A method for calculating eddy current loss of magnetic materials based on magnetic induction principle

Abstract

The present invention relates to the field of electromagnetic computing technology, and in particular to a method for calculating eddy current loss of magnetic materials based on the principle of magnetic induction. The method comprises the following steps: scanning the magnetic field distribution of magnetic materials to obtain multi-scale magnetic field distribution data of the materials; mapping the magnetic field intensity of the magnetic materials based on the multi-scale magnetic field distribution data of the materials to obtain a material magnetic field intensity distribution diagram; analyzing the dynamic characteristics of the magnetic materials to obtain material parameter change data; dynamically modeling the magnetic materials according to the material parameter change data to obtain a dynamic magnetic material model; tracing the eddy current path of the magnetic materials based on the dynamic magnetic material model and according to the material magnetic field intensity distribution diagram, and analyzing the eddy current propagation path to obtain a material eddy current distribution diagram and material eddy current propagation path data. The present invention significantly improves the accuracy and efficiency of eddy current loss calculation, while enhancing the applicability of the model and the reliability of industrial application.

Description

Technical Field

[0001] The invention relates to the field of electromagnetic calculation technology, and in particular to a method for calculating eddy current loss of magnetic materials based on the magnetic induction principle. Background Art

[0002] In the field of electromagnetic computing technology, the calculation of eddy current loss of magnetic materials is an important research direction. Eddy current loss refers to the energy loss caused by the movement of electrons inside the magnetic material under the action of an alternating magnetic field. This loss not only affects the performance of the material, but also limits its use in certain applications. Traditional eddy current loss calculation methods have some limitations, such as low calculation accuracy, low calculation efficiency, and inability to accurately simulate the multi-physical field coupling effect of the material.

[0003] Traditional eddy current loss calculation methods often rely on simplified physical models and assumptions, which limits their applicability under complex magnetic field conditions. For example, many methods assume that the magnetic field is uniformly distributed, while in fact the distribution of the magnetic field inside the material is very complex. This simplification leads to a large deviation between the calculation results and the actual situation, and cannot accurately reflect the actual loss of the material.

[0004] The dynamic characteristics of magnetic materials and the multi-physics coupling effects are often ignored in traditional methods. Magnetic materials are affected by many factors in practical applications, such as force fields and temperature fields, which change the magnetization characteristics and eddy current distribution of the materials. However, existing calculation methods rarely take into account the interaction of these multi-physics fields, making it impossible to fully evaluate the eddy current losses of materials under actual working conditions.

[0005] In addition, existing eddy current loss calculation methods are also insufficient when dealing with nonlinear characteristics and anisotropy of materials. The magnetization process of magnetic materials often exhibits nonlinear characteristics, and the anisotropy of materials also significantly affects the distribution and propagation of eddy currents. These characteristics are often ignored or simplified in traditional methods, resulting in inaccurate calculation results. Summary of the invention

[0006] Based on this, it is necessary for the present invention to provide a method for calculating eddy current loss of magnetic materials based on the principle of magnetic induction to solve at least one of the above technical problems.

[0007] To achieve the above purpose, a method for calculating eddy current loss of magnetic materials based on the magnetic induction principle comprises the following steps:

[0008] Step S1: Scanning the magnetic field distribution of the magnetic material to obtain multi-scale magnetic field distribution data of the material; mapping the magnetic field intensity of the magnetic material based on the multi-scale magnetic field distribution data of the material to obtain a magnetic field intensity distribution diagram of the material;

[0009] Step S2: Analyze the dynamic characteristics of the magnetic material to obtain material parameter change data; dynamically model the magnetic material according to the material parameter change data to obtain a dynamic magnetic material model;

[0010] Step S3: based on the dynamic magnetic material model and according to the material magnetic field intensity distribution map, eddy current path tracking is performed on the magnetic material, and eddy current propagation path analysis is performed to obtain the material eddy current distribution map and material eddy current propagation path data; based on the material eddy current distribution map and the material eddy current propagation path data, eddy current distribution of the magnetic material is reconstructed to obtain eddy current distribution reconstruction result data;

[0011] Step S4: Based on the dynamic magnetic material model and according to the material magnetic field intensity distribution diagram, a multi-physical field coupling model is performed on the magnetic material to obtain a material electromagnetic-thermal-stress coupling model; based on the material electromagnetic-thermal-stress coupling model and according to the eddy current distribution reconstruction result data, a numerical simulation is performed on the magnetic material to obtain the material multi-physical field dynamic response data;

[0012] Step S5: Monitor the real-time operating parameters of the magnetic material to obtain the real-time operating parameters of the material; evaluate and calculate the eddy current loss of the magnetic material based on the electromagnetic-thermal-stress coupling model of the material and according to the multi-physical field dynamic response data of the material and the real-time operating parameters of the material to obtain the eddy current loss data of the material.

[0013] The present invention can accurately map the magnetic field intensity distribution inside the material through magnetic field distribution scanning and acquisition of multi-scale magnetic field data, effectively overcome the simplified assumptions of traditional methods, and lay the foundation for the accurate prediction of eddy current loss. Secondly, through the in-depth analysis of the dynamic characteristics of the material and the construction of the dynamic magnetic material model, the present solution can capture the changes of material parameters over time and environmental conditions, thereby more truly reflecting the dynamic behavior of the material in actual use. In addition, the present invention comprehensively considers the influence of multi-physical field factors (such as force field, temperature field) on eddy current loss by constructing an electromagnetic-thermal-stress coupling model, thereby improving the comprehensiveness of the evaluation. The understanding of the eddy current propagation mechanism inside the material is further deepened by tracking and reconstructing the eddy current path. Through real-time monitoring and evaluation capabilities, the present solution can quickly respond to changes in material properties in industrial applications, and timely monitor performance and prevent faults. The application of parallel computing technology and numerical simulation methods greatly improves the computing efficiency and shortens the computing time in engineering applications. The introduction of nonlinear characteristics and anisotropic analysis enhances the applicability of the model to different magnetic materials. In summary, the present invention significantly improves the calculation accuracy and efficiency of eddy current losses, while enhancing the applicability of the model and the reliability of industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0015] Figure 1 A schematic flow chart of the steps of a method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to an embodiment is shown.

[0016] Figure 2 A detailed flow chart of step S26 of an embodiment is shown.

[0017] Figure 3 A detailed flow chart of step S45 of an embodiment is shown. DETAILED DESCRIPTION

[0018] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0020] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0021] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for calculating eddy current loss of magnetic materials based on the magnetic induction principle, comprising the following steps:

[0022] Step S1: Scanning the magnetic field distribution of the magnetic material to obtain multi-scale magnetic field distribution data of the material; mapping the magnetic field intensity of the magnetic material based on the multi-scale magnetic field distribution data of the material to obtain a magnetic field intensity distribution diagram of the material;

[0023] Step S2: Analyze the dynamic characteristics of the magnetic material to obtain material parameter change data; dynamically model the magnetic material according to the material parameter change data to obtain a dynamic magnetic material model;

[0024] Step S3: based on the dynamic magnetic material model and according to the material magnetic field intensity distribution map, eddy current path tracking is performed on the magnetic material, and eddy current propagation path analysis is performed to obtain the material eddy current distribution map and material eddy current propagation path data; based on the material eddy current distribution map and the material eddy current propagation path data, eddy current distribution of the magnetic material is reconstructed to obtain eddy current distribution reconstruction result data;

[0025] Step S4: Based on the dynamic magnetic material model and according to the material magnetic field intensity distribution diagram, a multi-physical field coupling model is performed on the magnetic material to obtain a material electromagnetic-thermal-stress coupling model; based on the material electromagnetic-thermal-stress coupling model and according to the eddy current distribution reconstruction result data, a numerical simulation is performed on the magnetic material to obtain the material multi-physical field dynamic response data;

[0026] Step S5: Monitor the real-time operating parameters of the magnetic material to obtain the real-time operating parameters of the material; evaluate and calculate the eddy current loss of the magnetic material based on the electromagnetic-thermal-stress coupling model of the material and according to the multi-physical field dynamic response data of the material and the real-time operating parameters of the material to obtain the eddy current loss data of the material.

