A microwave heating combined simulation method and system of multi-physics field fusion

Abstract

The application discloses a microwave heating combined simulation method and system of fusion of multiple physical fields, obtains initial distribution data of electromagnetic fields, temperature fields and flow fields in a microwave heating system through a pre-established multiple physical field model, sets boundary conditions according to the characteristics of layered interfaces, and obtains preliminary continuity constraint results of each physical field at the interfaces; according to the preliminary continuity constraint results, each physical field is meshed by using a finite element method, spatial discretization processing is performed on the grid mismatch problem, and the distribution mapping relationship of each physical field in space is determined; through the spatial distribution mapping relationship, physical quantity values of each physical field at the layered interfaces are obtained, the data between different grids are reconstructed by using a linear interpolation method for the case that spatial synchronization is difficult, and a spatial distribution data set is obtained. The application effectively solves the spatial and time synchronization problems in the multiple physical field coupling analysis, and improves the precision and efficiency of the performance analysis of the microwave heating system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of microwave heating technology, and particularly discloses a microwave heating joint simulation method and system fusing multiple physical fields. BACKGROUND

[0002] Microwave heating technology plays a key role in food processing, material preparation, and medical treatment, etc., which realizes rapid and uniform heating effect through electromagnetic wave excitation of molecular movement. However, the complex interaction of multiple physical fields such as electromagnetic field, temperature field and flow field involved in actual application determines the efficiency and quality of the heating process.

[0003] The existing multi-physical field simulation method often has difficulty in accurately capturing the dynamic coupling characteristics of different physical fields at the material interface when dealing with complex systems, especially in multi-layer materials or complex structures, the data exchange and boundary condition processing often have problems of insufficient accuracy or unstable calculation. This not only limits the reliability of the simulation, but also affects the optimization of the actual process. The core of multi-physical field interaction lies in the continuity and coordination at the interface. The calculation grids of different physical fields are usually inconsistent, leading to difficulty in synchronization of data in time and space. Especially when the layered structure of the material involves multiple physical property differences, the traditional method has difficulty in effectively handling the layered characteristics of each physical field at the interface, causing deviation of the simulation results from the actual process. In addition, the imperfect grid mapping and data transfer mechanism further exacerbates the problem of coordination and consistency of multi-physical fields in complex structures. These technical factors are interrelated, and grid mismatch will directly affect the accuracy of data transfer, while unstable data transfer will lead to distortion of the coupling calculation between physical fields.

[0004] Therefore, how to develop an algorithm that can efficiently handle the continuity conditions of multi-physical fields at the layered interface and realize data transfer and synchronization between different grids has become a key problem to improve the accuracy of microwave heating joint simulation. SUMMARY

[0005] The present application provides a microwave heating joint simulation method and system fusing multiple physical fields, which aims to solve at least one of the defects in the prior art.

[0006] One aspect of the present application relates to a microwave heating joint simulation method fusing multiple physical fields, comprising the following steps:

[0007] Through the pre-established multi-physical field model, the initial distribution data of electromagnetic field, temperature field and flow field in the microwave heating system are obtained, the boundary conditions are set according to the layered interface characteristics, and the preliminary continuity constraint results of each physical field at the interface are obtained;

[0008] Based on the preliminary continuity constraint results, the finite element method is used to mesh each physical field, and spatial discretization is performed to address the mesh mismatch problem, and the spatial distribution mapping relationship of each physical field is determined;

[0009] Through the spatial distribution mapping relationship, the physical value of each physical field at the layered interface is obtained. In the case of difficulty in spatial synchronization, the linear interpolation method is applied to reconstruct the data between different grids to obtain a spatial distribution data set;

[0010] Based on the spatially distributed data set, the changing trend of each physical field within the time step is obtained. To address the time synchronization challenge, the time step parameters are adjusted to match the evolution rate of each physical field, and the dynamic update data after time synchronization is determined;

[0011] By dynamically updating data after time synchronization, the driving influence of the electromagnetic field distribution characteristics on the dynamic changes of the temperature field is obtained. According to the dynamic changes of the temperature field, the gradient distribution of the temperature field at the interface is calculated to obtain the real-time evolution results of the temperature field;

[0012] According to the real-time evolution results of the temperature field, the impact data of the dynamic change of the temperature field on the flow field coupling is obtained. In view of the influence of the flow field coupling, the velocity distribution change of the flow field at the interface is calculated to determine the dynamic response data of the flow field.

[0013] Furthermore, the steps of obtaining initial distribution data of the electromagnetic field, temperature field, and flow field in the microwave heating system through a pre-established multi-physics field model, setting boundary conditions based on the layered interface characteristics, and obtaining preliminary continuity constraint results of each physical field at the interface include:

[0014] Through the pre-established multi-physics field model, the finite element method is used to numerically simulate the microwave heating environment, obtain the electromagnetic field distribution, temperature field distribution and flow field distribution from the input initial data, and obtain the preliminary distribution data of each physical field;

[0015] Aiming at the characteristics of the layered interface, an interface tracking algorithm is used to extract the electromagnetic field, temperature field and flow field values ​​at the interface from the preliminary distribution data to determine the physical field distribution characteristics at the interface;

[0016] According to the physical field distribution characteristics at the interface, the electromagnetic continuity conditions, heat flow continuity conditions, and flow field velocity and pressure continuity conditions are set to obtain the boundary conditions of each physical field at the interface;

[0017] If the boundary conditions meet the preset continuity threshold, the boundary conditions are optimized and adjusted through the iterative solver to obtain the preliminary continuity constraint results of each physical field at the interface.

[0018] Further, according to the preliminary continuity constraint result, the finite element method is adopted to divide the grid of each physical field, and the space discretization processing is performed for the grid mismatch problem, and the steps of determining the distribution mapping relationship of each physical field in space include:

[0019] According to the preliminary continuity constraint result, the finite element method is adopted to divide the grid of each physical field, and the space region is segmented by the grid generation tool to obtain the grid distribution data of each physical field;

[0020] For the grid distribution data, the space discretization tool is adopted to locally adjust the grid mismatch area, and if the grid unit connection error exceeds the preset threshold, the interpolation calculation tool is used to smooth the unit boundary, and the adjusted grid structure is determined;

[0021] According to the adjusted grid structure, the data mapping tool is used to map the preliminary distribution data of each physical field to the new grid unit, and if data is missing during mapping, the neighborhood average method is used to fill the data to obtain complete physical field distribution data;

[0022] For the complete physical field distribution data, the field value calculation tool is used to analyze the distribution relationship in space, and by comparing the field value difference of adjacent grid units, the distribution mapping relationship of each physical field in space is determined, and the final spatial distribution mapping result is obtained.

[0023] Further, through the spatial distribution mapping relationship, the physical quantity value of each physical field at the layered interface is obtained, and for the case of spatial synchronization difficulty, the linear interpolation method is applied to reconstruct the data between different grids to obtain the spatial distribution data set. The steps include:

[0024] According to the spatial distribution mapping relationship, the data extraction tool is used to obtain the physical quantity value data from the layered interface of each physical field, and if the physical quantity value data is missing at the interface, the neighborhood average method is used to fill the missing part to obtain the complete interface physical quantity data set;

[0025] For the complete interface physical quantity data set, the linear interpolation tool is used to reconstruct the physical quantity value between different grids, and if the physical quantity value difference between grids exceeds the preset threshold, the weighted average method is used to adjust the interpolation result to determine the reconstructed physical quantity distribution data;

[0026] According to the reconstructed physical quantity distribution data, the grid adjustment tool is used to redivide the grid mismatch area, and the grid unit is aligned by the space synchronization algorithm to obtain the synchronized grid structure data;

[0027] For the synchronized grid structure data, the reconstructed physical quantity distribution data is mapped to the new grid cells using a data mapping tool, and the field value difference of adjacent cells is analyzed through a field value calculation tool to obtain the final spatial distribution data set.

[0028] Further, according to the spatial distribution data set, the change trend of each physical field within the time step is obtained, and for the time synchronization challenge, the time step parameter is adjusted to match the evolution rate of each physical field. The steps of determining the dynamically updated data after time synchronization include:

[0029] According to the spatial distribution data set, the field value fluctuation data within the time step is obtained from each physical field using a data extraction tool. If the evolution rate difference of the field value fluctuation data exceeds a preset threshold, the time step is corrected through a step adjustment tool to obtain adjusted step parameter data.

[0030] For the adjusted step parameter data, the evolution rate of each physical field is matched by using a parameter optimization tool combined with the change trend data. If the time synchronization deviation value does not reach the preset threshold, the time step parameter is adjusted through a parameter iteration tool to determine the matched time step configuration.

[0031] According to the matched time step configuration, the physical quantity distribution data is dynamically refreshed using a data update tool, and the data verification tool is used to determine whether the field value fluctuation is consistent to generate the synchronized dynamically updated data set.

[0032] For the synchronized dynamically updated data set, the change trend data is associated with the time scale using a data mapping tool, and the physical quantity distribution data is fused using a data storage tool to obtain the dynamically updated data after time synchronization.

[0033] Further, through the dynamically updated data after time synchronization, the driving influence of electromagnetic field distribution characteristics on the dynamic change of temperature field is obtained. For the dynamic change of temperature field, the gradient distribution of temperature field at the interface is calculated to obtain the real-time evolution result of temperature field.

