Silicon carbide vacuum sintering furnace optimization method based on computational heat transfer theory simulation

Abstract

The invention discloses a silicon carbide vacuum sintering furnace optimization method based on computational heat transfer theory simulation, which is used in the field of artificial intelligence and comprises the following steps: establishing a heat transfer model; the heat effect in the silicon carbide vacuum sintering furnace is predicted by setting the boundary conditions of initial temperature, heat flow implementation and convective heat transfer and solving the heat transfer model by using an implicit numerical method; based on the optimized heat transfer model, simulating the physical process in the silicon carbide vacuum sintering furnace by using a multi-physical field simulation technology, and analyzing the influence of the operation parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process; comparing the operation data with a simulation result, and based on a comparison result and an analysis result; and comparing and analyzing the actual performance data and the simulation result, and evaluating the adjustment effect of the operation parameter data. Through quality detection and energy efficiency evaluation, the energy utilization efficiency in the sintering process is measured and optimized, and the link of energy waste is identified.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation. Background Art

[0002] Silicon carbide is a very hard material, usually used in high temperature, high power, high frequency and high wear-resistant applications. Its chemical formula is SiC, which is composed of two elements, silicon and carbon. Silicon carbide has a variety of crystal structures, each structure is called a polymorph. This material is widely used in abrasives, sand making, refractory materials, semiconductors and photovoltaic industries due to its advantages such as high hardness, high thermal conductivity, good heat resistance, strong corrosion resistance and high electrical conductivity. A vacuum sintering furnace is a device that uses a vacuum environment for high-temperature sintering. In this furnace, the material can be heated in an environment with almost no oxygen, thereby avoiding or reducing oxidation and improving the purity and performance of the product. Vacuum sintering is one of the commonly used processes in fields such as powder metallurgy and ceramic materials. It is particularly suitable for the preparation of high-performance ceramics and metal materials. In a vacuum or controlled atmosphere, the powder particles are sintered together by thermal energy to form a dense solid.

[0003] Computational heat transfer is a discipline that studies and predicts heat transfer between objects through mathematical models and computer simulations. In industrial applications, such as thermal efficiency analysis of silicon carbide sintering processes and equipment design optimization, computational heat transfer simulation is an indispensable tool. By setting specific physical models, boundary conditions and initial conditions, computational heat transfer simulation can accurately predict important thermal properties of materials during heating, such as temperature changes and heat flux distribution, and provide theoretical analysis and solutions for engineering and technical problems.

[0004] There is often a large deviation between the simulation and actual operation of traditional sintering furnaces. This is mainly because the model cannot accurately reflect the actual operating conditions and complex physical processes. In the absence of real-time feedback and automatic control systems, the adjustment of operating parameters depends on the operator's experience and judgment, which may lead to inefficiency, energy waste and even product quality problems. Traditional sintering processes often face problems such as low energy utilization efficiency and waste of raw materials, especially in the absence of precise control and evaluation systems. Traditional systems often lack real-time fault monitoring and early warning mechanisms, resulting in an increased risk of equipment failure and production interruption, and high maintenance costs. In a rapidly changing market environment, it is difficult for traditional sintering processes to quickly adjust production strategies to adapt to changes in market demand.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In order to overcome the above problems, the present invention aims to propose a silicon carbide vacuum sintering furnace optimization method based on computational heat transfer simulation, with the aim of solving the problems of low energy utilization efficiency and waste of raw materials often faced by traditional sintering processes, especially in the absence of precise control and evaluation systems. Traditional systems often lack real-time fault monitoring and early warning mechanisms, resulting in an increased risk of equipment failure and production interruption, and high maintenance costs. In a rapidly changing market environment, it is difficult for traditional sintering processes to quickly adjust production strategies to adapt to changes in market demand.

[0007] To this end, the specific technical solution adopted by the present invention is as follows: A method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation, the method comprising the following steps: S1. Collecting operation data and environmental data of the silicon carbide vacuum sintering furnace, and establishing a heat transfer model using a heat transfer equation based on the operation data and the environmental data; S2. Predict the thermal effect in the silicon carbide vacuum sintering furnace by setting the initial temperature, applying the boundary conditions of heat flow and convection heat transfer, and solving the heat transfer model using the implicit numerical method, and optimize the heat transfer model parameters according to the prediction results; S3. Based on the optimized heat transfer model, the physical process in the silicon carbide vacuum sintering furnace is simulated by using multi-physics field simulation technology. Based on the simulation results, the influence of the operating parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process is analyzed; S4. Collect the operation data of the silicon carbide vacuum sintering furnace in real time, and compare the operation data with the simulation results. Based on the comparison results and the analysis results, use the feedback control mechanism to adjust the operation parameter data; S5. Conduct quality inspection and energy efficiency evaluation on the sintered silicon carbide products, obtain the actual performance data of the silicon carbide products, compare and analyze the actual performance data with the simulation results, and evaluate the adjustment effect of the operating parameter data. Iteratively update the heat transfer model based on the evaluation results.

[0008] Optionally, collecting operation data and environmental data of the silicon carbide vacuum sintering furnace, and establishing a heat transfer model using a heat transfer equation based on the operation data and the environmental data comprises the following steps: S11, pre-configuring sensors at various positions of the silicon carbide vacuum sintering furnace, and collecting operation data and environmental data of the silicon carbide vacuum sintering furnace based on the pre-configured sensors; S12, defining boundary conditions and initial conditions of the heat transfer model according to the operation data and environmental data of the silicon carbide vacuum sintering furnace, and setting physical parameters of the heat transfer model based on the boundary conditions and initial conditions; S13. Construct a heat transfer equation and describe the heat conduction process through the internal materials of the silicon carbide vacuum sintering furnace and the heat exchange process between the furnace surface and the environment; S14. Use numerical methods to construct a heat transfer model based on the set physical parameters and heat transfer equations.

[0009] Optionally, constructing a heat transfer equation and describing the heat conduction process through the internal materials of the silicon carbide vacuum sintering furnace and the heat exchange process between the furnace surface and the environment includes the following steps: S131, constructing a heat transfer equation, the heat transfer equation comprising Fourier's law and Newton's law of cooling; S132. Based on the set physical parameters, set the thermal conductivity of Fourier's law, and use Fourier's law to describe the heat conduction process in the material inside the sintering furnace; S133, based on the set physical parameters, setting the heat exchange coefficient of Newton's cooling law and the ambient temperature, and using Newton's cooling law to simulate the heat exchange between the furnace surface and the environment; Among them, the expression using Fourier's law to describe the heat conduction process in the internal material of the sintering furnace is: ; In the formula, q Represents the heat flux density of the conduction process; k represents thermal conductivity; Represents the temperature gradient.