[0027] The present invention can accurately map the magnetic field intensity distribution inside the material through magnetic field distribution scanning and acquisition of multi-scale magnetic field data, effectively overcome the simplified assumptions of traditional methods, and lay the foundation for the accurate prediction of eddy current loss. Secondly, through the in-depth analysis of the dynamic characteristics of the material and the construction of the dynamic magnetic material model, the present solution can capture the changes of material parameters over time and environmental conditions, thereby more truly reflecting the dynamic behavior of the material in actual use. In addition, the present invention comprehensively considers the influence of multi-physical field factors (such as force field, temperature field) on eddy current loss by constructing an electromagnetic-thermal-stress coupling model, thereby improving the comprehensiveness of the evaluation. The understanding of the eddy current propagation mechanism inside the material is further deepened by tracking and reconstructing the eddy current path. Through real-time monitoring and evaluation capabilities, the present solution can quickly respond to changes in material properties in industrial applications, and timely monitor performance and prevent faults. The application of parallel computing technology and numerical simulation methods greatly improves the computing efficiency and shortens the computing time in engineering applications. The introduction of nonlinear characteristics and anisotropic analysis enhances the applicability of the model to different magnetic materials. In summary, the present invention significantly improves the calculation accuracy and efficiency of eddy current losses, while enhancing the applicability of the model and the reliability of industrial applications.

[0028] In this embodiment, the magnetic field scanning device is first started to perform a comprehensive scan on the magnetic material, and the multi-scale magnetic field distribution data of the magnetic material is collected in detail. Using FEA software, these data are imported and the magnetic field intensity mapping is performed to generate an accurate magnetic field intensity distribution map. Next, the dynamic characteristics of the material are analyzed through multi-spectrum electromagnetic excitation and microscopic strain monitoring, and the response data under different excitation states are collected to obtain the material parameter change data. Combined with the three-dimensional geometric model of the material, dynamic modeling is performed in the FEA software to create a dynamic magnetic material model that describes the dynamic behavior of the material. Using this model and the magnetic field intensity distribution map, the eddy current path and the propagation path are tracked in the software through the eddy current analysis tool to obtain the eddy current distribution map and the propagation path data, and the eddy current distribution is reconstructed based on this to obtain the reconstruction result data. Further, based on the dynamic magnetic material model and the magnetic field intensity distribution map, multi-physics field coupling modeling is performed to construct an electromagnetic-thermal-stress coupling model, and numerical simulation technology is used to combine the eddy current distribution reconstruction result data to perform simulation analysis to obtain multi-physics field dynamic response data. Finally, the real-time operating parameters of the material are obtained through monitoring by the sensor array, and these parameters are combined with the dynamic response data to evaluate and calculate the eddy current loss in the coupling model to obtain the material eddy current loss data.

[0029] Preferably, step S1 comprises the following steps:

[0030] Step S11: Scan the geometric features of the magnetic material to obtain a three-dimensional geometric model of the material;

[0031] Specifically, a 3D laser scanner can be used to perform a full-scale scan of the surface of a magnetic material. The scanner can emit a laser beam and receive the laser reflected from the surface of the material, and determine the exact position of each point on the surface by calculating the time it takes for the laser to travel back and forth. During the scanning process, the device rotates around the magnetic material to ensure that all geometric features of the material are fully captured. After the scan is completed, professional 3D modeling software such as Autodesk 3ds Max or SolidWorks is used to convert and reconstruct the collected data points into a 3D geometric model of the material.

[0032] Step S12: configuring a detection array for the magnetic material according to the three-dimensional geometric model of the material to obtain a detector array spatial distribution scheme;

[0033] Specifically, the configuration of the detection array can be designed based on the three-dimensional geometric model of the material using professional magnetic field simulation software, such as ANSYS Maxwell. The software can simulate and optimize the spatial distribution of detectors based on the geometric characteristics of the material and the expected magnetic field characteristics. During the design process, the type and number of detectors and their positions relative to the material are considered. After the design is completed, a spatial distribution plan of the detector array is generated, which describes in detail the precise position and orientation of each detector.

[0034] Step S13: deploying a multi-spectral magnetic field detector array on the magnetic material according to the detector array spatial distribution plan to obtain actual state data of the detector array;

[0035] Specifically, multi-spectral magnetic field detectors can be deployed using automated robotic arms and precise positioning systems according to the spatial distribution scheme of the detector array. These detectors are able to respond to magnetic field changes at different frequencies and have the characteristics of high sensitivity and wide bandwidth. The robotic arm places each detector in the predetermined position according to the guidance in the scheme and uses a fine-tuning mechanism to ensure the precise alignment of the detectors. After deployment, a test scan is performed to verify whether the actual state of the detector array is consistent with expectations. The test data will be fed back to the control system. If there is any deviation, the system will automatically adjust the detector position until the required accuracy is achieved. Finally, the actual state data of the detector array is collected.

[0036] Step S14: measuring the initial magnetization curve of the magnetic material to obtain the initial magnetization curve of the material;

[0037] Specifically, the magnetic material can be placed in a high-precision magnetic measurement device, such as a vibrating sample magnetometer (VSM). This device can accurately control the movement of the sample in a magnetic field and measure its magnetization response. By gradually changing the strength of the external magnetic field and recording the change in the magnetization intensity of the material, the initial magnetization curve of the material is obtained. During the measurement process, the device will gradually increase the magnetic field from zero until it reaches the saturated magnetization state, then gradually reduce the magnetic field to zero or the reverse saturation state, and then return to zero. Through this process, the magnetization intensity value of the material under different magnetic field strengths can be obtained, and the initial magnetization curve can be drawn to reflect the magnetization characteristics of the material.

[0038] Step S15: Based on the three-dimensional geometric model of the material and according to the initial magnetization curve of the material, the magnetization characteristic model of the magnetic material is estimated and modeled to obtain the magnetization characteristic model of the material;

[0039] Specifically, the three-dimensional geometric model of the material can be imported into numerical simulation software, such as COMSOL Multiphysics, and the magnetization behavior of the material can be defined based on the initial magnetization curve of the material. Appropriate physical fields and boundary conditions are set in the software, for example, the magnetic field is discretized using the finite element method (FEM), and then the magnetization data of the material is applied as input parameters. Through the calculation of the simulation software, the magnetization characteristics of the material under different magnetic field conditions can be obtained, and a material magnetization characteristic model can be generated.

[0040] Step S16: generating a magnetic field scanning sequence for the magnetic material based on the material magnetization characteristic model to obtain material magnetic field scanning sequence data;

[0041] Specifically, the magnetic field scanning sequence can be designed by using the material magnetization characteristic model in combination with the magnetic field scanning sequence generation software, such as LabVIEW or MATLAB. First, the magnetic field variation range and step size in the scanning sequence are determined according to the magnetization behavior predicted by the material magnetization characteristic model. Then, a program is written to control the magnetic field generator to change according to a predetermined sequence. The scanning parameters, such as magnetic field intensity, frequency, and duration, are set in the software. Through simulation and optimization, the material magnetic field scanning sequence data is generated.

[0042] Step S17: Based on the actual state data of the detector array and the material magnetic field scanning sequence data, multi-spectral dynamic magnetic field data of the magnetic material are collected to obtain multi-scale magnetic field distribution data of the material, and the magnetic field intensity of the magnetic material is mapped based on the multi-scale magnetic field distribution data of the material to obtain a material magnetic field intensity distribution diagram.

[0043] Specifically, please refer to the sub-steps of step S17 for the detailed implementation process of this embodiment.

[0044] The three-dimensional geometric model constructed by the present invention through geometric feature scanning provides an accurate spatial reference frame for magnetic field distribution analysis. Secondly, the optimized detection array configuration is carried out according to the model, which not only improves the comprehensiveness and uniformity of magnetic field data acquisition, but also increases the coverage and accuracy of measurement. In addition, through the application of multi-spectral magnetic field detection technology, the magnetic field characteristics of materials at different frequencies can be captured. Through the measurement of the initial magnetization curve and the magnetization characteristic estimation modeling based on this, a solid foundation is provided for understanding the magnetization behavior of the material. Through the orderly generation of magnetic field scanning sequences, the systematic nature of data acquisition and the effective acquisition of multi-scale magnetic field distribution are further ensured. Through the collection of multi-spectral dynamic magnetic field data, the magnetic field distribution information is enriched. Finally, the precise mapping of magnetic field intensity generates a more accurate magnetic field intensity distribution map, which provides reliable magnetic field parameters for the calculation of eddy current loss.

[0045] Preferably, step S17 comprises the following steps:

[0046] Step S171: performing a dynamic magnetic field scan on the magnetic material based on the actual state data of the detector array and the material magnetic field scan sequence data to obtain original multi-spectral magnetic field data of the material;

[0047] Specifically, the magnetic field scanning system can be started according to the actual state data of the detector array. The system consists of multiple high-precision magnetic field detectors that can measure the magnetic field strength of magnetic materials in different positions and directions in real time. Using the material magnetic field scanning sequence data, the magnetic field generator is controlled to change according to a predetermined sequence, thereby generating a dynamically changing magnetic field around the magnetic material. The detector array synchronously collects the magnetic field response of the material at various time and space points based on the actual state data. During the data acquisition process, a data acquisition card (DAQ) and professional signal processing software, such as National Instruments' LabVIEW, are used to record the output signal of the detector in real time and store it as the original multi-spectral magnetic field data of the material. These data reflect the magnetization state of the material under the action of magnetic fields of different frequencies and intensities.

