DCT type superconducting coil and optimization method thereof

Abstract

The invention provides an optimization method for a DCT type superconducting coil, and the method comprises the steps: dividing a multi-layer coil of the DCT type superconducting coil into a basic field coil and an adjustment field coil, the basic field coil being used for generating a basic field, and the adjustment field coil being used for generating an adjustment field; carrying out primary modeling on the basic field coil, and obtaining first magnetic field data of the basic field coil based on a basic field coil model obtained through modeling; modeling the adjusting field coil, and performing iterative optimization based on the first magnetic field data and an adjusting field coil model obtained by modeling until the magnetic field generated by the multi-layer coil meets a preset condition; and determining the corresponding parameters of the multi-layer coil when the magnetic field meets the preset condition as target parameters.

Description

Technical Field

[0001] This disclosure relates to the field of medical device technology, specifically to the field of superconducting magnets for medical ion therapy accelerators, and particularly to a DCT-type superconducting coil and its optimization method. Background Technology

[0002] Discrete Cosine Theta (DCT) type superconducting magnets are widely used in medical ion accelerators due to their advantages such as short ends, high excitation efficiency, and the ability to combine multiple poles. For DCT type superconducting magnets used in miniaturized medical ion accelerators, a uniform magnetic field of approximately 4T is typically required within a relatively large aperture. This necessitates the use of multi-layered nested DCT type superconducting coil structures to generate sufficient magnetic field. Optimizing magnetic field uniformity usually requires modeling and optimizing all multiple coil layers, resulting in a complex structure and a slow optimization process. Summary of the Invention

[0003] In view of this, this disclosure provides a DCT type superconducting coil and its optimization method.

[0004] This disclosure provides an optimization method for a DCT-type superconducting coil, comprising: dividing a multilayer coil of a DCT-type superconducting coil into a base field coil and a regulating field coil, wherein the base field coil is used to generate a base field and the regulating field coil is used to generate a regulating field; performing a first modeling of the base field coil, and obtaining first magnetic field data of the base field coil based on the modeling of the base field coil; performing a modeling of the regulating field coil, and performing iterative optimization based on the first magnetic field data and the modeling of the regulating field coil, until the magnetic field generated by the multilayer coil meets a preset condition; and determining the parameters of the multilayer coil corresponding to the magnetic field meeting the preset condition as target parameters.

[0005] According to embodiments of this disclosure, dividing the multilayer coil of a DCT-type superconducting coil into a base field coil and a regulating field coil includes: determining the outermost coil of the multilayer coil as the regulating field coil; and determining the other coils in the multilayer coil other than the outermost coil as the base field coil.

[0006] According to embodiments of this disclosure, a regulating field coil is modeled, and iterative optimization is performed based on first magnetic field data and the modeled regulating field coil model until the magnetic field generated by the multilayer coil meets preset conditions. This includes: iterative optimization of the coil end size parameters and cross-sectional current density distribution parameters of the regulating field coil based on the first magnetic field data until the magnetic field jointly generated by the base field coil and the regulating field coil meets preset conditions.

[0007] According to embodiments of this disclosure, the coil end dimension parameters include the end length of the regulating field coil and the number of coil turns, and the cross-sectional current density distribution parameters include the two-pole field component, the four-pole field component, the six-pole field component, and the eight-pole field component.

[0008] According to embodiments of this disclosure, based on first magnetic field data, the coil end size parameters and cross-sectional current density distribution parameters of the adjusting field coil are iteratively optimized until the magnetic field jointly generated by the base field coil and the adjusting field coil meets preset conditions. This includes: adjusting the end length and number of turns of the field coil based on the adjusting field coil model so that the effective length of the DCT type superconducting coil meets the design requirements; loading the first magnetic field data, and based on the adjusting field coil model, adjusting the dipole field component, quadrupole field component, hexapole field component, and octapole field component within a preset range to change the cross-sectional current density distribution of the adjusting field coil, so as to optimize the magnetic field uniformity of the multilayer coil until the magnetic field generated by the multilayer coil meets preset conditions.

