Induction heating structure optimization method and system based on adaptive differential evolutionary algorithm

Abstract

The invention provides an induction heating structure optimization method and system based on an adaptive differential evolution algorithm, and the method employs the differential evolution algorithm to achieve the comprehensive global optimization of the parameters of an induction heating structure, and enables the design and calculation parameters of the induction heating structure to be coupled with each other. The method solves the problem that the multi-parameter coupling calculation of the inductor is difficult to optimize by a traditional method, improves the total efficiency of the induction heating structure, reduces the design time, and provides a theoretical basis for the preliminary optimization design of the induction heating structure.

Description

Technical Field

[0001] The present application belongs to the technical field of induction heating design, and in particular, relates to an induction heating structure optimization method and system based on an adaptive differential evolution algorithm. Background Art

[0002] Induction heating is based on the development of electromagnetic theory. Through a deep understanding of electromagnetic induction phenomena, key parameters such as magnetic field distribution and induced current can be determined. Advances in numerical calculation methods, such as the finite element method and the boundary element method, can accurately solve complex electromagnetic field problems and provide accurate results for optimization. However, finite element methods are basically simulations performed when parameters such as the structure are known to verify the rationality of the design.

[0003] The calculation of induction heating structure parameters is an important part of induction heating structure design. The general calculation of induction heating structure parameters assumes that the parameters obtain reasonable results, and does not optimize the design results. Moreover, the design and calculation parameters of the induction heating structure are coupled with each other, and it is difficult for traditional optimization methods to obtain the global optimal solution of the parameters. Therefore, it is very promising to study a new optimization method. Summary of the invention

[0004] The purpose of this application is to solve the problems of the prior art and to provide an induction heating structure optimization method and system based on an adaptive differential evolution algorithm.

[0005] In order to solve the technical problem, the technical solution of the present application is: an induction heating structure optimization method based on an adaptive differential evolution algorithm, comprising the following steps: Step 1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, determine the variable parameters that need to be optimized, determine the boundary conditions of the variable parameters, initialize the population with the variable parameters as the target individuals, and generate multiple initial target individuals; Step 2: Using fixed basic parameters and variable parameters, the total efficiency of the sensor is derived through the principle of electromagnetic induction heating, and the objective function is determined to obtain the fitness value of the target individual; Step 3: Perform mutation operation on each initial target individual or the target individual optimized last time to generate mutant individuals; Step 4: Use the objective function of step 2 to calculate the fitness value of the target individual in step 3, obtain the crossover probability according to the adaptive crossover strategy, and perform a crossover operation between the mutant individual and the target individual to generate a test individual; Step 5: Use the objective function in step 2 to calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values ​​as the target individuals for optimization; Step 6: Repeat steps 2 to 5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

[0006] Preferably, step 1: determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, and determine the variable parameters that need to be optimized, where the variable parameters are the power frequency. , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness , determine the boundary conditions of the variable parameters, initialize the population with the variable parameters as the target individuals, and generate multiple initial target individuals; Step 2: Using fixed basic parameters and variable parameters, the total efficiency of the sensor is derived through the principle of electromagnetic induction heating, and the objective function is determined to obtain the fitness value of the target individual; The objective function is: in, Power frequency, Hz; Coil insulation filling factor; Outer diameter of insulation layer, m; Coil diameter, m; Coil wall thickness, mm; Total sensor efficiency, dimensionless; Step 3: Perform mutation operation on each initial target individual or the target individual optimized last time to generate mutant individuals; Step 4: Use the objective function of step 2 to calculate the fitness value of the target individual in step 3, obtain the crossover probability according to the adaptive crossover strategy, and perform a crossover operation between the mutant individual and the target individual to generate a test individual; Step 5: Use the objective function in step 2 to calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values ​​as the target individuals for optimization; Step 6: Repeat steps 2 to 5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

[0007] Preferably, the step 1 is specifically: Step 1.1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements. The fixed basic parameters include: starting temperature , Maximum temperature , Production capacity Specific heat capacity of the material ; Muffle specific heat capacity , resistivity , relative magnetic permeability , inner diameter , outer diameter ,length ; Thermal conductivity of the insulation layer , inner diameter ,length ; Resistivity of the coil , relative magnetic permeability ,length ; and vacuum permeability ; Step 1.2: Determine the variable parameters that need to be optimized, including: power frequency , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness ; Step 1.3: Determine the boundary conditions of the variable parameters, including: power frequency , Coil insulation filling factor , the outer diameter of the insulation layer and the coil diameter meet , coil wall thickness ; Step 1.4: Randomly initialize the boundary conditions The population of target individuals , each initial target individual is expressed as: .

