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

Abstract

The invention relates to the technical field of thermoelectric magnetic field numerical simulation, in particular to a numerical simulation method for liquid metal flow heat transfer under thermal electromagnetic multi-field coupling. The method comprises the following steps: acquiring metal structure physical parameters and thermal electromagnetic multi-field coupling simulation control parameters of metal to be tested; based on the metal structure physical parameters and the thermal electromagnetic multi-field coupling simulation control parameters, liquid metal thermal electromagnetic multi-field reaction numerical simulation and verification are carried out, and liquid metal thermal electromagnetic multi-field reaction verification data are generated; setting single-factor working condition simulation control parameters; and carrying out working condition simulation liquid metal flow heat transfer global characteristic analysis on the liquid metal thermoelectromagnetic multi-field reaction verification data based on the single-factor working condition simulation control parameters so as to establish a liquid metal thermoelectromagnetic flow heat transfer global characteristic model. According to the invention, comprehensive and accurate analysis of flowing and heat transfer of the liquid metal in the closed cavity under the thermal electromagnetic coupling effect by various factors is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of thermo-electromagnetic field numerical simulation, and in particular to a numerical simulation method for liquid metal flow and heat transfer under thermo-electromagnetic multi-field coupling. Background Art

[0002] In industrial fields such as the liquid blanket of magnetic confinement fusion reactors and electromagnetic metallurgy, the flow and heat transfer of liquid metal in a closed cavity under the coupling of thermo-electromagnetic multi-fields are very common. The flow and heat transfer process of liquid metal in a thermo-electromagnetic multi-field coupled environment is very complex, involving the interaction of multiple physical effects. Simulations that simply consider idealized flow without considering factors such as heat transfer and electromagnetics cannot accurately reflect the actual situation. Numerical simulation technology can comprehensively consider these factors and accurately describe the flow and heat transfer process of liquid metal. However, the magnetohydrodynamic problems in existing liquid metal multi-field simulation methods are themselves difficult problems in computational fluid dynamics. It is difficult to ensure the conservation of mass, momentum, and charge under multi-field reactions, which can easily lead to inaccurate calculations or divergent solutions. In addition, the flow and heat transfer of thermo-electromagnetic convection systems are affected by a combination of factors such as magnetic field strength, magnetic field direction, liquid metal Seebeck coefficient, and wall conductivity, making it difficult to fully understand the variation of heat transfer efficiency with various parameters. Summary of the Invention

[0003] Based on this, the present invention provides a numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling is provided, comprising the following steps: Step S1: obtaining the metal structure physical parameters and thermo-electromagnetic multi-field coupling simulation control parameters of the metal to be tested; performing a numerical simulation analysis of the thermo-electromagnetic multi-field response of the liquid metal based on the metal structure physical parameters and the thermo-electromagnetic multi-field coupling simulation control parameters, and generating numerical simulation data of the thermo-electromagnetic multi-field response of the liquid metal; Step S2: performing liquid metal thermo-electromagnetic multi-field response verification processing based on the liquid metal thermo-electromagnetic multi-field response numerical simulation data to generate liquid metal thermo-electromagnetic multi-field response verification data; Step S3: setting single-factor working condition simulation control parameters; performing a correlation analysis of the liquid metal flow and heat transfer characteristics affected by each working condition on the liquid metal thermal electromagnetic multi-field response verification data based on the single-factor working condition simulation control parameters, generating liquid metal working condition influence-flow and heat transfer characteristic correlation data, and establishing a liquid metal multi-source working condition-flow and heat transfer correlation model based on the liquid metal working condition influence-flow and heat transfer characteristic correlation data; Step S4: Analyzing the dimensionless stress characteristics and heat transfer efficiency influencing characteristics of the multi-source working condition based on the liquid metal multi-source working condition-flow and heat transfer correlation model, generating characteristic data of the dimensionless stress characteristics and heat transfer efficiency influencing characteristics of the multi-source working condition; establishing a global characteristic model of liquid metal thermal electromagnetic flow and heat transfer based on the dimensionless stress characteristics and heat transfer efficiency influencing characteristic data of the multi-source working condition and the liquid metal multi-source working condition-flow and heat transfer correlation model; Step S5: Execute the intelligent deduction operation of liquid metal flow and heat transfer with thermo-electromagnetic multi-field coupling through the global characteristic model of liquid metal thermo-electromagnetic flow and heat transfer.

[0005] Furthermore, step S1 includes the following steps: Step S11: obtaining the physical parameters of the metal structure of the metal to be tested; Step S12: obtaining thermo-electromagnetic multi-field coupling simulation control parameters, wherein the thermo-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: performing geometric modeling processing based on the physical parameters of the metal structure to generate a geometric model of the metal structure; Step S14: Designing a liquid metal flow and heat transfer calculation domain based on the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, and generating liquid metal flow and heat transfer calculation domain data; Step S15: performing preliminary numerical simulation analysis of the liquid metal thermal electromagnetic multi-field response on the metal structure geometric model based on the thermal electromagnetic multi-field coupling simulation control parameters and the liquid metal flow and heat transfer calculation domain data, and generating preliminary liquid metal thermal electromagnetic multi-field response numerical simulation data; Step S16: performing compatible conservation optimization processing of the thermo-electromagnetic numerical simulation on the preliminary liquid metal thermo-electromagnetic multi-field response numerical simulation data to generate the liquid metal thermo-electromagnetic multi-field response numerical simulation data.

[0006] Furthermore, step S14 includes the following steps: Step S141: setting boundary constraint data of the liquid metal flow heat transfer calculation domain according to the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model; Step S142: defining 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 to obtain 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 regional complex characteristic analysis based on the analysis region fixed-point identification data to generate analysis region complex characteristic data; Step S143: Designing an unstructured metal grid to be tested based on the analysis area fixed point identification data and the analysis area complex characteristic data to generate unstructured metal grid data to be tested; Step S144: Designing the 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 measured, and generating liquid metal flow and heat transfer calculation domain data.

[0007] Furthermore, step S16 includes the following steps: Step S161: performing numerical discretization processing on the preliminary numerical simulation data of liquid metal thermal electromagnetic multi-field response to generate liquid metal thermal electromagnetic multi-field response discretization data; Step S162: extracting current, potential, and thermoelectric boundary reaction characteristics based on the discretized data of the liquid metal thermo-electromagnetic multi-field reaction, and generating liquid metal current-potential-thermoelectric boundary reaction characteristic data; Step S163: performing Seebeck effect characteristic analysis of the liquid metal current-potential-thermoelectric boundary reaction on the liquid metal current-potential-thermoelectric boundary reaction characteristic data to generate Seebeck effect characteristic data of the liquid metal current-potential-thermoelectric boundary reaction; Step S164: Based on the Seebeck effect characteristic data of the liquid metal current-potential-thermoelectric boundary reaction, the discretized data of the liquid metal thermo-electromagnetic multi-field reaction are subjected to compatible conservation optimization processing of the liquid metal thermo-electromagnetic multi-field reaction to generate numerical simulation data of the liquid metal thermo-electromagnetic multi-field reaction.

[0008] Furthermore, step S2 includes the following steps: Step S21: collecting 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 response numerical simulation data; Step S22: Modeling a closed cavity for liquid metal simulation using the liquid metal thermal electromagnetic multi-field response 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 in the absence of a magnetic field, so as to obtain closed cavity flow field heat distribution verification data in the absence of a magnetic field; Step S24: using the liquid metal magnetic field driven simulation data to perform magnetic field closed cavity convection and thermal distribution verification analysis on the non-magnetic field closed cavity flow field thermal distribution data to obtain magnetic field closed cavity flow field thermal distribution verification data; Step S25: performing a magnetic field closed cavity thermal current analysis on the magnetic field closed cavity flow field thermal distribution verification data to generate magnetic field closed cavity thermal current data, and performing closed cavity thermal electromagnetic coupling effect process and thermal distribution verification analysis on the magnetic field closed cavity flow field thermal distribution verification data using the magnetic field closed cavity thermal current data to generate thermal electromagnetic effect closed cavity flow field thermal distribution verification data; Step S26: Perform liquid metal thermo-electromagnetic multi-field response verification processing 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 thermo-electromagnetic effect closed cavity flow field thermal distribution verification data to generate liquid metal thermo-electromagnetic multi-field response verification data.

[0009] Furthermore, step S3 includes the following steps: Step S31: setting single-factor working condition simulation control parameters, wherein the single-factor working condition simulation control parameters include magnetic field intensity simulation control parameters, magnetic field direction simulation control parameters, Seebeck effect simulation control parameters, and wall conductivity simulation control parameters; Step S32: performing single-factor working condition simulation control processing of liquid metal thermo-electromagnetic reaction on the bulk metal thermo-electromagnetic multi-field response verification data based on the single-factor working condition simulation control parameters to generate liquid metal thermo-electromagnetic single-factor working condition simulation data; Step S33: analyzing the liquid metal flow and heat transfer characteristics of the thermal electromagnetic reaction of each working condition based on the liquid metal thermal electromagnetic single-factor working condition simulation data, and generating the liquid metal flow and heat transfer characteristic data of the thermal electromagnetic reaction of each working condition; Step S34: performing correlation processing on the liquid metal flow and heat transfer characteristics affected by each working condition based on the liquid metal flow and heat transfer characteristic data of the thermal electromagnetic response of each working condition, and generating liquid metal working condition influence-flow and heat transfer characteristic correlation data; Step S35: Modeling and processing the liquid metal flow and heat transfer associated data of multi-source working condition thermal electromagnetic response is performed through the liquid metal working condition influence-flow and heat transfer characteristic associated data to generate a liquid metal multi-source working condition-flow and heat transfer associated model.

