Method for predicting solidification structure distribution of continuous casting billet based on solidification heat transfer model optimization

Abstract

The present application relates to the technical field of metallurgical continuous casting, and provides a method for predicting the solidification structure distribution of continuous casting billets based on a solidification heat transfer model optimization, which comprises the following steps: obtaining the actual process parameters, the CET starting position and the actual distance from the CET ending position to the surface of the casting billet of a reference scheme, and obtaining the thermal physical property parameters of the steel billet through material calculation; multiplying the basic thermal conductivity coefficient by a thermal conductivity conversion coefficient to obtain an equivalent thermal conductivity coefficient, combining the reference scheme parameters, establishing a solidification heat transfer model and performing simulation, extracting the temperature gradient G and the solidification rate R in the thickness direction of the casting billet, calculating the G 2 / R curve, and determining the critical values of the CET starting and complete conversion according to the actual CET starting and ending positions; inputting the target process parameters into the solidification heat transfer model for simulation to obtain the G 2 / R curve, and predicting the CET starting and ending positions under the target scheme through the critical values. The present application can realize the accurate prediction of the CET conversion in the solidification process of the continuous casting billet, and improve the numerical simulation efficiency and the process design efficiency.

Description

Technical Field

[0001] This invention relates to the field of metallurgical continuous casting technology, and in particular to a method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization. Background Technology

[0002] The columnar to equiaxed transition (CET) is a key microstructural transformation phenomenon during the solidification process of continuously cast billets. The distribution of columnar and equiaxed crystals has a crucial impact on the quality control and defect distribution of continuously cast billets. Therefore, research on the CET transformation of continuously cast billets has significant practical application value in optimizing the solidification microstructure of billets, improving the material properties of billets, and expanding effective process control measures.

[0003] Currently, research methods for the CET transformation during continuous casting solidification can be broadly categorized into three main types: direct experimental methods, physical simulation methods, and numerical simulation methods. Among the direct experimental methods, the classical pour-out method obtains relevant data by directly observing the solidification process; however, due to limitations in experimental conditions, it is difficult to precisely control solidification parameters, thus limiting the accuracy of the research.

[0004] Physical simulation methods played an important role in the early development of solidification theory. By observing the crystallization process of low-melting-point transparent crystals, they provided an intuitive basis for understanding the crystal growth mechanism. However, this method has the problem that the material properties are quite different from those of the actual continuous casting billets, resulting in insufficient correlation between the simulation results and the actual process.

[0005] The rise and development of numerical simulation technology benefited from the complex requirements of the continuous casting solidification process and the advancement of computer technology. This method is mainly divided into three categories: deterministic models, statistical models, and phase-field models. Deterministic models are based on classical nucleation and dendrite growth theories, simplifying grain morphology to spherical or cylindrical shapes and experimentally determining nucleation density and growth rate functions. However, due to its simplified treatment of microscopic physical processes and its strong sensitivity to initial conditions, its prediction accuracy is limited.

[0006] Statistical models, including the Monte Carlo method and cellular automata method, consider the randomness of nucleation location and growth direction, and can simulate solidification phenomena at multiple scales. However, they suffer from a lack of clear physical basis and significant difficulty in three-dimensional simulation. The phase-field method describes the solid-liquid interface state through phase-field variables, effectively avoiding the problem of interface tracking. However, it suffers from high computational cost and a limited scale that can be simulated.

[0007] Overall, existing research methods all have significant limitations: deterministic models cannot effectively handle stochastic factors in the solidification process; statistical models are more realistic in describing the CET transformation process, but lack sufficient physical mechanism support; while the phase-field method can accurately capture complex solid-liquid interface changes, it suffers from low computational efficiency. Therefore, future research should focus on optimizing algorithms to reduce computational load, improving relevant physical mechanisms, and integrating the advantages of various models to achieve quantitative simulation of alloy CET transformation. This will improve the consistency between simulation results and actual processes, and promote the effective application of CET transformation research findings in industrial production. Summary of the Invention

[0008] To address the above issues, this invention provides a method for predicting the solidification structure distribution of continuously cast billets based on a solidification heat transfer model optimization. By predicting the CET transformation during the solidification process of continuously cast billets based on the solidification heat transfer model, this method can flexibly and effectively adapt to different steel grades, billet shapes, different casting parameters, and application scenarios using additional control technologies such as electromagnetic stirring in the continuous casting process, providing a unified solution for diverse production needs.

