CN114997012A - Ferrite magnetic field optimization device and method based on genetic algorithm - Google Patents
Ferrite magnetic field optimization device and method based on genetic algorithm Download PDFInfo
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- 230000005291 magnetic effect Effects 0.000 title claims abstract description 89
- 229910000859 α-Fe Inorganic materials 0.000 title claims abstract description 55
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- 230000002068 genetic effect Effects 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000035699 permeability Effects 0.000 claims description 8
- 239000000463 material Substances 0.000 claims description 6
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- 229910001289 Manganese-zinc ferrite Inorganic materials 0.000 claims description 3
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Abstract
The invention relates to a ferrite magnetic field optimization method based on a genetic algorithm, and firstly, an improved ferrite magnetic field generating device is provided; then, a uniformity function and a genetic algorithm are introduced to optimize the magnetic field distribution in the air gap of the ferrite core, so that the uniformity of the magnetic field distribution is improved; the coil of the improved ferrite magnetic field generating device is mainly used for increasing the magnetic field intensity in the air gap at two ends of the air gap and optimizing the magnetic field in the air gap through a genetic algorithm. The invention can effectively increase the practicability and uniformity of the ferrite magnetic field.
Description
Technical Field
The invention relates to the technical field of modeling of ferrite magnetic fields, in particular to a ferrite magnetic field optimization device and method based on a genetic algorithm.
Background
Currently, there are three main methods for generating magnetic fields: a power-on solenoid, a Helmholtz coil, and a ferrite core wrapped with a current-carrying coil. The magnetic field generated by adopting the methods does not meet the application requirements, and the problems of small magnetic field intensity, uneven magnetic field distribution and the like exist; therefore, there are studies on magnetic fields and methods for improving the magnetic fields, but the existing studies are basically to improve the magnetic fields generated by the two ways of energizing the solenoid and the helmholtz coil, but the existing studies are to improve the magnetic fields generated by the ferrite core wound with the current-carrying coil. Therefore, research and improvement of the ferrite magnetic field are necessary and significant.
Disclosure of Invention
In view of the above, the present invention provides a ferrite magnetic field optimization apparatus and method based on genetic algorithm, which solve the problems of low uniformity and small magnetic field strength of the magnetic field generated by the ferrite core wound with the current-carrying coil.
In order to achieve the purpose, the invention adopts the following technical scheme:
a ferrite magnetic field optimization device based on a genetic algorithm comprises a ferrite magnetic core and two current-carrying coils, wherein the two current-carrying coils are symmetrically distributed at two ends of an air gap and used for increasing the magnetic field intensity in the air gap.
A ferrite magnetic field optimization device optimization method based on genetic algorithm comprises the following steps:
step S1, constructing a geometric model of the ferrite magnetic core and the current-carrying coil;
step S2: deducing a steady-state partial differential equation of the ferrite magnetic field according to the Maxwell equation set;
step S3: setting material parameters and setting boundary conditions according to geometric models of the ferrite magnetic core and the current-carrying coil;
step S4: constructing a finite element grid according to the precision requirement of problem solution, calculating a steady-state partial differential equation according to a finite element method to obtain the distribution and initial value of the magnetic field in the air gap of the ferrite core, and simultaneously obtaining a uniformity function of the magnetic field;
step S5: and optimizing parameters of the ferrite core and the current of the coil by adopting a genetic algorithm, obtaining the optimal parameters of the ferrite core and the coil after iteration is completed, and applying the optimal parameters to the magnetic field to obtain the optimal distribution of the magnetic field.
Furthermore, the ferrite magnetic core is made of manganese-zinc ferrite.
Further, the steady-state partial differential equation is specifically:
in the formula, mu r Is the relative permeability of the medium, E is the electric field strength, k 0 Is wave number, ε r Is complex and σ represents the loss.
Further, the material parameters include relative permittivity, permeability, and conductivity.
Further, the finite element mesh adopts a free tetrahedral mesh, and the boundary condition adopts a dirichlet boundary condition.