[0010] Furthermore, step S32 includes the following steps: Step S321: performing wall characteristic division processing on the wall conductivity simulation control parameters to obtain wall insulation simulation control parameters and wall conductivity simulation control parameters respectively; Step S322: performing differentiated current transmission characteristic analysis between the insulating wall and the non-insulating wall based on the wall insulation simulation control parameters and the wall conductivity simulation control parameters, and generating differentiated wall current characteristic data; Step S323: performing a wall surface differential magnetic damping effect characteristic analysis based on the wall surface differential current characteristic data to generate wall surface differential magnetic damping effect characteristic data; Step S324: performing single-factor working condition simulation control processing of liquid metal thermo-electromagnetic response on the liquid metal thermo-electromagnetic multi-field response verification data using the wall surface differentiated magnetic damping effect characteristic data and the single-factor working condition simulation control parameters to generate liquid metal thermo-electromagnetic single-factor working condition simulation data.

[0011] Furthermore, step S35 includes the following steps: Based on the liquid metal working condition influence-flow heat transfer characteristic correlation data, composite working condition influence characteristic analysis is performed to generate liquid metal composite working condition influence characteristic data; liquid metal flow heat transfer correlation data of multi-source working condition thermal electromagnetic response is modeled and processed using the liquid metal composite working condition influence characteristic data and liquid metal working condition influence-flow heat transfer characteristic correlation data to generate a liquid metal multi-source working condition-flow heat transfer correlation model.

[0012] Furthermore, step S4 includes the following steps: Step S41: analyzing the stress characteristics of the liquid metal multi-source working condition according to the liquid metal multi-source working condition-flow and heat transfer correlation model, and generating liquid metal multi-source working condition stress characteristic data; Step S42: performing dimensionless characteristic analysis of the force under the multi-source working condition based on the force characteristic data of the liquid metal multi-source working condition to generate dimensionless characteristic data of the force under the multi-source working condition; Step S43: Analyzing the dimensionless stress characteristics of the multi-source working condition and the heat transfer efficiency impact characteristics based on the dimensionless stress characteristic data of the multi-source working condition, and generating the dimensionless stress characteristic-heat transfer efficiency impact characteristic data of the multi-source working condition; Step S44: Map the dimensionless characteristics of the multi-source working condition force-heat transfer efficiency influence characteristic data to the liquid metal multi-source working condition-flow heat transfer correlation model to perform liquid metal thermal electromagnetic coupled flow heat transfer global characteristic modeling processing to generate a liquid metal thermal electromagnetic flow heat transfer global characteristic model.

[0013] Furthermore, the force characteristic data of the liquid metal multi-source working condition in step S41 includes magnetic damping force effect data of the liquid metal multi-source working condition, thermal electromagnetic force effect data of the liquid metal multi-source working condition, and buoyancy effect data of the liquid metal multi-source working condition.

[0014] The present invention can provide accurate basic data support for subsequent numerical simulations by obtaining the metal structure physical parameters and thermal electromagnetic multi-field coupling simulation control parameters of the metal to be tested, ensuring the pertinence and reliability of the simulation analysis. The geometric modeling and calculation domain design steps can construct a metal structure model that fits the actual situation and the flow and heat transfer calculation range, and define reasonable boundaries for the simulation analysis; and the compatible conservation optimization processing, especially the optimization of the discretized data in combination with the Seebeck effect characteristics, effectively solves the problems of inaccurate Lorentz force solution and difficulty in ensuring mass and momentum conservation in magnetohydrodynamic calculations when the Hartmann number is large, thereby improving the accuracy and stability of the numerical simulation results and providing high-quality preliminary simulation data for subsequent research. The phased collection of different field-driven simulation data and the closed cavity modeling verification can systematically test the validity of the numerical simulation results. From no magnetic field to magnetic field and then to the flow field thermal distribution verification under the thermal electromagnetic coupling effect, the progressive verification process can comprehensively check the errors that may exist in the simulation process and ensure the reliability of the simulation data in different scenarios. At the same time, this verification process can intuitively reflect the impact of thermo-electromagnetic multi-field coupling on liquid metal flow and heat transfer, laying a solid data foundation for subsequent analysis of the interaction between various factors and reducing the risk of inaccurate research conclusions due to data bias. By setting the single-factor operating condition simulation control parameters (covering key factors such as magnetic field intensity, magnetic field direction, Seebeck effect, and wall conductivity), it is possible to specifically analyze the impact of a single variable on liquid metal flow and heat transfer, avoiding the problem of blurred patterns caused by the mixing of multiple factors. With the characteristic data generated by single-factor simulation control, the core characteristics of flow and heat transfer under various operating conditions (such as velocity distribution and temperature gradient) can be clearly extracted. The multi-source operating condition-flow and heat transfer correlation model established through correlation analysis can further integrate the interaction between single and complex factors, intuitively presenting the corresponding relationship between different operating condition parameters and flow and heat transfer characteristics. This provides structured data support for the subsequent revelation of the influence mechanism of various factors on the thermo-electromagnetic coupling system and lays the foundation for accurately predicting the flow and heat transfer state under specific operating conditions. Based on the multi-source working condition-flow heat transfer correlation model, the force characteristics of liquid metal under multi-source working conditions (including magnetic damping force, thermo-electromagnetic force, and buoyancy, etc.) are deeply analyzed. The complex forces are converted into quantifiable characteristic parameters through dimensionless characteristic analysis, solving the problem of difficult direct comparison of forces and heat transfer efficiency under different working conditions. Establishing the correlation between dimensionless force characteristics and heat transfer efficiency can clarify the influence of the relative strength of magnetic damping effect and thermo-electromagnetic dynamic effect on heat transfer efficiency. The resulting global characteristic model can integrate the flow heat transfer laws under the action of multiple factors, realize the accurate characterization of the overall characteristics of the thermo-electromagnetic multi-field coupling system, and provide a quantitative basis and theoretical support for the efficient regulation of heat transfer efficiency.By performing intelligent deduction operations through the global characteristic model of liquid metal thermal electromagnetic flow and heat transfer, it is possible to utilize the established multi-factor correlation laws and global characteristics to quickly respond to the input of different thermal electromagnetic working parameters, and efficiently derive the corresponding liquid metal flow and heat transfer state (such as velocity field, temperature field distribution and heat transfer efficiency, etc.), without the need to repeat complex numerical simulation calculations, greatly improving the analysis efficiency. At the same time, relying on the global characteristics of the model, it can ensure the reliability of the derivation results in multi-factor coupling scenarios, providing a convenient and accurate decision support tool for the design optimization and parameter debugging of related industrial fields such as magnetic confinement fusion reactor blanket and electromagnetic metallurgy.

[0015] The beneficial effect of the present application is that the numerical simulation method of liquid metal flow and heat transfer under thermo-electromagnetic multi-field coupling of the present invention processes the current, potential and thermoelectric boundary considering the Seebeck effect based on the compatible conservation format, and adds corrections to non-orthogonal grids and oblique grids when solving the pressure and potential Poisson equations, effectively solving the problems of inaccurate solution of the Lorentz force, difficulty in ensuring conservation of mass and momentum, and easy divergence of the solution when the Hartmann number is large in magnetohydrodynamic calculations, thereby improving the accuracy and stability of the numerical simulation. By setting the single-factor working condition simulation control parameters, the influence of factors such as magnetic field intensity, magnetic field direction, liquid metal Seebeck coefficient, wall conductivity, etc. is systematically studied, and a multi-source working condition-flow heat transfer correlation model is established, which overcomes the defect of insufficient systematic research on these factors in existing studies and can clearly reveal the influence of various factors on the flow and heat transfer of liquid metal under thermo-electromagnetic coupling. By analyzing the stress characteristics of multi-source working conditions and extracting dimensionless characteristic parameters, characteristic parameters characterizing the relative sizes of magnetic damping effect and thermo-electromagnetic dynamic effect were established, and the variation law of heat transfer efficiency with each parameter was clarified, providing strong theoretical guidance for related industrial applications. Moreover, through the global characteristic model of liquid metal thermo-electromagnetic flow and heat transfer, intelligent deduction operations of flow and heat transfer can be efficiently performed, providing precise support for realizing characteristics such as flow velocity and heat transfer in liquid metal convection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the steps of a numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling of the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0017] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a numerical simulation method for liquid metal flow and heat transfer under thermal electromagnetic multi-field coupling. In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a flow chart of the steps of a numerical simulation method for liquid metal flow and heat transfer under the coupling of thermo-electromagnetic multi-fields according to the present invention. The numerical simulation method for liquid metal flow and heat transfer under the coupling of thermo-electromagnetic multi-fields comprises the following steps: Based on this, the present invention provides a numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling to solve at least one of the above technical problems.

