Multi-objective design method for transducer magnetic circuit component containing magnetic flux density of multi-magnetic circuit model

Abstract

The application discloses a kind of transducer magnetic circuit component multi-objective optimization design methods of containing multi-magnetic circuit model magnetic flux density, comprising: first, the size of magnetic gap, magnetic flux density average and magnetic flux density variance are determined as the function of three optimization targets of magnetic circuit characterization, second, by establishing the finite element model of each type of magnetic circuit component, and according to the finite element model, design variables, variable value range and constraint conditions etc. are established, on this basis, the multi-objective optimization model of transducer magnetic circuit component is established, finally, the Pareto front solution set of optimization model is obtained by calculation, the objective function is screened, and the multi-objective optimization design of magnetic circuit component is completed.The application can solve the discrete / continuous variable mixed nonlinear magnetic flux density multi-objective optimization problem of multiple magnetic circuit models under multiple constraint conditions, and the optimal design of multiple magnetic circuit models under constraint conditions can be realized by solving the Pareto front solution set.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic underwater acoustic emission transducer engineering technology, and specifically relates to a multi-objective optimization design method for transducer magnetic circuit components with magnetic flux density of multiple magnetic circuit models. Background Technology

[0002] Electromagnetic underwater acoustic transducers are transducers that convert electrical energy into vibration through a magnetic field, and then radiate sound waves into the water medium. They are highly valued by researchers due to their low operating frequency, high radiation sound source level, and small weight load. The most typical type is the moving coil transducer, whose coil, radiating surface, and connecting parts together constitute the oscillator system of this type of transducer. Its working principle is that when alternating current passes through the coil, it will generate a reciprocating Lorentz force in the magnetic field environment, which forces the radiating surface to vibrate and radiate sound waves into the water medium, realizing the energy conversion of "electricity-force-sound".

[0003] Therefore, for moving-coil transducers, common magnetic circuit structures include internal magnetic circuits, external magnetic circuits, and radial magnetic circuits, as well as novel composite magnetic circuits. The quality of the magnetic field environment greatly determines the acoustic radiation performance of the transducer. The physical quantity characterizing the magnetic field environment is magnetic flux density. The higher the amplitude of the magnetic flux density in the magnetic circuit components of a moving-coil transducer, the higher its energy conversion efficiency. Similarly, the more uniform the magnetic flux density distribution in the magnetic gap, the more stable the Lorentz force converted from electrical energy, and the more stable the transducer performance. Finally, the larger the magnetic gap in the magnetic circuit, the more coils the moving-coil transducer cuts magnetic field lines, and the better the transducer performance.

[0004] In summary, high magnetic flux density amplitude, uniform magnetic flux density distribution, and large magnetic gap size are among the primary design goals of magnetic circuit components for moving coil transducers. However, magnetic circuit materials with different structures, sizes, and materials exhibit varying magnetic flux density distributions. Furthermore, the smaller the magnetic gap size, the higher the magnetic flux density amplitude. Therefore, magnetic circuit component design is also a multi-objective optimization problem, and how to scientifically balance these three design goals has become a major challenge in magnetic circuit design. Summary of the Invention

[0005] The technical problem this invention aims to solve is to overcome the shortcomings of the prior art. For the multi-objective optimization design problem of magnetic flux density amplitude, uniformity, and gap size of transducer magnetic circuit components with different structures, this invention provides a multi-objective optimization design method for transducer magnetic circuit components with magnetic flux density based on multiple magnetic circuit models. First, this invention determines the gap size, mean magnetic flux density, and variance of magnetic flux density as functions characterizing the three optimization objectives of the magnetic circuit. Second, it establishes finite element models of various magnetic circuit components and, based on these models, establishes design variables (specifically, the design finite element model symbol is used as a variable), variable value ranges, and constraints. On this basis, it establishes a multi-objective optimization model for the transducer magnetic circuit component. Finally, it calculates the Pareto front solution set of the optimization model, selects the objective function, and completes the multi-objective optimization design of the magnetic circuit component. The specific steps are as follows:

[0006] (1) Determination of magnetic circuit component model and objective function

[0007] Simplified models of magnetic circuits such as internal magnetic circuits, external magnetic circuits, and radial magnetic circuits are summarized. First, based on the magnetic circuit structure, the parameter variables under each magnetic circuit structure are selected and determined. Then, the average value, variance, and magnetic gap size on the magnetic gap center line of the corresponding magnetic circuit structure are selected as the objective function for magnetic circuit design.

