Method and system for predicting effective complex permeability of soft magnetic compound

Abstract

The invention discloses a soft magnetic compound effective complex permeability prediction method and system, and relates to the technical field of soft magnetic compound effective complex permeability prediction. The technical defect that a universal, accurate and effective method for predicting the complex permeability of the soft magnetic composite material is lacked is overcome, and the prediction method comprises the following steps that (1) intrinsic material parameters of magnetic particles are substituted into an LLG equation, and the intrinsic permeability of the single magnetic particle is calculated; (2) simulating the distribution of the magnetic moments of the magnetic particles in the soft magnetic compound by using a magnetic moment distribution model based on a Boltzmann distribution function, and calculating to obtain the average complex permeability of a magnetic particle set; (3) performing skin effect correction on the average complex permeability of the magnetic particle set by using a skin effect correction model; and (4) determining the effective complex magnetic conductivity of the soft magnetic composite material. The effective complex permeability of soft magnetic compounds with different volume fractions is predicted through a small number of experiments, and the problems of high cost and low efficiency of material optimization and application are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of predicting the effective complex permeability of soft magnetic composites. Background Art

[0002] In the contemporary field of materials science and engineering, soft magnetic composites (SMCs), as a new type of material with great potential, are gradually emerging. Such materials are mainly composed of magnetic particles and insulating binders, and their unique and excellent properties have attracted much attention in many key fields. Specifically, soft magnetic composites have significant characteristics such as high resistivity and low loss, which show extremely broad application prospects in the manufacture of electronic components, the optimization of electromagnetic energy conversion processes, the improvement of microwave absorption technology, and the forefront development of information technology, attracting a large number of scientific researchers and engineering technicians' high attention and in-depth research. In the application research process of soft magnetic composites, the complex permeability always plays a crucial role. It is directly related to the performance of materials in the electromagnetic field and is a key parameter for optimizing the performance of magnetic materials. Establishing an accurate model to calculate the complex permeability of soft magnetic composites is of irreplaceable significance for guiding the performance optimization of soft magnetic composites in practical applications.

[0003] The magnetization M of the magnetic particle is described by the Landau-Lifshitz-Gilbert (LLG) equation:

[0004]

[0005] where γ is the gyromagnetic ratio, α is the Gilbert damping factor, and M s is the saturation magnetization of the particle. The total effective field H eff = H a + h, including the static magnetic field H a and the microwave field h.

[0006] For uniaxial magnetic particles, the total effective field H eff is expressed as:

[0007] H eff = h + H a = h1e1 + h2e2 + (h3 + H a )e3.

[0008] where H a is the component of the static magnetic field H a on the 3-axis of the principal axis coordinate system, and h i (i = 1, 2, 3) are the components of the microwave field h on the 1, 2, 3 axes of the principal axis coordinate system.

[0009] m1 = χ 11 h1 + χ 12h2

[0010] m2 = χ 22 h2 - χ 12 h1

[0011] m3 = 0.

[0012] Where:

[0013]

[0014] For easy-plane magnetic particles, the total effective field H eff is expressed as:

[0015]

[0016] Where, H ⊥ and H ∥ are the in-plane and out-of-plane anisotropy fields, M i (i = 1, 2, 3) are the components of the magnetization M along the 1, 2, 3 axes of the principal axis coordinate system, h i (i = 1, 2, 3) are the components of the microwave field h along the 1, 2, 3 axes of the principal axis coordinate system.

[0017] Substituting H eff into the LLG equation and solving gives:

[0018] m1 = χ 11 h1 + χ 12 h2

[0019] m2 = χ 22 h2 - χ 12 h1

[0020] m3 = 0.

[0021] Where:

[0022]

[0023] It should be noted that all the above derivations are carried out in the principal axis coordinate system o-123 of the particle. In actual calculations, we need to perform a coordinate transformation to unify the results to the laboratory coordinate system. After the transformation, the intrinsic magnetic permeability of the easy-plane particle is [1]:

[0024]

[0025] Where:

[0026]

[0027] Where, θ, φ, δ are the direction angles of the particle magnetic moment. are the polar angle and azimuth angle of the microwave field in the spherical coordinate system.