[0020] To achieve the above objectives, a numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling is provided, comprising the following steps: Step S1: obtaining the metal structure physical parameters and thermo-electromagnetic multi-field coupling simulation control parameters of the metal to be tested; performing a numerical simulation analysis of the thermo-electromagnetic multi-field response of the liquid metal based on the metal structure physical parameters and the thermo-electromagnetic multi-field coupling simulation control parameters, and generating numerical simulation data of the thermo-electromagnetic multi-field response of the liquid metal; In the embodiment of the present invention, the physical parameters of the metal structure of the metal to be tested are obtained. For example, for liquid lithium-lead alloy, the relevant parameters are measured experimentally in a constant temperature environment of 25°C: the density is measured by the density bottle method to be 2600 kg / m3, the dynamic viscosity is measured by the rotational viscometer to be 0.0012 Pa·s, the thermal conductivity is measured by the hot wire method to be 35 W / (m·K), and the electrical conductivity is measured by the four-probe method to be 2.8×10 6S / m, and the Seebeck coefficient measured by the thermoelectromotive force measurement device was 18 μV / K. The geometric parameters of the metal under test were also recorded. The control parameters for the thermoelectromagnetic multi-field coupling simulation were obtained: the temperature field was set to 350K on the bottom wall and 300K on the top wall (temperature difference of 50K); the electric field was set to zero for the normal component of the wall-induced current; the magnetic field was set to a uniform magnetic field of 0.2 T along the y-axis; the flow field was set to a time step of 0.005 s and a simulation duration of 20 s. The convergence criterion was that the change in the physical quantity between adjacent time steps was less than 10 -5 . Based on the physical parameters of the metal structure, a metal geometric model is constructed using a three-dimensional modeling tool, such as the geometric conditions of the metal being driven from solid to liquid, ensuring that the dimensional error is ≤0.001m. The calculation domain is designed based on the simulation control parameters and the geometric model: the boundary constraints are 350K at the bottom and 300K at the top, the side is insulated, and the wall flow velocity is 0; the flow area is divided into a 0.28m×0.28m×0.28m space inside the cavity, a 0.0005m grid is used in the 0.005m area near the wall, and a 0.002m grid is used inside, for a total of 1.4 million grids. Based on the control parameters and calculation domain data, a preliminary simulation is performed using the finite volume method to solve the momentum, energy, and electromagnetic field equations, and record flow rate, temperature, and other data every 0.1s. The preliminary simulation data was optimized for compatibility conservation, boundary current, potential and temperature gradient were extracted, the Seebeck effect was analyzed (such as the relationship between the bottom grid current density of 100A / square meter and the temperature gradient of 1000K / m), the Lorentz force calculation was modified to satisfy the conservation of mass and momentum, and finally the numerical simulation data of the liquid metal thermal electromagnetic multi-field response were generated.

[0021] Step S2: performing liquid metal thermo-electromagnetic multi-field response verification processing based on the liquid metal thermo-electromagnetic multi-field response numerical simulation data to generate liquid metal thermo-electromagnetic multi-field response verification data; In this embodiment of the present invention, three types of driving data are collected based on numerical simulation data of the thermo-electromagnetic multi-field response of liquid metal: thermal field data includes the temperature change from 300K to 350K and the corresponding flow velocity from 0 to 20 seconds; electric field data includes current density, electric potential, and related flow velocity; and magnetic field data includes the Lorentz force, magnetic field intensity, and affected flow velocity. All data are stored by time and space classification. Using the geometric parameters in the simulation data, a simulated closed cavity model is constructed using a three-dimensional modeling method to restore the cavity structure, wall material, and flow area, ensuring that the geometric parameters are consistent with the simulation data. Using the thermal and electric field data, a non-magnetic field environment is set in the model to simulate natural convection. The simulated flow velocity value of 0.12m / s at the center of a certain cross section deviates by 9% from the theoretical value of 0.11m / s. The heat distribution conforms to the law of heat conduction, generating non-magnetic field verification data. Applying a 0.2T magnetic field, the simulation showed a decrease in flow velocity from 0.12 m / s to 0.07 m / s and an increase in temperature gradient from 1000 K / m to 1200 K / m. The deviation from experimental data was ≤8%, generating magnetic field validation data. A thermocurrent analysis was performed on the magnetic field validation data. Based on a Seebeck coefficient of 18 μV / K and a temperature gradient of 1200 K / m, a thermocurrent density of 21.6 mA / m² was calculated. Substituting these values ​​into the simulation, the flow velocity increased back to 0.085 m / s and the temperature gradient decreased to 1100 K / m, validating the coupling effect and generating thermoelectromagnetic effect validation data. The simulation validation data for each reaction group was integrated to generate validation data for the multi-field thermoelectromagnetic response of liquid metals.

[0022] Step S3: setting single-factor working condition simulation control parameters; performing a correlation analysis of the liquid metal flow and heat transfer characteristics affected by each working condition on the liquid metal thermal electromagnetic multi-field response verification data based on the single-factor working condition simulation control parameters, generating liquid metal working condition influence-flow and heat transfer characteristic correlation data, and establishing a liquid metal multi-source working condition-flow and heat transfer correlation model based on the liquid metal working condition influence-flow and heat transfer characteristic correlation data; In an embodiment of the present invention, the influencing factors that require separate analysis are first identified. Different variation states are then set for each factor, with only the state of that factor being varied at a time, while other conditions remain unchanged. For example, the magnetic field direction, Seebeck effect-related properties, and wall conductivity are first fixed, while only the magnetic field strength is varied. Other conditions, such as the magnetic field strength, are then fixed, while only the magnetic field direction is varied. The same method is then used to vary the Seebeck effect-related properties and wall conductivity. Based on these defined variation states for each individual factor and combined with validation data from liquid metal thermoelectromagnetic multi-field responses, the flow and heat transfer of liquid metal under each state are simulated, and characteristics such as the flow velocity and heat transfer efficiency of the liquid under different states are recorded. These simulation results are then analyzed to identify the relationships between the different states of each factor and the flow and heat transfer characteristics, such as how the flow velocity changes and how the heat transfer efficiency is affected when the magnetic field strengthens. These relationships are then organized into systematic data. Finally, based on this correlation data, a model is constructed that reflects the flow and heat transfer patterns of liquid metal under the combined action of multiple factors. This model can predict the corresponding flow and heat transfer conditions based on the input states of the multiple factors.

[0023] Step S4: Analyzing the dimensionless stress characteristics and heat transfer efficiency influencing characteristics of the multi-source working condition based on the liquid metal multi-source working condition-flow and heat transfer correlation model, generating characteristic data of the dimensionless stress characteristics and heat transfer efficiency influencing characteristics of the multi-source working condition; establishing a global characteristic model of liquid metal thermal electromagnetic flow and heat transfer based on the dimensionless stress characteristics and heat transfer efficiency influencing characteristic data of the multi-source working condition and the liquid metal multi-source working condition-flow and heat transfer correlation model; In an embodiment of the present invention, relying on the established liquid metal multi-source working condition-flow heat transfer correlation model, a typical scenario in which multiple factors act together is selected to analyze the various forces acting on the liquid metal in this scenario, including the damping force generated by the magnetic field, the thermo-electromagnetic force generated by the combined action of the temperature gradient and the magnetic field, and the buoyancy caused by the temperature difference, etc., to clarify the magnitude, direction and distribution of these forces in the liquid. Afterwards, the magnitude of these forces is compared with a certain reference force to obtain dimensionless force characteristics, and at the same time, dimensionless parameters that can reflect the intensity of the magnetic field, etc., as well as the proportional relationship between different forces, are calculated to reflect the relative intensity of various forces. Next, the relationship between these dimensionless force characteristics and heat transfer efficiency is analyzed, such as how the heat transfer efficiency changes when a certain dimensionless force increases, to clarify the influence law between them, and to form relevant data. Finally, these data are combined with the previous liquid metal multi-source working condition-flow heat transfer correlation model to construct a global characteristic model. The model can comprehensively consider the effects of multiple factors and output the flow state, temperature distribution, heat transfer efficiency and other contents of liquid metal based on the input working condition information, thereby realizing a comprehensive prediction of the flow and heat transfer of liquid metal under the coupling of thermal and electromagnetic fields.

[0024] Step S5: Execute the intelligent deduction operation of liquid metal flow and heat transfer with thermo-electromagnetic multi-field coupling through the global characteristic model of liquid metal thermo-electromagnetic flow and heat transfer.

[0025] In an embodiment of the present invention, an established global characteristic model of liquid metal thermal electromagnetic flow and heat transfer is called. This model has integrated the influence of various factors on flow and heat transfer, as well as the correlation between force characteristics and heat transfer efficiency. Afterwards, the thermal electromagnetic multi-field coupling scenario information that needs to be analyzed in actual applications is input, such as the specific magnetic field application method, the temperature environment of the liquid metal, the conductivity of the container wall, etc. Based on the internal existing correlation rules and global characteristics, the model will automatically deduce the flow state of the liquid metal in this scenario, including the distribution of flow velocity, changes in flow direction, etc.; at the same time, it will deduce the heat transfer situation, such as the efficiency of heat transfer and the distribution of temperature in the liquid.

[0026] Furthermore, step S1 includes the following steps: Step S11: obtaining the physical parameters of the metal structure of the metal to be tested; In an embodiment of the present invention, when obtaining the physical parameters of the metal structure of the metal to be tested, it is necessary to collect the density, dynamic viscosity, thermal conductivity, electrical conductivity, and Seebeck coefficient of the liquid metal, and simultaneously record the geometric parameters of the closed cavity, including the cavity length, width, height, and wall thickness, material, and electrical conductivity. These parameters are measured through physical experiments, such as using the density bottle method to measure the density of the liquid metal at operating temperature, using a rotational viscometer to determine the dynamic viscosity, using the hot wire method to obtain the thermal conductivity, using the four-probe method to obtain the electrical conductivity, and using a thermoelectromotive force measurement device to determine the Seebeck coefficient. All parameter measurements must be averaged over three or more repeated experiments to ensure that the data error is controlled within 5%.

[0027] Step S12: obtaining thermo-electromagnetic multi-field coupling simulation control parameters, wherein the thermo-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; In an embodiment of the present invention, thermo-electromagnetic multi-field coupling simulation control parameters are obtained. The thermo-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 are all adjustable. For example, when determining the thermo-electromagnetic multi-field coupling simulation control parameters, the temperature field simulation control parameters are set to a closed cavity bottom wall temperature of 350K and a top wall temperature of 300K, with a temperature difference of 50K, and the wall temperature remains constant; the electric field simulation control parameters are set to a component of the induced current at the wall perpendicular to the wall direction of 0, and the boundary condition of the thermal current at the wall is continuous; the magnetic field simulation control parameters are set to a uniform magnetic field along the y-axis direction, an initial magnetic field strength of 0.2T, and a magnetic field direction that remains unchanged; the integrated flow field simulation parameters are set to a time step of 0.005s for flow velocity calculation, a total simulation time of 20s, and a flow field convergence criterion that the change in physical quantities in adjacent time steps is less than the set value.

