A design method and apparatus for an electromagnetic device

Abstract

This invention discloses a design method and apparatus for electromagnetic devices, belonging to the field of electromagnetic sensing technology. The design method considers specific actual usage scenarios when designing electromagnetic devices, comprehensively considers multiple electromagnetic property parameters of candidate materials and the mutual constraints between different electromagnetic property parameters, constructs a multi-objective optimization model of the electromagnetic device based on its structure and materials, solves this model to obtain the Pareto optimal solution to calculate the sensitivity, uses the sensitivity to determine the objective function, minimizes the objective function to find the stable optimal solution under different candidate materials, and finally uses a state evaluation method to select the optimal material. The proposed method has good generalization and reference value, and has certain engineering promotion value, thereby solving the technical problem that existing electromagnetic device designs often only consider a single performance, resulting in poor performance and limited application scenarios.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic sensing technology, and more specifically, relates to a design method and apparatus for an electromagnetic device. Background Technology

[0002] High-current sensing and measurement technology is widely used in power systems, national defense, industrial production, testing and inspection, and scientific research. These fields have certain requirements for the accuracy, spectral characteristics, and speed of current measurement. Currently, high-current sensing and measurement technology mainly relies on four types of devices: shunts, transformers, sensors, and Rogowski coils. Among them, shunts are favored due to their advantages such as simple measurement principle, low stray inductance, stable resistance, and no need for additional power supply.

[0003] During the operation of electromagnetic devices, various coupling processes involving electricity, magnetism, heat, and force occur. Taking a shunt as an example, when a large current passes through it, the high-frequency components of the current exacerbate the skin effect, affecting the resistance value. This instability in resistance further increases measurement uncertainty and impacts the dynamic response characteristics of the shunt. Simultaneously, the shunt may deform or even be damaged under the influence of strong electromagnetic forces; this deformation also causes changes in resistance. Furthermore, the thermal effect of large currents cannot be ignored. The generated heat not only leads to insulation degradation but also alters the resistance value through material nonlinearity. Therefore, designing electromagnetic devices while considering performance is an extremely complex and challenging problem.

[0004] However, current research on electromagnetic devices focuses primarily on improving single performance characteristics (measurement accuracy, measurement range, dynamic response characteristics), without considering the constraints that improvement in a single performance has on other performance characteristics and the mutual coupling relationships between multiple physical fields. As a result, electromagnetic devices often have poor performance and limited application scenarios. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a design method and apparatus for electromagnetic devices. The aim is to consider specific actual usage scenarios when designing electromagnetic devices, comprehensively consider multiple electromagnetic property parameters of candidate materials and the interrelationships between different electromagnetic property parameters, construct a multi-objective optimization model for the electromagnetic device based on its structure and materials, solve this model to obtain the Pareto optimal solution to calculate the sensitivity, use the sensitivity to determine the objective function, minimize the objective function to find the stable optimal solution under different candidate materials, and finally use a state evaluation method to select the optimal material. The proposed method has good generalization and reference value, and has certain engineering application value, thereby solving the technical problem that existing electromagnetic device designs often only consider a single performance, resulting in poor performance and limited application scenarios.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for designing an electromagnetic device is provided, comprising:

[0007] S1: Obtain the electromagnetic properties of each candidate material;

[0008] S2: Design multiple optimization variables based on the structural parameters of each candidate material, design multiple optimization objectives based on the electromagnetic properties of each candidate material, and design constraints to construct a multi-objective optimization model;

[0009] S3: Solve the multi-objective optimization model to obtain multiple sets of Pareto optimal solutions; each set of Pareto optimal solutions is a combination of the optimization objectives; based on the multiple sets of Pareto optimal solutions, fit the mapping relationship between each optimization variable and each optimization objective;

[0010] S4: Calculate the sensitivity of each of the optimization variables using the mapping relationship, determine the objective function using the sensitivity of each of the optimization variables, and minimize the objective function to determine the stable optimal solution of each of the candidate materials from multiple sets of Pareto optimal solutions;

[0011] S5: Evaluate the stable optimal solutions of each of the candidate materials to select the optimal material from the multiple candidate materials, and design an electromagnetic device using the stable optimal solution of the optimal material.

