A method and system for optimizing the magnetic field of a pole-type high temperature superconducting magnet device

Abstract

This invention discloses a method and system for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device, relating to the field of induction heating technology. The method includes: acquiring induction heating characteristic numbers of the counterpolar high-temperature superconducting magnet device to be optimized; determining the type of the counterpolar high-temperature superconducting magnet device based on the induction heating characteristic numbers, and determining the core topology and optimization parameter set within the counterpolar high-temperature superconducting magnet device based on the type of the device; and determining the target parameter combination for magnetic field optimization from the optimization parameter set according to preset rules, using the temperature distribution of the counterpolar high-temperature superconducting magnet device after heating as the optimization objective. This invention solves the problems of poor adaptability, low magnetic field control precision, and low efficiency of multi-parameter optimization in existing technologies for optimizing the magnetic field of counterpolar high-temperature superconducting magnet devices.

Description

Technical Field

[0001] This invention relates to the field of induction heating technology, and in particular to a method and system for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device. Background Technology

[0002] Non-ferrous metal hot working and melting technology has indispensable application value in high-end industrial fields such as aerospace, new energy vehicles, and high-end equipment manufacturing. The uniformity and efficiency of its heating process directly determine the quality of billet processing and the performance of finished products. Traditional induction heating technology relies on conventional electromagnets to achieve magnetic field excitation. However, it is limited by the upper limit of the magnetic field strength of conventional electromagnets, resulting in weak magnetic field control capability and large energy loss. At the same time, due to the lack of adaptive design for the electromagnetic-thermal characteristics of different materials, the billet temperature distribution is uneven and the thermal gradient is large during the heating process, making it difficult to meet the requirements of high-precision and high-efficiency modern heat treatment processes.

[0003] With the development of superconducting material preparation and strong magnetic field application technologies, high-temperature superconducting magnets, due to their significant advantages of high field strength and low energy consumption, have become a core technology direction for improving induction heating efficiency. DC induction heating technology based on antipolar high-temperature superconducting magnets generates eddy currents through the relative motion of the magnetic field and the metal billet, providing a new path for achieving efficient and controllable heating processes. However, the research and application of existing antipolar high-temperature superconducting magnet induction heating technology still faces many key technical bottlenecks: First, there is no established adaptation criterion for different conductor materials. Different materials have significantly different electromagnetic energy absorption and internal heat transfer capabilities. Existing technologies use uniform magnetic field control and heating parameters, which cannot achieve precise matching between materials and heating processes. Second, the core, as the core component for magnetic field distribution, relies heavily on experience in its shape design, lacking a quantitative geometric parameter optimization system, making it difficult to achieve precise control of magnetic field distribution through adjustments to the core topology. Third, the coordinated optimization of multiple parameters such as magnet current, air gap, and billet rotation speed lacks efficient experimental design methods. The process development involves numerous experiments and long cycles, making it difficult to quickly obtain the optimal parameter combination that balances heating uniformity and efficiency.

[0004] In summary, there is an urgent need to develop a magnetic field optimization method for counterpolar high-temperature superconducting magnet devices that is adaptable to different material properties, allows for quantitative control of magnetic field distribution, and enables efficient multi-parameter synergistic optimization. This method would address issues such as lack of material compatibility, lack of quantitative standards for core design, and difficulties in multi-parameter synergy in existing technologies, thereby improving the magnetic field regulation capability, heating uniformity, and process development efficiency of superconducting induction heating. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for optimizing the magnetic field of a polarized high-temperature superconducting magnet device, which aims to solve at least one of the problems mentioned in the background art.

[0006] This invention proposes a method for optimizing the magnetic field of a high-temperature superconducting magnet device with polarity, the method comprising: Obtain the induction heating characteristic numbers of the counter-polar high-temperature superconducting magnet device to be optimized; The type of antipolar high-temperature superconducting magnet device is determined based on the induction heating characteristic number, and the core topology and optimized parameter set in the antipolar high-temperature superconducting magnet device are determined based on the type of antipolar high-temperature superconducting magnet device. Taking the temperature distribution after heating the polar high-temperature superconducting magnet device as the optimization target, the target parameter combination for magnetic field optimization is determined from the optimization parameter set according to preset rules.

[0007] Furthermore, in the above-mentioned method for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device, the step of obtaining the induction heating characteristic number of the counterpolar high-temperature superconducting magnet device to be optimized further includes: A simulation model of a counterpolar high-temperature superconducting magnet device is constructed. The simulation model of the counterpolar high-temperature superconducting magnet device includes a mechanical frame, support seats arranged relatively distributed within the mechanical frame, iron cores respectively arranged on the support seats, and superconducting coils sleeved on the iron cores. An air gap is formed between the two iron cores to accommodate the conductor rod material.

[0008] Furthermore, in the above-mentioned method for optimizing the magnetic field of the high-temperature superconducting magnet device, the induction heating characteristic number represents the ratio of the skin depth to the heat diffusion depth of the conductor rod within one electromagnetic cycle, and the formula is:

[0009] in, For deep skin care, Where is the diffusion coefficient. The equivalent angular frequency, is the vacuum permeability.