[0010] Optionally, using a numerical method and constructing a heat transfer model based on set physical parameters and heat transfer equations includes the following steps: S141, dividing the internal materials of the silicon carbide vacuum sintering furnace and the space between the furnace surface and the environment into discretized grids, and determining the size and distribution of each grid point in the discretized grid according to the size of the silicon carbide and the preset accuracy requirements; S142, setting a time step based on the size and distribution of each grid point; S143. Use the finite difference method to discretize Fourier's law and Newton's cooling law in space and time, form discretized equations, and construct a heat transfer model.

[0011] Optionally, by setting the initial temperature, applying the boundary conditions of heat flow and convection heat transfer, and solving the heat transfer model using an implicit numerical method, the thermal effect in the silicon carbide vacuum sintering furnace is predicted, and the heat transfer model parameters are optimized according to the prediction results, including the following steps: S21. In the constructed heat transfer model, setting initial temperatures for grid points of the discretized grid; S22. Based on the constructed heat transfer model, implement the boundary conditions of heat flow and convection heat transfer; S23. Using implicit numerical method (Crank-Nicolson method) to solve the discretized equations of the boundary conditions, the temperature distribution of the internal material and the furnace surface of the silicon carbide vacuum sintering furnace is analyzed based on the calculation results; S24. Optimize the heat transfer model parameters according to the analysis results of the temperature distribution of the internal material and the surface of the silicon carbide vacuum sintering furnace.

[0012] Optionally, using an implicit numerical method (Crank-Nicolson method) to solve the discretized equations for implementing boundary conditions, and analyzing the temperature distribution of the internal material and the furnace surface of the silicon carbide vacuum sintering furnace based on the calculation results includes the following steps: S231. Based on the constructed discretized equations, thermal boundary conditions are applied at the boundaries of the heat transfer model to reflect the heat exchange in the actual sintering process; S232, configure the implicit numerical (Crank-Nicolson) solver by setting the initial temperature configuration and discretizing the time derivative; S233, running the iterative solution process, advancing time step by time step, updating the temperature values ​​of the grid points, and checking convergence after each time step; S234. Analyze the calculated temperature distribution data to obtain the thermal effect of each area in the grid point.

[0013] Optionally, according to the analysis results of the temperature distribution of the internal material and the surface of the furnace body of the silicon carbide vacuum sintering furnace, optimizing the heat transfer model parameters includes the following steps: S241, collecting temperature distribution data of internal materials and furnace surface of silicon carbide vacuum sintering furnace; S242, establishing a mapping relationship between heat transfer model parameters and temperature distribution; S243. Optimize the heat transfer model parameters based on the mapping relationship; and evaluate the performance of the optimized heat transfer model.

[0014] Optionally, based on the optimized heat transfer model, multi-physics field simulation technology is used to simulate the physical process in the silicon carbide vacuum sintering furnace, and based on the simulation results, the influence of the operating parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process is analyzed, including the following steps: S31. According to the actual physical process in the silicon carbide vacuum sintering furnace, set the multi-physics field simulation parameters, run the multi-physics field simulation, and monitor the changes of various physical quantities in real time; S32, collecting the operating parameter data obtained by multi-physics field simulation, and analyzing the simulated operating parameter data by using statistical analysis method, and evaluating the influence of different operating parameter data (temperature, pressure, power input, etc.) on the sintering process; S33. Analyze the performance of the silicon carbide vacuum sintering furnace based on the evaluation results.

[0015] Optionally, collecting the operating parameter data obtained by multi-physics field simulation, analyzing the simulated operating parameter data by statistical analysis, and evaluating the influence of different operating parameter data on the sintering process include the following steps: S321, collecting each operating parameter data from the multi-physics field simulation, calculating the basic statistics of each operating parameter data, analyzing the distribution of each operating parameter data, and determining the skewness and peak state of each operating parameter data; S322, selecting a membership function based on the skewness and kurtosis of each operating parameter data, and adjusting the node positions in the membership function; S323, constructing a fuzzy logic model using the adjusted membership function, and mapping the operating parameter data to the degree of influence on the sintering process, and using a visual display interface to display the relationship between the operating parameter data and the sintering effect; S324. Based on the fuzzy logic model analysis results, evaluate the impact of different operating parameters on the sintering process.

[0016] Optionally, the sintered silicon carbide product is subjected to quality inspection and energy efficiency evaluation, actual performance data of the silicon carbide product is obtained, the actual performance data is compared and analyzed with the simulation results, and the adjustment effect of the operating parameter data is evaluated. The heat transfer model is iteratively updated based on the evaluation results, including the following steps: S51. Conduct quality inspection on the sintered silicon carbide products, evaluate the energy efficiency of the silicon carbide vacuum sintering furnace, and determine the energy utilization efficiency during the sintering process; S52. Based on the results of quality inspection and the energy efficiency during sintering, determine whether the actual performance of silicon carbide products meets expectations, and check the impact of adjustment of operating parameter data on product quality and energy efficiency; S53, comparing the actual performance data with the simulation results, evaluating the accuracy of the simulation and the effectiveness of the heat transfer model, determining the differences between the simulation results and the actual data, and analyzing the reasons for the differences; S54. Based on the comparison results, evaluate whether the adjustment of the operating parameters has a positive impact on the quality and energy efficiency of the silicon carbide product; S55. Based on the evaluation results of the operating parameter adjustment effects, the heat transfer model is iteratively updated.

[0017] Compared with the prior art, this application has the following beneficial effects: 1. The present invention pre-configures sensors at key positions of the silicon carbide vacuum sintering furnace to collect operating data and environmental data, thereby ensuring that the model is established based on actual operating conditions and improving the accuracy and reliability of model predictions; by defining the boundary conditions and initial conditions of the heat transfer model and adjusting the physical parameters, precise control of the sintering furnace operation can be achieved, which helps to improve product quality, optimize energy use, and reduce operating costs. Based on the collected operating data, the heat transfer model can be flexibly adjusted to adapt to changes in the production process. By real-time monitoring and analysis of the operating data of the sintering furnace, potential problems and anomalies can be identified in a timely manner, thereby performing fault diagnosis and preventive maintenance, avoiding expensive equipment failures and production interruptions, and ensuring production efficiency and the maximum service life of the equipment.

[0018] 2. The present invention uses an implicit numerical method to solve the heat transfer equation, especially the Crank-Nicolson method, which can provide high numerical stability and accuracy in the time stepping process, balance the characteristics of forward difference and backward difference, optimize stability and convergence speed, and is suitable for processing complex boundary conditions and initial states in heat transfer. Through the precise setting of model parameters and boundary conditions, the simulated temperature distribution can reflect the thermal effects inside and on the surface of the sintering furnace in detail, including the identification of hot spots and cold zones, which has direct guiding significance for adjusting the layout of heating elements, optimizing sintering process parameters and improving product quality; according to the simulation results, the heat transfer model parameters, such as thermal conductivity, heat exchange coefficient, etc., can be optimized in a targeted manner to more accurately simulate the actual sintering process. Through accurate thermal effect simulation, unnecessary energy waste can be identified and reduced, and thermal energy utilization efficiency can be optimized. Accurate temperature distribution simulation can help predict and identify potential equipment failure areas.

