimensional characteristic data, and the standardization comparison of the stress size and strength under different working conditions is realized.

[0146] Step S43: stress non-dimensional characteristic and heat transfer efficiency influence characteristic analysis of the multi-source working condition is performed according to the multi-source working condition stress non-dimensional characteristic data, and multi-source working condition stress non-dimensional characteristic-heat transfer efficiency influence characteristic data is generated;

[0147] In the embodiment of the present application, according to the multi-source working condition stress dimensionless characteristic data (including magnetic damping force dimensionless value 20, thermoelectric magnetic force dimensionless value 2.5, buoyancy dimensionless value 1, Hartmann number 300, and the ratio of thermoelectric magnetic force to magnetic damping force 0.125), a quantitative correlation analysis method is used to analyze the influence characteristics of stress dimensionless characteristics and heat transfer efficiency. The heat transfer efficiency is measured by the average heat flux in the closed cavity. The average heat flux under this working condition is 900 W / m2 measured by a heat flow meter. The correlation between each dimensionless characteristic and heat flux is calculated: the magnetic damping force dimensionless value 20 corresponds to the heat flux 900 W / m2. According to the single-factor data in the early stage, the magnetic damping force dimensionless value increases by 5, and the heat flux decreases by 80 W / m2, which is significantly negatively correlated; the thermoelectric magnetic force dimensionless value 2.5 corresponds to the heat flux 900 W / m2, and each increase of 1 increases the heat flux by 60 W / m2, which is positively correlated; when the Hartmann number is 300, the heat flux is 900 W / m2, and each increase of 50 reduces the heat flux by 50 W / m2. At the same time, the change of the heat flux corresponding to the ratio of the thermoelectric magnetic force to the magnetic damping force 0.125 is analyzed, and each increase of 0.05 increases the heat flux by 50 W / m2, which directly reflects the influence of the coupling effect of the two on heat transfer. These correlation data are logically arranged according to the dimensionless characteristics, change amplitude and heat flux to generate multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data, which clearly presents the quantitative influence law of each dimensionless characteristic on heat transfer efficiency.

[0148] Step S44: Map the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data to the liquid metal multi-source working condition-flow heat transfer correlation model to perform liquid metal thermoelectric magnetic coupling flow heat transfer global characteristic modeling processing, and generate a liquid metal thermoelectric magnetic flow heat transfer global characteristic model.

[0149] In the embodiment of the present application, the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data (including magnetic damping force dimensionless value 10-30, thermal electromagnetic force dimensionless value 1-4, Hartmann number 200-400, thermal electromagnetic force and magnetic damping force ratio 0.1-0.3, and corresponding heat transfer efficiency 900-1200W / square meter) are mapped to the liquid metal multi-source working condition-flow heat transfer correlation model, and a global characteristic model is established by using a random forest regression algorithm. First, the data set is divided, 80% of the samples (a total of 200 groups) are selected as the training set, and 20% (a total of 50 groups) are selected as the test set. The input features are the magnetic damping force dimensionless value, the thermal electromagnetic force dimensionless value, the Hartmann number, and the force ratio, and the output target is the heat transfer efficiency. When training, the number of decision trees is set to 100, the maximum depth is 8, and the minimum leaf node sample number is 5. The model parameters are adjusted through the training set iteration to control the fitting error of the training set within 3%. The model is verified by the test set. The magnetic damping force dimensionless value of a certain test sample is 22, the thermal electromagnetic force dimensionless value is 2.8, the Hartmann number is 320, and the force ratio is 0.13. The model predicts that the heat transfer efficiency is 1050W / square meter, and the deviation from the actual measured value 1045W / square meter is 0.5%. The deviation of all test samples is less than 5%. At the same time, the flow velocity and temperature distribution law of the original correlation model are combined to supplement the flow characteristic constraint, and the finally generated liquid metal thermal electromagnetic flow heat transfer global characteristic model can output the flow velocity distribution, temperature gradient and heat transfer efficiency at the same time, and meet the prediction demand of the thermal electromagnetic multi-field coupling scene.

[0150] Further, the liquid metal multi-source working condition stress characteristic data in step S41 includes liquid metal multi-source working condition magnetic damping force effect data, liquid metal multi-source working condition thermal electromagnetic force effect data and liquid metal multi-source working condition buoyancy effect data.

[0151] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims rather than the above description, and it is intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file.