[0034] The field value fluctuation data is obtained from the electromagnetic field distribution characteristics using a data extraction tool, and the field value fluctuation data is reconstructed in time series using a linear interpolation tool to obtain the electromagnetic field driven temperature field basic data.

[0035] For the electromagnetic field driven temperature field basic data, the gradient distribution of temperature field at the interface is calculated using a finite difference tool. If the fluctuation amplitude of the gradient distribution exceeds a preset threshold, the gradient distribution is corrected through a smoothing filter tool to obtain the smoothed gradient distribution result.

[0036] According to the smoothed gradient distribution results, the time step adjustment tool is used to match the evolution rate of the temperature field, and the dynamic refresh tool is used to update the spatiotemporal distribution of the temperature field to obtain real-time evolution distribution information;

[0037] For the real-time evolution distribution information, data mapping tools are used to associate the changing trend of the temperature field with the time scale. The correlation results are fused with the physical quantity distribution data through data storage tools to obtain the real-time evolution data of the temperature field after time synchronization.

[0038] Furthermore, based on the real-time evolution results of the temperature field, the influence data of the dynamic change of the temperature field on the flow field coupling is obtained. Based on the influence of the flow field coupling, the velocity distribution change of the flow field at the interface is calculated. The steps of determining the dynamic response data of the flow field include:

[0039] The temporal and spatial distribution data of the temperature field are obtained from the real-time evolution data of the temperature field using data extraction tools. The temporal and spatial distribution data are reconstructed into time series using linear interpolation tools to obtain the basic flow field data driven by the temperature field.

[0040] Based on the basic flow field data, the finite difference tool is used to calculate the velocity distribution of the flow field at the interface. If the fluctuation amplitude of the velocity distribution exceeds the preset threshold, the smoothing filter tool is used to correct the velocity distribution to obtain the smoothed velocity distribution result;

[0041] According to the smoothed velocity distribution results, the time step adjustment tool is used to match the dynamic response rate of the flow field, and the dynamic refresh tool is used to update the spatiotemporal distribution of the flow field to obtain the dynamic response data of the flow field.

[0042] For the dynamic response data of the flow field, a data mapping tool is used to associate the flow field change trend with the time scale. The correlation results are fused with the spatiotemporal distribution data of the temperature field through a data storage tool to obtain the time-synchronized flow field coupling impact data.

[0043] Furthermore, the finite difference tool is used to calculate the velocity distribution of the flow field at the interface based on the basic flow field data. If the fluctuation amplitude of the velocity distribution exceeds the preset threshold, the velocity distribution is corrected by the smoothing filter tool. In the step of obtaining the smoothed velocity distribution result, the velocity distribution at the interface is obtained by the following formula:

[0044]

[0045] in, express The velocity component in the direction, express The velocity component in the direction, Indicates time, and represents the spatial coordinates, represents the fluid density, Indicates pressure, represents the kinematic viscosity coefficient;

[0046] The fluctuation amplitude of the velocity distribution is calculated by the following formula:

[0047]

[0048] in, represents the fluctuation amplitude of the velocity distribution, represents the total number of sampling points, Indicates the The velocity value of the position, Represents the average speed, when Smoothing is required when the preset threshold is exceeded;

[0049] The smoothed velocity distribution is obtained by the following formula:

[0050]

[0051] in, represents the smoothed velocity distribution, represents the original velocity distribution, represents the weight coefficient of the smoothing filter, represents the half-width of the filter window, represents a calculated variable, Indicates the grid spacing.

[0052] Furthermore, based on the smoothed velocity distribution results, the time step adjustment tool is used to match the dynamic response rate of the flow field. The dynamic refresh tool is used to update the spatiotemporal distribution of the flow field. In the step of obtaining the dynamic response data of the flow field, the time step adjustment is obtained by the following formula:

[0053]

[0054] in, represents the time step of the next moment, represents the current time step, represents the target Courant number, represents the current Courant number, represents the smoothed velocity distribution, represents the original velocity distribution;

[0055] The dynamic response rate of the flow field is given by the following formula:

[0056]

[0057] wherein, denotes a dynamic response rate, denotes a time derivative of the velocity field, denotes a reference length scale, denotes a reference velocity, denotes a velocity gradient, denotes a response characteristic parameter;

[0058] The spatio-temporal distribution of the flow field is updated by the following equation:

[0059]

[0060] wherein, denotes the updated flow field distribution, denotes the flow field distribution at the previous time instant, denotes the flow field distribution calculated at the current time instant, denotes a refresh coefficient, and denotes a spatial coordinate, denotes a time.

[0061] Another aspect of the present application relates to a microwave heating combined simulation system fusing multiple physical fields, used for executing the microwave heating combined simulation method fusing multiple physical fields, and the microwave heating combined simulation system fusing multiple physical fields comprises:

[0062] A constraint result acquisition module is configured to acquire initial distribution data of electromagnetic fields, temperature fields and flow fields in a microwave heating system through a pre-established multiple physical field model, set boundary conditions for layered interface characteristics, and obtain preliminary continuity constraint results of each physical field at the interface.

[0063] A distribution mapping relationship determination module is configured to perform grid division on each physical field by using a finite element method according to the preliminary continuity constraint results, perform spatial discretization processing for grid mismatch problems, and determine the distribution mapping relationship of each physical field in space.

[0064] A distribution data set acquisition module is configured to acquire physical quantity values of each physical field at the layered interface through the spatial distribution mapping relationship, apply a linear interpolation method to reconstruct data between different grids for the case of spatial synchronization difficulty, and obtain a spatial distribution data set.

[0065] A dynamic update data determination module is configured to acquire the change trend of each physical field within a time step according to the spatial distribution data set, adjust the time step parameter to match the evolution rate of each physical field for the time synchronization challenge, and determine dynamic update data after time synchronization.

[0066] The real-time evolution result acquisition module is configured to acquire the driving influence of the electromagnetic field distribution characteristic on the temperature field dynamic change through the time-synchronized dynamic update data, calculate the gradient distribution of the temperature field at the interface according to the temperature field dynamic change, and obtain the real-time evolution result of the temperature field.

[0067] The dynamic response data determination module is configured to acquire the influence data of the temperature field dynamic change on the flow field coupling according to the real-time evolution result of the temperature field, calculate the speed distribution change of the flow field at the interface according to the flow field coupling influence, and determine the dynamic response data of the flow field.

[0068] The present application has the following beneficial effects:

[0069] The present application provides a microwave heating combined simulation method and system fusing multiple physical fields, which pre-establishes a multiple physical field model of electromagnetic field, temperature field and flow field, sets a boundary condition according to the layered interface characteristic, adopts a finite element method to perform grid division and space discretization processing, solves the grid mismatching problem, applies a linear interpolation method to reconstruct the data between different grids, and adjusts a time step parameter to match the evolution rates of the physical fields, realizes the synchronization in space and time, analyzes the driving influence of the electromagnetic field distribution on the temperature field dynamic change and the influence of the temperature field change on the flow field coupling, and thus accurately calculates the dynamic changes of the temperature field gradient distribution and the flow field speed distribution, effectively solves the space and time synchronization problem in the multiple physical field coupling analysis, and improves the precision and efficiency of the performance analysis of the microwave heating system. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 FIG. 1 is a flowchart of an embodiment of the microwave heating combined simulation method fusing multiple physical fields of the present application;

[0071] Figure 2 FIG. 2 is a functional block diagram of an embodiment of the microwave heating combined simulation system fusing multiple physical fields of the present application.

[0072] REFERENCE SIGNS:

[0073] 10, constraint result acquisition module; 20, distribution mapping relationship determination module; 30, distribution data set acquisition module; 40, dynamic update data determination module; 50, real-time evolution result acquisition module; 60, dynamic response data determination module. DETAILED DESCRIPTION

[0074] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0075] As Figure 1As shown, the first embodiment of the present application proposes a microwave heating combined simulation method of multi-physical field, including the following steps:

[0076] In step S100, the initial distribution data of electromagnetic field, temperature field and flow field in the microwave heating system is obtained through the pre-established multi-physical field model, the boundary conditions are set according to the characteristics of the layered interface, and the preliminary continuity constraint results of each physical field at the interface are obtained.

[0077] The multi-physical field model of the microwave heating system is a cross-scale dynamic model that simultaneously couples electromagnetic field, temperature field and flow field. The core is to describe the interaction of the three fields: electromagnetic field drives temperature field change through microwave energy absorption (such as Joule heat); temperature field reacts on electromagnetic field and flow field by changing medium properties (such as dielectric constant, viscosity); flow field changes energy distribution through medium motion (such as natural convection), further affecting electromagnetic field and temperature field.

[0078] The initial distribution data refers to the initial state parameters of electromagnetic field, temperature field and flow field before the interface boundary conditions are applied and the multi-physical fields are strongly coupled. The layered interface refers to the interface between two or more different media (such as solid-liquid, liquid-gas, different density liquids) in the microwave heating system. Under the boundary conditions, the preliminary continuity constraint results of each physical field at the interface refer to the continuity (or discontinuity) relationship that the field quantities must satisfy at the interface through mathematical expressions or physical rules, which is the basic constraint for solving the multi-physical field coupling.

[0079] In step S200, according to the preliminary continuity constraint results, the finite element method is used to divide the grid of each physical field, the spatial discretization processing is carried out for the grid mismatch problem, and the distribution mapping relationship of each physical field in space is determined.