[0012] In one embodiment, S2 includes:

[0013] S21: Design optimization variables based on the structural parameters of each candidate material;

[0014] S22: Determine the optimization objectives based on the electromagnetic properties of each candidate material. The optimization objectives include minimizing the scale factor uncertainty, minimizing the dynamic response characteristics, and maximizing the current amplitude.

[0015] S23: Design constraints include structural parameter constraints and electromagnetic property parameter constraints.

[0016] In one embodiment, the electromagnetic property parameter constraints in S23 include: the thickness must not exceed half of the skin depth and the temperature must not exceed a preset temperature threshold.

[0017] In one embodiment, the scale factor uncertainty is represented by the resistance change ΔR: ΔR = Δθ·R0·α; where Δθ is the temperature rise. R0 is the design resistance value, α is the temperature coefficient, W is the energy consumed by the electromagnetic equipment, m is the mass of the resistive material, and c is the specific heat capacity.

[0018] In one embodiment, S3 includes:

[0019] S31: Use a multi-objective optimization algorithm to solve the multi-objective optimization model to obtain the Pareto optimal solution; the multi-objective optimization algorithm includes any one of the following: non-dominated sorting genetic algorithm with elitist strategy, multi-objective evolutionary algorithm, multi-objective particle swarm optimization algorithm, niche Pareto genetic algorithm, and dominant Pareto evolutionary algorithm;

[0020] S32: Based on the Pareto optimal solution, obtain the mapping relationship between each optimization variable and each optimization objective through curve fitting.

[0021] In one embodiment, S4 includes:

[0022] S41: Calculate the sensitivity of each optimization variable using the mapping relationship between each optimization variable and the corresponding optimization objective;

[0023] S42: Use the sum of the squares of the sensitivities of each of the optimization variables as the objective function;

[0024] S43: Minimize the objective function to find a stable optimal solution among the Pareto optimal solutions.

[0025] In one embodiment, S41 includes: based on the expression y = f(x) representing the mapping relationship between each of the optimization variables and each of the optimization objectives, using the formula... Calculate the sensitivity S(y,x) of each of the optimization variables; where x is the optimization variable and y is the optimization objective.

[0026] In one embodiment, y is the optimization objective, including the resistance change ΔR and the rise time t. r and current amplitude I m ;

[0027] x is the optimization variable, including the resistor length l, inner diameter a, outer diameter b, and thickness h;

[0028] S42 includes: taking the sum of the squares of the sensitivities S of each of the optimization variables as the objective function Q, expressed as:

[0029] In one embodiment, S5 includes:

[0030] A state evaluation method is used to evaluate the stable optimal solution of each of the candidate materials, thereby selecting the optimal material from the multiple candidate materials for the design of electromagnetic devices;

[0031] The state assessment method includes at least one of principal component analysis, weighted rank sum ratio, entropy weight method, and coefficient of variation method.

[0032] According to another aspect of the present invention, a design apparatus for an electromagnetic device is provided, comprising:

[0033] The acquisition module is used to acquire the electromagnetic property parameters of each candidate material;

[0034] The module is used to design multiple optimization variables based on the structural parameters of each candidate material, design multiple optimization objectives based on the electromagnetic property parameters of each candidate material, and design constraints to construct a multi-objective optimization model.

[0035] The calculation module is used to solve the multi-objective optimization model to obtain multiple sets of Pareto optimal solutions; each set of Pareto optimal solutions is a combination of the optimization objectives; and based on the multiple sets of Pareto optimal solutions, a mapping relationship between each optimization variable and each optimization objective is fitted.

[0036] The search module is used to calculate the sensitivity of each of the optimization variables using the mapping relationship, determine the objective function using the sensitivity of each of the optimization variables, and minimize the objective function to determine the stable optimal solution of each of the candidate materials from multiple sets of Pareto optimal solutions;

[0037] The design module is used to evaluate the stable optimal solutions of each of the candidate materials in order to select the optimal material from the multiple candidate materials, and to design an electromagnetic device using the stable optimal solution of the optimal material.

