Method and device for predicting thermal physical parameters of continuous casting billet, storage medium and electronic equipment

Abstract

The application discloses a continuous casting billet thermal physical property parameter prediction method and device, a storage medium and an electronic device, relates to the technical field of continuous casting solidification simulation, and comprises the following steps: establishing a billet calculation model based on initial parameters transmitted by a client; performing heat conduction analysis on the billet calculation model to obtain a dynamic cooling rate; correcting a solute balance distribution coefficient based on a preset microsegregation model and the dynamic cooling rate to obtain solute distribution parameters under non-equilibrium solidification; combining the solute distribution parameters and the microsegregation model to calculate the inclusion precipitation amount of multiple inclusions in the billet, correct the liquid-phase solute concentration, and synchronously calculate the corrected phase fraction; and calculating thermal physical property parameters by using a phase weighting method according to the corrected phase fraction and the liquid-phase solute concentration, iteratively updating the dynamic cooling rate, the solute balance distribution coefficient, the inclusion precipitation amount, the corrected phase fraction and the thermal physical property parameters until a termination condition is met, and outputting a thermal physical property parameter prediction result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of continuous casting solidification simulation, and in particular to a continuous casting billet thermal property parameter prediction method and device, a storage medium and an electronic device. BACKGROUND

[0002] Continuous casting is the core link of realizing the transformation of molten steel from liquid to solid in steel production, and the solidification structure quality and solidification defect control directly determine the final performance of the steel, which is the key process to guarantee the quality of steel products from the source. Since the numerical simulation method was first applied to the calculation of continuous casting solidification process in the 1960s, the solidification heat transfer calculation technology has gradually matured, and the thermal tracking model has been widely popularized in industrial production, providing an important support for continuous casting process optimization and quality prediction. However, the accuracy of thermal property parameters (including specific heat capacity, thermal conductivity, density, etc.) is a key factor that determines the reliability of the prediction of the solidification process. Such parameters directly affect the calculation results of the heat transfer model, and are further related to the judgment of the solidification end position, the phase change interval range and the defect generation risk. Therefore, accurately obtaining and calculating the thermal property parameters of the continuous casting billet has been a key technical pain point in the field of continuous casting solidification simulation for a long time.

[0003] The current industry widely uses a phase weighting method based on a microsegregation model to calculate the thermal property parameters of the continuous casting billet. This scheme is applied to high-precision continuous casting solidification simulation scenarios, such as predicting the influence of the δ / γ phase change interval of microalloyed steel on thermal properties. The core principle is to calculate the phase fraction evolution law of the δ phase, the γ phase and the liquid phase in the solidification process through a microsegregation model (such as the Scheil model, the Brody-Flemings model, the Ueshima model, etc.), and then to obtain the thermal property parameters of the whole billet by using a phase weighting method according to the inherent thermal property parameters of each phase. Subsequently, an integrated microsegregation model is developed to realize the collaborative calculation of the solidus temperature, the solute distribution and other parameters by coupling a thermodynamic database. However, this technical scheme has a core technical defect: all classic microsegregation models and subsequent integrated models take a fixed cooling rate as the core assumption, that is, a constant cooling rate (such as the average cooling rate of the billet center) is used to carry out the calculation of the phase fraction and the solute concentration distribution in the calculation process. In actual continuous casting production, the thermal history of different positions of the billet has significant differences, and the cooling rate not only changes with the position of the billet, but also dynamically changes with the solidification process. The mismatch between this assumption and the actual working condition directly leads to the calculation deviation of the solute equilibrium partition coefficient, and further causes the prediction error of the solute segregation and the phase fraction, and finally causes the insufficient calculation accuracy of the thermal property parameters, which is difficult to meet the demand of modern continuous casting production for high-precision solidification simulation. SUMMARY

[0004] Therefore, the present application provides a continuous casting billet thermal property parameter prediction method and device, a storage medium and an electronic device, which can improve the prediction accuracy of the thermal property parameters.

[0005] According to a first aspect of the present application, a continuous casting billet thermal property parameter prediction method is provided, the method is applied to a server side, and the method comprises the following steps:

[0006] Receiving initial parameters transmitted by a client, and establishing a billet calculation model based on the initial parameters;

[0007] Performing heat conduction analysis on the billet calculation model, calculating dynamic cooling rates at different positions of the billet, and obtaining billet thermal history data containing the dynamic cooling rates;

[0008] Based on a preset microsegregation model and the dynamic cooling rates in the billet thermal history data, correcting a solute balance distribution coefficient to obtain a solute distribution parameter under non-equilibrium solidification;

[0009] Combining the solute distribution parameter and the microsegregation model, calculating inclusion precipitation amounts of multiple inclusions in the billet, correcting liquid phase solute concentrations, and synchronously calculating corrected phase fraction rates;

[0010] According to the corrected phase fraction rates and the corrected liquid phase solute concentrations, calculating thermal property parameters by using a phase weighting method, iteratively updating the dynamic cooling rates, the solute balance distribution coefficient, the inclusion precipitation amounts, the corrected phase fraction rates, and the thermal property parameters until a termination condition is met, and outputting a thermal property parameter prediction result.

[0011] According to a second aspect of the present application, a continuous casting billet thermal property parameter prediction device is provided, the device is applied to a server side, and the device comprises the following components:

[0012] A receiving module is configured to receive initial parameters transmitted by a client, and establish a billet calculation model based on the initial parameters;

[0013] A calculation module is configured to perform heat conduction analysis on the billet calculation model, calculate dynamic cooling rates at different positions of the billet, and obtain billet thermal history data containing the dynamic cooling rates;

[0014] A correction module is configured to correct a solute balance distribution coefficient based on a preset microsegregation model and the dynamic cooling rates in the billet thermal history data, and obtain a solute distribution parameter under non-equilibrium solidification;

[0015] The correction module is further configured to combine the solute distribution parameter and the microsegregation model, calculate inclusion precipitation amounts of multiple inclusions in the billet, correct liquid phase solute concentrations, and synchronously calculate corrected phase fraction rates;

[0016] An updating module is configured to calculate the thermophysical parameters by using a phase weighting method according to the corrected phase fraction and the corrected liquid solute concentration, and to update the dynamic cooling rate, the solute partitioning coefficient, the inclusion precipitation amount, the corrected phase fraction and the thermophysical parameters by iteration until a termination condition is met, and to output a prediction result of the thermophysical parameters.

[0017] According to a third aspect of the present application, a storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the above-mentioned continuous casting billet thermophysical parameter prediction method.

[0018] According to a fourth aspect of the present application, an electronic device is provided, which comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, and the processor implements the above-mentioned continuous casting billet thermophysical parameter prediction method when executing the program.

[0019] By the above technical solution, the continuous casting billet thermophysical parameter prediction method, device, storage medium and electronic device provided by the present application can break through the core assumption of the fixed cooling rate of the classical microsegregation model by establishing a billet calculation model and obtaining the dynamic cooling rate and thermal history data of the billet at different positions through heat conduction analysis, and can obtain accurate solute partitioning parameters under non-equilibrium solidification based on the dynamic cooling rate and the preset microsegregation model to correct the solute partitioning coefficient, and can calculate the amounts of various inclusions by the microsegregation model in combination with the parameters to correct the liquid solute concentration and the phase fraction, and finally can calculate the thermophysical parameters by using the phase weighting method and update the dynamic cooling rate, the solute partitioning coefficient, the inclusion precipitation amount, the corrected phase fraction and the thermophysical parameters by iteration to ensure that the parameters match the actual solidification process, which can effectively correct the deviation of the solute partitioning coefficient caused by the fixed cooling rate, reduce the prediction error of the solute segregation and the phase fraction, significantly improve the calculation accuracy of the thermophysical parameters of the continuous casting billet, and meet the needs of the high-precision solidification simulation in modern continuous casting production.

[0020] The above description is only a summary of the technical solutions of the present application, and in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented in accordance with the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 Fig. 1 shows a flowchart of a continuous casting billet thermophysical parameter prediction method provided by an embodiment of the present application;

[0023] Figure 2 A flowchart of a continuous casting billet thermal physical property parameter prediction method provided by another embodiment of the application is shown;

[0024] Figure 3 A structural schematic diagram of a continuous casting billet thermal physical property parameter prediction device provided by an embodiment of the application is shown. DETAILED DESCRIPTION

[0025] Hereinafter, the application will be described in detail with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0026] The current industry widely uses a phase weighting method based on a microscopic segregation model to calculate the thermal physical property parameters of a continuous casting billet. This scheme is applied to a medium-high precision continuous casting solidification simulation scene, for example, to predict the influence of a microalloy steel δ / γ phase transformation interval on thermal physical properties. The core principle is to calculate the phase fraction evolution law of δ phase, γ phase and liquid phase in the solidification process through a microscopic segregation model (such as Scheil model, Brody-Flemings model, Ueshima model, etc.), and then to obtain the thermal physical property parameters of the whole billet by using a phase weighting method according to the inherent thermal physical property parameters of each phase; a subsequent integrated microscopic segregation model is also developed to realize the collaborative calculation of solidus temperature, solute distribution and other parameters by coupling a thermodynamic database. However, this technical scheme has a core technical defect: all classical microscopic segregation models and subsequent integrated models take a fixed cooling rate as the core assumption, that is, a constant cooling rate (such as the average cooling rate of the billet center) is used to carry out the calculation of phase fraction and solute concentration distribution in the calculation process. In actual continuous casting production, the thermal history of different positions of the billet has a significant difference, and the cooling rate not only changes with the position of the billet, but also dynamically changes with the solidification process. This mismatch between the assumption and the actual working condition directly leads to the deviation of the solute equilibrium partition coefficient calculation, and further causes the prediction error of solute segregation and phase fraction, and finally causes the insufficient calculation precision of thermal physical property parameters, which is difficult to meet the demand of modern continuous casting production for high-precision solidification simulation.

[0027] To solve the above technical problems, an embodiment of the application provides a continuous casting billet thermal physical property parameter prediction method, as shown in Figure 1 The method comprises the following steps.

[0028] Step 110: receiving an initial parameter transmitted by a client, and establishing a billet calculation model based on the initial parameter.

[0029] The initial parameters refer to the basic data transmitted by the client to the server and used for building the billet calculation model and subsequent simulation analysis. The core can include steel composition (such as the content of elements such as Mn, S, Ti, N), process parameters (such as billet size, mold and secondary cooling zone process parameters), calculation position information (key positions of the billet where thermal physical parameters need to be predicted), and model initialization basic parameters (such as element basic characteristic parameters and secondary dendrite spacing related calculation parameters). The billet calculation model refers to a simulation model built by the server based on the received initial parameters, which is used for conducting thermal conduction and micro-solidification analysis of the continuous casting billet. The core components include a geometric model matching the actual billet size, a discrete grid element set covering the key positions, and a calculation module integrating steel characteristics, solidification thermodynamics and other basic parameters. It is the core carrier for subsequent thermal conduction analysis, inclusion precipitation calculation and other steps.

[0030] For the embodiments of the present disclosure, the server can first receive the initial parameters encrypted and transmitted by the client, which include steel composition, process parameters such as billet size, key calculation position information, and model initialization parameters such as element basic characteristics. Then, based on the billet size in the initial parameters, a quarter billet geometric model reflecting the symmetry characteristics and key regions of the billet is constructed. In order to reduce the amount of calculation and ensure the accuracy of the key position analysis, the geometric model is divided into quadrilateral meshes to obtain a discrete grid element set covering the key positions such as the center and edge of the billet. Finally, the quarter billet geometric model, the discrete grid element set, and the initialization model basic parameters such as the element basic characteristics and the secondary dendrite spacing related calculation parameters in the initial parameters are integrated to form a billet calculation model that can support subsequent thermal conduction analysis and micro-solidification simulation.

[0031] This technical step can provide an accurate simulation carrier for subsequent steps such as obtaining dynamic cooling rate for thermal conduction analysis and correcting solute parameters based on micro-segregation model by receiving initial parameters containing core information such as steel and process and constructing a billet calculation model integrating geometry, grid and basic characteristic parameters. This can effectively avoid subsequent analysis errors caused by model geometry distortion, insufficient grid coverage or missing basic parameters. At the same time, the encryption transmission of the initial parameters can ensure data security, and the construction of the quarter billet geometric model can balance the calculation accuracy and efficiency, laying a core foundation for the accuracy and reliability of the overall thermal physical parameter prediction.

[0032] Step 120, conducting thermal conduction analysis on the billet calculation model, calculating the dynamic cooling rate of the billet at different positions, and obtaining the billet thermal history data containing the dynamic cooling rate.

[0033] The heat conduction analysis refers to a process of analyzing the heat transfer law inside the casting blank, solving the temperature distribution at different positions and different time points, based on the heat conduction law (such as a two-dimensional heat conduction equation), using a numerical calculation method (such as a finite difference method), and combining the actual working conditions such as the mold strong cooling and the spray cooling in the secondary cooling zone during the solidification process of the casting blank, and the boundary conditions (heat flux, heat exchange coefficient, etc.) are required to correct the analysis results to match the actual situation; the dynamic cooling rate refers to the temperature change rate of different discrete grid units of the casting blank at each solidification time (calculation step), and the numerical value dynamically changes (such as the cooling rate of the edge of the casting blank is faster than the center, and the cooling rate in the later solidification stage is slower than that in the early stage) with the position of the casting blank (different grid units) and the solidification process (different calculation steps), which is not a constant value assumed by the traditional model, and is a key parameter reflecting the real thermal state of the casting blank; the casting blank thermal history data refers to a data set that completely records the thermal state of the casting blank during the solidification process, and the core includes two parts: one is the temperature information (i.e., the temperature change curve) of the casting blank at different positions (each discrete grid unit) changing with time, and the other is the dynamic cooling rate corresponding to each position at each time point, which can completely trace the thermal state evolution process of the casting blank from high-temperature liquid phase to low-temperature solid phase.