[0080] Finite element meshing is the process of discretizing the calculation domain (including layered interface) of the microwave heating system into a large number of "elements" and "nodes", which aims to convert the partial differential equations of continuous physical fields (such as electromagnetic field, temperature field) into algebraic equation groups for solving.

[0081] Spatial discretization processing is the process of converting continuous physical space and physical fields (such as temperature field, flow field, electromagnetic field, etc.) on it into a finite number of discrete elements (or nodes), and approximating the overall physical behavior by the state of these discrete elements.

[0082] The mapping relationship of the distribution of each physical field in space refers to the rules of the physical quantities of different physical fields (such as temperature field, flow field, electromagnetic field, etc.) in the same spatial domain or different sub-domains being associated, transmitted or mutually influenced through the spatial position. The core is to establish the corresponding relationship of the discretized expressions (such as the physical quantities of the grid nodes) of different physical fields in space to realize the transmission and coupled calculation of the physical quantities across the fields.

[0083] In step S300, the physical quantity values of each physical field at the layered interface are obtained through the spatial distribution mapping relationship. For the case of spatial synchronization difficulty, the linear interpolation method is applied to reconstruct the data between different grids to obtain the spatial distribution data set.

[0084] The spatial distribution data set is a discrete data set that can uniformly describe the continuous distribution of each physical field at the layered interface and in the global space, which is obtained by positioning the interface position through the preset spatial distribution mapping relationship and then reconstructing the discrete data of different grids in space by using the linear interpolation method, due to the discontinuity of the physical quantity at the layered interface or the mismatch of different physical field grids leading to "spatial synchronization difficulty" (i.e. the nodes / cells of different grids cannot be directly corresponded).

[0085] In step S400, the variation trend of each physical field within the time step is obtained according to the spatial distribution data set. For the time synchronization challenge, the time step parameter is adjusted to match the evolution rate of each physical field to determine the dynamic update data after time synchronization.

[0086] The dynamic update data after time synchronization refers to the time-sequenced data set that can dynamically record the evolution process of each physical field on the unified time axis, which is finally formed by adaptively adjusting the time step parameter to match the time scale of each field based on the spatial distribution data set, for the "time synchronization challenge" (i.e. the speed of change of each physical field with time is different, such as the flow field may change dramatically in milliseconds, and the thermal field may change slowly in minutes) caused by the difference in evolution rate of different physical fields. The core is to solve the problem of time asynchronization to ensure that the dynamic changes of each physical field can be correlated and coupled in the time dimension.

[0087] In step S500, the driving influence of the electromagnetic field distribution characteristics on the dynamic change of the temperature field is obtained through the dynamic update data after time synchronization. For the dynamic change of the temperature field, the gradient distribution of the temperature field at the interface is calculated to obtain the real-time evolution result of the temperature field.

[0088] The real-time evolution result of the temperature field refers to time-synchronized dynamic update data, quantification of electromagnetic field distribution characteristics (such as current density, magnetic field intensity), driving effect of the temperature field through energy conversion (such as Joule heat effect), and finally formed time-sequenced data results that can reflect the continuous change of the temperature field in the whole space (including the interface) with time. The core is to realize the real-time tracking and quantitative characterization of the dynamic change of the temperature field through the closed loop of "electromagnetic field driving-temperature field response-interface gradient feedback".

[0089] Step S600, according to the real-time evolution result of the temperature field, the influence data of the dynamic change of the temperature field on the flow field coupling is obtained, the velocity distribution change of the flow field at the interface is calculated for the flow field coupling influence, and the dynamic response data of the flow field is determined.

[0090] The dynamic response data of the flow field refers to the real-time evolution result of the temperature field, the driving effect of the dynamic change of the temperature field (such as density gradient, property parameter change) on the flow field, and finally formed time-sequenced spatial data set that can reflect the dynamic adjustment of the flow field (velocity, pressure, etc.) with the evolution of the temperature field. The core is to capture the causal relationship of "temperature field change→flow field response", especially focusing on the flow characteristics (such as velocity gradient, shear force) at the interface, and fully presenting the real-time feedback of the flow field to the dynamic change of the temperature field.

[0091] Further, the microwave heating combined simulation method fusing multiple physical fields provided by the embodiment includes steps S100:

[0092] Step S110, through the pre-established multi-physical field model, the finite element method is used to numerically simulate the microwave heating environment, the electromagnetic field distribution, the temperature field distribution and the flow field distribution are obtained from the input initial data, and the preliminary distribution data of each physical field is obtained.

[0093] When studying the multi-physical field coupling problem under the microwave heating environment, the distribution data of the electromagnetic field, the temperature field and the flow field are obtained through numerical simulation. For the simulation of electromagnetic field distribution, the finite element method is used to calculate the electric field intensity in the microwave cavity. Assuming that the input power is 500 watts and the frequency is 2.45 GHz, the spatial distribution characteristics of the electric field intensity in the cavity are obtained by solving Maxwell's equations, especially in the electric field concentration area of the material surface. This distribution data provides a basis for subsequent analysis, especially in the study of uneven heating.

[0094] In the acquisition of temperature field distribution, based on the heat conduction equation, the temperature changes of the material inside and the surrounding environment are calculated by combining the electromagnetic field energy loss as a heat source. Assuming the initial temperature is 25 degrees Celsius and the heating time is 60 seconds, the simulation results show that the center temperature of the material rises to 80 degrees Celsius, while the surface temperature reaches 100 degrees Celsius. This temperature difference distribution reveals the penetration characteristics of microwave heating, which helps to optimize the heating uniformity.

[0095] For the flow field distribution, if the material is a fluid, the convection phenomenon caused by heating is simulated by the Navier-Stokes equation. Assuming the initial velocity of the fluid is 0, after heating, convection is formed due to temperature difference, and the surface flow rate can reach 0.1 meters per second. This flow field data is crucial for understanding heat transfer and material mixing behavior, and can effectively guide process parameter adjustment.

[0096] Step S120, for the layered interface characteristics, the interface tracking algorithm is used to extract the electromagnetic field, temperature field and flow field values at the interface from the preliminary distribution data, and determine the physical field distribution characteristics at the interface.

[0097] The following formula is used to extract and determine the distribution characteristics of the layered interface temperature field:

[0098] (1)

[0099] In formula (1), represents the temperature, represents the time, represents the velocity vector, represents the thermal diffusion coefficient, represents the heat source term, represents the density, represents the specific heat capacity at constant pressure.

[0100] In the analysis of layered interface characteristics, the interface tracking algorithm is used to extract the physical field values at the interface. Assuming that the research object is a layered structure of two materials with different dielectric constants, the electric field intensity at the interface may experience a sudden change, jumping from 1000 volts per meter on one side to 500 volts per meter on the other side. By extracting these data, the physical field distribution characteristics at the interface are determined, providing a basis for subsequent boundary condition setting.

[0101] Step S130, according to the physical field distribution characteristics at the interface, set the electromagnetic continuity condition, heat flow continuity condition and flow field velocity pressure continuity condition, to obtain the boundary conditions of each physical field at the interface.

[0102] The continuity condition of the electromagnetic field at the interface is obtained by the following formula:

[0103] (2)

[0104] In formula (2), represents the interface normal vector, and represent the magnetic field intensity vectors on both sides of the interface, represents the surface current density at the interface.

[0105] The continuity condition for heat flow at the interface is given by the following formula:

[0106] (3)

[0107] In formula (3), and represent the thermal conductivity of the materials on both sides of the interface, and represent the temperature fields on both sides of the interface, Represents the interface normal vector.

[0108] The continuity condition of the flow field at the interface is given by the following formula:

[0109] (4)

[0110] In formula (4), and represent the density of the fluid on both sides of the interface, and They represent the velocity of the fluid on both sides of the interface, Represents the interface normal vector.

[0111] When setting interface boundary conditions, the electromagnetic continuity condition requires the tangential components of the electric field to be equal on both sides of the interface, the heat flow continuity condition requires the heat flux density to be continuous, and the flow field condition requires velocity and pressure continuity. Assuming the temperature difference at the interface is less than 1°C, the heat flow continuity condition meets the preset threshold. This condition ensures a proper transition of the physical field across the interface and reduces simulation errors.

[0112] Step S140: If the boundary conditions meet the preset continuity threshold, the boundary conditions are optimized and adjusted through an iterative solver to obtain preliminary continuity constraint results of each physical field at the interface.

[0113] The physical field values ​​at the interface after optimization by the iterative solver are given by the following formula:

[0114] (5)

[0115] In formula (5), Represents the first The physical field value at the interface, represents the physical field value of the current iteration step, represents the iteration step parameter, denotes the gradient of the objective function with respect to the physical field, denotes the velocity field component, denotes the pressure field component.

[0116] If the boundary conditions meet the continuity threshold, the adjustment is optimized by the iterative solver. Assuming that the initial boundary conditions result in a temperature jump exceeding the threshold of 2 degrees Celsius at the interface, the iterative solver adjusts the heat flow parameters multiple times to reduce the jump to within 0.5 degrees Celsius. This optimization process significantly improves the simulation accuracy and helps obtain more realistic physical field distribution results.

[0117] Further, the microwave heating combined simulation method of the fusion of multiple physical fields provided by the embodiment comprises the following steps:

[0118] In step S210, according to the preliminary continuity constraint result, the finite element method is used to divide the grid of each physical field, the space region is segmented by a grid generation tool, and the grid distribution data of each physical field is obtained.