[0038] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0039] (1) This invention provides a design method for electromagnetic devices. When designing electromagnetic devices, the method considers specific actual usage scenarios, integrates multiple electromagnetic property parameters of candidate materials, and takes into account the mutual constraints between different electromagnetic property parameters. A multi-objective optimization model of the electromagnetic device is constructed by combining the structure and materials of the device. The Pareto optimal solution is obtained by solving this model, thereby calculating the sensitivity. The objective function is determined using the sensitivity, and the stable optimal solution under different candidate materials is found by minimizing the objective function. Finally, the optimal material is selected using a state evaluation method. The proposed method has good generalization and reference value, and has certain engineering promotion value. This solves the technical problem that existing electromagnetic device designs often only consider a single performance, resulting in poor performance and limited application scenarios. Previous research has not focused on the perspective of multiple electromagnetic property parameters, comprehensively considering the mutual constraints between the accuracy, dynamic response characteristics, and upper limit of current measurement of electromagnetic devices under the electro-magnetic-thermal-mechanical coupling effect. This method, however, is the first to propose a solution to this problem, achieving a comprehensive consideration of sensor performance, improving overall performance, and broadening application scenarios. It not only provides the Pareto optimal solution but also allows the selection of the most stable solution, which has significant reference value in engineering practice.

[0040] (2) This scheme determines the optimization objectives based on the electromagnetic property parameters of each candidate material. The optimization objectives include minimizing the scale factor uncertainty, minimizing the dynamic response characteristics, and maximizing the current amplitude. By minimizing the resistance change, the uncertainty of the scale factor can be reduced, making the electromagnetic device parameters more stable. Considering the influence of inductance and resistance on the response time, the electromagnetic device can achieve a fast response. By limiting the temperature rise, the current amplitude can be increased. The above optimization objectives are crucial to the performance design of the entire electromagnetic device.

[0041] (3) The constraints described in this design include: the thickness must not exceed half of the skin depth and the temperature must not exceed the preset temperature threshold. Considering the constraints of electromagnetic property parameters, the designed electromagnetic equipment can be made more stable and applicable to a wider range of scenarios.

[0042] (4) In this scheme, the uncertainty of the scale factor is represented by the resistance change ΔR: ΔR=ΔθR0·α; considering the influence of temperature rise on the resistance of this type of material, the resistance change can be calculated by combining the resistance temperature coefficient α of the material, which has low computational complexity and thus improves the execution efficiency of the whole algorithm.

[0043] (5) This scheme uses a multi-objective optimization algorithm to solve the multi-objective optimization model to obtain the Pareto optimal solution; it can obtain the Pareto front and realize multi-objective optimization under various materials.

[0044] (6) This scheme uses the sum of the squares of the sensitivity of each of the optimization variables as the objective function; the computational complexity is low, thereby improving the execution efficiency of the entire algorithm.

[0045] (7) This scheme utilizes the formula The sensitivity S(y,x) of each optimization variable can be calculated without considering the dimensions between the optimization objectives, thus achieving the purpose of direct calculation.

[0046] (8) In this scheme, the sum of the squares of the sensitivities S of each of the optimization variables is taken as the objective function Q, which is expressed as: Minimizing the sum of sensitivities is considered as the optimization objective, thus finding the most stable optimal situation on the Pareto front. Optimizing the above parameters as optimization variables and objectives ensures the performance of the designed electromagnetic device while maintaining low computational complexity.

[0047] (9) The state assessment method described in this scheme includes one of the principal component analysis method, weighted rank sum ratio method, entropy weight method and coefficient of variation method. The above assessment methods are selected to consider the optimal stability of different materials, and can even be combined with the properties of different materials, thereby achieving the optimal comprehensive performance under different materials. Attached Figure Description

[0048] Figure 1 This is a flowchart of a shunt design method provided in Embodiment 1 of the present invention.