[0034] For the embodiments of the present disclosure, the completed casting blank calculation model can be used as the basis. Firstly, the finite difference method is used to solve the heat conduction equation to preliminarily obtain the initial temperature values of different discrete grid units of the casting blank at each calculation step and the surface temperature of the casting blank; then, the boundary condition parameters (including the surface heat flux of the casting blank, the spray heat exchange coefficient between the cooling water and the surface of the casting blank, and the surface radiation heat exchange coefficient of the casting blank) of the mold and the secondary cooling zone are calculated through a preset formula based on the process parameters (such as the cooling water temperature and the water flow density) in the initial parameters and the surface temperature of the casting blank, and these boundary condition parameters are substituted into the two-dimensional heat conduction equation to correct the initial temperature values to eliminate the deviation of the unconstrained calculation, so as to obtain the corrected temperature values of each discrete grid unit at each calculation step that match the actual working conditions; then, based on the corrected temperature values at different calculation steps, the dynamic cooling rate corresponding to each discrete grid unit is extracted by calculating the ratio of the temperature difference value of the same grid unit at adjacent calculation steps to the time step; finally, the temperature change curve of each discrete grid unit and the corresponding dynamic cooling rate are integrated in chronological order to form the casting blank thermal history data that completely reflects the solidification thermal state of the casting blank at different positions.

[0035] By conducting heat conduction analysis on the casting billet calculation model and introducing boundary condition correction, the deviation of unconstrained temperature calculation from the actual cooling condition can be effectively avoided, and the accuracy of the temperature data at different positions of the casting billet can be ensured; on this basis, the dynamic cooling rate obtained can break through the assumption of fixed cooling rate of the traditional model, and can truly reflect the thermal state difference of the casting billet at different positions with the solidification process, providing actual thermal state input for subsequent correction of the solute balance distribution coefficient; and the casting billet thermal history data integrating the temperature change curve and the dynamic cooling rate can completely record the thermal evolution law of the casting billet during the solidification process, which not only can provide accurate basic data support for subsequent inclusion precipitation calculation, phase fraction correction and other steps, but also can reduce the calculation error of thermal physical property parameters caused by the distortion of thermal state parameters from the source, significantly improving the overall accuracy of the prediction of the thermal physical property parameters of the continuous casting billet.

[0036] Step 130, based on the preset microsegregation model and the dynamic cooling rate in the casting billet thermal history data, the solute balance distribution coefficient is corrected to obtain the solute distribution parameter under non-equilibrium solidification.

[0037] The preset microsegregation model refers to a numerical model constructed in advance for simulating the distribution of solute elements and the phase transformation law during the solidification process of the casting billet, which mainly includes geometric assumptions (such as columnar dendrites being regular hexagons, taking 1 / 6 section as the calculation domain), diffusion rules (such as complete diffusion of solute in liquid phase, partial diffusion in solid phase) and integrated core algorithms (such as Aziz interface dynamics equation), which is the calculation carrier for the correction of the solute distribution coefficient; the solute balance distribution coefficient refers to the ratio of the mass fraction of solute elements in the solid phase to that in the liquid phase when the casting billet solidification reaches the thermodynamic equilibrium state, which is the basic parameter representing the distribution law of solute in solid-liquid phase, and the initial value is the model preset thermodynamic equilibrium benchmark value; the solute distribution parameter under non-equilibrium solidification refers to the solute distribution coefficient after correction based on the dynamic cooling rate, which is consistent with the actual non-equilibrium solidification process of the casting billet, is the optimization of the solute balance distribution coefficient under the thermodynamic equilibrium state, and can reflect the influence of cooling rate and phase state change on solute distribution, providing accurate parameter support for subsequent inclusion precipitation calculation.

[0038] For the embodiments of the present disclosure, the dynamic cooling rate in the previously obtained slab thermal history data can be first loaded in a preset microsegregation model, and a solute balance distribution coefficient base value (reference value in a thermodynamic equilibrium state) built in the model is initialized; then the Aziz interface kinetics equation integrated in the model is called, the solute balance distribution coefficient base value is dynamically corrected by deriving a dimensionless solidification rate parameter in combination with the solidification interface moving speed calculated from the dynamic cooling rate and the slab temperature gradient, to obtain a preliminarily corrected solute balance distribution coefficient; finally, whether the slab solidification system is in a three-phase coexistence state of δ phase, γ phase and liquid phase is judged by the phase state monitoring function of the model, and if so, the preliminarily corrected coefficient is further adjusted according to the weight ratio of δ phase and γ phase in the solid phase, to finally obtain the solute distribution parameter under non-equilibrium solidification.

[0039] By introducing the actual dynamic cooling rate of the slab into the preset microsegregation model to correct the solute balance distribution coefficient, the limitation of the traditional model relying on constant cooling rate can be broken through, and the correction process can be fully fitted to the real cooling conditions at different positions and different solidification stages of the slab; in combination with the Aziz interface kinetics equation and the weight adjustment under the three-phase coexistence state, the deviation between the thermodynamic equilibrium reference value and the actual non-equilibrium solidification process can be effectively eliminated, and the accuracy of the solute distribution parameter can be significantly improved; the corrected solute distribution parameter can provide reliable core input for subsequent inclusion precipitation calculation, liquid solute concentration correction and phase fraction calculation, reduce the subsequent thermal physical property parameter calculation error caused by the distortion of the solute distribution parameter from the source, and lay a key foundation for improving the overall prediction accuracy.

[0040] Step 140, in combination with the solute distribution parameter and the microsegregation model, the inclusion precipitation amount of multiple inclusions in the slab is calculated, the liquid solute concentration is corrected, and the corrected phase fraction is calculated.

[0041] The multiple inclusions refer to typical inclusions that are easy to precipitate in the solidification process of the slab and have a significant impact on solute concentration and thermal physical properties, and at least can include MnS inclusions and TiN inclusions, which consume the corresponding solute elements in the liquid phase; the inclusion precipitation amount refers to the total amount of solute elements precipitated from the liquid phase by a specific inclusion (such as MnS, TiN) in the solidification process, which is calculated by the mass conservation equation combined with the activity coefficient, and is the key basis for correcting the liquid solute concentration; the liquid solute concentration refers to the mass fraction of each solute element (such as Mn, S, Ti, N, etc.) in the liquid phase in the solidification process of the slab, and the corrected value is obtained after deducting the solute content consumed by the inclusion precipitation, which directly affects the phase transition temperature and the phase fraction calculation; the corrected phase fraction refers to the proportion of δ phase, γ phase and liquid phase in the slab after considering the inclusion precipitation and the dynamic cooling condition, which is calculated by re-calculating the phase transition temperature based on the corrected liquid solute concentration and the dynamic cooling rate, and can truly reflect the phase state evolution in the solidification process.

[0042] In this embodiment of the disclosure, the solute partition parameters obtained above can be loaded into a preset microsegregation model. First, for MnS inclusions, the standard Gibbs free energy change is calculated using the Gibbs free energy calculation algorithm built into the model, expressed as ΔG. θ The precipitation condition is determined by setting ΔG ≤ 0. Then, the activity coefficients of Mn and S are calculated based on the initial steel composition and the model's activity interaction coefficient database. The amount of MnS precipitation is calculated using the mass conservation equation, and the concentrations of Mn and S in the liquid phase are corrected. Subsequently, for TiN inclusions, the activity coefficients of Ti and N are calculated based on the initial composition and activity database. These are then substituted into the Gibbs free energy expression integrated by the model to determine the precipitation condition with ΔG ≤ 0. The amount of TiN precipitation is calculated based on the mass conservation equation, and the concentrations of Ti and N in the liquid phase are corrected. The corrected concentrations of Mn, S, Ti, and N are integrated to obtain the complete corrected liquid phase solute concentration. The phase transition temperature calculation module of the model is then called to recalculate the solid-liquid phase line temperature and the δ / γ phase transition temperature based on the solute distribution parameters. Finally, the dynamic cooling rate in the thermal history data is combined with the phase fraction calculation module of the model to calculate the proportions of the δ phase, γ phase, and liquid phase at each temperature, and the corrected phase fraction is obtained.

[0043] By combining the corrected accurate solute distribution parameters with the microsegregation model, the precipitation amount of various inclusions is calculated in a targeted manner and the liquid phase solute concentration is corrected. This avoids the concentration distortion problem caused by traditional models ignoring the influence of inclusions or only considering a single inclusion. Simultaneously, the phase transition temperature is recalculated based on the corrected concentration and the phase fraction is obtained by combining it with the dynamic cooling rate. This allows the phase fraction to truly reflect the solute consumption of inclusion precipitation and the influence of actual cooling conditions on phase evolution, effectively reducing the prediction error of the phase fraction. The corrected liquid phase solute concentration and phase fraction can provide accurate core inputs for the subsequent phase weighting method to calculate thermophysical parameters, ensuring the accuracy of thermophysical parameter prediction from the perspective of key parameters and meeting the needs of high-precision solidification simulation.

[0044] Step 150: Based on the corrected phase fraction and the corrected liquid solute concentration, calculate the thermophysical parameters using the phase weighting method. Iterate and update the dynamic cooling rate, solute equilibrium distribution coefficient, inclusion precipitation amount, corrected phase fraction, and thermophysical parameters until the termination condition is met, and output the predicted thermophysical parameters.

[0045] The phase addition weighting method refers to calculating the numerical value of the overall thermal physical property parameter of the casting blank according to the phase fraction of each phase (delta phase, gamma phase and liquid phase) as a weight, weighting the thermal physical property basic parameters (specific heat capacity, thermal conductivity coefficient and density) corresponding to each phase respectively, and the core is to reflect the contribution proportion of each phase to the overall thermal physical property; the thermal physical property parameter refers to a key parameter representing the thermal physical property of the casting blank, and the core includes specific heat capacity (heat required for 1 ℃ temperature rise per unit mass), effective thermal conductivity coefficient (representing heat transfer capacity, including steel liquid flow correction) and density (mass per unit volume), which is a core input parameter of solidification simulation; the iterative update refers to the process of matching each parameter to the actual working condition by cyclically updating the dynamic cooling rate, the solute balance distribution coefficient, the inclusion precipitation amount, the corrected phase fraction and the thermal physical property parameter, because the parameters in the solidification process of the casting blank have a chain effect, which is a key means to eliminate the single calculation deviation; the termination condition refers to the core basis for judging whether the iterative calculation can be stopped, which can be that the phase composition of all discrete grid elements in the calculation domain of the casting blank is completely converted into the gamma phase (the casting blank completes solidification, and there is no subsequent phase change and inclusion precipitation), so as to ensure that the iteration is sufficient and there is no invalid calculation; the thermal physical property parameter prediction result refers to the curve output after the iteration is terminated, which records the corresponding relationship between temperature and thermal physical property parameters (specific heat capacity, effective thermal conductivity coefficient and density) in the solidification process of the casting blank, and can be directly used for medium and high precision continuous casting solidification simulation.

[0046] For the embodiments of the present disclosure, the corrected phase fraction and the corrected liquid phase solute concentration are the core inputs, the phase addition weighting method is used to calculate the overall specific heat capacity, effective thermal conductivity coefficient and density of the casting blank respectively, and the thermal physical property parameter set of the calculation step is obtained; then the calculation step of the microscopic segregation model and the update step of the thermal history data are set, and after each round of calculation iteration of the microscopic segregation model is completed, the dynamic cooling rate in the thermal history data is updated based on the current solidification process (temperature field change and solid phase zone expansion) of the casting blank, and then the solute balance distribution coefficient is re-corrected in combination with the updated dynamic cooling rate, and the inclusion precipitation amount, the corrected phase fraction and the thermal physical property parameter are synchronously updated, and the corresponding relationship between the current temperature and the thermal physical property parameter is recorded; the phase composition of the casting blank calculation domain is monitored in real time, and the iteration is stopped when all the units are completely converted into the gamma phase, the corresponding relationship recorded in the iteration process is integrated into a complete temperature-thermal physical property parameter curve, and the curve is output as the prediction result.

[0047] The thermal physical property parameters are calculated by the addition weighting method combined with the corrected phase fraction and the liquid phase solute concentration, so that the contribution of each phase to the overall thermal physical property can be ensured to be consistent with the actual solidification phase state; the core parameters such as the dynamic cooling rate and the solute balance distribution coefficient are iteratively updated, so that the deviation accumulation of a single calculation link can be effectively eliminated, and each parameter can be matched with the real-time solidification process of the casting blank; the full gamma phase is taken as the termination condition, so that the complete coverage of the thermal physical property evolution law in the solidification process can be ensured, and invalid iteration can be avoided, and the accuracy of the finally output temperature-thermal physical property parameter curve is significantly improved, and the core needs of the medium and high-precision solidification simulation in the modern continuous casting production can be met.