[0028] For the intrinsic complex permeability of a single magnetic particle, the above two models can be selected for calculation according to the particle type. However, for the prediction of the complex permeability of soft magnetic composites, there is still a lack of a general and accurate method. This gap in the algorithm seriously restricts the performance optimization process of soft magnetic composites in practical applications, and also limits their application expansion and in-depth exploration in a wider range of fields. Summary of the Invention

[0029] In summary, the object of the present invention is to solve the technical deficiency that there is a lack of a general and accurate effective method for predicting the complex permeability of soft magnetic composites, and to propose a method and system for predicting the effective complex permeability of soft magnetic composites.

[0030] To solve the technical deficiencies proposed by the present invention, the technical solutions adopted are as follows:

[0031] A method for predicting the effective complex permeability of soft magnetic composites, characterized in that the prediction method adopts the following steps:

[0032] (1), According to the type of magnetic particles in the soft magnetic composite of the easy-plane type or easy-axis type, substitute the intrinsic material parameters of the magnetic particles into the LLG equation, and calculate the intrinsic permeability of a single magnetic particle from the modified LLG equation;

[0033] (2), Use the magnetic moment distribution model based on the Boltzmann distribution function to simulate the distribution of the magnetic moments of the magnetic particles in the soft magnetic composite, and substitute the obtained magnetic moment orientation data into the calculation formula for the average permeability of the particle ensemble to calculate the average complex permeability of the magnetic particle ensemble;

[0034] (3), According to the equivalent particle size of the magnetic particles in the soft magnetic composite, use the skin effect correction model to correct the average complex permeability of the magnetic particle ensemble for the skin effect;

[0035] (4), According to the volume fraction parameter, use the HEMT model to calculate the effective complex permeability of the soft magnetic composite and determine the effective complex permeability of the soft magnetic composite.

[0036] The technical features for further limiting the present invention include:

[0037] The intrinsic material parameters of the magnetic particles include: saturation magnetization M s 、magnetocrystalline anisotropy equivalent field H eff 、natural resonance damping factor α.

[0038] The calculation formula for the average permeability of the particle ensemble is:

[0039]

[0040] Among them, represents the intrinsic permeability of a single particle, and N is the total number of simulated particles.

[0041] Substitute it into the probability density function of the magnetic moment distribution model of the Boltzmann distribution function. The probability density function of the backward magnetic moment is expressed as:

[0042] P(θ)∝sin(θ)e ξcos(θ)

[0043] Among them, is a dimensionless parameter representing the relative intensity of the interaction between the magnetic moment and the external field.

[0044] When the magnetic moment distribution model of the Boltzmann distribution function is used to simulate the magnetic moment distribution of magnetic particles in the soft magnetic composite, in order to simulate the magnetic moment distribution with different degrees of orientation, the inverse transform sampling method is used for calculation, and random samples are generated through the known cumulative distribution function;

[0045] The cumulative distribution function of the backward magnetic moment is expressed as:

[0046]

[0047] The value range of F(θ) is [0, 1]. The inverse cumulative distribution function is the inverse function of the cumulative distribution function and satisfies:

[0048] F(F -1 (u))=u, u∈[0, 1]

[0049] For any given probability u (between 0 and 1), F -1 (u) can return an angular value θ such that the cumulative distribution function F(θ) = u.

[0050] The inverse cumulative distribution function is numerically solved by the interpolation method. Define the degree of orientation D as the percentage of the projection size of the magnetic moment in the xy plane to the total size of the magnetic moment. Its value range is from 0 to 100. The degree of orientation D is calculated by the following formula:

[0051]

[0052] Among them, M xy is the projection value of the magnetic moment in the xy plane, is the total magnetic moment vector; by generating 10,000 uniformly distributed random numbers, using the inverse cumulative distribution function to inversely find the corresponding θ values to obtain 10,000 magnetic moment samples. Finally, calculate the degree of orientation of these samples and adjust the parameter ξ in the probability density function so that the final distribution conforms to the expected degree of orientation.