[0008] If the magnetic flux density distribution on the center line is {B i The average magnetic flux density of {i=1,2,3,…,N} is:

[0009] (1)

[0010] The variance of magnetic flux density is:

[0011] (2)

[0012] (2) Establish a finite element proxy model of the magnetic circuit

[0013] The core of the finite element model of the magnetic circuit component is to obtain its finite element governing equations. The transducer is discretized into a finite number of elements, and the Hamiltonian variational principle is applied to the system's Lagrangian function to obtain the finite element governing equations for the steady-state solution of the magnetic circuit. The model establishment process is as follows: Figure 1 As shown.

[0014] In particular, this invention summarizes the finite element proxy models of different magnetic circuit components and assigns them code values. For the structural parameters of different components, corresponding design variables are established. At the same time, different magnetic circuit structure codes mean that different finite element proxy models must be used for simulation analysis.

[0015] (3) Establish a multi-objective optimization model

[0016] The three essential elements of a multi-objective optimization model are: design variables, constraints, and objective function. Therefore, based on the completion of the finite element proxy model design, design variables must be established according to the proxy model and design inputs.

[0017] Specifically, for the structural parameters of different components, corresponding design variables must be established. Different magnetic circuit structure codes imply different simulation proxy models. Constraints on the variables must be designed according to the actual situation. Finally, a set of objective functions is established based on the design inputs. The specific steps are as follows:

[0018] First, design variables for a multi-objective optimization model are established using various parameters such as geometric dimensions, material codes, and structural proxies to be optimized in the finite element proxy model of the magnetic circuit component. These variables can be divided into continuous design variables (such as geometric dimensions) and discrete design variables (such as material codes and structural codes) according to their type.

[0019] Secondly, establish a set of constraints for the model based on the range of values ​​of the design variables (such as size processing limitations, material selection range, etc.) or the range of values ​​of a certain objective function.

[0020] Next, the data on the magnetic gap center line is extracted through the finite element proxy model, and the average value and variance of the magnetic flux density are calculated according to equations (1) and (2). The design variables and the objective function calculated by the finite element model are then imported into the multi-objective optimization simulation platform.

[0021] Finally, the Pareto front solution set is obtained by using a multi-objective optimization simulation platform and employing multi-objective algorithms (including evolutionary algorithms, particle swarm optimization algorithms, artificial neural network optimization algorithms, etc.) to complete the solution. Based on the solution set, the design and selection of each design variable are completed.

[0022] The beneficial effects of this invention are:

[0023] 1. This invention proposes a multi-objective optimization design method for magnetic flux density with multiple magnetic circuit models. It summarizes the finite element proxy models of different magnetic circuits, assigns code values ​​to different magnetic circuit models, and introduces the code values ​​into the multi-objective model to realize the comprehensive analysis of the multi-magnetic circuit finite element proxy model.

[0024] 2. The optimization design method proposed in this invention can solve the multi-objective optimization problem of magnetic gap flux density of various magnetic circuit models under multiple constraints, which is a mixed type of nonlinearity with discrete (transducer material and boundary condition symbols, etc.) and continuous (geometric structural parameters, etc.) variables. The optimal design of multiple magnetic circuit models under constraints can be achieved by solving the Pareto front solution set.

[0025] 3. The objective function proposed in this invention, namely the average value and variance of the magnetic flux density along the center line of the magnetic gap and the magnetic gap size, can quantify the magnetic field performance of the transducer magnetic circuit components. Attached Figure Description

[0026] Figure 1 This is a simulation flowchart.

[0027] Figure 2 The diagram shows three types of magnetic circuit components and their center lines: (a) internal magnetic circuit structure, (b) external magnetic circuit structure, and (c) radial magnetic circuit structure; the dotted line indicated by "DL" in the diagram is the center line of the magnetic gap.

[0028] Figure 3 The simulation cloud maps are for three types of magnetic circuit finite element models: (a) magnetic induction intensity distribution cloud map of internal magnetic circuit, (b) magnetic induction intensity distribution cloud map of external magnetic circuit, and (c) magnetic induction intensity distribution cloud map of radial magnetic circuit.