[0010] Furthermore, in the above-mentioned method for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device, the step of determining the type of the counterpolar high-temperature superconducting magnet device based on the induction heating characteristic number includes: when The high-temperature superconducting magnet device with opposing poles was determined to be Type I, with a convex core topology. The optimized parameter set included the convex core pole cutting depth. convex iron core cutting width , Magnet current I, Air gap distance Rotation speed v ; when The type II high-temperature superconducting magnet device was determined, and the optimized parameter set included magnet current I and air gap distance. Rotation speedv ; when The high-temperature superconducting magnet device with opposing poles was determined to be Type III, with a concave core topology. The optimized parameter set included the depth of cut in the middle of the concave core. The width of the concave iron core is cut in the middle. , Magnet current I, Air gap distance Rotation speed v .

[0011] Furthermore, in the above-mentioned method for optimizing the magnetic field of a high-temperature superconducting magnet device with opposing poles, the formula for calculating the magnet current I is as follows: ; in, Gapair This is the air gap distance. The permeability of free space, P For the target heating power, For electrical conductivity, f The equivalent frequency of rotational speed. V The effective heating volume of the conductor rod material. N This represents the equivalent ampere-turns of a single magnetic pole coil. This is the effective factor for electromagnetic coupling, and its value varies depending on the characteristic number of induction heating.

[0012] Furthermore, in the above-mentioned method for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device, the step of determining the target parameter combination for magnetic field optimization from the optimization parameter set according to preset rules, with the temperature distribution of the counterpolar high-temperature superconducting magnet device after heating as the optimization target, includes: An orthogonal arrangement table experiment was constructed based on the number of optimization parameters in the optimization parameter set. The target parameter combination for magnetic field optimization was determined with the standard deviation of the conductor rod cross-sectional temperature as the optimization objective and the signal-to-noise ratio as the evaluation index.

[0013] Furthermore, in the above-mentioned method for optimizing the magnetic field of a high-temperature superconducting magnet device with opposing poles, the depth of the convex iron core pole cutting is... convex iron core cut width ; Cutting depth in the middle of the concave iron core The width of the concave iron core is cut in the middle. ; air gap distance The adjustment range is: ; The adjustment range of the magnet current I is: The initial value is adjusted within ±20%. The rotational speed range is (300 RPM, 1200 RPM); Where D is the diameter of the conductor rod, and L is the length of the conductor rod, and , .

[0014] Another object of the present invention is to provide a magnetic field optimization system for a counterpolar high-temperature superconducting magnet device, the system comprising: The acquisition module is used to acquire the induction heating characteristic numbers of the counter-polar high-temperature superconducting magnet device to be optimized; The determination module is used to determine the type of the counter-polar high-temperature superconducting magnet device based on the induction heating characteristic number, and to determine the core topology and optimized parameter set in the counter-polar high-temperature superconducting magnet device based on the type of the counter-polar high-temperature superconducting magnet device. The optimization module is used to determine the target parameter combination for magnetic field optimization from the optimization parameter set according to preset rules, with the temperature distribution after heating the polarized high-temperature superconducting magnet device as the optimization target.

[0015] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0016] Another object of the present invention is to provide an electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0017] This invention obtains the induction heating characteristic number of the anti-polarity high-temperature superconducting magnet device to be optimized. Based on this characteristic number, it achieves precise classification of different conductor materials, establishes an adaptation criterion between the electromagnetic-thermal properties of the material and the heating process, breaks through the limitations of the unified parameter control in the prior art, and achieves precise matching between materials and heating processes. The device type is determined according to the induction heating characteristic number, and the corresponding iron core topology is matched. Geometric parameters such as the pole cutting depth and pole cutting width of the iron core are incorporated into a quantitative optimization system, replacing the traditional empirical iron core design method, and achieving precise control of the magnetic field distribution through the adjustment of the iron core topology. Taking the temperature distribution after heating as the optimization target, the target parameter combination for magnetic field optimization is determined from the matched optimization parameter set according to preset rules, and a highly efficient collaborative optimization logic for multiple parameters such as magnet current, air gap, and rotation speed is established. The optimal parameter combination that balances heating uniformity and efficiency can be obtained without a large number of repeated experiments. By utilizing induction heating characteristic numbers to achieve precise matching of materials and processes, and combining quantified iron core topology parameters to optimize magnetic field distribution, this technology also achieves efficient multi-parameter collaborative optimization through targeted parameter combination screening. This fundamentally solves the technical problems of material mismatch, lack of quantitative standards for iron core design, and difficulty in multi-parameter coordination in existing technologies for induction heating of polar high-temperature superconducting magnets. It also solves the problems of poor adaptability, low magnetic field control precision, and low efficiency of multi-parameter optimization in existing technologies for magnetic field optimization of polar high-temperature superconducting magnet devices. Attached Figure Description

[0018] Figure 1 This is a flowchart of the magnetic field optimization method for the polar high-temperature superconducting magnet device in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the high-temperature superconducting magnet device with a counterpolar configuration in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of the superconducting coil, iron core, and support base in the high-temperature superconducting magnet device with a polarity in this embodiment of the invention; Figure 4 This is a schematic diagram of the convex iron core in the high-temperature superconducting magnet device with a polarity in this embodiment of the invention; Figure 5 This is a schematic diagram of the standard iron core in the polarized high-temperature superconducting magnet device in this embodiment of the invention; Figure 6 This is a schematic diagram of the concave iron core in the high-temperature superconducting magnet device with a polarity in this invention. Figure 7 This is a simulation diagram of the magnetic field of the convex iron core under the first working condition in the embodiment of the present invention; Figure 8 This is a simulation diagram of the magnetic field of the standard iron core under the first working condition in the embodiment of the present invention; Figure 9 This is a simulation diagram of the magnetic field of the concave iron core under the first working condition in the embodiment of the present invention; Figure 10 This is a thermal simulation comparison diagram of the rod material before and after optimization of the Type I high-temperature superconducting magnet device in this embodiment of the invention; Figure 11 This is a thermal simulation comparison diagram of the rod material of the Type II high-temperature superconducting magnet device in the embodiments of this application before and after optimization; Figure 12 This is a thermal simulation comparison diagram of the rod material of the Type III high-temperature superconducting magnet device in the embodiments of this application before and after optimization; Figure 13 This is a structural block diagram of the magnetic field optimization system for the polarized high-temperature superconducting magnet device in the third embodiment of the present invention.