[0119] The grid distribution data of the physical field is obtained by the following formula:

[0120] (6)

[0121] In formula (6), denotes the global grid distribution matrix, denotes the total number of grid elements, denotes the coordinate transformation matrix of the element, denotes the grid distribution data matrix at the element level.

[0122] For the grid division using the finite element method, the space region in the microwave heating system is segmented by a professional grid generation tool such as Gmsh or ANSYS. Assuming that a cylindrical microwave cavity with a diameter of 0.2 meters is studied, and a material with a thickness of 0.05 meters is placed inside. Using the finite element method, the cavity is divided into about 100,000 tetrahedral grid elements to ensure the calculation accuracy of the electromagnetic field, temperature field and flow field. When dividing the grid, the grid is preferentially encrypted at the material surface and the cavity boundary to capture the rapidly changing regions of electric field intensity and temperature gradient. This refined grid division can effectively improve the simulation resolution of the physical field distribution.

[0123] In step S220, for the grid distribution data, a spatial discretization tool is used to locally adjust the grid mismatch region, and if the grid element connection error exceeds the preset threshold, an interpolation calculation tool is used to smooth the element boundary to determine the adjusted grid structure.

[0124] The connection error between adjacent grid cells is obtained by the following formula:

[0125] (7)

[0126] In formula (7), denotes the connection error between adjacent grid cells, and denotes the node position of the grid cell in the first coordinate direction, denotes the node position of the grid cell in the second coordinate direction, denotes the node position of the grid cell in the third coordinate direction, denotes the preset error threshold, and when is greater than zero, it indicates that the connection error exceeds the threshold and needs to be adjusted.

[0127] The adjusted grid structure is obtained by the following formula:

[0128] (8)

[0129] In formula (8), denotes the new grid node coordinates after interpolation smoothing processing, denotes the original grid node coordinates, and denotes the interpolation basis function, denotes the corresponding weight coefficient, denotes the interpolation order, and v denotes the parameter coordinates.

[0130] For local adjustment of the grid mismatch area, the geometric connection error of adjacent grid cells is detected. Assuming that the preset error threshold is 0.001 meters, if it is found that the length difference of the grid cells in a certain area exceeds the threshold, the interpolation calculation tool is used to smooth the boundary. Specifically, at the interface between the material and the air, due to the difference in dielectric constant, the grid may be discontinuous. The Laplace interpolation method is used to smooth and adjust the node position at the boundary, so that the error is reduced to within 0.0005 meters. This adjustment ensures the geometric consistency of subsequent physical field calculation.

[0131] Step S230, according to the adjusted grid structure, the preliminary distribution data of each physical field is mapped to the new grid cell through the data mapping tool, if data is missing during mapping, the neighborhood average method is used for data filling, and complete physical field distribution data is obtained. ​

[0132] The physical field distribution data is derived by the following equation:

[0133] (9)

[0134] In equation (9), represents the complete physical field distribution data mapped to the new grid, represents the preliminary distribution data of the original physical field, represents the original grid structure before adjustment, represents the new grid structure after adjustment, represents the mapping function of the data mapping tool.

[0135] For the process of data mapping to the new grid cells, the preliminary distribution data of electromagnetic field, temperature field and flow field is redistributed using the data mapping tool. Assuming that the electromagnetic field simulation shows that the material surface electric field strength is 800 volts per meter, through the data mapping tool, the value is accurately distributed to the adjusted grid nodes. If part of the node data is missing, the neighborhood average method is used to fill in the average value based on the electric field values of the surrounding 4 nodes to generate a complete data set. This method can effectively reduce the data loss problem caused by grid adjustment.

[0136] Step S240, for the complete physical field distribution data, a field value calculation tool is used to analyze the distribution relationship in space, by comparing the field value difference of adjacent grid cells, the distribution mapping relationship of each physical field in space is judged, and the final spatial distribution mapping result is obtained.

[0137] The field value difference of adjacent grid cells is calculated by the following equation:

[0138] (10)

[0139] In equation (10), represents the physical field value of the th grid cell, represents the physical field value of the adjacent th grid cell, represents the spatial distance between the two grid cells, represents the field value change rate per unit distance, which is used to quantify the degree of field value difference between adjacent grid cells.

[0140] The distribution mapping relationship of multiple physical fields in three-dimensional space is established, and the distribution mapping relationship is derived by the following equation:

[0141] (11)

[0142] In equation (11), represents the spatial coordinates a mapping field value at a spatial position, representing a total number of physical fields participating in mapping, representing a weight coefficient of the th physical field, representing a field value distribution function of the th physical field at a spatial position .

[0143] The spatial distribution relationship is analyzed by the field value calculation tool. By comparing the field value difference of adjacent grid units, the continuity of the physical field distribution is determined. Assuming that the temperature field simulation shows that the center temperature of the material is 75 degrees Celsius and the surface temperature is 95 degrees Celsius, by calculating the temperature difference of adjacent grid units, it is found that the maximum temperature difference is 5 degrees Celsius per millimeter, indicating that there is a significant temperature gradient. This analysis helps to identify uneven heating areas and provides a basis for optimizing microwave power distribution.

[0144] Further, the microwave heating joint simulation method fusing multiple physical fields provided by the embodiment comprises the following steps:

[0145] In step S310, according to the spatial distribution mapping relationship, a data extraction tool is used to obtain physical quantity value data from the layered interface of each physical field. If the physical quantity value data is missing at the interface, the missing part is filled by the neighborhood average method to obtain a complete interface physical quantity data set.

[0146] The physical quantity value data is extracted from the layered interface of each physical field according to the spatial distribution mapping relationship, which is realized by the following formula:

[0147] (12)

[0148] In formula (12), represents a mapping physical quantity value at a spatial position, represents a physical quantity value of the th physical field at the position, represents a weight coefficient of the th physical field, represents a total number of physical fields. The missing part is filled by the neighborhood average method, which is realized by the following formula:

[0149]

[0150] (13) In formula (13),

[0151] represents a filled physical quantity value at an interface position, represents a position within the neighborhood, represents a position within the neighborhood, represents a position within the neighborhood, known physical quantity values at the interface, representing a position a neighborhood range around, representing the number of valid data points in the neighborhood.

[0152] The complete interface physical quantity data set is obtained by the following formula:

[0153] (14)

[0154] In formula (14), representing the complete interface physical quantity data set, representing the original physical quantity data directly extracted from the layered interface, representing the interpolation data filled by the neighborhood averaging method. Formula (14) indicates that the complete interface physical quantity data set is the union of the original physical quantity data and the interpolation data.

[0155] For obtaining the physical quantity value data from the layered interface of each physical field, the data extraction tool extracts the physical quantity values of the electromagnetic field, temperature field and flow field at the interface of the material and air in the microwave heating system. Assuming that a cylindrical microwave cavity with a diameter of 0.3 meters is studied, and a piece of material with a thickness of 0.06 meters is placed inside. The data extraction tool scans the nodes at the interface to obtain the electric field intensity, temperature and flow rate values. If it is found that some node data is missing, for example, the temperature value is missing in some area of the material surface due to irregular grid, the neighborhood averaging method is used to fill in the average value based on the temperature values of the surrounding six nodes. Assuming that the neighborhood node temperature values are 80, 82, 79, 81, 83 and 80 degrees Celsius, the filling value can be taken as 81 degrees Celsius, thereby generating a complete interface data set. This method ensures the integrity of the data, which is convenient for subsequent analysis.

[0156] In step S320, for the complete interface physical quantity data set, a linear interpolation tool is used to reconstruct the physical quantity values between different grids, and if the difference between the physical quantity values between the grids exceeds a preset threshold, the interpolation result is adjusted by a weighted averaging method to determine the reconstructed physical quantity distribution data.

[0157] The linear interpolation is performed between two grid points to reconstruct the physical quantity distribution, and the physical quantity value at the interpolation point is obtained by the following formula:

[0158] (15)

[0159] In formula (15), representing the physical quantity value at the interpolation point, and representing the physical quantity values of the known grid points, representing the coordinate position of the interpolation point, and Represent the coordinate positions of known grid points respectively.

[0160] When the difference in physical quantities exceeds the threshold, the interpolation result is adjusted by weighted average. The adjusted physical quantity value is obtained by the following formula:

[0161] (16)

[0162] In formula (16), Indicates the adjusted physical value, Indicates the The weight coefficient of the grid points, Indicates the The physical value of the grid point, Indicates the total number of grid points participating in the weighted average calculation.

[0163] For the complete interface physical quantity data set, a linear interpolation tool is used to reconstruct the physical quantity values ​​between grids. Assuming that the electric field strength in a certain area on the material surface is 900 volts / meter and 850 volts / meter between two grid nodes, the electric field value of the intermediate node is calculated by linear interpolation. If the electric field difference between grids exceeds a preset threshold, such as 50 volts / meter, the interpolation result is adjusted by weighted averaging, giving priority to the weights of nodes close to the material boundary. For example, when the weights are set to 0.6 and 0.4, the adjusted electric field values ​​are closer to the high-intensity areas. This reconstruction method improves the smoothness of the physical quantity distribution and helps to accurately describe the physical behavior at the interface.

[0164] Step S330: Based on the reconstructed physical quantity distribution data, a grid adjustment tool is used to re-divide the grid mismatch area, and the grid units are aligned using a spatial synchronization algorithm to obtain synchronized grid structure data.