Further, the step S5 is specifically:
step S51: initializing parameters such as current in a coil to be used in the optimization process, length and width of a ferrite core on sections on two sides of an air gap and the like, and encoding the parameters to form an initial group;
step S52, carrying out fitness evaluation on individuals in the group and judging whether the fitness evaluation meets the termination criterion;
step S53: if the termination criterion is not met, selecting, crossing and performing mutation operation on the population to obtain a new population;
step S54: repeating the steps S52, S53 until the termination criteria are met, and stopping loop iteration by the algorithm;
step S55: and after the algorithm optimization is completed, applying parameters corresponding to the optimal values to the magnetized ferromagnetic core with the current-carrying coil to obtain the optimal distribution of the magnetic field.
Compared with the prior art, the invention has the following beneficial effects:
the ferrite magnetic field generating device can enhance the magnetic field intensity in the air gap, optimize the parameters of the ferrite magnetic core and the coil through a genetic algorithm, and obviously improve the uniformity of the magnetic field distribution.
Drawings
FIG. 1 is a magnetic field strength profile of an unmodified device;
FIG. 2 is a graph of the magnetic field strength profile of the improved apparatus of the present invention;
FIG. 3 is a flow chart of a method in one embodiment of the present invention;
FIG. 4 is an unoptimized field strength profile in an example of the invention;
FIG. 5 is a graph of an unoptimized magnetic field strength in an example of the present invention;
FIG. 6 is a graph of magnetic field strength distribution after optimization in an example of the invention;
FIG. 7 is a graph of magnetic field strength after optimization in an example of the invention;
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 2, the present invention provides a ferrite magnetic field optimization device based on genetic algorithm, which includes a ferrite core and two current-carrying coils symmetrically distributed at two ends of an air gap for increasing the magnetic field strength in the air gap. The magnetic field generated by the magnetic field generating device before the modification is smaller than that shown in fig. 1. While after the improvement, as shown in fig. 2, the magnetic field strength in the air gap is significantly increased (darker color); and optimizing the magnetic field intensity distribution in the air gap by adopting a genetic algorithm on the basis of the improved magnetic field generating device.
As shown in fig. 3, the ferrite magnetic field optimization method based on the genetic algorithm includes the following steps:
step S1: constructing a geometric model of a ferrite magnetic core and two current-carrying coils;
step S2: deducing a steady-state partial differential equation of the ferrite magnetic field according to the Maxwell equation set;
step S3: setting material parameters and setting boundary conditions according to geometric models of the ferrite magnetic core and the current-carrying coil;
step S4: constructing a finite element grid according to the precision requirement of problem solution, calculating a steady-state partial differential equation according to a finite element method to obtain the distribution and initial value of the magnetic field in the air gap of the ferrite core, and simultaneously obtaining a uniformity function of the magnetic field;
step S5: and optimizing parameters of the ferrite core and the current of the coil by adopting a genetic algorithm, obtaining the optimal parameters of the ferrite core and the coil after iteration is completed, and applying the optimal parameters to the magnetic field to obtain the optimal distribution of the magnetic field.
In the present embodiment, the ferrite core used is manganese-zinc ferrite having a permeability of more than 2000, a small loss and a high curie temperature point.
In this embodiment, the steady state partial differential equation gram can be described as:
in the formula, mu r Is the relative permeability of the medium, E is the electric field strength, k 0 Is wave number, ε r Is complex and σ represents the loss.
In this embodiment, the material parameters include relative permittivity, permeability, and conductivity. The magnetic core has the following parameters: the relative permittivity is 1, the magnetic permeability is 2300, and the electric conductivity is 10S/m.
In this embodiment, the finite element mesh is a free tetrahedral mesh, the complete mesh includes 12 domain units and 140 boundary units, and the boundary condition is a dirichlet boundary condition.
In this embodiment, the step S5 may specifically be:
step S51: initializing parameters such as current in a coil to be used in the optimization process, length and width of a ferrite core on sections on two sides of an air gap and the like, and encoding the parameters to form an initial group;
step S52, evaluating the fitness (uniformity function) of the individuals in the group and judging whether the termination criterion is met;
step S53: if the termination criterion is not met, selecting, crossing and mutating the population to obtain a new population;
step S54: repeating the steps S52, S53 until the termination criteria are met, and stopping loop iteration by the algorithm;
step S55: after the algorithm optimization is completed, parameters corresponding to the optimal values are applied to the magnetized ferromagnetic core with the current-carrying coil, and the magnetic field in the air gap can be found to have the optimal distribution.