[0008] Preferably, in step 2, the total efficiency of the inductor is derived by using fixed basic parameters and variable parameters through the principle of electromagnetic induction heating, and the objective function is determined as follows: Step 2.1 Calculate the material heating power: , if gas is introduced, calculate the gas heating power at the same time: ; in, is the average specific heat capacity of the gas, J / (kg·K); is the average density of the gas, kg / m³; is the gas flow rate, m 3 / h; The total power of furnace charge heating is: ; Step 2.2 Calculate the heat dissipation power: ; Step 2.3 Calculate the average total power absorbed by the charge: ; in, is the power coefficient, and ; Step 2.4 Obtain by looking up the cylindrical spiral coil inductance correction factor diagram and ; Step 2.5 Calculate the current penetration depth: Coil current penetration depth: ; Muffle current penetration depth: ; Step 2.6 calculates the relative muffle radius, including the following: Relative radius of the muffle cavity: ; Relative radius of the muffle outer periphery: ; The average relative radius of the muffle: ; Muffle relative wall thickness: ; Step 2.7 Calculate the impedance of a single-turn coil, including the following: Single turn resistance: , single-turn reactance: ; in, and It is expressed as: , ; Step 2.8 Calculate the external reactance and leakage reactance: External reactance: ; Leakage reactance: ; Among them, the angular frequency , coil cross-sectional area , muffle cross-sectional area ; Step 2.9 Calculate the charge conversion impedance: Charge conversion impedance: ; Among them, the impedance conversion factor is: ; Step 2.10 Calculate the coil inducing conductor impedance: Coil inducing conductor impedance: ; Among them, the AC impedance coefficient is obtained by checking the AC impedance coefficient diagram: , , coil circumference ; Step 2.11 Calculate the system equivalent impedance: System equivalent impedance: ; Step 2.12 Calculate the total inductor efficiency, including: Electrical efficiency of the sensor: ; Thermal efficiency of the sensor: ; The total efficiency of the sensor is: ; Step 2.13 Determine the objective function as: .

[0009] Preferably, the step 3 is specifically: In the During the optimization iteration calculation, for each target individual Perform mutation operation to generate mutant individuals , the operation rules are as follows: in, , , are three different target individuals randomly selected from the current population, is the mutation factor. As the number of iterations increases, an adaptive strategy is used to change. The specific rules are as follows: in, is the maximum factor of variation; is a natural number; is the coefficient of variation; is the maximum number of iterations.

[0010] Preferably, the step 4 is specifically: The target individual With variant individuals Perform crossover operation to generate experimental individuals , the operation rules are as follows: for , randomly generate a The number between ,if or ,but ;otherwise, ;in for No. values, for No. values, for No. Values; in, is the crossover probability, is from A dimension index randomly selected from , the crossover probability uses an adaptive crossover strategy, the specific rules are as follows: in, is the maximum crossover probability; is the adaptive coefficient; is the fitness value of the target individual.

[0011] Preferably, the step 5 is specifically: Calculate target individuals and experimental individuals If the fitness value of the test individual is better than that of the target individual, the test individual is selected as the target individual in the next optimization population; otherwise, the target individual is selected; specifically: if ,but ;otherwise, .

[0012] Preferably, the step 6 is specifically: Repeat steps 2 to 5 until the termination condition is reached to obtain the global optimal value of the population after the final iteration, and use the variable parameters represented by the target individual corresponding to the current global optimal value as the optimal parameters, wherein the termination condition includes: reaching the set maximum number of iterations, the number of consecutive iterations in which the objective function does not decrease is greater than the set threshold, or the calculation time reaches the set threshold.