[0165] The re-divided grid structure is obtained by the following formula:

[0166] (17)

[0167] In formula (17), represents the mesh structure function after re-division, represents the total number of physical quantity distributions, Indicates the The weight coefficient of each physical quantity, Indicates the Physical quantities in coordinates The distribution function at Indicates the Physical quantities in coordinates The density distribution at .

[0168] The synchronized mesh structure data is obtained by the following formula:

[0169] (18)

[0170] In formula (18), represents the spatial synchronization function at time , represents the total volume of the mesh region, represents the position function of the mesh node at time , represents the Laplacian operator for describing spatial diffusion, represents the time synchronization adjustment parameter, represents the rate of change of the mesh position over time.

[0171] For the redivision of the mismatched region of the mesh, the irregular mesh in the microwave cavity is optimized by the mesh adjustment tool. It is assumed that the difference in the length of the grid cell at the interface between the material and the air reaches 0.002 meters, which exceeds the preset threshold of 0.001 meters. The spatial synchronization algorithm is used to realign the grid cells to ensure geometric consistency. For example, the grid on the surface of the material is densified to generate about 120,000 tetrahedral cells, and the grid length at the boundary is controlled within 0.0008 meters. This synchronized mesh structure improves the stability of the calculation and provides a reliable foundation for physical field mapping.

[0172] In step S340, for the synchronized mesh structure data, the reconstructed physical quantity distribution data is mapped to the new grid cells using the data mapping tool, and the field value difference between adjacent cells is analyzed by the field value calculation tool to obtain the final spatial distribution data set.

[0173] The physical quantity distribution value in the new grid cell is obtained by the following formula:

[0174] (19)

[0175] In formula (19), represents the physical quantity distribution value in the new grid cell, represents the total number of original grid cells participating in the mapping, represents the weight coefficient of the th original grid cell, represents the original physical quantity value of the th original grid cell, represents the shape function contribution of the th cell to the target position.

[0176] The field value difference is obtained by the following formula:

[0177] (20)

[0178] In formula (20), represents the field value difference measure between adjacent units and represents the dimension number of physical quantities, represents the field value of the first physical quantity component in the unit represents the field value of the first physical quantity component in the unit represents the first physical quantity component.

[0179] The final spatial distribution data set is obtained by the following formula:

[0180] (21)

[0181] In formula (21), represents the field value distribution function of any point in three-dimensional space, represents the maximum order of spherical harmonic expansion, represents the spherical harmonic coefficient, represents the order spherical harmonic function, represents the radial basis function.

[0182] For the synchronized grid structure data, the reconstructed physical quantity distribution data is mapped to the new grid unit by using a data mapping tool. Assuming that the reconstructed temperature field shows that the surface temperature of the material is 90 degrees Celsius and the center is 70 degrees Celsius, the data mapping tool will accurately distribute these values to the new grid nodes. If some node data is missing, it can be filled based on the neighborhood average method, for example, taking the average temperature of the surrounding 5 nodes. Further analysis of the temperature difference between adjacent units is carried out by using a field value calculation tool, and it is assumed that the maximum temperature difference is 4 degrees Celsius per millimeter, indicating that there is a significant gradient. This analysis reveals the temperature distribution characteristics in the heating process, providing data support for optimizing microwave power distribution.

[0183] Further, the microwave heating joint simulation method fusing multiple physical fields provided in the embodiment comprises the following steps:

[0184] In step S410, according to the spatial distribution data set, a data extraction tool is used to obtain the field value fluctuation data within the time step from each physical field. If the evolution rate difference of the field value fluctuation data exceeds the preset threshold value, a step adjustment tool is used to modify the time step to obtain adjusted step parameter data.

[0185] ​​​​​​​​The evolution rate of the field value fluctuation data in each physical field is obtained by the following formula:

[0186] (22)

[0187] In formula (22), denotes the evolution rate of the i-th physical field, denotes the field value of the i-th physical field at time t, denotes the field value of the i-th physical field at time t, denotes the field value of the i-th physical field at time t, denotes the field value of the i-th physical field at time t, denotes the field value of the i-th physical field at time t, denotes the field value of the i-th physical field at time t, denotes the field value of the i-th physical field at time t, denotes the current time step.

[0188] According to the difference in evolution rate, the time step is adaptively adjusted, and the new time step after adjustment is calculated by the following formula:

[0189] (23)

[0190] In formula (23), denotes the new time step after adjustment, denotes the original time step, denotes the target evolution rate difference value, denotes the preset evolution rate difference threshold.

[0191] In the study of the evolution of physical fields at the interface between the material and the air in the microwave heating system, the field value fluctuation data within the time step is obtained by the data extraction tool. Assuming that in a cylindrical microwave cavity with a diameter of 0.3 meters, for a material with a thickness of 0.05 meters, the fluctuation data of the electromagnetic field and the temperature field are extracted. The initial time step is set to 0.1 seconds, and it is found that the electromagnetic field strength fluctuation rate is fast, with a change of 100 volts per meter within 1 second, while the temperature field changes slowly, with a change of only 2 degrees Celsius within 1 second, with a difference exceeding the preset threshold of 10%. At this time, the time step is corrected to 0.05 seconds by the step adjustment tool to capture finer electromagnetic field changes, and the adjusted step parameter data is obtained.

[0192] In step S420, for the adjusted step parameter data, the parameter optimization tool is used to match the evolution rate of each physical field in combination with the change trend data, and if the deviation value of time synchronization does not reach the preset threshold, the time step parameter is adjusted by the parameter iteration tool to determine the matched time step configuration.

[0193] The optimal time step is determined by comparing the changes in the evolution rates of each physical field, and the optimized time step is obtained by the following formula:

[0194] (twenty four)

[0195] In formula (24), represents the optimized time step, Indicates the index of different physical fields, Indicates the The current time step of the physics field, Indicates the preset threshold parameter, Indicates the The state variables of the physical field, represents the time derivative, i.e. the evolution rate, Indicates the number of time steps.

[0196] The time synchronization deviation is calculated using the following formula:

[0197] (25)

[0198] In formula (25), Indicates the deviation value of time synchronization. represents the total number of physical fields involved in synchronization, Indicates the The current time of the physics field, Represents the average value over all physics times.

[0199] The time step parameter after iteration is calculated by the following formula:

[0200] (26)

[0201] In formula (26), Indicates the The time step parameter after iterations, Indicates the The time step parameter for the iteration, represents the iterative learning rate, represents the time synchronization error function, represents the gradient of the error function with respect to the time step.

[0202] Based on the adjusted step size parameter data, a parameter optimization tool was used, combined with the trend data, to match the evolution rates of each physical field. Assuming the evolution rates of the electromagnetic and temperature fields need to be synchronized, if the time synchronization deviation exceeds the preset threshold of 0.01 seconds, the step size parameter is further adjusted using the parameter iteration tool. For example, the step size was refined to 0.03 seconds. After multiple iterations, the matched time step configuration was determined to be 0.04 seconds, ensuring consistency in the time scales of the electromagnetic and temperature fields.

[0203] Step S430: According to the matched time step configuration, the data update tool is used to dynamically refresh the physical quantity distribution data, and the data verification tool is used to determine whether the field value fluctuations are consistent, and a synchronized dynamically updated data set is generated.

[0204] The data verification tool is used to determine whether the field value fluctuation meets the consistency requirements. The field value fluctuation consistency test is implemented using the following formula:

[0205] (27)

[0206] In formula (27), It represents the field value fluctuation consistency test index, represents the total number of test points, Indicates the current moment The field value of the test point, Indicates the previous moment The field value of each test point.

[0207] The dynamically updated dataset after synchronization is obtained by:

[0208] (28)

[0209] In formula (28), Represents the final generated synchronous dynamic update data set, Represents the original physical quantity distribution data, represents the data after interpolation processing, represents the corrected data, 、 、 Respectively represent the weight coefficient of each data source and meet the normalization conditions.

[0210] Based on the matched time step configuration, a data update tool is used to dynamically refresh the physical quantity distribution data. For example, on the material surface, the temperature field data needs to be updated every 0.04 seconds. A data verification tool is used to determine whether the field value fluctuations are consistent. If a mismatch between the temperature fluctuations and the electromagnetic field fluctuations is detected at a certain point in time—for example, if the temperature suddenly changes to 85 degrees Celsius while the electromagnetic field intensity does not change significantly—the data update frequency is adjusted to generate a synchronized, dynamically updated dataset. This approach helps capture changes in the coupling between physical fields.

[0211] Step S440: For the synchronized dynamically updated data set, use a data mapping tool to associate the change trend data with the time scale, and fuse the physical quantity distribution data through a data storage tool to obtain the dynamically updated data after time synchronization.

[0212] The dynamic update data after time synchronization is obtained by the following formula:

[0213] (29)

[0214] In formula (29), denotes the time of the synchronized dynamic update data, denotes the time of the change trend data, denotes the time weight function, denotes the initial time.

[0215] For the synchronized dynamic update data set, the data mapping tool is used to associate the change trend data with the time scale. Assuming that the temperature field presents a linear rising trend over time, from 70 degrees Celsius to 90 degrees Celsius, taking 10 seconds, the data mapping tool corresponds to a time step of 0.04 seconds, and accurately records the change of each step. At the same time, through the data storage tool, the physical quantity distribution data is fused, for example, the electromagnetic field intensity distribution and the temperature distribution data are integrated, and the time-synchronized dynamic update data is obtained. This method is convenient for subsequent analysis of the dynamic response characteristics of the material in the microwave heating process.