[0048] Step S172: performing high-dimensional tensor decomposition and fusion on the original multi-spectral magnetic field data of the material to obtain material fused multi-dimensional magnetic field data;

[0049] Specifically, mathematical software, such as MATLAB, can be used to perform high-dimensional tensor decomposition on the original multi-spectral magnetic field data of the material. First, the original multi-spectral magnetic field data of the material is regarded as a multidimensional array, in which each dimension represents a different frequency or spatial position. Using a tensor decomposition algorithm, such as parallel factor (PARAFAC) or high-order singular value decomposition (HOSVD), the original data is decomposed into several independent components, which represent the magnetic field characteristics under different modes. Then, by fusing these components, the fused multi-dimensional magnetic field data of the material is obtained.

[0050] Step S173: reconstructing the magnetic field of the magnetic material by compressed sensing based on the three-dimensional geometric model of the material and the fusion multi-dimensional magnetic field data of the material to obtain multi-scale magnetic field distribution data of the material;

[0051] Specifically, compressed sensing algorithms, such as L1-minimization or gradient projection, can be used based on the three-dimensional geometric model of the material to sparsely represent and reconstruct the material's fused multi-dimensional magnetic field data. These algorithms can accurately restore the multi-scale distribution of the magnetic field when the data is sparse or incomplete. In software, such as Python's SciPy library, appropriate sparsity and regularization parameters are set, and compressed sensing algorithms are run to obtain multi-scale magnetic field distribution data of the material. These data not only contain the macroscopic distribution characteristics of the magnetic field, but also reveal the microscopic structure of the magnetic field inside the material.

[0052] Step S174: performing magnetic field intensity modeling on the material multi-scale magnetic field distribution data based on the material three-dimensional geometric model to obtain a material magnetic field intensity calculation model;

[0053] Specifically, professional finite element analysis (FEA) software, such as ANSYS or COMSOL Multiphysics, can be used to import the three-dimensional geometric model of the material. Then, the multi-scale magnetic field distribution data of the material is used as input conditions to define the magnetic property parameters of the material, such as magnetic permeability and magnetic saturation. Appropriate physical field equations are set in the software, such as the magnetic vector potential equation in Maxwell's equations, to simulate the distribution of the magnetic field in the material. Then, the boundary conditions are applied and the multi-scale magnetic field distribution data of the loading material is loaded to perform a numerical simulation of the magnetic field intensity. After the calculation is completed, the software will output a material magnetic field strength calculation model, which describes in detail the distribution law of the magnetic field intensity inside the material.

[0054] Step S175: performing parallel calculation of magnetic field strength on the material magnetic field strength calculation model to obtain spatial distribution data of the material magnetic field strength;

[0055] Specifically, a high-performance computing cluster (HPC) or cloud computing resources, such as Amazon AWS or Google Cloud Platform, can be selected to perform the computing task. First, the material magnetic field strength calculation model is decomposed into subtasks that can be processed in parallel. Use parallel computing libraries, such as OpenMP or MPI, to assign these subtasks to different processors or computing nodes. Through parallel algorithms, such as multi-threading or multi-process technology, numerical calculations of magnetic field strength are performed simultaneously. After the calculation is completed, the results of each subtask are collected and merged into complete spatial distribution data of material magnetic field intensity.

[0056] Step S176: Perform three-dimensional volume rendering and visualization on the spatial distribution data of the material magnetic field intensity to obtain a material magnetic field intensity distribution map.

[0057] Specifically, the spatial distribution data of the material magnetic field intensity can be imported into visualization software, such as ParaView or MayaVi2. In the software, appropriate visualization technology, such as volume rendering technology or isosurface technology, is selected according to the characteristics of the data. By adjusting the transparency, color mapping and lighting condition parameters, the material magnetic field intensity distribution map is generated.

[0058] The present invention realizes the real-time capture of multi-spectral magnetic field data of magnetic materials under different scanning sequences through the application of dynamic magnetic field scanning technology. Furthermore, the magnetic field data of different sources and frequencies are effectively integrated through high-dimensional tensor decomposition and fusion, which enhances the consistency and reliability of the data. The application of compressed sensing technology shows its advantages in magnetic field reconstruction, and can accurately restore the multi-scale magnetic field distribution of materials even in the case of sparse data, maintaining high resolution. In addition, the magnetic field strength calculation model established based on three-dimensional geometric model and multi-scale magnetic field distribution data provides a solid theoretical support for the accurate calculation of magnetic field strength. The introduction of parallel computing technology greatly improves the calculation speed of magnetic field intensity spatial distribution data, shortens data processing time, and improves work efficiency. The spatial distribution of magnetic field intensity is intuitively visible through the application of three-dimensional volume rendering visualization technology. The present invention improves the flexibility and adaptability of magnetic field measurement, and can adjust the detection array configuration and magnetic field scanning sequence according to material characteristics and actual needs.

[0059] Preferably, step S2 comprises the following steps:

[0060] Step S21: performing multi-spectrum electromagnetic excitation on the magnetic material, and simultaneously performing force field and temperature field dynamic loading on the magnetic material to obtain material multi-physical field excitation state data;

[0061] Specifically, a multi-spectrum electromagnetic excitation system can be prepared, which can generate electromagnetic fields of different frequencies and intensities. A signal generator is used to generate the required electromagnetic wave signal, and the signal is enhanced by a power amplifier. At the same time, a temperature controller and a force field loading device are used to apply varying temperature and force field conditions to the magnetic material. These devices can accurately control the loading parameters, such as the temperature range, the magnitude of the loading force, and the duration. The magnetic material is placed in an electromagnetic coil and connected to the temperature controller and the force field loading device. In the above process, the loading parameters of the multi-spectrum electromagnetic excitation, force field, and temperature field, as well as the response data of the material are recorded synchronously. These data are collected in real time through a data acquisition system (such as LabVIEW software with a DAQ card) to obtain the material's multi-physical field excitation state data.

[0062] Step S22: measuring the dynamic magnetization characteristics of the magnetic material according to the material multi-physical field excitation state data to obtain a dynamic magnetization curve of the material;

[0063] Specifically, dynamic magnetic property measurement equipment, such as a superconducting quantum interference device (SQUID) magnetometer, can be used to measure the magnetization characteristics of magnetic materials under multi-physical field excitation states. The magnetic material is placed in the measurement area of ​​the SQUID magnetometer, which is able to detect very weak magnetic field changes. During the measurement process, the corresponding electromagnetic, force field, and temperature field conditions are applied synchronously according to the multi-physical field excitation state data of the material. At the same time, the magnetization response of the material is recorded, including the changes in magnetization intensity over time and external conditions. By dynamically tracking these data, the dynamic magnetization curves of the material can be obtained, which reflect the magnetization behavior of the material under different excitation conditions.

[0064] Step S23: performing microscopic strain monitoring on the magnetic material according to the material multi-physical field excitation state data to obtain material microscopic strain distribution data;

[0065] Specifically, digital image correlation (DIC) technology can be used. First, a tracer material with a random pattern is sprayed or coated on the surface of the magnetic material for observation under a microscope. Then, the sample is placed under a microscope with high-resolution imaging capabilities and connected to the camera of the DIC system. During the experiment, the corresponding conditions are applied synchronously according to the multi-physics field excitation state data of the material. At the same time, the software of the DIC system is used to capture the image of the material surface in real time, and the changes in the pattern in the image are analyzed to calculate the strain distribution on the material surface. In this way, the microscopic strain distribution number of the material can be obtained.

[0066] Step S24: monitoring the temperature distribution of the magnetic material according to the material multi-physical field excitation state data to obtain the material dynamic temperature distribution data;

[0067] Specifically, a high-precision temperature monitoring system can be used to monitor the temperature distribution of magnetic materials in real time under multi-physics field excitation. This system includes thermocouples, infrared thermal imagers, or other non-contact temperature measurement devices. For example, if an infrared thermal imager is used, first place the magnetic material in the field of view of the thermal imager and ensure that the device can capture the thermal radiation on the surface of the material. Under multi-physics field excitation, the temperature of the material will change, and the thermal imager will record these changes in real time and generate a temperature distribution map. By analyzing these images, the temperature values ​​of the material at different times and locations can be obtained, thereby obtaining the dynamic temperature distribution data of the material.