[0009] According to an embodiment of the present invention, a method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization is provided.

[0010] In a first aspect of the invention, a method for predicting the solidification microstructure distribution of continuously cast billets based on solidification heat transfer model optimization is provided. The method includes: Step S01: Obtain the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and obtain the thermal properties of the billet through material calculations; Step S02: Multiply the basic thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with the parameters of the benchmark scheme, establish a solidification heat transfer model and perform simulation. Extract the temperature gradient G and solidification rate R in the thickness direction of the billet, and calculate G. 2 / R curve, and determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET; Step S03: Input the target process parameters into the solidification heat transfer model for simulation to obtain its G. 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

[0011] Further, the process of obtaining the thermal properties of the steel billet through material calculation in step S01 specifically involves using JMatPro software to obtain the thermal properties of the steel billet, including the basic thermal conductivity. and Equivalent specific heat capacity C eff and central solidity fs .

[0012] Furthermore, the method for solving the thermal conductivity conversion coefficient mentioned in step S02 is as follows: the critical value CET for the start and complete conversion is obtained under the baseline scheme conditions. s and CET f To standardize the criteria for judgment, the values ​​of the thermal conductivity conversion coefficient were adjusted, and the calculated subcooling under different EMS parameters were matched with the measured subcooling for trial calculation until the calculated value matched the measured value, thereby determining the corresponding thermal conductivity conversion coefficient for each scheme.

[0013] Furthermore, the governing equations of the solidification heat transfer model described in step S02 are improved as follows: , , , in, Density of molten steel, kg·m -3 ; The equivalent thermal conductivity is W·m. -1 ·℃ -1 ; The temperature at a certain moment, in °C; For time, s; The width of the billet is in meters (m). The value is in the thickness direction of the billet, in meters (m). For latent heat of solidification, W / m 3 ; The thermal conductivity conversion coefficient; The fundamental thermal conductivity of the solid phase is W·m. -1 ·℃ -1 ; The fundamental thermal conductivity of the liquid phase is W·m. -1 ·℃ -1 .

[0014] Further, in step S02, the temperature gradient G and solidification rate R along the thickness direction of the cast billet are extracted, and G is calculated. 2 The specific steps for the / R curve are as follows: [Calculate the equivalent thermal conductivity...] Equivalent specific heat capacity C eff Central solidity f s The actual process parameters of the baseline scheme are input into Procast software to obtain the billet thickness versus temperature gradient G curve and the billet thickness versus solidification rate R curve under the baseline scheme parameters. Then, the billet thickness and G curve under the baseline scheme parameters are calculated. 2 / R curve.

[0015] Furthermore, the specific steps in step S02 for determining the critical value between the start and complete transition of CET based on the actual start and end positions of CET are as follows: In G 2 The two intersection points on the / R curve, representing the start and end positions of the actual CET in the benchmark scheme, are respectively the G points. 2 The / R value is the critical value between the start and complete conversion of CET. s and CET f .

[0016] Further, the specific steps of step S03 are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Equivalent specific heat capacity C eff Central solidity f s The target process parameters are input into Procast software to obtain the G curve of billet thickness versus temperature gradient and the R curve of billet thickness versus solidification rate under the target process parameters. Then, the relationship between billet thickness and G under the target process parameters is calculated. 2 The curve of / R shows the relationship between the billet thickness and G under the target process parameters. 2 The CET curve obtained from the / R curve is taken from the baseline scheme parameters. s and CET f The two intersection points are used to obtain the predicted start and end positions of CET.