[0029] Figure 4 The following are cloud maps showing the magnetic flux density distribution of three types of magnetic circuits: (a) magnetic flux density distribution of the gap in the internal magnetic circuit, (b) magnetic flux density distribution of the gap in the external magnetic circuit, and (c) magnetic flux density distribution of the gap in the radial magnetic circuit.

[0030] Figure 5 The figure shows the Pareto front solution set for a multi-objective optimization model with different finite element models. In the figure, "D" represents the optimal solution under this solution set.

[0031] Figure 6 The magnetic flux density distribution diagram of the radial magnetic circuit after optimization design. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0033] This invention is described in detail using the multi-objective optimization design of magnetic circuits with three magnetic circuit structures—internal, external, and radial—as an example. First, a simplified model summarizing the three magnetic circuits (internal, external, and radial) is established, and the mean and variance of the magnetic flux density along the corresponding magnetic gap centerline are plotted as functions representing the three optimization objectives of the magnetic circuit, along with the magnetic gap size. Second, finite element proxy models of the three magnetic circuit structures are established, and design variables, variable ranges, and constraints are established based on these proxy models. On this basis, a multi-objective optimization model for the transducer magnetic circuit assembly is established. Finally, the Pareto front solution set of the optimization model is calculated, the objective function is selected, and the multi-objective optimization design of the magnetic circuit assembly is completed. Figure 1 As shown, the specific steps are as follows:

[0034] (1) Determination of magnetic circuit component model and objective function

[0035] Simplified models of internal magnetic type, external magnetic type, and radial type are shown below. Figure 2 As shown, secondly according to Figure 2 As shown, the magnetic gap centerline corresponding to the magnetic circuit structure is selected ( Figure 2 The average value, variance, and gap size of the magnetic flux density on the magnetic circuit (DL) are used as objective functions for magnetic circuit design.

[0036] (2) Establish a finite element proxy model of the magnetic circuit

[0037] according to Figure 2 The simplified models of internal magnetic, external magnetic and radial magnetic circuits shown are used to establish three finite element proxy models. The simplified model variables involved are mainly divided into structural parameters and material properties, as shown in Table 1.

[0038] Table 1. Variables of the Finite Element Model

[0039]

[0040] After establishing the simplified model, the first step is to assign values ​​to the materials of the magnetic circuit. For example, the top plate and the magnetic circuit can be made of metals such as magnetic alloys and soft iron, while the permanent magnets can be made of permanent magnet materials such as neodymium iron boron N35 and N45.

[0041] Secondly, the boundary conditions of the magnetic field domain must be set, including the model settings of the top plate, magnetic circuit and permanent magnet.

[0042] Furthermore, after setting and dividing the mesh model to meet the requirements of finite element mesh generation, the calculation parameters of the surrogate model steady-state calculator can be designed, including the calculation frequency, solver method, etc.

[0043] Finally, the obtained data undergoes post-processing. The centerline dataset is extracted and processed to obtain the average and variance of the magnetic flux density along the centerline of the magnetic gap in the magnetic circuit assembly, as well as the gap size. The specific process is as follows. Figure 1 As shown. Figure 3 and Figure 4 The image shows a simulated cloud map of the magnetic flux density of the magnetic gaps in each structural magnetic circuit.

[0044] (3) Establish a multi-objective optimization model

[0045] Based on the three elements of a multi-objective optimization model—design variables, constraints, and objective functions—after completing the finite element proxy model design of the magnetic circuit components, it is necessary to establish design variables according to the proxy model and design inputs, design constraints for these variables based on actual conditions, and finally establish a set of objective functions based on the design inputs. The specific steps are as follows:

[0046] First, design variables for a multi-objective optimization model are established using different surrogate model codes and various parameters such as geometric dimensions and material codes to be optimized in the surrogate models, as shown in Table 2.

[0047] Table 2 Design Variables for Multi-Objective Optimization Model

[0048]

[0049] Secondly, a set of constraints for the model is established based on the range of values ​​of the design variables (such as the processing technology limitations of the dimensions, the range of material selection, etc.) or the range of values ​​of a certain objective function, as shown in Table 3.

[0050] Table 3 Summary of Variable Values ​​and Constraints for Multi-Objective Optimization Model

[0051]

[0052] Finally, based on the design input, a set of objective functions for the multi-objective model is established. In this embodiment, the average value, variance, and gap size of the magnetic flux density on the center line of the magnetic gap of the magnetic circuit component are selected, and the design range of the sub-objective parameters is established based on the design input.