[0053] When using the skin effect correction model to correct the skin effect of the average complex magnetic permeability of a magnetic particle assembly, an alternating microwave magnetic field h0 enters the magnet along the Z-axis, and the microwave field h in the magnet can be expressed as:

[0054] h = h0e -z / δ e i(z / δ-ωt)

[0055] where ω is the angular frequency of the microwave, e -z / δ is the attenuation factor, and δ is the skin depth, which is expressed as:

[0056] where ρ is the resistivity, μ i is the intrinsic magnetic permeability, f is the microwave frequency, and the magnitude of the microwave field conducted into the particle

[0057] inside is:

[0058] |h| = h0e -(R-r) / δ

[0059] where R is the particle radius, and the dynamic magnetization of the particle is:

[0060]

[0061] χ i is the intrinsic magnetic susceptibility of the particle, and the intrinsic magnetic permeability of a single particle after skin effect correction is:

[0062]

[0063] The average magnetic permeability of the particle assembly after skin effect correction is:

[0064]

[0065] The described HEMT model is expressed as:

[0066]

[0067] where, φ is the volume fraction of the magnetic particles, μ1 is the average magnetic permeability of the particle assembly, μ0 is the magnetic permeability of the matrix, and for a non-magnetic matrix, μ0 = 1, μ eff is the effective magnetic permeability of the composite material.

[0068] A soft magnetic composite effective complex magnetic permeability prediction system includes:

[0069] An input module for inputting the intrinsic parameters of the magnetic particles. For easy-axis type particles, the magnetocrystalline anisotropy field H a , the natural resonance damping coefficient α, and the saturation magnetization M s, resistivity ρ, particle size R; for easy-plane particles, input the anisotropy field H ⊥ , H ∥ , natural resonance damping coefficient α, saturation magnetization M s , resistivity ρ, particle size R;

[0070] The data analysis module performs simulations according to a method for predicting the effective complex permeability of a soft magnetic composite as described in claims 1-8;

[0071] The output module is used to display the complex permeability magnetic spectrum diagram of the soft magnetic composite based on the simulation data of the data analysis module and export the calculated complex permeability magnetic spectrum data of the soft magnetic composite.

[0072] The described prediction system uses matlab appdesigner simulation software.

[0073] The beneficial effects of the present invention are as follows: The present invention calculates the effective complex permeability of a soft magnetic composite based on the intrinsic parameters of the magnetic composite material, predicts the effective complex permeability of soft magnetic composites with different volume fractions on the premise of conducting a small number of experiments, reduces the high cost and low efficiency problems of material optimization and application, provides a solid theoretical basis and clear guiding direction for the performance optimization of soft magnetic composite materials, and promotes the further development of soft magnetic composite materials in various application fields and gives full play to their great potential in modern science and technology and industry. Description of the Drawings

[0074] Figure 1 It is a schematic diagram of the working process of the method and system for calculating the effective complex permeability of a soft magnetic composite;

[0075] Figure 2 It is a schematic diagram of the simulated distribution of randomly oriented magnetic moments;

[0076] Figure 3 It is a schematic diagram of the simulated distribution of magnetic moments with an orientation degree of 30%;

[0077] Figure 4 It is a schematic diagram of the simulated distribution of magnetic moments with an orientation degree of 60%;

[0078] Figure 5 It is a schematic diagram of the simulated distribution of magnetic moments with an orientation degree of 90%;

[0079] Figure 6 It is a curve graph of the distribution probability of magnetic moments with different orientation degrees along the θ angle;

[0080] Figure 7 It is an interface diagram of simulating the distribution of magnetic moments with a specific orientation based on the magnetic moment distribution model of the Boltzmann distribution function;

[0081] Figure 8The interface diagram of the effective complex permeability of the soft magnetic composite calculated by the system of the present invention. Detailed implementation manners

[0082] The technical solution of the present invention will be further described below in conjunction with the preferred specific embodiments of the present invention with reference to the drawings.

