Superconducting magnet coil design method, apparatus, device, medium, and product

Abstract

The application relates to the technical field of superconducting magnet design optimization, and discloses a superconducting magnet coil design method, device, equipment, medium and product, the method comprising the following steps: obtaining multiple optimization targets and constraint conditions of the multiple optimization targets, wherein the multiple optimization targets comprise at least two of the following: a coil center magnetic field strength, a magnetic field uniformity of a coil target area, a coil inductance and a coil material consumption; obtaining an initial setting range of multiple coil design parameters; constructing multiple fitness functions according to the multiple optimization targets; performing optimization processing on the multiple coil design parameters in the initial setting range of the multiple coil design parameters based on the multiple fitness functions to obtain a target coil design parameter combination, wherein the target coil design parameter combination is a configuration scheme of the multiple coil design parameters meeting the constraint conditions of the multiple optimization targets.The application realizes comprehensive automatic optimization of multiple performance indexes such as a magnetic field strength, a uniformity, an inductance and a material consumption in superconducting magnet coil design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of superconducting magnet design optimization, and particularly relates to a superconducting magnet coil design method, device, equipment, medium and product. BACKGROUND

[0002] The superconducting magnet coil is a core functional component of the superconducting magnet, is made of superconducting material, and generates a super-strong and stable magnetic field by passing a large current inside the superconducting magnet coil. It is the fundamental to realize the high magnetic field and low loss characteristics of the superconducting magnet. The superconducting magnet coil is widely used in high-tech fields such as nuclear fusion reaction devices (such as tokamak), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), particle accelerators, and has very high requirements for the strength, uniformity and electrical performance of the magnetic field.

[0003] At present, the design of the superconducting magnet coil is based on a method combining classical electromagnetism theory (such as Biot-Savart law) and engineering experience, and the magnetic field distribution is approximately calculated by analytical formula, and the design personnel manually adjusts the coil parameters (such as conductor specifications, number of turns, winding layer number, structure size, etc.) multiple times to meet the design requirements. The above design method has certain operability in some single-target optimization tasks, but focuses on single target optimization, and it is difficult to effectively realize the comprehensive optimization of multiple performance indicators such as magnetic field strength, uniformity and low inductance. SUMMARY

[0004] The present application provides a superconducting magnet coil design method, device, equipment, medium and product to solve the problem that focusing on single target optimization makes it difficult to effectively realize the comprehensive optimization of multiple performance indicators such as magnetic field strength, uniformity and low inductance.

[0005] In a first aspect, the present application provides a superconducting magnet coil design method, which comprises: obtaining a plurality of optimization targets and constraint conditions of the plurality of optimization targets, wherein the plurality of optimization targets comprises at least two of the following: coil center magnetic field strength, coil target region magnetic field uniformity, coil inductance and coil material usage; obtaining an initial setting range of a plurality of coil design parameters; constructing a plurality of fitness functions according to the plurality of optimization targets, wherein the plurality of optimization targets and the plurality of fitness functions correspond one by one; based on the plurality of fitness functions, optimizing the plurality of coil design parameters in the initial setting range of the plurality of coil design parameters to obtain a target coil design parameter combination, wherein the target coil design parameter combination is a configuration scheme of the plurality of coil design parameters meeting the constraint conditions of the plurality of optimization targets.

[0006] The superconducting magnet coil design method provided in the embodiment, after obtaining a plurality of optimization objectives, constraint conditions of the plurality of optimization objectives, and an initial setting range of a plurality of coil design parameters, a plurality of fitness functions are constructed according to the plurality of optimization objectives, and then the plurality of coil design parameters are optimized based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters to obtain a target coil design parameter combination, thereby realizing comprehensive and automatic optimization of multiple performance indicators such as magnetic field strength, uniformity, inductance, and material usage in the superconducting magnet coil optimization design, significantly improving design efficiency and performance indicators, being suitable for a wide range of engineering application scenarios, solving the technical bottleneck that the traditional method is difficult to simultaneously consider multiple objectives, and realizing a reasonable balance between performance and economy.

[0007] In an optional implementation, the optimization processing of the plurality of coil design parameters based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters to obtain the target coil design parameter combination includes: generating an initial population according to the initial setting range of the plurality of coil design parameters, wherein the initial population includes a plurality of individuals, and each individual corresponds to a configuration scheme of the plurality of coil design parameters; determining the fitness of each individual in the plurality of individuals according to the plurality of fitness functions; performing cross operation, mutation operation, and elite operation on the initial population according to the fitness of each individual to generate a next-generation population; determining whether the next-generation population reaches a convergence condition or a maximum iteration number; and determining the configuration scheme corresponding to the optimal individual in the next-generation population as the target coil design parameter combination when the next-generation population reaches the convergence condition or the maximum iteration number.

[0008] In the embodiment, the NSGA-II multi-objective genetic algorithm is introduced, has good global search capability, can quickly find the Pareto optimal solution set of the superconducting magnet coil design, significantly improves the design efficiency, and avoids the design process from falling into local optimization.

[0009] In an optional implementation, the plurality of optimization objectives include a coil center magnetic field strength, and the coil center magnetic field strength is determined by a coil space magnetic field distribution function represented by the Biot-Savart law.

[0010] In an optional embodiment, before the initial population is subjected to the crossover operation, the mutation operation and the elite operation according to the fitness of each individual to generate the next generation population, the method further comprises: determining the deviation between the first theoretical magnetic field strength of each individual and the first simulated magnetic field strength of each individual, wherein the first theoretical magnetic field strength is the coil center magnetic field strength of each individual determined by the Biot-Savart law, and the first simulated magnetic field strength is the coil center magnetic field strength of each individual determined by the finite element simulation model; and the initial population is subjected to the crossover operation, the mutation operation and the elite operation according to the fitness of each individual to generate the next generation population, comprising: when the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than a preset deviation value, the initial population is subjected to the crossover operation, the mutation operation and the elite operation according to the fitness of each individual to generate the next generation population.

[0011] In the present embodiment, the theoretical calculation based on the Biot-Savart law is cross-verified with the CAE simulation results, which can effectively improve the accuracy of the magnetic field distribution calculation, and verify the feasibility of the optimized parameters through simulation analysis, so as to ensure that the design results are highly consistent with the actual operating conditions and improve the design accuracy and reliability. In an optional embodiment, before the configuration scheme corresponding to the optimal individual in the next generation population is determined as the target coil design parameter combination, the method further comprises: determining the deviation between the second theoretical magnetic field strength of the optimal individual and the second simulated magnetic field strength of the optimal individual; and the configuration scheme corresponding to the optimal individual in the next generation population is determined as the target coil design parameter combination, comprising: when the deviation between the second theoretical magnetic field strength and the second simulated magnetic field strength is less than a preset deviation value, the configuration scheme corresponding to the optimal individual is determined as the target coil design parameter combination.

[0012] In an optional embodiment, the plurality of optimization objectives include the coil center magnetic field strength, the magnetic field uniformity of the coil target region, the coil inductance and the coil material usage, and the plurality of coil design parameters are optimized based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters to obtain the target coil design parameter combination, comprising: within the initial setting range of the plurality of coil design parameters, the plurality of coil design parameters are optimized based on the fitness function of the coil center magnetic field strength and the fitness function of the magnetic field uniformity of the coil target region, with the constraint condition of satisfying the coil center magnetic field strength and the constraint condition of satisfying the magnetic field uniformity of the coil target region as the target, to obtain an intermediate population; and the intermediate population is optimized based on the fitness function corresponding to the coil inductance and the fitness function corresponding to the coil material usage, with the minimization of the coil inductance and the minimization of the coil material usage as the target, to obtain the target coil design parameter combination.