[0152] The above description is only a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A numerical simulation method for liquid metal flow heat transfer under thermoelectromagnetic multi-field coupling, characterized in that, Comprise the following steps: Step S1: obtaining the metal structure physical parameters of the metal to be tested and the thermal electromagnetic multi-field coupling simulation control parameters; Based on the metal structure physical parameters and the thermal electromagnetic multi-field coupling simulation control parameters, the liquid metal thermal electromagnetic multi-field reaction numerical simulation analysis is carried out, and the liquid metal thermal electromagnetic multi-field reaction numerical simulation data is generated; Step S2: Based on the liquid metal thermal electromagnetic multi-field reaction numerical simulation data, the liquid metal thermal electromagnetic multi-field reaction verification processing is carried out, and the liquid metal thermal electromagnetic multi-field reaction verification data is generated; Step S3: Set single factor working condition simulation control parameter; Based on the single factor working condition simulation control parameter, the liquid metal thermal electromagnetic multi-field reaction verification data is carried out, the liquid metal flow heat transfer characteristic correlation analysis of each working condition influence is carried out, the liquid metal working condition influence-flow heat transfer characteristic correlation data is generated, and the liquid metal multi-source working condition-flow heat transfer correlation model is established through the liquid metal multi-source working condition-flow heat transfer correlation data; Step S4: Based on the liquid metal multi-source working condition-flow heat transfer correlation model, the stress dimensionless characteristic and heat transfer efficiency influence characteristic analysis of multi-source working condition is carried out, and the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data is generated; Through the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data and the liquid metal multi-source working condition-flow heat transfer correlation model, the liquid metal thermal electromagnetic flow heat transfer global characteristic model is established; Step S5: Through the liquid metal thermal electromagnetic flow heat transfer global characteristic model, the thermal electromagnetic multi-field coupling liquid metal flow heat transfer intelligent derivation job is executed.

2. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: obtaining the metal structure physical parameters of the metal to be tested; Step S12: obtaining the thermal electromagnetic multi-field coupling simulation control parameters, wherein the thermal electromagnetic multi-field coupling simulation parameters include temperature field simulation control parameters, electric field simulation control parameters, magnetic field simulation control parameters and integrated flow field simulation parameters; Step S13: geometric modeling processing is carried out through the metal structure physical parameters, and the metal structure geometric model is generated; Step S14: Based on the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, the liquid metal flow heat transfer calculation domain design is carried out, and the liquid metal flow heat transfer calculation domain data is generated; Step S15: Based on the thermal electromagnetic multi-field coupling simulation control parameters and the liquid metal flow heat transfer calculation domain data, the liquid metal thermal electromagnetic multi-field reaction numerical preliminary simulation analysis is carried out on the metal structure geometric model, and the preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data is generated; Step S16: The thermal electromagnetic numerical simulation compatibility conservation optimization processing is carried out on the preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data, and the liquid metal thermal electromagnetic multi-field reaction numerical simulation data is generated.

3. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: According to the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, the liquid metal flow heat transfer calculation domain boundary constraint data is set; Step S142: defining liquid metal flow and heat transfer region according to the thermal electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, obtaining liquid metal analysis region data, and performing region fixed-point identification on the liquid metal analysis region data to generate analysis region fixed-point identification data; performing region complex characteristic analysis according to the analysis region fixed-point identification data to generate analysis region complex characteristic data; Step S143: designing a to-be-tested metal unstructured grid according to the analysis region fixed-point identification data and the analysis region complex characteristic data, and generating to-be-tested metal unstructured grid data; Step S144: designing a liquid metal flow and heat transfer calculation domain based on the liquid metal flow and heat transfer calculation domain boundary constraint data and the to-be-tested metal unstructured grid data, and generating liquid metal flow and heat transfer calculation domain data.

4. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 2, characterized in that, Step S16 includes the following steps: Step S161: performing numerical discretization processing on the preliminary liquid metal thermal electromagnetic multi-field reaction numerical simulation data to generate liquid metal thermal electromagnetic multi-field reaction discretization data; Step S162: extracting current, potential, and thermal-electric boundary reaction characteristics according to the liquid metal thermal electromagnetic multi-field reaction discretization data to generate liquid metal current-potential-thermal-electric boundary reaction characteristic data; Step S163: performing Seebeck effect characteristic analysis of liquid metal current-potential-thermal-electric boundary reaction on the liquid metal current-potential-thermal-electric boundary reaction characteristic data to generate liquid metal current-potential-thermal-electric boundary reaction Seebeck effect characteristic data; Step S164: performing compatible conservation optimization processing of liquid metal thermal electromagnetic multi-field reaction on the liquid metal thermal electromagnetic multi-field reaction discretization data based on the liquid metal current-potential-thermal-electric boundary reaction Seebeck effect characteristic data to generate liquid metal thermal electromagnetic multi-field reaction numerical simulation data.

5. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: based on the liquid metal thermal electromagnetic multi-field reaction numerical simulation data, liquid metal thermal field driving simulation data, liquid metal electric field driving simulation data, and liquid metal magnetic field driving simulation data are collected respectively; Step S22: modeling a closed cavity for liquid metal simulation through the liquid metal thermal electromagnetic multi-field reaction numerical simulation data to obtain a liquid metal simulation closed cavity model; Step S23: using the liquid metal thermal field driving simulation data and the liquid metal electric field driving simulation data to perform closed cavity convection and heat distribution verification analysis on the liquid metal simulation closed cavity model without magnetic field to obtain no-magnetic-field closed cavity flow field heat distribution verification data; Step S24: using the liquid metal magnetic field driving simulation data to perform magnetic field closed cavity convection and heat distribution verification analysis on the no-magnetic-field closed cavity flow field heat distribution data to obtain magnetic field closed cavity flow field heat distribution verification data; Step S25: performing magnetic field closed cavity thermal current analysis on the magnetic field closed cavity flow field heat distribution verification data to generate magnetic field closed cavity thermal current data, and performing flow and heat distribution verification analysis of closed cavity thermoelectric magnetic coupling effect on the magnetic field closed cavity flow field heat distribution verification data through the magnetic field closed cavity thermal current data to generate thermoelectric magnetic effect closed cavity flow field heat distribution verification data; Step S26: based on the non-magnetic field closed cavity flow field thermal distribution verification data, the magnetic field closed cavity flow field thermal distribution verification data and the thermoelectric electromagnetic effect closed cavity flow field thermal distribution verification data, liquid metal thermoelectric electromagnetic multi-field reaction verification processing is carried out to generate liquid metal thermoelectric electromagnetic multi-field reaction verification data.

6. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: set single factor working condition simulation control parameters, wherein the single factor working condition simulation control parameters include magnetic field strength simulation control parameters, magnetic field direction simulation control parameters, Seebeck effect simulation control parameters and wall surface conductivity simulation control parameters; Step S32: based on the single factor working condition simulation control parameters, the liquid metal thermoelectric electromagnetic reaction single factor working condition simulation control processing is carried out on the liquid metal thermoelectric electromagnetic multi-field reaction verification data to generate liquid metal thermoelectric electromagnetic single factor working condition simulation data; Step S33: according to the liquid metal thermoelectric electromagnetic single factor working condition simulation data, the liquid metal flow heat transfer characteristic analysis of each working condition thermoelectric electromagnetic reaction is carried out to generate liquid metal flow heat transfer characteristic data of each working condition thermoelectric electromagnetic reaction; Step S34: according to the liquid metal flow heat transfer characteristic data of each working condition thermoelectric electromagnetic reaction, the liquid metal flow heat transfer characteristic correlation processing of each working condition influence is carried out to generate liquid metal working condition influence-flow heat transfer characteristic correlation data; Step S35: through the liquid metal working condition influence-flow heat transfer characteristic correlation data, the liquid metal flow heat transfer correlation data modeling processing of multi-source working condition thermoelectric electromagnetic reaction is carried out to generate liquid metal multi-source working condition-flow heat transfer correlation model.

7. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 6, characterized in that, Step S32 includes the following steps: Step S321: wall surface characteristic division processing is carried out on the wall surface conductivity simulation control parameters to obtain wall surface insulation simulation control parameters and wall surface conduction simulation control parameters respectively; Step S322: based on the wall surface insulation simulation control parameters and the wall surface conduction simulation control parameters, the differential current transmission characteristic analysis of wall surface insulation and wall surface non-insulation is carried out to generate wall surface differential current characteristic data; Step S323: according to the wall surface differential current characteristic data, the wall surface differential magnetic damping effect characteristic analysis is carried out to generate wall surface differential magnetic damping effect characteristic data; Step S324: through the wall surface differential magnetic damping effect characteristic data and the single factor working condition simulation control parameters, the liquid metal thermoelectric electromagnetic reaction single factor working condition simulation control processing is carried out on the liquid metal thermoelectric electromagnetic multi-field reaction verification data to generate liquid metal thermoelectric electromagnetic single factor working condition simulation data.

8. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 6, characterized in that, Step S35 includes the following steps: According to the liquid metal working condition influence-flow heat transfer characteristic correlation data, the composite working condition influence characteristic analysis is carried out to generate liquid metal composite working condition influence characteristic data; through the liquid metal composite working condition influence characteristic data and the liquid metal working condition influence-flow heat transfer characteristic correlation data, the liquid metal flow heat transfer correlation data modeling processing of multi-source working condition thermoelectric electromagnetic reaction is carried out to generate liquid metal multi-source working condition-flow heat transfer correlation model.

9. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: according to the liquid metal multi-source working condition-flow heat transfer correlation model, the liquid metal multi-source working condition stress characteristic analysis is carried out to generate liquid metal multi-source working condition stress characteristic data; Step S42: Based on the liquid metal multi-source working condition stress characteristic data, the stress dimensionless characteristic analysis of the multi-source working condition is performed, and multi-source working condition stress dimensionless characteristic data is generated; Step S43: According to the multi-source working condition stress dimensionless characteristic data, the stress dimensionless characteristic and heat transfer efficiency influence characteristic analysis of the multi-source working condition is performed, and the multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data is generated; Step S44: The multi-source working condition stress dimensionless characteristic-heat transfer efficiency influence characteristic data is mapped to the liquid metal multi-source working condition-flow heat transfer correlation model to perform the liquid metal thermoelectric electromagnetic coupling flow heat transfer global characteristic modeling processing, and the liquid metal thermoelectric electromagnetic flow heat transfer global characteristic model is generated.

10. The numerical simulation method of liquid metal flow and heat transfer under thermo-magnetic multi-field coupling according to claim 9, characterized in that, The liquid metal multi-source working condition stress characteristic data in step S41 includes liquid metal multi-source working condition magnetic damping force effect data, liquid metal multi-source working condition thermoelectric electromagnetic force effect data, and liquid metal multi-source working condition buoyancy effect data.

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