[0028] Step S13: performing geometric modeling processing based on the physical parameters of the metal structure to generate a geometric model of the metal structure; In the embodiment of the present invention, a three-dimensional modeling tool is used to construct a closed cavity geometric model based on the morphological characteristics of the measured physical parameters of the metal structure. The closed cavity is assumed to be a rectangular parallelepiped structure with a length of 0.3m, a width of 0.3m, and a height of 0.3m. The cavity wall thickness is 0.01m, and the wall material is aluminum oxide (its electrical conductivity is known to be 1×10 -10 S / m), the interior of the cavity is a liquid metal-filled area. During the modeling process, the geometric dimension 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.

[0029] Step S14: Designing a liquid metal flow and heat transfer calculation domain based on the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, and generating liquid metal flow and heat transfer calculation domain data; In an embodiment of the present invention, when designing the calculation domain for liquid metal flow and heat transfer based on the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, the calculation domain range is determined to be the liquid metal flow area inside the cavity, excluding the solid part of the wall surface. The grid is divided according to the flow field simulation requirements, and the calculation domain is discretized using an unstructured grid. The grid is encrypted near the wall where the flow velocity changes dramatically, and the grid size is set to 0.5mm. 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 are temperature boundaries, the left and right walls are adiabatic boundaries, and the external boundary is a magnetic field boundary, to generate liquid metal flow and heat transfer calculation domain data containing grid information, boundary type and parameters.

[0030] Step S15: performing preliminary numerical simulation analysis of the liquid metal thermal electromagnetic multi-field response on the metal structure geometric model based on the thermal electromagnetic multi-field coupling simulation control parameters and the liquid metal flow and heat transfer calculation domain data, and generating preliminary liquid metal thermal electromagnetic multi-field response numerical simulation data; In an embodiment of the present invention, when performing a preliminary numerical simulation analysis of the liquid metal thermo-electromagnetic multi-field response on a metal structure geometric model based on the thermo-electromagnetic multi-field coupling simulation control parameters and the liquid metal flow and heat transfer calculation domain data, a computational fluid dynamics solver is called and the physical parameters of the liquid metal and the calculation domain mesh 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 solution process, the velocity and pressure field of the flow field, the temperature distribution of the temperature field, the 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 every 100 steps to generate preliminary numerical simulation data of the liquid metal thermo-electromagnetic multi-field response containing the distribution data of each physical field.

[0031] Step S16: performing compatible conservation optimization processing of the thermo-electromagnetic numerical simulation on the preliminary liquid metal thermo-electromagnetic multi-field response numerical simulation data to generate the liquid metal thermo-electromagnetic multi-field response numerical simulation data.

[0032] In an embodiment of the present invention, when performing compatible conservation optimization processing of the preliminary numerical simulation data of the thermo-electromagnetic multi-field reaction of liquid metal, a 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 to check whether the conservation conditions are met. If there is a deviation, the coefficients in the calculation format are adjusted to make corrections. 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 error in the calculation of the Lorentz force is large, the boundary condition processing method for solving the electric potential is corrected. After optimization, recalculation and verification are required to ensure that the errors in the conservation of mass, momentum, and charge are all controlled within the conservation set value (such as 1e-6), and finally the optimized numerical simulation data of the thermo-electromagnetic multi-field reaction of liquid metal are generated.

[0033] Furthermore, step S14 includes the following steps: Step S141: setting boundary constraint data of the liquid metal flow heat transfer calculation domain according to the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model; In this embodiment of the present invention, computational domain boundary constraint data is set based on the established control parameters for the thermo-electromagnetic multi-field coupling simulation (e.g., a 50K temperature gradient and a 0.2T uniform magnetic field along the y-axis) and the geometric model of the metal structure (a 0.3m×0.3m×0.3m enclosed cavity). The temperature boundary constraints are 350K for the bottom wall and 300K for the top wall, with the side walls adiabatic. The electric field boundary constraint is that the normal component of the induced current on the wall is zero, and the thermal current is continuous. The magnetic field boundary constraint is that the magnetic field intensity remains constant at 0.2T at the wall, with no direction change. The flow field boundary constraint is that the liquid metal velocity at the wall is zero (no slip condition). All boundary constraint data is recorded according to the wall coordinates of the geometric model to ensure precise matching of the constraint parameters at each boundary location.

[0034] Step S142: defining 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 to obtain 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 regional complex characteristic analysis based on the analysis region fixed-point identification data to generate analysis region complex characteristic data; In this embodiment of the present invention, based on the control parameters of the thermo-electromagnetic multi-field coupling simulation and the closed cavity geometry model, the liquid metal flow and heat transfer region is defined as a 0.28m × 0.28m × 0.28m cubic space within the closed cavity (excluding the wall thickness). Using a coordinate positioning method, a near-wall region (including the bottom, top, and four sides) 0.005m from the wall is identified with fixed points spaced 0.001m apart, forming a continuous boundary layer identification region. Previous flow characteristics analysis revealed that this identified region exhibits large velocity and temperature gradients and complex flow conditions due to the combined effects of wall friction and electromagnetic forces. Based on this, complex characteristic data for the analysis region is generated, clarifying that this region requires higher-precision simulation.

[0035] Step S143: Designing an unstructured metal grid to be tested based on the analysis area fixed point identification data and the analysis area complex characteristic data to generate unstructured metal grid data to be tested; In an embodiment of the present invention, an unstructured grid generation method is used to design a grid based on the fixed-point identification data of the analysis area (0.005m identification area near the wall) and complex characteristic data (high gradient flow). In the identification area, tetrahedral grid cells are used with a grid side length of 0.0005m to ensure that the flow details in the boundary layer can be captured; in the non-identification area (inner area), hexahedral grid cells are used with a grid side length of 0.002m to balance the calculation accuracy and efficiency. During the grid generation process, the grid quality inspection tool is used to verify that the grid distortion rate is less than 0.2 and the aspect ratio is less than 5. The grid size is adjusted based on the complexity of the liquid metal flow to ensure that its detailed features can be accurately collected. Finally, unstructured grid data of the metal to be tested containing grid cells is generated, and each grid cell corresponds to unique coordinates and size information.

[0036] Step S144: Designing the 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 measured, and generating liquid metal flow and heat transfer calculation domain data.

[0037] In an embodiment of the present invention, the boundary constraint data (temperature, electric field, magnetic field, and flow field constraints) of the liquid metal flow and heat transfer calculation domain are associated and integrated with the unstructured mesh data of the metal to be measured. Through a geometric mapping method, the boundary constraint parameters are assigned to the boundary nodes of the corresponding mesh, such as binding the bottom wall mesh nodes to a 350K temperature constraint and binding the wall mesh nodes to a flow velocity constraint of 0. At the same time, the spatial coordinates, dimensions, and area (identified area / non-identified area) of the mesh cells are recorded to form a complete calculation domain data structure. The resulting liquid metal flow and heat transfer calculation domain data not only contains the spatial distribution information of the mesh but also integrates the constraint parameters of each boundary, providing a precise calculation range and condition setting for subsequent numerical simulations.

[0038] Furthermore, step S16 includes the following steps: Step S161: performing numerical discretization processing on the preliminary numerical simulation data of liquid metal thermal electromagnetic multi-field response to generate liquid metal thermal electromagnetic multi-field response discretization data; In an embodiment of the present invention, the preliminary numerical simulation data of liquid metal thermo-electromagnetic multi-field response (including continuous distribution data such as velocity, temperature, current density, etc. of liquid metal within 0-20s) is numerically discretized. The finite volume method is used to divide the calculation domain into discrete control volumes according to the generated unstructured grid (1.5 million grid cells), and the continuous control equations are converted into discrete algebraic equations by integration. The convection term of the velocity field is discretized using the second-order upwind format, the diffusion term of the temperature field is discretized using the central difference format, and the current density is distributed to each grid node using the linear interpolation method. After discretization, each grid cell corresponds to a set of physical quantity values. For example, the velocity of a grid cell at 5s is 0.1m / s, the temperature is 320K, and the current density is 100A / square meter. Finally, the discretized data of liquid metal thermo-electromagnetic multi-field response are generated to ensure that the discrete error is controlled within 10 -4 Within.

[0039] Step S162: extracting current, potential, and thermoelectric boundary reaction characteristics based on the discretized data of the liquid metal thermo-electromagnetic multi-field reaction, and generating liquid metal current-potential-thermoelectric boundary reaction characteristic data; In an embodiment of the present invention, the spatial extent of the thermoelectric boundary is determined based on the discretized data of the liquid metal's thermo-electromagnetic multi-field response (including physical quantity data such as current, electric potential, and temperature for each grid cell). This is the set of grid cells corresponding to the wall of the closed cavity in the discretized data (e.g., grids within 0.001 m from the wall). For these boundary grid cells, vector information of their current density (including components along the tangent direction of the wall and perpendicular to the normal direction of the wall), scalar values ​​of the electric potential, and temperature gradient data near the wall (calculated using the temperature difference and distance between adjacent grid cells). For example, at a boundary grid cell on the bottom wall, the extracted current density has a tangential component of 80 A / m² and a normal component of 0 A / m² (complying with the wall current boundary constraint), an electric potential of 3.2 V, and a temperature gradient of 1200 K / m² (calculated based on the temperature difference of 3 K between the grid and the adjacent internal grid and a distance of 0.0025 m). After performing the same extraction operation on all boundary mesh cells, the data is sorted and organized by wall position (bottom, top, and four sides) to form the liquid metal current-potential-thermoelectric boundary reaction characteristic data. This data can intuitively reflect the current distribution, potential level, and temperature gradient at different wall positions.