[0049] Figure 2 This is a schematic diagram of the coupling of multiple physical fields (electromagnetic, thermal, and mechanical) during the operation of the shunt provided in Embodiment 1 of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0051] The following embodiments are described using the design of a shunt:

[0052] Example 1

[0053] like Figure 1As shown, this embodiment provides a method for designing a shunt, including: obtaining the electromagnetic property parameters of each candidate material; combining the structural parameters of various candidate materials, constructing a multi-objective optimization model based on the electromagnetic property parameters of each candidate material, including the optimization variables, optimization objectives, and constraints corresponding to the multi-objective optimization model; solving the multi-objective optimization model to obtain multiple sets of Pareto optimal solutions; fitting the mapping relationship between each optimization variable and each optimization objective based on the multiple sets of Pareto optimal solutions; calculating the sensitivity of each optimization variable using the mapping relationship between each optimization variable and the corresponding optimization objective; calculating the objective function using the sensitivity of each optimization variable, minimizing the objective function to find the stable optimal solution for each candidate material from the multiple sets of Pareto optimal solutions; evaluating the stable optimal solution corresponding to each candidate material, thereby selecting the optimal material from multiple candidate materials for designing the shunt.

[0054] Specifically, candidate materials include thin film materials such as constantan, manganin, stainless steel, nickel-copper, and nickel-chromium; electromagnetic properties mainly include resistivity ρ and relative permeability μ. r Specific heat capacity c, temperature coefficient α, and density γ, etc.

[0055] The solution yields multiple Pareto optimal solutions, each representing a set of solutions for multiple optimization objectives. Since we've used three objectives as an example, the graphical representation would be a three-dimensional surface, technically called the Pareto front. Projecting this front onto each objective reveals that an improvement in one objective might lead to a deterioration in another. For instance, response time and resistance changes might exhibit the following trend: a decrease in response time (i.e., an improvement in this metric) could potentially increase resistance or decrease current. Therefore, a Pareto optimal solution represents the value of the independent variable corresponding to that objective state. Different optimization objectives will result in different values ​​for these independent variables.

[0056] The specific process of fitting the mapping relationship between each optimization variable and each optimization objective is as follows: For example, the response time can be used as a dependent variable, and the length of the resistive element can be used as an independent variable. Then, a polynomial is used for fitting. Of course, other formulas can also be used for fitting, as long as the goodness of fit (including the minimum root mean square error or the maximum coefficient of determination) is good. Finally, the mapping relationship between the response time and the length of the resistive element can be obtained.

[0057] This embodiment considers specific application scenarios when designing the shunt, comprehensively considering multiple electromagnetic properties of candidate materials and the interrelationships between different electromagnetic properties. A multi-objective optimization model for the shunt is constructed based on its structure and materials. Solving this model yields the Pareto optimal solution, thereby calculating the sensitivity. The sensitivity is then used to determine the objective function. Minimizing the objective function finds a stable optimal solution for different candidate materials. Finally, a state evaluation method is used to select the optimal material. The proposed method has good generalization and reference value, and has certain engineering application value. This solves the technical problem that existing shunt designs often only consider a single performance aspect, resulting in poor shunt performance and limited application scenarios. Previously, no research has focused on multiple electromagnetic properties to comprehensively consider the electro-magnetic-thermal-mechanical coupling effects of the shunt (such as...). Figure 2 As shown in the figure, the interrelationship between accuracy, dynamic response characteristics and upper limit of current measurement is a key factor. This method is the first to propose a solution to this problem, achieving a comprehensive balance of sensor performance, improving overall performance and broadening application scenarios. It not only provides the Pareto optimal solution, but also allows the selection of the most stable solution, which has significant reference value in engineering practice.

[0058] Example 2

[0059] In this embodiment, S2 includes: S21: designing optimization variables based on the structural parameters of each candidate material; S22: determining optimization objectives based on the electromagnetic properties of each candidate material, including minimizing the scale factor uncertainty, minimizing dynamic response characteristics, and maximizing the current amplitude; S23: designing constraints including structural parameter constraints and electromagnetic property parameter constraints. The optimization variables include structural parameters such as the resistor length l, inner diameter a, outer diameter b, and thickness h.

[0060] Example 3

[0061] In this embodiment, S23 includes: design constraints including: the thickness must not exceed half of the skin depth, the temperature must not exceed a preset temperature threshold, and restrictions are placed on the structural parameters of the resistor length l, inner diameter a, outer diameter b, and thickness h.