[0019] 3. The present invention not only focuses on heat transfer through multi-physical field simulation, but also integrates other related physical processes such as fluid dynamics, structural mechanics and possible electromagnetic fields. Comprehensive analysis helps to understand the complex interactions and influences, ensuring that the simulation results more comprehensively reflect the actual operating environment. Through data analysis and fuzzy logic models, it not only provides scientific operating parameter adjustment suggestions, but also demonstrates the intuitive relationship between operating parameters and sintering effects. By simulating different operating scenarios, potential problems and risks can be predicted, so that risk management and response measures can be formulated in advance.

[0020] 4. The present invention realizes rapid response and precise adjustment by real-time monitoring of key operating parameters such as temperature, pressure and power input, and comparing them with preset simulation results. The real-time data feedback mechanism makes the operation of the sintering furnace more precise and can respond immediately to changes and demands in the production process. The automatic control system dynamically adjusts the operating parameters according to real-time data, which can continuously optimize the sintering process and improve production efficiency and product quality. The automated control system reduces the reliance on manual adjustments by operators and reduces the risks and costs caused by human operational errors. The feedback control system integrated with real-time data processing and analysis provides data-based decision support to help operators and managers make decisions based on accurate data insights. Through precise parameter control, energy and materials can be used more efficiently and waste can be reduced.

[0021] 5. The present invention can ensure that the sintered silicon carbide products meet the relevant quality standards and performance requirements through quality inspection. Through energy efficiency evaluation, it can measure and optimize the energy utilization efficiency in the sintering process, identify the links of energy waste, and reduce energy consumption by adjusting operating parameters. By comparing the actual performance data with the simulation results, the accuracy and applicability of the heat transfer model can be evaluated. Based on the results of comparative analysis, the parameters of the heat transfer model can be continuously adjusted and optimized to more accurately simulate the actual sintering process. Through the feedback of actual performance data, it can better adapt to changes in market demand and production conditions, adjust the production strategy in time, and improve the market competitiveness of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown: Figure 1 The present invention is a flowchart of a method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] According to an embodiment of the present invention, a method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation is provided.

[0025] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an optimization method for a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to an embodiment of the present invention, the optimization method for a silicon carbide vacuum sintering furnace comprises the following steps: S1. Collect operating data and environmental data of the silicon carbide vacuum sintering furnace, and establish a heat transfer model using a heat transfer equation based on the operating data and the environmental data.

[0026] Preferably, collecting the operation data and environmental data of the silicon carbide vacuum sintering furnace, and establishing a heat transfer model using a heat transfer equation based on the operation data and the environmental data comprises the following steps: S11. Pre-configure sensors at various locations of the silicon carbide vacuum sintering furnace, and collect operating data and environmental data of the silicon carbide vacuum sintering furnace based on the pre-configured sensors.

[0027] S12. Define boundary conditions and initial conditions of the heat transfer model according to the operation data and environmental data of the silicon carbide vacuum sintering furnace, and set physical parameters of the heat transfer model based on the boundary conditions and initial conditions.

[0028] It needs to be explained that the appropriate type of sensor should be selected according to the physical quantity that needs to be monitored (such as temperature, pressure, gas composition, etc.). Commonly used sensors include thermocouples (temperature measurement), pressure sensors, gas analyzers, etc. Configuration location: Sensors are configured at key locations of the sintering furnace, such as different levels in the furnace, feed and discharge ports, near heating elements, etc., to ensure that the sensors can capture key changes in the sintering process, use the installed sensor data to collect physical parameters, so as to collect, store and transmit data obtained from the sensors in real time, ensure data communication security and stability, use appropriate communication protocols and network configurations, perform system tests before the sintering furnace is operated, check whether all sensors and data acquisition systems are functioning properly, adjust and calibrate sensors, and ensure data accuracy and reliability.

[0029] Analyze the data collected from step S11 to determine the typical operating status and environmental conditions of the sintering furnace, such as temperature distribution, pressure changes, etc., identify key features in the data, such as temperature peaks, pressure fluctuations, etc., which are used to define the boundaries and initial conditions of the model. Set the boundary conditions of the heat transfer model according to the collected data, such as the heat flow and convection heat transfer conditions of the sintering furnace wall. The boundary conditions include fixed temperature, fixed heat flow, convection heat transfer coefficient, etc. Set the initial temperature distribution and other related initial states of the model, such as pressure, chemical component concentration, etc. Use professional software (such as ANSYS, COMSOL Multiphysics) to perform numerical solution and simulation of the heat transfer model, and adjust the physical parameters of the model, such as thermal conductivity, specific heat capacity, density, etc. of the material to ensure that the output of the model matches the actual observed data.

[0030] When setting the physical parameters of the heat transfer model, the physical properties of the materials and related components in the silicon carbide vacuum sintering furnace are usually involved. Specifically, the physical parameters include: thermal conductivity of the sintering furnace material, specific heat capacity of the sintering furnace material and density of the sintering furnace material; 1) Thermal conductivity of sintering furnace materials: Thermal conductivity is a measure of the heat flow transfer capacity of a material per unit length due to the temperature gradient. For silicon carbide sintering furnaces, the thermal conductivity of the furnace body material (including high-temperature refractory materials, silicon carbide or other ceramic materials) needs to be considered.

[0031] 2) Specific heat capacity of sintering furnace materials: Specific heat capacity is the energy required to heat a unit mass of material by 1 degree Celsius, which has an important influence on the calculation of material temperature changes during the sintering process.

[0032] 3) Density of sintering furnace materials: Density affects the storage and transfer of heat in the heat transfer model and is an important variable in calculating the heat conduction equation.

[0033] The accurate setting of the above physical parameters is crucial to the predictive accuracy and efficiency of the heat transfer model. Usually, the physical parameters are obtained from material manuals, scientific research literature or related experimental data. In actual operation, it is also necessary to consider the changes in the physical properties of the material due to changes in environmental factors such as temperature and pressure. This means that in a high-temperature operating environment, the physical parameters may be different from those under standard conditions and need to be accurately obtained through experiments.

[0034] S13. Construct a heat transfer equation and describe the heat conduction process through the internal materials of the silicon carbide vacuum sintering furnace and the heat exchange process between the furnace surface and the environment.

[0035] Preferably, constructing a heat transfer equation and describing the heat conduction process through the internal materials of the silicon carbide vacuum sintering furnace and the heat exchange process between the furnace surface and the environment includes the following steps: S131, constructing a heat transfer equation, the heat transfer equation comprising Fourier's law and Newton's law of cooling; S132. Based on the set physical parameters, set the thermal conductivity of Fourier's law, and use Fourier's law to describe the heat conduction process in the material inside the sintering furnace; S133, based on the set physical parameters, setting the heat exchange coefficient of Newton's cooling law and the ambient temperature, and using Newton's cooling law to simulate the heat exchange between the furnace surface and the environment; Among them, the expression using Fourier's law to describe the heat conduction process in the internal material of the sintering furnace is: ; In the formula, q Represents the heat flux density of the conduction process; k represents thermal conductivity; Represents the temperature gradient.