[0040] Step S163: performing Seebeck effect characteristic analysis of the liquid metal current-potential-thermoelectric boundary reaction on the liquid metal current-potential-thermoelectric boundary reaction characteristic data to generate Seebeck effect characteristic data of the liquid metal current-potential-thermoelectric boundary reaction; In an embodiment of the present invention, when analyzing the Seebeck effect characteristics of liquid metal current-potential-thermoelectric boundary reaction data, the physical mechanism of the Seebeck effect is that thermocurrent is induced by temperature gradient through the Seebeck coefficient. Current data in regions with non-zero temperature gradients (primarily near the bottom and top walls due to temperature differences) are screened from the characteristic data. The ratio of current density to temperature gradient in these regions is calculated to obtain a parameter characterizing the strength of the Seebeck effect (e.g., a current density of 100 A / m² and a temperature gradient of 1000 K / m in a bottom grid cell gives a ratio of 0.1 A·m / (K·m²)). The specific value of the Seebeck coefficient is then determined based on the inherent properties of the liquid metal. Next, the differences in this ratio at different boundary locations are compared to analyze the enhancement effect of the Seebeck effect in regions with larger temperature gradients (e.g., the bottom wall). The variation of potential with temperature gradient is observed (e.g., the potential increases linearly with increasing temperature gradient), clarifying the relationship between current, potential, and temperature gradient under the Seebeck effect. Finally, these correlation rules and 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 characteristics of the Seebeck effect at the thermoelectric boundary.

[0041] Step S164: Based on the Seebeck effect characteristic data of the liquid metal current-potential-thermoelectric boundary reaction, the discretized data of the liquid metal thermo-electromagnetic multi-field reaction are subjected to compatible conservation optimization processing of the liquid metal thermo-electromagnetic multi-field reaction to generate numerical simulation data of the liquid metal thermo-electromagnetic multi-field reaction.

[0042] In an embodiment of the present invention, based on the characteristic data of the Seebeck effect of the liquid metal current-potential-thermoelectric boundary reaction (such as the correlation between the Seebeck coefficient, thermocurrent and temperature gradient), the discretized data of the liquid metal thermoelectromagnetic multi-field reaction is subjected to compatible conservation optimization. The thermocurrent generated by the Seebeck effect is incorporated into the calculation of the Lorentz force, and the electromagnetic force term in the discretized data is corrected to ensure that the solution of the Lorentz force is consistent with the influence of the Seebeck effect while ensuring accuracy. Then, the mass conservation equation is verified. By comparing the mass difference between the inflow and outflow of each grid unit, the discrete value of the relevant physical quantity is adjusted to control the mass conservation error within 10 -6 Then, for momentum conservation, check whether the change of momentum in the discretized data matches the impulse of external forces such as Lorentz force and viscous force, and correct the mismatched grid unit data to ensure that the momentum conservation error does not exceed 10 -5At the same time, the conservation of kinetic energy is verified. By calculating the difference between the change in kinetic energy and the work done by the external force, possible deviations are corrected to ensure that the conservation of kinetic energy is within a reasonable range. For example, for a grid unit whose thermal current increases due to the Seebeck effect, its Lorentz force value is corrected so that it can reflect the driving effect of the thermoelectromagnetic force without destroying the momentum transfer balance between the unit and the adjacent units, so as to generate numerical simulation data of the thermoelectromagnetic multi-field response of liquid metal. This data can accurately reflect the physical laws under the thermoelectromagnetic multi-field coupling and provide a reliable basis for subsequent verification and analysis.

[0043] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment, step S2 includes the following steps: Step S21: collecting 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 response numerical simulation data; In an embodiment of the present invention, a data screening method is used to collect three types of driving simulation data based on numerical simulation data of liquid metal's thermo-electromagnetic multi-field response. When collecting liquid metal thermal field-driven simulation data, physical quantities in the simulation data driven solely by temperature differences are selected, such as the temperature value, heat flux, and natural convection velocity of each grid cell. For example, data on the temperature change from 300K to 350K within 0-20s and the corresponding flow velocity distribution are extracted. When collecting liquid metal electric field-driven simulation data, the current density, potential distribution, and related flow velocity data generated by the electric field are screened to clarify the driving effect of current on flow. When collecting liquid metal magnetic field-driven simulation data, the Lorentz force distribution, magnetic field strength, and flow velocity data affected by the magnetic field are extracted. All collected data are categorized and stored by time series and spatial coordinates to ensure that each type of data corresponds to a unique driving field parameter.

[0044] Step S22: Modeling a closed cavity for liquid metal simulation using the liquid metal thermal electromagnetic multi-field response numerical simulation data to obtain a liquid metal simulation closed cavity model; In this embodiment of the present invention, a three-dimensional modeling method is used to construct a liquid metal simulated closed cavity model using geometric parameters from numerical simulation data of liquid metal's thermo-electromagnetic multi-field response. Based on the closed cavity dimensions (0.3m long, 0.3m wide, and 0.3m high) in the simulation data, the spatial structure of the cavity is restored, the wall material (aluminum oxide) and thickness (0.01m) are determined, and the liquid metal-filled area (0.28m×0.28m×0.28m internal space) in the simulation data is used as the model's flow region. During the modeling process, the wall positions and internal space dimensions in the simulation data are precisely matched to ensure that the model's geometric parameters are completely consistent with the simulation data, ultimately resulting in a liquid metal simulated closed cavity model that includes the cavity structure, material properties, and flow region.

[0045] 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 in the absence of a magnetic field, so as to obtain closed cavity flow field heat distribution verification data in the absence of a magnetic field; In this embodiment of the present invention, a liquid metal simulation closed cavity model was verified and analyzed using liquid metal thermal field-driven simulation data (thermal field data with a temperature difference of 50K) and liquid metal electric field-driven simulation data (electric field data in the absence of a magnetic field). A non-magnetic field environment (magnetic field strength of 0T) was set up in the model, and thermal and electric field data were input to simulate the natural convection of liquid metal within the closed cavity. The deviations of the convection flow rate and temperature distribution from theoretical values ​​(e.g., the results calculated using the natural convection flow rate formula under a known temperature difference) were calculated. For example, the deviation between the simulated flow rate value of 0.12 m / s at the center of a cross section and the theoretical value of 0.11 m / s was 9%, which is within the allowable range. Simultaneously, the heat distribution was verified to ensure compliance with the laws of heat conduction. Ultimately, verification data for the thermal distribution of the closed cavity flow field without a magnetic field was generated, and the verification results for the flow rate and temperature were recorded.

[0046] Step S24: using the liquid metal magnetic field driven simulation data to perform magnetic field closed cavity convection and thermal distribution verification analysis on the non-magnetic field closed cavity flow field thermal distribution data to obtain magnetic field closed cavity flow field thermal distribution verification data; In an embodiment of the present invention, a liquid metal simulated closed cavity model was verified and analyzed using liquid metal magnetic field driven simulation data (including uniform magnetic field parameters with an intensity of 0.2T along the y-axis) and referenced by the thermal distribution data of the closed cavity flow field without a magnetic field (such as the basic data of a flow velocity of 0.12m / s and a temperature gradient of 1000K / m in the closed cavity). First, the magnetic field driving parameters were loaded into the model, and the temperature field (350K at the bottom and 300K at the top) and electric field boundary conditions were kept consistent with those in the absence of a magnetic field. The flow velocity distribution and temperature distribution of the liquid metal in the closed cavity under the action of a magnetic field were calculated through numerical simulation. During the simulation process, the damping effect of the magnetic field on the flow was monitored. For example, the difference between the flow velocity of a grid cell of 0.12m / s in the absence of a magnetic field and the flow velocity of 0.07m / s in the cell after the magnetic field was applied was compared to verify whether the flow velocity reduction conforms to the positive correlation between the magnetic field strength and the Lorentz force. At the same time, the changes in temperature distribution are analyzed, such as whether the temperature gradient under the action of the magnetic field increases due to the slowdown of the flow (for example, from 1000K / m to 1200K / m). By comparing with the experimental data of flow heat transfer under the action of a known magnetic field, it is ensured that the simulation deviation of the flow velocity and temperature distribution is controlled within 8%. Finally, the thermal distribution verification data of the flow field in the magnetic field closed cavity is generated, which includes the flow velocity, temperature and change of each grid unit after the action of the magnetic field.

[0047] Step S25: performing a magnetic field closed cavity thermal current analysis on the magnetic field closed cavity flow field thermal distribution verification data to generate magnetic field closed cavity thermal current data, and performing closed cavity thermal electromagnetic coupling effect process and thermal distribution verification analysis on the magnetic field closed cavity flow field thermal distribution verification data using the magnetic field closed cavity thermal current data to generate thermal electromagnetic effect closed cavity flow field thermal distribution verification data; In this embodiment of the present invention, a thermocurrent analysis is performed using a Seebeck effect-based calculation method based on the validation data for the thermal distribution of the flow field in a magnetically confined cavity (including flow velocity, temperature gradient, and magnetic field strength data). Based on the liquid metal Seebeck coefficient (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 (e.g., 20 μV / K × 1200 K / m = 24 mA / m²) using the formula (where thermocurrent density is the sum of the product of the Seebeck coefficient and the temperature gradient) to generate thermocurrent data for the magnetically confined cavity. This thermocurrent data is combined with the magnetic field parameter (0.2 T) to calculate the thermoelectromagnetic force generated by the thermocurrent in the magnetic field (the direction of which is determined by the left-hand rule). This force is then substituted into the validation data for the thermal distribution of the flow field in the magnetically confined cavity to re-simulate the flow field and thermal distribution. For example, in a certain area, due to the thermoelectromagnetic 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 thermoelectromagnetic force on flow is consistent with theoretical derivation (thermoelectromagnetic force increases when thermal current increases), and whether the changes in flow field and heat distribution conform to the law of conservation of energy, we finally generate thermal distribution verification data of the flow field in a closed cavity with thermoelectromagnetic effect, and record the flow velocity, temperature and thermal current distribution under thermoelectromagnetic coupling.