[0216] Further, the microwave heating joint simulation method provided by the embodiment fuses multiple physical fields, and step S500 includes:

[0217] Step S510, using a data extraction tool to obtain field value fluctuation data from the electromagnetic field distribution characteristics, and using a linear interpolation tool to reconstruct the time series of the field value fluctuation data to obtain electromagnetic field driven temperature field basic data.

[0218] The electromagnetic field driven temperature field basic data is obtained by the following formula:

[0219] (30)

[0220] In formula (30), denotes the electromagnetic field driven temperature field basic data, denotes the electromagnetic power to temperature conversion coefficient denotes the electromagnetic power density distribution, denotes the thermal conductivity coefficient, denotes the Laplace operator, denotes the initial temperature distribution, denotes the heat capacity correction coefficient, denotes the material density, denotes the specific heat capacity, denotes the rate of change of temperature with time.

[0221] In the study of the interaction between electromagnetic field and temperature field at the interface of material and air in a microwave heating system, the data extraction tool is used to obtain the field value fluctuation data in the electromagnetic field distribution characteristics. Assuming that in a cylindrical microwave cavity with a diameter of 0.4 meters, for a material with a thickness of 0.06 meters, the data extraction tool records the fluctuation of electromagnetic field intensity within 0.1 seconds, and it is found that the field value changes from 1000 volts / meter to 1200 volts / meter. Linear interpolation tool is used to reconstruct the time series of these data, and the discrete field value data is smoothly connected to generate continuous time series data. For example, through interpolation processing, the electromagnetic field intensity change trend every 0.01 seconds is obtained, which provides basic data for subsequent temperature field analysis. This method can effectively capture the characteristics of rapid changes in electromagnetic field, and provide reliable basis for the analysis of the driving mechanism of temperature field.

[0222] In step S520, for the electromagnetic field driven temperature field basic data, the finite difference tool is used to calculate the gradient distribution of the temperature field at the interface, and if the fluctuation amplitude of the gradient distribution exceeds the preset threshold, the gradient distribution is modified by the smoothing filter tool to obtain the smoothed gradient distribution result.

[0223] The gradient distribution of the temperature field at the interface is calculated by the following formula:

[0224] (31)

[0225] In formula (31), represents the temperature field distribution function, represents the temperature field gradient vector, and represent the temperature values of adjacent grid points, and represent the finite difference grid spacing, and represent the grid node index, represents the component of the temperature gradient in the direction, represents the component of the temperature gradient in the direction.

[0226] The smoothed gradient distribution result is obtained by the following formula:

[0227] (32)

[0228] In formula (32), represents the smoothed gradient distribution, represents the filter kernel weight coefficient, represents the radius size of the filter window, and represent the relative position index within the filter window, It is a double summation, which is used to traverse all points in the window. Indicates The original temperature gradient at this point. Formula (32) realizes the smooth correction of the gradient distribution through weighted averaging.

[0229] Based on the basic data of the electromagnetic field-driven temperature field, a finite difference tool is used to calculate the temperature gradient distribution at the interface. Assuming that the material surface temperature varies from 80°C to 82°C with a spatial resolution of 0.01 meter, the finite difference tool calculates a temperature gradient of 200°C / meter. If the gradient fluctuation exceeds a preset threshold of 15%, the data is corrected using a smoothing filter tool. For example, a Gaussian filter is used to smooth out unusually sharp gradient fluctuations, resulting in a stable distribution with a gradient value of 180°C / meter. This smoothing process can reduce the interference of data noise on subsequent analysis and improve the accuracy of the temperature field evolution rate calculation.

[0230] Step S530: According to the smoothed gradient distribution result, the evolution rate of the temperature field is matched using a time step adjustment tool, and the spatiotemporal distribution of the temperature field is updated using a dynamic refresh tool to obtain real-time evolution distribution information.

[0231] The time step of the evolution rate is dynamically adjusted according to the change amplitude of the temperature field gradient. The adjusted time step of the evolution rate is calculated by the following formula:

[0232] (33)

[0233] In formula (33), represents the time step of the next moment, represents the current time step, represents the target error threshold, Represents the second-order gradient of the current temperature field, Indicates the maximum adjustment factor.

[0234] Based on the smoothed gradient distribution, the time step adjustment tool is used to match the evolution rate of the temperature field. Assuming an initial time step of 0.05 seconds, the temperature field changes slowly, rising only 1.5 degrees Celsius per second, while the electromagnetic field changes more rapidly. The time step adjustment tool refines the time step to 0.02 seconds, and the dynamic refresh tool updates the spatiotemporal distribution of the temperature field accordingly. For example, at a point on the material surface, the temperature field data is updated every 0.02 seconds, recording the real-time evolution distribution of the smooth transition from 80 degrees Celsius to 81 degrees Celsius. This refined step adjustment better reflects the coupled dynamics between physical fields.

[0235] Step S540: For the real-time evolution distribution information, a data mapping tool is used to associate the change trend of the temperature field with the time scale, and the association result is integrated with the physical quantity distribution data through a data storage tool to obtain the time-synchronized real-time evolution data of the temperature field.

[0236] The real-time evolution data of the temperature field after time synchronization is obtained by the following formula:

[0237] (34)

[0238] In formula (34), Indicates the The temperature field data after synchronization at each moment, Indicates the The original temperature field data at the moment, Indicates the time correction amount, Indicates the The temperature change rate at a moment, Indicates the The alignment error compensation term at the moment.

[0239] For real-time evolution distribution information, a data mapping tool is used to associate the temperature field change trend with the time scale. Assuming that the temperature field rises linearly from 75 degrees Celsius to 95 degrees Celsius within 10 seconds, the data mapping tool will correspond the temperature change every 0.02 seconds to the time point to generate a continuous trend curve. Through the data storage tool, this trend data is fused with the electromagnetic field intensity distribution data to form the real-time evolution data of the temperature field after time synchronization. For example, the stored fusion data records that when the electromagnetic field intensity is 1100 volts / meter at a certain time point, the temperature is 85 degrees Celsius. This fusion process facilitates the analysis of the mutual influence characteristics of the electromagnetic field and the temperature field during microwave heating, providing data support for optimizing the heating process.

[0240] Furthermore, the microwave heating joint simulation method integrating multiple physical fields provided in this embodiment includes step S600:

[0241] Step S610: Using a data extraction tool to obtain the spatiotemporal distribution data of the temperature field from the real-time evolution data of the temperature field, and reconstructing the spatiotemporal distribution data into time series using a linear interpolation tool to obtain basic flow field data driven by the temperature field.

[0242] The basic data of the flow field driven by the temperature field is obtained by the following formula:

[0243] (35)

[0244] In formula (35), represents the flow field velocity vector driven by the temperature field, represents the dynamic viscosity coefficient of the fluid, represents the fluid density, represents the spatial gradient of the driving temperature field, represents the thermal expansion coefficient of the fluid, represents the gravitational acceleration vector, represents the temperature distribution of the driving flow field, Indicates the reference temperature value.

[0245] When studying the multi-field coupling characteristics of the material-air interface in a microwave heating system, a series of data processing methods are used to obtain dynamic interaction data between the temperature field and the flow field, thereby supporting the optimization of the heating process. For example, to obtain spatiotemporal distribution data from the real-time evolution of the temperature field, a data extraction tool is used to extract temperature values ​​at specific time points and spatial locations from the stored temperature field data. Assuming that the material thickness is 0.06 meters in a cylindrical microwave cavity with a diameter of 0.4 meters, the data extraction tool records the temperature change at a point on the material surface over a period of 5 seconds, from 75 degrees Celsius to 80 degrees Celsius. By extracting temperature values ​​every 0.1 seconds, preliminary spatiotemporal distribution data is generated, laying the foundation for subsequent analysis.

[0246] When reconstructing time series from spatiotemporal distribution data, linear interpolation is used to fill in gaps between data points. Assuming the data points are extracted at 0.1 second intervals, interpolation generates temperature changes every 0.02 seconds, such as a smooth transition from 76°C to 77°C over a certain period. This reconstruction method generates continuous temperature field driving data, providing more refined input for flow field analysis.

[0247] Step S620: Based on the basic flow field data, a finite difference tool is used to calculate the velocity distribution of the flow field at the interface. If the fluctuation amplitude of the velocity distribution exceeds a preset threshold, the velocity distribution is corrected by a smoothing filter tool to obtain a smoothed velocity distribution result.

[0248] The velocity distribution at the interface is given by the following formula:

[0249] (36)

[0250] In formula (36), express The velocity component in the direction, express The velocity component in the direction, Indicates time, and represents the spatial coordinates, represents the fluid density, Indicates pressure, represents the kinematic viscosity coefficient;

[0251] The fluctuation amplitude of the velocity distribution is calculated by the following formula:

[0252] (37)

[0253] In formula (37), represents the fluctuation amplitude of the velocity distribution, represents the total number of sampling points, represents the velocity value at the th position, represents the average value of the velocity, and smoothing needs to be performed when σ exceeds a preset threshold.

[0254] The smoothed velocity distribution result is obtained by the following formula:

[0255] (38)

[0256] In formula (38), represents the smoothed velocity distribution, represents the original velocity distribution, represents the weight coefficient of the smoothing filter, represents the half-width of the filter window, represents the calculation variable, represents the grid spacing.