[0062] The constraints are clearly defined, including: the thickness must not exceed half the skin depth, i.e., h ≤ δ / 2 and Where f is the current frequency and μ0 is the permeability in vacuum. The temperature must not exceed the maximum temperature of 100℃, i.e., Δθ≤100; in addition, structural parameters such as the length l, inner diameter a, outer diameter b, and thickness h of the resistive element also need to be limited, i.e.:

[0063] l down ≤l≤l up ;a down ≤a≤aup b down ≤b≤b up h down ≤h≤h up ;

[0064] Among them, l up and l down These are the upper and lower limits of the length, a. up and a down These are the upper and lower limits of the inner diameter, respectively, b up and b down These are the upper and lower limits of the outer diameter, h, respectively. up and h down These represent the upper and lower limits of the thickness, respectively.

[0065] Example 4

[0066] In this embodiment, the scale factor uncertainty is represented by the resistance change ΔR: ΔR = Δθ·R0·α; where Δθ is the temperature rise. R0 is the design resistance value, α is the temperature coefficient, W is the energy consumed by the shunt, m is the mass of the resistive material, and c is the specific heat capacity.

[0067] The optimization objectives include minimizing the scale factor uncertainty, minimizing the dynamic response characteristics, and maximizing the current amplitude; specifically, the scale factor uncertainty is represented by the resistance change ΔR, while the dynamic response characteristics are expressed using the rise time t. r Characterized by I, the current amplitude m The entire objective optimization model is represented as follows:

[0068]

[0069] Where R0 is the designed resistance value, R is the actual resistance value, L0 is the inductance, Δθ is the temperature rise, W is the energy consumed by the shunt, m is the mass of the resistor material, and τ is the time interval.

[0070] Example 5

[0071] In this embodiment, S3 includes: S31: using a multi-objective optimization algorithm to solve the multi-objective optimization model to obtain the Pareto optimal solution; the multi-objective optimization algorithm includes any one of the following: non-dominated sorting genetic algorithm with elitist strategy, multi-objective evolutionary algorithm, multi-objective particle swarm optimization algorithm, niche Pareto genetic algorithm, and dominant Pareto evolutionary algorithm; S32: based on the Pareto optimal solution, obtaining the mapping relationship between each optimization variable and each optimization objective through curve fitting.

[0072] Example 6

[0073] In this embodiment, S4 includes: S41: calculating the sensitivity of each optimization variable using the mapping relationship between each optimization variable and the corresponding optimization objective; S42: using the sum of the squares of the sensitivity of each optimization variable as the objective function; S43: minimizing the objective function, thereby finding a stable optimal solution in the Pareto optimal solution.

[0074] Specifically, Pareto optimality involves a combination of many optimization objectives, thus corresponding to multiple solutions. Changes in each variable within each solution will perturb the optimization objective to varying degrees. Therefore, by calculating the sensitivity of the optimization objective to the optimization variables, multiple sensitivities can be obtained. For example, if there are four objectives involving five optimization variables, then 20 sensitivities need to be calculated. That is, for one combination of optimization objectives, 20 sensitivities can be obtained. The objective is to minimize these sensitivities; otherwise, the optimization objective may fluctuate significantly with changes in the optimization variables, which is undesirable. A simple approach is to square these multiple sensitivities and sum them. An advantage here is that sensitivity is dimensionless, allowing multiple objectives to be considered together in relation to changes in variables. At the Pareto front, the sum of squares of these sensitivities also varies with the value of the sum. The optimization objective with the smallest sum of squares is selected, resulting in the most stable optimization outcome.

[0075] Example 7

[0076] In this embodiment, S41 includes: based on the expression y = f(x) representing the mapping relationship between each optimization variable and each optimization objective, using the formula... Calculate the sensitivity S(y,x) of each optimization variable; where x is the optimization variable and y is the optimization objective.