[0036] It should be explained that Fourier's law in the heat transfer equation is used to describe the conduction process of heat through solid materials.

[0037] Newton's law of cooling is used in the heat transfer equation to describe the heat exchange process between heat and the surrounding environment. The expression of Newton's law of cooling is yes ; in, represents the heat exchange coefficient; T s Indicates the surface temperature; T ∞ Indicates the far-field ambient temperature.

[0038] Select the appropriate heat transfer coefficient ℎ, based on experimental data or literature values. For a specific working environment and material, the heat transfer coefficient needs to be adjusted to match the experimental measurement. Set the ambient temperature T ∞ Usually the laboratory or factory ambient temperature, the external boundary conditions are set in the numerical simulation using Newton's cooling law to analyze the heat exchange effect between the furnace surface and the environment, and to evaluate the heat dissipation efficiency and cooling rate.

[0039] S14. Use numerical methods to construct a heat transfer model based on the set physical parameters and heat transfer equations.

[0040] Preferably, using numerical methods and constructing a heat transfer model based on set physical parameters and heat transfer equations includes the following steps: S141, dividing the internal materials of the silicon carbide vacuum sintering furnace and the space between the furnace surface and the environment into discretized grids, and determining the size and distribution of each grid point in the discretized grid according to the size of the silicon carbide and the preset accuracy requirements; S142, setting a time step based on the size and distribution of each grid point; S143. Use the finite difference method to discretize Fourier's law and Newton's cooling law in space and time, form discretized equations, and construct a heat transfer model.

[0041] It should be explained that the geometric model of the sintering furnace is defined using computer-aided design (CAD) software; the CAD model is imported using numerical simulation software (such as ANSYS and COMSOL), and meshing is performed in the simulation software. The density and distribution of the mesh depend on the complexity of the model and the required simulation accuracy; fine meshing is performed in key areas (such as around heating elements and material interfaces) to improve simulation accuracy, and the mesh is appropriately dense in geometrically complex areas (such as edges and corners) to capture thermal gradients and local effects. Mesh independence tests are run to ensure that the results do not change significantly with mesh density.

[0042] Select an appropriate time step to ensure numerical stability and computational efficiency. The time step is usually related to the spatial grid size and can be estimated by the CFL condition (Courant-Friedrichs-Lewy condition). A time step that is too small will increase the calculation time, while a time step that is too large may lead to instability in the numerical solution. Perform several preliminary tests before simulation to test the impact of different time steps on the results to ensure that the selected time step can provide a stable numerical solution while ensuring accuracy.

[0043] Finite difference equations for Fourier's law and Newton's law of cooling are set up in numerical simulation software. This includes converting continuous partial differential equations into discrete algebraic equations, applying difference equations at each grid point, using central difference method to approximate spatial derivatives, using forward or backward difference method to approximate time derivatives, setting appropriate initial conditions and boundary conditions, using iterative solvers (such as Gauss-Seidel iteration, conjugate gradient method, etc.) to solve the discretized equations, obtaining the temperature distribution at each time step, verifying the simulation results, comparing with experimental data or known results, and adjusting model parameters (such as thermal conductivity, heat exchange coefficient) or boundary conditions according to the comparison results to improve model accuracy and applicability.

[0044] S2. By setting the initial temperature, applying the boundary conditions of heat flow and convection heat transfer, and solving the heat transfer model using the implicit numerical method, the thermal effect in the silicon carbide vacuum sintering furnace is predicted, and the heat transfer model parameters are optimized based on the prediction results.

[0045] Preferably, by setting the initial temperature, implementing the boundary conditions of heat flow and convection heat transfer, and solving the heat transfer model using an implicit numerical method, the thermal effect in the silicon carbide vacuum sintering furnace is predicted, and the heat transfer model parameters are optimized according to the prediction results, including the following steps: S21. In the constructed heat transfer model, set the initial temperature for the grid points of the discretized grid.

[0046] S22. Based on the constructed heat transfer model, boundary conditions for heat flow and convection heat transfer are implemented.

[0047] It should be explained that, according to the actual operation data or design specifications of the sintering furnace, the temperature information at the start-up of the sintering furnace is collected, and the initial temperature is set for each discrete grid point in the model in the numerical simulation software. The actually measured temperature or the theoretically calculated temperature is applied to the corresponding position of the model. The initial temperature data is input or imported using the interface of the numerical simulation software (such as ANSYS, COMSOL, etc.), and the initial conditions are set in the software to ensure that the temperatures of all grid points are set correctly so that the simulation starts from the initial state. The heat flux density of a specific part of the model, such as the heat generated by the heating element of the sintering furnace, is determined, and the heat flux density of the specific part of the model, such as the heat generated by the heating element of the sintering furnace, is set in the software. Flow boundary conditions, fixed heat flow or heat flow that changes according to a certain rule, set the corresponding heat exchange coefficient and ambient temperature according to the convective heat transfer characteristics between the furnace and the environment; for example, there may be natural convection or forced convection with the air on the surface of the furnace. In the simulation software, enter the specific values ​​of the above boundary conditions and select the appropriate boundary type, such as fixed heat flow, fixed temperature, convection boundary, etc., to ensure that each boundary condition accurately reflects the physical process and that all boundary conditions are correctly set. These settings will directly affect the accuracy of the simulation and the reliability of the results. Perform a final check before simulation, including logical and physical tests of the boundary conditions.

[0048] S23. The implicit numerical method (Crank-Nicolson method) is used to solve the discretized equations of the implementation boundary conditions, and the temperature distribution of the internal material and the furnace surface of the silicon carbide vacuum sintering furnace is analyzed based on the calculation results.

[0049] Preferably, using an implicit numerical method (Crank-Nicolson method) to solve the discretized equations for implementing boundary conditions, and analyzing the temperature distribution of the internal material and the furnace surface of the silicon carbide vacuum sintering furnace based on the calculation results includes the following steps: S231. Based on the constructed discretized equations, thermal boundary conditions are applied at the boundaries of the heat transfer model to reflect the heat exchange in the actual sintering process; S232, configure an implicit numerical (Crank-Nicolson) solver, set the initial temperature configuration and discretize the time derivatives, and process the derivatives in Crank-Nicolson format (arithmetic mean of forward and backward differences in time); S233, running the iterative solution process, advancing time step by time step, updating the temperature values ​​of the grid points, and checking convergence after each time step; S234. Analyze the calculated temperature distribution data to obtain the thermal effect of each area in the grid point.