[0013] In an optional implementation, after obtaining the target coil design parameter combination, the method further includes: coupling the superconducting magnet coil constructed based on the target coil design parameter combination with the thermal simulation model of the cooling system, and optimizing the target coil design parameter combination to maximize the cooling efficiency of the superconducting magnet coil.

[0014] In an optional implementation, the method further includes: obtaining an operation parameter of the superconducting magnet coil constructed based on the target coil design parameter combination; and optimizing the target coil design parameter combination based on the operation parameter.

[0015] In a second aspect, the present application provides a superconducting magnet coil design device, the device comprising: a target acquisition module configured to acquire a plurality of optimization targets and constraint conditions of the plurality of optimization targets, wherein the plurality of optimization targets comprises at least two of a coil center magnetic field strength, a magnetic field uniformity of a coil target area, a coil inductance, and a coil material usage; a range acquisition module configured to acquire an initial setting range of a plurality of coil design parameters; a function construction module configured to construct a plurality of fitness functions according to the plurality of optimization targets, wherein the plurality of optimization targets and the plurality of fitness functions correspond to each other in a one-to-one manner; and an optimization processing module configured to perform optimization processing on the plurality of coil design parameters based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters to obtain a target coil design parameter combination, wherein the target coil design parameter combination is a configuration scheme of the plurality of coil design parameters satisfying the constraint conditions of the plurality of optimization targets.

[0016] In a third aspect, the present application provides an electronic device, comprising: a memory and a processor, which are communicatively connected to each other, and the memory stores computer instructions; the processor executes the computer instructions to perform the superconducting magnet coil design method of the first aspect or any of the corresponding embodiments thereof.

[0017] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for causing a computer to perform the superconducting magnet coil design method of the first aspect or any of the corresponding embodiments thereof.

[0018] In a fifth aspect, the present application provides a computer program product comprising computer instructions for causing a computer to perform the superconducting magnet coil design method of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative effort based on these drawings.

[0020] Figure 1 is a schematic diagram of an application scenario according to an embodiment of the present application;

[0021] Figure 2 is a first flowchart of a superconducting magnet coil design method according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of magnetic field distribution of an overall calculation region according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of magnetic field distribution of a target region according to an embodiment of the present application;

[0024] Figure 5 is a schematic diagram of magnetic field distribution of a coil surface according to an embodiment of the present application;

[0025] Figure 6 is a second flowchart of a superconducting magnet coil design method according to an embodiment of the present application;

[0026] Figure 7 is a comparison diagram of coil current and conductor limit current according to an embodiment of the present application;

[0027] Figure 8 is a third flowchart of a superconducting magnet coil design method according to an embodiment of the present application;

[0028] Figure 9 is a flowchart of a superconducting magnet coil design method combining multi-objective genetic optimization and CAE simulation according to an embodiment of the present application;

[0029] Figure 10 is a structural block diagram of a superconducting magnet coil design device according to an embodiment of the present application;

[0030] Figure 11 is a hardware structure schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0032] It can be understood that, before using the technical solutions disclosed in the embodiments of the present application, the type of personal information, the use range, the use scenario and the like involved in the present application should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0033] The terms "first", "second" are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more than two, unless otherwise specifically limited.

[0034] The execution of the superconducting magnet coil design method depends on a specific application environment architecture or a specific hardware architecture, and the specific application environment architecture or the specific hardware architecture is described here.

[0035] As an optional application scenario of the embodiments of the present application, as shown in the figure, Figure 1 The architecture can include at least one terminal device and at least one server, Figure 1 The architecture is exemplarily shown in the figure to include a computer 101, a mobile terminal 102 and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0036] The terminal device can be specifically a smart phone, a tablet computer, a notebook computer, a palm computer, and can also be a desktop computer, a game console, a smart television, a smart wearable device, a vehicle-mounted terminal, a VR (Virtual Reality) device, an AR (Augmented Reality) device, etc. The server 103 can be an independent physical server, can also be a server cluster or a distributed system, and can also be a cloud server providing cloud services. The network 110 can be a wired network or a wireless network, and examples thereof include but are not limited to the Internet, an enterprise intranet, a local area network, a wide area network, a mobile communication network and a combination thereof.

[0037] In the technical fields of nuclear fusion reaction devices (such as tokamak), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), particle accelerators and the like, the strength, uniformity and electrical performance of the magnetic field are required to be high. When the superconducting magnet coil is applied to the above technical fields, the design of the superconducting magnet coil needs to take into account high magnetic field strength, high magnetic field uniformity, low inductance, compact structure size and lower material cost.

[0038] At present, the design of the superconducting magnet coil generally relies on theoretical calculation based on empirical formula or single-target numerical simulation method. Although this design method has certain operability in some single-target optimization tasks, it often has the following shortcomings in actual engineering:

[0039] (1) The optimization target is single, and it is difficult to take into account the multiple performance requirements required at present.

[0040] Taking a single target (such as maximum magnetic field strength) as the optimization focus, the design result is often better in local performance, but the overall performance is difficult to achieve the best balance. There is often a mutual restriction or even conflict between multiple targets such as high magnetic field strength, high magnetic field uniformity, low inductance, compact structure size and lower material cost. Therefore, the traditional design method based on a single target or relying on engineering experience often cannot effectively realize the comprehensive optimization of various performance indicators.

[0041] (2) The design process highly depends on human experience, and the efficiency is low.

[0042] The traditional design process requires the designer to repeatedly adjust the design parameters and perform calculation verification by experience, which is long in cycle, low in iteration efficiency, and it is difficult to ensure that the global optimal solution is found within a limited number of adjustments.

[0043] (3) Lack of intelligent optimization means, difficult to fully explore the design space.

[0044] The existing scheme based on analytical formula or finite element simulation alone often cannot automatically search and optimize multiple targets for large-scale design variables, and the exploration range of the design space is limited, which is easy to fall into local optimum.

[0045] Therefore, the present application provides a superconducting magnet coil design method, device, equipment, medium and product, which optimizes the center magnetic field strength, magnetic field uniformity, coil inductance and material consumption of the coil through an optimization algorithm, solves the technical bottleneck that the traditional method cannot simultaneously take into account multiple targets, realizes a reasonable balance between performance and economy, has good global search capability, can quickly find the optimal solution set of the superconducting magnet coil design, significantly improves the design efficiency, and avoids the design process from falling into local optimum.

[0046] The superconducting magnet coil design method provided by the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0047] The present application provides a superconducting magnet coil design method, which can be used in the terminal device or the functional module in the terminal device, Figure 2 is a flowchart of a superconducting magnet coil design method according to an embodiment of the present application, as Figure 2 shown, the flow includes the following steps:

[0048] Step S201, obtaining a plurality of optimization objectives and constraint conditions of the plurality of optimization objectives.

[0049] Among them, the optimization objective is the performance index that the superconducting magnet coil needs to meet, and the plurality of optimization objectives includes at least two of the coil center magnetic field strength, the magnetic field uniformity of the coil target area, the coil inductance and the coil material usage.

[0050] The target area can be defined by the user, for example, the coil target area can be less than 25 mm in the radial direction and within the axial ±100 mm range. The magnetic field uniformity can refer to the deviation degree of the magnetic field strength of any point in the target area from the average magnetic field strength or the center magnetic field strength of the region.

[0051] Specifically, the optimization objectives and the constraint conditions of the optimization objectives can be input by the user into the terminal device, so that the terminal device can obtain a plurality of optimization objectives and the constraint conditions corresponding to each optimization objective.