[0009] According to embodiments of this disclosure, the cross-sectional current density of the regulating field coil exhibits a cosine distribution across the cross-section of the regulating field coil.

[0010] According to embodiments of this disclosure, the preset ranges for the two-pole field component, the four-pole field component, the six-pole field component, and the eight-pole field component are all -0.1 to 0.1; the preset range for the end length of the adjusting field coil is 100 mm to 150 mm, and the number of turns of the adjusting field coil is 18 to 25.

[0011] According to embodiments of this disclosure, the current load rate in the DCT type superconducting coil does not exceed 70%.

[0012] According to embodiments of this disclosure, the magnetic field uniformity of the multilayer coil reaches the level of 100,000 components.

[0013] Another aspect of this disclosure provides a DCT type superconducting coil, which is obtained by optimizing the optimization method of this disclosure.

[0014] The DCT-type superconducting coil and its optimization method disclosed herein have at least the following technical advantages:

[0015] This method decomposes the multilayer coil modeling optimization of DCT-type superconducting coils into a base field coil and a regulating field coil. After the base field coil is established, only its magnetic field data is retained; it is not modeled again in subsequent multi-stage optimizations. Only the regulating field coil is modeled and optimized. The base field data is loaded during post-processing to achieve overall coil optimization. This method simplifies multilayer magnetic field modeling to a single layer, significantly reducing the computational load of simulation modeling and improving simulation time by an order of magnitude. It avoids the reliance on high computing power and long processing times in complex modeling during magnetic field optimization. Simplifying the multilayer coil model to a single-layer coil model ensures that the superconducting coil performance remains approximately the same after simplification, greatly improving magnetic field optimization efficiency while maintaining accuracy. This method is applicable to various coil-dominated superconducting magnets, demonstrating strong versatility and practicality. Attached Figure Description

[0016] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0017] Figure 1 A flowchart illustrating an optimization method for a DCT-type superconducting coil according to an embodiment of the present disclosure is shown.

[0018] Figure 2 A schematic diagram of modeling parameters for a DCT-type superconducting coil according to an embodiment of the present disclosure is shown.

[0019] Figure 3 A schematic diagram illustrating the optimization of a DCT-type superconducting coil according to an embodiment of the present disclosure is shown.

[0020] Figure 4 A flowchart illustrating the optimization process of a DCT-type superconducting coil according to an embodiment of the present disclosure is shown.

[0021] The attached figures are labeled as follows:

[0022] 1. Length of the straight section of the coil Lst, 2. Length of the end of the coil Le, 3. Basic field data and adjustment field modeling, 4. Number of turns of the outermost coil N_out, 5. Current distribution of the coil cross section. Detailed Implementation

[0023] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0024] Regarding the current design of DCT-type superconducting magnets for medical ion therapy accelerators, this disclosure provides an optimization method for DCT-type superconducting coils. The optimization process eliminates the need for full-model building; instead, it divides the process into basic field modeling and adjustment magnetic field modeling. Adjustment magnetic field modeling only requires modeling one layer of the coil. Compared to full-model building, this significantly accelerates the electromagnetic optimization of the superconducting coil and expedites the development of DCT superconducting coils.

[0025] Figure 1 A flowchart illustrating an optimization method for a DCT-type superconducting coil according to an embodiment of the present disclosure is shown.

[0026] like Figure 1 As shown, the optimization method for the DCT type superconducting coil in this embodiment may include operations S110 to S140.

[0027] In operation of S110, the multilayer coil of the DCT type superconducting coil is divided into a base field coil and a conditioning field coil. The base field coil is used to generate the base field, and the conditioning field coil is used to generate the conditioning field.

[0028] In operation S120, the basic field coil is modeled once, and the first magnetic field data of the basic field coil is obtained based on the model of the basic field coil.