[0013] Preferably, an induction heating structure optimization system based on an adaptive differential evolution algorithm is used to implement an induction heating structure optimization method based on an adaptive differential evolution algorithm, and the induction heating structure optimization system includes a data storage medium and a data calculation module, the data storage medium is used to store the data calculation module and all parameters; the data calculation module is used to calculate and obtain the optimal parameters of the induction heating structure.

[0014] Compared with the prior art, the advantages of this application are: (1) This application discloses the use of differential evolution algorithm to achieve comprehensive global optimization of induction heating structure parameters, so that the parameters of the induction heating structure design calculation are coupled with each other, solving the problem that the multi-parameter coupling calculation of the inductor is difficult to optimize using traditional methods, and improving the overall efficiency of the induction heating structure and reducing the design time, providing a theoretical basis for the preliminary optimization design of the induction heating structure; (2) This application uses the differential evolution algorithm with its powerful search capability to obtain the optimal parameters and perform global optimization of the induction heating structure. Through multidisciplinary integration, involving fields such as electromagnetics and thermal science, the application integrates knowledge from various disciplines to optimize the design, thereby improving the performance and reliability of the induction heating structure to meet production needs. (3) This application adopts an adaptive strategy to improve the search capability of the differential evolution algorithm, greatly reducing the number of iterations and computing time, and at the same time provides an adaptive computing strategy to improve the performance of the differential evolution algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a flow chart of the induction heating structure optimization method based on the adaptive differential evolution algorithm of this application; Figure 2 A simplified diagram of the structure of an induction heating furnace for this application; Figure 3 This is a diagram of the inductance correction coefficient of the cylindrical spiral coil in Example 1 of the present application; Figure 4 This is the AC impedance coefficient diagram in Example 1 of the present application.

[0017] Description of reference numerals: 1. Muffle, 2. Material, 3. Insulation layer, 4. Induction coil. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] like Figure 1 As shown, the present application discloses an induction heating structure optimization method based on an adaptive differential evolution algorithm, comprising the following steps: S1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, determine the variable parameters that need to be optimized, determine the boundary conditions of the variable parameters, initialize the population with the variable parameters as the target individuals, and generate multiple initial target individuals; S2: Using fixed basic parameters and variable parameters to derive the total efficiency of the sensor through the principle of electromagnetic induction heating, determine the objective function, and obtain the fitness value of the target individual; S3: Perform mutation operation on each initial target individual or the target individual optimized last time to generate mutant individuals; S4: Use the objective function of S2 to calculate the fitness value of the target individual of S3, obtain the crossover probability according to the adaptive crossover strategy, and perform a crossover operation between the mutant individual and the target individual to generate a test individual; S5: Use the objective function of S2 to calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values ​​as the target individuals for optimization; S6: Repeat S2-S5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

[0021] Preferably, the present application includes the following steps: Step 1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, and determine the variable parameters that need to be optimized, where the variable parameters are the power frequency. , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness , determine the boundary conditions of the variable parameters, initialize the population with the variable parameters as the target individuals, and generate multiple initial target individuals; Step 2: Using fixed basic parameters and variable parameters, the total efficiency of the sensor is derived through the principle of electromagnetic induction heating, and the objective function is determined to obtain the fitness value of the target individual; The objective function is: in, Power frequency, Hz; Coil insulation filling factor; Outer diameter of insulation layer, m; Coil diameter, m; Coil wall thickness, mm; Overall sensor efficiency; Step 3: Perform mutation operation on each initial target individual or the target individual optimized last time to generate mutant individuals; Step 4: Use the objective function of step 2 to calculate the fitness value of the target individual in step 3, obtain the crossover probability according to the adaptive crossover strategy, and perform a crossover operation between the mutant individual and the target individual to generate a test individual; Step 5: Use the objective function in step 2 to calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values ​​as the target individuals for optimization; Step 6: Repeat steps 2 to 5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