[0052] For example, when the optimization objective is the coil center magnetic field strength , the constraint condition can be ; when the optimization objective is the magnetic field uniformity of the coil target area , the constraint condition can be ; when the optimization objective is the coil inductance , the constraint condition can be that the coil inductance is less than the preset inductance, or the inductance of the superconducting magnet coil is minimized; when the optimization objective is the coil material usage (such as the actual wire length of the coil ), the constraint condition can be that the actual wire length of the coil is less than the preset length, or the wire length of the superconducting magnet coil is minimized.

[0053] Step S202, obtaining an initial setting range of a plurality of coil design parameters.

[0054] wherein the coil design parameters can refer to the structure, material, electrical properties and geometry of the coil, and are key variables that directly or indirectly affect the optimization objectives (e.g. magnetic field strength, uniformity, etc.). For example, the coil design parameters can include conductor gauge, radial winding layer number, axial winding layer number, coil current, coil inner diameter size, coil outer diameter size, coil height, and other coil size structures, etc.

[0055] Each coil design parameter corresponds to an initial setting range, which can refer to a reasonable and feasible numerical interval pre-defined for the corresponding coil design parameter according to engineering experience, physical constraints, performance requirements, and manufacturing feasibility, etc.

[0056] Specifically, the coil design parameters and the initial setting ranges of the coil design parameters can be defined and input by the user into the terminal device, so that the terminal device can obtain the initial setting ranges of the plurality of coil design parameters.

[0057] The conductor gauge (diameter) can refer to the material of the superconducting wire used in the superconducting magnet coil and the diameter of the cross section of the superconducting wire. When the coil design parameter is the conductor gauge, the initial setting range corresponding to the conductor gauge can be determined according to the superconducting conductor performance curve. For example, when a niobium-titanium (NbTi) superconducting conductor is selected, the circular cross-sectional gauge can include 0.55 mm, 0.65 mm, 0.77 mm, 0.80 mm, and 0.90 mm.

[0058] The superconducting conductor performance curve refers to a relationship curve or surface that characterizes the critical working state of the superconducting material under different external conditions (magnetic field, temperature, current density, etc.). The superconducting conductor performance curve can be described by an empirical formula (commonly known as the NbTi superconducting conductor performance curve empirical formula) or an experimental curve (experimental test data provided by the conductor manufacturer). When designing the parameters, the superconducting conductor performance curve is used to guide the determination of the conductor diameter, the allowable current, and the operating parameters.

[0059] The superconducting conductor performance curve is composed of three basic critical parameters: critical temperature T c , critical magnetic field B c , and critical current density J c . The critical temperature refers to the highest temperature at which the material transitions from a superconducting state to a normal conducting state, with units of K; the critical magnetic field refers to the maximum magnetic field strength at which the superconducting material can maintain superconductivity at a specific temperature, with units of T; and the critical current density refers to the maximum current density that the conductor can carry at a given temperature and magnetic field, with units of A / mm 2 .

[0060] The initial setting range corresponding to the number of radial winding layers and the initial setting range corresponding to the number of axial winding layers can be determined according to size restrictions. For example, the initial setting range of the number of radial winding layers can be 2 layers to 20 layers; and the initial setting range of the number of axial winding layers can be 2 layers to 600 layers.

[0061] The coil current is a direct excitation source for the superconducting magnet coil to generate a magnetic field, and the initial setting range of the coil current can be determined according to the current and magnetic field performance relationship of the material and the maximum allowable current limit. For example, according to the current-magnetic field performance relationship of NbTi conductor, the coil current is preliminarily limited to not more than 100 A.

[0062] For example, the initial setting range of the plurality of coil design parameters can also include: the coil inner diameter size is greater than 230 mm, the radial reserved dewar cold shield installation space is 30 mm, the conductor interlayer insulation layer thickness is 0.6 mm, and / or the outer armor thickness is 2 mm.

[0063] In step S203, a plurality of fitness functions are constructed according to a plurality of optimization objectives.

[0064] The plurality of optimization objectives and the plurality of fitness functions correspond one-to-one. The fitness function can quantify the influence of different coil design parameter combinations (candidate solutions or individuals) on the plurality of optimization objectives, and the design of the fitness function directly affects the convergence speed of the subsequent optimization algorithm and whether the optimal solution can be found. The fitness function is also called the evaluation function, which is used to determine the fitness of the candidate solution (different combinations of coil design parameters). The fitness can evaluate the degree of excellence of the candidate solution, and provide a basis for the optimization direction.

[0065] Specifically, the terminal device can be configured with a coil space magnetic field distribution function characterized based on the Biot-Savart law, which can be as shown in formula (1):

[0066]

[0067] In the formula, is the magnetic field intensity vector, with the unit of Tesla (T); is the vacuum permeability, H / m; is the wire current, with the unit of Ampere (A); is the conductor microelement length vector; is the unit vector from the conductor microelement to the magnetic field calculation point; is the distance from the conductor microelement to the magnetic field calculation point, with the unit of meters (m). The Biot-Savart law can obtain the magnetic field distribution characteristics of a specified region inside the coil by integrating each turn of the coil.

[0068] In the case that the plurality of optimization objectives include the coil center magnetic field strength, the above formula (1) can be determined as the fitness function corresponding to the coil center magnetic field strength, and after obtaining the candidate solution, the position of the coil center can be determined based on the structure parameters in the candidate solution, and then the coil center magnetic field strength can be determined by formula (1) and the position of the coil center.

[0069] The plurality of optimization objectives can include the magnetic field uniformity of the coil target region, and the magnetic field uniformity can be represented by the following formula (2):

[0070]

[0071] In the formula, is the maximum value of the magnetic field strength in the target region, and the unit is Tesla (T); is the minimum value of the magnetic field strength in the target region, and the unit is Tesla (T).

[0072] In the case that the plurality of optimization objectives include the magnetic field uniformity of the coil target region, the above formula (2) can be determined as the fitness function corresponding to the magnetic field uniformity of the coil target region. After obtaining the candidate solution, the maximum value and the minimum value of the magnetic field strength in the target region can be determined by the above formula (1), and then the magnetic field uniformity of the coil target region can be determined based on formula (2).

[0073] Specifically, the magnetic field distribution of the entire region of the superconducting magnet coil can be as shown in Figure 3 , the magnetic field distribution of the target region of the superconducting magnet coil can be as shown in Figure 4 , and the magnetic field distribution on the surface of the superconducting magnet coil can be as shown in Figure 5 It can be seen from Figure 3 that the maximum value of the magnetic field strength of the entire region of the superconducting magnet coil is 0.719T, and the minimum value of the magnetic field strength of the entire region of the superconducting magnet coil is 0.0607T; from Figure 4 It can be seen that the maximum value of the magnetic field strength of the target region of the superconducting magnet coil is 0.503T, and the minimum value of the magnetic field strength of the entire region of the superconducting magnet coil is 0.404T; from Figure 5 It can be seen that the maximum value of the magnetic field strength on the surface of the superconducting magnet coil is 0.721T, and the minimum value of the magnetic field strength on the surface of the superconducting magnet coil is 0.0453T.

[0074] In the case that the plurality of optimization objectives include the coil inductance, the fitness function corresponding to the coil inductance can refer to the long solenoid approximation formula, which can be as shown in formula (3):

[0075]

[0076] In the formula, is the coil inductance, and the unit is H; Ntotal is the total number of turns of the coil; Acoil is the cross-sectional area of the coil, in m2. 2 Hcoil is the axial height of the coil, in m.

[0077] When the multiple optimization objectives include the amount of coil material, the fitness function corresponding to the amount of coil material can be as shown in equation (4):

[0078]

[0079] In the formula, Lcoil is the actual wire length of the coil, in m; Dcoil is the inner diameter of the coil, in m; Dcoil is the outer diameter of the coil, in m.

[0080] Step S204: Based on the multiple fitness functions, the multiple coil design parameters are optimized within the initial setting range of the multiple coil design parameters, to obtain a target coil design parameter combination.