[0029] In operation S130, the regulating field coil is modeled, and iterative optimization is performed based on the first magnetic field data and the modeled regulating field coil model until the magnetic field generated by the multi-layer coil meets the preset conditions.

[0030] In operation S140, the parameters of the multilayer coil corresponding to the magnetic field meeting the preset conditions are determined as the target parameters.

[0031] According to embodiments of this disclosure, a base field coil is used to generate the base magnetic field required by the system. The base magnetic field is fundamental to the normal operation of the system, and its strength and distribution need to meet specific design requirements. An adjustment field coil is used to fine-tune the base magnetic field to meet more precise or varying needs. The design of the adjustment field coil can be more flexible to adapt to different operating conditions or experimental requirements.

[0032] According to embodiments of this disclosure, a basic field coil can be modeled using computer-aided design software or specialized electromagnetic field simulation software. The modeling process needs to consider factors such as the geometry, material properties, and current distribution of the basic field coil. Based on the modeling results, the electromagnetic field simulation software is used to calculate the magnetic field distribution and intensity generated by the basic field coil, obtaining first magnetic field data. This data will serve as the basis for subsequent optimization processes. The magnetic field data of the basic field coil is retained only after it has been modeled once, and it will not be modeled again in subsequent optimization processes. During post-processing, the first magnetic field data corresponding to the basic field coil is loaded to achieve overall optimization of the coil.

[0033] According to embodiments of this disclosure, an electromagnetic field simulation software can be used to model the regulating field coil. The interaction between the regulating field coil and the base field coil needs to be considered during the modeling process. Based on the first magnetic field data and the regulating field coil model, multiple simulation calculations are performed by adjusting the parameters of the regulating field coil (such as current magnitude, coil shape, etc.) to find the optimal parameter combination that allows the magnetic field generated by the multi-layer coil to meet preset conditions. This process may require multiple iterations and adjustments until a satisfactory optimization result is achieved.

[0034] During the iterative optimization process, when the magnetic field generated by the multilayer coil meets the preset conditions, all relevant parameters of the multilayer coil at this time (such as coil shape, size, material properties, current distribution, etc.) are recorded. These parameters are the design targets, i.e., target parameters. The target parameters will be used to guide the manufacturing and installation of the actual coil to ensure that the magnetic field generated by the actual system is consistent with the design requirements.

[0035] In some embodiments, dividing the multilayer coils of a DCT-type superconducting coil into a base field coil and a regulating field coil includes: determining the outermost coil of the multilayer coil as the regulating field coil; and determining the other coils in the multilayer coil other than the outermost coil as the base field coils.

[0036] According to embodiments of this disclosure, the base field coil refers to the inner coils of a multi-layered nested DCT-type superconducting coil, excluding the outermost coil. The magnetic field generated by the base field coil is called the base field. The tuning field coil refers to the outermost coil, typically consisting of only one layer, and its magnetic field is called the tuning field. Because the base field coil is located in the inner layers of the coil, it contributes significantly to the overall magnetic field and is the main component generating the base magnetic field. The tuning field coil, located in the outermost layer, has a relatively smaller impact on the overall magnetic field, but its magnetic field distribution can be fine-tuned by adjusting its current or shape.

[0037] The magnetic field performance of the entire DCT superconducting coil can be optimized through multi-parameter collaborative optimization of the regulating field coil. Multi-parameter collaboration refers to parametric 3D modeling of key components of the regulating field coil, including coil end dimensions and cross-sectional current density distribution, during the finite element modeling of the DCT superconducting coil. After setting their respective variation ranges, these variations are simultaneously applied during the optimization iteration process to seek the possible optimal solution within a large parameter space.

[0038] In some embodiments, the regulating field coil is modeled, and iterative optimization is performed based on the first magnetic field data and the modeled regulating field coil model until the magnetic field generated by the multilayer coil meets the preset conditions. This includes: iterative optimization of the coil end size parameters and cross-sectional current density distribution parameters of the regulating field coil based on the first magnetic field data until the magnetic field jointly generated by the base field coil and the regulating field coil meets the preset conditions.

