CN114997012B - 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 79
- 229910000859 α-Fe Inorganic materials 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 230000002068 genetic effect Effects 0.000 title claims abstract description 21
- 230000035699 permeability Effects 0.000 claims description 8
- 239000000463 material Substances 0.000 claims description 6
- 229910001289 Manganese-zinc ferrite Inorganic materials 0.000 claims description 3
- JIYIUPFAJUGHNL-UHFFFAOYSA-N [O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[Mn++].[Mn++].[Mn++].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Zn++].[Zn++] Chemical group [O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[O--].[Mn++].[Mn++].[Mn++].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Fe+3].[Zn++].[Zn++] JIYIUPFAJUGHNL-UHFFFAOYSA-N 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, firstly providing an improved ferrite magnetic field generating device; introducing a uniformity function and a genetic algorithm, optimizing the magnetic field distribution in the ferrite core air gap, and improving the uniformity of the magnetic field distribution; 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 then the magnetic field in the air gap is optimized 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 ferrite magnetic field modeling, 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: an energized solenoid, a helmholtz coil, a ferrite core wound with a current carrying coil. The magnetic field generated by the methods generally 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 continuous studies on magnetic fields and methods for improving magnetic fields, however, the prior studies are basically to improve magnetic fields generated by two modes of an energized solenoid and a helmholtz coil, but rarely improve magnetic fields generated by a ferrite core around which a current-carrying coil is wound. Therefore, research and improvement of ferrite magnetic fields are very necessary and significant.
Disclosure of Invention
In view of the above, the present invention aims to provide a ferrite magnetic field optimizing device and method based on a genetic algorithm, which solve the problems of low uniformity and small magnetic field strength of a magnetic field generated by a ferrite core wound with a current-carrying coil.
In order to achieve the above purpose, the invention adopts the following technical scheme:
A ferrite magnetic field optimizing device based on a genetic algorithm comprises a ferrite magnetic core and two current-carrying coils, wherein the coils are symmetrically distributed at two ends of an air gap and used for increasing the magnetic field intensity in the air gap.
The optimizing method of the ferrite magnetic field optimizing device based on the genetic algorithm comprises the following steps:
s1, constructing a geometric model of a ferrite core and a current-carrying coil;
step S2: deducing a steady partial differential equation of the ferrite magnetic field according to the Maxwell equation set;
step S3: setting material parameters and boundary conditions according to geometric models of the ferrite core and the current-carrying coil;
step S4: constructing a finite element grid according to the precision requirement of problem solving, calculating a steady partial differential equation according to a finite element method to obtain the distribution and initial value of a magnetic field in an air gap of a ferrite core, and simultaneously obtaining a uniformity function of the magnetic field;
Step S5: and optimizing parameters of the ferrite core and current of the coil by adopting a genetic algorithm, obtaining optimal parameters of the ferrite core and the coil after iteration is completed, and applying the optimal parameters to a magnetic field to obtain optimal distribution of the magnetic field.
Further, the ferrite core adopts manganese zinc ferrite.
Further, the steady-state partial differential equation is specifically:
Where μ r is the relative permeability of the medium, E is the electric field strength, k 0 is the wave number, ε r is the complex number, 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 specifically includes:
Step S51: initializing parameters such as current in coils to be used in the optimization process, length and width of ferrite cores of sections at two sides of an air gap and the like, and coding the parameters to form an initial group;
Step S52, carrying out fitness evaluation on individuals in the group and judging whether the termination criterion is met;
step S53: if the termination criterion is not met, selecting the group, crossing, and performing mutation operation to obtain a new group;
step S54: repeating the steps S52 and S53 until the termination criterion is met, and stopping the loop iteration by the algorithm;
step S55: after the algorithm optimization is completed, the parameters corresponding to the optimal values are applied to the magnetized ferromagnetic core with the current-carrying coil, and the optimal distribution of the magnetic field is obtained.
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 genetic algorithm, and obviously improve the uniformity of magnetic field distribution.
Drawings
FIG. 1 is a graph of the magnetic field strength profile of an unmodified device;
FIG. 2 is a graph showing the magnetic field strength profile of the improved apparatus of the present invention;
FIG. 3 is a flow chart of a method in an example of the invention;
FIG. 4 is a graph of unoptimized magnetic field strength profile in an example of the invention;
FIG. 5 is a graph of unoptimized magnetic field strength in an example of the invention;
FIG. 6 is a graph of optimized magnetic field strength profile in an example of the present invention;
FIG. 7 is a graph of optimized magnetic field strength in an example of the invention;
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
Referring to fig. 2, the invention provides a ferrite magnetic field optimizing device based on a genetic algorithm, which comprises a ferrite magnetic core and two current-carrying coils, wherein the coils are symmetrically distributed at two ends of an air gap and are used for increasing the magnetic field intensity in the air gap. The magnetic field generated by the unmodified magnetic field generating device is smaller than that shown in fig. 1. After modification, the magnetic field strength in the air gap is significantly enhanced (darker color) as shown in fig. 2; 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 comprises the following steps:
Step S1: constructing a geometric model of the ferrite core and the two current-carrying coils;
step S2: deducing a steady partial differential equation of the ferrite magnetic field according to the Maxwell equation set;
step S3: setting material parameters and boundary conditions according to geometric models of the ferrite core and the current-carrying coil;
step S4: constructing a finite element grid according to the precision requirement of problem solving, calculating a steady partial differential equation according to a finite element method to obtain the distribution and initial value of a magnetic field in an air gap of a ferrite core, and simultaneously obtaining a uniformity function of the magnetic field;
Step S5: and optimizing parameters of the ferrite core and current of the coil by adopting a genetic algorithm, obtaining optimal parameters of the ferrite core and the coil after iteration is completed, and applying the optimal parameters to a magnetic field to obtain optimal distribution of the magnetic field.