[0068] Step S25: collecting material parameters of the magnetic material in a state without a magnetic field to obtain material parameters in a state without a magnetic field;

[0069] Specifically, various physical property measuring instruments can be used to obtain the basic physical parameters of the material, such as magnetic permeability, resistivity, and thermal conductivity. For example, an impedance analyzer is used to measure the resistivity and magnetic permeability of the material. By measuring the impedance of the material at different frequencies, the electromagnetic properties of the material can be obtained. In addition, a thermal conductivity meter is used to determine the thermal conductivity of the material. During the measurement process, ensure that the environmental conditions are stable, such as temperature and humidity, to avoid the influence of external factors on the measurement results. Through these measurements, a series of parameters of the material in the state without a magnetic field can be obtained, and finally the parameters of the material in the state without a magnetic field can be obtained.

[0070] Step S26: Perform multi-physical field coupling parameter inversion analysis on the magnetic material according to the material's no-magnetic-field state parameters, the material's dynamic magnetization curve, the material's microscopic strain distribution data, and the material's dynamic temperature distribution data to obtain material parameter change data, and dynamically model the magnetic material according to the material parameter change data based on the material's three-dimensional geometric model to obtain a dynamic magnetic material model.

[0071] Specifically, please refer to the sub-steps of step S26 for the detailed implementation process of this embodiment.

[0072] The present invention can simulate the complex environment of materials in actual use by combining multi-spectrum electromagnetic excitation with dynamic loading of force field and temperature field. The magnetization behavior of the material under different excitation conditions is captured by precise dynamic magnetization characteristic measurement technology, providing key dynamic magnetization curves. By real-time monitoring of microscopic strain distribution, it is possible to gain insight into the local response of the internal structure of the material under the action of multiple physical fields, while precise temperature distribution monitoring provides data support for analyzing the impact of temperature on material properties. The material parameters accurately collected in the absence of a magnetic field provide a benchmark for multi-physical field coupling analysis, and further reveal the changing laws of material parameters through coupling parameter inversion analysis. Ultimately, the dynamic magnetic material model constructed based on these in-depth analyses provides a solid foundation for eddy current loss calculation and performance prediction. These measures together improve the accuracy of material property analysis and enhance the understanding of the internal response of the material.

[0073] Preferably, step S26 comprises the following steps:

[0074] Step S261: performing multi-physics field data synchronization on the material dynamic magnetization curve, the material microscopic strain distribution data and the material dynamic temperature distribution data to obtain the material multi-physics field coupling response data;

[0075] Specifically, data synchronization software, such as LabVIEW or MATLAB, can be used to time-align the material dynamic magnetization curve, material microscopic strain distribution data, and material dynamic temperature distribution data to ensure consistent time resolution of data acquisition. By writing scripts or using the built-in synchronization tools in the software, data from different physical fields can be integrated in the same time series to form material multi-physical field coupling response data.

[0076] Step S262: extracting material parameter dynamic characteristics from the material multi-physics field coupling response data to obtain material parameter dynamic characteristic data;

[0077] Specifically, data analysis software, such as MATLAB or Python, combined with NumPy and SciPy libraries, can be used to conduct in-depth analysis of the material multi-physics field coupling response data. First, signal processing techniques, such as Fourier transform or wavelet transform, are applied to analyze the frequency characteristics and time-varying characteristics of the data. Then, through statistical analysis methods, such as regression analysis or principal component analysis, the dynamic characteristics of material parameters are extracted, such as the law of magnetic permeability changing with temperature and the influence of strain on magnetization characteristics. Through these analyses, material parameter dynamic characteristic data can be obtained, which reflects the intrinsic physical mechanism of the material under the action of multiple physical fields.

[0078] Step S263: performing parameter coupling relationship analysis on the material parameter dynamic characteristic data to obtain a material parameter coupling relationship network;

[0079] Specifically, data mining and network analysis tools, such as Python's NetworkX library or Gephi software, can be used to analyze the coupling relationship of material parameter dynamic characteristic data. Define the coupling relationship between parameters, such as the relationship between magnetic permeability and temperature, and the relationship between strain and magnetization. Then, by constructing a network model, these parameters are regarded as nodes in the network, and the coupling relationship between parameters is regarded as edges. Use network analysis methods, such as cluster analysis or centrality analysis, to identify direct and indirect interactions between parameters and their importance in the coupling network. Through this analysis, a material parameter coupling relationship network can be obtained, which reveals the interdependence between different physical quantities and the overall coupling characteristics.

[0080] Step S264: performing multi-scale material property modeling on the magnetic material based on the material parameter coupling relationship network and the material multi-physics field coupling response data to obtain a material parameter coupling dynamics model;

[0081] Specifically, the knowledge of materials science and computational physics can be used to combine the material parameter coupling relationship network with the material multi-physics field coupling response data. Use computational material science software, such as COMSOL Multiphysics or ANSYS, to build a multi-scale material property model. In the software, define the multi-scale structure of the material, from the microscopic lattice structure to the macroscopic sample size. Input the parameter interactions determined in the material parameter coupling relationship network and the multi-physics field coupling response data to simulate the physical behavior of materials at different scales. By adjusting the model parameters and boundary conditions, iterative calculations are performed until the results predicted by the model match the experimental data. Finally, a model that can describe the coupled dynamic behavior of materials at multiple scales is obtained, namely the material parameter coupling dynamic model.

[0082] Step S265: constructing a dynamic constitutive relationship for the material parameter coupling dynamics model to obtain a material multi-scale characteristic model;

[0083] Specifically, finite element analysis (FEA) software, such as ABAQUS, can be used to define the stress-strain relationship of the material and integrate it into the material parameter coupled dynamic model. By setting appropriate material behavior parameters, such as elastic modulus, yield strength, and plasticity parameters, a constitutive model of the material is constructed. Then, numerical simulation is performed to simulate the behavior of the material under different loading conditions, such as tension, compression, or multiaxial loading. By adjusting the parameters in the constitutive model until the simulation results are consistent with the experimental observations, a multi-scale property model of the material is obtained.

[0084] Step S266: performing iso-geometric coupling on the material multi-scale characteristic model and the material three-dimensional geometric model to obtain a dynamic magnetic material model.

[0085] Specifically, the three-dimensional geometric model of the material can be imported into finite element analysis software, such as ANSYS or COMSOL Multiphysics, for meshing. In the software, the properties of the mesh, such as unit type, size, and material properties, are defined according to the multi-scale characteristic model of the material. The isogeometric coupling technology is used to ensure that the material properties can be accurately represented at each scale of the geometric model. In this way, a dynamic magnetic material model that integrates the multi-scale characteristics of the material and the three-dimensional geometric information, namely the dynamic magnetic material model, can be obtained.

[0086] The present invention provides a solid data foundation for accurately capturing the comprehensive response of materials in electromagnetic, mechanical and thermal environments through data synchronization and coupled response analysis. Furthermore, the in-depth extraction of dynamic characteristics can identify and quantify the changes in material parameters over time, thereby enhancing the understanding of the dynamic behavior of materials. By clarifying the parameter coupling relationship, the interaction between different physical quantities is revealed by establishing a network. Based on these data, a dynamic model reflecting the characteristics of materials at all scales from micro to macro is constructed for multi-scale characteristic modeling. The construction of dynamic constitutive relations further enhances the model's ability to predict the mechanical behavior of materials in a multi-physical field environment. The geometric and physical consistency of the multi-scale characteristic model and the three-dimensional geometric model of the material is ensured by the application of isogeometric coupling technology. Finally, the precise construction of the dynamic magnetic material model provides a reliable basis for eddy current loss evaluation and performance prediction. The present invention improves the applicability and flexibility of the model and promotes the understanding of the internal mechanism of the material.

[0087] Preferably, step S3 comprises the following steps:

[0088] Step S31: performing grid eddy current initialization simulation on the magnetic material based on the dynamic magnetic material model and the material magnetic field intensity distribution diagram to obtain initial eddy current distribution data of the material;

[0089] Specifically, computational fluid dynamics (CFD) software, such as ANSYS or COMSOL Multiphysics, can be used to import dynamic magnetic material models and material magnetic field intensity distribution maps. Set appropriate physical fields, such as electromagnetic fields, and corresponding boundary conditions and initial conditions in the software. Use the software's meshing tool to divide the magnetic material model into finite grid cells for numerical simulation. Initialize the eddy current distribution on the grid, which can be achieved by setting the initial current density or magnetic field conditions. Then, run the simulation program to calculate the initial eddy current distribution inside the magnetic material under the action of the magnetic field. The software will provide the initial eddy current distribution data of the material.