[0039] Among them, the coil end dimension parameters include the end length of the regulating field coil and the number of coil turns, and the cross-sectional current density distribution parameters include the two-pole field component, the four-pole field component, the six-pole field component, and the eight-pole field component.

[0040] Furthermore, based on the first magnetic field data, the coil end size parameters and cross-sectional current density distribution parameters of the regulating field coil are iteratively optimized until the magnetic field jointly generated by the base field coil and the regulating field coil meets the preset conditions. This includes: adjusting the end length and number of turns of the regulating field coil based on the regulating field coil model so that the effective length of the DCT type superconducting coil meets the design requirements; loading the first magnetic field data, and based on the regulating field coil model, adjusting the dipole field component, quadrupole field component, hexapole field component, and octapole field component within a preset range to change the cross-sectional current density distribution of the regulating field coil, so as to optimize the magnetic field uniformity of the multilayer coil until the magnetic field generated by the multilayer coil meets the preset conditions.

[0041] Among them, the cross-sectional current density of the regulating field coil exhibits a cosine distribution on the cross-section of the regulating field coil.

[0042] Figure 2 A schematic diagram of modeling parameters for a DCT-type superconducting coil according to an embodiment of the present disclosure is shown. Figure 3 A schematic diagram illustrating the optimization of a DCT-type superconducting coil according to an embodiment of the present disclosure is shown. Figure 4 A flowchart illustrating the optimization process of a DCT-type superconducting coil according to an embodiment of the present disclosure is shown.

[0043] like Figures 2-4 As shown, for a DCT type superconducting coil, its coil distribution can be described by a set of shape functions:

[0044]

[0045]

[0046] The coil distribution is determined by 10 parameters: m1~m4, nn, N, N_out, Le, Le_out, and Lst. nn, N, N_out, Le, Le_out, and Lst represent the number of coil layers, the number of turns in the inner multi-layer coils, the number of turns in the outermost coil, the end length of the inner multi-layer coils, the end length of the outermost coil, and the length of the straight segment of the coil, respectively. The four parameters—the number of coil layers nn, the number of turns in the inner layer N, the end length of the inner layer Le, and the length of the straight segment Lst—must be determined in advance based on the superconducting magnet design requirements (such as magnetic field value, effective length, and deflection angle) to determine the main structural dimensions of the DCT superconducting coil. The first step is to establish a full-size coil model and adjust it so that the main magnetic field meets the design requirements. Only the basic field coil model is established; after solving, only the magnetic field data file (usually the integrated magnetic field values) is retained, and the coil model is no longer retained.

[0047] The second step is to establish and solve the adjustment field coil. In the data processing, the basic field magnetic field data is loaded to obtain the overall magnetic field performance of the coil. In this way, the magnetic field uniformity of the superconducting magnet can be finely optimized by adjusting the parameters of the outermost coil.

[0048] In some embodiments, as shown in Table 1, the preset ranges for the two-pole field component, the four-pole field component, the six-pole field component, and the eight-pole field component are all -0.1 to 0.1; the preset range for the end length of the adjustment field coil is 100 mm to 150 mm, and the number of turns of the adjustment field coil is 18 to 25.

[0049] Within the aforementioned parameter range, by adjusting N_out and Le_out, the effective length of the superconducting coil is ensured to meet design requirements, and the current load in the superconducting wire is kept below 70%. The magnetic field uniformity of the superconducting coil is optimized by combining genetic algorithms, particle swarm optimization, and other optimization methods with only the four parameters m1~m4 of the field coil. Adjusting the m1~m4 parameters can change the current distribution across the coil cross-section, thereby optimizing the magnetic field uniformity. This method simplifies multi-layer coils to two-layer coils, significantly reducing modeling difficulty and increasing the single-optimization rate to 1 / 10 of the original. Furthermore, this method can optimize the magnetic field uniformity of the superconducting coil to the level of hundreds of thousands of components.