[0050] It needs to be explained that boundary conditions are set in the simulation software, which reflect the heat flow and convection conditions in the actual sintering process, including setting heat flow boundaries (fixed or variable heat flow) and convection boundaries (setting ambient temperature and heat exchange coefficient); use numerical simulation software (such as ANSYS, COMSOL) to input or adjust these boundary conditions to ensure that all conditions are accurately mapped to the corresponding parts of the model, select or write the Crank-Nicolson solver in the simulation software, the arithmetic average of the forward and backward differences of the time derivative, provide numerical stability and accuracy in time stepping, set initial conditions and time steps, ensure that time discretization is coordinated with space discretization, and process time derivatives in Crank-Nicolson format, which involves calculating the derivatives of the current and next time steps, and then taking their average, starting the solution process, and updating the temperature values ​​of the grid points at each time step. In each time step, the Crank-Nicolson method is used to solve the temperature distribution, and the convergence of the solution process is monitored to ensure that the numerical solution tends to be stable as the time step increases. After each time step, the convergence of the solution is checked to ensure that there is no numerical oscillation or divergence. If necessary, adjust the time step or mesh density; collect and analyze the obtained temperature distribution data, use data visualization tools (such as MATLAB charts, COMSOL post-processing modules) to display the temperature distribution and thermal effects, identify hot spots and cold areas in the model, analyze the influence of the points and cold areas on the entire sintering process, and evaluate the thermal performance of the silicon carbide vacuum sintering furnace based on the temperature distribution to ensure that the thermal management strategy can effectively control the temperature inside the sintering furnace and optimize the sintering quality and energy efficiency.

[0051] Ensuring the numerical stability and accuracy of the heat transfer model while being able to obtain detailed information about the temperature distribution inside the sintering furnace is crucial for optimizing the sintering process and improving product quality.

[0052] S24. Optimize the heat transfer model parameters according to the analysis results of the temperature distribution of the internal material and the surface of the silicon carbide vacuum sintering furnace.

[0053] Preferably, according to the analysis results of the temperature distribution of the internal material and the surface of the furnace body of the silicon carbide vacuum sintering furnace, optimizing the heat transfer model parameters includes the following steps: S241, collecting temperature distribution data of internal materials and furnace surface of silicon carbide vacuum sintering furnace; S242, establishing a mapping relationship between heat transfer model parameters and temperature distribution; S243. Optimize the heat transfer model parameters based on the mapping relationship; and evaluate the performance of the optimized heat transfer model.

[0054] It needs to be explained that the data export function in the numerical simulation software (such as ANSYS, COMSOL) is used to collect the temperature distribution data after the simulation is completed. The data should include the temperature value of each grid point to ensure that the time point and spatial position of the data collection correspond to the key stages and parts of the actual sintering process. The collected data is sorted and preprocessed, such as format conversion, data cleaning, etc., to facilitate further analysis and processing, and analyze the correlation between the temperature distribution data and the model parameters. This may involve statistical analysis methods, such as regression analysis, or machine learning techniques, such as neural networks, to identify how parameters affect temperature distribution. Use professional data analysis software (such as MATLAB, Python, etc.) to perform these analyses. Adjust model parameters based on the analysis results. For example, if the temperature in a certain area is found to be persistently high, the thermal conductivity or thermal boundary conditions of the material in that area need to be adjusted. Apply parameter optimization techniques (such as gradient descent, genetic algorithms, etc.) to adjust parameters based on the established mapping relationship. The technology can find the optimal solution among multiple parameters to achieve the best temperature distribution simulation. Re-run the model, apply the optimized parameters, and view the changes in temperature distribution. Use simulation results to compare with actual operating data or expected goals to evaluate the effectiveness of model optimization. If necessary, continue to adjust parameters, or consider improvements in other aspects of the model, such as meshing accuracy, time step setting, etc. Prepare a detailed technical report to record the optimization process of model parameters, the techniques used, the comparison results before and after improvement, and any further recommendations.

[0055] S3. Based on the optimized heat transfer model, multi-physics field simulation technology is used to simulate the physical process in the silicon carbide vacuum sintering furnace. Based on the simulation results, the influence of the operating parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process is analyzed.

[0056] Preferably, based on the optimized heat transfer model, the physical process in the silicon carbide vacuum sintering furnace is simulated by using multi-physics field simulation technology, and based on the simulation results, the influence of the operating parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process is analyzed, including the following steps: S31. According to the actual physical process in the silicon carbide vacuum sintering furnace, set the multi-physics field simulation parameters, run the multi-physics field simulation, and monitor the changes of various physical quantities in real time.

[0057] It should be explained that the use of numerical simulation software that can handle multi-physics problems, such as COMSOL Multiphysics or ANSYS Workbench, numerical simulation software is used to simulate physical fields such as heat transfer, fluid dynamics, structural mechanics and electromagnetic fields. A three-dimensional model of the sintering furnace is constructed in the software, including the furnace structure, heating elements and other key components. The heat transfer model is set, including heat sources, thermal boundary conditions, thermal conductivity of materials, etc. If gas flow is involved in the sintering process, the corresponding fluid dynamics parameters, such as flow rate, pressure, temperature, etc., are set. Consider the influence of effects such as thermal expansion on the structure and set the relevant structural mechanics parameters. As needed, simulation parameters of other physical fields such as electromagnetic fields may also need to be set. The coupling relationship of multiple physical fields is set in the software to ensure that the interactions between different physical fields are handled correctly. The appropriate time step and solver are selected to ensure the stability and accuracy of the simulation. The software is configured to monitor key physical quantities such as temperature, pressure, displacement, etc. in real time, which can be achieved through monitoring tools in the software or by writing specific scripts. Data automatic recording is set for subsequent analysis and verification. After the simulation is completed, the changes in each physical quantity are analyzed to evaluate the rationality and accuracy of the simulation results. If possible, the simulation results are compared with experimental data to verify the predictive ability of the model.

[0058] S32. Collect the operating parameter data obtained from the multi-physics field simulation, and use statistical analysis to analyze the simulated operating parameter data to evaluate the impact of different operating parameter data (temperature, pressure, power input, etc.) on the sintering process.

[0059] Preferably, collecting the operating parameter data obtained by multi-physical field simulation, analyzing the simulated operating parameter data by statistical analysis, and evaluating the influence of different operating parameter data on the sintering process include the following steps: S321, collecting each operating parameter data from the multi-physics field simulation, calculating the basic statistics of each operating parameter data, analyzing the distribution of each operating parameter data, and determining the skewness and peak state of each operating parameter data; S322, selecting a membership function based on the skewness and kurtosis of each operating parameter data, and adjusting the node positions in the membership function; S323, constructing a fuzzy logic model using the adjusted membership function, and mapping the operating parameter data to the degree of influence on the sintering process, and using a visual display interface to display the relationship between the operating parameter data and the sintering effect; S324. Based on the fuzzy logic model analysis results, evaluate the impact of different operating parameters on the sintering process.