[0048] In summary, according to the continuous casting blank thermal physical property parameter prediction method provided by the present application, by establishing a casting blank calculation model and obtaining dynamic cooling rates and thermal history data at different positions of the casting blank through heat conduction analysis, the core assumption of fixed cooling rate of the classical microsegregation model can be broken, the accurate solute distribution parameters under non-equilibrium solidification are obtained based on the dynamic cooling rate and the preset microsegregation model to correct the solute balance distribution coefficient, and then the microsegregation model is combined with the parameters to calculate the amount of various inclusions and correct the liquid phase solute concentration and the phase fraction, and finally the thermal physical property parameters are calculated by the addition weighting method, and the dynamic cooling rate, the solute balance distribution coefficient, the amount of inclusions, the corrected phase fraction and the thermal physical property parameters are iteratively updated to ensure that each parameter is matched with the actual solidification process, so that the deviation of the solute balance distribution coefficient caused by the fixed cooling rate can be effectively corrected, the prediction error of the solute segregation and the phase fraction can be reduced, the calculation accuracy of the thermal physical property parameters of the continuous casting blank can be significantly improved, and the needs of the medium and high-precision solidification simulation in the modern continuous casting production can be met.

[0049] Further, as a refinement and extension of the above embodiment, in order to fully describe the embodiment, the embodiment also provides another continuous casting blank thermal physical property parameter prediction method as shown in Figure 2 The method comprises the following steps:

[0050] Step 210, receiving the initial parameters transmitted by the client, and establishing a casting blank calculation model based on the initial parameters.

[0051] For the embodiments of the present disclosure, the embodiment step 210 can include the following steps:

[0052] Step 210-1, receiving the steel grade composition, process parameters and calculation position information transmitted by the client in encrypted form.

[0053] In a specific application scenario, the user can input the following information in the client interface: ① Steel grade composition (mass fraction of elements such as C, Si, Mn, P, S, Ti, N, %); ② Process parameters (casting size, mm2; drawing speed, m / min; etc.); ③ Calculation position (surface, 5 mm under the skin, 1 / 4, etc.); After input, the client will encrypt and transmit the above parameters to the server. Correspondingly, the server can receive the steel grade composition, process parameters and calculation position information encrypted and transmitted by the client.

[0054] For example, the steel grade information input by the client can be as shown in Table 1.

[0055] Table 1 Steel grade information

[0056]

[0057] Step 210-2, according to the casting size in the process parameters, a quarter casting geometric model is established.

[0058] The quarter casting geometric model refers to a simplified three-dimensional model constructed by using the biaxial symmetry (symmetry about the horizontal and vertical center lines) of the casting cross section (width x thickness) and the uniformity in the length direction, which only contains 1 / 4 area of the original casting cross section and the complete length, and is the core carrier that takes into account the calculation efficiency and accuracy.

[0059] For the embodiments of the present disclosure, the core size parameters (length L, width W, thickness H) of the casting can be extracted from the received process parameters, and the geometric quantification reference is determined according to the commonly used geometric modeling formula for casting solidification simulation (cross-sectional area S = W x H, unit: mm2; casting volume V = W x H x L, unit: mm3, wherein W is the maximum size of the casting cross section in the horizontal direction, H is the maximum size in the vertical direction, and L is the pouring direction length). Then a three-dimensional coordinate system is established with the length direction of the casting as the z-axis and the geometric center of the cross section as the origin (0, 0, 0), based on the biaxial symmetry characteristics of the casting cross section, the space region of x∈[0, W / 2], y∈[0, H / 2], z∈[0, L] is selected, and the three-dimensional solid structure is accurately constructed according to the extracted size, forming a quarter casting geometric model. During the modeling process, the model length is ensured to be consistent with the original casting, and the cross-sectional boundary is completely coincided with the symmetry axis of the original casting.

[0060] By extracting the casting size in the process parameters and using the biaxial symmetry to construct the quarter casting geometry model, the model calculation scale is reduced to 1 / 4 of the original casting model while fully retaining the geometric characteristics of the key areas such as the center and edge of the casting (completely consistent with the original casting), which can greatly reduce the computational power consumption and calculation time of subsequent grid division, heat conduction analysis and other links, and improve the overall simulation efficiency. At the same time, the simplification logic of biaxial symmetry can avoid the risk of geometric distortion and ensure the modeling accuracy of the key positions, providing accurate geometric basis for subsequent dynamic cooling rate calculation, solute distribution parameter correction and other core steps.

[0061] Step 210-3, quadrilateral mesh division is performed on the quarter casting geometry model to obtain a discrete grid cell set covering the key positions of the casting.

[0062] Among them, the quadrilateral mesh division refers to a numerical modeling method that uses quadrilateral elements (a planar element surrounded by four edges) to discretize the geometric model, and the grid structure covering the model is formed by connecting the element nodes, which is a prerequisite for subsequent numerical calculation (such as heat conduction analysis); the key positions of the casting refer to the areas with representative thermal state, solute distribution and phase transition during the solidification process of the casting, which include the casting center (easy to appear center segregation), the edge (the fastest cooling rate), the 1 / 4 thickness (the active phase transition area) and other positions that significantly affect the prediction accuracy of thermal physical parameters; the discrete grid cell set is the whole of all quadrilateral elements formed after grid division, each element contains clear geometric dimensions, node coordinates and physical property association information, and can be used as an independent calculation unit to participate in subsequent simulation analysis.

[0063] For the disclosed embodiment, the completed quarter casting geometry model can be used as the object, and finite element grid division tools (such as ANSYS, ABAQUS) are used to carry out quadrilateral mesh division. First, a differentiated division strategy can be developed according to the calculation accuracy requirements of the key positions (center, edge, 1 / 4 thickness) and non-key positions of the casting. The key positions need to ensure the calculation accuracy by using dense grids, and the non-key positions need to consider the efficiency by using sparse grids; during the division process, the node alignment is used to ensure the regularity of the quadrilateral element form (the aspect ratio is controlled within 1:3), and at the same time, the unqualified elements are removed through grid quality inspection (such as element distortion rate ≤5%), and finally the discrete grid cell set covering the key positions such as the center and edge of the casting and meeting the quality standards is formed.

[0064] By dividing the quarter slab geometry model into quadrilateral meshes and using encryption strategy for key positions, the accuracy of subsequent heat conduction analysis and solute segregation calculation can be improved by taking advantage of the regularity of quadrilateral elements and the good convergence of numerical calculation. At the same time, by designing different grid sizes for key positions and non-key positions, the overall grid number can be reduced while ensuring the reliability of core area calculation, balancing calculation efficiency; The discrete grid element set formed by the quarter slab key position can provide a high-quality discretization calculation carrier for subsequent dynamic cooling rate and accurate calculation of thermal physical property parameters, effectively avoiding analysis errors caused by insufficient grid coverage or quality defects.

[0065] Step 210-4, based on the quarter slab geometry model, the discrete grid element set and the initialization model basic parameters, a slab calculation model is constructed, and the initialization model basic parameters at least include element basic characteristic parameters and secondary dendrite arm spacing related calculation parameters.

[0066] Among them, the initialization model basic parameters refer to the core set of physical and chemical and structural parameters required for constructing the slab calculation model, which provides initial input for subsequent simulation analysis and is the key support for the model to have calculation function; The element basic characteristic parameter is used to represent the parameter of the physical and chemical property of each solute element (such as C, Mn, S, Ti, N, etc.) in the slab, at least including element equilibrium distribution coefficient (k0), solid phase diffusion coefficient (D s ), liquid phase diffusion coefficient (D l ) and other parameters, which determine the distribution and diffusion law of solute in the solidification process; The secondary dendrite arm spacing related calculation parameter is used to calculate the basic parameter of the secondary dendrite arm spacing (d2) in the slab solidification process, which includes the solidification time (t), the dendrite growth coefficient (k) and other parameters, and the secondary dendrite arm spacing is a key structural parameter affecting microsegregation and inclusion precipitation.

[0067] For the embodiments of the present disclosure, the quarter slab geometry model can be constructed as a structure basis, the discrete grid element set is used as a discretization carrier for numerical calculation, and the initialization model basic parameters are imported synchronously; The geometry model, discrete grid and the above initialization parameters are coupled and integrated through a numerical simulation platform (such as a finite element / finite difference platform), the association mapping of each grid element and physical and chemical parameters is established, the calculation logic of the model (such as the solution rule of heat conduction equation, solute diffusion calculation method) is defined, and finally the slab calculation model with complete calculation function is formed.

[0068] The casting billet calculation model is constructed by integrating the quarter casting billet geometric model, the discrete grid element set and the initialized model basic parameters, so as to consider the calculation efficiency and the core region calculation accuracy by means of the simplified geometric model and the discrete grid, and to provide accurate physical and chemical and structure basis for the subsequent heat conduction, solute segregation, inclusion precipitation and other calculations of the solidification process by importing the element basic characteristic parameters and the secondary dendrite inter-wall distance related calculation parameters; the constructed casting billet calculation model realizes the organic unification of the geometric structure, the discrete carrier and the physical parameters, can accurately reproduce the actual solidification working condition of the casting billet, and can effectively avoid the calculation error caused by the missing parameters or the disconnection between the structure and the parameters, so as to provide a reliable core calculation carrier for the subsequent dynamic cooling rate acquisition and the accurate prediction of the thermal physical parameters.

[0069] Step 220, performing heat conduction analysis on the casting billet calculation model, calculating the dynamic cooling rate of the casting billet at different positions, and acquiring the casting billet thermal history data containing the dynamic cooling rate.

[0070] For the embodiments of the present disclosure, the embodiment step 220 can include the following steps:

[0071] Step 220-1, based on the casting billet calculation model, solving the heat conduction equation by using the finite difference method to obtain the initial temperature value of the different discrete grid elements of the casting billet at each calculation step and the surface temperature of the casting billet.

[0072] The finite difference method is a numerical calculation method for discretizing a continuous physical equation (such as a heat conduction equation) into an algebraic equation set, and the physical quantity (such as temperature) of each node of the model discrete grid element is obtained by numerical solving, which has the characteristics of fast convergence speed and adaptation to discrete grid; the heat conduction equation is a control equation for describing the heat transfer law inside the casting billet, which reflects the change relationship between temperature and time and space, and is the theoretical basis for calculating the temperature distribution of the casting billet; the calculation step is a time interval (Δt) set in numerical calculation, and the temperature is dynamically updated with the solidification process by iteratively solving the heat conduction equation according to the calculation step; the initial temperature value is the temperature data of each discrete grid element at the initial calculation step (t=0), which is usually set based on the casting initial temperature (such as the liquidus temperature of the liquid steel) and is the initial input for the iterative solution of the heat conduction equation; the surface temperature of the casting billet is the temperature value of the surface discrete grid element of the casting billet geometric model, which directly participates in the calculation of the boundary condition parameters (such as heat flux and heat transfer coefficient) of the subsequent crystallizer and secondary cooling zone.

[0073] For the embodiments of the present disclosure, based on the completed casting blank calculation model, the finite difference method is used to discretize the two-dimensional heat conduction equation, the initial pouring temperature of the casting blank is assigned as the initial boundary condition to all discrete grid cells, the calculation step Δt (unit s, 0.01-0.1 s according to the calculation accuracy requirement) is set, the node temperature of each discrete grid cell is iteratively solved by the finite difference method, and the temperature data (i.e. initial temperature value) of each discrete grid cell at each calculation step is obtained. At the same time, the temperature data of the surface discrete grid cells of the casting blank geometry model is extracted as the surface temperature of the casting blank.

[0074] By combining the casting blank calculation model with the finite difference method to solve the heat conduction equation, the advantages of the finite difference method in adapting discrete grids and high solving efficiency are fully utilized, and the initial temperature value of each discrete grid cell at each calculation step and the surface temperature of the casting blank are quickly obtained, which can provide accurate temperature input for subsequent boundary condition parameter calculation (heat flux, heat transfer coefficient, etc.). The global coverage of the initial temperature value can ensure the integrity of the internal temperature distribution calculation of the casting blank, and the accurate extraction of the surface temperature of the casting blank can ensure the reliability of the subsequent boundary condition correction, laying a solid temperature foundation for the dynamic cooling rate calculation and heat history data acquisition from the source, which can effectively avoid the subsequent analysis errors caused by the distortion of the initial temperature.

[0075] In step 220-2, based on the process parameters in the initial parameters and the surface temperature of the casting blank, the boundary condition parameters of the mold and the secondary cooling zone are calculated respectively by a preset formula. The boundary condition parameters include the surface heat flux of the casting blank, the spray heat transfer coefficient between the cooling water and the casting blank surface, and the radiation heat transfer coefficient of the casting blank surface.

[0076] The preset formula is a quantitative calculation formula pre-set for different heat transfer forms of the mold (contact heat transfer) and the secondary cooling zone (spray convection and radiation heat transfer), which is the core calculation tool of the boundary condition parameters. The mold is a device that makes the molten steel initially solidify into a shell in continuous casting, and is the key area for initial cooling of the casting blank. The contact heat transfer between the mold and the casting blank is one of the main sources of the surface heat flux of the casting blank. The secondary cooling zone is a region for secondary spray cooling of the casting blank after the mold, and is a core cooling unit for subsequent solidification of the casting blank, which includes spray convection, radiation and other heat transfer forms. The boundary condition parameter is a parameter representing the heat transfer characteristics between the casting blank and the external environment (mold, cooling water, air), which is a boundary constraint condition for solving the heat conduction equation and determines the external constraint strength of the heat transfer of the casting blank. The surface heat flux of the casting blank is the heat per unit time through a unit surface area of the casting blank, which is a quantitative index of the heat transfer strength between the casting blank and the mold in the mold region. The spray heat transfer coefficient between the cooling water and the casting blank surface is used to represent the convective heat transfer capacity when the cooling water is sprayed to the surface of the casting blank in the secondary cooling zone, and reflects the heat transfer efficiency of the spray cooling. The radiation heat transfer coefficient of the casting blank surface is used to represent the heat dissipation capacity of the casting blank surface to the surrounding environment, which is a quantitative parameter of the radiation heat transfer between the casting blank and the environment.