In this embodiment, the uniformity function is designed as (H) max -H min )/H mean In which H is max ,H min And H mean The maximum value, the minimum value and the average value of the magnetic field intensity in the air gap area are respectively.
To illustrate the optimization effect, fig. 4 and 5 are a magnetic field intensity distribution diagram and a magnetic field intensity graph before optimization, and it can be seen that the magnetic field intensity distribution in the air gap is obviously uneven, and the difference between the maximum value and the minimum value can reach 5000A/m, and fig. 6 and 7 are a magnetic field intensity distribution diagram and a magnetic field intensity graph after optimization, and it can be seen that the magnetic field intensity distribution in the air gap is obviously improved, and the difference between the maximum value and the minimum value is only 500A/m.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (8)
1. A ferrite magnetic field optimization device based on a genetic algorithm is characterized by comprising a ferrite magnetic core and two current-carrying coils, wherein the two current-carrying coils are symmetrically distributed at two ends of an air gap and used for increasing the magnetic field intensity in the air gap.
2. The optimization method of the ferrite magnetic field optimization device based on the genetic algorithm according to claim 1, characterized by comprising the following steps:
step S1, constructing a geometric model of the ferrite magnetic core and the current-carrying coil;
step S2: deducing a steady-state partial differential equation of the ferrite magnetic field according to the Maxwell equation;
step S3: setting material parameters and setting boundary conditions according to geometric models of the ferrite magnetic core and the current-carrying coil;
step S4: constructing a finite element grid according to the precision requirement of problem solution, calculating a steady-state partial differential equation according to a finite element method to obtain the distribution and initial value of the magnetic field in the air gap of the ferrite core, and simultaneously obtaining a uniformity function of the magnetic field;
step S5: and optimizing parameters of the ferrite core and the current of the coil by adopting a genetic algorithm, obtaining the optimal parameters of the ferrite core and the coil after iteration is completed, and applying the optimal parameters to the magnetic field to obtain the optimal distribution of the magnetic field.
3. The optimization method of ferrite magnetic field optimization device based on genetic algorithm as claimed in claim 2, wherein said ferrite core is manganese zinc ferrite.
4. The optimization method of the ferrite magnetic field optimization device based on the genetic algorithm as claimed in claim 2, wherein the steady state partial differential equation is specifically:
in the formula, mu r Is the relative permeability of the medium, E is the electric field strength, k 0 Is wave number, ε r Is complex and σ represents the loss.
5. The optimizing method of a ferrite magnetic field optimizing device based on a genetic algorithm as claimed in claim 2, wherein the material parameters include relative permittivity, permeability and conductivity.
6. The optimization method of ferrite magnetic field optimization device based on genetic algorithm as claimed in claim 2, wherein the finite element mesh adopts free tetrahedral mesh, and the boundary condition adopts Dirichlet boundary condition.
7. The optimization method of ferrite magnetic field optimization device based on genetic algorithm as claimed in claim 2, wherein said step S5 is specifically:
step S51: initializing relevant parameters to be used in the optimization process, and coding the parameters to form an initial group;
step S52, carrying out fitness evaluation on individuals in the group and judging whether the fitness evaluation meets the termination criterion;
step S53: if the termination criterion is not met, selecting, crossing and mutating the population to obtain a new population;
step S54: repeating the steps S52, S53 until a termination criterion is met, the algorithm stopping loop iteration;
step S55: and after the algorithm optimization is completed, applying parameters corresponding to the optimal values to the magnetized ferromagnetic core with the current-carrying coil to obtain the optimal distribution of the magnetic field.
8. The optimizing method of ferrite magnetic field optimizing device based on genetic algorithm as claimed in claim 7, wherein the related parameters include current in the coil, length and width of the ferrite core in cross section at both sides of the air gap.
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