[0083] Refer to Figure 1 As shown in , a method for predicting the effective complex permeability of a soft magnetic composite disclosed by the present invention adopts the following steps:

[0084] (1) Calculation of the intrinsic permeability of magnetic particles; according to the types of magnetic particles in the easy-plane type or easy-axis type soft magnetic composite, the intrinsic material parameters of the magnetic particles are substituted into the LLG equation, and the intrinsic permeability of a single magnetic particle is calculated by the modified LLG equation; that is, according to different material types, different magnetic crystal anisotropy equivalent fields H eff Modify the Landau-Lifshitz-Gilbert (LLG) equation. Substitute the intrinsic parameters of the material into the modified LLG equation to calculate the intrinsic permeability of a single magnetic particle. The intrinsic material parameters of the magnetic particles include: saturation magnetization Ms, magnetic crystal anisotropy equivalent field Heff, and natural resonance damping factor α.

[0085] (2) Calculation of the average permeability of the particle assembly; use the magnetic moment distribution model based on the Boltzmann distribution function to simulate the distribution of the magnetic moments of the magnetic particles in the soft magnetic composite, and substitute the simulated magnetic moment orientation data into the calculation formula of the average permeability of the particle assembly to calculate the average complex permeability of the magnetic particle assembly;

[0086] Since the soft magnetic composite contains a large number of magnetic particles inside, each particle contributes to the overall permeability; the permeability of a single particle is affected by the angle between the microwave field and its magnetic moment direction. To calculate the effective permeability of the soft magnetic composite material, it is necessary to consider the contribution of each particle in the assembly to obtain the average permeability of the particle assembly. The average permeability of the particle assembly can be written as:

[0087]

[0088] Among them, represents the intrinsic permeability of a single particle, and N is the total number of simulated particles.

[0089] By different sample preparation methods, samples with different degrees of orientation can be obtained. In order to simulate the particle magnetic moment distribution of samples with different degrees of orientation, the present invention proposes a magnetic moment distribution model based on the Boltzmann distribution function; specifically: when magnetic particles rotate and orient in a magnetic field, the distribution of the magnetic moment direction is mainly affected by the combined effect of thermodynamic equilibrium and the applied magnetic field. According to the thermal equilibrium theory in statistical mechanics, the orientation of the magnetic moment should follow the Boltzmann distribution. When the magnetic moment m of the magnetic particle is under the action of the applied magnetic field H, its energy E can be expressed as:

[0090] E = -m·H = -mH cos(θ),

[0091] where m = |m| is the magnitude of the magnetic moment, H = |H| is the intensity of the applied magnetic field, and θ is the angle between the magnetic moment and the magnetic field direction.

[0092] Due to the rotational symmetry of the system, the probability density function depends only on the polar angle θ and is independent of the azimuthal angle . Substituting the probability density function of the Boltzmann distribution model, the probability density function of the backward magnetic moment is expressed as:

[0093]

[0094] where k B is the Boltzmann constant, T is the absolute temperature. Z is the partition function, expressed as: For convenience, the probability density function of the backward magnetic moment is expressed as:

[0095] P(θ) ∝ sin(θ)e ξcos(θ)

[0096] where is a dimensionless parameter representing the relative intensity of the magnetic moment and the external field interaction. In order to simulate the magnetic moment distribution of different degrees of orientation, the present invention uses the inverse transform sampling method for calculation. Generate random samples with a specific distribution through the known Cumulative Distribution Function (CDF).

[0097] The cumulative distribution function (CDF) of the backward magnetic moment is expressed as:

[0098]

[0099] Considering that P(θ) is the probability density function, the value range of F(θ) is [0, 1]. The Inverse Cumulative Distribution Function (ICDF) is the inverse function of the cumulative distribution function, satisfying:

[0100] F(F -1F(u)) = u, u ∈ [0, 1]

[0101] That is, for any given probability u (between 0 and 1), F -1 (u) can return an angular value θ such that the cumulative distribution function F(θ) = u. Usually, it is not possible to directly sample the random variables of the target distribution, but it is easy to generate random numbers u that follow a uniform distribution. Substituting the uniform random number u into the inverse cumulative distribution function can obtain the sample x of the target distribution.