[0077] For the embodiments of the present disclosure, the process parameters in the initial parameters (such as the mold size, the secondary cooling zone cooling water flow rate / temperature, and the nozzle type) and the casting blank surface temperature obtained by previous solving are input, and each boundary condition parameter is calculated through a preset formula. First, for the mold region, the formula is used to calculate the casting blank surface heat flux q (in the formula, B is a coefficient related to the pouring condition, which is determined by the process parameters such as the pouring speed; t is the cooling time, which is calculated from the casting blank moving speed and the region length); then the formula is used to calculate the spray heat transfer coefficient (in the formula, W i is the water flow density, which is from the cooling water flow rate in the process parameters and the spray area; T w is the cooling water temperature, which is taken from the process parameters; a i is a correction coefficient, which is determined by the process parameters such as the nozzle type); and the formula is used to calculate the radiation heat transfer coefficient (in the formula, e is the radiation coefficient, which is 0.8; s is the Spitzer constant, which is 5.67x10 -8 W m - ² K -4 ; T surf is the casting blank surface temperature, and T env is the environmental temperature, which is taken from the process parameters), and finally the boundary condition parameters corresponding to the mold and the secondary cooling zone are obtained.

[0078] By combining the actual process parameters and the casting blank surface temperature, the boundary condition parameters of the mold and the secondary cooling zone are calculated by using the preset formula, so that the boundary constraints of the heat conduction analysis are fully matched with the actual heat transfer conditions of the continuous casting production, and the deviation of the fixed boundary parameters from the actual cooling intensity can be avoided. At the same time, the accurately quantified heat flux, spray heat transfer coefficient, and radiation heat transfer coefficient can provide the actual heat transfer intensity basis for the subsequent casting blank temperature value correction, effectively improve the accuracy of the casting blank internal temperature distribution and the dynamic cooling rate calculation, and guarantee the overall precision of the subsequent solute distribution parameter correction and the thermal physical property parameter prediction from the boundary constraint level.

[0079] In step 220-3, the boundary condition parameters are substituted into the two-dimensional heat conduction equation to correct the initial temperature value, and the corrected temperature value of each discrete grid element at each calculation step is obtained.

[0080] Among them, the two-dimensional heat conduction equation is used to describe the control equation of the heat transfer law of the casting blank cross section (x, y direction), which considers the influence of temperature, time and solidification latent heat, and the core form is , is the theoretical basis for calculating the temperature distribution of the casting blank; the initial temperature value is the temperature data of each discrete grid element at each calculation step obtained by preliminarily solving the heat conduction equation by the finite difference method, and is the basis input for subsequent temperature correction; the discrete grid element is an independent calculation element formed by quadrilateral mesh division in the casting blank calculation model, each element corresponds to a local area of the casting blank, and is the basic unit of temperature calculation; the calculation step is a time interval set in numerical calculation, and the dynamic evolution of the temperature in the solidification process of the casting blank is realized by updating the temperature according to the calculation step; the corrected temperature value is the temperature data of each discrete grid element at each calculation step obtained by re-solution after substituting the actual boundary condition parameters into the heat conduction equation, which is more in line with the actual solidification temperature state of the casting blank.

[0081] For the embodiments of the present disclosure, the two-dimensional heat conduction equation (where ρ(T, w C ) is the density of the casting blank, c(T, w C ) is the specific heat capacity, T is the temperature, t is the time, λ(T, w C ) is the thermal conductivity, x and y are the horizontal and vertical coordinates (unit: m) of the cross section of the casting blank, L is the solidification latent heat, and fs is the liquid phase fraction) is used as the basis, and the boundary condition parameters obtained in the previous calculation are substituted into the boundary term of the equation as external constraints: the surface heat flux of the casting blank is used as the heat flow boundary condition in the mold area, and the spray heat transfer coefficient, the radiation heat transfer coefficient and the ambient temperature are used as the convective-radiative composite boundary condition in the secondary cooling zone; the initial temperature value is kept as the initial input for iterative solution, and the finite difference method is used to re-iterate the temperature of each discrete grid element, and the element temperature is updated in each calculation step in combination with the heat transfer constraints of the boundary condition parameters, and finally the corrected temperature value of each discrete grid element in each calculation step is obtained, which is in line with the actual heat transfer working condition.

[0082] By substituting the boundary condition parameters corresponding to the actual working condition into the two-dimensional heat conduction equation to correct the initial temperature value, the temperature data of each discrete grid element of the casting blank can fully reflect the actual cooling intensity of the mold and the secondary cooling zone, and the deviation between the initial temperature value and the actual solidification state without boundary constraints can be effectively eliminated; the accuracy of the corrected temperature value can directly improve the reliability of the subsequent dynamic cooling rate extraction and heat history data integration, provide a more actual temperature basis for subsequent solute distribution parameter correction and thermal property parameter calculation, and ensure the accuracy of the overall prediction scheme from the heat state input level.

[0083] Step 220-4, based on the corrected temperature value at different calculation steps, extracting the dynamic cooling rate corresponding to each discrete grid element in the discrete grid element set.

[0084] wherein the corrected temperature value is the temperature data of each discrete grid element at the corresponding calculation step obtained by substituting the actual boundary condition parameter into the heat conduction equation, which fits the actual solidification temperature state of the casting blank, and the unit is ℃; the discrete grid element set is the whole of independent calculation elements formed after the quadrilateral grid division of the quarter casting blank geometric model, each element corresponds to a local area of the casting blank, and is the basic unit of temperature and cooling rate calculation; the dynamic cooling rate is the temperature change rate of the casting blank at the corresponding calculation step of a discrete grid element, which dynamically fluctuates with the casting position (different grid elements) and the solidification process (different calculation steps), is different from the constant cooling rate of the traditional model, and the unit is ℃ / s, which is a key parameter reflecting the real thermal state of the casting blank.

[0085] For the embodiments of the present disclosure, the dynamic cooling rate calculation formula can be used based on the corrected temperature values of each discrete grid element at different calculation steps (in the formula, C R is the dynamic cooling rate, the unit is ℃ / s; , are the corrected temperature values of a discrete grid element at the i-th calculation step and the i-1-th calculation step, respectively, the unit is ℃; is the calculation step length, that is, the time interval of adjacent two calculation steps, the unit is s), the corrected temperature values of each discrete grid element at adjacent calculation steps are extracted in turn, the ratio of the absolute value of the temperature difference value to the calculation step length is calculated, the dynamic cooling rate of the discrete grid element at the corresponding calculation step is obtained, and finally the extraction of the dynamic cooling rates of all discrete grid elements is completed.

[0086] By extracting the corresponding dynamic cooling rate of each discrete grid element from the corrected temperature value, the limitation of using the constant cooling rate in the traditional model can be broken through, the cooling rate can fully reflect the real thermal state difference of the casting blank at different positions and different solidification stages, the dynamic cooling rate can provide actual thermal state input for the subsequent core steps of correcting the solute balance distribution coefficient and calculating the inclusion precipitation amount, effectively reduce the subsequent parameter calculation error caused by the distortion of the cooling rate, and lay a key foundation for improving the overall accuracy of the thermal physical property parameter prediction.

[0087] Step 220-5, integrating the temperature values of each discrete grid element in time sequence to form the temperature change curve of the casting blank at different positions, and integrating the dynamic cooling rates of each grid element, the temperature change curve and the dynamic cooling rate are stored as complete casting blank thermal history data.

[0088] For the embodiments of the present disclosure, the step number n can be calculated as a time index based on a time conversion formula t = n x At (where t is the actual solidification time, unit s; n is the calculation step number; At is the calculation step length, unit s, which is a preset numerical calculation time interval), the modified temperature value of each discrete grid element at each calculation step is sequentially associated in the order of t to construct a temperature change curve of different positions of the casting blank (positions corresponding to each grid element) with time as the horizontal axis and temperature as the vertical axis; at the same time, the dynamic cooling rate (C R ) of each discrete grid element at each calculation step is associated and matched with the actual solidification time t of the corresponding calculation step and the grid element position information, and finally the temperature change curves of all positions and the corresponding dynamic cooling rates are integrated and collected to form the casting blank thermal history data which fully reflects the thermal state evolution of the whole solidification process of the casting blank and are stored.

[0089] By integrating the temperature value and the dynamic cooling rate of each discrete grid element in chronological order, the complete casting blank thermal history data including the temperature change curve and the dynamic cooling rate are formed, which can realize the systematic collection of the thermal state data of the casting blank during the solidification process and avoid the fragmentation of the temperature and cooling rate data. The data fully reproduces the thermal state evolution law of different positions of the casting blank with the solidification process, which can provide continuous and accurate thermal state input for the subsequent core steps of correcting the solute partitioning coefficient based on the dynamic cooling rate and calculating the inclusion precipitation amount, thereby ensuring the calculation accuracy and reliability of the overall technical solution from the data level.

[0090] Step 230, in the preset microsegregation model, the dynamic cooling rate in the thermal history data is loaded, and the solute partitioning coefficient basic value is initialized.

[0091] The preset microsegregation model refers to a numerical model that is constructed in advance to simulate the solute diffusion, phase transition and inclusion precipitation law during the solidification process of the casting blank, integrates geometric assumptions, diffusion rules and core algorithms (such as Aziz interface dynamics equation), and is a core calculation carrier for carrying out the correction of the solute partitioning coefficient. The dynamic cooling rate in the thermal history data refers to the temperature change rate of each discrete grid element at the corresponding calculation step extracted from the casting blank thermal history data, which dynamically fluctuates with the position of the casting blank and the solidification process, and the unit is ℃ / s, which is a key parameter reflecting the real thermal state of the casting blank. The initialized solute partitioning coefficient basic value refers to the ratio of the solid phase mass fraction to the liquid phase mass fraction of the solute element in the thermodynamic equilibrium state, which is preset based on the composition and thermodynamic properties of the steel grade, and is a reference value for the subsequent dynamic correction of the solute partitioning coefficient.

[0092] For the embodiments of the present disclosure, the casting blank thermal history data can be first loaded in the preset microsegregation model to extract the dynamic cooling rate (C R) and the spatial position and solidification time corresponding to the grid unit; meanwhile, based on the thermodynamic properties of each solute element (such as C, Mn, S, Ti, N, etc.) in the steel composition, the initial solute balance distribution coefficient of each solute element is set, and the loading and initialization of the dynamic cooling rate and the basic distribution coefficient in the microsegregation model are completed.

[0093] By loading the real dynamic cooling rate of the casting blank and the initial solute balance distribution coefficient based on the steel characteristics in the microsegregation model, the limitations of the traditional model relying on the constant cooling rate can be broken through, the model input can be fitted to the actual thermal state of the casting blank, and the accurate thermodynamic benchmark for subsequent correction of the solute distribution coefficient based on the dynamic cooling rate can be provided, so that the subsequent calculation deviation caused by the distortion of the initial parameters can be effectively avoided, a reliable foundation for accurate acquisition of the solute distribution parameters under non-equilibrium solidification is laid, and then the overall precision of the subsequent inclusion precipitation calculation, phase fraction correction and thermal physical property parameter prediction is ensured.

[0094] In step 240, based on the Aziz interface dynamics equation integrated in the microsegregation model, the solidification interface moving speed and the dynamic cooling rate are combined to dynamically correct the solute balance distribution coefficient basis value, and the corrected solute balance distribution coefficient is obtained.

[0095] The Aziz interface dynamics equation refers to a core equation built in the preset microsegregation model and used to describe the relationship between the solute distribution coefficient and the solidification interface moving characteristics under non-equilibrium solidification, can quantify the influence of dynamic working conditions on the distribution rule of solute between solid and liquid phases, and is a key tool for realizing dynamic correction of the distribution coefficient; the solidification interface moving speed refers to the speed at which the solid-liquid interface advances to the liquid phase area in the solidification process of the casting blank, is directly related to the dynamic cooling rate and the temperature gradient, and is a core parameter reflecting the speed of the solidification process; and the corrected solute balance distribution coefficient refers to the solute distribution coefficient fitted to the non-equilibrium solidification process after being corrected by the Aziz interface dynamics equation in combination with the actual working condition parameters.

[0096] For the embodiments of the present disclosure, the solidification interface moving speed can be calculated in the microsegregation model by the formula R wherein v is the solidification interface moving speed, Cis the dynamic cooling rate extracted from the thermal history data; and G is the temperature gradient at the solid-liquid interface of the casting blank, which is calculated from the temperature distribution of each discrete grid unit), and then the calculated solidification interface moving speed and dynamic cooling rate are taken as inputs to dynamically correct the solute balance distribution coefficient basis value by substituting the equation, and finally the corrected solute balance distribution coefficient of each discrete grid unit in the corresponding solidification process is obtained.

[0097] The solute balance distribution coefficient basic value is corrected by the Aziz interface kinetic equation combined with the solidification interface moving speed and the dynamic cooling rate, which can break through the limitation of the traditional model based on the equilibrium state or constant parameter to calculate the distribution coefficient, so that the corrected distribution coefficient can accurately reflect the influence of dynamic working conditions on solute distribution under non-equilibrium solidification, and effectively eliminate the deviation between the equilibrium benchmark value and the actual working condition. The accurate distribution coefficient can provide core input for subsequent inclusion precipitation calculation, liquid solute concentration correction and phase fraction calculation, reduce the calculation error from the key parameter level, and provide an important guarantee for improving the overall accuracy of the prediction of thermophysical parameters.