[0048] Step S26: Perform liquid metal thermo-electromagnetic multi-field response verification processing 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 thermo-electromagnetic effect closed cavity flow field thermal distribution verification data to generate liquid metal thermo-electromagnetic multi-field response verification data.

[0049] In this embodiment of the present invention, a comprehensive validation process was performed based on three sets of flow field thermal distribution validation data under conditions without a magnetic field, with a magnetic field, and with thermo-electromagnetic effects. The flow velocity trends in the three data sets (0.12 m / s, 0.08 m / s, and 0.09 m / s for the three conditions without a magnetic field, with a magnetic field, and with thermo-electromagnetic coupling) were compared to verify compliance with physical logic (magnetic field damping reduces flow velocity, while thermo-electromagnetic forces partially offset the damping). The rationality of the temperature distribution under different conditions was also verified, for example, the more uniform temperature mixing under thermo-electromagnetic coupling. The overall deviations between the three data sets and the corresponding theoretical models were calculated and found to be within 10%, confirming the reliability of the simulation data. Finally, validation data for the thermo-electromagnetic multi-field response of liquid metal was generated, and the three validation results were integrated to serve as valid data for subsequent analysis.

[0050] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S3 in FIG. 1 , in this embodiment, step S2 includes the following steps: Step S31: setting single-factor working condition simulation control parameters, wherein the single-factor working condition simulation control parameters include magnetic field intensity simulation control parameters, magnetic field direction simulation control parameters, Seebeck effect simulation control parameters, and wall conductivity simulation control parameters; In the embodiment of the present invention, when setting the single-factor working condition simulation control parameters, for the magnetic field intensity simulation control parameter, three gradient values ​​of 0.1T, 0.2T, and 0.3T are set, and the magnetic field direction is fixed along the y-axis; the magnetic field direction simulation control parameter is set along the x-axis, y-axis, and z-axis, and the magnetic field intensity is fixed at 0.2T; the Seebeck effect simulation control parameter is achieved by adjusting the Seebeck coefficient, and is set to three values ​​of 10μV / K, 20μV / K, and 30μV / K, while other parameters remain unchanged; the wall conductivity simulation control parameter is set to the wall insulation (conductivity 1×10 -10 S / m) and wall conductivity (conductivity 1×10 5 The remaining parameters remain unchanged. All parameter settings are based on the key influencing factors to be analyzed in the project research, and only a single variable is changed when each parameter is changed to ensure the validity of the single-factor analysis.

[0051] Step S32: performing single-factor working condition simulation control processing of liquid metal thermo-electromagnetic reaction on the bulk metal thermo-electromagnetic multi-field response verification data based on the single-factor working condition simulation control parameters to generate liquid metal thermo-electromagnetic single-factor working condition simulation data; In an embodiment of the present invention, simulation control processing is performed on the verification data of the liquid metal thermo-electromagnetic multi-field response based on the set single-factor working condition simulation control parameters. Taking the single-factor simulation of magnetic field intensity as an example, while keeping the magnetic field direction along the y-axis, the Seebeck coefficient at 20μV / K, and the wall insulation, the magnetic field intensity parameters of 0.1T, 0.2T, and 0.3T are input respectively, and the simulation program is run to obtain the flow field and thermal distribution data under the corresponding magnetic field intensity. Similarly, the magnetic field direction, Seebeck coefficient, and wall conductivity are adjusted and simulated as single variables. For example, when the magnetic field direction is along the x-axis, other parameters such as the magnetic field intensity of 0.2T are kept unchanged. Each simulation records complete flow rate, temperature, current, and other data to generate liquid metal thermo-electromagnetic single-factor working condition simulation data, ensuring that each working condition data corresponds to a unique variable parameter.

[0052] Step S33: analyzing the liquid metal flow and heat transfer characteristics of the thermal electromagnetic reaction of each working condition based on the liquid metal thermal electromagnetic single-factor working condition simulation data, and generating the liquid metal flow and heat transfer characteristic data of the thermal electromagnetic reaction of each working condition; In this embodiment, a feature extraction method is used to analyze flow and heat transfer characteristics based on data from single-factor thermo-electromagnetic simulations of liquid metal. For data from magnetic field intensity conditions, the average flow velocity (e.g., 0.1 m / s at 0.1 T, decreasing to 0.05 m / s at 0.3 T), maximum temperature gradient, and heat transfer efficiency (measured as heat flux) are calculated at different intensities to clarify the inhibitory effect of increasing magnetic field intensity on flow. For data from magnetic field direction conditions, the velocity vector distributions along the x-, y-, and z-axes are compared, revealing a more uniform velocity distribution across a cross-section when the magnetic field is along the z-axis. For data from Seebeck coefficient and wall conductivity conditions, the effects of thermal current changes on flow velocity and the effect of current transmission paths on heat transfer are analyzed. These characteristics are quantified and recorded to generate characteristic data for liquid metal flow and heat transfer under various thermo-electromagnetic conditions.

[0053] Step S34: performing correlation processing on the liquid metal flow and heat transfer characteristics affected by each working condition based on the liquid metal flow and heat transfer characteristic data of the thermal electromagnetic response of each working condition, and generating liquid metal working condition influence-flow and heat transfer characteristic correlation data; In this embodiment of the present invention, a correlation analysis method is used to process data on the flow and heat transfer characteristics of liquid metal derived from various thermoelectromagnetic reactions. For example, a linear fit between magnetic field strength and average flow velocity reveals a correlation relationship: for every 0.1T increase in magnetic field strength, the average flow velocity decreases by 0.025 m / s. Correlating the Seebeck coefficient with heat transfer efficiency reveals a 15% increase in heat transfer efficiency when the Seebeck coefficient increases from 10 μV / K to 30 μV / K. Comparing flow velocity data under insulating and conductive wall conditions reveals a correlation result: "The average flow velocity is 0.03 m / s higher when the wall is conductive than when it is insulating." This method quantitatively analyzes the relationship between all single factors and flow and heat transfer characteristics, categorizes and organizes them by variable type, and generates correlation data between the influence of liquid metal operating conditions and flow and heat transfer characteristics, visually presenting the influence patterns of each factor.

[0054] Step S35: Modeling and processing the liquid metal flow and heat transfer associated data of multi-source working condition thermal electromagnetic response is performed through the liquid metal working condition influence-flow and heat transfer characteristic associated data to generate a liquid metal multi-source working condition-flow and heat transfer associated model.

[0055] In one embodiment of the present invention, a correlation model is constructed using data correlating the effects of liquid metal operating conditions with flow and heat transfer characteristics. Magnetic field strength, direction, Seebeck coefficient, and wall conductivity are used as input parameters, and flow and heat transfer characteristics (such as average flow velocity and heat transfer efficiency) are used as output parameters. A mathematical model is established based on quantitative relationships in the correlation data (such as the linear relationship between magnetic field strength and flow velocity, and the proportional relationship between the Seebeck coefficient and heat transfer efficiency). For example, in the model, average flow velocity = 0.15-0.25 × magnetic field strength + 0.001 × Seebeck coefficient (when the wall is insulated). The model's accuracy is verified using validation data (with the deviation between calculated and simulated values ​​being less than 5%). This ultimately generates a correlation model between liquid metal multi-source operating conditions and flow and heat transfer, which can predict flow and heat transfer characteristics based on the input multi-factor parameters.

[0056] Furthermore, step S32 includes the following steps: Step S321: performing wall characteristic division processing on the wall conductivity simulation control parameters to obtain wall insulation simulation control parameters and wall conductivity simulation control parameters respectively; In the embodiment of the present invention, when the wall conductivity simulation control parameter is used to classify the wall characteristics, the characteristic classification standard is defined based on the conductivity value. When the wall is insulating, the induced current is mainly concentrated in the Hartmann layer, and the magnetic damping effect is large (large Joule dissipation). However, 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 insulation simulation control parameter is set to wall conductivity ≤ 1×10 -8 S / m. Under this parameter, the current cannot be transmitted through the wall. The control parameter of the wall conductivity simulation is that the wall conductivity is ≥1×10 4 S / m, a parameter that allows current to flow along the wall. During the classification process, conductivity measuring instruments were used to confirm the actual conductivity values ​​of the two materials, ensuring that the parameter settings were consistent with the inherent properties of the materials. Ultimately, clear control parameters for wall insulation and wall conductivity simulation were obtained, providing a foundation for subsequent differentiated analysis.

[0057] Step S322: performing differentiated current transmission characteristic analysis between the insulating wall and the non-insulating wall based on the wall insulation simulation control parameters and the wall conductivity simulation control parameters, and generating differentiated wall current characteristic data; In the embodiment of the present invention, based on the wall insulation simulation control parameters (aluminum oxide wall, conductivity 1×10 -10The current distribution measurement method was used to analyze differentiated current transmission characteristics by adjusting the control parameters of the wall conductivity simulation (0.2T along the y-axis) and wall conductivity. Under the same magnetic field (0.2T along the y-axis) and temperature field (50K temperature difference), the current path and density within the closed cavity were measured under two different wall conditions. When the wall was insulating, the current was concentrated in the Hartmann layer within the liquid metal, with a peak current density of 200A / m2. When the wall was conductive, 30% of the current was transmitted through the wall, and the peak current density within the liquid metal dropped to 140A / m2, with a more uniform current distribution. The current transmission path, density distribution, and peak value data were recorded for both conditions to generate differentiated wall current characteristic data, clearly demonstrating the impact of wall conductivity on current transmission.