[0102] Generally, the form of the inverse cumulative distribution function (ICDF) is complex and difficult to solve analytically. Considering the monotonicity of the cumulative distribution function (CDF), the ICDF can be numerically solved by the interpolation method to improve the calculation efficiency. Define the degree of orientation D as the percentage ratio of the projection size of the magnetic moment in the xy plane (easy plane) to the total size of the magnetic moment, and its value range is from 0 to 100. Specifically, the degree of orientation D can be calculated by the following formula:

[0103]

[0104] where M xy is the projection value of the magnetic moment in the xy plane (easy plane), is the total magnetic moment vector. By generating 10,000 random numbers that follow a uniform distribution and using the ICDF to inversely find the corresponding θ values, 10,000 magnetic moment samples are obtained. Finally, calculate the degree of orientation of these samples and adjust the parameter ξ in the probability density function so that the final distribution conforms to the expected degree of orientation. Through the above method, the magnetic moment distributions with various degrees of orientation can be conveniently simulated, as shown in Figures 2 to 6 shown.

[0105] Substituting the simulated magnetic moment orientation data into the calculation formula of the average magnetic permeability of the particle assembly can calculate the average complex magnetic permeability of the magnetic particle assembly.

[0106] (3) Skin effect correction; According to the equivalent particle size of the magnetic particles in the soft magnetic composite, use the skin effect correction model to correct the skin effect of the average complex magnetic permeability of the magnetic particle assembly;

[0107] Metal soft magnetic particles will inevitably have the skin effect under high-frequency microwave fields. When the particle size is greater than the critical size, the skin effect will become obvious, which will significantly affect the complex magnetic permeability spectrum. Therefore, it is necessary to consider the skin effect in the calculation.

[0108] The alternating microwave magnetic field h0 enters the magnet along the Z axis, and the microwave magnetic field h in the magnet can be expressed as:

[0109] h = h0e -z / δ e i(z / δ-ωt)

[0110] where ω is the angular frequency of the microwave. e -z / δ is the attenuation factor. δ is the skin depth, expressed as: where ρ is the resistivity, μ i is the intrinsic permeability, and f is the microwave frequency. As the depth increases, the microwave field conducted into the interior of the magnet gradually attenuates. For spherical particles, the magnitude of the microwave field conducted into the interior of the particles can be written as:

[0111] |h| = h0e -(R-r) / δ .

[0112] where R is the radius of the particle. The dynamic magnetization of the particle can be written as:

[0113]

[0114] χ i is the intrinsic magnetic susceptibility of the particle. Therefore, the intrinsic permeability of a single particle after skin effect correction is written as:

[0115]

[0116] The average magnetic permeability of the particle assembly after skin effect correction is written as:

[0117]

[0118] (4) Calculation of the effective magnetic permeability of the soft magnetic composite; According to the volume fraction parameter, use the HEMT model to calculate the effective complex magnetic permeability of the soft magnetic composite to determine the effective complex magnetic permeability of the soft magnetic composite material.

[0119] Essentially, the soft magnetic composite material can be regarded as magnetic particles distributed in a non-magnetic insulating matrix. For the effective magnetic permeability of such a system, the effective medium theory is generally used for calculation. Currently, the widely used theory is the Bruggeman effective medium theory. The Bruggeman effective medium theory is expressed as:

[0120]

[0121] where f1 is the volume fraction of the magnetic particles, μ1 is the average magnetic permeability of the particle assembly, μ2 is the magnetic permeability of the matrix (for a non-magnetic matrix, μ2 = 1), and μ eff is the effective magnetic permeability of the composite material.