[0019] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0020] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0021] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] Example 1 Please see Figure 1 The figure shows a magnetic field optimization method for a counter-polar high-temperature superconducting magnet device in the first embodiment of the present invention, the method including steps S10 to S12.

[0024] Step S10: Obtain the induction heating characteristic number of the counter-polar high-temperature superconducting magnet device to be optimized.

[0025] In this embodiment of the invention, the device type is determined by the number of induction heating features, and then the corresponding iron core topology and optimization parameters are matched. Finally, the optimal combination of magnetic field parameters is obtained, and the magnetic field optimization of the polar high-temperature superconducting magnet device is completed. This solves the problems of lack of material matching criteria and difficulty in multi-parameter coordination in superconducting induction heating, and improves the magnetic field adjustment capability and heating uniformity.

[0026] Specifically, the induction heating characteristic number represents the ratio of the skin depth to the heat diffusion depth of the conductor rod within one electromagnetic cycle, and the formula is:

[0027] in, For deep skin care, Where is the diffusion coefficient. The equivalent angular frequency, is the vacuum permeability.

[0028] First, determine the relevant parameters of the material properties of the conductor rod to be heated, including core parameters such as skin depth, diffusion coefficient, and equivalent angular frequency. Then, calculate the characteristic number using the formula for induction heating characteristic number. This step is the core basis for subsequent device type determination, enabling accurate classification of conductor rods with different electromagnetic and thermal conductivity characteristics. This lays the foundation for subsequent differentiated optimization and avoids the problem of poor heating uniformity caused by using a single optimization strategy for different materials in traditional optimization methods.

[0029] In addition, in some optional embodiments of the present invention, in the preliminary step of obtaining the induction heating characteristic number, a simulation model is constructed to allow the entire magnetic field optimization process to complete parameter optimization and verification in the simulation environment before conducting physical experiments. This significantly reduces experimental costs, improves the accuracy and efficiency of parameter optimization, and makes the structure and working principle of the antipolar high-temperature superconducting magnet device clearer, facilitating subsequent adjustment and optimization of various parameters.

[0030] Constructing a simulation model of a counter-polarity high-temperature superconducting magnet device refers to building a corresponding digital model in simulation software based on the actual structural composition of the counter-polarity high-temperature superconducting magnet device. Specifically, for example... Figure 2As shown in Figure 6, the model fully includes core structures such as the mechanical skeleton 3, support base 4, iron core 2, and superconducting coil 1, and the materials, dimensions, and relative positions of each structure are consistent with the actual device. The mechanical skeleton provides basic support and fixation for the entire device. The support bases are symmetrically arranged at both ends of the mechanical skeleton, and the iron core is slidably supported on the two support bases. The superconducting coil is slidably fitted onto the outside of the iron core along its axial direction. The gap between the two iron cores is an air gap, which is used to accommodate the conductor rod 5 to be heated and is the core area where the magnetic field and the conductor rod 5 generate electromagnetic induction. In the simulation model, the parameters of each structure can be adjusted independently, providing an operable digital carrier for subsequent parameter optimization experiments. Simultaneously, it can accurately simulate the magnetic field distribution and heating process of the actual device, making the optimization results more consistent with practical application scenarios.

[0031] Step S11: Determine the type of the antipolar high-temperature superconducting magnet device based on the induction heating characteristic number, and determine the core topology and optimized parameter set in the antipolar high-temperature superconducting magnet device based on the type of the antipolar high-temperature superconducting magnet device.

[0032] The calculated induction heating characteristic number is compared with a preset numerical threshold to classify the corresponding device type. Different device types are adapted to different iron core topologies. At the same time, all parameters that need to participate in magnetic field optimization under this type are determined to form an optimization parameter set.

[0033] Specifically, the threshold for classifying induction heating feature numbers into device types, the corresponding core topology and optimization parameter set for each type are specifically limited to achieve a one-to-one correspondence between feature numbers, device types, core structures and optimization parameters; When the calculated induction heating characteristic number φ < 0.8, the high-temperature superconducting magnet device with opposing poles is determined to be Type I. The conductor rod of this type exhibits a strong skin effect, with the heat source highly concentrated in the very shallow surface layer of the conductor rod, while heat diffusion is relatively fast. The suitable core topology at this time is a convex core, and the corresponding optimized parameter set includes the convex core pole cutting depth H1, the convex core pole cutting width W1, the magnet current I, and the air gap distance. Gapair Rotation speed v By introducing the shaving dimension parameter of the convex iron core as an optimization term, the magnetic field distribution can be changed by adjusting the iron core structure, making the heat source concentrated on the surface more uniform and compensating for the uneven heating problem caused by the strong skin effect.