[0077] Example 8

[0078] In this embodiment, y is the optimization objective, including the resistance change ΔR and the rise time t. r and current amplitude I m ; x is the optimization variable, including the resistor length l, inner diameter a, outer diameter b, and thickness h; S42 includes: taking the sum of the squares of the sensitivity S of each optimization variable as the objective function Q, expressed as:

[0079]

[0080] Specifically, sensitivity is obtained using the following general formula: Where y is the objective, namely the change in resistance ΔR and the rise time t. r、 Current amplitude I m And so on, where x is the optimization variable, including the resistor length l, inner diameter a, outer diameter b, thickness h, etc., from which S(ΔR,l), S(ΔR,a), S(ΔR,b), S(ΔR,h), S(t) can be obtained.r ,l),S(t) r ,a),S(t) r ,b),S(t) r ,h),S(I m ,l),S(I m ,a),S(I m ,b),S(I m ,h).

[0081] Let the sum of the squares of each sensitivity be taken as the objective function, that is:

[0082]

[0083] Based on this, the objective function Q is minimized, thereby finding a stable optimal solution among the Pareto optimal solutions.

[0084] Example 9

[0085] In this embodiment, S5 includes: evaluating the stable optimal solution of each candidate material using a state evaluation method, thereby selecting the optimal material from multiple candidate materials for designing a shunt; wherein the state evaluation method includes at least one of principal component analysis, weighted rank sum ratio method, entropy weight method and coefficient of variation method.

[0086] Specifically, for example, material 1 corresponds to a most stable optimal case 1 (target 11, target 12, target 13); material 2 also corresponds to a most stable optimal case 2 (target 21, target 22, target 23)... So how do we evaluate which material is the best? We need to combine these optimal cases, and even the price or other properties of the materials, to comprehensively rank these materials. The evaluation result should be a ranking, that is, material 1 has the best overall performance, material 2 is second, material 3... Based on the evaluation results of each candidate material, the optimal material is determined, and the electromagnetic equipment is designed using the stable optimal solution of the optimal material.

[0087] Example 10

[0088] According to another aspect of the present invention, a design apparatus for a shunt is provided, comprising:

[0089] The acquisition module is used to acquire the electromagnetic property parameters of each candidate material;

[0090] The module is used to design multiple optimization variables based on the structural parameters of each candidate material, design multiple optimization objectives based on the electromagnetic property parameters of each candidate material, and design constraints to construct a multi-objective optimization model.

[0091] The calculation module is used to solve the multi-objective optimization model to obtain multiple sets of Pareto optimal solutions; each set of Pareto optimal solutions is a combination of the optimization objectives; and based on the multiple sets of Pareto optimal solutions, a mapping relationship between each optimization variable and each optimization objective is fitted.

[0092] The search module is used to calculate the sensitivity of each of the optimization variables using the mapping relationship, determine the objective function using the sensitivity of each of the optimization variables, and minimize the objective function to determine the stable optimal solution of each of the candidate materials from multiple sets of Pareto optimal solutions;

[0093] The design module is used to evaluate the stable optimal solutions of each of the candidate materials in order to select the optimal material from the multiple candidate materials, and to design an electromagnetic device using the stable optimal solution of the optimal material.

[0094] Example 11

[0095] This embodiment provides a design system, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0096] The design system includes a processor and a memory connected via a system bus. The processor provides computational and control capabilities to support the operation of the entire design system. The memory may include non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. These computer programs can be executed by the processor to implement the electromagnetic device design method provided in the various embodiments described above. The internal memory provides a cached runtime environment for the operating system computer programs in the non-volatile storage media.

[0097] Example 12

[0098] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described electromagnetic device design method.