[0017] In a second aspect of the invention, an apparatus for predicting the solidification microstructure distribution of continuously cast billets based on an optimized solidification heat transfer model is provided. The apparatus includes: Actual data acquisition module: used to acquire the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and to obtain the thermal properties of the billet through material calculations; CET Critical Value Acquisition Module: This module multiplies the base thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with baseline parameters, it establishes and simulates a solidification heat transfer model, extracts the temperature gradient G and solidification rate R along the billet thickness direction, and calculates G. 2 / R curve, and determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET; Target Scheme Prediction Module: Used to input target process parameters into the solidification heat transfer model for simulation, and obtain its G... 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

[0018] In a third aspect of the invention, an electronic device is provided. The electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the program to implement the method according to a first aspect of the invention.

[0019] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to a first aspect of the invention.

[0020] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description.

[0021] The beneficial effects of this invention are: 1. Based on the solidification heat transfer model, the CET transformation during the solidification process of continuously cast billets can be accurately predicted, laying a solid foundation for it to become a core technology for optimizing modern continuous casting processes. 2. This method is highly applicable and can be flexibly adapted to different steel grades, billet specifications, casting process parameters, and external field control technologies such as electromagnetic stirring, providing a unified solution for diverse industrial production needs. 3. The model has excellent computational simplicity. Through numerical simulation and optimization algorithms, it greatly simplifies the solution process of traditional numerical models and significantly improves the efficiency of numerical simulation and process design. 4. This calculation method has high simulation accuracy. It can not only accurately predict the distribution boundaries of columnar crystals and equiaxed crystals, but also finely depict the transition zone characteristics of columnar crystals to equiaxed crystals during the solidification process of continuously cast billets. It is highly consistent with the detection results of the solidification structure of actual cast billets. Attached Figure Description

[0022] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Wherein: Figure 1 A flowchart of a method for predicting the solidification structure distribution of continuously cast billets based on a solidification heat transfer model optimization according to an embodiment of the present invention is shown. Figure 2 A graph showing the relationship between billet thickness and temperature gradient under baseline parameters according to an embodiment of the present invention is shown. Figure 3 A graph showing the relationship between billet thickness and solidification rate under baseline parameters according to an embodiment of the present invention is shown. Figure 4 The billet thickness and G are shown under the baseline scheme parameters according to an embodiment of the present invention. 2 / R curve; Figure 5 CET under baseline scheme parameters according to an embodiment of the present invention is shown. s and CET f Schematic diagram; Figure 6 The graph shows the billet thickness versus temperature gradient under the target process parameters according to an embodiment of the present invention. Figure 7 The graph shows the relationship between billet thickness and solidification rate under the target process parameters according to an embodiment of the present invention. Figure 8 The billet thickness and G under the target process parameters according to an embodiment of the present invention are shown. 2 / R curve; Figure 9 A schematic diagram showing the predicted start and end positions of CET under target process parameters according to an embodiment of the present invention is shown; Figure 10 A block diagram of an apparatus for predicting the solidification structure distribution of continuously cast billets based on a solidification heat transfer model optimization according to an embodiment of the present invention is shown. Figure 11 A schematic diagram of an apparatus for predicting the solidification structure distribution of continuously cast billets based on a solidification heat transfer model optimized according to an embodiment of the present invention is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] According to embodiments of the present invention, a method for predicting the solidification microstructure distribution of continuously cast billets based on solidification heat transfer model optimization is proposed. The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments.

[0025] Figure 1 This is a schematic flowchart illustrating a method for predicting the solidification microstructure distribution of continuously cast billets based on a solidification heat transfer model optimization, according to an embodiment of the present invention. The method includes: Step S01: Obtain the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and obtain the thermal properties of the billet through material calculations; Step S02: Multiply the basic thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with the parameters of the benchmark scheme, establish a solidification heat transfer model and perform simulation. Extract the temperature gradient G and solidification rate R in the thickness direction of the billet, and calculate G. 2 / R curve, and determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET; Step S03: Input the target process parameters into the solidification heat transfer model for simulation to obtain its G. 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

[0026] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0027] To provide a clearer explanation of the method for predicting the solidification structure distribution of continuously cast billets based on the optimization of the solidification heat transfer model, a specific embodiment is described below. However, it is worth noting that this embodiment is only for better illustrating the present invention and does not constitute an improper limitation of the present invention.