[0034] When the calculated induction heating characteristic number satisfies 0.8 ≤ φ ≤ 1.5, the high-temperature superconducting magnet device with opposing poles is determined to be Type II. The conductor rod of this type exhibits a balanced behavior, with the depth scale of heat source generation and heat diffusion being comparable. This type is most conducive to achieving efficient and uniform heating, and no special pole-cutting treatment is required for the core structure; a standard flat core is sufficient. The corresponding optimized parameter set includes magnet current I and air gap distance. Gapair Rotation speed v By adjusting these three core process parameters, precise control of the magnetic field can be achieved, which can further improve the uniformity of temperature distribution while ensuring heating efficiency.

[0035] When the calculated induction heating characteristic number φ > 1.5, the high-temperature superconducting magnet device with a counterpolar configuration is determined to be Type III. The conductor rod of this type exhibits deep-penetration heating, with heat primarily generated within the deeper volume of the conductor rod, and heat is not easily homogenized internally. The suitable core topology at this time is a concave core, and the corresponding optimized parameter set includes the concave core center cutting depth H3, the concave core center cutting width W3, the magnet current I, and the air gap distance. Gapair Rotation speed v By incorporating the core-shaped electrode dimension parameters into the optimization process, the distribution of the magnetic field deep within the conductor rod can be altered by adjusting the core structure, allowing for more uniform heat transfer and resolving the problem of uneven heating in deep-penetrating materials.

[0036] Specifically, the formula for calculating the magnet current I is: ; in, Gapair This is the air gap distance. The permeability of free space, P For the target heating power, For electrical conductivity, f The equivalent frequency of rotational speed. V The effective heating volume of the conductor rod material. N This represents the equivalent ampere-turns of a single magnetic pole coil. This is the effective factor for electromagnetic coupling, and its value varies depending on the characteristic number of induction heating.

[0037] More specifically, the magnetic field at the center of the air gap of a counterpolar magnet is approximated as (ignoring the magnetic reluctance of the iron core):

[0038] Eddy current power per unit volume of conductor rod in a time-varying magnetic field (low-frequency condition):

[0039]

[0040] constants are merged into :

[0041] Incorporate all constants :

[0042] in, I For magnetic current, Gapair This is the air gap distance. The permeability of free space, P For the target heating power, For conductivity, f The equivalent frequency of rotational speed. V The effective heating volume of the conductor rod material. N This represents the equivalent ampere-turns of a single magnetic pole coil.

[0043] Based on the electromagnetic coupling effectiveness factor, and according to the characteristic number of induction heating, it is divided into types I, II, and III: .

[0044] Step S12: Taking the temperature distribution after heating the polarized high-temperature superconducting magnet device as the optimization target, determine the target parameter combination for magnetic field optimization from the optimization parameter set according to the preset rules.

[0045] The core optimization objective is to ensure the uniformity of temperature distribution after heating the conductor rod. Using pre-defined experimental design rules and evaluation indicators, combined experiments and analyses are conducted on various parameters within the optimization parameter set to select the parameter combination that achieves the most uniform temperature distribution as the target parameter combination. This step, by directly optimizing temperature distribution, fundamentally improves the heating uniformity of the conductor rod. Furthermore, the parameter optimization through pre-defined rules solves the challenge of multi-parameter synergistic optimization and shortens the process development cycle.

[0046] In addition, in some optional embodiments of the present invention, the value range of each optimization parameter is specifically defined, and the adjustable range of each parameter is clearly defined. This allows those skilled in the art to have a clear range of parameter adjustment when conducting optimization experiments, avoiding the blindness of parameter adjustment. At the same time, the value range is determined based on a large amount of simulation experiments and engineering practice data. Adjusting the parameters within this range can ensure the working safety and optimization effectiveness of the device. It will not cause device failure due to parameters exceeding the range, and it can also ensure that the goal of magnetic field optimization and heating uniformity improvement is achieved through parameter adjustment.

[0047] For example, the depth of the convex core cut. convex iron core cut width ; Cutting depth in the middle of the concave iron core The width of the concave iron core is cut in the middle. ; air gap distance The adjustment range is: ; The adjustment range of the magnet current I is: The initial value is adjusted within ±20%. The rotational speed range is (300 RPM, 1200 RPM); Where D is the diameter of the conductor rod, and L is the length of the conductor rod, and , .

[0048] In summary, the magnetic field optimization method for the anti-polarity high-temperature superconducting magnet device in the above embodiments of the present invention obtains the induction heating characteristic number of the anti-polarity high-temperature superconducting magnet device to be optimized. Based on this characteristic number, it achieves accurate classification of different conductor materials, establishes the adaptation criteria between the electromagnetic-thermal properties of materials and the heating process, breaks through the limitations of the unified parameter control in the prior art, and achieves precise matching between materials and heating processes. The device type is determined according to the induction heating characteristic number and the corresponding iron core topology is matched. Geometric parameters such as the pole cutting depth and pole cutting width of the iron core are incorporated into the quantitative optimization system, replacing the traditional empirical iron core design method, and achieving precise control of the magnetic field distribution by adjusting the iron core topology. Taking the temperature distribution after heating as the optimization target, the target parameter combination for magnetic field optimization is determined from the matched optimization parameter set according to preset rules. An efficient collaborative optimization logic for multiple parameters such as magnet current, air gap, and rotation speed is established, which can obtain the optimal parameter combination that takes into account both heating uniformity and efficiency without a large number of repeated experiments. By utilizing induction heating characteristic numbers to achieve precise matching of materials and processes, and combining quantified iron core topology parameters to optimize magnetic field distribution, this technology also achieves efficient multi-parameter collaborative optimization through targeted parameter combination screening. This fundamentally solves the technical problems of material mismatch, lack of quantitative standards for iron core design, and difficulty in multi-parameter coordination in existing technologies for induction heating of polar high-temperature superconducting magnets. It also solves the problems of poor adaptability, low magnetic field control precision, and low efficiency of multi-parameter optimization in existing technologies for magnetic field optimization of polar high-temperature superconducting magnet devices.