[0098] Step 250, when it is judged that the casting blank is in a three-phase coexistence state of δ phase, γ phase and liquid phase, the corrected solute balance distribution coefficient is adjusted according to the proportion weight of each solid phase to obtain the final solute distribution parameter under non-equilibrium solidification.

[0099] The three-phase coexistence state of δ phase, γ phase and liquid phase is a specific phase state stage in the solidification process of the casting blank, which means that the casting blank has δ ferrite phase (high-temperature solid phase), γ austenite phase (medium-temperature solid phase) and un-solidified liquid phase at the same time. At this time, the fraction of the three phases is greater than 0, which is the key solidification stage affecting the solute distribution rule. The proportion weight of each solid phase refers to the proportion of δ phase and γ phase in the total solid phase under the three-phase coexistence state, i.e. the proportion weight of δ phase f_δ and the proportion weight of γ phase f_γ, which satisfy f_δ+ f_γ = 1 (total solid phase fraction), and are the core basis for weighted adjustment of the solute distribution coefficient. The corrected solute balance distribution coefficient refers to the solute distribution coefficient (k_δ, k_γ) of δ phase and γ phase respectively after correction by the Aziz interface kinetic equation combined with the dynamic cooling rate and the solidification interface moving speed, which reflects the solute distribution rule of a single solid phase under non-equilibrium solidification. The solute distribution parameter under non-equilibrium solidification refers to the final comprehensive solute distribution coefficient (k_final) obtained after considering the three-phase coexistence characteristics and the proportion weight of each solid phase, which is dimensionless and can fully reflect the solute distribution rule of non-equilibrium solidification under three-phase coexistence state, and is the core input for subsequent calculation.

[0100] Through the phase state monitoring module of the microsegregation model, whether the casting blank is in the three-phase coexistence state is judged according to the phase fraction of each discrete grid element. If the three-phase coexistence condition is met, the proportion weight of each solid phase can be obtained first, and then the corrected solute balance distribution coefficients k_δ and k_γ of δ phase and γ phase are retrieved, and the final solute distribution parameter k_final under non-equilibrium solidification is obtained by weighted adjustment formula k_final = f_δ k_δ+ f_γ k_final is the final solute partition parameter under non-equilibrium solidification; f_δ, f_γ are the weight of each solid phase; k_δ, k_γ are the modified solute equilibrium partition coefficients of δ phase and γ phase), the partition coefficient is comprehensively adjusted, and finally the solute partition parameter that fits the actual three-phase coexistence non-equilibrium solidification is obtained.

[0101] By adjusting the modified solute equilibrium partition coefficient according to the weight of each solid phase under the three-phase coexistence state, the influence of the solute partition difference between δ phase and γ phase on the overall solute distribution can be fully considered, and the parameter distortion problem caused by the traditional model ignoring the diversity of phase state or using a single partition coefficient can be avoided; the final solute partition parameter under non-equilibrium solidification can accurately reproduce the real solute partition rule in the three-phase coexistence stage, providing more actual core parameter support for subsequent inclusion precipitation calculation, liquid solute concentration correction and phase fraction optimization, and further improving the accuracy of thermophysical property prediction.

[0102] Step 260, combine the solute partition parameter with the microsegregation model to calculate the inclusion precipitation amount of multiple inclusions in the casting blank, correct the liquid solute concentration, and simultaneously calculate the corrected phase fraction.

[0103] Among them, the multiple inclusions at least include MnS inclusions and TiN inclusions.

[0104] Correspondingly, the embodiment step 260 can include the following steps:

[0105] Step 260-1, for MnS inclusions, determine the MnS precipitation condition by calculating the standard Gibbs free energy change.

[0106] Among them, the MnS inclusion refers to the sulfide inclusion formed by the chemical reaction of manganese (Mn) and sulfur (S) in the liquid phase during the solidification process of the casting blank, which consumes the corresponding solute elements in the liquid phase and directly affects the liquid solute concentration and the thermophysical properties of the casting blank, and is one of the typical harmful inclusions in the continuous casting blank; the standard Gibbs free energy change (ΔG°) refers to the Gibbs free energy change of the chemical reaction (Mn + S → MnS) under standard conditions (constant temperature, constant pressure, and activity of each substance is 1), which is a core thermodynamic criterion for judging whether the reaction (i.e. MnS precipitation) can proceed spontaneously; the MnS precipitation condition refers to the thermodynamic critical condition for the spontaneous precipitation of MnS inclusions from the liquid phase of the casting blank, which is specifically ΔG°≤0 (ΔG°≤0), when the condition is met, Mn and S elements in the liquid phase will spontaneously combine to form MnS inclusions.

[0107] ​​In this embodiment of the present disclosure, the activities of Mn and S elements in the liquid phase of the billet can be calculated based on the solute concentration and element activity coefficients in the liquid phase. Simultaneously, the billet temperature corresponding to the calculation step is extracted from the billet thermal history data. These parameters are substituted into the standard Gibbs free energy change calculation formula for the MnS formation reaction, and the standard Gibbs free energy change of the reaction is obtained through calculation. ),like If the value is ≤0, then the precipitation condition of MnS inclusions is satisfied.

[0108] The precipitation conditions of MnS inclusions are determined by calculating the standard Gibbs free energy change. The spontaneous trend of MnS precipitation can be quantified by using thermodynamic criteria. Combined with the actual liquid phase solute activity and temperature state of the billet, the timing deviation caused by traditional empirical precipitation judgment is avoided. This method can accurately capture the thermodynamic precipitation critical state of MnS inclusions, and can provide a reliable basis for subsequent accurate calculation of MnS precipitation amount and correction of liquid phase solute concentration. It ensures the accuracy of subsequent phase fraction calculation and thermophysical parameter prediction from the source of inclusion precipitation.

[0109] Step 260-2: Based on the MnS precipitation conditions and the Mn and S element content in the initial steel composition, calculate the activity coefficients of Mn and S elements through the activity interaction coefficients of each solute element, calculate the solute content consumed by precipitation using the mass conservation equation, and then correct the concentration of the remaining Mn and S solutes in the liquid phase.

[0110] Among them, the Mn and S element content in the initial steel composition refers to the mass fraction of manganese (Mn) and sulfur (S) elements in the original molten steel of the billet, which is the basic benchmark for calculating the total element stock and precipitation consumption; the activity interaction coefficient is a thermodynamic parameter characterizing the influence of other solute elements in the molten phase of the billet on the activity of the target element, which is determined by the characteristics of the steel composition and is used to correct the deviation between the actual solution and the ideal solution; the activity coefficient is used to reflect the correlation between the activity and mass fraction of Mn and S elements in the actual solution, which is the core parameter for converting element concentration into activity and directly affects the accuracy of thermodynamic calculation of precipitation reaction; the mass conservation equation The formula is based on the principle that the total content of Mn and S elements in the billet remains unchanged. It is used to correlate the balance between the initial element content, the remaining liquid phase element content, and the content of elements consumed by precipitation. The content of solute consumed by precipitation refers to the mass fraction of Mn and S elements in the liquid phase that are consumed to form MnS inclusions when the MnS precipitation conditions are met. It is determined by the difference between the total element content and the remaining content. The corrected liquid phase Mn and S solute concentration refers to the mass fraction of Mn and S elements remaining in the liquid phase of the billet after deducting the Mn and S content consumed by precipitation. It is a key basic data for subsequent phase fraction calculation and thermophysical parameter prediction.

[0111] For the embodiments of the present disclosure, when it is determined that the MnS precipitation condition is met, the activity coefficients of Mn and S elements can be calculated based on the mass fractions of Mn and S elements in the initial steel composition by means of the activity interaction coefficient; the solute contents of Mn and S consumed in the precipitation are calculated by combining the stoichiometric ratio of MnS and the mass conservation equation; and finally, the remaining solute concentrations of Mn and S in the liquid phase after correction are obtained by deducting the contents consumed in the precipitation.

[0112] By combining the MnS precipitation condition, the activity interaction coefficient and the mass conservation equation to correct the solute concentrations of Mn and S in the liquid phase, the mutual influence between elements in the actual solution can be fully considered, the solute contents consumed in the precipitation can be accurately quantified based on the thermodynamic equilibrium and the mass conservation, and the corrected liquid phase concentration can truly reflect the actual remaining state of Mn and S elements in the casting billet solidification process.

[0113] In step 260-3, for TiN inclusions, the activity coefficients of Ti and N elements are calculated by combining the contents of Ti and N elements in the initial steel composition and the activity interaction coefficients of solute elements, the activity coefficients are substituted into the Gibbs free energy expression of TiN to determine the TiN precipitation condition, the contents of Ti and N consumed in the TiN precipitation process are calculated based on the mass conservation equation, and then the remaining solute concentrations of Ti and N in the liquid phase are corrected.

[0114] The TiN inclusions are nitride inclusions generated by chemical reaction of titanium (Ti) and nitrogen (N) elements in the liquid phase during the solidification of the casting blank, and the precipitation thereof consumes corresponding solute elements in the liquid phase, affecting the solute concentration in the liquid phase and the thermal physical properties of the casting blank; the Ti and N element contents in the initial steel composition are the mass fractions of Ti and N elements in the original molten steel of the casting blank, and are the basic reference for calculating the total inventory, precipitation consumption and residual concentration of the elements; the activity interaction coefficient is a thermodynamic parameter for representing the influence degree of other solute elements in the liquid phase of the casting blank on the activity of the target element (Ti or N), and is determined by the composition characteristics of the steel, and is used to correct the deviation of the actual solution from the ideal solution; the activity coefficient is a coefficient for reflecting the correlation between the activity and the mass fraction of Ti and N elements in the actual solution, and is a core parameter for converting the element concentration into activity, and directly affects the accuracy of the thermodynamic calculation of the TiN precipitation reaction; the Gibbs free energy expression of TiN is a thermodynamic formula for describing the relationship between the Gibbs free energy change and the temperature and element activity of the TiN reaction, and is a core basis for judging whether TiN can spontaneously precipitate; the TiN precipitation condition is a thermodynamic critical condition for spontaneous precipitation of TiN inclusions from the liquid phase of the casting blank, that is, the Gibbs free energy change of the TiN generation reaction is less than or equal to 0, and when the condition is met, Ti and N elements will spontaneously combine to form TiN inclusions; the mass conservation equation is a calculation formula established based on the principle that the total content of Ti and N elements in the casting blank is constant, and is used to associate the balance relationship among the initial element content, the residual liquid phase element content and the precipitated consumed element content; the precipitated consumed Ti and N element content is the mass fraction consumed by the combination of Ti and N elements in the liquid phase to form TiN inclusions when the TiN precipitation condition is met, and is determined by the difference between the total content and the residual content of the elements; the corrected Ti and N solute concentration in the liquid phase is the residual Ti and N element mass fraction in the liquid phase of the casting blank after deducting the precipitated consumed Ti and N content, and is key basic data for subsequent phase fraction calculation and thermal physical property parameter prediction.

[0115] For the embodiments of the present disclosure, the activity coefficient of Ti and N elements can be calculated by taking the Ti and N element contents in the initial steel composition as the basis and combining the activity interaction coefficients of each solute element, the TiN Gibbs free energy expression is substituted by the activity coefficient, whether the TiN precipitation condition is met is judged by calculating the Gibbs free energy change, if the condition is met, the Ti and N element contents consumed in the TiN precipitation process are calculated according to the mass conservation equation and the stoichiometric ratio of TiN, and finally the residual Ti and N solute concentration in the corrected liquid phase is obtained by deducting the consumed content.

[0116] By combining the initial steel composition, activity interaction coefficient and Gibbs free energy expression to accurately determine the TiN precipitation condition, and then using the mass conservation equation to quantify the element content consumed by precipitation, both the mutual interaction between elements in the actual solution and the deviation caused by the ideal solution assumption can be fully considered, and the modified liquid Ti, N solute concentration can truly reflect the actual residual state of the elements during solidification, forming a complete liquid solute concentration system with the previously modified Mn, S concentration, which can provide comprehensive and reliable input for subsequent phase transition temperature calculation, phase fraction correction and weighted calculation of thermophysical parameters, effectively reducing the parameter distortion error caused by inclusion precipitation, and further improving the overall accuracy of thermophysical parameter prediction.

[0117] Step 260-4, integrate the corrected Mn, S concentration and the corrected Ti, N concentration to obtain the corrected liquid solute concentration; based on the corrected liquid solute concentration and the solute distribution parameter, recalculate the solid-liquid phase line temperature and the δ / γ phase transition temperature of the casting blank; combine the solid-liquid phase line temperature, the δ / γ phase transition temperature and the dynamic cooling rate to calculate the proportion of δ phase, γ phase and liquid phase at each temperature through the microsegregation model, and determine the corrected phase fraction of δ phase, γ phase and liquid phase.