In this embodiment, the ferrite core used is manganese-zinc ferrite, which has a permeability of more than 2000, low loss and high curie temperature point.
In this embodiment, the steady state partial differential equation g can be described as:
Where μ r is the relative permeability of the medium, E is the electric field strength, k 0 is the wave number, ε r is the complex number, σ represents the loss.
In this embodiment, the material parameters include relative permittivity, permeability, and conductivity. The parameters corresponding to the magnetic core are as follows: the relative permittivity is 1, the permeability is 2300, and the conductivity is 10[ S/m ].
In this embodiment, the finite element mesh is selected from a free tetrahedral mesh, and the complete mesh includes 12 domain units and 140 boundary units, and the boundary condition adopts dirichlet boundary condition.
In this embodiment, the step S5 may specifically be:
Step S51: initializing parameters such as current in coils to be used in the optimization process, length and width of ferrite cores of sections at two sides of an air gap and the like, and coding the parameters to form an initial group;
step S52, evaluating fitness (uniformity function) of individuals in the population and judging whether termination criteria are met;
step S53: if the termination criterion is not met, selecting the group, crossing, and performing mutation operation to obtain a new group;
step S54: repeating the steps S52 and S53 until the termination criterion is met, and stopping the loop iteration by the algorithm;
Step S55: after the algorithm optimization is completed, the 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-Hmin)/Hmean, where H max,Hmin and H mean are the maximum, minimum and average values, respectively, of the air gap field strength.
For the purpose of illustrating the optimization effect, fig. 4 and 5 are magnetic field intensity distribution diagrams and magnetic field intensity diagrams before optimization, it can be seen that the magnetic field intensity distribution in the air gap is significantly nonuniform, and the maximum value and the minimum value differ by 5000A/m, and fig. 6 and 7 are magnetic field intensity distribution diagrams and magnetic field intensity diagrams after optimization, it can be seen that the magnetic field intensity distribution in the air gap is significantly improved, and the difference between the maximum value and the minimum value is only 500A/m.
The foregoing description is only of the preferred embodiments of the invention, and all changes and modifications that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (5)
1. The optimizing method of the ferrite magnetic field optimizing device based on the genetic algorithm is characterized in that the device comprises a ferrite magnetic core and two current-carrying coils, wherein the coils are symmetrically distributed at two ends of an air gap and used for increasing the magnetic field intensity in the air gap;
the optimization method comprises the following steps:
s1, constructing a geometric model of a ferrite core and a current-carrying coil;
step S2: deducing a steady partial differential equation of the ferrite magnetic field according to the Maxwell equation set;
step S3: setting material parameters and boundary conditions according to geometric models of the ferrite core and the current-carrying coil;
step S4: constructing a finite element grid according to the precision requirement of problem solving, calculating a steady partial differential equation according to a finite element method to obtain the distribution and initial value of a magnetic field in an air gap of a ferrite core, and simultaneously obtaining a uniformity function of the magnetic field;
Step S5: optimizing parameters of the ferrite core and current of the coil by adopting a genetic algorithm, obtaining optimal parameters of the ferrite core and the coil after iteration is completed, and applying the optimal parameters to a magnetic field to obtain optimal distribution of the magnetic field;
the step S5 specifically comprises the following steps:
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 termination criterion is met;
step S53: if the termination criterion is not met, selecting the group, crossing, and performing mutation operation to obtain a new group;
step S54: repeating the steps S52 and S53 until the termination criterion is met, and stopping the loop iteration by the algorithm;
Step S55: after the algorithm optimization is completed, applying parameters corresponding to the optimal values to a magnetized ferromagnetic core with a current-carrying coil to obtain the optimal distribution of the magnetic field;
The relevant parameters include the current in the coil, the length and width of the ferrite core of the cross section of the air gap on both sides.
2. The optimization method of a ferrite magnetic field optimization device based on a genetic algorithm according to claim 1, wherein the ferrite core is manganese zinc ferrite.
3. The optimization method of a ferrite magnetic field optimization device based on a genetic algorithm according to claim 1, wherein the steady-state partial differential equation is specifically:
Where μ r is the relative permeability of the medium, E is the electric field strength, k 0 is the wave number, ε r is the complex number, σ represents the loss.
4. The optimization method of a ferrite magnetic field optimization device based on a genetic algorithm according to claim 1, wherein the material parameters include relative permittivity, permeability and conductivity.
5. The optimization method of a ferrite magnetic field optimization device based on a genetic algorithm according to claim 1, wherein the finite element mesh adopts a free tetrahedral mesh, and the boundary condition adopts a dirichlet boundary condition.
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