[0049] Example 2 This embodiment also proposes a method for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device. The difference between the method for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device in this embodiment and the method for optimizing the magnetic field of a counterpolar high-temperature superconducting magnet device in Embodiment 1 is as follows: An orthogonal arrangement table experiment was constructed based on the number of optimization parameters in the optimization parameter set. The target parameter combination for magnetic field optimization was determined with the standard deviation of the conductor rod cross-sectional temperature as the optimization objective and the signal-to-noise ratio as the evaluation index.

[0050] Among them, the orthogonal arrangement table experiment combined with specific optimization objectives and evaluation indicators achieved efficient collaborative optimization of multiple parameters. At the same time, using the standard deviation of cross-sectional temperature as the optimization objective and the signal-to-noise ratio as the evaluation indicator, the judgment of optimization results is more objective and quantitative, ensuring that the combination of target parameters can achieve the optimal temperature distribution uniformity.

[0051] First, count the number of parameters in the optimization parameter set corresponding to each device type. If there are 3 parameters, select the L9(3³) orthogonal array; if there are 5 parameters, select the L18(3³) orthogonal array. 5 Orthogonal arrays are used to set different levels of each parameter according to the design rules of orthogonal arrays, and to conduct combined experiments. Orthogonal arrays can cover different combinations of parameter levels with the fewest number of experiments, greatly reducing the workload of experiments and improving the efficiency of parameter optimization. In the simulation model, the device parameters are adjusted sequentially according to the parameter combinations of the orthogonal array to simulate the heating process of the conductor rod, and the heating results of each group of experiments are recorded.

[0052] Using the standard deviation of the conductor rod's cross-sectional temperature as the optimization objective means calculating the standard deviation of the temperature values ​​at various locations along the conductor rod's cross-section. A smaller standard deviation indicates a more uniform temperature distribution across the cross-section. The core objective of optimization is to find the parameter combination that minimizes this standard deviation. This optimization objective directly addresses the core requirement of heating uniformity, making the optimization results more aligned with the process requirements of practical applications. Using the signal-to-noise ratio (SNR) as the evaluation index means converting the temperature standard deviation into a signal-to-noise ratio. The magnitude of the SNR is used to evaluate the quality of each experimental parameter combination. A higher SNR indicates stronger anti-interference capability and more stable optimization results, avoiding the randomness associated with evaluating solely based on the temperature standard deviation. By performing SNR and range analysis on the results of orthogonal experiments, the optimal levels of each parameter are selected. The resulting combination constitutes the target parameter combination for magnetic field optimization. This parameter combination enables the magnetic field generated by the anti-polarity high-temperature superconducting magnet device to achieve the most uniform temperature distribution after heating the conductor rod.

[0053] For example, when At that time, the optimized parameters included the depth of the convex core cutoff. convex iron core cutting width Magnetic current I air gap distance Rotation speed v Select L18(3) 5 The orthogonal arrangement table is shown in Table 1.

[0054] Table 1

[0055] when At that time, the optimized parameters include magnet current. I air gap distance Rotation speed v Select L9(3) 3 The orthogonal arrangement table is shown in Table 2.

[0056] Table 2

[0057] when At that time, the optimization parameters included the cutting depth in the middle of the concave iron core. The width of the concave iron core is cut in the middle. Magnetic current I air gap distance Rotation speed v Select L18(3) 5 The orthogonal arrangement table is shown in Table 3.

[0058] Table 3

[0059] The selection of magnet current and rotation speed in Table 3 differs from that in Tables 1 and 2. The values ​​are determined based on simulation and experience to balance experimental efficiency and accuracy.

[0060] Please see Figures 7 to 9 With all other optimization parameters remaining identical, changing only the core shape reveals completely different magnetic field distributions under the three core types, particularly with significant variations in the magnetic field near the core, leading to substantial differences in the heating effect of the conductor rod. Details are as follows: like Figure 8 As shown, due to the tip effect, the magnetic field of a flat (standard) iron core structure is mainly concentrated at the edges of the core. Mag_B is located at the center of the air gap, 2 mm from the left side of the core; Mag_B_1 is located at the center of the air gap, 2 mm from the right side of the core; and Mag_B_2 is located at the center of the air gap, equidistant from both the left and right sides of the core. It can be seen that the magnetic fields of Mag_B_1 and Mag_B are higher at the edges of the core at both ends (0 mm and 500 mm on the X-axis). The highest magnetic field at the center of Mag_B_2 is approximately 1.45 T. Meanwhile, the average magnetic field of Mag_B_1 and Mag_B is around 2.0 T. This magnetic field distribution results in higher heat at the ends of the conductor material when heating it.