[0058] Step S323: performing a wall surface differential magnetic damping effect characteristic analysis based on the wall surface differential current characteristic data to generate wall surface differential magnetic damping effect characteristic data; In this embodiment of the present invention, an electromagnetic force calculation method is used to analyze the characteristics of the differentiated magnetic damping effect on the wall surface based on the differentiated current characteristic data of the wall surface (current concentration in the Hartmann layer when insulating and current dispersion when conducting). The magnetic damping effect is determined by the Lorentz force generated by the interaction between current and magnetic field. The Lorentz force formula shows that when the wall surface is insulating, the Lorentz force generated by the interaction between the concentrated current (200 A / m²) in the Hartmann layer and the magnetic field (0.2 T) is 40 N / m³, significantly damping the flow. When the wall surface is conducting, the dispersed current (140 A / m²) generates a Lorentz force of 28 N / m³, which weakens the damping effect. The flow velocity decay rates under the two conditions are also measured (30% for insulating and 20% for conducting) to quantify the difference in magnetic damping effect and generate characteristic data for the differentiated magnetic damping effect on the wall surface.

[0059] Step S324: performing single-factor working condition simulation control processing of liquid metal thermo-electromagnetic response on the liquid metal thermo-electromagnetic multi-field response verification data using the wall surface differentiated magnetic damping effect characteristic data and the single-factor working condition simulation control parameters to generate liquid metal thermo-electromagnetic single-factor working condition simulation data.

[0060] In this embodiment of the present invention, simulation control processing of liquid metal thermoelectromagnetic multi-field response verification data was performed using characteristic data of the wall's differentiated magnetic damping effect (Lorentz force 40 N / m³ for insulation and 28 N / m³ for conductivity) and single-factor simulation control parameters (only the wall conductivity was varied, with other parameters fixed at a magnetic field of 0.2 T and a Seebeck coefficient of 20 μV / K). The simulation was performed by applying the wall insulation and conductive parameters separately, while keeping all other conditions constant. The simulation was then run to obtain flow field and heat distribution data under the two operating conditions. For example, the central flow velocity in the closed chamber was 0.07 m / s and the heat flux was 80 W / m² when the wall was insulating; the central flow velocity was 0.1 m / s and the heat flux was 95 W / m² when the wall was conductive. This data was categorized by operating condition, and key parameters such as flow velocity, temperature, and heat flux were recorded to generate single-factor simulation data for liquid metal thermoelectromagnetic conditions, ensuring that the data directly reflects the influence of wall conductivity on flow and heat transfer.

[0061] Furthermore, step S35 includes the following steps: Based on the liquid metal working condition influence-flow heat transfer characteristic correlation data, composite working condition influence characteristic analysis is performed to generate liquid metal composite working condition influence characteristic data; liquid metal flow heat transfer correlation data of multi-source working condition thermal electromagnetic response is modeled and processed using the liquid metal composite working condition influence characteristic data and liquid metal working condition influence-flow heat transfer characteristic correlation data to generate a liquid metal multi-source working condition-flow heat transfer correlation model.

[0062] In the embodiment of the present invention, when performing composite working condition influence characteristic analysis based on the liquid metal working condition influence-flow heat transfer characteristic correlation data, the magnetic field intensity of 0.2T, Seebeck coefficient of 20μV / K, and wall conductivity of 1×10 5A composite operating condition (S / m) is formed (based on core influencing factors to be studied, such as the magnetic field, Seebeck coefficient, and wall conductivity). The flow and heat transfer characteristics are derived using the control variable superposition method. The control parameters for the single-factor operating condition simulation are first set to multiple sets of values, and the composite characteristics of the interactions between these sets of values ​​are analyzed. Through numerical simulation of the actual composite operating condition, the average flow rate measured increases compared to the baseline value. The deviation is due to the multi-factor coupling and the combined effect of the magnetic field and the Seebeck effect, where the thermal electromagnetic force slightly outweighs the magnetic damping effect, compared to the single-factor superposition effect. The actual flow rate, heat transfer efficiency, and deviation data under the composite operating condition are recorded to generate liquid metal composite operating condition influence characteristic data. Modeling is performed using the liquid metal composite operating condition influence characteristic data and the correlation data between operating condition influence and flow and heat transfer characteristics. A multivariate regression method is used to construct the model, with magnetic field intensity, Seebeck coefficient, and wall conductivity as input parameters and average flow rate and heat transfer efficiency as output parameters. First, a basic formula was established based on the correlation between the operating condition effects and flow heat transfer characteristics of liquid metal operating condition-flow heat transfer characteristics. Then, coupling correction terms were introduced into the composite operating condition characteristic data, such as the "magnetic field intensity × Seebeck coefficient" interaction term (coefficient 0.001). After this correction, the average flow velocity calculated by the composite operating condition model was kept within 3% of the actual measured value. A similar model was also established for heat transfer efficiency, incorporating the influence of various factors and coupling effects on heat flux. The resulting liquid metal multi-source operating condition-flow heat transfer correlation model accurately reflects the flow and heat transfer characteristics under the combined influence of multiple factors, providing support for related analysis of scenarios such as magnetic confinement fusion reactor blankets.

[0063] Furthermore, step S4 includes the following steps: Step S41: analyzing the stress characteristics of the liquid metal multi-source working condition according to the liquid metal multi-source working condition-flow and heat transfer correlation model, and generating liquid metal multi-source working condition stress characteristic data; In an embodiment of the present invention, a force decomposition method is used to analyze the force characteristics based on a liquid metal multi-source working condition-flow heat transfer correlation model (which already includes the correlation between parameters such as magnetic field intensity and Seebeck coefficient and flow velocity and temperature). A typical composite working condition (magnetic field intensity, Seebeck coefficient, and wall conductivity) in the model is selected. Based on the flow velocity and temperature gradient data output by the model, three types of forces acting on the liquid metal are calculated: magnetic damping force (generated by the interaction between current and magnetic field, calculated using the Lorentz force formula, and the force generated by the interaction between the current density and magnetic field of a certain grid unit), thermoelectromagnetic force (generated by the interaction between thermal current induced by the Seebeck effect and magnetic field, calculated in combination with the Seebeck coefficient and temperature gradient), and buoyancy (generated by density changes caused by temperature differences). The magnitude, direction, and distribution of the three types of forces are recorded for all grid units to generate force characteristic data for liquid metal multi-source working conditions, clarifying the contribution ratio of each force under the composite working condition.

[0064] Step S42: performing dimensionless characteristic analysis of the force under the multi-source working condition based on the force characteristic data of the liquid metal multi-source working condition to generate dimensionless characteristic data of the force under the multi-source working condition; In the embodiment of the present invention, based on the force characteristic data of liquid metal multi-source working conditions (including magnetic damping force 40N / cubic meter, thermal electromagnetic force 5N / cubic meter, and buoyancy 2N / cubic meter), a dimensionless calculation method is used to perform dimensionless force characteristic analysis. Buoyancy is selected as the reference force (because buoyancy is the basic driving force of natural convection), and the dimensionless values ​​of each force are calculated: dimensionless value of magnetic damping force = 40N / cubic meter ÷ 2N / cubic meter = 20, dimensionless value of thermal electromagnetic force = 5N / cubic meter ÷ 2N / cubic meter = 2.5, dimensionless value of buoyancy = 2N / cubic meter ÷ 2N / cubic meter = 1. At the same time, the Hartmann number (dimensionless number) that characterizes the intensity of the magnetic field is calculated. Based on the magnetic field intensity of 0.3T and the liquid metal conductivity of 3×10 6 S / m, characteristic length 0.3m and dynamic viscosity 0.001Pa·s, according to the Hartmann number formula , calculated as 300 (where B is the magnetic field strength, L is the characteristic length, σ is the conductivity, and μ is the dynamic viscosity). Furthermore, the ratio of the thermal electromagnetic force to the magnetic damping force is calculated (5 N / m³ ÷ 40 N / m³ = 0.125). This ratio directly reflects the degree to which the thermal electromagnetic force offsets the magnetic damping force. These dimensionless parameters are organized according to the calculation logic to generate dimensionless characteristic force data for multiple source working conditions, enabling standardized comparison of force magnitudes and intensities under different working conditions.

[0065] Step S43: Analyzing the dimensionless stress characteristics of the multi-source working condition and the heat transfer efficiency impact characteristics based on the dimensionless stress characteristic data of the multi-source working condition, and generating the dimensionless stress characteristic-heat transfer efficiency impact characteristic data of the multi-source working condition; In this embodiment of the present invention, a quantitative correlation analysis method was used to analyze the influence of dimensionless force characteristics on heat transfer efficiency based on dimensionless force characteristic data for a multi-source operating condition (including a dimensionless magnetic damping force value of 20, a dimensionless thermal electromagnetic force value of 2.5, a dimensionless buoyancy force value of 1, a Hartmann number of 300, and a ratio of thermal electromagnetic force to magnetic damping force of 0.125). Heat transfer efficiency was measured as the average heat flux within the closed cavity. A heat flow meter measured the average heat flux under this operating condition to be 900 W / m2. The correlation between each dimensionless characteristic and heat flux was calculated: a dimensionless magnetic damping force value of 20 corresponds to a heat flux of 900 W / m². Combined with previous single-factor data, it was found that for every increase of 5 in the dimensionless magnetic damping force value, the heat flux decreased by 80 W / m², showing a significant negative correlation. A dimensionless thermoelectromagnetic force value of 2.5 corresponds to a heat flux of 900 W / m², and for every increase of 1 in the value, the heat flux increased by 60 W / m², showing a positive correlation. At a Hartmann number of 300, the heat flux was 900 W / m², and for every increase of 50 in the Hartmann number, the heat flux decreased by 50 W / m². Furthermore, the change in heat flux corresponding to a ratio of 0.125 between the thermoelectromagnetic force and the magnetic damping force was analyzed. For every 0.05 increase in this ratio, the heat flux increased by 50 W / m², directly reflecting the influence of the coupling effect between the two on heat transfer. These related data are organized according to the logic between dimensionless characteristics, variation amplitude and heat flux to generate characteristic data of dimensionless stress characteristics and heat transfer efficiency impact under multi-source working conditions, clearly presenting the quantitative influence of each dimensionless characteristic on heat transfer efficiency.