[0022] Preferably, the step 1 is specifically: Step 1.1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements. The fixed basic parameters include: starting temperature , Maximum temperature , Production capacity Specific heat capacity of the material ; Muffle specific heat capacity , resistivity , relative magnetic permeability , inner diameter , outer diameter ,length ; Thermal conductivity of the insulation layer , inner diameter ,length ; Resistivity of the coil , relative magnetic permeability ,length ; and vacuum permeability ; Step 1.2: Determine the variable parameters that need to be optimized, including: power frequency , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness ; Step 1.3: Determine the boundary conditions of the variable parameters, including: power frequency , Coil insulation filling factor , the outer diameter of the insulation layer and the coil diameter meet , coil wall thickness ; Step 1.4: Randomly initialize the boundary conditions The population of target individuals , each initial target individual is expressed as: .

[0023] Preferably, in step 2, the total efficiency of the inductor is derived by using fixed basic parameters and variable parameters through the principle of electromagnetic induction heating, and the objective function is determined as follows: Step 2.1 Calculate the material heating power: , if gas is introduced, calculate the gas heating power at the same time: ; in, is the average specific heat capacity of the gas, J / (kg·K); is the average density of the gas, kg / m³; is the gas flow rate, m 3 / h; The total power of furnace charge heating is: ; Step 2.2 Calculate the heat dissipation power: ; Step 2.3 Calculate the average total power absorbed by the charge: ; in, is the power coefficient, and ; Step 2.4 Obtain by looking up the cylindrical spiral coil inductance correction factor diagram and ; Step 2.5 Calculate the current penetration depth: Coil current penetration depth: ; Muffle current penetration depth: ; Step 2.6 calculates the relative muffle radius, including the following: Relative radius of the muffle cavity: ; Relative radius of the muffle outer periphery: ; The average relative radius of the muffle: ; Muffle relative wall thickness: ; Step 2.7 Calculate the impedance of a single-turn coil, including the following: Single turn resistance: , single-turn reactance: ; in, and It is expressed as: , ; Step 2.8 Calculate the external reactance and leakage reactance: External reactance: ; Leakage reactance: ; Among them, the angular frequency , coil cross-sectional area , muffle cross-sectional area ; Step 2.9 Calculate the charge conversion impedance: Charge conversion impedance: ; Among them, the impedance conversion factor is: ; Step 2.10 Calculate the coil inducing conductor impedance: Coil inducing conductor impedance: ; Among them, the AC impedance coefficient is obtained by checking the AC impedance coefficient diagram: , , coil circumference ; Step 2.11 Calculate the system equivalent impedance: System equivalent impedance: ; Step 2.12 Calculate the total inductor efficiency, including: Electrical efficiency of the sensor: ; Thermal efficiency of the sensor: ; The total efficiency of the sensor is: ; Step 2.13 Determine the objective function as: .

[0024] Preferably, the step 3 is specifically: In the During the optimization iteration calculation, for each target individual Perform mutation operation to generate mutant individuals , the operation rules are as follows: in, , , are three different target individuals randomly selected from the current population, is the mutation factor. As the number of iterations increases, an adaptive strategy is used to change. The specific rules are as follows: in, is the maximum factor of variation; is a natural number; is the coefficient of variation; is the maximum number of iterations.

[0025] Preferably, the step 4 is specifically: The target individual With variant individuals Perform crossover operation to generate experimental individuals , the operation rules are as follows: for , randomly generate a The number between ,if or ,but ;otherwise, ;in for No. values, for No. values, for No. Values; in, is the crossover probability, is from A dimension index randomly selected from , the crossover probability uses an adaptive crossover strategy, the specific rules are as follows: in, is the maximum crossover probability; is the adaptive coefficient; is the fitness value of the target individual.

[0026] Preferably, the step 5 is specifically: Calculate target individuals and experimental individuals If the fitness value of the test individual is better than that of the target individual, the test individual is selected as the target individual in the next optimization population; otherwise, the target individual is selected; specifically: if ,but ;otherwise, .

[0027] Preferably, the step 6 is specifically: Repeat steps 2 to 5 until the termination condition is reached to obtain the global optimal value of the population after the final iteration, and use the variable parameters represented by the target individual corresponding to the current global optimal value as the optimal parameters, wherein the termination condition includes: reaching the set maximum number of iterations, the number of consecutive iterations in which the objective function does not decrease is greater than the set threshold, or the calculation time reaches the set threshold.