[0099] Any references to memory, storage, databases, or other media used in this embodiment may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which is used as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0100] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for an electromagnetic device, characterized in that, include: S1: Obtain the electromagnetic properties of each candidate material; S2: Design multiple optimization variables based on the structural parameters of each candidate material, design multiple optimization objectives based on the electromagnetic properties of each candidate material, and design constraints to construct a multi-objective optimization model; S3: Solve the multi-objective optimization model to obtain multiple sets of Pareto optimal solutions; each set of Pareto optimal solutions is a combination of the optimization objectives; based on the multiple sets of Pareto optimal solutions, fit the mapping relationship between each optimization variable and each optimization objective; S4: Calculate the sensitivity of each of the optimization variables using the mapping relationship, determine the objective function using the sensitivity of each of the optimization variables, and minimize the objective function to determine the stable optimal solution of each of the candidate materials from multiple sets of Pareto optimal solutions; S5: Evaluate the stable optimal solutions of each of the candidate materials to select the optimal material from the multiple candidate materials, and design an electromagnetic device using the stable optimal solution of the optimal material; S4 includes: S41: calculating the sensitivity of each optimization variable using the mapping relationship between each optimization variable and the corresponding optimization objective; S42: using the sum of the squares of the sensitivity of each optimization variable as the objective function; S43: minimizing the objective function to find a stable optimal solution among the Pareto optimal solutions; S41 includes: based on the expression y=f(x) of the mapping relationship between each of the optimization variables and each of the optimization objectives, using the formula... Calculate the sensitivity of each of the optimization variables. Where x is the optimization variable and y is the optimization objective; y is the optimization objective, including the resistance change ΔR and the rise time t. r and current amplitude I m ; x is the optimization variable, including the resistor length l and inner diameter. outer diameter b and thickness h; S42 includes: taking the sum of the squares of the sensitivities S of each of the optimization variables as the objective function Q, expressed as: .

2. The design method of the electromagnetic device as described in claim 1, characterized in that, S2 includes: S21: Design optimization variables based on the structural parameters of each candidate material; S22: Determine the optimization objectives based on the electromagnetic properties of each candidate material. The optimization objectives include minimizing the scale factor uncertainty, minimizing the dynamic response characteristics, and maximizing the current amplitude. S23: Design constraints include structural parameter constraints and electromagnetic property parameter constraints.

3. The design method for the electromagnetic device as described in claim 2, characterized in that, The electromagnetic property parameter constraints in S23 include: the thickness must not exceed half of the skin depth and the temperature must not exceed a preset temperature threshold.

4. The design method of the electromagnetic device as described in claim 2, characterized in that, The uncertainty of the scale factor is determined by the change in resistance. express: ; in, For the temperature rise, , To design the resistance value, Temperature coefficient; Energy is consumed by electromagnetic devices. For the quality of the resistor material, Specific heat capacity.

5. The design method of the electromagnetic device as described in claim 1, characterized in that, S3 includes: S31: Use a multi-objective optimization algorithm to solve the multi-objective optimization model to obtain the Pareto optimal solution; the multi-objective optimization algorithm includes any one of the following: non-dominated sorting genetic algorithm with elitist strategy, multi-objective evolutionary algorithm, multi-objective particle swarm optimization algorithm, niche Pareto genetic algorithm, and dominant Pareto evolutionary algorithm; S32: Based on the Pareto optimal solution, obtain the mapping relationship between each optimization variable and each optimization objective through curve fitting.

6. The design method of the electromagnetic device according to any one of claims 1-5, characterized in that, S5 includes: A state evaluation method is used to evaluate the stable optimal solution of each of the candidate materials, thereby selecting the optimal material from the multiple candidate materials for the design of electromagnetic devices; The state assessment method includes one of principal component analysis, weighted rank sum ratio, entropy weight method, and coefficient of variation method.

7. A design apparatus for an electromagnetic device, characterized in that, A design method for performing the electromagnetic device according to any one of claims 1-6, comprising: The acquisition module is used to acquire the electromagnetic property parameters of each candidate material; The module is used to design multiple optimization variables based on the structural parameters of each candidate material, design multiple optimization objectives based on the electromagnetic property parameters of each candidate material, and design constraints to construct a multi-objective optimization model. The calculation module is used to solve the multi-objective optimization model to obtain multiple sets of Pareto optimal solutions; each set of Pareto optimal solutions is a combination of the optimization objectives; and based on the multiple sets of Pareto optimal solutions, a mapping relationship between each optimization variable and each optimization objective is fitted. The search module is used to calculate the sensitivity of each of the optimization variables using the mapping relationship, determine the objective function using the sensitivity of each of the optimization variables, and minimize the objective function to determine the stable optimal solution of each of the candidate materials from multiple sets of Pareto optimal solutions; The design module is used to evaluate the stable optimal solutions of each of the candidate materials in order to select the optimal material from the multiple candidate materials, and to design an electromagnetic device using the stable optimal solution of the optimal material.

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