[0060] It should be explained that the data export function of numerical simulation software (such as COMSOL, ANSYS) is used to collect key operating parameters in the simulation process, such as temperature, pressure, power input, etc., and ensure that the collected data includes time series data throughout the sintering cycle for detailed analysis. The basic statistics of each operating parameter, such as mean, standard deviation, maximum and minimum, are calculated using statistical software (such as Excel, MATLAB, or Pandas library in Python). The distribution characteristics of each operating parameter, including skewness and peak state, are analyzed, which helps to understand the shape and trend of the data distribution. According to the skewness and peak state of the data, the appropriate membership function shape is selected, such as triangular, trapezoidal or Gaussian function. Functions are used to define fuzzy sets of variables in fuzzy logic. Fuzzy logic tools (such as MATLAB's Fuzzy Logic) are used. Toolbox or Python's skfuzzy library) to set the membership function, adjust the key parameters or node positions in the membership function to better reflect the influence of the operating parameters, and adjust the node positions based on the statistical analysis results of the operating parameters and professional knowledge; use the adjusted membership function to build a fuzzy logic model, map the operating parameter data to the degree of influence on the sintering process, set fuzzy logic rules, and the fuzzy logic rules describe the expected effect of the sintering process under different operating parameter combinations; use data visualization tools (such as Tableau, Power BI, or Python's Matplotlib library) to create an interactive interface to display the relationship between operating parameters and sintering effects, help users understand how operating parameters affect the sintering process through visualization, and provide intuitive decision support; run the fuzzy logic model, use real-time or historical data to test the output of the model, analyze the sintering effects predicted by the model under different operating parameter settings, and evaluate the specific effects of different operating parameters such as temperature, pressure, and power input on the sintering process based on the model analysis results, and put forward suggestions for optimizing operating parameters to improve the performance and sintering of the sintering furnace.

[0061] S33. Analyze the performance of the silicon carbide vacuum sintering furnace based on the evaluation results.

[0062] It is necessary to explain that the multi-physics simulation results are integrated with the actual operation data, and data analysis software (such as MATLAB or Python) is used to compare the simulation results with the actual operation data, identify the reasons for performance differences, and analyze the impact of key operating parameters such as temperature, pressure, and power input on the performance of the sintering furnace. Focus on those parameters that have a significant impact on product quality and energy efficiency. According to the performance analysis results, specific parameter adjustment suggestions are made, including increasing or decreasing the temperature setting of certain areas, adjusting the pressure setting, or optimizing the power input distribution. After adjusting the parameters, the same multi-physics model is used for re-simulation to predict how the adjusted operating parameters will affect the sintering process. The recommended parameter adjustments are implemented in the actual sintering furnace, and small-scale tests are conducted to verify the improvement effect. These adjustments are implemented in a wider production process, and key performance indicators are continuously tracked using sensors and real-time data monitoring systems. The production data after adjusting the operating parameters is collected, and the actual effects of these changes are evaluated. Based on the actual production data and continuous performance evaluation, the operating parameters are further fine-tuned to ensure that the sintering furnace reaches the optimal operating state, and a detailed report is prepared to explain the performance analysis results, the operating parameter adjustment suggestions and their expected impact, and the steps and results of implementing these adjustments.

[0063] S4. Collect the operating data of the silicon carbide vacuum sintering furnace in real time, and compare the operating data with the simulation results. Based on the comparison results and the analysis results, use the feedback control mechanism to adjust the operating parameter data.

[0064] It should be explained that sensors such as temperature, pressure, and power input are installed at key locations of the sintering furnace. These sensors can monitor and record key operating parameters in the sintering process in real time, collect sensor data in real time, integrate real-time collected data and previous simulation data, use data processing software (such as MATLAB or Python) for real-time data processing and analysis, compare the differences between real-time data and simulation results, especially focus on those parameters with large deviations, analyze possible causes and consequences, and use automatic control systems to adjust operating parameters based on data comparison results and previously set adjustment suggestions. For example, if the actual temperature is lower than the simulated temperature, the control system may automatically increase the power of the heating element, configure a PID controller or other type of controller to automatically adjust the parameters to the predetermined target value, which involves setting appropriate gains and time constants to ensure fast and stable responses. Using a monitoring interface in the control room to display the operating status and key parameters of the sintering furnace in real time can help operators understand the current operating conditions and required adjustments. The control system continuously optimizes parameter settings based on feedback data to adapt to changes and abnormalities that may occur in the production process.

[0065] S5. Conduct quality inspection and energy efficiency evaluation on the sintered silicon carbide products, obtain the actual performance data of the silicon carbide products, compare and analyze the actual performance data with the simulation results, and evaluate the adjustment effect of the operating parameter data. Iteratively update the heat transfer model based on the evaluation results.

[0066] Preferably, the sintered silicon carbide product is subjected to quality inspection and energy efficiency evaluation, the actual performance data of the silicon carbide product is obtained, the actual performance data is compared and analyzed with the simulation results, and the adjustment effect of the operating parameter data is evaluated. The heat transfer model is iteratively updated based on the evaluation results, including the following steps: S51. Conduct quality inspection on the sintered silicon carbide products, evaluate the energy efficiency of the silicon carbide vacuum sintering furnace, and determine the energy utilization efficiency during the sintering process; S52. Based on the results of quality inspection and the energy efficiency during sintering, determine whether the actual performance of silicon carbide products meets expectations, and check the impact of adjustment of operating parameter data on product quality and energy efficiency; S53, comparing the actual performance data with the simulation results, evaluating the accuracy of the simulation and the effectiveness of the heat transfer model, determining the differences between the simulation results and the actual data, and analyzing the reasons for the differences; S54. Based on the comparison results, evaluate whether the adjustment of the operating parameters has a positive impact on the quality and energy efficiency of the silicon carbide product; S55. Based on the evaluation results of the operating parameter adjustment effects, the heat transfer model is iteratively updated.

[0067] It should be explained that the hardness, microstructure and phase composition of the sintered silicon carbide products are tested by hardness testing machines, microscopes, X-ray diffractometers and other equipment, and the test results are recorded, including hardness values, uniformity of microstructures and purity of phases. The energy consumption data of the entire sintering cycle is recorded through the energy consumption monitoring system of the sintering furnace, and the energy efficiency of the sintering process, such as the energy consumed per unit product, is calculated. The actual quality parameters tested are compared with the design goals or industry standards to evaluate whether the products have reached the expected quality. The actual energy efficiency is analyzed compared with the expected or industry average level to determine the efficiency of energy utilization. The data obtained from the actual performance test are compared with the simulation results to analyze the differences between the two and explore the possible reasons for the differences between the actual results and the simulations, such as inaccurate model assumptions, incorrect parameter settings, etc. It is evaluated whether the adjustment of the operating parameters has improved the product quality and whether the adjustment of the operating parameters has improved the energy efficiency. According to the comparison between the actual data and the simulation results, the key parameters in the heat transfer model are adjusted, and a new round of simulation is carried out using the updated model to verify the effect of the parameter adjustment.

[0068] Take the xx factory as an example. The xx factory has a vacuum sintering furnace for producing high-performance silicon carbide ceramics. The vacuum sintering furnace uses high-frequency induction heating and has temperature control and pressure control systems. Step 1: Sensor configuration and data collection; Sensor configuration: Install sensors at key locations of the sintering furnace, including: Thermocouples are installed at multiple levels inside the furnace to measure the temperature distribution.