[0028] The following specific example will further illustrate the method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization: Step S01: Obtain the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and obtain the thermal properties of the billet through material calculation.

[0029] In this embodiment, 160×160mm is selected. 2 The cross-section of grade 70 steel has the following chemical composition: 0.70% C by weight, 0.25% Si by weight, 0.55% Mn by weight, 0.012% P by weight, 0.008% S by weight, and the remainder is Fe.

[0030] The actual process parameters of the baseline scheme are: pulling speed: 2.3m / min, secondary cooling intensity: 0.35L / kg, superheat: 25℃, M-EMS (crystallizer electromagnetic stirring): current 100A, frequency 3Hz, F-EMS (terminal electromagnetic stirring): current 0A, frequency 0Hz.

[0031] Under the baseline scheme, the actual CET start position is 43 mm from the surface of the billet, and the CET end position is 57 mm from the surface of the billet.

[0032] The thermal properties of the steel billet were obtained using JMatPro software: basic thermal conductivity. and Equivalent specific heat capacity C eff and central solidity f s .

[0033] Step S02: Multiply the basic thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with the parameters of the benchmark scheme, establish a solidification heat transfer model and perform simulation. Extract the temperature gradient G and solidification rate R in the thickness direction of the billet, and calculate G. 2 The / R curve is used to determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET.

[0034] To facilitate calculations, the effects of M-EMS and F-EMS on the solidification heat transfer process of the high-carbon steel small billet under study are mainly converted into their effects on the thermal conductivity of the billet. EMS has no effect on the solid-phase thermal conductivity of specific steel grades, therefore the solid-phase thermal conductivity is not considered. The thermal conductivity values ​​for different temperature ranges were uniformly calculated using Jmatpro software; while the thermal conductivity of molten steel under static conditions... The calculations were also performed using Jmatpro software. Under electromagnetic stirring, the liquid core is in motion, which drives the internal molten steel to move continuously. This movement of the molten steel enhances the heat transfer effect; therefore, the liquid phase equivalent thermal conductivity is used here. and thermal conductivity conversion coefficient α To comprehensively characterize the effect of electromagnetic stirring on the flow of molten steel within the liquid phase cavity: , Conversion coefficients under EMS setting parameters in each scheme To solve this, we first need to determine the critical value CET for the initial and complete conversion under the baseline scheme conditions. s and CET f To standardize the criteria for judgment, the thermal conductivity conversion coefficient was subsequently adjusted. The calculated subcooling under different EMS parameters is matched with the measured subcooling through trial calculations until the calculated value matches the measured value, thereby determining the corresponding conversion coefficient for each scheme. This section is detailed in the paper Yubo Gao; Yanping Bao; Min Wang; Ying Wang. "Study on the synergistic effect of electromagnetic stirring and mechanical reduction on the improvement of the internal homogeneity of high-carbon steel billet.", and will not be repeated here.

[0035] Therefore, the governing equations of the solidification heat transfer model are improved as follows: , , in, Density of molten steel, kg·m -3 ; The equivalent thermal conductivity is W·m. -1 ·℃ -1 ; The temperature at a certain moment, in °C; For time, s; The width of the billet is in meters (m). The value is in the thickness direction of the billet, in meters (m). For latent heat of solidification, W / m 3 The basic governing equations and boundary conditions of the solidification heat transfer model of the billet described in this invention, as well as other related calculation formulas, are as described in patent CN202510391895.4, and will not be repeated here.