[0118] Among them, the corrected liquid solute concentration is the comprehensive mass fraction system of each core solute element (Mn, S, Ti, N, etc.) in the liquid phase of the casting blank obtained by integrating the corrected Mn, S concentration and Ti, N concentration; the solute distribution parameter is the final determined comprehensive solute distribution coefficient considering the non-equilibrium solidification characteristics of the casting blank (including the weight of each solid phase in the three-phase coexistence state), which reflects the distribution law of solute between solid and liquid phases; the solid-liquid phase line temperature is the liquidus temperature (L) at which the casting blank begins to solidify and the solidus temperature (S) at which it completely solidifies, which is determined by the liquid solute concentration and the solute distribution law, and is the key temperature reference for dividing the solidification stage; the δ / γ phase transition temperature is the critical temperature at which the high-temperature solid phase δ ferrite phase transforms into the medium-temperature solid phase γ austenite phase during the solidification process of the casting blank, which is affected by the liquid solute concentration and the cooling condition, and is the core parameter for judging the phase evolution; the phase fraction is the proportion of δ phase, γ phase and liquid phase in the total volume of the casting blank, including δ phase fraction, γ phase fraction and liquid phase fraction, which is the core index for representing the phase distribution of the casting blank during solidification; the corrected phase fraction is the δ phase, γ phase and liquid phase fraction calculated by the microsegregation model based on the corrected liquid solute concentration, key phase transition temperature and dynamic cooling rate, which can truly reflect the phase evolution law of the casting blank.

[0119] For the embodiments of the present disclosure, the revised Mn and S concentrations can be integrated with the revised Ti and N concentrations to form a revised liquid solute concentration system containing each core solute element, and then the solid-liquid phase line temperature and the δ / γ phase transition temperature of the casting blank are recalculated based on the liquid solute concentration and the solute distribution parameter under non-equilibrium solidification. Subsequently, the calculated key temperatures are combined with the dynamic cooling rate of each discrete grid unit of the casting blank, and the phase evolution process at different temperatures is simulated through a microsegregation model to calculate the proportion of the δ phase, the γ phase and the liquid phase corresponding to each temperature, and finally the phase fraction of the revised δ phase, the γ phase and the liquid phase is determined.

[0120] By integrating the revised concentrations of the core solute elements to form a complete liquid solute concentration system, combining the solute distribution parameter to accurately calculate the key phase transition temperature, and relying on the microsegregation model and the dynamic cooling rate to quantify the phase fraction, it can not only ensure that the input data for phase fraction calculation is comprehensive and realistic, but also fully consider the comprehensive influence of solute concentration, phase transition temperature and cooling condition on phase evolution, so that the revised phase fraction can truly reflect the phase distribution rule in the solidification process of the casting blank, provide accurate phase state proportion basis for subsequent calculation of thermophysical parameters by using the phase weighting method, effectively reduce the prediction error of thermophysical parameters caused by the distortion of phase fraction, and further improve the reliability and accuracy of the overall technical scheme.

[0121] Step 270, according to the revised phase fraction and the revised liquid solute concentration, the thermophysical parameters are calculated by using the phase weighting method.

[0122] For the embodiments of the present disclosure, the embodiment step 270 can include the following steps:

[0123] Step 270-1, based on the revised liquid solute concentration and the solute distribution parameter, the carbon content in the δ phase and the γ phase is determined, the specific heat capacity characteristics of each phase are matched according to the revised phase fraction of the δ phase, the γ phase and the liquid phase, the specific heat capacity characteristics of each phase are weighted and summed according to the phase fraction, and the specific heat capacity of the casting blank is obtained.

[0124] The solute distribution parameter is a comprehensive solute distribution coefficient determined by considering non-equilibrium solidification and three-phase coexistence characteristics, and reflects the distribution law of solute between the delta phase, the gamma phase and the liquid phase; the carbon content in the delta phase and the gamma phase is the mass fraction of carbon elements in the delta ferrite phase and the gamma austenite phase determined based on the modified liquid solute concentration and the solute distribution parameter, and directly affects the specific heat capacity characteristics of each phase; the modified phase fraction of the delta phase, the gamma phase and the liquid phase is the proportion of each phase in the total volume of the casting blank calculated by combining the liquid solute concentration, the phase change temperature and the dynamic cooling rate, and is the core weight basis of the weighted calculation; the specific heat capacity characteristics of each phase are the specific heat capacity data of the delta phase, the gamma phase and the liquid phase under the corresponding temperature and composition conditions, which are determined by the material structure and element content of each phase; the phase fraction weighted summation is a weighted average calculation method for the specific heat capacity characteristics of the corresponding phase by taking the phase fraction of each phase as the weight, which is used to integrate the comprehensive specific heat capacity of the multi-phase system; and the specific heat capacity of the casting blank is the overall specific heat capacity of the casting blank obtained by the phase fraction weighted summation, which is the core thermal property parameter representing the overall heat absorption or heat release capacity of the casting blank.

[0125] According to the embodiments of the present disclosure, the carbon content in the delta phase and the gamma phase can be determined based on the modified liquid solute concentration and the solute distribution parameter, and then the specific heat capacity characteristics of each phase under the corresponding composition and temperature can be matched according to the modified phase fraction of the delta phase, the gamma phase and the liquid phase, and then the specific heat capacity characteristics of each phase matched can be weighted and summed by taking the phase fraction of each phase as the weight, and finally the overall specific heat capacity of the casting blank can be obtained.

[0126] By accurately determining the carbon content of each phase by combining the liquid solute concentration and the solute distribution parameter, and then matching the corresponding specific heat capacity characteristics, and then weighting and summing by taking the phase fraction as the weight, the influence of the composition difference of each phase on the heat capacity can be fully considered, and the contribution of the proportion of each phase to the overall heat capacity of the casting blank can be reflected, the errors caused by single phase state or average processing can be avoided, the specific heat capacity of the casting blank calculated can truly reflect the comprehensive thermal physical characteristics under the multi-phase coexistence state in the solidification process, and accurate and reliable core data support can be provided for subsequent heat conduction analysis and overall thermal property parameter system construction.

[0127] In step 270-2, the carbon content in the delta phase is determined based on the modified phase fraction and the modified liquid solute concentration, the basic thermal conductivity coefficient of each phase is calculated according to the modified phase fraction, the basic thermal conductivity coefficient of the liquid phase is multiplied by an empirical constant to obtain the effective thermal conductivity coefficient of the liquid phase, and the effective thermal conductivity coefficient is obtained by phase fraction weighted summation of the basic thermal conductivity coefficient of the delta phase, the basic thermal conductivity coefficient of the gamma phase and the effective thermal conductivity coefficient of the liquid phase.

[0128] The carbon content in the delta phase is determined based on the corrected phase fraction and the solute concentration of the liquid phase, is a key component parameter related to the thermal conductivity of the delta phase, the basic thermal conductivity of each phase is the inherent thermal conductivity of the delta phase, the gamma phase and the liquid phase under the corresponding composition and temperature conditions, is determined by the material structure and element content of each phase, and is basic data for calculating the effective thermal conductivity, the empirical constant is a coefficient obtained based on the practice of continuous casting process and the fitting of thermal conduction test data, is used for correcting the basic thermal conductivity of the liquid phase to match the heat transfer characteristics of the liquid phase in the actual solidification process, the effective thermal conductivity of the liquid phase is the thermal conductivity obtained by multiplying the basic thermal conductivity of the liquid phase by the empirical constant, and reflects the actual heat transfer capacity of the liquid phase, and the effective thermal conductivity is the overall thermal conductivity of the casting blank obtained by phase fraction weighted summation, and comprehensively reflects the overall heat transfer capacity of the casting blank in the coexistence state of the delta phase, the gamma phase and the liquid phase, and is one of the core thermal physical parameters.

[0129] For the embodiments of the present disclosure, the carbon content in the delta phase can be determined based on the corrected phase fraction and the corrected solute concentration of the liquid phase, and the basic thermal conductivities of the delta phase, the gamma phase and the liquid phase are matched and calculated respectively according to the corrected phase fractions, the effective thermal conductivity of the liquid phase is obtained by multiplying the basic thermal conductivity of the liquid phase by a predetermined empirical constant, and finally the delta phase basic thermal conductivity, the gamma phase basic thermal conductivity and the effective thermal conductivity of the liquid phase are weighted and summed with the phase fraction as the weight, and the effective thermal conductivity of the casting blank is finally obtained.

[0130] By accurately correlating the carbon content in the delta phase and the basic thermal conductivities of each phase by combining the corrected phase fraction and the solute concentration of the liquid phase, the empirical constant is used to correct the thermal conductivity of the liquid phase to match the actual working condition, and the multi-phase thermal conductivity contribution is integrated by phase fraction weighted summation, which can not only fully consider the inherent thermal conductivity characteristics, composition influence and actual heat transfer deviation of each phase, but also reflect the comprehensive effect of different phase state proportions on the overall thermal conductivity of the casting blank, avoid errors caused by single phase state thermal conductivity coefficient or ideal state assumption, and make the calculated effective thermal conductivity truly reflect the actual heat transfer law of the coexistence of multiple phases in the solidification process, thereby providing reliable core thermal physical support for subsequent accurate thermal conduction analysis and process optimization.

[0131] Step 270-3, based on the corrected phase fraction and the corrected solute concentration of the liquid phase, the carbon content in the delta phase, the gamma phase and the liquid phase is determined, the density characteristics of each phase are matched based on the carbon content and the corrected phase fraction, the density characteristics of each phase are weighted and summed with the phase fraction, and the density of the casting blank is obtained.

[0132] The carbon content in the δ phase, the γ phase and the liquid phase is determined based on the corrected phase fraction and the liquid solute concentration, and directly affects the density characteristics of each phase; the density characteristics of each phase are inherent density data of the δ phase, the γ phase and the liquid phase under corresponding composition (carbon content) and temperature conditions, which are determined by the material structure and element composition of each phase, and are basic data for calculating the overall density of the casting blank; the phase fraction weighted summation is a weighted average calculation of the density characteristics of the corresponding phase by taking the phase fraction of each phase as the weight, and the core logic is to quantify the contribution of each phase to the overall density of the casting blank by the phase ratio, so as to integrate the comprehensive density of the multi-phase system; the casting blank density is the overall density of the casting blank obtained by the phase fraction weighted summation, which is a core thermophysical parameter representing the mass per unit volume of the casting blank, and reflects the overall material density characteristics of the casting blank under the coexistence of multiple phases.

[0133] For the embodiments of the present disclosure, the specific content of carbon elements in the δ phase, the γ phase and the liquid phase can be determined through the correlation between the corrected phase fraction and the corrected liquid solute concentration, and then the inherent density characteristics of each phase under the corresponding composition and temperature conditions can be matched according to the carbon content of each phase, and at the same time, the contribution weight of each phase to the overall density of the casting blank can be determined in combination with the corrected phase fraction, and finally the density characteristics of the δ phase, the γ phase and the liquid phase can be integrated through the phase fraction weighted summation, and the overall density of the casting blank is finally obtained.

[0134] By accurately locking the carbon content of each phase in combination with the corrected phase fraction and the liquid solute concentration, and then matching the corresponding density characteristics, and integrating the multi-phase density contribution by taking the phase fraction as the weight, the direct influence of carbon content on the density of each phase can be fully considered, and the comprehensive effect of the proportion of different phases on the overall density of the casting blank can be objectively reflected, the error caused by the single phase density substitution or the average processing can be effectively avoided, the casting blank density calculated can truly reproduce the actual material characteristics of the coexistence of multiple phases in the solidification process, and accurate and reliable core thermophysical data support can be provided for subsequent heat conduction analysis, casting blank quality evaluation and process optimization.

[0135] Step 270-4: integrate the specific heat capacity of the casting blank, the effective thermal conductivity and the casting blank density to form a set of thermophysical parameters of the calculation step.

[0136] Step 280: iteratively update the dynamic cooling rate, the solute balance distribution coefficient, the inclusion precipitation amount, the corrected phase fraction and the thermophysical parameters until the termination condition is met, and output the thermophysical parameter prediction result.

[0137] For the embodiments of the present disclosure, the embodiment step 280 can include the following steps:

[0138] Step 280-1: set the calculation step length of the microsegregation model and the update step length of the thermal history data.

[0139] The calculation step of the microsegregation model refers to a preset time interval during numerical calculation of the microsegregation model, is a time unit for iterative solution of microprocesses such as solute diffusion and phase transition of the model, and directly determines the capturing accuracy of the model on microsolidification phenomena; and the updating step of the thermal history data refers to a time interval for recording, storing and updating the thermal history data (including temperature change curve, dynamic cooling rate, etc.) of the casting blank, is a key parameter for associating the thermal state data and the calculation rhythm of the microsegregation model, and ensures that the model can call real-time and effective thermal state input.

[0140] For the embodiments of the present disclosure, the calculation step of the microsegregation model can be determined based on the severity of the thermal state change during the solidification process of the casting blank, the accuracy requirement of the microsegregation model on the solute diffusion and phase transition process, and the calculation efficiency, so as to ensure that the step can accurately capture the change of microsolidification characteristics in a short time; and then the updating step of the thermal history data is set synchronously based on the calculation step of the microsegregation model, so that the updating frequency of the thermal history data is consistent with the calculation frequency of the model, and the latest thermal history data at the corresponding time node can be called by the model during each iterative calculation.

[0141] By synchronously setting the calculation step of the microsegregation model and the updating step of the thermal history data, it is ensured that the model can perform calculation with accuracy adapted to the microsolidification process, and it is avoided that key microchanges are omitted due to too large step; and it is ensured that the rhythm of the thermal state data and the model calculation is matched, and it is prevented that calculation deviation is caused by data updating lag or redundancy, so that the calculation efficiency is considered, and accurate and synchronous input support is provided for subsequent solute distribution parameter correction, phase fraction calculation and other links, and the coherence of the overall technical scheme and the reliability of the calculation result are ensured.