[0069] Pressure sensors are installed on the furnace door and furnace bottom to monitor the pressure inside the furnace.

[0070] A gas analyzer is installed in the exhaust system to analyze the composition changes of the gas during the sintering process.

[0071] Data Collection: All sensors are connected to a data acquisition system to collect temperature, pressure and gas composition data in real time. The data is transmitted to a central database via a secure network for storage and preliminary analysis.

[0072] Step 2: Establishment of heat transfer model; Model definition: Using ANSYS software, a heat transfer model is constructed based on the design parameters and physical properties of the sintering furnace. The heat source is set as an induction heating element, and the thermal boundary conditions and material properties (thermal conductivity, specific heat capacity, density) are determined.

[0073] Setting of initial and boundary conditions: Setting the initial and boundary conditions of the model according to the actual collected data, such as the heat flow and convection heat transfer conditions of the sintering furnace wall.

[0074] Step 3: Model simulation and analysis; Numerical simulation: Finite element analysis is applied in the model to simulate heat transfer and resolve the temporal and spatial distribution of temperature and pressure.

[0075] Results Analysis: The simulation results are used to identify any areas of abnormal temperature or pressure and analyze possible causes (such as inaccurate material properties, incorrect boundary conditions, etc.).

[0076] Step 4: Model optimization and iterative update; Parameter adjustment: Based on the comparison between simulation results and actual measurement data, key parameters in the heat transfer model are adjusted, such as thermal conductivity and convection heat transfer coefficient.

[0077] Iterative simulation: Use the adjusted parameters to re-simulate and verify the effect of the adjustment. If the simulation results match the actual data better, confirm the model update; if not, continue to adjust and test, as product quality is highly dependent on the precise control of temperature and pressure during the sintering process.

[0078] In summary, with the aid of the above technical solutions of the present invention, the present invention pre-configures sensors at key positions of the silicon carbide vacuum sintering furnace to collect operating data and environmental data, thereby ensuring that the establishment of the model is based on actual operating conditions and improving the accuracy and reliability of model prediction; by defining the boundary conditions and initial conditions of the heat transfer model and adjusting the physical parameters, precise control of the sintering furnace operation can be achieved, which helps to improve product quality, optimize energy use, and reduce operating costs. Based on the collected operating data, the heat transfer model can be flexibly adjusted to adapt to changes in the production process. By real-time monitoring and analysis of the operating data of the sintering furnace, potential problems and anomalies can be identified in a timely manner, thereby performing fault diagnosis and preventive maintenance, avoiding expensive equipment failures and production interruptions, and ensuring production efficiency and the longest service life of the equipment; the present invention uses implicit numerical methods to solve the heat transfer equation, especially the Crank-Nicolson method, which can provide high numerical stability and accuracy in the time stepping process, balance the characteristics of forward differences and backward differences, optimize stability and convergence speed, and is suitable for processing complex boundary conditions and initial states in heat transfer. The set model parameters and boundary conditions, the simulated temperature distribution can reflect the thermal effects inside and on the surface of the sintering furnace in detail, including the identification of hot spots and cold zones, which has direct guiding significance for adjusting the layout of heating elements, optimizing sintering process parameters and improving product quality. According to the simulation results, the heat transfer model parameters, such as thermal conductivity, heat exchange coefficient, etc., can be optimized in a targeted manner to simulate the actual sintering process more accurately. Through accurate thermal effect simulation, unnecessary energy waste can be identified and reduced, and thermal energy utilization efficiency can be optimized. Accurate temperature distribution simulation can help predict and identify potential equipment failure areas. The present invention not only focuses on heat transfer through multi-physical field simulation, but also integrates other related physical processes such as fluid dynamics, structural mechanics and possible electromagnetic fields. Comprehensive analysis helps understand complex interactions and influences, ensuring that the simulation results more comprehensively reflect the actual operating environment. Through data analysis and fuzzy logic models, not only scientific operating parameter adjustment suggestions are provided, but also the intuitive relationship between operating parameters and sintering effects can be demonstrated. By simulating different operating scenarios, potential problems and risks can be predicted, so that risk management and countermeasures can be formulated in advance.The present invention realizes rapid response and precise adjustment by real-time monitoring of key operating parameters such as temperature, pressure and power input, and comparing them with preset simulation results. The real-time data feedback mechanism makes the operation of the sintering furnace more precise and can respond to changes and demands in the production process immediately. The automatic control system is used to dynamically adjust the operating parameters according to real-time data, which can continuously optimize the sintering process and improve production efficiency and product quality. The automatic control system reduces the reliance on manual adjustment by operators and reduces the risks and costs caused by human operating errors. The feedback control system integrated with real-time data processing and analysis provides data-based decision support, helping operators and managers make decisions based on accurate data insights. Parameter control can use energy and materials more effectively and reduce waste; the present invention can ensure that the sintered silicon carbide products meet relevant quality standards and performance requirements through quality inspection, measure and optimize the energy utilization efficiency in the sintering process through energy efficiency evaluation, identify the energy waste link, reduce energy consumption by adjusting operating parameters, compare actual performance data with simulation results, evaluate the accuracy and applicability of the heat transfer model, and based on the results of comparative analysis, continuously adjust and optimize the parameters of the heat transfer model to more accurately simulate the actual sintering process. Through the feedback of actual performance data, it can better adapt to changes in market demand and production conditions, adjust production strategies in time, and improve the market competitiveness of products. ;

[0079] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. A method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation, characterized in that: The silicon carbide vacuum sintering furnace optimization method comprises the following steps: S1. Collecting operation data and environmental data of the silicon carbide vacuum sintering furnace, and establishing a heat transfer model using a heat transfer equation based on the operation data and the environmental data; S2. Predict the thermal effect in the silicon carbide vacuum sintering furnace by setting the initial temperature, applying the boundary conditions of heat flow and convection heat transfer, and solving the heat transfer model using the implicit numerical method, and optimize the heat transfer model parameters according to the prediction results; S3. Based on the optimized heat transfer model, the physical process in the silicon carbide vacuum sintering furnace is simulated using multi-physics field simulation technology. Based on the simulation results, the influence of the operating parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process is analyzed; S4. Collect the operation data of the silicon carbide vacuum sintering furnace in real time, and compare the operation data with the simulation results. Based on the comparison results and the analysis results, use the feedback control mechanism to adjust the operation parameter data; S5. Conduct quality inspection and energy efficiency evaluation on the sintered silicon carbide products, obtain the actual performance data of the silicon carbide products, compare and analyze the actual performance data with the simulation results, and evaluate the adjustment effect of the operating parameter data. Iteratively update the heat transfer model based on the evaluation results.

2. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 1, characterized in that: The collecting of the operation data and the environmental data of the silicon carbide vacuum sintering furnace and establishing the heat transfer model by using the heat transfer equation based on the operation data and the environmental data comprises the following steps: S11, pre-configuring sensors at various positions of the silicon carbide vacuum sintering furnace, and collecting operation data and environmental data of the silicon carbide vacuum sintering furnace based on the pre-configured sensors; S12, defining boundary conditions and initial conditions of the heat transfer model according to the operation data and environmental data of the silicon carbide vacuum sintering furnace, and setting physical parameters of the heat transfer model based on the boundary conditions and initial conditions; S13. Construct a heat transfer equation and describe the heat conduction process through the internal materials of the silicon carbide vacuum sintering furnace and the heat exchange process between the furnace surface and the environment; S14. Use numerical methods to construct a heat transfer model based on the set physical parameters and heat transfer equations.

3. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 2, characterized in that: The construction of the heat transfer equation and the description of the heat conduction process through the internal materials of the silicon carbide vacuum sintering furnace and the heat exchange process between the furnace surface and the environment include the following steps: S131, constructing a heat transfer equation, wherein the heat transfer equation includes Fourier's law and Newton's cooling law; S132. Based on the set physical parameters, set the thermal conductivity of Fourier's law, and use Fourier's law to describe the heat conduction process in the material inside the sintering furnace; S133, based on the set physical parameters, setting the heat exchange coefficient of Newton's cooling law and the ambient temperature, and using Newton's cooling law to simulate the heat exchange between the furnace surface and the environment; The expression for describing the heat conduction process in the material inside the sintering furnace using Fourier's law is: ； In the formula, q Represents the heat flux density of the conduction process; k represents thermal conductivity; Represents the temperature gradient.

4. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 3, characterized in that: The method of constructing a heat transfer model using a numerical method based on set physical parameters and heat transfer equations includes the following steps: S141, dividing the internal materials of the silicon carbide vacuum sintering furnace and the space between the furnace surface and the environment into discretized grids, and determining the size and distribution of each grid point in the discretized grid according to the size of the silicon carbide and the preset accuracy requirements; S142, setting a time step based on the size and distribution of each grid point; S143. Use the finite difference method to discretize Fourier's law and Newton's cooling law in space and time, form discretized equations, and construct a heat transfer model.

5. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 4, characterized in that: The method of setting the initial temperature, implementing the boundary conditions of heat flow and convection heat transfer, solving the heat transfer model by implicit numerical method, predicting the thermal effect in the silicon carbide vacuum sintering furnace, and optimizing the heat transfer model parameters according to the prediction results includes the following steps: S21. In the constructed heat transfer model, setting initial temperatures for grid points of the discretized grid; S22. Based on the constructed heat transfer model, implement the boundary conditions of heat flow and convection heat transfer; S23, using an implicit numerical method to solve the discretized equations for implementing boundary conditions, and analyzing the temperature distribution of the internal material and the furnace surface of the silicon carbide vacuum sintering furnace based on the calculation results; S24. Optimize the heat transfer model parameters according to the analysis results of the temperature distribution of the internal material and the surface of the silicon carbide vacuum sintering furnace.

6. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 5, characterized in that: The method of solving the discretized equations for implementing the boundary conditions by using an implicit numerical method and analyzing the temperature distribution of the internal material and the surface of the furnace body of the silicon carbide vacuum sintering furnace based on the calculation results comprises the following steps: S231. Based on the constructed discretized equations, thermal boundary conditions are applied at the boundaries of the heat transfer model to reflect the heat exchange in the actual sintering process; S232, configuring an implicit numerical solver, configuring and discretizing the time derivative by setting an initial temperature; S233, running the iterative solution process, advancing time step by time step, updating the temperature values ​​of the grid points, and checking convergence after each time step; S234. Analyze the calculated temperature distribution data to obtain the thermal effect of each area in the grid point.

7. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 6, characterized in that: The optimization of the heat transfer model parameters according to the analysis results of the temperature distribution of the internal material and the surface of the silicon carbide vacuum sintering furnace includes the following steps: S241, collecting temperature distribution data of internal materials and furnace surface of silicon carbide vacuum sintering furnace; S242, establishing a mapping relationship between heat transfer model parameters and temperature distribution; S243. Optimize the heat transfer model parameters based on the mapping relationship; and evaluate the performance of the optimized heat transfer model.

8. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 1, characterized in that: The method of simulating the physical process in the silicon carbide vacuum sintering furnace by using multi-physics field simulation technology based on the optimized heat transfer model and analyzing the influence of the operating parameter data of the silicon carbide vacuum sintering furnace on the heat transfer process based on the simulation results includes the following steps: S31. According to the actual physical process in the silicon carbide vacuum sintering furnace, set the multi-physics field simulation parameters, run the multi-physics field simulation, and monitor the changes of various physical quantities in real time; S32, collecting the operating parameter data obtained by multi-physical field simulation, and analyzing the simulated operating parameter data by using a statistical analysis method to evaluate the influence of different operating parameter data on the sintering process; S33. Analyze the performance of the silicon carbide vacuum sintering furnace based on the evaluation results.

9. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 8, characterized in that: The collecting of the operating parameter data obtained by multi-physical field simulation, analyzing the simulated operating parameter data by statistical analysis, and evaluating the influence of different operating parameter data on the sintering process include the following steps: S321, collecting each operating parameter data from the multi-physics field simulation, calculating the basic statistics of each operating parameter data, analyzing the distribution of each operating parameter data, and determining the skewness and peak state of each operating parameter data; S322, selecting a membership function based on the skewness and kurtosis of each operating parameter data, and adjusting the node positions in the membership function; S323, constructing a fuzzy logic model using the adjusted membership function, and mapping the operating parameter data to the degree of influence on the sintering process, and using a visual display interface to display the relationship between the operating parameter data and the sintering effect; S324. Based on the fuzzy logic model analysis results, evaluate the impact of different operating parameters on the sintering process.

10. The method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation according to claim 1, characterized in that: The method of performing quality inspection and energy efficiency evaluation on the sintered silicon carbide product, obtaining actual performance data of the silicon carbide product, comparing and analyzing the actual performance data with the simulation results, and evaluating the adjustment effect of the operating parameter data, and iteratively updating the heat transfer model based on the evaluation results includes the following steps: S51. Conduct quality inspection on the sintered silicon carbide products, evaluate the energy efficiency of the silicon carbide vacuum sintering furnace, and determine the energy utilization efficiency during the sintering process; S52. Based on the results of quality inspection and the energy efficiency during sintering, determine whether the actual performance of silicon carbide products meets expectations, and check the impact of adjustment of operating parameter data on product quality and energy efficiency; S53, comparing the actual performance data with the simulation results, evaluating the accuracy of the simulation and the effectiveness of the heat transfer model, determining the differences between the simulation results and the actual data, and analyzing the reasons for the differences; S54. Based on the comparison results, evaluate whether the adjustment of the operating parameters has a positive impact on the quality and energy efficiency of the silicon carbide product; S55. Based on the evaluation results of the operating parameter adjustment effects, the heat transfer model is iteratively updated.

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