[0081] The target coil design parameter combination is a configuration scheme of the multiple coil design parameters that satisfies the constraint conditions of the multiple optimization objectives.

[0082] Specifically, the multiple coil design parameters can be optimized within the initial setting range of the multiple coil design parameters based on the multiple fitness functions by using an optimization algorithm, so as to obtain the target coil design parameter combination. The multiple fitness functions are input into the optimization algorithm to evaluate the performance indicators of multiple solutions formed by the optimization algorithm within the initial setting range, and the optimal solution (i.e., the target coil design parameter combination) is selected based on the evaluation results.

[0083] For example, the optimization algorithm can be a multi-objective genetic algorithm NSGA-II or a particle swarm optimization algorithm.

[0084] The superconducting magnet coil design method provided in this embodiment comprises the following steps: after obtaining the multiple optimization objectives, the constraint conditions of the multiple optimization objectives, and the initial setting range of the multiple coil design parameters, multiple fitness functions are constructed according to the multiple optimization objectives, and then the multiple coil design parameters are optimized within the initial setting range of the multiple coil design parameters based on the multiple fitness functions, to obtain a target coil design parameter combination. The method realizes the comprehensive and automatic optimization of multiple performance indicators such as magnetic field strength, uniformity, inductance, and material amount in the optimization design of the superconducting magnet coil, significantly improves the design efficiency and performance indicators, is suitable for a wide range of engineering application scenarios, solves the technical bottleneck that the traditional method is difficult to simultaneously consider multiple objectives, and realizes a reasonable balance between performance and economy.

[0085] ​The application further provides another superconducting magnet coil design method, which can be used for the terminal device or the functional module in the terminal device, Figure 6 is a flowchart of another superconducting magnet coil design method according to an embodiment of the application, as shown in the figure, and the flowchart comprises the following steps: Figure 6

[0086] In step S601, a plurality of optimization objectives and constraint conditions of the plurality of optimization objectives are obtained.

[0087] For details, refer to step S201 of the embodiment shown in Figure 2 , which will not be repeated here.

[0088] In step S602, an initial setting range of a plurality of coil design parameters is obtained.

[0089] For details, refer to step S202 of the embodiment shown in Figure 2 , which will not be repeated here.

[0090] In step S603, a plurality of fitness functions are constructed according to the plurality of optimization objectives.

[0091] For details, refer to step S203 of the embodiment shown in Figure 2 , which will not be repeated here.

[0092] In step S604, the plurality of coil design parameters are optimized based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters, and a target coil design parameter combination is obtained.

[0093] In this step, the multi-objective genetic algorithm NSGA-II is used for design parameter optimization. At this time, the plurality of coil design parameters are gene-coded in a real number coding mode for optimization calculation of the multi-objective genetic algorithm.

[0094] Specifically, step S604 can comprise:

[0095] In step S6041, an initial population is generated according to the initial setting range of the plurality of coil design parameters.

[0096] The initial population comprises a plurality of individuals, and each individual corresponds to a configuration scheme of the plurality of coil design parameters.

[0097] In some embodiments, a machine learning or neural network model can be introduced to guide the generation of the initial population under the limitation of the initial setting range of the plurality of coil design parameters, so as to improve the convergence speed of the optimization algorithm and the globality of the optimization result, and reduce the iteration number and calculation amount of the algorithm.

[0098] ​The initial population size can be set to 100, i.e., the initial population includes 100 individuals, the individual code adopts real number coding, and the coding parameters can include: conductor specification (conductor diameter), radial winding layer number, axial winding layer number, and coil current size.

[0099] In some other embodiments, after obtaining the initial setting range of the plurality of coil design parameters, the initial population can be randomly generated under the limitation of the initial setting range.

[0100] Step S6042, according to the plurality of fitness functions, determine the fitness corresponding to each individual in the plurality of individuals.

[0101] Specifically, taking the plurality of optimization targets including the coil central magnetic field strength, the magnetic field uniformity of the coil target area, the coil inductance, and the coil material usage as an example, after generating the initial population, the four target function values (fitness) corresponding to each individual in the initial population can be determined according to the formulas (1) to (4), so as to evaluate the performance of each individual on the optimization targets (indicators) of the magnetic field strength, the magnetic field uniformity, the inductance, and the material usage.

[0102] Step S6043, according to the fitness of each individual, perform cross operation, mutation operation, and elite operation on the initial population to generate the next generation population.

[0103] Specifically, after determining the fitness of each individual, the NSGA-II algorithm can be used to perform non-dominated sorting according to the fitness, and the individuals in the initial population can be divided into different levels, such as filtering out the individuals in the Pareto front, the second level, the third level, …, and the nth level. The Pareto front is the first level, which refers to a set of optimal solutions that cannot be further improved, and the individuals in the Pareto front are preferentially selected to enter the next generation population.

[0104] After dividing the individuals in the initial population into different levels, the candidate solutions (individuals) in the higher levels can be reserved as the parents, and then new 100 individuals (the next generation population) can be generated from the plurality of individuals (taking 100 as an example) in the initial population through cross operation (randomly combining the parent parameters to generate new candidate solutions), mutation operation (randomly fine-tuning part of the child parameters), and elite strategy (reserving the candidate solutions in the Pareto optimal front in the last generation).

[0105] For example, a selected parent individual is subjected to a simulated binary crossover (SBX) operation with a crossover probability of 0.8 to generate a new offspring population. A polynomial mutation operation is performed on the new offspring population with a mutation probability of 0.1 to increase the diversity of solutions and prevent premature convergence. An elitist strategy is used to retain solutions on the Pareto optimal front in the previous generation, ensuring that good solutions are not eliminated and thus continuously improving the optimization effect.

[0106] The simulated binary crossover operation is used to describe a real-coded genetic algorithm crossover operator that simulates the behavior of binary-coded single-point crossover in continuous search space. The core is to generate offspring through a specific probability distribution, so that the offspring have a controllable mutation range while inheriting the characteristics of the parents, thereby balancing the exploration and utilization capabilities in multi-objective optimization.

[0107] In some embodiments, before the step S6043 described above, the superconducting magnet coil design method further includes determining the deviation between the first theoretical magnetic field strength of each individual and the first simulated magnetic field strength of each individual. At this time, the step S6043 described above is specifically: when the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than a preset deviation value, performing a crossover operation, a mutation operation and an elite operation on the initial population according to the fitness of each individual to generate a next generation population.

[0108] The first theoretical magnetic field strength is the coil center magnetic field strength determined by each individual through the Biot-Savart law, and the first simulated magnetic field strength is the coil center magnetic field strength determined by each individual through the finite element simulation model. The preset deviation value is used to represent the upper limit of the deviation allowed by the theoretical magnetic field strength, which can be set by the user, for example, the preset deviation value can be 3% or 5%, etc.

[0109] Specifically, a finite element simulation model of the superconducting magnet coil can be established in a finite element simulation software (such as COMSOL Multiphysics software) according to the coil design parameters corresponding to the individual, and the magnetic field distribution of the superconducting magnet coil is simulated and determined through the finite element simulation model, to verify the accuracy of the Biot-Savart theoretical magnetic field calculation result.

[0110] For example, if the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than 5%, it is considered that the Biot-Savart theoretical magnetic field calculation result is accurate, and the next step operation can be performed; if the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength exceeds (is greater than or equal to) 5%, it is considered that the magnetic field calculation result is inaccurate, the Biot-Savart theoretical magnetic field calculation model (such as formula (1)) is adjusted, and the above process is repeated until the error between the simulation and the theoretical calculation is less than 5%.