[0257] For the velocity distribution calculation of the flow field basic data, the finite difference tool is used to analyze the flow field characteristics at the interface. Assuming that the air flow velocity near the material surface changes from 0.5 m / s to 0.8 m / s within 0.2 seconds, the tool calculates the fluctuation of the velocity distribution. If the fluctuation amplitude exceeds a preset threshold, such as 20%, further processing is required. This method can capture the subtle changes of the flow field at the interface.

[0258] If the velocity distribution fluctuation is too large, a smoothing filter tool is used to correct the data. Assuming that the velocity fluctuation in a certain area presents a sharp change, the Gaussian filtering method is used to smooth the abnormal value, and finally the velocity distribution is stabilized at about 0.6 m / s. This processing helps to reduce noise interference and improve data reliability.

[0259] In step S630, according to the smoothed velocity distribution result, a time step adjustment tool is used to match the dynamic response rate of the flow field, and a dynamic refresh tool is used to update the space-time distribution of the flow field to obtain the flow field dynamic response data.

[0260] The adjustment of the time step is obtained by the following formula:

[0261] (39)

[0262] In formula (39), denotes the time step of the next moment, denotes the current time step, denotes the target Courant number, denotes the current Courant number, denotes the smoothed velocity distribution, denotes the original velocity distribution;

[0263] The dynamic response rate of the flow field is obtained by the following formula:

[0264] (40)

[0265] In formula (40), denotes the dynamic response rate, denotes the time derivative of the velocity field, denotes the reference length scale, denotes the reference velocity, denotes the velocity gradient, denotes the response characteristic parameter.

[0266] The spatio-temporal distribution of the flow field is updated by the following formula:

[0267] (41)

[0268] In formula (41), denotes the updated flow field distribution, denotes the flow field distribution at the previous moment, denotes the flow field distribution calculated at the current moment, denotes the refresh coefficient, and denotes the spatial coordinates, denotes the current moment.

[0269] When adjusting the dynamic response rate of the flow field, the time step adjustment tool matches the temperature field variation characteristics. Assuming that the initial step is 0.05 seconds, it is found that the flow field response is slow, and it is adjusted to 0.02 seconds. The dynamic refresh tool updates the spatio-temporal distribution of the flow field accordingly, such as recording that the air flow velocity of a certain point gradually transitions from 0.6 meters / second to 0.7 meters / second. This adjustment can better reflect the coupling characteristics of the temperature field and the flow field.

[0270] Step S640, for the flow field dynamic response data, the data mapping tool is used to associate the flow field variation trend with the time scale, and the data storage tool is used to fuse the associated results and the temperature field spatio-temporal distribution data, to obtain the time-synchronized flow field coupling influence data.

[0271] The flow field coupling influence data is calculated by the following formula:

[0272] (42)

[0273] In formula (42), represents the flow field coupling influence data, represents the number of time sampling points, represents the first flow field state vector, represents the first temperature field state vector, represents the tensor product operation, represents the time decay factor, represents the current time, represents the reference time point, represents the time scale parameter.

[0274] For the correlation processing of flow field dynamic response data, the data mapping tool corresponds the flow field change trend to the time scale. Assuming that the flow field speed linearly rises from 0.5 m / s to 0.9 m / s within 10 seconds, the data mapping tool generates a speed change curve every 0.02 seconds, which facilitates subsequent analysis of the interaction rules between the flow field and the temperature field.

[0275] Through the data storage tool, the flow field and the temperature field data are fused to form time-synchronized coupling influence data. Assuming that at a certain time point, the temperature is 78 degrees Celsius and the flow field speed is 0.7 m / s, the fusion data records this corresponding relationship. This fusion method facilitates in-depth study of the dynamic influence of multi-field coupling in the microwave heating process and provides data support for process improvement.

[0276] See Figure 2The embodiment relates to a microwave heating combined simulation system of fused multi-physical fields, which is used for executing the microwave heating combined simulation method of fused multi-physical fields, and the microwave heating combined simulation system of fused multi-physical fields comprises a constraint result acquisition module 10, a distribution mapping relationship determination module 20, a distribution data set acquisition module 30, a dynamic update data determination module 40 and a real-time evolution result acquisition module 50. The constraint result acquisition module 10 is used for acquiring initial distribution data of electromagnetic fields, temperature fields and flow fields in a microwave heating system through a pre-established multi-physical field model, setting boundary conditions according to layering interface characteristics, and obtaining preliminary continuity constraint results of the physical fields at the interface. The distribution mapping relationship determination module 20 is used for performing grid division on the physical fields by adopting a finite element method according to the preliminary continuity constraint results, performing space discretization processing on the grid mismatch problem, and determining distribution mapping relationships of the physical fields in space. The distribution data set acquisition module 30 is used for acquiring physical quantity values of the physical fields at the layering interface through the space distribution mapping relationships, reconstructing data between different grids by applying a linear interpolation method for the space synchronization difficulty, and obtaining a space distribution data set. The dynamic update data determination module 40 is used for acquiring variation trends of the physical fields within a time step according to the space distribution data set, adjusting a time step parameter to match evolution rates of the physical fields for the time synchronization challenge, and determining dynamic update data after time synchronization. The real-time evolution result acquisition module 50 is used for acquiring driving influence of electromagnetic field distribution characteristics on dynamic changes of temperature fields through the dynamic update data after time synchronization, calculating gradient distribution of the temperature fields at the interface for the dynamic change of the temperature fields, and obtaining real-time evolution results of the temperature fields. The dynamic response data determination module 60 is used for acquiring influence data of temperature field dynamic changes on flow field coupling according to the real-time evolution results of the temperature fields, calculating speed distribution changes of the flow fields at the interface for the flow field coupling influence, and determining dynamic response data of the flow fields.

[0277] Compared with the prior art, the microwave heating combined simulation method and system of fused multi-physical fields provided by the embodiment pre-establish a multi-physical field model of electromagnetic fields, temperature fields and flow fields, set boundary conditions according to layering interface characteristics, perform grid division and space discretization processing by adopting a finite element method, and solve the grid mismatch problem. Linear interpolation is applied to reconstruct data between different grids, and time step parameters are adjusted to match evolution rates of the physical fields, so that space and time synchronization is realized. The driving influence of electromagnetic field distribution on dynamic changes of temperature fields and the influence of temperature field changes on flow field coupling are analyzed, so that dynamic changes of temperature field gradient distribution and flow field speed distribution are accurately calculated, the space and time synchronization difficulty in multi-physical field coupling analysis is effectively solved, and the precision and efficiency of performance analysis of the microwave heating system are improved.

[0278] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the foregoing description without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims be interpreted as including all such variations and modifications as fall within the spirit and scope of the application. It is further intended that the disclosure of all such modifications and variations be included within the scope of the application, the terms used herein being defined solely for purposes of the description being applied thereto unless otherwise indicated.

Claims

1. A microwave heating joint simulation method integrating multiple physical fields, characterized in that: The following steps are involved: The pre-established multi-physics model is used to obtain the initial distribution data of the electromagnetic field, temperature field, and flow field in the microwave heating system. Boundary conditions are set based on the layered interface characteristics to obtain preliminary continuity constraint results for each physical field at the interface. Based on the preliminary continuity constraint results, the finite element method is used to mesh each physical field, spatial discretization is performed to address the mesh mismatch problem, and the spatial distribution mapping relationship of each physical field is determined; Through the spatial distribution mapping relationship, the physical value of each physical field at the layered interface is obtained. In the case of difficulty in spatial synchronization, the linear interpolation method is applied to reconstruct the data between different grids to obtain a spatial distribution data set; According to the spatially distributed dataset, the changing trend of each physical field within the time step is obtained. To address the time synchronization challenge, the time step parameters are adjusted to match the evolution rate of each physical field, and the dynamic update data after time synchronization is determined; By dynamically updating data after time synchronization, the driving influence of the electromagnetic field distribution characteristics on the dynamic change of the temperature field is obtained. According to the dynamic change of the temperature field, the gradient distribution of the temperature field at the interface is calculated to obtain the real-time evolution result of the temperature field; According to the real-time evolution results of the temperature field, the influence data of the dynamic change of the temperature field on the flow field coupling is obtained. In view of the influence of the flow field coupling, the velocity distribution change of the flow field at the interface is calculated to determine the dynamic response data of the flow field; The steps of obtaining the influence data of the dynamic change of the temperature field on the flow field coupling based on the real-time evolution result of the temperature field, calculating the velocity distribution change of the flow field at the interface based on the influence of the flow field coupling, and determining the dynamic response data of the flow field include: Using a data extraction tool to obtain the temporal and spatial distribution data of the temperature field from the real-time evolution data of the temperature field, and reconstructing the temporal and spatial distribution data into time series using a linear interpolation tool to obtain the basic flow field data driven by the temperature field; Based on the basic flow field data, a finite difference tool is used to calculate the velocity distribution of the flow field at the interface. If the fluctuation amplitude of the velocity distribution exceeds a preset threshold, the velocity distribution is corrected by a smoothing filter tool to obtain a smoothed velocity distribution result; The velocity distribution at the interface is given by the following formula: in, express The velocity component in the direction, express The velocity component in the direction, Indicates time, and represents the spatial coordinates, represents the fluid density, Indicates pressure, represents the kinematic viscosity coefficient; The fluctuation amplitude of the velocity distribution is calculated by the following formula: in, represents the fluctuation amplitude of the velocity distribution, represents the total number of sampling points, Indicates the The velocity value of the position, Represents the average value of the speed. When σ exceeds the preset threshold, smoothing is required. The smoothed velocity distribution is obtained by the following formula: in, represents the smoothed velocity distribution, represents the original velocity distribution, represents the weight coefficient of the smoothing filter, represents the half-width of the filter window, represents a calculated variable, Indicates the grid spacing; According to the smoothed velocity distribution result, the dynamic response rate of the flow field is matched by using the time step adjustment tool, and the spatiotemporal distribution of the flow field is updated by the dynamic refresh tool to obtain the dynamic response data of the flow field; The adjustment of the time step is given by the following formula: in, represents the time step of the next moment, represents the current time step, represents the target Courant number, represents the current Courant number, represents the smoothed velocity distribution, represents the original velocity distribution; The dynamic response rate of the flow field is given by the following formula: in, represents the dynamic response rate, represents the time derivative of the velocity field, represents the reference length scale, represents the reference speed, represents the velocity gradient, Represents the response characteristic parameter; The spatiotemporal distribution of the flow field is updated by the following formula: in, represents the updated flow field distribution, represents the flow field distribution at the previous moment, represents the flow field distribution calculated at the current moment, Represents the refresh coefficient, and represents the spatial coordinates, Indicates time; For the flow field dynamic response data, a data mapping tool is used to associate the flow field change trend with the time scale, and the correlation results are fused with the temperature field spatiotemporal distribution data through a data storage tool to obtain the time-synchronized flow field coupling impact data.