[0061] and Figure 8 resemblance, Figure 7Due to the tip effect, the magnetic field of a convex iron core structure is mainly concentrated at the edges of the core. Mag_B is located at the center of the air gap, 2mm from the left side of the core; Mag_B_1 is located at the center of the air gap, 2mm from the right side of the core; and Mag_B_2 is located at the center of the air gap, equidistant from both the left and right sides of the core. It can be seen that the magnetic fields of Mag_B_1 and Mag_B are higher at the chamfered corners at both ends of the core (50mm and 450mm on the X-axis). The highest magnetic field at the center of Mag_B_2 is approximately 1.45T. Meanwhile, the average magnetic field of Mag_B_1 and Mag_B is around 2.0T. This magnetic field distribution results in higher heat at the chamfered edges of the conductor material when heating, while the overall heat at the chamfered edges is less than that of a flat iron core.

[0062] same Figure 7 , Figure 8 , Figure 9 Due to the tip effect, the magnetic field of the concave iron core structure is mainly concentrated at the edge of the core. Mag_B (red line) is located at the center of the air gap, 2mm from the left iron core; Mag_B_1 (green line) is located at the center of the air gap, 2mm from the right iron core; and Mag_B_2 (blue line) is located at the center of the air gap, equidistant from both the left and right iron cores. It can be seen that the magnetic fields of Mag_B_1 and Mag_B are higher at the grooves cut in the middle of the iron core (160mm and 340mm on the X-axis). The highest magnetic field at the center of Mag_B_2 is approximately 1.40T. Meanwhile, the average magnetic field of Mag_B_1 and Mag_B is 2.1T. This magnetic field distribution results in less heat being generated at the grooves of the conductor material when heating it.

[0063] In summary, it is proven that the magnetic field distribution and heating effect can be optimized by changing the shape of the iron core.

[0064] Please see Figures 10 to 12 Taking one example each from type I, II, and III high-temperature superconducting magnet devices, a comparison of the heat distribution before and after optimization reveals that the magnetic field optimization method for high-temperature superconducting magnet devices based on the induction heating characteristic number method of this invention can effectively optimize the heat distribution and improve heating uniformity. Specifically: like Figure 10 Before optimization, the heat on the conductor material was mainly distributed in the middle (528℃), with less heat at both ends (471℃). After optimization, the heat distribution on the conductor material is more uniform, with 519℃ in the middle and 528℃ at the ends.

[0065] like Figure 11 Before optimization, the heat on the conductor material was mainly distributed at the ends (528℃), with less heat in the middle (502℃). After optimization, the heat distribution on the conductor material is more uniform, with 526℃ in the middle and 516℃ at the ends.

[0066] like Figure 12 Before optimization, the heat on the conductor material was mainly distributed in the middle (401℃), with less heat at the ends (367℃). After optimization, the heat distribution on the conductor material is more uniform, with 396℃ in the middle and 401℃ at the ends.

[0067] In addition, in some optional embodiments of the present invention, the step of determining the target parameter combination for magnetic field optimization from the optimization parameter set according to preset rules, with the temperature distribution after heating the counterpolar high-temperature superconducting magnet device as the optimization target, further includes: First, a coupled mapping model of magnetic field distribution and temperature field distribution is constructed based on the device type. The parameters in the optimization parameter set are used as input variables of the coupled mapping model, and the three-dimensional temperature distribution uniformity of the conductor rod is used as the output variable. The coupled mapping model is trained by magnetic field simulation data and thermal simulation data under multiple sets of different parameter combinations. Then, Latin hypercube sampling is performed within the preset value range of each parameter to obtain multiple sets of initial parameter samples, which are then input into the coupled mapping model to output the corresponding three-dimensional temperature distribution uniformity evaluation value. Next, with the goal of maximizing the evaluation value of three-dimensional temperature distribution uniformity, an improved particle swarm optimization algorithm is used to iteratively optimize the initial parameter samples. During the iteration process, the adaptive inertial weight and learning factor of the particles are set according to the device type. Finally, the optimal parameter sample obtained by iterative optimization is verified by joint magnetic field-thermal field simulation. If the verification result meets the preset temperature uniformity threshold, the optimal parameter sample is determined as the target parameter combination for magnetic field optimization. If it does not meet the threshold, the verification data is added to the coupled mapping model for retraining and iterative optimization is performed again until the target parameter combination that meets the threshold requirement is obtained.

[0068] Furthermore, in some optional embodiments of the present invention, the step of iteratively optimizing the initial parameter samples using an improved particle swarm optimization algorithm, wherein the adaptive inertia weight and learning factor of the particles are set according to the device type during the iteration process includes: For the Type I high-temperature superconducting magnet device, a nonlinear decreasing adaptive inertial weight is adopted, and a core cutting geometric parameter constraint term is introduced to correct the particle position, so that the particle optimization process always falls within the safe design range of the convex core cutting depth and cutting width. For the Type II high-temperature superconducting magnet device, a fixed inertial weight combined with a dynamic learning factor is adopted. The coupling sensitivity between the magnet current and the air gap distance is used as the basis for adjusting the learning factor to suppress the coupling oscillation between parameters. For the Type III high-temperature superconducting magnet device, a segmented adaptive inertial weight is adopted to expand the search range of concave core structure parameters in the early stage of iteration, tighten the optimization step size of the middle cutting depth and width in the later stage of iteration, and introduce temperature uniformity gradient information to correct the particle velocity. Meanwhile, a magnetic field distortion penalty factor is introduced during the iterative optimization process. Parameter combinations with air gap magnetic field distortion rates exceeding a preset threshold are marked as non-dominated solutions and automatically eliminated, thus avoiding local overheating of the conductor rod due to excessively strong local magnetic fields in the optimization results.