[0028] Preferably, an induction heating structure optimization system based on an adaptive differential evolution algorithm is used to implement an induction heating structure optimization method based on an adaptive differential evolution algorithm, and the induction heating structure optimization system includes a data storage medium and a data calculation module, the data storage medium is used to store the data calculation module and all parameters; the data calculation module is used to calculate and obtain the optimal parameters of the induction heating structure.

[0029] Embodiment 1: like Figure 2 As shown, a simplified diagram of an induction heating furnace structure is disclosed. Figure 2 It includes muffle 1, material 2, insulation layer 3, known fixed basic parameters have been marked, and unknown parameters (variable parameters) need to be optimized; like Figure 1 As shown, Figure 1 A flowchart of an induction heating structure optimization method based on an adaptive differential evolution algorithm provided in an embodiment of the present invention. The induction heating structure optimization design method of this embodiment includes: Step 1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, the variable parameters that need to be optimized, determine the boundary conditions of the variable parameters, and initialize the population with the variable parameters as the target individuals; Specifically, step 1 includes: Step 1.1 Determine the fixed basic parameters of materials, muffle, insulation layer, and coil according to process requirements, including: starting temperature , Maximum temperature , Production capacity Specific heat capacity of the material ; Muffle specific heat capacity , resistivity , relative magnetic permeability , inner diameter , outer diameter ,length ; Thermal conductivity of the insulation layer , inner diameter ,length ; Resistivity of the coil , relative magnetic permeability ,length ; Vacuum permeability .

[0030] Step 1.2 The variable parameters that need to be optimized include: power supply frequency , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness , .

[0031] Step 1.3 Determine the boundary conditions of the parameters, including: power frequency , Coil insulation filling factor , the outer diameter of the insulation layer and the coil diameter meet , coil wall thickness .

[0032] Step 1.4 Randomly initialize the boundary conditions The population of target individuals , each initial target individual is expressed as: .

[0033] Step 2: Derive the objective function of optimizing coil design to obtain the fitness value of the target individual; Specifically, in step S2, the objective function is derived as follows: Step 2.1 Calculate the material heating power: , if gas is introduced, calculate the gas heating power at the same time: ; in, is the average specific heat capacity of the gas, is the average density of the gas, is the gas flow rate.

[0034] The total power of furnace charge heating is: .

[0035] Step 2.2 Calculate the heat dissipation power: , contains unknown parameters and is not calculated for the time being.

[0036] Step 2.3 Calculate the average total power absorbed by the charge: , contains unknown parameters and is not calculated for the time being.

[0037] in, is the power coefficient, and .

[0038] Step 2.4 Check Figure 3 Cylindrical spiral coil inductance correction factor diagram obtained and .

[0039] Specifically, the coil and muffle are calculated separately. The value of , using this as the horizontal axis, find the corresponding vertical axis The coil contains unknown parameters. , not calculated for now The value of muffle ,check Figure 3 Corresponding to the vertical axis value, get This operation is automatically completed by the calculation module.

[0040] Step 2.5 Calculate the current penetration depth: and , contains unknown parameters and is not calculated for the time being.

[0041] Step 2.6 calculates the relative radius of the muffle, which contains unknown parameters and will not be calculated for now. The specific parameters include the following: Relative radius of the muffle cavity: ; Relative radius of the muffle outer periphery: ; The average relative radius of the muffle: ; Muffle relative wall thickness: ; Step 2.7 calculates the impedance of a single-turn coil, which contains unknown parameters and will not be calculated for now. The specific parameters include the following: Single turn resistance: , single-turn reactance: ; in, and It can be expressed as: , .

[0042] Step 2.8 calculates the external reactance and leakage reactance, which contains unknown parameters and will not be calculated for now.

[0043] External reactance: Leakage reactance: Among them, the angular frequency , coil cross-sectional area , muffle cross-sectional area .

[0044] Step 2.9 calculates the charge conversion impedance, which contains unknown parameters and will not be calculated for now.