[0111] The theoretical magnetic field calculation model parameters can be geometric parameters and / or numerical integral and discrete precision. The geometric parameters include equivalent radius / equivalent cross section, such as radius correction of the simplified "line current" replaced by a finite cross section conductor, the geometric parameters can also include spiral pitch / real track, such as changing the simplified "concentric circle turn" to "spiral turn" approximation, and the geometric parameters can also include interlayer / interturn radius increment, such as considering the insulation, armor, gap into the turn radius. The numerical integral and discrete precision includes single-turn integral segmentation number, such as adjusting the discrete segment number of each turn ring integral, for example, from 360 to 720.

[0112] Step S6044, determine whether the next generation population reaches the convergence condition or the maximum iteration number.

[0113] Step S6045, when the next generation population reaches the convergence condition or the maximum iteration number, determine the configuration scheme corresponding to the optimal individual in the next generation population as the target coil design parameter combination.

[0114] Specifically, after obtaining the next generation population, it is judged whether the next generation population reaches the set convergence condition or the iteration number corresponding to the next generation population reaches the set maximum iteration number (such as 100 times). If the convergence condition or the maximum iteration number is not reached, return to the above step S6042 to continue optimization iteration, at this time, the individual in step S6042 is the individual included in the newly generated next generation population; if the convergence condition or the maximum iteration number is reached, obtain the optimal coil design parameter combination satisfying the design target and the constraint condition from the next generation population.

[0115] For example, after reaching the convergence condition or the maximum iteration number, select the comprehensive optimization scheme with coil center magnetic field intensity ≥0.5T, coil target region magnetic field uniformity ≤15%, minimum inductance and lowest material usage from the Pareto frontier solution set of the next generation population, and determine the comprehensive optimization scheme as the target coil design parameter combination.

[0116] The target coil design parameter combination can include conductor specification, radial winding layer number, axial winding layer number, coil current, coil inner diameter size, coil outer diameter size, coil height, actual wire usage, coil maximum magnetic field intensity, target region magnetic field uniformity and coil inductance.

[0117] Specifically, the comparison between the coil current and the conductor limit current can be as shown in Figure 7 The first line segment 71 (black line segment) represents the conductor limit current under different magnetic field intensities B, and the second line segment 72 (red line segment) represents the coil current. The safety margin is verified by the area between the first line segment 71 and the second line segment 72. Figure 7For example, the limit current of a superconducting magnet coil formed by NbTi superconducting strands with a diameter of 0.487 mm to maintain a superconducting state is taken as an example. The data of the first line segment 71 is from the manufacturer. The first part 721 (green line segment) of the second line segment 72 represents the coil current at different magnetic field strengths B obtained by simulation after the parameters and structure of the designed superconducting magnet coil are determined. Because the limit current of the conductor may not be reached in actual operation, the second part 722 (red dashed line) of the second line segment 72 is a fitted extension of the first part 721 to determine the limit current that the designed superconducting magnet coil can withstand (i.e., the current corresponding to the intersection of the second part 722 and the first line segment 71).

[0118] For example, the safety margin can be determined by the following formula (5) The greater the safety margin, the higher the safety of the operation of the superconducting magnet coil.

[0119]

[0120] In the formula, represents the limit current that the designed superconducting magnet coil can withstand, i.e., the current corresponding to the intersection of the second part 722 and the first line segment 71, represents the maximum current of the designed superconducting magnet coil in actual operation, i.e., the current corresponding to the upper end point of the first part 721.

[0121] In some embodiments, after obtaining the target coil design parameter combination, the superconducting magnet coil design method further comprises: determining the deviation between the optimal individual's second theoretical magnetic field strength and the optimal individual's second simulated magnetic field strength.

[0122] At this time, the above step S6045 is specifically: when the deviation between the second theoretical magnetic field strength and the second simulated magnetic field strength is less than a preset deviation value, the optimal individual's configuration scheme is determined as the target coil design parameter combination.

[0123] In the formula, the second theoretical magnetic field strength is the coil center magnetic field strength calculated by the Biot-Savart law by the optimal individual, and the second simulated magnetic field strength is the coil center magnetic field strength simulated by the optimal individual through the finite element simulation model.

[0124] Specifically, after the target coil design parameter combination is determined, the selected scheme is finally simulated and verified by using CAE simulation software. If the deviation between the simulation result and the theoretical calculation result is less than a preset deviation value, the determined target coil design parameter combination is determined as the final optimization result, ensuring that the performance indicators of the final scheme meet the design requirements.

[0125] The superconducting magnet coil design method provided by the embodiment can cross-verify theoretical calculation based on the Biot-Savart law and CAE simulation results, effectively improve the accuracy of magnetic field distribution calculation, verify the feasibility of the optimized parameters through simulation analysis, ensure that the design result is highly consistent with the actual operation condition, and improve the design accuracy and reliability. Meanwhile, the NSGA-II multi-objective genetic algorithm is introduced, which has good global search capability, can quickly find the Pareto optimal solution set of the superconducting magnet coil design, significantly improve the design efficiency, and avoid the design process from falling into local optimum.

[0126] The application further provides another superconducting magnet coil design method, which can be used for the terminal device or the functional module in the terminal device, Figure 8 is a flowchart of another superconducting magnet coil design method according to an embodiment of the application, as shown in the figure, the flowchart comprises the following steps: Figure 8

[0127] In step S801, a plurality of optimization objectives and constraint conditions of the plurality of optimization objectives are obtained.

[0128] For details, refer to step S201 of the embodiment shown in Figure 2 and will not be repeated here.

[0129] In step S802, the initial setting range of a plurality of coil design parameters is obtained.

[0130] For details, refer to step S202 of the embodiment shown in Figure 2 and will not be repeated here.

[0131] In step S803, a plurality of fitness functions are constructed according to the plurality of optimization objectives.

[0132] For details, refer to step S203 of the embodiment shown in Figure 2 and will not be repeated here.

[0133] In step S804, the plurality of coil design parameters are optimized based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters, and a target coil design parameter combination is obtained.

[0134] Specifically, the plurality of optimization objectives include coil center magnetic field intensity, coil target region magnetic field uniformity, coil inductance and coil material usage, at this time, the above step S804 comprises:

[0135] ​Step S8041, within the initial setting range of the plurality of coil design parameters, based on the fitness function of the coil center magnetic field strength and the fitness function of the magnetic field uniformity of the coil target area, the plurality of coil design parameters are optimized to obtain an intermediate population, with the constraint condition of satisfying the coil center magnetic field strength and the constraint condition of satisfying the magnetic field uniformity of the coil target area as the target.

[0136] Step S8042, on the basis of the intermediate population, with the coil inductance minimization and the coil material usage minimization as the target, based on the fitness function corresponding to the coil inductance and the fitness function corresponding to the coil material usage, the intermediate population is optimized to obtain the target coil design parameter combination.

[0137] Specifically, the optimization target of the embodiment includes four indexes of coil center magnetic field strength, magnetic field uniformity, coil inductance and material usage, wherein the magnetic field strength and the magnetic field uniformity are constraint conditions that must be satisfied, and the inductance and the material usage are targets for further optimization. Therefore, the fitness function is constructed as two parts of constraint conditions and objective functions.

[0138] For example, the constraint conditions include constraint one and constraint two, constraint one is the condition that the magnetic field strength needs to satisfy, as shown in formula (6), and constraint two is the condition that the magnetic field uniformity needs to satisfy, as shown in formula (7).

[0139]

[0140] In the formula, It can be the maximum value of the magnetic field in the area with a radial less than 25 mm and an axial ±100 mm, with the unit of Tesla (T); It can be the minimum value of the magnetic field in the same area, with the unit of Tesla (T).