[0036] Equivalent thermal conductivity Equivalent specific heat capacity C eff Central solidity f s The actual process parameters of the baseline scheme are input into the Procast software to obtain the billet thickness versus temperature gradient G curve under the baseline scheme parameters (e.g., Figure 2 (as shown) and the R-curve of billet thickness versus solidification rate (as shown) Figure 3 (As shown). This further yields the billet thickness and G under the baseline scheme parameters. 2 The curve of / R, such as Figure 4 As shown.

[0037] Based on the actual start and end positions of CET under the benchmark scheme (43mm and 57mm), the billet thickness under the benchmark scheme parameters and G 2 Find the corresponding CET threshold between the start and complete conversion on the / R curve. s and CET f ,like Figure 5 As shown, CET s and CET f The values ​​are 485 and 160 respectively.

[0038] Step S03: Input the target process parameters into the solidification heat transfer model for simulation to obtain its G. 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

[0039] In this embodiment, the target process parameters are: pulling speed: 2.4 m / min, secondary cooling strength: 0.52 L / kg, superheat: 22℃, M-EMS (crystallizer electromagnetic stirring): current 200A, frequency 3Hz, F-EMS (terminal electromagnetic stirring): current 150A, frequency 6Hz.

[0040] Equivalent thermal conductivity Equivalent specific heat capacity C eff Central solidity f s The target process parameters are input into the Procast software to obtain the G-curve of billet thickness versus temperature gradient under the target process parameters (e.g., ...). Figure 6 (as shown) and the R-curve of billet thickness versus solidification rate (as shown) Figure 7 (As shown). This further yields the billet thickness and G under the target process parameters. 2 The curve of / R, such as Figure 8 As shown.

[0041] The billet thickness and G under the target process parameters 2 The CET curve obtained from the / R curve is taken from the baseline scheme parameters. s and CET f The two intersection points are used to obtain the predicted start and end positions of CET, such as... Figure 9 As shown, the predicted start and end positions of CET under the target process parameters are 26 mm and 48 mm, respectively.

[0042] The present invention has conducted actual measurements on the target process parameters, as shown in Table 1. The calculation results by the solidification heat transfer model are very close to the actual solidification structure transformation position of the cast billet, with errors within 5%, which further illustrates the reliability and practicality of the method.

[0043] Table 1 Based on the same inventive concept, this invention also proposes a device for predicting the solidification microstructure distribution of continuously cast billets based on solidification heat transfer model optimization. The implementation of this device can be found in the implementation of the method described above; repeated details will not be repeated. Figure 10 As shown, the device 100 includes: Actual data acquisition module 101: used to acquire the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and to obtain the thermal properties of the billet through material calculations; CET Critical Value Acquisition Module 102: This module multiplies the basic thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with baseline parameters, it establishes and simulates a solidification heat transfer model, extracts the temperature gradient G and solidification rate R along the billet thickness direction, and calculates G.2 / R curve, and determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET; Target Scheme Prediction Module 103: Used to input the target process parameters into the solidification heat transfer model for simulation, and obtain its G. 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

[0044] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0045] like Figure 11 As shown, the device includes a central processing unit (CPU), which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0046] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0047] The processing unit executes the various methods and processes described above, such as method steps S01 to S03. For example, in some embodiments, method steps S01 to S03 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of method steps S01 to S03 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute method steps S01 to S03 by any other suitable means (e.g., by means of firmware).

[0048] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0049] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0050] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0051] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0052] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization, characterized in that, The method includes: Step S01: Obtain the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and obtain the thermal properties of the billet through material calculations; Step S02: Multiply the basic thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with the parameters of the benchmark scheme, establish a solidification heat transfer model and perform simulation. Extract the temperature gradient G and solidification rate R in the thickness direction of the billet, and calculate G. 2 / R curve, and determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET; Step S03: Input the target process parameters into the solidification heat transfer model for simulation to obtain its G. 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

2. The method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization according to claim 1, characterized in that, The process of obtaining the thermal properties of the steel billet through material calculations in step S01 specifically involves using JMatPro software to obtain the thermal properties of the steel billet, including the basic thermal conductivity. and Equivalent specific heat capacity C eff and central solidity f s .