[0045] Charge conversion impedance: .

[0046] Among them, the impedance conversion factor is: .

[0047] Step 2.10 calculates the coil-inducing conductor impedance, which contains unknown parameters and will not be calculated for now.

[0048] Coil inducing conductor impedance: .

[0049] Among them, Figure 4 AC impedance coefficient diagram to obtain the AC impedance coefficient: , , coil circumference .

[0050] Step 2.11 calculates the system equivalent impedance, which contains unknown parameters and will not be calculated for now.

[0051] System equivalent impedance: .

[0052] Step 2.12 calculates the total efficiency of the sensor, including unknown parameters, which will not be calculated for now. Specifically include: Electrical efficiency of the sensor: ; Thermal efficiency of the sensor: ; The total efficiency of the sensor is: .

[0053] Step 2.13 Determine the objective function as: The above uncalculated parameters will be used together as part of the objective function. Since they are more coupled with unknown parameters, it is impossible to give specific calculation results, but they are all considered together in the calculation module.

[0054] Step 3: Perform mutation operation on each target individual in the population to generate mutant individuals; Specifically, step S3 includes: In the In the iteration calculation, for each individual Perform mutation operation to generate mutant individuals The operation rules are as follows: in, , , are three different individuals randomly selected from the current population, is the mutation factor. As the number of iterations increases, an adaptive strategy is used to change. The specific rules are as follows: in, is the maximum factor of variation; is a natural number; is the coefficient of variation; is the maximum number of iterations. , , .

[0055] Step 4: Perform a crossover operation between the mutant individual and the target individual to generate the test individual; Specifically, step S4 includes: The target individual With variant individuals Perform crossover operation to generate experimental individuals , the operation rules are as follows: for , randomly generate a The number between ,if or ,but ;otherwise, ;in for No. values, for No. values, for No. Values; in, is the crossover probability, is from A dimension index randomly selected from , the crossover probability uses an adaptive crossover strategy, the specific rules are as follows: in, is the maximum crossover probability; is the adaptive coefficient; is the fitness value of the target individual. , .

[0056] Step 5: Calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values; Specifically, step S5 includes: Calculate target individuals and experimental individuals If the fitness value of the test individual is better than that of the target individual, the test individual is selected as the individual in the next generation population; otherwise, the target individual is selected. Specifically: if ,but ;otherwise, .

[0057] Step 6: Repeat steps 2 to 5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

[0058] Specifically, the step 6 includes: Repeat steps 2 to 5 until the termination condition is reached, obtain the global optimal value of the population after the final iteration, and take the variable parameters represented by the individuals corresponding to the current global optimal value as the optimal result, wherein the termination condition includes: reaching the set maximum number of iterations, the number of consecutive iterations without the objective function decreasing is greater than the set threshold, or the calculation time reaches the set threshold. In this embodiment, the maximum number of iterations is 1000, and the number of consecutive iterations without the objective function decreasing is greater than the set threshold. , the calculation time reaches the set threshold The actual number of iterations in this embodiment is 136, and the calculation time is , the optimization result is: power supply frequency , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness , the objective function value is , then the optimal total efficiency is .

[0059] The method of the present application is compared with the traditional differential evolution algorithm, and the above embodiment is calculated to compare the number of iterations and calculation time of the method of the present application and the traditional differential evolution algorithm. The above embodiment is calculated 9 times using the method of the present application and the traditional method, and the calculation results are as follows: The results show that the average number of iterations of the traditional differential evolution algorithm is 916, which basically reaches the maximum number of iterations set. The average number of iterations of the method in this application is 137, which is 85% lower than the number of iterations of the traditional differential evolution algorithm. The average calculation time of the traditional differential evolution algorithm is 1.12s, and the average calculation time of the method in this application is 0.36s, which is 68% lower than the number of iterations of the traditional differential evolution algorithm. Therefore, the method in this application has a lower number of iterations and a lower calculation time than the traditional differential evolution algorithm.