[0141] After generating the initial population, the initial population is optimized based on formula (6) and formula (7) to obtain an intermediate population. The above two constraint conditions are strong constraints. If a certain individual (design parameter combination) cannot satisfy any one of the constraints (constraint one and constraint two), the individual is determined as an invalid solution and is excluded from the optimization population. The intermediate population is the optimized initial population that simultaneously satisfies constraint one and constraint two.

[0142] On the premise of satisfying the above two constraint conditions, two objective functions are established for further optimization. The objective function one is the coil inductance optimization target, as shown in formula (8), and the objective function two is the material usage optimization target, as shown in formula (9).

[0143]

[0144] In the formula, f1 represents the objective function one, and f2 represents the objective function two.

[0145] After obtaining the intermediate population, the individuals in the intermediate population are further optimized in the optimization direction of minimizing the above two objective functions, to obtain the target coil design parameter combination.

[0146] Specifically, in the optimization calculation process, firstly, it is ensured that the two constraint conditions of magnetic field strength and magnetic field uniformity are always met, and then the solution set (i.e., the intermediate population) meeting the conditions is further optimized in terms of inductance and material consumption, so as to finally realize the compromise and balance of various performance indicators of the coil design, and obtain the design scheme with the optimal comprehensive performance.

[0147] Step S805: coupling the thermal simulation model of the superconducting magnet coil and the cooling system constructed based on the target coil design parameter combination, and optimizing the target coil design parameter combination with the maximum cooling efficiency of the superconducting magnet coil as the target.

[0148] Specifically, the present application can further optimize the coil cooling efficiency, reduce the operating temperature fluctuation of the superconducting conductor, and ensure that the magnet performance is more stable and reliable by coupling the coil design parameter combination meeting the multiple optimization target constraint conditions with the thermal simulation model of the cooling system.

[0149] The coil cooling efficiency is strongly related to the coil size structure, but needs to be comprehensively considered in combination with the cooling loop design, material thermal performance and operating conditions.

[0150] Step S806: obtaining the operating parameters of the superconducting magnet coil constructed based on the target coil design parameter combination.

[0151] Specifically, the operating parameters can refer to the key physical quantities that can be monitored and recorded in real time, and can directly or indirectly reflect the coil performance state, operating stability and safety risk in the process of realizing the intended function (such as MRI imaging, nuclear fusion confinement, particle acceleration, etc.) of the superconducting magnet coil. For example, the operating parameters can include real-time current of the coil, coil terminal voltage / leakage current and coil body temperature, etc.

[0152] Step S807: optimizing the target coil design parameter combination based on the operating parameters.

[0153] Specifically, based on the operating parameters, the target coil design parameter combination can be optimized by using an optimization algorithm.

[0154] The present application can establish a coil operation data feedback mechanism by monitoring the actual operating parameters of the coil in real time, continuously optimize the subsequent design scheme of the coil based on the operation data, and form a closed-loop optimization system of design-manufacturing-operation-feedback.

[0155] Optionally, the application can also combine the structure optimization result with the coil manufacturing process simulation to predict possible problems in the manufacturing process in advance and provide targeted process improvement solutions, thereby improving the process feasibility and production efficiency in the actual manufacturing process.

[0156] Specifically, by combining the coil structure optimization result with the manufacturing process simulation (including winding tension simulation, vacuum pressure impregnation flow simulation, resin curing thermal stress simulation, etc.), possible geometric deviations, process defects and thermal stress mismatch problems in the manufacturing process can be predicted in advance. For example, uneven tension can be found through winding simulation and the tension control process can be improved; potential voids can be identified through impregnation simulation and the glue injection process can be optimized; stress concentration can be predicted through curing and thermal cycle simulation and the curing curve and material matching can be optimized. This can effectively improve the process feasibility, reduce the defect rate, and improve the coil production efficiency and reliability.

[0157] Optionally, the application can also further extend the multi-objective optimization algorithm to include more factors such as coil magnetic field characteristics, electrical performance, structural reliability, manufacturing cost, and maintenance convenience into the optimization target, further improving the comprehensive advantages of the design scheme and the economic benefits of actual application.

[0158] The superconducting magnet coil design method provided by the embodiment can combine cooling system thermal simulation, manufacturing process simulation, and actual operation data feedback to form a closed-loop optimization process of design-manufacturing-operation-feedback, further improving the manufacturability, operation stability, and post-optimization capability of the coil design scheme, and is suitable for various high-performance superconducting magnet application scenarios such as nuclear fusion, nuclear magnetic resonance, and particle accelerators, and has strong engineering promotion value. At the same time, by optimizing the material usage, reducing the inductance, and verifying the manufacturability in the design stage, the material cost and manufacturing risk can be reduced, the design cycle can be shortened, and the economic benefits can be improved.

[0159] The superconducting magnet coil design method provided by the application will be described in detail below in combination with the flowchart under the condition of combining multi-objective genetic optimization with CAE simulation.

[0160] As shown in Figure 9 , first, the optimization target and the constraint condition are determined, the optimization target includes the magnetic field strength, the uniformity, the inductance, and the material usage, etc.; then the initial range of the coil design parameters is determined, and the coil design parameters are gene coded, that is, the design parameter range of the conductor specification, the winding layer number, the current size, etc. is preliminarily determined, and the gene coding is performed.

[0161] After that, the model of the theoretical calculation of the Biot-Savart law magnetic field is obtained, the magnetic field distribution corresponding to the initial design parameter combination is calculated, and then the accuracy of the theoretical calculation result is verified through CAE simulation, that is, the coil model is established by using CAE simulation, the coil magnetic field distribution is simulated, and the accuracy of the theoretical magnetic field calculation result is verified based on the coil magnetic field distribution.

[0162] If the difference between the simulation result and the theoretical calculation result is less than 5%, the constraint condition (the above constraint one and constraint two) and the optimization fitness function (the objective function one and the objective function two) are constructed, specifically, the constraint condition and the fitness function containing the magnetic field strength, the uniformity, the inductance and the material consumption are constructed according to the optimization target; if it is greater than or equal to 5%, the parameters of the theoretical calculation model are adjusted until the difference between the simulation result and the theoretical calculation result is less than 5%.

[0163] After the constraint condition and the optimization fitness function are constructed, the multi-objective genetic algorithm is used for optimization iteration, that is, the multi-objective genetic algorithm NSGA-II is used for optimization iteration of the design parameters. Then, it is judged whether the optimization algorithm reaches the convergence condition, if not, the multi-objective genetic algorithm is continued to be used for optimization iteration; if the convergence condition is reached, the Pareto optimal solution set satisfying the design target is obtained, specifically, the Pareto optimal solution set is obtained, and one or several optimal solutions are selected for CAE verification. The accuracy and effectiveness of the obtained Pareto optimal solution are verified through CAE simulation verification.

[0164] Then, it is judged whether the verified simulation result meets the design requirement, if not, the multi-objective genetic algorithm is continued to be used for optimization iteration; if it meets, the final optimization design scheme is obtained, specifically, the final superconducting magnet coil optimization design scheme is determined according to the simulation verification result.

[0165] The present application combines the multi-objective genetic optimization algorithm with the CAE simulation technology, and takes the Biot-Savart law as the theoretical basis, and proposes a kind of efficient, feasible comprehensive optimization method for the multi-objective contradiction and parameter coupling relationship in the design process of superconducting magnet coil, with the following beneficial effects:

[0166] (1) Multi-objective collaborative optimization

[0167] The present application can simultaneously optimize the center magnetic field strength, the magnetic field uniformity, the coil inductance and the material consumption and other multiple key performance indicators of the coil, solves the technical bottleneck that the traditional method is difficult to simultaneously consider multiple targets, and realizes the reasonable balance of performance and economy.