[0142] In step 280-2, the calculation iteration of the microsegregation model is performed based on the calculation step and the updating step; after each round of calculation iteration of the microsegregation model, the dynamic cooling rate in the thermal history data is updated based on the solidification progress of the casting blank, and the solute balance distribution coefficient is re-corrected based on the updated dynamic cooling rate.

[0143] The calculation iteration of the microsegregation model refers to the repeated calculation behavior of the core processes such as solute distribution and phase fraction calculation of the microsegregation model according to the set calculation step, and gradually approaches the actual solidification state of the casting blank; the solidification progress of the casting blank is a dynamic process in which the casting blank gradually changes from liquid phase to solid phase, and the advancing state is determined by factors such as temperature change and phase change; and the solute balance distribution coefficient is a parameter for representing the distribution rule of solute elements between solid and liquid phases, and its value needs to be dynamically adjusted according to the actual thermal state of the casting blank to fit the non-equilibrium solidification characteristics.

[0144] For the embodiments of the present disclosure, the calculation iteration of the microsegregation model can be started according to the preset calculation step and update step. After completing a round of calculation, the dynamic cooling rate in the thermal history data is updated according to the advancement of the casting blank solidification process (such as temperature change and phase evolution), and then the solute balance distribution coefficient is adjusted and corrected again based on the updated dynamic cooling rate, so as to provide key parameter input fitting the current solidification state for the next round of model calculation iteration.

[0145] By carrying out model calculation iteration in matched steps and updating the dynamic cooling rate and the solute balance distribution coefficient based on the solidification process after each iteration, real-time linkage optimization of the thermal state data and the core thermodynamic parameters can be realized, calculation deviation caused by fixed or lagging parameters can be avoided, and it is ensured that the solute distribution coefficient always fits the actual solidification characteristics of the casting blank, thereby providing dynamic and accurate input support for subsequent phase fraction calculation, thermal physical property parameter solving and other links, and the coherence of the overall technical scheme and the reliability of the calculation results are significantly improved.

[0146] Step 280-3: The inclusion precipitation amount, phase fraction and thermal physical property parameter are simultaneously updated according to the solute balance distribution coefficient corrected again, and the correspondence between the temperature and the thermal physical property parameter is recorded.

[0147] For the embodiments of the present disclosure, the solute balance distribution coefficient corrected again is taken as the core logic starting point. Since this coefficient directly determines the distribution rule of solute among phases and then affects the inclusion precipitation and phase evolution, the inclusion precipitation amount is first updated synchronously, then the phase fraction of the delta phase, the gamma phase and the liquid phase is adjusted based on the coefficient, then the thermal physical property parameters such as the specific heat capacity, the effective thermal conductivity and the density are simultaneously optimized according to the updated phase fraction and the solute concentration, and finally each temperature node is associated with the corresponding updated thermal physical property parameter, and the correspondence between the two is recorded completely.

[0148] By taking the solute balance distribution coefficient corrected again as the link, simultaneously updating the inclusion precipitation amount, the phase fraction and the thermal physical property parameter and recording the correspondence between the temperature and the thermal physical property parameter, the logical consistency and data correlation among the core parameters can be ensured, the system deviation caused by single parameter update can be avoided, the thermal physical property parameter can be real-time fitted to the current solidification state of the casting blank, and the correspondence between the temperature and the thermal physical property parameter established can provide comprehensive and reliable data support for subsequent precise analysis of heat conduction, optimization of process parameters and prediction of casting blank quality.

[0149] Step 280-4: The phase composition of the calculation domain in the casting blank calculation model is judged in real time. When all the units in the calculation domain are completely converted into the gamma phase, the iteration calculation is stopped, and the complete relationship curve between the temperature and the thermal physical property parameter is transmitted to the client as the thermal physical property parameter prediction result.

[0150] In the iterative calculation process of the microsegregation model, the phase composition of the calculation domain in the slab calculation model can be monitored in real time, it is judged whether the phase state of each discrete grid element only exists γ phase, when it is confirmed that all elements in the calculation domain have been completely converted into γ phase, the iterative calculation of the model is stopped, and then the relationship curve of the temperature and the thermal physical property parameters integrated in the whole process of the slab solidification is transmitted to the corresponding client as the final prediction result of the thermal physical property parameters.

[0151] By judging the phase composition of the calculation domain in real time and taking the complete conversion of all elements into γ phase as the iteration stopping condition, the phase state evolution and the change of the thermal physical property parameters in the slab solidification process can be ensured to be completely simulated, the incomplete data caused by early stopping can be avoided, the calculation redundancy caused by excessive iteration can be prevented, and the calculation integrity and efficiency are taken into account; the relationship curve finally transmitted to the client can comprehensively and accurately reflect the dynamic law of the thermal physical property parameters of the slab with the temperature, and can provide direct and usable core data support for the client to carry out subsequent process optimization, quality prediction and other work, and improve the practicality and landing value of the technical scheme.

[0152] In summary, according to the continuous casting slab thermal physical property parameter prediction method provided by the present application, by establishing a slab calculation model and obtaining the dynamic cooling rate and thermal history data of the slab at different positions through heat conduction analysis, the core assumption of the fixed cooling rate of the classical microsegregation model can be broken, the accurate solute distribution parameters under non-equilibrium solidification are obtained based on the dynamic cooling rate and the preset microsegregation model correction solute balance distribution coefficient, and then the solute distribution parameters are combined to calculate the amount of precipitation of multiple inclusions through the microsegregation model and correct the liquid solute concentration and phase fraction, and finally the thermal physical property parameters are calculated by using the phase weighting method and the dynamic cooling rate, the solute balance distribution coefficient, the amount of precipitation of inclusions, the corrected phase fraction and the thermal physical property parameters are updated through iteration to ensure that each parameter matches the actual solidification process, which can effectively correct the solute balance distribution coefficient deviation caused by the fixed cooling rate, reduce the solute segregation and phase fraction prediction error, significantly improve the calculation accuracy of the thermal physical property parameters of the continuous casting slab, and meet the needs of modern continuous casting production in high-precision solidification simulation.

[0153] Further, as a specific implementation of the method shown in Figure 1 and Figure 2 , the present embodiment provides a continuous casting slab thermal physical property parameter prediction device, as shown in Figure 3 , which can include:

[0154] The receiving module 31 can be used to receive the initial parameters transmitted by the client, and establish a slab calculation model based on the initial parameters;

[0155] The calculation module 32 can be used for heat conduction analysis of the slab calculation model, and calculating the dynamic cooling rate of the slab at different positions to obtain the slab thermal history data containing the dynamic cooling rate;

[0156] The correction module 33 can be used to correct the solute partitioning coefficient based on the preset microsegregation model and the dynamic cooling rate in the slab thermal history data, to obtain the solute partitioning parameter under non-equilibrium solidification;

[0157] The correction module 33 can also be used to calculate the inclusion precipitation amount of multiple inclusions in the slab by combining the solute partitioning parameter and the microsegregation model, correct the liquid solute concentration, and synchronously calculate the corrected phase fraction;

[0158] The updating module 34 can be used to calculate the thermophysical parameters by using the phase weighting method according to the corrected phase fraction and the corrected liquid solute concentration, and update the dynamic cooling rate, the solute partitioning coefficient, the inclusion precipitation amount, the corrected phase fraction and the thermophysical parameters by iteration until the termination condition is met, and output the thermophysical parameter prediction result.

[0159] In some embodiments of the present application, the receiving module 31 can be specifically used to receive the steel grade composition, the process parameters and the calculation position information encrypted and transmitted by the client; a quarter slab geometry model is established according to the slab size in the process parameters; a quadrilateral mesh division is performed on the quarter slab geometry model to obtain a discrete mesh element set covering the key positions of the slab; and a slab calculation model is constructed based on the quarter slab geometry model, the discrete mesh element set and the initialization model basic parameters, the initialization model basic parameters at least including the element basic characteristic parameters and the secondary dendrite arm spacing related calculation parameters.

[0160] In some embodiments of the present application, the calculation module 32 can be specifically used to solve the heat conduction equation by using the finite difference method based on the slab calculation model, to obtain the initial temperature values of different discrete mesh elements of the slab at each calculation step and the slab surface temperature; the boundary condition parameters of the mold and the secondary cooling zone are calculated by a preset formula based on the process parameters in the initial parameters and the slab surface temperature, the boundary condition parameters including the slab surface heat flux, the spray heat transfer coefficient between the cooling water and the slab surface, and the slab surface radiation heat transfer coefficient; the boundary condition parameters are substituted into the two-dimensional heat conduction equation to correct the initial temperature values, to obtain the corrected temperature values of each discrete mesh element at each calculation step; the dynamic cooling rate corresponding to each discrete mesh element in the discrete mesh element set is extracted based on the corrected temperature values at different calculation steps; the temperature values of each discrete mesh element are integrated in time sequence to form the temperature change curve of different positions of the slab, and the dynamic cooling rate of each mesh element is integrated to store the temperature change curve and the dynamic cooling rate as the complete slab thermal history data.

[0161] In some embodiments of the present application, the correction module 33 can be specifically used to load the dynamic cooling rate in the thermal history data in the preset microsegregation model, and initialize the solute balance distribution coefficient basic value; based on the integrated Aziz interface dynamics equation in the microsegregation model, the solute balance distribution coefficient basic value is dynamically corrected combined with the solidification interface moving speed and the dynamic cooling rate, to obtain the corrected solute balance distribution coefficient; when it is judged that the casting blank is in a three-phase coexistence state of δ phase, γ phase and liquid phase, the corrected solute balance distribution coefficient is adjusted according to the proportion weight of each solid phase, to obtain the final solute distribution parameter under non-equilibrium solidification.

[0162] In some embodiments of the present application, the plurality of inclusions at least includes MnS inclusions and TiN inclusions; the correction module 33 can also be used to calculate the inclusion precipitation amount of the plurality of inclusions in the casting blank combined with the solute distribution parameter and the microsegregation model, correct the liquid phase solute concentration and synchronously calculate the corrected phase fraction, including: for the MnS inclusions, the MnS precipitation condition is determined by calculating the standard Gibbs free energy change; based on the MnS precipitation condition, combined with the Mn and S element content in the initial steel composition, the activity coefficient of Mn and S elements is calculated through the activity interaction coefficient of each solute element, the solute content consumed in the precipitation is calculated combined with the mass conservation equation, and then the remaining Mn and S solute concentration in the liquid phase is corrected; for the TiN inclusions, combined with the Ti and N element content in the initial steel composition and the activity interaction coefficient of each solute element, the activity coefficient of Ti and N elements is calculated, the activity coefficient is substituted into the Gibbs free energy expression of TiN to determine the TiN precipitation condition, based on the TiN precipitation condition, the Ti and N element content consumed in the TiN precipitation process is calculated according to the mass conservation equation, and then the remaining Ti and N solute concentration in the liquid phase is corrected; the corrected Mn and S concentrations and the corrected Ti and N concentrations are integrated to obtain the corrected liquid phase solute concentration; based on the corrected liquid phase solute concentration and the solute distribution parameter, the solid-liquid phase line temperature and the δ / γ phase transition temperature of the casting blank are recalculated; combined with the solid-liquid phase line temperature, the δ / γ phase transition temperature and the dynamic cooling rate, the proportion of δ phase, γ phase and liquid phase at each temperature is calculated through the microsegregation model to determine the corrected phase fraction of δ phase, γ phase and liquid phase.

[0163] In some embodiments of the present application, the updating module 34 can be specifically used to determine the carbon content in the δ phase and the γ phase based on the corrected liquid-phase solute concentration and the solute distribution parameter, match the specific heat capacity characteristics of each phase according to the corrected phase fractions of the δ phase, the γ phase and the liquid phase, perform phase fraction weighted summation on the specific heat capacity characteristics of each phase, and obtain the specific heat capacity of the casting blank; determine the carbon content in the δ phase based on the corrected phase fractions and the corrected liquid-phase solute concentration, calculate the basic thermal conductivity coefficients of each phase according to the corrected phase fractions, multiply the basic thermal conductivity coefficient of the liquid phase by an empirical constant to obtain the effective thermal conductivity coefficient of the liquid phase, and perform phase fraction weighted summation on the basic thermal conductivity coefficients of the δ phase and the γ phase and the effective thermal conductivity coefficient of the liquid phase to obtain the effective thermal conductivity coefficient; determine the carbon content in the δ phase, the γ phase and the liquid phase based on the corrected phase fractions and the corrected liquid-phase solute concentration, match the density characteristics of each phase based on the carbon content and the corrected phase fractions, perform phase fraction weighted summation on the density characteristics of each phase, and obtain the density of the casting blank; and integrate the specific heat capacity, the effective thermal conductivity coefficient and the density of the casting blank to form a set of thermal physical property parameters of the calculation step.

[0164] In some embodiments of the present application, the updating module 34 can be specifically used to set the calculation step of the microsegregation model and the update step of the thermal history data; perform calculation iteration of the microsegregation model based on the calculation step and the update step, update the dynamic cooling rate in the thermal history data based on the solidification progress of the casting blank after each round of calculation iteration of the microsegregation model, and re-correct the solute balance distribution coefficient based on the updated dynamic cooling rate; synchronously update the inclusion precipitation amount, the phase fraction and the thermal physical property parameters according to the re-corrected solute balance distribution coefficient, and record the corresponding relationship between the temperature and the thermal physical property parameters; and judge the phase composition of the calculation domain in the casting blank calculation model in real time, stop the iteration calculation when all the units in the calculation domain are completely converted into the γ phase, and transmit the complete relationship curve between the temperature and the thermal physical property parameters as the thermal physical property parameter prediction result to the client.