[0053] Based on the multi-objective model, a suitable multi-objective optimization algorithm (including evolutionary algorithms, particle swarm optimization, artificial neural network optimization algorithms, etc.) is selected to solve the model and obtain the Pareto front solution set of the multi-objective model. In this embodiment, the NSGA-II algorithm among evolutionary algorithms is selected. The Pareto front solution set of the magnetic flux density optimization of the multi-surrogate model is obtained by solving the algorithm. Figure 5 As shown.

[0054] Furthermore, based on the Pareto front solution set, the magnetic circuit structure is completed, and the corresponding geometric dimensions, material codes, and boundary condition type codes are selected, thereby completing the design, fabrication, and testing.

[0055] Based on the Pareto front solution set, this embodiment selected a radial magnetic circuit structure, and the test results of its centerline magnetic flux density amplitude are as follows: Figure 6 As shown.

Claims

1. A multi-objective design method for transducer magnetic circuit components with magnetic flux density based on multiple magnetic circuit models, characterized in that, Includes the following steps: S1 Magnetic Circuit Component Model and Objective Function Determination Simplified magnetic circuit models such as internal magnetic circuit, external magnetic circuit, and radial magnetic circuit are summarized. First, based on the magnetic circuit structure, the parameter variables under each magnetic circuit structure are selected and determined. The average value, variance, and magnetic gap size on the magnetic gap center line of the corresponding magnetic circuit structure are selected as the objective function for magnetic circuit design. S2 establishes a finite element proxy model of the magnetic circuit. The transducer is discretized into a finite number of elements, and the Hamiltonian variational principle is applied to the system's Lagrangian function to obtain the finite element control equations for the steady-state solution of the magnetic circuit. S3 Establish a multi-objective optimization model The elements of a multi-objective optimization model include design variables, constraints, and objective functions. Design variables are established based on the surrogate model and design inputs. Finally, the multi-objective optimization simulation platform is used to solve the problem using a multi-objective algorithm, obtaining the Pareto front solution set. The design and selection of each design variable are then completed based on the solution set.

2. The method according to claim 1, characterized in that, In step S1, the magnetic flux density distribution along the center line of the magnetic gap is {Bi, i=1,2,3,…,N}, and the average magnetic flux density is: （1） The variance of magnetic flux density is: （2）。 3. The method according to claim 2, characterized in that, In step S2, The finite element proxy models of different magnetic circuit components are summarized and assigned code values. For the structural parameters of different components, corresponding design variables are established. At the same time, different magnetic circuit structure codes mean that different finite element proxy models must be used for simulation analysis.

4. The method according to claim 3, characterized in that, In step S3, corresponding design variables are established for the structural parameters of different components. Different magnetic circuit structure codes mean different simulation proxy models. The constraints of the variables are designed according to the actual situation. Finally, a set of objective functions is established based on the design input.

5. The method according to claim 4, characterized in that, The specific steps are as follows: First, design variables for a multi-objective optimization model are established using various parameters such as geometric dimensions, material codes, and structural proxies to be optimized in the finite element proxy model of the magnetic circuit component. These variables can be divided into continuous design variables and discrete design variables according to their type. Secondly, establish a set of constraints for the model based on the range of values ​​of the design variables or the range of values ​​of a certain objective function; Next, the data on the magnetic gap center line is extracted through the finite element proxy model, and the average value and variance of the magnetic flux density are calculated according to equations (1) and (2). The design variables and the objective function calculated by the finite element model are then imported into the multi-objective optimization simulation platform. Finally, the Pareto front solution set was obtained by using a multi-objective optimization simulation platform and a multi-objective algorithm to solve the problem. Based on the solution set, the design and selection of each design variable were completed.

6. The method according to claim 5, characterized in that: The continuous design variables include geometric dimensions, and the discrete design variables include material designations and structural designations.

7. The method according to claim 5, characterized in that: The range of values ​​for the design variables includes the limitations of the dimensional processing technology and the range of material selection.

8. The method according to claim 5, characterized in that: The multi-objective algorithm includes any one of the following: evolutionary algorithm, particle swarm optimization algorithm, and artificial neural network optimization algorithm.

9. The method according to any one of claims 1-8, characterized in that, The finite element model designation and design inputs are used as the design variables.

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