[0168] (2) Improve design accuracy and reliability

[0169] The present application cross-verify the theoretical calculation based on Biot-Savart law and the CAE simulation result, can effectively improve the accuracy of the magnetic field distribution calculation, and verify the feasibility of the optimized parameters through simulation analysis, ensure that the design result is highly consistent with the actual operation condition.

[0170] (3) Improve optimization efficiency and globality

[0171] Introduce NSGA-II multi-objective genetic algorithm, which has good global search ability, can quickly find the Pareto optimal solution set of superconducting magnet coil design, significantly improve the design efficiency, and avoid the design process into local optimum.

[0172] (4) Good engineering applicability and scalability

[0173] The present application can combine cooling system thermal simulation, manufacturing process simulation and operation data feedback to form a closed-loop optimization system of design-manufacturing-operation-feedback, further improve the manufacturability, operation stability and post-optimization capability of the coil design scheme, suitable for various high-performance superconducting magnet application scenarios such as nuclear fusion, nuclear magnetic resonance, particle accelerator, etc., and has strong engineering popularization value.

[0174] (5) Reduce cost and risk

[0175] By optimizing the material quantity, reducing the inductance and verifying the manufacturability in the design stage, it helps to reduce the material cost and manufacturing risk, shorten the design cycle and improve the economic benefit.

[0176] The present application closely combines multi-objective genetic algorithm (such as NSGA-II), theoretical calculation based on Biot-Savart law and CAE finite element simulation to build a complete "theoretical calculation-simulation verification-iterative optimization" technical path, effectively realizing the collaborative optimization of superconducting magnet coil multi-objective parameters.

[0177] The superconducting magnet coil designed by the present application is applied to high-performance superconducting magnet fields such as nuclear fusion device, nuclear magnetic resonance, particle accelerator, etc.

[0178] In the present embodiment, a superconducting magnet coil design device is also provided, which is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiments is preferably realized in software, hardware, or a combination of software and hardware is also possible and is conceived.

[0179] The present embodiment provides a superconducting magnet coil design device, as shown in Figure 10 , comprising:

[0180] The target acquisition module 1001 is configured to acquire a plurality of optimization targets and constraint conditions of the plurality of optimization targets, wherein the plurality of optimization targets comprises at least two of a coil center magnetic field strength, a magnetic field uniformity of a coil target region, a coil inductance, and a coil material usage;

[0181] The range acquisition module 1002 is configured to acquire an initial setting range of a plurality of coil design parameters;

[0182] The function construction module 1003 is configured to construct a plurality of fitness functions according to the plurality of optimization targets, wherein the plurality of optimization targets and the plurality of fitness functions are in one-to-one correspondence.

[0183] The optimization processing module 1004 is configured to perform optimization processing on the plurality of coil design parameters based on the plurality of fitness functions within the initial setting range of the plurality of coil design parameters, to obtain a target coil design parameter combination, wherein the target coil design parameter combination is a configuration scheme of the plurality of coil design parameters that satisfies the constraint conditions of the plurality of optimization targets.

[0184] In some optional embodiments, the optimization processing module 1004 comprises:

[0185] The first generation unit is configured to generate an initial population according to the initial setting range of the plurality of coil design parameters, wherein the initial population comprises a plurality of individuals, and each individual corresponds to a configuration scheme of the plurality of coil design parameters;

[0186] The first determination unit is configured to determine a fitness of each individual in the plurality of individuals according to the plurality of fitness functions.

[0187] The second generation unit is configured to perform a crossover operation, a mutation operation, and an elite operation on the initial population according to the fitness of each individual, to generate a next-generation population.

[0188] The second determination unit is configured to determine whether the next-generation population reaches a convergence condition or a maximum iteration number.

[0189] The third determination unit is configured to determine, when the next-generation population reaches the convergence condition or the maximum iteration number, a configuration scheme corresponding to an optimal individual in the next-generation population as the target coil design parameter combination.

[0190] In some optional embodiments, the plurality of optimization targets comprises the coil center magnetic field strength, and the coil center magnetic field strength is determined by a coil spatial magnetic field distribution function characterized by the Biot-Savart law.

[0191] In some optional embodiments, the apparatus further comprises:

[0192] The first magnetic field determination module is configured to determine a deviation between a first theoretical magnetic field intensity of each individual and a first simulated magnetic field intensity of each individual, wherein the first theoretical magnetic field intensity is a coil center magnetic field intensity of each individual determined by Biot-Savart law, and the first simulated magnetic field intensity is a coil center magnetic field intensity of each individual simulated by a finite element simulation model.

[0193] The second generation unit comprises:

[0194] The first generation subunit is configured to, when the deviation between the first theoretical magnetic field intensity and the first simulated magnetic field intensity is less than a preset deviation value, perform a crossover operation, a mutation operation and an elite operation on the initial population according to the fitness of each individual, to generate a next generation population.

[0195] In some optional embodiments, the apparatus further comprises:

[0196] The second magnetic field determination module is configured to determine a deviation between a second theoretical magnetic field intensity of the optimal individual and a second simulated magnetic field intensity of the optimal individual.

[0197] The third determination unit comprises:

[0198] The first determination subunit is configured to, when the deviation between the second theoretical magnetic field intensity and the second simulated magnetic field intensity is less than a preset deviation value, determine the configuration scheme corresponding to the optimal individual as the target coil design parameter combination.

[0199] In some optional embodiments, the optimization processing module 1004 comprises:

[0200] The first optimization unit is configured to, within an initial setting range of the plurality of coil design parameters, perform optimization processing on the plurality of coil design parameters based on a fitness function of the coil center magnetic field intensity and a fitness function of the magnetic field uniformity of the coil target region, to obtain an intermediate population, with a constraint condition of satisfying the coil center magnetic field intensity and a constraint condition of satisfying the magnetic field uniformity of the coil target region as the target.

[0201] The second optimization unit is configured to, based on the intermediate population, perform optimization on the intermediate population based on a fitness function corresponding to the coil inductance and a fitness function corresponding to the coil material usage, to obtain the target coil design parameter combination, with minimization of the coil inductance and minimization of the coil material usage as the target.

[0202] In some optional embodiments, the apparatus further comprises:

[0203] The first optimization module is configured to couple a superconducting magnet coil constructed based on the target coil design parameter combination with a thermal simulation model of a cooling system, to optimize the target coil design parameter combination with maximization of a cooling efficiency of the superconducting magnet coil as the target.

[0204] In some optional embodiments, the apparatus further comprises:

[0205] a parameter obtaining module configured to obtain an operating parameter of the superconducting magnet coil constructed based on the target coil design parameter combination;

[0206] a second optimization module configured to optimize the target coil design parameter combination based on the operating parameter.

[0207] The superconducting magnet coil design apparatus provided by the embodiments of the present application can perform the superconducting magnet coil design method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method. The further function description of each of the above modules and units is the same as that of the corresponding embodiments, which will not be repeated here.

[0208] Figure 11 A structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0209] The following will be specifically described with reference to Figure 11 which shows a structural schematic diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present application. The electronic device can include a processor (for example, a central processor, a graphics processor, etc.) 1101, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1102 or loaded from a storage 1108 into a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the electronic device are also stored. The processor 1101, the ROM 1102, and the RAM 1103 are connected to each other through a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0210] Generally, the following apparatuses can be connected to the I / O interface 1105: an input apparatus 1106 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output apparatus 1107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage 1108 including, for example, a magnetic tape, a hard disk, etc.; and a communication apparatus 1109. The communication apparatus 1109 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 11 The electronic device with various apparatuses is shown, but it should be understood that it is not required to implement or have all the shown apparatuses, and more or less apparatuses can be alternatively implemented or had.

[0211] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program in accordance with embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for carrying out the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication device 1109, or installed from the memory 1108, or installed from the ROM 1102. When the computer program is executed by the processor 1101, the above-mentioned functions defined in the superconducting magnet coil design method of embodiments of the present application are performed.