[0069] Specifically, a nonlinear decreasing adaptive inertia weight is employed. A larger inertia weight is set in the early stages of iterative optimization to ensure that particles can perform a global search within a larger parameter space, preventing premature convergence. As the number of iterations increases, the inertia weight gradually decreases according to a nonlinear function, allowing particles to focus more on fine-grained local searches in the later stages of optimization, thus improving the accuracy of parameter optimization.

[0070] Simultaneously, a core cutting geometry parameter constraint is introduced to correct the particle position in real time. This constraint takes the cutting depth and width of the convex core as the core constraint objects, and after each particle position update, it is determined whether the current parameters exceed the preset safe design range. If they exceed the range, the constraint will force the particle position back into the legal range, ensuring that the particle always falls within the safe design range of the convex core cutting depth and width throughout the entire optimization process, thus avoiding problems such as unmanufacturable structures, abnormal magnetic field distribution, or insufficient mechanical strength.

[0071] Fixed inertia weights are used to ensure the stability of the iteration process and avoid parameter oscillations caused by frequent weight changes. Simultaneously, a dynamic learning factor is used for adjustment, with the coupling sensitivity between the magnet current and the air gap distance serving as the basis for dynamic adjustment of the learning factor.

[0072] When the coupling sensitivity between the magnet current and the air gap distance is high, it indicates that changes in these two parameters will strongly influence each other, easily causing system oscillations. In this case, reducing the learning factor weakens the particle update amplitude. When the coupling sensitivity is low, appropriately increasing the learning factor accelerates the optimization convergence speed. By suppressing the coupling oscillations between parameters in this way, the optimization process becomes smoother, and the final combination of magnet current and air gap distance parameters ensures magnetic field stability without drastic fluctuations. A piecewise adaptive inertia weighting method is adopted, dividing the entire iteration process into two stages: an early stage and a late stage. In the early stage, a larger inertia weight is used to expand the search range of the concave core structure parameters, increasing the likelihood of finding the global optimum. In the late stage, the inertia weight is reduced, tightening the optimization step size for the central cutting depth and width, allowing the particles to perform a fine search within a smaller range, thus improving the accuracy of the final parameters.

[0073] Simultaneously, temperature uniformity gradient information is introduced to provide feedback correction for particle velocity. The temperature uniformity gradient is obtained by real-time calculation or prediction of temperature field distribution. When the temperature gradient is large, it indicates that the local temperature difference is significant. At this time, the particle velocity is corrected to reduce the change amplitude of the corresponding structural parameters, so that the optimization direction tends to the region with more uniform temperature distribution, thereby improving the thermal stability of the superconducting magnet during operation.

[0074] In the iterative optimization process, a magnetic field distortion penalty factor is introduced to calculate the air gap magnetic field distortion rate corresponding to the current parameter combination in real time. Parameter combinations with air gap magnetic field distortion rates exceeding a preset threshold are marked as non-dominated solutions and automatically removed from the optimization set.

[0075] This method filters out parameter combinations that can cause severe magnetic field distortion, avoiding situations where the optimization results are excessively strong in local areas. This prevents problems such as local overheating of the conductor rod, increased risk of quenching, or reduced service life caused by excessively high local magnetic fields, ensuring that the final optimized parameters simultaneously meet the requirements of magnetic field quality, structural safety, and thermal stability.

[0076] In summary, the magnetic field optimization method for the anti-polarity high-temperature superconducting magnet device in the above embodiments of the present invention obtains the induction heating characteristic number of the anti-polarity high-temperature superconducting magnet device to be optimized. Based on this characteristic number, it achieves accurate classification of different conductor materials, establishes the adaptation criteria between the electromagnetic-thermal properties of materials and the heating process, breaks through the limitations of the unified parameter control in the prior art, and achieves precise matching between materials and heating processes. The device type is determined according to the induction heating characteristic number and the corresponding iron core topology is matched. Geometric parameters such as the pole cutting depth and pole cutting width of the iron core are incorporated into the quantitative optimization system, replacing the traditional empirical iron core design method, and achieving precise control of the magnetic field distribution by adjusting the iron core topology. Taking the temperature distribution after heating as the optimization target, the target parameter combination for magnetic field optimization is determined from the matched optimization parameter set according to preset rules. An efficient collaborative optimization logic for multiple parameters such as magnet current, air gap, and rotation speed is established, which can obtain the optimal parameter combination that takes into account both heating uniformity and efficiency without a large number of repeated experiments. By utilizing induction heating characteristic numbers to achieve precise matching of materials and processes, and combining quantified iron core topology parameters to optimize magnetic field distribution, this technology also achieves efficient multi-parameter collaborative optimization through targeted parameter combination screening. This fundamentally solves the technical problems of material mismatch, lack of quantitative standards for iron core design, and difficulty in multi-parameter coordination in existing technologies for induction heating of polar high-temperature superconducting magnets. It also solves the problems of poor adaptability, low magnetic field control precision, and low efficiency of multi-parameter optimization in existing technologies for magnetic field optimization of polar high-temperature superconducting magnet devices.

[0077] Example 3 Please see Figure 13 The figure shows a magnetic field optimization system for a counterpolar high-temperature superconducting magnet device proposed in the third embodiment of the present invention. The system includes: The acquisition module 100 is used to acquire the induction heating characteristic number of the counter-polar high-temperature superconducting magnet device to be optimized; The determination module 200 is used to determine the type of the counter pole high-temperature superconducting magnet device based on the induction heating characteristic number, and to determine the core topology and optimized parameter set in the counter pole high-temperature superconducting magnet device based on the type of the counter pole high-temperature superconducting magnet device. The optimization module 300 is used to determine the target parameter combination for magnetic field optimization from the optimization parameter set according to preset rules, with the temperature distribution after heating the anti-polar high-temperature superconducting magnet device as the optimization target.