[0090] Step S32: performing material nonlinear characteristic analysis on the magnetic material to obtain material nonlinear characteristic data, and performing material anisotropy analysis on the magnetic material to obtain material anisotropy data;

[0091] Specifically, physical property measurement equipment, such as a vibrating sample magnetometer (VSM) or a superconducting quantum interference device (SQUID), can be used to measure the magnetization curve of a magnetic material under different magnetic field conditions, thereby obtaining material nonlinear characteristic data. At the same time, the crystal orientation of the material is analyzed by X-ray diffraction (XRD) technology, and its anisotropy is evaluated to obtain material anisotropy data.

[0092] Step S33: performing eddy current dynamics spatiotemporal evolution calculation on the magnetic material based on the dynamic magnetic material model according to the material initial eddy current distribution data, the material nonlinear characteristic data and the material anisotropy data, to obtain the material eddy current spatiotemporal evolution data;

[0093] Specifically, finite element analysis software, such as ABAQUS or COMSOL Multiphysics, can be used to calculate the spatiotemporal evolution of eddy current dynamics based on the initial eddy current distribution data of the material, the nonlinear characteristic data of the material, and the anisotropy data of the material. The governing equations of eddy current dynamics, such as Maxwell's equations and Ohm's law, are defined in the software, and the material properties and initial conditions are input into the dynamic magnetic material model. Time-stepping solution is performed to simulate the evolution of eddy currents in the material over time. Through simulation, data on the variation of eddy currents over time and space, i.e., the spatiotemporal evolution data of eddy currents in the material, can be obtained.

[0094] Step S34: performing multi-scale eddy current path tracking analysis on the material eddy current spatiotemporal evolution data to obtain multi-scale eddy current path data of the material;

[0095] Specifically, data processing and visualization software, such as MATLAB or ParaView, can be used to process and analyze the spatiotemporal evolution data of material eddy currents. Through streamline visualization technology, the propagation path of the eddy current can be intuitively observed. In addition, path tracking algorithms, such as particle tracking or path integral method, are applied to quantitatively analyze the propagation characteristics of the eddy current. Through multi-scale analysis, the propagation mechanism of the eddy current can be understood from different scales from microscopic to macroscopic. Finally, multi-scale eddy current path data of the material is obtained.

[0096] Step S35: performing eddy current energy propagation network modeling based on the material eddy current spatiotemporal evolution data and the material multi-scale eddy current path data to obtain a material eddy current energy propagation network model;

[0097] Specifically, computational simulation software and network analysis tools can be used to establish an eddy current energy propagation network model. Using finite element analysis (FEA) software, such as ANSYS or COMSOL Multiphysics, the material eddy current spatiotemporal evolution data and the material multi-scale eddy current path data are imported. The physical process of energy propagation is defined in the software, for example, by setting the conductivity, magnetic permeability and thermal property parameters of the material to simulate the energy dissipation and propagation of the eddy current. Then, using graph theory and network analysis methods, the eddy current path is regarded as an edge in the network, and the eddy current dense area is regarded as a node in the network to construct an energy propagation network. By calculating the energy flow in the network and the interaction between the nodes, the material eddy current energy propagation network model is obtained.

[0098] Step S36: performing three-dimensional eddy current distribution visualization processing on the material eddy current spatiotemporal evolution data and the material eddy current energy propagation network model to obtain a material eddy current distribution map;

[0099] Specifically, the material eddy current spatiotemporal evolution data and the material eddy current energy propagation network model can be imported into visualization software, such as ParaView or Tecplot. In the software, select appropriate visualization techniques, such as volume rendering or isosurface technology, to display the three-dimensional distribution of eddy currents in the material. By adjusting transparency, color mapping and lighting effects, the intensity and direction of the eddy current can be clearly displayed. In addition, the animation function can be used to display the dynamic change process of the eddy current over time. Finally, a three-dimensional material eddy current distribution map is generated.

[0100] Step S37: extracting key eddy current propagation channels from the material eddy current distribution diagram to obtain material eddy current propagation path data;

[0101] Specifically, areas with high eddy current intensity can be identified from the material eddy current distribution map. These areas are hot spots or key channels for eddy current propagation. Use image processing software, such as MATLAB or ImageJ, to analyze these areas and extract eddy current propagation paths by threshold segmentation, edge detection or feature extraction. Then, use path analysis tools, such as fluid dynamics analysis software or custom scripts, to track these paths and analyze their propagation characteristics. By identifying the dominant path and branch path of eddy current propagation, material eddy current propagation path data can be obtained.

[0102] Step S38: reconstructing the eddy current distribution of the magnetic material based on the material eddy current distribution diagram and the material eddy current propagation path data to obtain eddy current distribution reconstruction result data.

[0103] Specifically, please refer to the sub-steps of step S38 for the detailed implementation process of this embodiment.

[0104] The present invention ensures the accuracy of the initial eddy current distribution data by performing eddy current initialization simulation based on a dynamic magnetic material model and a material magnetic field intensity distribution diagram. Through in-depth material property analysis, including nonlinear characteristics and anisotropy considerations, comprehensive material property data is provided for calculation, capturing the differences in the magnetization behavior of the material in different directions. The complex dynamic process of eddy currents inside the material is simulated by dynamic calculation of the spatiotemporal evolution of eddy currents, while the tracking analysis of multi-scale eddy current paths identifies the propagation path of eddy currents and enhances the understanding of the eddy current propagation mechanism. The modeling of the eddy current energy propagation network provides a new perspective for analyzing eddy current energy propagation and loss, while the visualization of the three-dimensional eddy current distribution intuitively displays the distribution state of eddy currents inside the material. The extraction of key eddy current propagation channels provides targeted improvement directions for material design and optimization, and the reconstruction of eddy current distribution allows a comprehensive re-evaluation of the eddy current distribution inside the material. Overall, the present invention significantly improves the accuracy of eddy current loss calculations through precise simulation and multi-dimensional analysis.

[0105] Preferably, step S38 includes the following steps:

[0106] Step S381: performing eddy current structure feature recognition on the material eddy current distribution diagram to obtain eddy current structure feature classification data;

[0107] Specifically, visualization software such as ParaView or MATLAB can be used to load the material eddy current distribution map. The morphological characteristics of the eddy current can be identified through image processing techniques such as filtering, edge detection, and feature extraction algorithms. For example, the Canny edge detection algorithm can be applied to highlight the boundary of the eddy current, or the Hough transform can be used to identify the straight line features of the eddy current. Then, these features are classified using machine learning classification algorithms such as support vector machines (SVM) or random forests to obtain eddy current structure feature classification data.

[0108] Step S382: performing eddy current propagation mode recognition on the material eddy current propagation path data to obtain eddy current propagation mode classification data;

[0109] Specifically, different eddy current propagation pattern characteristics can be defined in software, such as Python with the Scikit-learn library, such as the path length, curvature or branch complexity of the eddy current. Then, clustering algorithms, such as K-means or hierarchical clustering, are applied to group the material eddy current propagation path data to identify different propagation modes. By analyzing the distribution and characteristics of these modes, eddy current propagation mode classification data can be obtained.

[0110] Step S383: obtaining a dynamic magnetic material model;

[0111] Specifically, the dynamic magnetic material model established in the previous step can be reused. In this step, no new operation or analysis is required, but the established dynamic magnetic material model is simply retrieved from the database or model library. This model is obtained through multi-physics field coupling modeling in the previous step, and includes the electromagnetic-thermal-stress coupling characteristics of the material. In a software environment, such as ANSYS or COMSOL Multiphysics, the dynamic magnetic material model is accessed and obtained through the model management tool.

[0112] Step S384: reconstructing the eddy current distribution of the magnetic material according to the eddy current structure feature classification data and the eddy current propagation mode classification data based on the dynamic magnetic material model to obtain eddy current distribution reconstruction result data.

[0113] Specifically, a dynamic magnetic material model can be loaded in software such as COMSOL Multiphysics. Then, the eddy current structure feature classification data and the eddy current propagation mode classification data are used as input conditions to adjust relevant parameters in the model, such as current density distribution, material properties or boundary conditions. The model is run and iterative calculations are performed until the eddy current distribution predicted by the model is consistent with the actual observed data. In this way, the eddy current distribution reconstruction result data can be obtained.

[0114] Through the in-depth understanding of eddy current structure, the present invention can effectively classify the distribution of eddy currents in materials and identify key propagation modes. By applying the dynamic magnetic material model, the accuracy and practicality of the eddy current distribution reconstruction process are further ensured, reflecting the dynamic characteristics of the material under actual working conditions. The precise eddy current distribution reconstruction not only optimizes the material design and reduces eddy current losses, but also improves the efficiency and reliability of magnetic materials in high-frequency applications.