[0050] Table 1 schematically illustrates the parameters and their corresponding preset ranges.

[0051] Table 1

[0052]

[0053] Based on the above-described optimization method for DCT type superconducting coils, this disclosure also provides a DCT type superconducting coil, the structure of which is as follows: Figure 2 As shown. The DCT type superconducting coil was optimized using the optimization method provided in the embodiments of this disclosure; specific details will not be repeated here.

[0054] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. An optimization method for DCT type superconducting coils, characterized in that, include: The multilayer coils of the DCT type superconducting coil are divided into a base field coil and a regulating field coil. The base field coil is used to generate the base field, and the regulating field coil is used to generate the regulating field. The basic field coil is modeled once, and the first magnetic field data of the basic field coil is obtained based on the model of the basic field coil. The regulating field coil is modeled, and the regulating field coil model is iteratively optimized based on the first magnetic field data and the modeled regulating field coil model until the magnetic field generated by the multi-layer coil meets the preset conditions. The parameters of the multilayer coil corresponding to the magnetic field meeting the preset conditions are determined as the target parameters.

2. The optimization method according to claim 1, characterized in that, The division of the multilayer coil of the DCT type superconducting coil into a base field coil and a regulating field coil includes: The outermost coil of the multilayer coil is defined as the adjustment field coil; The coils other than the outermost coil in the multilayer coil are defined as the basic field coils.

3. The optimization method according to claim 1 or 2, characterized in that, The process of modeling the regulating field coil, iteratively optimizing the model based on the first magnetic field data and the resulting regulating field coil model, until the magnetic field generated by the multi-layer coil meets preset conditions includes: Based on the first magnetic field data, the coil end size parameters and cross-sectional current density distribution parameters of the regulating field coil are iteratively optimized until the magnetic field jointly generated by the base field coil and the regulating field coil meets the preset conditions.

4. The optimization method according to claim 3, characterized in that, The coil end dimension parameters include the end length of the regulating field coil and the number of coil turns, and the cross-sectional current density distribution parameters include the two-pole field component, the four-pole field component, the six-pole field component, and the eight-pole field component.

5. The optimization method according to claim 4, characterized in that, Based on the first magnetic field data, the coil end size parameters and cross-sectional current density distribution parameters of the regulating field coil are iteratively optimized until the magnetic field jointly generated by the base field coil and the regulating field coil meets preset conditions, including: Based on the adjustment field coil model, the end length and number of turns of the adjustment field coil are adjusted so that the effective length of the DCT type superconducting coil meets the design requirements. Load the first magnetic field data, and based on the adjustment field coil model, adjust the two-pole field component, four-pole field component, six-pole field component, and eight-pole field component within a preset range to change the cross-sectional current density distribution of the adjustment field coil, so as to optimize the magnetic field uniformity of the multilayer coil until the magnetic field generated by the multilayer coil meets the preset conditions.

6. The optimization method according to claim 5, characterized in that, The cross-sectional current density of the regulating field coil exhibits a cosine distribution across its cross-section.

7. The optimization method according to claim 5, characterized in that, The preset ranges for the two-pole field component, the four-pole field component, the six-pole field component, and the eight-pole field component are all -0.1 to 0.

1. The preset range of the end length of the regulating field coil is 100 mm to 150 mm, and the number of coil turns of the regulating field coil is 18 to 25.

8. The optimization method according to claim 1, characterized in that, The current load rate in the DCT type superconducting coil does not exceed 70%.

9. The optimization method according to claim 1, characterized in that, The magnetic field uniformity of the multilayer coil reaches the level of 100,000 components.

10. A DCT type superconducting coil, characterized in that, The DCT type superconducting coil is obtained by optimization using the optimization method described in any one of claims 1 to 9.

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