2. The microwave heating joint simulation method integrating multiple physical fields according to claim 1, characterized in that: The steps of obtaining initial distribution data of electromagnetic fields, temperature fields, and flow fields in the microwave heating system through a pre-established multi-physics field model, setting boundary conditions based on the layered interface characteristics, and obtaining preliminary continuity constraint results of each physical field at the interface include: Through the pre-established multi-physics field model, the finite element method is used to numerically simulate the microwave heating environment, obtain the electromagnetic field distribution, temperature field distribution and flow field distribution from the input initial data, and obtain the preliminary distribution data of each physical field; Aiming at the characteristics of the layered interface, an interface tracking algorithm is used to extract the electromagnetic field, temperature field and flow field values ​​at the interface from the preliminary distribution data to determine the physical field distribution characteristics at the interface; According to the physical field distribution characteristics at the interface, electromagnetic continuity conditions, heat flow continuity conditions, and flow field velocity and pressure continuity conditions are set to obtain the boundary conditions of each physical field at the interface; If the boundary condition meets the preset continuity threshold, the boundary condition is optimized and adjusted through an iterative solver to obtain preliminary continuity constraint results of each physical field at the interface.

3. The microwave heating joint simulation method integrating multiple physical fields according to claim 1, characterized in that: Based on the preliminary continuity constraint results, the finite element method is used to mesh each physical field, spatial discretization is performed to address the mesh mismatch problem, and the steps of determining the spatial distribution mapping relationship of each physical field include: Based on the preliminary continuity constraint results, the finite element method is used to mesh each physical field, and the spatial region is segmented using a mesh generation tool to obtain mesh distribution data for each physical field; Based on the grid distribution data, a spatial discretization tool is used to locally adjust the grid mismatch area. If the grid cell connection error exceeds a preset threshold, the cell boundary is smoothed using an interpolation calculation tool to determine the adjusted grid structure. According to the adjusted grid structure, the preliminary distribution data of each physical field is mapped to the new grid unit through the data mapping tool. If data is missing during mapping, the neighborhood averaging method is used to fill in the data to obtain the complete physical field distribution data; For the complete physical field distribution data, the field value calculation tool is used to analyze the distribution relationship in space. By comparing the field value differences between adjacent grid cells, the distribution mapping relationship of each physical field in space is judged to obtain the final spatial distribution mapping result.

4. The microwave heating joint simulation method integrating multiple physical fields according to claim 1, characterized in that: The steps of obtaining the physical value of each physical field at the layered interface through the spatial distribution mapping relationship, and reconstructing the data between different grids using the linear interpolation method in the case of difficulty in spatial synchronization to obtain the spatial distribution data set include: According to the spatial distribution mapping relationship, a data extraction tool is used to obtain physical quantity value data from the hierarchical interface of each physical field. If the physical quantity value data is missing at the interface, the missing part is filled by the neighborhood averaging method to obtain a complete interface physical quantity data set; For the complete interface physical quantity data set, a linear interpolation tool is used to reconstruct the physical quantity values ​​between different grids. If the difference in physical quantity values ​​between grids exceeds a preset threshold, the interpolation result is adjusted using a weighted average method to determine the reconstructed physical quantity distribution data. According to the reconstructed physical quantity distribution data, the grid mismatch area is re-divided using the grid adjustment tool, and the grid units are aligned using the spatial synchronization algorithm to obtain the synchronized grid structure data; For the synchronized grid structure data, the data mapping tool is used to map the reconstructed physical quantity distribution data to the new grid unit, and the field value calculation tool is used to analyze the field value differences between adjacent units to obtain the final spatial distribution data set.

5. The microwave heating joint simulation method integrating multiple physical fields according to claim 1, characterized in that: The steps of obtaining the change trend of each physical field within the time step according to the spatially distributed data set, adjusting the time step parameters to match the evolution rate of each physical field to address the time synchronization challenge, and determining the dynamically updated data after time synchronization include: According to the spatially distributed data set, a data extraction tool is used to obtain field value fluctuation data within a time step from each physical field. If the evolution rate difference of the field value fluctuation data exceeds a preset threshold, the time step is corrected using a step adjustment tool to obtain adjusted step parameter data; For the adjusted step parameter data, the parameter optimization tool is used in combination with the change trend data to match the evolution rate of each physical field. If the time synchronization deviation value does not reach the preset threshold, the time step parameter is adjusted through the parameter iteration tool to determine the matched time step configuration; According to the matched time step configuration, the data update tool is used to dynamically refresh the physical quantity distribution data, and the data verification tool is used to determine whether the field value fluctuations are consistent, and a synchronized dynamic update data set is generated; For the dynamically updated data set after synchronization, the data mapping tool is used to associate the change trend data with the time scale, and the physical quantity distribution data is integrated through the data storage tool to obtain the dynamically updated data after time synchronization.

6. The microwave heating joint simulation method integrating multiple physical fields according to claim 1, characterized in that: The steps of obtaining the driving influence of the electromagnetic field distribution characteristics on the dynamic change of the temperature field by dynamically updating the data after time synchronization, calculating the gradient distribution of the temperature field at the interface according to the dynamic change of the temperature field, and obtaining the real-time evolution result of the temperature field include: Using a data extraction tool to obtain field value fluctuation data from the electromagnetic field distribution characteristics, and using a linear interpolation tool to reconstruct the field value fluctuation data in time series to obtain basic temperature field data driven by the electromagnetic field; Based on the basic data of the temperature field driven by the electromagnetic field, the finite difference tool is used to calculate the gradient distribution of the temperature field at the interface. If the fluctuation amplitude of the gradient distribution exceeds the preset threshold, the gradient distribution is corrected by the smoothing filter tool to obtain the smoothed gradient distribution result; According to the smoothed gradient distribution results, the evolution rate of the temperature field is matched using a time step adjustment tool, and the spatiotemporal distribution of the temperature field is updated using a dynamic refresh tool to obtain real-time evolution distribution information; For the real-time evolution distribution information, a data mapping tool is used to associate the change trend of the temperature field with the time scale, and the correlation result is fused with the physical quantity distribution data through a data storage tool to obtain the time-synchronized real-time evolution data of the temperature field.

7. A microwave heating joint simulation system integrating multiple physical fields, used to execute the microwave heating joint simulation method integrating multiple physical fields according to any one of claims 1 to 6, characterized in that: The microwave heating joint simulation system integrating multiple physical fields includes: A constraint result acquisition module (10) is used to obtain initial distribution data of electromagnetic field, temperature field and flow field in the microwave heating system through a pre-established multi-physics field model, set boundary conditions according to the layered interface characteristics, and obtain preliminary continuity constraint results of each physical field at the interface; A distribution mapping relationship determination module (20) is used to perform grid division on each physical field using a finite element method based on the preliminary continuity constraint result, perform spatial discretization processing on the grid mismatch problem, and determine the distribution mapping relationship of each physical field in space; A distribution data set acquisition module (30) is used to obtain the physical value of each physical field at the layered interface through a spatial distribution mapping relationship, and in the case of difficulty in spatial synchronization, a linear interpolation method is applied to reconstruct the data between different grids to obtain a spatial distribution data set; A dynamic update data determination module (40) is used to obtain the change trend of each physical field within the time step according to the spatial distribution data set, adjust the time step parameter to match the evolution rate of each physical field in response to the time synchronization challenge, and determine the dynamic update data after time synchronization; A real-time evolution result acquisition module (50) is used to obtain the driving influence of the electromagnetic field distribution characteristics on the dynamic change of the temperature field through dynamic update data after time synchronization, and calculate the gradient distribution of the temperature field at the interface according to the dynamic change of the temperature field to obtain the real-time evolution result of the temperature field; The dynamic response data determination module (60) is used to obtain the influence data of the dynamic change of the temperature field on the flow field coupling according to the real-time evolution result of the temperature field, calculate the velocity distribution change of the flow field at the interface based on the flow field coupling influence, and determine the dynamic response data of the flow field.

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