[0212] Figure 11 The electronic device shown is merely an example and should not impose any limitation on the functions and use range of embodiments of the present application.

[0213] Embodiments of the present application also provide a computer-readable storage medium, the above-mentioned method according to embodiments of the present application can be implemented in hardware, firmware, or as computer code recordable on a storage medium, or as computer code originally stored in a remote storage medium or non-transitory machine-readable storage medium and to be stored in a local storage medium downloaded through a network, so that the method described herein can be processed by such software stored on a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor or hardware, implements the superconducting magnet coil design method shown in the above embodiments.

[0214] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be invoked or provided. Those skilled in the art should understand that the form of computer program instructions in computer-readable medium includes but is not limited to source files, executable files, installation package files, etc., and accordingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer executes the corresponding compiled program after compiling the instructions, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installation program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0215] While embodiments of the application have been described in connection with the preferred embodiments of the various figures, those of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the application, and that such modifications and changes fall within the scope of the appended claims.

Claims

1. A method for designing a superconducting magnet coil, characterized in that, The method includes: Multiple optimization objectives and constraints for the multiple optimization objectives are obtained, wherein the multiple optimization objectives include the magnetic field strength at the center of the coil, the magnetic field uniformity in the target region of the coil, the coil inductance, and the amount of coil material used. The magnetic field strength at the center of the coil is determined by the coil spatial magnetic field distribution function characterized by the Biot-Savart law. Obtain the initial setting range of multiple coil design parameters; Based on the plurality of optimization objectives, a plurality of fitness functions are constructed, wherein the plurality of optimization objectives and the plurality of fitness functions correspond one-to-one; Within the initial setting range of the multiple coil design parameters, with the goal of satisfying the constraints of the magnetic field strength at the center of the coil and the magnetic field uniformity of the target region of the coil, the multiple coil design parameters are optimized based on the fitness function of the magnetic field strength at the center of the coil and the fitness function of the magnetic field uniformity of the target region of the coil to obtain an intermediate population. Based on the intermediate population, with the goals of minimizing coil inductance and minimizing coil material usage, the intermediate population is optimized based on the fitness function corresponding to the coil inductance and the fitness function corresponding to the coil material usage to obtain the target coil design parameter combination. The target coil design parameter combination is a configuration scheme of multiple coil design parameters that satisfies the constraints of the multiple optimization goals. Within the initial setting range of the plurality of coil design parameters, the plurality of coil design parameters are optimized based on the plurality of fitness functions, including: An initial population is generated based on the initial setting range of the multiple coil design parameters, wherein the initial population includes multiple individuals, and each individual corresponds to a configuration scheme of the multiple coil design parameters; Based on the multiple fitness functions, determine the fitness of each of the multiple individuals; The deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength of each individual is determined, wherein the first theoretical magnetic field strength is the magnetic field strength at the center of the coil calculated for each individual using the Biot-Savart law, and the first simulated magnetic field strength is the magnetic field strength at the center of the coil simulated for each individual using a finite element simulation model. When the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than a preset deviation value, crossover, mutation, and elite operations are performed on the initial population according to the fitness of each individual to generate the next generation population. Determine whether the next generation population has reached the convergence condition or the maximum number of iterations; When the next generation population reaches the convergence condition or the maximum number of iterations, the configuration scheme corresponding to the optimal individual in the next generation population is determined as the target coil design parameter combination. The intermediate population is the optimized initial population in which all individuals simultaneously satisfy the constraint conditions of the magnetic field strength at the center of the coil and the magnetic field uniformity of the target region of the coil. When the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is greater than or equal to the preset deviation value, the parameters of the coil spatial magnetic field distribution function characterized by the Biot-Savart law are adjusted until the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than the preset deviation value.

2. The method according to claim 1, characterized in that, Before determining the optimal configuration scheme corresponding to the individual in the next generation population as the target coil design parameter combination, the method further includes: Determine the deviation between the optimal individual's second theoretical magnetic field strength and the optimal individual's second simulated magnetic field strength; The step of determining the optimal configuration scheme corresponding to the individual in the next generation population as the target coil design parameter combination includes: When the deviation between the second theoretical magnetic field strength and the second simulated magnetic field strength is less than a preset deviation value, the optimal configuration scheme corresponding to the individual is determined as the target coil design parameter combination.

3. The method according to claim 1, characterized in that, After obtaining the target coil design parameter combination, the method further includes: The superconducting magnet coil, constructed based on the combination of design parameters of the target coil, is coupled with the thermal simulation model of the cooling system. The combination of design parameters of the target coil is optimized with the goal of maximizing the cooling efficiency of the superconducting magnet coil.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain the operating parameters of the superconducting magnet coil constructed based on the combination of design parameters of the target coil; Based on the operating parameters, optimize the combination of design parameters for the target coil.

5. A design device for a superconducting magnet coil, characterized in that, The device includes: The target acquisition module is used to acquire multiple optimization targets and the constraints of the multiple optimization targets. The multiple optimization targets include the magnetic field strength at the center of the coil, the magnetic field uniformity of the target area of ​​the coil, the coil inductance, and the amount of coil material. The magnetic field strength at the center of the coil is determined by the coil spatial magnetic field distribution function characterized by the Biot-Savart law. The range acquisition module is used to obtain the initial setting range of multiple coil design parameters; The function construction module is used to construct multiple fitness functions based on the multiple optimization objectives, wherein the multiple optimization objectives and the multiple fitness functions correspond one-to-one; An optimization processing module is configured to optimize the multiple coil design parameters within the initial setting range of the multiple coil design parameters, with the objectives of satisfying the constraints of the magnetic field strength at the center of the coil and the magnetic field uniformity of the target region of the coil, based on the fitness function of the magnetic field strength at the center of the coil and the fitness function of the magnetic field uniformity of the target region of the coil, to obtain an intermediate population; and to further optimize the intermediate population based on the intermediate population, with the objectives of minimizing the coil inductance and minimizing the coil material usage, based on the fitness function corresponding to the coil inductance and the fitness function corresponding to the coil material usage, to obtain a target coil design parameter combination, wherein the target coil design parameter combination is a configuration scheme of multiple coil design parameters that satisfies the constraints of the multiple optimization objectives; The optimization processing module specifically includes: generating an initial population based on the initial setting range of the multiple coil design parameters, wherein the initial population includes multiple individuals, each corresponding to a configuration scheme of the multiple coil design parameters; determining the fitness of each individual based on the multiple fitness functions; determining the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength of each individual, wherein the first theoretical magnetic field strength is the coil center magnetic field strength calculated by each individual using the Biot-Savart law, and the first simulated magnetic field strength is the coil center magnetic field strength simulated by each individual using a finite element simulation model; when the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than a preset deviation value, adjusting the initial population based on the fitness of each individual. The initial population undergoes crossover, mutation, and elite operations to generate the next generation population. It is determined whether the next generation population has reached the convergence condition or the maximum number of iterations. When the next generation population reaches the convergence condition or the maximum number of iterations, the optimal configuration scheme corresponding to the best individual in the next generation population is determined as the target coil design parameter combination. The intermediate population is the optimized initial population where all individuals simultaneously satisfy the constraints of the coil's central magnetic field strength and the magnetic field uniformity of the coil's target region. When the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is greater than or equal to the preset deviation value, the parameters of the coil's spatial magnetic field distribution function, characterized by the Biot-Savart law, are adjusted until the deviation between the first theoretical magnetic field strength and the first simulated magnetic field strength is less than the preset deviation value.

6. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the superconducting magnet coil design method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the superconducting magnet coil design method according to any one of claims 1 to 4.

8. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the superconducting magnet coil design method according to any one of claims 1 to 4.

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