[0078] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0079] Example 4 In another aspect, the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in any one of Embodiments 1 to 2 above.

[0080] Example 5 In another aspect, the present invention provides an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of any one of the methods described in Embodiments 1 to 2 above.

[0081] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0083] More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable storage media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0084] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0086] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method of optimizing the magnetic field of a pole-type high temperature superconducting magnet device, characterized by, The method comprises: obtaining an inductive heating characteristic number of a paired-pole high-temperature superconducting magnet device to be optimized; determining the type of the paired-pole high-temperature superconducting magnet device according to the inductive heating characteristic number, and determining the core topology and an optimization parameter set in the paired-pole high-temperature superconducting magnet device based on the type of the paired-pole high-temperature superconducting magnet device; determining a target parameter combination for magnetic field optimization from the optimization parameter set according to a preset rule, with the temperature distribution of the paired-pole high-temperature superconducting magnet device after heating as an optimization target.

2. The method of magnetic field optimization for a homopolar high temperature superconducting magnet device according to claim 1, wherein, The method further comprises, before the step of obtaining the inductive heating characteristic number of the paired-pole high-temperature superconducting magnet device to be optimized: constructing a simulation model of the paired-pole high-temperature superconducting magnet device, wherein the simulation model of the paired-pole high-temperature superconducting magnet device comprises a mechanical skeleton, support seats arranged in a relative distribution in the mechanical skeleton, iron cores respectively arranged on the support seats, and superconducting coils sleeved on the iron cores, and an air gap for accommodating a conductor bar is formed between the two iron cores.

3. The method of magnetic field optimization for a homopolar high temperature superconducting magnet device according to claim 2, wherein, The inductive heating characteristic number represents the ratio of the skin depth of the conductor bar to the heat diffusion depth in one electromagnetic period, and the formula is: wherein, is the skin depth, is the diffusion coefficient, is the equivalent angular frequency, is the vacuum permeability.

4. The method of magnetic field optimization for a homopolar high temperature superconducting magnet device according to claim 3, wherein, The step of determining the type of the paired-pole high-temperature superconducting magnet device according to the inductive heating characteristic number comprises: When , it is determined that the pole type high temperature superconducting magnet device is type I, the core topological structure is a convex core, and the optimization parameter set includes a convex core pole cutting depth , a convex core pole cutting width , a magnet current I, an air gap distance , and a rotating speed v ; When , the high-temperature superconducting magnet device is determined as type II, and the optimization parameter set includes a magnet current I, an air gap distance , and a rotating speed v ; When , it is determined that the pole type high temperature superconducting magnet device is type III, the core topological structure is a concave core, and the optimization parameter set includes a concave core middle depth , a concave core middle width , a magnet current I, an air gap distance , and a rotating speed v .

5. The method of magnetic field optimization for a HTS magnet of the opposed-pole type according to claim 4, characterized in that, The formula for calculating the magnet current I is: ； wherein, Gapair is the air gap distance, is the vacuum permeability, P is the target heating power, is the electrical conductivity, f is the rotational speed equivalent frequency, V is the effective heating volume of the conductor rod, N is the equivalent ampere-turn factor of the single magnetic pole coil, is the electromagnetic coupling effective factor, which is taken from different ranges according to the induction heating characteristic number.

6. The method of magnetic field optimization for a homopolar high temperature superconducting magnet device of claim 5, wherein, The step of determining the target parameter combination for magnetic field optimization from the optimization parameter set according to a preset rule, with the temperature distribution of the paired-pole high-temperature superconducting magnet device after heating as an optimization target, comprises: constructing an orthogonal arrangement table experiment according to the number of optimization parameters in the optimization parameter set, and determining the target parameter combination for magnetic field optimization with the conductor bar cross-section temperature standard deviation as an optimization target and the signal-to-noise ratio as an evaluation index.

7. The method of magnetic field optimization for a homopolar high temperature superconducting magnet device of claim 4, wherein, Convex core pole shaving depth Convex core pole shaving width ; Depth of middle portion of recessed core Width of middle portion of recessed core ; Air gap distance The adjustment range is: ; The magnet current I is regulated in the range of 0 to 2.5 A. with an initial value of 0 A and a regulation range of ±20%. The rotation speed value range is (300 Rpm, 1200 Rpm). where D is the diameter of the conductor bar stock, and L is the length of the conductor bar stock, and , .

8. A magnetic field optimization system for a pole-type high temperature superconducting magnet device, characterized by, The system comprises: an acquisition module configured to obtain an inductive heating characteristic number of a paired-pole high-temperature superconducting magnet device to be optimized; a determination module configured to determine the type of the paired-pole high-temperature superconducting magnet device according to the inductive heating characteristic number, and determine the core topology and an optimization parameter set in the paired-pole high-temperature superconducting magnet device based on the type of the paired-pole high-temperature superconducting magnet device; an optimization module configured to determine a target parameter combination for magnetic field optimization from the optimization parameter set according to a preset rule, with the temperature distribution of the paired-pole high-temperature superconducting magnet device after heating as an optimization target.

9. A readable storage medium, having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the method of any one of claims 1 to 7.

10. An electronic device, comprising: A computer program product comprises a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor implements the steps of the method of any one of claims 1 to 7 when executing the program.

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