[0115] Preferably, step S4 comprises the following steps:

[0116] Step S41: simulating the thermal fluctuation effect of magnetic moment of the magnetic material based on the dynamic magnetic material model and the eddy current distribution reconstruction result data to obtain the thermal fluctuation simulation data of the magnetic moment of the material;

[0117] Specifically, computational physics simulation software, such as LAMMPS or COMSOL Multiphysics, can be used to simulate the thermal fluctuation effect of the magnetic moment of magnetic materials. Import the dynamic magnetic material model and the eddy current distribution reconstruction result data as initial conditions. Set the simulation parameters in the software, including temperature, time step, and system boundary conditions. Select an appropriate force field, such as Lennard-Jones or Morse potential, to describe the interaction between magnetic moments. Run the simulation program, and the software will calculate the random fluctuations of the magnetic moment under thermal excitation, thereby obtaining the simulation data of the thermal fluctuation of the material magnetic moment.

[0118] Step S42: performing real-time thermal imaging of the thermocouple array on the magnetic material to obtain material temperature distribution time series data, and solving the non-Fourier heat conduction equation for the magnetic material according to the material temperature distribution time series data to obtain the material non-equilibrium temperature field;

[0119] Specifically, a thermocouple array can be used to perform real-time thermal imaging of magnetic materials. Place the magnetic material within the measurement range of the thermocouple array and ensure that all thermocouples are properly connected to the data acquisition system. Use an infrared thermal imager or similar imaging technology to capture the temperature distribution on the surface of the material. Through software such as LabVIEW, the temperature data collected by the thermocouple array is recorded in real time to obtain the material temperature distribution time series data. Then, use this data as input conditions to solve the non-Fourier heat conduction equation in the computational fluid dynamics (CFD) software. Set appropriate physical models and boundary conditions, perform numerical simulations, and obtain the non-equilibrium temperature field of the material.

[0120] Step S43: performing digital image correlation strain measurement on the magnetic material to obtain material strain distribution time series data, and performing nonlinear finite element analysis on the magnetic material according to the material strain distribution time series data to obtain the material nonlinear stress field;

[0121] Specifically, a digital image correlation (DIC) system can be used to measure strain on magnetic materials. The surface of the material is sprayed or coated with a random pattern, and then a high-speed camera is used to capture images of the material surface during loading. The DIC software analyzes these images and calculates the displacement and strain field on the material surface. These strain distribution time series data are imported into finite element analysis (FEA) software such as ABAQUS or ANSYS. In the software, the material model and the corresponding constitutive relationship are defined according to the nonlinear properties of the material, such as plasticity or creep behavior. Appropriate boundary conditions and loading paths are set to perform nonlinear finite element analysis. Through simulation, the nonlinear stress field of the material can be obtained.

[0122] Step S44: coupling the material magnetic moment thermal fluctuation simulation data, the material non-equilibrium temperature field and the material nonlinear stress field based on the dynamic magnetic material model to obtain the material electromagnetic-thermal-stress coupling model;

[0123] Specifically, you can start COMSOL Multiphysics and create a new model document, import the thermal fluctuation simulation data of the material magnetic moment to set the initial conditions of the magnetic field, and define the magnetization curve and magnetic loss parameters. Subsequently, the material non-equilibrium temperature field data is imported as the initial temperature distribution, the heat transfer equation is defined, and the thermal boundary conditions of the conduction, convection and radiation heat transfer mechanisms are set. Next, the material nonlinear stress field data is integrated, the solid mechanics equations are defined, including the stress-strain relationship and the nonlinear properties of the material, such as plastic deformation or creep, and the mechanical boundary conditions of the displacement boundary or force boundary are set. Finally, the coupling setting is performed to obtain the material electromagnetic-thermal-stress coupling model.

[0124] Step S45: Based on the electromagnetic-thermal-stress coupling model of the material and according to the eddy current distribution reconstruction result data, the magnetic material is numerically simulated to obtain the material multi-physical field dynamic response data.

[0125] Specifically, please refer to the sub-steps of step S45 for the detailed implementation process of this embodiment.

[0126] The present invention provides a new perspective for exploring the dynamic magnetothermal properties of materials from a microscopic scale through the precise simulation of the thermal fluctuation effect of magnetic moment. Secondly, by accurately acquiring the real-time temperature distribution and effectively solving the non-equilibrium temperature field, the thermal conduction behavior of the material under non-steady-state conditions can be deeply analyzed. Through the precise measurement of strain distribution and the in-depth analysis of the nonlinear stress field, solid experimental data and theoretical support are provided for revealing the mechanical behavior and stress state inside the material. Furthermore, through the construction of the electromagnetic-thermal-stress coupling model, the data of the three physical fields of magnetism, heat and force are integrated to provide a multi-dimensional comprehensive framework for the comprehensive characteristic analysis of magnetic materials. Finally, through the comprehensive simulation of the dynamic response of multiple physical fields, the present invention is able to evaluate the multi-physical field coupling effect of materials in complex environments.

[0127] Preferably, step S45 includes the following steps:

[0128] Step S451: performing eddy current distribution dense area identification on the eddy current distribution reconstruction result data to obtain a eddy current dense area topology map;

[0129] Specifically, data visualization and analysis software, such as MATLAB or Python's SciPy library, can be used to process and analyze the eddy distribution reconstruction result data. Using these tools, image processing techniques, such as threshold segmentation or clustering algorithms, are used to identify areas with dense eddy distribution. After identification, topological analysis methods, such as Topological Data Analysis (TDA), are further used to construct a topological map of eddy-dense areas. This topological map will show the connectivity, holes, and other important topological features of eddy-dense areas.

[0130] Step S452: refining the mesh of the eddy current dense area of ​​the material electromagnetic-thermal-stress coupling model according to the eddy current dense area topology map to obtain a material multi-resolution coupling calculation model;

[0131] Specifically, based on the topological map of the eddy current-intensive area, the electromagnetic-thermal-stress coupling model of the material can be loaded into the finite element analysis (FEA) software, such as ANSYS or COMSOL Multiphysics, and then the corresponding area can be selected for mesh refinement according to the location and topological characteristics of the eddy current-intensive area. This involves adjusting the size, shape and density of the mesh. After the mesh refinement is completed, a material multi-resolution coupling calculation model is obtained, which has a higher mesh resolution in the eddy current-intensive area, and a coarser mesh is used in other areas to save computing resources.

[0132] Step S453: performing harmonic analysis on the eddy current distribution reconstruction result data to obtain an eddy current harmonic characteristic spectrum;

[0133] Specifically, signal processing software, such as MATLAB's Signal Processing Toolbox, can be used to perform harmonic analysis on the eddy current distribution reconstruction result data. First, the eddy current distribution reconstruction result data is regarded as a time series signal, and Fourier transform (FFT) or other harmonic analysis techniques are applied to decompose these signals. This can identify the periodic components and frequency characteristics in the eddy current distribution. By analyzing these components, an eddy current harmonic signature spectrum can be constructed, which will show the frequency, amplitude and phase information of different harmonics.

[0134] Step S454: constructing a multi-physics field solver for the material multi-resolution coupling calculation model according to the eddy current harmonic characteristic spectrum to obtain a material model multi-field coupling solver;

[0135] Specifically, multi-physics simulation software, such as COMSOL Multiphysics, can be used to build a multi-physics solver based on the eddy current harmonic characteristic spectrum. First, load the multi-resolution coupling calculation model of the material in the software, and define the control equations of each physical field, such as Maxwell's equations for the electromagnetic field, the heat conduction equations for the thermal field, and the stress-strain relationship for the mechanical field. Then, according to the key frequencies identified by the eddy current harmonic characteristic spectrum, set appropriate solver parameters, such as time step, frequency range, and solver type (such as direct solver or iterative solver). Next, configure the solver to handle coupling phenomena to ensure that the interactions between different physical fields are handled correctly. After completing the settings, build and verify the solver to finally obtain the material model multi-field coupling solver.

[0136] Step S455: Designing a time integration scheme for the multi-field coupling solver of the material model to obtain a time integration scheme for the magnetic material;

[0137] Specifically, the numerical analysis software MATLAB can be used to define and implement the time integration algorithm, select the implicit backward Euler method, and use its stability and accuracy to deal with nonlinear problems. Through the built-in ODE solver and other toolboxes in MATLAB, set the time step, total simulation time and tolerance parameters. The material model multi-field coupling solver is integrated into the time integration scheme, which can handle the complex interactions of the electromagnetic-thermal-stress coupling model. In MATLAB, the solver is called as a function or script to receive and update the physical field state in each time step. During the simulation process, the solver considers the physical field variables, boundary conditions and initial conditions, linearizes each field such as electromagnetic field, thermal field and mechanical field, and applies iterative methods to improve the solution accuracy. In this way, a time integration scheme that describes the dynamic response of the material is gradually constructed, and finally a magnetic material time integration scheme is obtained.