[0060] The present application discloses a method for realizing comprehensive global optimization of induction heating structure parameters by using differential evolution algorithm, so that the parameters of induction heating structure design calculation are mutually coupled, which solves the problem that multi-parameter coupling calculation of inductor is difficult to optimize by traditional methods, improves the overall efficiency of induction heating structure, reduces design time, and provides a theoretical basis for preliminary optimization design of induction heating structure.

[0061] This application uses the differential evolution algorithm with its powerful search capabilities to obtain the optimal parameters and perform global optimization of the induction heating structure. Through multidisciplinary integration, involving fields such as electromagnetism and thermal science, the knowledge of various disciplines is integrated to optimize the design, so as to enable the induction heating structure to improve performance and reliability to meet production needs.

[0062] This application adopts an adaptive strategy to improve the search capability of the differential evolution algorithm, greatly reducing the number of iterations and calculation time, and at the same time provides an adaptive computing strategy to improve the performance of the differential evolution algorithm.

[0063] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An induction heating structure optimization method based on an adaptive differential evolution algorithm is characterized in that: The following steps are involved: Step 1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, determine the variable parameters that need to be optimized, determine the boundary conditions of the variable parameters, initialize the population with the variable parameters as the target individuals, and generate multiple initial target individuals; Step 2: Using fixed basic parameters and variable parameters, the total efficiency of the sensor is derived through the principle of electromagnetic induction heating, and the objective function is determined to obtain the fitness value of the target individual; Step 3: Perform mutation operation on each initial target individual or the target individual optimized last time to generate mutant individuals; Step 4: Use the objective function of step 2 to calculate the fitness value of the target individual in step 3, obtain the crossover probability according to the adaptive crossover strategy, and perform a crossover operation between the mutant individual and the target individual to generate a test individual; Step 5: Use the objective function in step 2 to calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values ​​as the target individuals for optimization; Step 6: Repeat steps 2 to 5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

2. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 1 is characterized in that: The following steps are involved: Step 1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements, and determine the variable parameters that need to be optimized. The variable parameter is the power frequency. , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness , determine the boundary conditions of the variable parameters, initialize the population with the variable parameters as the target individuals, and generate multiple initial target individuals; Step 2: Using fixed basic parameters and variable parameters, the total efficiency of the sensor is derived through the principle of electromagnetic induction heating, and the objective function is determined to obtain the fitness value of the target individual; The objective function is: in, Power frequency, Hz; Coil insulation filling factor; Outer diameter of insulation layer, m; Coil diameter, m; Coil wall thickness, mm; Overall sensor efficiency; Step 3: Perform mutation operation on each initial target individual or the target individual optimized last time to generate mutant individuals; Step 4: Use the objective function of step 2 to calculate the fitness value of the target individual in step 3, obtain the crossover probability according to the adaptive crossover strategy, and perform a crossover operation between the mutant individual and the target individual to generate a test individual; Step 5: Use the objective function in step 2 to calculate the fitness values ​​of the test individuals and the target individuals, and retain the better individuals according to the fitness values ​​as the target individuals for optimization; Step 6: Repeat steps 2 to 5 until the termination condition is met to obtain the optimal parameters of the induction heating structure.

3. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 2 is characterized in that: The step 1 is specifically as follows: Step 1.1: Determine the fixed basic parameters of the material, muffle, insulation layer, and coil according to the process requirements. The fixed basic parameters include: starting temperature , Maximum temperature , Production capacity Specific heat capacity of the material ; Muffle specific heat capacity , resistivity , relative magnetic permeability , inner diameter , outer diameter ,length ; Thermal conductivity of the insulation layer , inner diameter ,length ; Resistivity of the coil , relative magnetic permeability ,length ; and vacuum permeability ; Step 1.2: Determine the variable parameters that need to be optimized, including: power frequency , Coil insulation filling factor , Outer diameter of insulation layer , coil diameter , coil wall thickness ; Step 1.3: Determine the boundary conditions of the variable parameters, including: power frequency , Coil insulation filling factor , the outer diameter of the insulation layer and the coil diameter meet , coil wall thickness ; Step 1.4: Randomly initialize the boundary conditions The population of target individuals , each initial target individual is expressed as: .

4. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 3 is characterized in that: In step 2, the total efficiency of the inductor is derived by using fixed basic parameters and variable parameters through the principle of electromagnetic induction heating, and the objective function is determined as follows: Step 2.1 Calculate the material heating power: , if gas is introduced, calculate the gas heating power at the same time: ； in, is the average specific heat capacity of the gas, J / (kg·K); is the average density of the gas, kg / m³; is the gas flow rate, m 3 / h; The total power of furnace charge heating is: ; Step 2.2 Calculate the heat dissipation power: ； Step 2.3 Calculate the average total power absorbed by the charge: ； in, is the power coefficient, and ; Step 2.4 Obtain by looking up the cylindrical spiral coil inductance correction factor diagram and ; Step 2.5 Calculate the current penetration depth: Coil current penetration depth: ; Muffle current penetration depth: ; Step 2.6 calculates the relative muffle radius, including the following: Relative radius of the muffle cavity: ; Relative radius of the muffle outer periphery: ; The average relative radius of the muffle: ; Muffle relative wall thickness: ; Step 2.7 Calculate the impedance of a single-turn coil, including the following: Single turn resistance: , single-turn reactance: ; in, and It is expressed as: , ; Step 2.8 Calculate the external reactance and leakage reactance: External reactance: ; Leakage reactance: ; Among them, the angular frequency , coil cross-sectional area , muffle cross-sectional area ; Step 2.9 Calculate the charge conversion impedance: Charge conversion impedance: ; Among them, the impedance conversion factor is: ; Step 2.10 Calculate the coil inducing conductor impedance: Coil inducing conductor impedance: ; Among them, the AC impedance coefficient is obtained by checking the AC impedance coefficient diagram: , , coil circumference ; Step 2.11 Calculate the system equivalent impedance: System equivalent impedance: ; Step 2.12 Calculate the total inductor efficiency, including: Electrical efficiency of the sensor: ; Thermal efficiency of the sensor: ; The total efficiency of the sensor is: ; Step 2.13 Determine the objective function as: .

5. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 4 is characterized in that: The step 3 is specifically as follows: In the During the optimization iteration calculation, for each target individual Perform mutation operation to generate mutant individuals , the operation rules are as follows: in, , , are three different target individuals randomly selected from the current population, is the mutation factor. As the number of iterations increases, an adaptive strategy is used to change. The specific rules are as follows: in, is the maximum factor of variation; is a natural number; is the coefficient of variation; is the maximum number of iterations.

6. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 5 is characterized in that: The step 4 is specifically as follows: The target individual With variant individuals Perform crossover operation to generate experimental individuals , the operation rules are as follows: for , randomly generate a The number between ,if or ,but ; otherwise, ;in for No. values, for No. values, for No. values; in, is the crossover probability, is from A dimension index randomly selected from , the crossover probability uses an adaptive crossover strategy, the specific rules are as follows: in, is the maximum crossover probability; is the adaptive coefficient; is the fitness value of the target individual.

7. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 6 is characterized in that: The step 5 is specifically as follows: Calculate target individuals and experimental individuals If the fitness value of the test individual is better than that of the target individual, the test individual is selected as the target individual in the next optimization population; otherwise, the target individual is selected; specifically: if ,but ; otherwise, .

8. The induction heating structure optimization method based on the adaptive differential evolution algorithm according to claim 7 is characterized in that: The step 6 is specifically as follows: Repeat steps 2 to 5 until the termination condition is reached to obtain the global optimal value of the population after the final iteration, and use the variable parameters represented by the target individual corresponding to the current global optimal value as the optimal parameters, wherein the termination condition includes: reaching the set maximum number of iterations, the number of consecutive iterations in which the objective function does not decrease is greater than the set threshold, or the calculation time reaches the set threshold.

9. The induction heating structure optimization system based on adaptive differential evolution algorithm is characterized by: Used to implement an induction heating structure optimization method based on an adaptive differential evolution algorithm, the induction heating structure optimization system includes a data storage medium and a data calculation module, the data storage medium is used to store the data calculation module and all parameters; the data calculation module is used to calculate and obtain the optimal parameters of the induction heating structure.

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