[0122] The Bruggeman effective medium theory mainly considers the mean-field effect, but its prediction accuracy is limited in the case of high volume fractions. H.M. Yin et al. considered the static magnetic interaction between particles and used the Green's function method to solve the local magnetic field, obtaining the mean field through integration, thus proposing a more accurate effective medium theory in the case of high volume fractions and complex microstructures, called the HEMT theory. The following is the expression of the HEMT theory:

[0123]

[0124] where, φ is the volume fraction of magnetic particles, μ1 is the average magnetic permeability of the particle assembly, μ0 is the magnetic permeability of the matrix (for a non-magnetic matrix, μ0 = 1), and μ eff is the effective magnetic permeability of the composite material. In this work, the HEMT theory is used to calculate the effective magnetic permeability of the composite.

[0125] After considering the skin effect correction, the HEMT is expressed as:

[0126]

[0127] where, φ is the volume fraction of magnetic particles, μ1 is the average magnetic permeability of the particle assembly, μ0 is the magnetic permeability of the matrix (for a non-magnetic matrix, μ0 = 1), and μ eff is the effective magnetic permeability of the composite material.

[0128] A soft magnetic composite effective complex magnetic permeability prediction system disclosed in the present invention includes:

[0129] An input module for inputting the intrinsic parameters of magnetic particles. For easy-axis particles, input the magnetocrystalline anisotropy field H a , the natural resonance damping coefficient α, the saturation magnetization M s , the resistivity ρ, and the particle size R; for easy-plane particles, input the anisotropy fields H ⊥ , H ∥ , the natural resonance damping coefficient α, the saturation magnetization M s , the resistivity ρ, and the particle size R; the intrinsic parameters of magnetic particles can be obtained by measurement or based on the data disclosed in existing materials. As shown in Figure 7 , when the prediction system of the present invention uses the matlab appdesigner simulation software, click on the "orientation" tab, use the magnetic moment distribution model based on the Boltzmann distribution function to substitute the required orientation degree parameters to simulate the magnetic moment distribution, and save the simulation results.

[0130] The data analysis module performs simulations according to the above-mentioned method for predicting the effective complex permeability of soft magnetic composites; as Figure 8 shown, select the corresponding model in the system according to the type of magnetic particles, input the measured parameters and import the magnetic moment distribution simulation data, adjust different volume fractions, and the system will automatically calculate the complex permeability of soft magnetic composites with different volume fractions and display the complex permeability magnetic spectrum diagram of soft magnetic composites in the "Permeability" panel.

[0131] The output module is used to display the complex permeability magnetic spectrum diagram of soft magnetic composites according to the simulation data of the data analysis module and export the calculated complex permeability magnetic spectrum data of soft magnetic composites. Click Figure 8 "Data Export" on the interface to export the calculated complex permeability magnetic spectrum data of soft magnetic composites.

Claims

1. A method for predicting the effective complex magnetic permeability of a soft magnetic composite, characterized in that The prediction method adopts the following steps: (1) According to the types of magnetic particles in the easy-plane or easy-axis type soft magnetic composite, substitute the intrinsic material parameters of the magnetic particles into the LLG equation, and calculate the intrinsic permeability of a single magnetic particle from the modified LLG equation; (2) Use the magnetic moment distribution model based on the Boltzmann distribution function to simulate the distribution of magnetic moments of the magnetic particles in the soft magnetic composite, and substitute the obtained magnetic moment orientation data into the calculation formula of the average complex permeability of the particle ensemble to calculate the average complex permeability of the magnetic particle ensemble; (3) According to the equivalent particle size of the magnetic particles in the soft magnetic composite, use the skin effect correction model to correct the average complex permeability of the magnetic particle ensemble for the skin effect; (4) According to the volume fraction parameter, use the HEMT model to calculate the effective complex permeability of the soft magnetic composite and determine the effective complex permeability of the soft magnetic composite material.

2. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 1, characterized in that: The intrinsic material parameters of the magnetic particles include: saturation magnetization M s , magnetic crystal anisotropy equivalent field H eff , and natural resonance damping factor α.

3. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 1, characterized in that: The calculation formula of the average complex permeability of the particle ensemble is: Among them, represents the intrinsic permeability of a single particle, and N is the total number of particles simulated.

4. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 1, characterized in that: Substitute the probability density function of the magnetic moment distribution model of the Boltzmann distribution function, and the probability density function of the backward magnetic moment is expressed as: P(θ) ∝ sin(θ)r ξco(θ) Among them, is a dimensionless parameter representing the relative strength of the interaction between the magnetic moment and the external field.

5. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 4, characterized in that: When the magnetic moment distribution model of the Boltzmann distribution function is used to simulate the distribution of magnetic moments of the magnetic particles in the soft magnetic composite, in order to simulate the magnetic moment distribution with different degrees of orientation, the inverse transform sampling method is used for calculation, and random samples are generated through the known cumulative distribution function; The cumulative distribution function of the backward magnetic moment is expressed as: The value range of F(θ) is [0,1]. The inverse cumulative distribution function is the inverse function of the cumulative distribution function and satisfies: G(G -1 (i)) = u, u ∈ [0, 1] For any given probability u (between 0 and 1), F -1 (u) can return an angular value θ such that the cumulative distribution function F(θ) = u.

6. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 5, characterized in that: The inverse cumulative distribution function is numerically solved by the interpolation method. Define the degree of orientation D as the percentage of the ratio of the projection size of the magnetic moment in the xy plane to the total size of the magnetic moment. Its value range is 0 to 100. The degree of orientation D is calculated by the following formula: where M xy is the projection value of the magnetic moment in the xy plane, is the total magnetic moment vector; by generating 10,000 uniformly distributed random numbers and using the inverse cumulative distribution function to inversely calculate the corresponding θ values to obtain 10,000 magnetic moment samples, finally, calculate the degree of orientation of these samples and adjust the parameter ξ in the probability density function so that the final distribution conforms to the expected degree of orientation.

7. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 1, characterized in that: When using the skin effect correction model to correct the average complex permeability of the magnetic particle ensemble for the skin effect, the alternating microwave magnetic field h0 enters the magnet along the Z axis, and the microwave field h in the magnet can be expressed as: h = h0e -z / δ e i(z / δ-ω) where ω is the angular frequency of the microwave, and e -z / δ is the attenuation factor, and δ is the skin depth, expressed as: where ρ is the resistivity, μ i is the intrinsic permeability, f is the microwave frequency, and the magnitude of the microwave field conducted into the particles is: |h| = h0e -(R-r) / δ where R is the particle radius, and the dynamic magnetization intensity of the particle is: χ i is the intrinsic magnetic susceptibility of the particle, and the intrinsic permeability of a single particle after skin effect correction is: After the skin effect correction of the average complex permeability of the particle ensemble, it is:

8. A method for predicting the effective complex permeability of a soft magnetic composite according to claim 1, characterized in that: The HEMT model is described as: Among them, φ is the volume fraction of magnetic particles, μ1 is the average magnetic permeability of the particle assembly, μ0 is the magnetic permeability of the matrix, and for a non-magnetic matrix, μ0 = 1, μ eff is the effective magnetic permeability of the composite material.

9. A prediction system for the effective complex permeability of a soft magnetic composite, characterized in that The system includes: An input module for inputting the intrinsic parameters of magnetic particles. For easy-axis particles, the magnetocrystalline anisotropy field H a , the natural resonance damping coefficient α, the saturation magnetization M s , the resistivity ρ, and the particle size R; for easy-plane particles, the anisotropy fields H ⊥ , H ∥ , the natural resonance damping coefficient α, the saturation magnetization M s , the resistivity ρ, and the particle size R; The data analysis module performs simulations according to a method for predicting the effective complex permeability of a soft magnetic composite as described in claims 1-8; The output module is used to display the complex permeability magnetic spectrum diagram of the soft magnetic composite according to the simulation data of the data analysis module and export the calculated complex permeability magnetic spectrum data of the soft magnetic composite.

10. A prediction system for the effective complex permeability of a soft magnetic composite according to claim 9, characterized in that: The prediction system uses the matlab appdesigner simulation software.

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