[0138] Step S456: Based on the material multi-resolution coupling calculation model, perform multi-physics field iterative solution according to the magnetic material time integration scheme and the material model multi-field coupling solver, and extract time-frequency domain response characteristics to obtain material multi-physics field dynamic response data.

[0139] Specifically, multi-physics simulation software and time integration schemes can be used to perform multi-physics iterative solutions. First, load the material multi-resolution coupling calculation model and the material model multi-field coupling solver in COMSOL Multiphysics. Then, apply the magnetic material time integration scheme and set the time range and step size of the simulation. Next, run the simulation and perform iterative solutions. Within each time step, the solver will update the physical field variables, process the changes in material properties, and calculate the multi-physics coupling effects. During the simulation process, the software will record the physical field data of each time step, such as electromagnetic field intensity, temperature distribution, and stress state. Finally, post-process the simulation results, and use time-frequency domain analysis techniques to extract response features, such as fast Fourier transform (FFT) to analyze frequency components, or use time domain analysis to evaluate the transient response of the system. Through these analyses, the material multi-physics dynamic response data is finally obtained.

[0140] The present invention provides a key basis for refined simulation through accurate identification of eddy current-dense areas, and the multi-resolution optimization of the computational model enhances the accurate reflection of physical phenomena inside the material, especially in eddy current-dense areas. By acquiring the eddy current harmonic characteristic spectrum, the understanding of the eddy current frequency characteristics is deepened. Through the efficient construction of the multi-physics field solver, based on the eddy current harmonic characteristic spectrum, the efficiency and accuracy of solving the multi-physics field problems of magnetic materials are greatly improved. Through the design of the time integration scheme, the optimization of numerical simulation in terms of stability and computational efficiency is ensured. Through the accurate simulation of the multi-physics field coupling effect, combined with the application of the multi-resolution coupling computational model and the multi-field coupling solver, the complex effects of materials under the action of multiple physical fields can be accurately simulated. Finally, through multi-physics field iterative solution and time-frequency domain response feature extraction, the present invention can comprehensively evaluate the response characteristics of materials under dynamic conditions.

[0141] Preferably, step S5 comprises the following steps:

[0142] Step S51: Perform real-time monitoring of the magnetic material using a sensor array to obtain original signals of multiple physical quantities of the material;

[0143] Specifically, a set of high-precision sensor arrays can be used to monitor magnetic materials in real time. These sensors include but are not limited to temperature sensors, stress sensors, and magnetic field sensors, which can detect multiple physical quantities of materials under actual working conditions. The sensor array is connected to a computer through a data acquisition system (DAQ) to collect data in real time. Software tools such as LabVIEW or MATLAB are used to control the sampling frequency, amplification gain parameters of the sensor array, and record raw signals. These raw signals are not limited to voltage, current, and temperature changes, and ultimately the raw signals of multiple physical quantities of the material are obtained.

[0144] Step S52: Perform material real-time working condition analysis on the original signal of the material's multiple physical quantities to obtain the material's real-time working condition parameters;

[0145] Specifically, signal processing software, such as MATLAB's Signal Processing Toolbox, can be used to perform denoising and filtering operations on the original signals of multiple physical quantities of the material. Then, analysis methods, such as Fourier transform, are applied to extract key features of the signal, such as frequency and amplitude. Through these features, the real-time operating parameters of the material, such as operating temperature, stress level, and magnetic field strength, can be determined, and finally the real-time operating parameters of the material can be obtained.

[0146] Step S53: Time synchronization and data fusion are performed on the real-time working condition parameters of the material and the dynamic response data of the multi-physical field of the material to obtain a comprehensive dynamic characteristic spectrum of the material working condition;

[0147] Specifically, data fusion techniques, such as Kalman filtering or least squares method, can be used to integrate the real-time material working parameters with the material multi-physics field dynamic response data. In software, such as Python's Pandas library, the data is synchronized according to the timestamp, and then a fusion algorithm is applied to generate a comprehensive characteristic spectrum, and finally a comprehensive dynamic characteristic spectrum of the material working condition is obtained, which reflects the dynamic characteristics of the material under different working conditions.

[0148] Step S54: mapping the material electromagnetic-thermal-stress coupling model according to the material working condition comprehensive dynamic characteristic spectrum to obtain a real-time dynamic coupling model of the material;

[0149] Specifically, simulation software such as COMSOL Multiphysics can be used to load the electromagnetic-thermal-stress coupling model of the material, and the model parameters can be adjusted according to the comprehensive dynamic characteristic spectrum of the material working condition, such as updating the material properties and boundary conditions. In this way, a real-time dynamic coupling model can be obtained, that is, a real-time dynamic coupling model of the material, which can reflect the behavior of the material under actual working conditions.

[0150] Step S55: Calculate the real-time eddy current loss of the magnetic material based on the real-time dynamic coupling model of the material to obtain the eddy current loss data of the material.

[0151] Specifically, the real-time dynamic coupling model of the material can be loaded and adjusted in the COMSOL Multiphysics simulation software to ensure that it accurately reflects the multi-physics characteristics of the magnetic material under actual working conditions. After the simulation is started, the software automatically performs numerical solutions, focusing on monitoring the distribution of Joule heat, which is a key indicator of eddy current loss. As the simulation progresses, Joule heat data at key time points are recorded and collected. Then, these time series data are imported for in-depth analysis using data processing software such as Excel or more professional scientific computing software. In these software, the average Joule heat value for each time step is calculated or numerical integration methods such as the trapezoidal rule or the Simpson rule are used to determine the cumulative eddy current loss. Finally, a set of material eddy current loss data is obtained, which are displayed in graphical or tabular form, accurately describing the loss changes of the magnetic material during the entire simulation.

[0152] The present invention ensures the instant acquisition of original signals of multiple physical quantities through real-time monitoring. The accurate acquisition of real-time operating parameters reflects the state of the material under actual use conditions, enhancing the accuracy of the evaluation. Through time synchronization and data fusion, the effective integration of real-time operating parameters and multi-physical field dynamic response data is achieved. The construction of the comprehensive dynamic characteristic spectrum of the operating conditions provides a comprehensive perspective for understanding the dynamic behavior of materials under different operating conditions. The real-time performance of the material under the electromagnetic-thermal-stress coupling state is captured through the precise mapping of the real-time dynamic coupling model. The real-time calculation of eddy current loss provides a dynamic evaluation for real-time monitoring and performance optimization of the material, ensuring the reliability and safety of the material use.

[0153] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0154] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for calculating eddy current loss of magnetic materials based on the principle of magnetic induction, characterized in that: The following steps are involved: Step S1: Scanning the magnetic field distribution of the magnetic material to obtain multi-scale magnetic field distribution data of the material; mapping the magnetic field intensity of the magnetic material based on the multi-scale magnetic field distribution data of the material to obtain a magnetic field intensity distribution diagram of the material; Step S2: Analyze the dynamic characteristics of the magnetic material to obtain material parameter change data; dynamically model the magnetic material according to the material parameter change data to obtain a dynamic magnetic material model; Step S3: based on the dynamic magnetic material model and according to the material magnetic field intensity distribution map, eddy current path tracking is performed on the magnetic material, and eddy current propagation path analysis is performed to obtain the material eddy current distribution map and material eddy current propagation path data; based on the material eddy current distribution map and the material eddy current propagation path data, eddy current distribution of the magnetic material is reconstructed to obtain eddy current distribution reconstruction result data; Step S4: Based on the dynamic magnetic material model and according to the material magnetic field intensity distribution diagram, a multi-physics field coupling model is performed on the magnetic material to obtain an electromagnetic-thermal-stress coupling model of the material; Based on the electromagnetic-thermal-stress coupling model of the material and according to the eddy current distribution reconstruction result data, the magnetic material is numerically simulated to obtain the material multi-physical field dynamic response data; wherein step S4 includes the following steps: Step S41: simulating the thermal fluctuation effect of magnetic moment of the magnetic material based on the dynamic magnetic material model and the eddy current distribution reconstruction result data to obtain the thermal fluctuation simulation data of the magnetic moment of the material; Step S42: performing real-time thermal imaging of the magnetic material using a thermocouple array to obtain material temperature distribution time series data, and solving a non-Fourier heat conduction equation for the magnetic material based on the material temperature distribution time series data to obtain a non-equilibrium temperature field of the material; Step S43: performing digital image correlation strain measurement on the magnetic material to obtain material strain distribution time series data, and performing nonlinear finite element analysis on the magnetic material according to the material strain distribution time series data to obtain the material nonlinear stress field; Step S44: coupling the material magnetic moment thermal fluctuation simulation data, the material non-equilibrium temperature field and the material nonlinear stress field based on the dynamic magnetic material model to obtain the material electromagnetic-thermal-stress coupling model; Step S45: numerically simulating the magnetic material based on the electromagnetic-thermal-stress coupling model of the material and according to the eddy current distribution reconstruction result data to obtain the material multi-physical field dynamic response data; Step S5: Monitor the real-time operating parameters of the magnetic material to obtain the real-time operating parameters of the material; evaluate and calculate the eddy current loss of the magnetic material based on the electromagnetic-thermal-stress coupling model of the material and according to the multi-physical field dynamic response data of the material and the real-time operating parameters of the material to obtain the eddy current loss data of the material.

2. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Scan the geometric features of the magnetic material to obtain a three-dimensional geometric model of the material; Step S12: configuring a detection array for the magnetic material according to the three-dimensional geometric model of the material to obtain a detector array spatial distribution scheme; Step S13: deploying a multi-spectral magnetic field detector array on the magnetic material according to the detector array spatial distribution plan to obtain actual state data of the detector array; Step S14: measuring the initial magnetization curve of the magnetic material to obtain the initial magnetization curve of the material; Step S15: Based on the three-dimensional geometric model of the material and according to the initial magnetization curve of the material, the magnetization characteristic model of the magnetic material is estimated and modeled to obtain the magnetization characteristic model of the material; Step S16: generating a magnetic field scanning sequence for the magnetic material based on the material magnetization characteristic model to obtain material magnetic field scanning sequence data; Step S17: Based on the actual state data of the detector array and the material magnetic field scanning sequence data, multi-spectral dynamic magnetic field data of the magnetic material are collected to obtain multi-scale magnetic field distribution data of the material, and the magnetic field intensity of the magnetic material is mapped based on the multi-scale magnetic field distribution data of the material to obtain a material magnetic field intensity distribution diagram.

3. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 2, characterized in that: Step S17 includes the following steps: Step S171: performing a dynamic magnetic field scan on the magnetic material based on the actual state data of the detector array and the material magnetic field scan sequence data to obtain original multi-spectral magnetic field data of the material; Step S172: performing high-dimensional tensor decomposition and fusion on the original multi-spectral magnetic field data of the material to obtain material fused multi-dimensional magnetic field data; Step S173: reconstructing the magnetic field of the magnetic material by compressed sensing based on the three-dimensional geometric model of the material and the fusion multi-dimensional magnetic field data of the material to obtain multi-scale magnetic field distribution data of the material; Step S174: performing magnetic field intensity modeling on the material multi-scale magnetic field distribution data based on the material three-dimensional geometric model to obtain a material magnetic field intensity calculation model; Step S175: performing parallel calculation of magnetic field strength on the material magnetic field strength calculation model to obtain spatial distribution data of the material magnetic field strength; Step S176: Perform three-dimensional volume rendering and visualization on the spatial distribution data of the material magnetic field intensity to obtain a material magnetic field intensity distribution map.

4. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing multi-spectrum electromagnetic excitation on the magnetic material, and simultaneously performing force field and temperature field dynamic loading on the magnetic material to obtain material multi-physical field excitation state data; Step S22: measuring the dynamic magnetization characteristics of the magnetic material according to the material multi-physical field excitation state data to obtain a dynamic magnetization curve of the material; Step S23: performing microscopic strain monitoring on the magnetic material according to the material multi-physical field excitation state data to obtain material microscopic strain distribution data; Step S24: monitoring the temperature distribution of the magnetic material according to the material multi-physical field excitation state data to obtain the material dynamic temperature distribution data; Step S25: collecting material parameters of the magnetic material in a state without a magnetic field to obtain material parameters in a state without a magnetic field; Step S26: Perform multi-physical field coupling parameter inversion analysis on the magnetic material according to the material's no-magnetic-field state parameters, the material's dynamic magnetization curve, the material's microscopic strain distribution data, and the material's dynamic temperature distribution data to obtain material parameter change data, and dynamically model the magnetic material according to the material parameter change data based on the material's three-dimensional geometric model to obtain a dynamic magnetic material model.

5. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 4, characterized in that: Step S26 includes the following steps: Step S261: performing multi-physics field data synchronization on the material dynamic magnetization curve, the material microscopic strain distribution data and the material dynamic temperature distribution data to obtain the material multi-physics field coupling response data; Step S262: extracting material parameter dynamic characteristics from the material multi-physics field coupling response data to obtain material parameter dynamic characteristic data; Step S263: performing parameter coupling relationship analysis on the material parameter dynamic characteristic data to obtain a material parameter coupling relationship network; Step S264: performing multi-scale material property modeling on the magnetic material based on the material parameter coupling relationship network and the material multi-physics field coupling response data to obtain a material parameter coupling dynamics model; Step S265: constructing a dynamic constitutive relationship for the material parameter coupling dynamics model to obtain a material multi-scale characteristic model; Step S266: performing iso-geometric coupling on the material multi-scale characteristic model and the material three-dimensional geometric model to obtain a dynamic magnetic material model.

6. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing grid eddy current initialization simulation on the magnetic material based on the dynamic magnetic material model and the material magnetic field intensity distribution diagram to obtain initial eddy current distribution data of the material; Step S32: performing material nonlinear characteristic analysis on the magnetic material to obtain material nonlinear characteristic data, and performing material anisotropy analysis on the magnetic material to obtain material anisotropy data; Step S33: performing eddy current dynamics spatiotemporal evolution calculation on the magnetic material based on the dynamic magnetic material model according to the material initial eddy current distribution data, the material nonlinear characteristic data and the material anisotropy data, to obtain the material eddy current spatiotemporal evolution data; Step S34: performing multi-scale eddy current path tracking analysis on the material eddy current spatiotemporal evolution data to obtain multi-scale eddy current path data of the material; Step S35: performing eddy current energy propagation network modeling based on the material eddy current spatiotemporal evolution data and the material multi-scale eddy current path data to obtain a material eddy current energy propagation network model; Step S36: performing three-dimensional eddy current distribution visualization processing on the material eddy current spatiotemporal evolution data and the material eddy current energy propagation network model to obtain a material eddy current distribution map; Step S37: extracting key eddy current propagation channels from the material eddy current distribution diagram to obtain material eddy current propagation path data; Step S38: reconstructing the eddy current distribution of the magnetic material based on the material eddy current distribution diagram and the material eddy current propagation path data to obtain eddy current distribution reconstruction result data.

7. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 6, characterized in that: Step S38 includes the following steps: Step S381: performing eddy current structure feature recognition on the material eddy current distribution diagram to obtain eddy current structure feature classification data; Step S382: performing eddy current propagation mode recognition on the material eddy current propagation path data to obtain eddy current propagation mode classification data; Step S383: obtaining a dynamic magnetic material model; Step S384: reconstructing the eddy current distribution of the magnetic material according to the eddy current structure feature classification data and the eddy current propagation mode classification data based on the dynamic magnetic material model to obtain eddy current distribution reconstruction result data.

8. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 1, characterized in that: Step S45 includes the following steps: Step S451: performing eddy current distribution dense area identification on the eddy current distribution reconstruction result data to obtain a eddy current dense area topology map; Step S452: refining the mesh of the eddy current dense area of ​​the material electromagnetic-thermal-stress coupling model according to the eddy current dense area topology map to obtain a material multi-resolution coupling calculation model; Step S453: performing harmonic analysis on the eddy current distribution reconstruction result data to obtain an eddy current harmonic characteristic spectrum; Step S454: constructing a multi-physics field solver for the material multi-resolution coupling calculation model according to the eddy current harmonic characteristic spectrum to obtain a material model multi-field coupling solver; Step S455: Designing a time integration scheme for the multi-field coupling solver of the material model to obtain a time integration scheme for the magnetic material; Step S456: Based on the material multi-resolution coupling calculation model, perform multi-physics field iterative solution according to the magnetic material time integration scheme and the material model multi-field coupling solver, and extract time-frequency domain response characteristics to obtain material multi-physics field dynamic response data.

9. The method for calculating eddy current loss of magnetic materials based on the magnetic induction principle according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Perform real-time monitoring of the magnetic material using a sensor array to obtain original signals of multiple physical quantities of the material; Step S52: Perform material real-time working condition analysis on the original signal of the material's multiple physical quantities to obtain the material's real-time working condition parameters; Step S53: Time synchronization and data fusion are performed on the real-time working condition parameters of the material and the dynamic response data of the multi-physical field of the material to obtain a comprehensive dynamic characteristic spectrum of the material working condition; Step S54: mapping the material electromagnetic-thermal-stress coupling model according to the material working condition comprehensive dynamic characteristic spectrum to obtain a real-time dynamic coupling model of the material; Step S55: Calculate the real-time eddy current loss of the magnetic material based on the real-time dynamic coupling model of the material to obtain the material eddy current loss data.

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