3. The method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization according to claim 1, characterized in that, The method for solving the thermal conductivity conversion coefficient in step S02 is as follows: using the critical values ​​CETs and CETf obtained under the baseline scheme as the unified judgment basis; adjusting the value of the thermal conductivity conversion coefficient, matching the calculated subcooling and the measured subcooling under different EMS parameters until the calculated value matches the measured value, thereby determining the corresponding thermal conductivity conversion coefficient under each scheme.

4. The method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization according to claim 3, characterized in that, The governing equations of the solidification heat transfer model described in step S02 are improved as follows: ， ， ， in, Density of molten steel, kg·m -3 ; The equivalent thermal conductivity is W·m. -1 ·℃ -1 ; The temperature at a certain moment, in °C; For time, s; The width of the billet is in meters (m). The value is in the thickness direction of the billet, in meters (m). For latent heat of solidification, W / m 3 ; The thermal conductivity conversion coefficient; The fundamental thermal conductivity of the solid phase is W·m. -1 ·℃ -1 ; The fundamental thermal conductivity of the liquid phase is W·m. -1 ·℃ -1 .

5. The method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization according to claim 1, characterized in that, In step S02, the temperature gradient G and solidification rate R in the thickness direction of the cast billet are extracted, and G is calculated. 2 The specific steps for the / R curve are as follows: [Calculate the equivalent thermal conductivity...] Equivalent specific heat capacity C eff Central solidity f s The actual process parameters of the baseline scheme are input into Procast software to obtain the billet thickness versus temperature gradient G curve and the billet thickness versus solidification rate R curve under the baseline scheme parameters. Then, the billet thickness and G curve under the baseline scheme parameters are calculated. 2 / R curve.

6. The method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization according to claim 1, characterized in that, The specific steps in step S02 to determine the critical value between the start and complete conversion of CET based on the actual start and end positions of CET are as follows: In G 2 The two intersection points on the / R curve, representing the start and end positions of the actual CET in the benchmark scheme, are respectively the G points. 2 The / R value is the critical value between the start and complete conversion of CET. s and CET f .

7. The method for predicting the solidification structure distribution of continuously cast billets based on solidification heat transfer model optimization according to claim 1, characterized in that, The specific steps of step S03 are as follows: The equivalent thermal conductivity... Equivalent specific heat capacity C eff Central solidity f s The target process parameters are input into Procast software to obtain the G curve of billet thickness versus temperature gradient and the R curve of billet thickness versus solidification rate under the target process parameters. Then, the relationship between billet thickness and G under the target process parameters is calculated. 2 The curve of / R shows the relationship between the billet thickness and G under the target process parameters. 2 The CET curve obtained from the / R curve is taken from the baseline scheme parameters. s and CET f The two intersection points are used to obtain the predicted start and end positions of CET.

8. A device for predicting the solidification structure distribution of continuously cast billets based on a solidification heat transfer model optimization, characterized in that, The device implements the method as described in any one of claims 1 to 7, comprising: Actual data acquisition module: used to acquire the actual process parameters of the benchmark scheme and the actual distances from the start and end positions of CET to the surface of the billet, and to obtain the thermal properties of the billet through material calculations; CET Critical Value Acquisition Module: This module multiplies the base thermal conductivity by the thermal conversion coefficient to obtain the equivalent thermal conductivity. Combined with baseline parameters, it establishes and simulates a solidification heat transfer model, extracts the temperature gradient G and solidification rate R along the billet thickness direction, and calculates G. 2 / R curve, and determine the critical value for the start and complete conversion of CET based on the actual start and end positions of CET; Target Scheme Prediction Module: Used to input target process parameters into the solidification heat transfer model for simulation, and obtain its G... 2 The / R curve is used to predict the start and end positions of CET under the target scheme through the critical value.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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