[0066] Step S44: Map the dimensionless characteristics of the multi-source working condition force-heat transfer efficiency influence characteristic data to the liquid metal multi-source working condition-flow heat transfer correlation model to perform liquid metal thermal electromagnetic coupled flow heat transfer global characteristic modeling processing to generate a liquid metal thermal electromagnetic flow heat transfer global characteristic model.

[0067] In this embodiment of the present invention, the dimensionless characteristics of multi-source force conditions and their impact on heat transfer efficiency (including dimensionless magnetic damping force values ​​of 10-30, dimensionless thermal electromagnetic force values ​​of 1-4, Hartmann number of 200-400, and a ratio of thermal electromagnetic force to magnetic damping force of 0.1-0.3, corresponding to a heat transfer efficiency of 900-1200 W / m2) are mapped to a liquid metal multi-source condition-flow heat transfer correlation model. A global characteristic model is then established using a random forest regression algorithm. First, the dataset is partitioned, with 80% of the samples (200 groups) selected as the training set and 20% (50 groups) as the test set. The input features are the dimensionless magnetic damping force values, dimensionless thermal electromagnetic force values, Hartmann number, and force ratio, and the output target is heat transfer efficiency. During training, the number of decision trees is set to 100, with a maximum depth of 8 and a minimum number of leaf nodes of 5. Model parameters are iteratively adjusted through the training set to keep the training set fitting error within 3%. The model was validated using a test set. For a test sample with an input dimensionless magnetic damping force value of 22, a dimensionless thermoelectromagnetic force value of 2.8, a Hartmann number of 320, and a force ratio of 0.13, the model predicted a heat transfer efficiency of 1050 W / m², deviating by 0.5% from the actual measured value of 1045 W / m². The deviation was less than 5% for all test samples. Furthermore, by combining the model with the velocity and temperature distribution patterns of the original correlation model and supplementing the flow characteristic constraints, the resulting global model for the thermoelectromagnetic flow and heat transfer of liquid metals simultaneously outputs velocity distribution, temperature gradient, and heat transfer efficiency, meeting the prediction requirements of thermoelectromagnetic multi-field coupling scenarios.

[0068] Furthermore, the force characteristic data of the liquid metal multi-source working condition in step S41 includes magnetic damping force effect data of the liquid metal multi-source working condition, thermal electromagnetic force effect data of the liquid metal multi-source working condition, and buoyancy effect data of the liquid metal multi-source working condition.

[0069] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0070] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling, characterized in that: The following steps are involved: Step S1: obtaining the metal structure physical parameters and thermal electromagnetic multi-field coupling simulation control parameters of the metal to be tested; Based on the physical parameters of the metal structure and the control parameters of the thermo-electromagnetic multi-field coupling simulation, the numerical simulation analysis of the thermo-electromagnetic multi-field response of the liquid metal is performed to generate the numerical simulation data of the thermo-electromagnetic multi-field response of the liquid metal; Step S2: performing liquid metal thermo-electromagnetic multi-field response verification processing based on the liquid metal thermo-electromagnetic multi-field response numerical simulation data to generate liquid metal thermo-electromagnetic multi-field response verification data; Step S3: setting single-factor working condition simulation control parameters; performing a correlation analysis of the liquid metal flow and heat transfer characteristics affected by each working condition on the liquid metal thermal electromagnetic multi-field response verification data based on the single-factor working condition simulation control parameters, generating liquid metal working condition influence-flow and heat transfer characteristic correlation data, and establishing a liquid metal multi-source working condition-flow and heat transfer correlation model based on the liquid metal working condition influence-flow and heat transfer characteristic correlation data; Step S4: Analyzing the dimensionless stress characteristics and heat transfer efficiency influencing characteristics of the multi-source working condition based on the liquid metal multi-source working condition-flow and heat transfer correlation model, generating characteristic data of the dimensionless stress characteristics and heat transfer efficiency influencing characteristics of the multi-source working condition; establishing a global characteristic model of liquid metal thermal electromagnetic flow and heat transfer based on the dimensionless stress characteristics and heat transfer efficiency influencing characteristic data of the multi-source working condition and the liquid metal multi-source working condition-flow and heat transfer correlation model; Step S5: Execute the intelligent deduction operation of liquid metal flow and heat transfer with thermo-electromagnetic multi-field coupling through the global characteristic model of liquid metal thermo-electromagnetic flow and heat transfer.

2. The numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: obtaining the physical parameters of the metal structure of the metal to be tested; Step S12: obtaining thermo-electromagnetic multi-field coupling simulation control parameters, wherein the thermo-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: performing geometric modeling processing based on the physical parameters of the metal structure to generate a geometric model of the metal structure; Step S14: Designing a liquid metal flow and heat transfer calculation domain based on the thermo-electromagnetic multi-field coupling simulation control parameters and the metal structure geometric model, and generating liquid metal flow and heat transfer calculation domain data; Step S15: performing preliminary numerical simulation analysis of the liquid metal thermal electromagnetic multi-field response on the metal structure geometric model based on the thermal electromagnetic multi-field coupling simulation control parameters and the liquid metal flow and heat transfer calculation domain data, and generating preliminary liquid metal thermal electromagnetic multi-field response numerical simulation data; Step S16: performing compatible conservation optimization processing of the thermo-electromagnetic numerical simulation on the preliminary liquid metal thermo-electromagnetic multi-field response numerical simulation data to generate the liquid metal thermo-electromagnetic multi-field response numerical simulation data.

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

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

5. The numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: collecting 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 response numerical simulation data; Step S22: Modeling a closed cavity for liquid metal simulation using the liquid metal thermal electromagnetic multi-field response 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 in the absence of a magnetic field, so as to obtain closed cavity flow field heat distribution verification data in the absence of a magnetic field; Step S24: using the liquid metal magnetic field driven simulation data to perform magnetic field closed cavity convection and thermal distribution verification analysis on the non-magnetic field closed cavity flow field thermal distribution data to obtain magnetic field closed cavity flow field thermal distribution verification data; Step S25: performing a magnetic field closed cavity thermal current analysis on the magnetic field closed cavity flow field thermal distribution verification data to generate magnetic field closed cavity thermal current data, and performing closed cavity thermal electromagnetic coupling effect process and thermal distribution verification analysis on the magnetic field closed cavity flow field thermal distribution verification data using the magnetic field closed cavity thermal current data to generate thermal electromagnetic effect closed cavity flow field thermal distribution verification data; Step S26: Perform liquid metal thermo-electromagnetic multi-field response verification processing 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 thermo-electromagnetic effect closed cavity flow field thermal distribution verification data to generate liquid metal thermo-electromagnetic multi-field response verification data.

6. The numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: setting single-factor working condition simulation control parameters, wherein the single-factor working condition simulation control parameters include magnetic field intensity simulation control parameters, magnetic field direction simulation control parameters, Seebeck effect simulation control parameters, and wall conductivity simulation control parameters; Step S32: performing single-factor working condition simulation control processing of liquid metal thermo-electromagnetic reaction on the bulk metal thermo-electromagnetic multi-field response verification data based on the single-factor working condition simulation control parameters to generate liquid metal thermo-electromagnetic single-factor working condition simulation data; Step S33: analyzing the liquid metal flow and heat transfer characteristics of the thermal electromagnetic reaction of each working condition based on the liquid metal thermal electromagnetic single-factor working condition simulation data, and generating the liquid metal flow and heat transfer characteristic data of the thermal electromagnetic reaction of each working condition; Step S34: performing correlation processing on the liquid metal flow and heat transfer characteristics affected by each working condition based on the liquid metal flow and heat transfer characteristic data of the thermal electromagnetic response of each working condition, and generating liquid metal working condition influence-flow and heat transfer characteristic correlation data; Step S35: Modeling and processing the liquid metal flow and heat transfer associated data of multi-source working condition thermal electromagnetic response is performed through the liquid metal working condition influence-flow and heat transfer characteristic associated data to generate a liquid metal multi-source working condition-flow and heat transfer associated model.

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

8. The numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling according to claim 6 is characterized in that: Step S35 includes the following steps: Based on the liquid metal working condition influence-flow heat transfer characteristic correlation data, composite working condition influence characteristic analysis is performed to generate liquid metal composite working condition influence characteristic data; liquid metal flow heat transfer correlation data of multi-source working condition thermal electromagnetic response is modeled and processed using the liquid metal composite working condition influence characteristic data and liquid metal working condition influence-flow heat transfer characteristic correlation data to generate a liquid metal multi-source working condition-flow heat transfer correlation model.

9. The numerical simulation method for liquid metal flow and heat transfer under thermal and electromagnetic multi-field coupling according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: analyzing the stress characteristics of the liquid metal multi-source working condition according to the liquid metal multi-source working condition-flow and heat transfer correlation model, and generating liquid metal multi-source working condition stress characteristic data; Step S42: performing dimensionless characteristic analysis of the force under the multi-source working condition based on the force characteristic data of the liquid metal multi-source working condition to generate dimensionless characteristic data of the force under the multi-source working condition; Step S43: Analyzing the dimensionless stress characteristics of the multi-source working condition and the heat transfer efficiency impact characteristics based on the dimensionless stress characteristic data of the multi-source working condition, and generating the dimensionless stress characteristic-heat transfer efficiency impact characteristic data of the multi-source working condition; Step S44: Map the dimensionless characteristics of the multi-source working condition force-heat transfer efficiency influence characteristic data to the liquid metal multi-source working condition-flow heat transfer correlation model to perform liquid metal thermal electromagnetic coupled flow heat transfer global characteristic modeling processing to generate a liquid metal thermal electromagnetic flow heat transfer global characteristic model.

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

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