[0165] Based on the above-mentioned methods as shown in Figure 1 and Figure 2 , correspondingly, the present embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the above-mentioned continuous casting blank thermal physical property parameter prediction method as shown in Figure 1 and Figure 2 .

[0166] Based on such understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), and includes a plurality of instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute the method of each implementation scenario of the present application.

[0167] Based on the above-mentioned methods as shown inFigure 1 and Figure 2 the virtual device embodiment shown in Figure 3 In order to achieve the above-mentioned purposes, the embodiments of the present application also provide an electronic device, which can be a personal computer, a tablet computer, a server, or other network devices, etc., comprising a storage medium and a processor; the storage medium is used for storing a computer program; the processor is used for executing the computer program to realize the above-mentioned method for predicting the thermal physical parameters of a continuous casting billet. Figure 1 and Figure 2 the method for predicting the thermal physical parameters of a continuous casting billet.

[0168] Optionally, the above-mentioned entity device can also comprise a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface can comprise a display screen (Display), an input unit such as a keyboard (Keyboard), etc. The optional user interface can also comprise a USB interface, a card reader interface, etc. The network interface can optionally comprise a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0169] Those skilled in the art can understand that the above-mentioned structure of the entity device provided by the embodiments does not constitute a limitation on the entity device, and can comprise more or fewer components, or combine certain components, or different component arrangements.

[0170] The storage medium can also comprise an operating system and a network communication module. The operating system is a program for managing the hardware and software resources of the above-mentioned entity device, and supports the running of the information processing program and other software and / or programs. The network communication module is used for realizing the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing entity device.

[0171] Those skilled in the art can clearly understand from the above description of the embodiments that the present application can be realized by means of software and necessary general hardware platforms, or by hardware.

[0172] The embodiment of the application can obtain the dynamic cooling rate and thermal history data of the casting blank at different positions by establishing a casting blank calculation model and conducting heat conduction analysis, can break through the core assumption of the fixed cooling rate of the classic microsegregation model, can correct the solute balance distribution coefficient based on the dynamic cooling rate and the preset microsegregation model to obtain accurate solute distribution parameters under non-equilibrium solidification, can combine the parameters to calculate the amounts of various inclusions precipitated by the microsegregation model and correct the liquid solute concentration and phase fraction, and finally can calculate the thermal physical property parameters by using the addition weighting method, and update the dynamic cooling rate, the solute balance distribution coefficient, the amount of inclusions precipitated, the corrected phase fraction and the thermal physical property parameters by iteration to ensure that each parameter matches the actual solidification process, can effectively correct the solute balance distribution coefficient deviation caused by the fixed cooling rate, can reduce the solute segregation and phase fraction prediction error, can significantly improve the calculation accuracy of the thermal physical property parameters of the continuous casting blank, and can meet the needs of the medium and high-precision solidification simulation in the modern continuous casting production.

[0173] Those skilled in the art can understand that the modules or processes in the drawings are not necessarily required for implementing the present application. Those skilled in the art can understand that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the description of the implementation scenario, or can be changed to be located in one or more devices different from the implementation scenario. The modules of the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0174] The above application serial numbers are only for description, and do not represent the advantages and disadvantages of the implementation scenario. The above disclosure is only some specific implementation scenarios of the present application, but the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present application.

Claims

1. A method for predicting the thermal properties of continuously cast billets, characterized in that, The method is applied to the server side, and the method includes: Receive initial parameters transmitted by the client, and establish a billet calculation model based on the initial parameters; A heat conduction analysis is performed on the billet calculation model to calculate the dynamic cooling rate at different locations on the billet and obtain the billet thermal history data containing the dynamic cooling rate. This includes: solving the heat conduction equation using the finite difference method based on the billet calculation model to obtain the initial temperature values ​​of different discrete grid cells of the billet at each calculation step and the surface temperature of the billet; and calculating the boundary condition parameters of the crystallizer and the secondary cooling zone using preset formulas based on the process parameters transmitted encrypted by the client and the surface temperature of the billet. The boundary condition parameters include the heat flux density of the billet surface and the spray heat transfer coefficient between the cooling water and the billet surface. The initial temperature value is corrected by substituting the boundary condition parameters into the two-dimensional heat conduction equation to obtain the corrected temperature value of each discrete grid cell in each calculation step. Based on the corrected temperature values ​​of different calculation steps, the dynamic cooling rate corresponding to each discrete grid cell in the discrete grid cell set is extracted. The temperature values ​​of each discrete grid cell are integrated in chronological order to form the temperature change curve of different positions of the billet. At the same time, the dynamic cooling rate of each grid cell is integrated, and the temperature change curve and the dynamic cooling rate are used as complete billet thermal history data storage. Based on the preset microsegregation model and the dynamic cooling rate in the billet thermal history data, the solute equilibrium distribution coefficient is corrected to obtain the solute distribution parameters under non-equilibrium solidification. By combining the solute distribution parameters and the microsegregation model, the amount of inclusions precipitated in the billet is calculated, the liquid phase solute concentration is corrected, and the corrected phase fraction is calculated simultaneously. Based on the corrected phase fraction and the corrected liquid solute concentration, the thermophysical parameters are calculated using the phase weighting method. The dynamic cooling rate, the solute equilibrium distribution coefficient, the amount of inclusion precipitation, the corrected phase fraction, and the thermophysical parameters are iteratively updated until the termination condition is met, and the predicted thermophysical parameters are output.

2. The method according to claim 1, characterized in that, The initial parameters transmitted by the receiving client, and the establishment of a billet calculation model based on the initial parameters, include: Receive initial parameters transmitted by the client in encrypted form, wherein the initial parameters include at least process parameters; Based on the billet dimensions in the process parameters, establish a geometric model of a quarter-cast billet; The geometric model of the quarter-slab is divided into quadrilateral meshes to obtain a set of discrete mesh elements covering the key locations of the slab; A billet calculation model is constructed based on the quarter-slab geometric model, the discrete mesh unit set, and the initialization model basic parameters. The initialization model basic parameters include at least the basic elemental property parameters and the calculation parameters related to the secondary dendrite wall spacing.

3. The method according to claim 1, characterized in that, Based on the preset microsegregation model and the dynamic cooling rate in the billet thermal history data, the solute equilibrium distribution coefficient is corrected to obtain the solute distribution parameters under non-equilibrium solidification, including: In the preset microsegregation model, the dynamic cooling rate from the thermal history data is loaded, and the basic value of the initial solute balance distribution coefficient is initialized. Based on the Aziz interface kinetic equation integrated in the microsegregation model, and combined with the solidification interface migration velocity and dynamic cooling rate, the basic value of the solute equilibrium distribution coefficient is dynamically corrected to obtain the corrected solute equilibrium distribution coefficient. When it is determined that the billet is in a state of coexistence of δ phase, γ phase and liquid phase, the corrected solute equilibrium distribution coefficient is adjusted according to the proportion weight of each solid phase to obtain the final solute distribution parameters under non-equilibrium solidification.

4. The method according to claim 1, characterized in that, The various inclusions include at least MnS inclusions and TiN inclusions; Combining the solute distribution parameters and the microsegregation model, the amount of inclusions precipitated in the billet is calculated, the liquid phase solute concentration is corrected, and the corrected phase fraction is calculated simultaneously, including: For the MnS inclusions, the MnS precipitation conditions were determined by calculating the standard Gibbs free energy change; Based on the MnS precipitation conditions, combined with the Mn and S element content in the initial steel composition, the activity coefficients of Mn and S elements are calculated through the activity interaction coefficients of each solute element, and the content of solute consumed by precipitation is calculated by combining the mass conservation equation, thereby correcting the concentration of remaining Mn and S solutes in the liquid phase. For the TiN inclusions, the activity coefficients of Ti and N elements are calculated by combining the content of Ti and N elements in the initial steel composition with the activity interaction coefficients of each solute element. The activity coefficients are then substituted into the Gibbs free energy expression of TiN to determine the TiN precipitation conditions. Based on the TiN precipitation conditions, the content of Ti and N elements consumed during the TiN precipitation process is calculated according to the mass conservation equation, and then the concentration of the remaining Ti and N solutes in the liquid phase is corrected. By integrating the corrected Mn and S concentrations with the corrected Ti and N concentrations, the corrected liquid phase solute concentration is obtained. Based on the corrected liquid phase solute concentration and the solute distribution parameters, the solid-liquid phase temperature and δ / γ phase transition temperature of the billet are recalculated. Combining the solid-liquid phase temperature, the δ / γ phase transition temperature, and the dynamic cooling rate, the proportions of δ phase, γ phase, and liquid phase at each temperature are calculated using the microsegregation model to determine the corrected phase fractions of δ phase, γ phase, and liquid phase.

5. The method according to claim 1, characterized in that, Based on the corrected phase fraction and the corrected liquid solute concentration, the thermophysical parameters are calculated using the phase weighting method, including: Based on the corrected liquid phase solute concentration and solute distribution parameters, the carbon content in the δ phase and γ phase is determined. According to the corrected δ phase, γ phase and liquid phase fraction, the specific heat capacity characteristics of each phase are matched respectively. The specific heat capacity characteristics of each phase are weighted and summed by phase fraction to obtain the specific heat capacity of the billet. Based on the corrected phase fraction and the corrected liquid phase solute concentration, the carbon content in the δ phase is determined. The basic thermal conductivity of each phase is calculated according to the corrected phase fraction. The basic thermal conductivity of the liquid phase is multiplied by an empirical constant to obtain the effective thermal conductivity of the liquid phase. The basic thermal conductivity of the δ phase, the basic thermal conductivity of the γ phase, and the effective thermal conductivity of the liquid phase are weighted by phase fraction to obtain the effective thermal conductivity. Based on the corrected phase fraction and the corrected liquid phase solute concentration, the carbon content in the δ phase, γ phase and liquid phase is determined. Based on the carbon content and the corrected phase fraction, the density characteristics of each phase are matched, and the density characteristics of each phase are weighted and summed by phase fraction to obtain the billet density. The specific heat capacity of the billet, the effective thermal conductivity, and the density of the billet are integrated to form a set of thermophysical parameters for this calculation step.

6. The method according to claim 1, characterized in that, The process iteratively updates the dynamic cooling rate, the solute equilibrium distribution coefficient, the amount of inclusion precipitation, the corrected phase fraction, and the thermophysical parameters until a termination condition is met, outputting the predicted thermophysical parameters, including: Set the calculation step size of the microsegregation model and the update step size of the thermal history data; The microsegregation model is iterated based on the calculation step size and the update step size. After each round of microsegregation model calculation iteration, the dynamic cooling rate in the thermal history data is updated based on the solidification process of the billet, and the solute balance distribution coefficient is corrected based on the updated dynamic cooling rate. Based on the revised solute equilibrium partition coefficient, the amount of inclusions precipitated, the phase fraction and the thermophysical parameters are updated synchronously, and the correspondence between temperature and thermophysical parameters is recorded. The phase composition of the calculation domain in the billet calculation model is determined in real time. When all units in the calculation domain are completely transformed into the γ phase, the iterative calculation is stopped, and the complete temperature-thermal property parameter relationship curve is transmitted to the client as the thermal property parameter prediction result.

7. A device for predicting the thermal properties of continuously cast billets, characterized in that, The device is used on a server side, and the device includes: The receiving module is used to receive initial parameters transmitted by the client and establish a billet calculation model based on the initial parameters; The calculation module is used to perform heat conduction analysis on the billet calculation model, calculate the dynamic cooling rate at different locations of the billet, and obtain billet thermal history data containing the dynamic cooling rate. This includes: based on the billet calculation model, solving the heat conduction equation using the finite difference method to obtain the initial temperature values ​​of different discrete mesh elements of the billet at each calculation step and the billet surface temperature; and based on the encrypted process parameters transmitted from the client and the billet surface temperature, calculating the boundary condition parameters of the crystallizer and the secondary cooling zone using preset formulas. These boundary condition parameters include the heat flux density of the billet surface and the spraying of cooling water onto the billet surface. The heat transfer coefficient and the radiation heat transfer coefficient of the billet surface are used as parameters. The boundary condition parameters are substituted into the two-dimensional heat conduction equation to correct the initial temperature value, thereby obtaining the corrected temperature value of each discrete grid cell in each calculation step. Based on the corrected temperature values ​​of different calculation steps, the dynamic cooling rate corresponding to each discrete grid cell in the discrete grid cell set is extracted. The temperature values ​​of each discrete grid cell are integrated in chronological order to form the temperature change curve of different positions of the billet. At the same time, the dynamic cooling rate of each grid cell is integrated, and the temperature change curve and the dynamic cooling rate are used as complete billet thermal history data storage. The correction module is used to correct the solute balance distribution coefficient based on the preset microsegregation model and the dynamic cooling rate in the billet thermal history data, so as to obtain the solute distribution parameters under non-equilibrium solidification. The correction module can also be used to combine the solute distribution parameters with the microsegregation model to calculate the amount of inclusion precipitation of various inclusions in the billet, correct the liquid phase solute concentration and simultaneously calculate the corrected phase fraction. The update module is used to calculate the thermophysical parameters using the phase weighting method based on the corrected phase fraction and the corrected liquid solute concentration. It iteratively updates the dynamic cooling rate, the solute equilibrium distribution coefficient, the amount of inclusion precipitation, the corrected phase fraction, and the thermophysical parameters until the termination condition is met, and outputs the predicted thermophysical parameters.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. An electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

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