Train frozen coal induction heating temperature rise uniformity optimization method based on response surface method and multi-parameter optimization

Abstract

The invention relates to the technical field of electromagnetic heating temperature rise uniformity optimization, and discloses a train frozen coal induction heating temperature rise uniformity optimization method based on a response surface method and multi-parameter optimization. A second-order polynomial response surface model is designed and established by adopting a central composite test, an axisymmetric coil layout based on a first constant parameterization linear dynamic tolerance sequence is designed, and temperature uniformity is represented by sample standard deviation by applying an NSGA-II multi-objective optimization algorithm under the condition that thawing temperature constraints are met. And carrying out Pareto optimization by taking temperature sample standard deviation minimization and average temperature maximization as targets, and finally determining an optimal parameter combination through a TOPSIS decision method. According to simulation verification, the method can effectively improve the uniformity of the electromagnetic heating temperature field of the inner surface of the frozen coal carriage, and a theoretical basis is provided for engineering application.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic heating temperature rise uniformity optimization, and specifically relates to a method for optimizing the temperature rise uniformity of train frozen coal induction heating based on response surface methodology and multi-parameter optimization. Background Art

[0002] Railway coal is prone to freezing in winter, especially when the moisture content is higher than 8%, and the freezing strength is high. The residual moisture of coal after washing in my country is 6.5% to 14%, which is easy to form frozen coal and difficult to clean. After the winter dumper operation, the frozen coal residue is 7% to 14%, which requires a lot of manual cleaning, which is inefficient and affects the company's benefits and vehicle turnover. Moreover, the cleaning of frozen coal depends on manual labor, which poses a great safety hazard. Some of the frozen coal residue may also cause the carriage to be overloaded, posing a risk of overturning.

[0003] In order to solve the problem of difficulty and low efficiency in cleaning frozen coal on trains, the industry has proposed electromagnetic induction heating technology to solve the problem of frozen coal. The electromagnetic induction heating method generates an alternating electromagnetic field to generate eddy currents, which generates heat in the carriage, thereby achieving a heating effect and thawing the frozen coal. This method of thawing frozen coal has high heating efficiency, controllable temperature rise and high safety. However, due to the structure of the carriage, the temperature rise uniformity of induction heating is poor, and there are problems of local overheating and inability to thaw local frozen coal.

[0004] Therefore, this application proposes a method for optimizing the uniformity of temperature rise of frozen coal induction heating on trains based on response surface methodology and multi-parameter optimization. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for optimizing the uniformity of temperature rise of frozen coal induction heating on trains based on response surface methodology and multi-parameter optimization, which solves the problems in the prior art of poor uniformity of temperature rise of induction heating due to the structure of the carriage, local overheating and inability to thaw local frozen coal.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization, the method comprising the following steps: S1. Determine the parameters required for modeling and the required material parameters according to the train carrying frozen coal and the induction heating coil; S2, using central composite experimental design to establish a second-order polynomial response surface model and generate a series of experimental parameter combinations; S3, establishing a proportional geometric model according to the parameters determined in step S1 and step S2 and the test parameter combination; S4, meshing all proportional geometric models; S5. Add the corresponding materials of each part of the model to the material parameters obtained in step 1, set the magnetic field boundary conditions and temperature field boundary conditions, set temperature probes at equal distances on the inner surface of the carriage, perform magnetic-thermal coupling calculations on the models under all test parameter combinations, and obtain the temperature value sequence after heating. ; S6, calculating the average temperature and the sample standard deviation according to the temperature value sequence, and fitting a second-order polynomial response surface model on this basis; S7. The quality of the obtained second-order polynomial response surface model was tested by variance analysis; S8. Evaluate the goodness of fit of the second-order polynomial response surface model after testing based on the corrected determination coefficient; S9, using the second-order polynomial response surface model obtained in step S7 as the objective function, and obtaining a Pareto solution set through an optimization algorithm; S10. Based on the TOPSIS method, the optimal decision solution is obtained; S11. The optimal decision solution is used as an input variable, and the fitting accuracy of the second-order polynomial response surface model is verified through simulation calculation to obtain the optimized heating effect.

[0007] As a further solution of the present invention: Step S2 is specifically that the induction heating coils are divided into 2n-1 groups, and a longitudinal magnetic flux layout is adopted, with the central coil as the central axis, and the two sides are axially symmetrical, and the spacing between each group of coils is wide in the middle and narrow on both sides; each group of coils is numbered, the central coil is numbered 1, and the numbering is sequentially from 1 to n outwards, the symmetrical coils on the left and right sides are the same, and the spacing between each group of induction heating coils is in accordance with the first constant parameterized linear dynamic tolerance sequence: set up, d is the distance between each set of induction heating coils, in m, i Number the coils, is the distance between coil 1 and coil 2, in m. fixed, d is the decreasing distance each time, i.e. the tolerance, in m. d The value of can be changed; select the response surface method to coil width, tolerance d , the coil current and current frequency are taken as input variables, their numerical ranges are determined, and a second-order polynomial response surface model is established using central composite experimental design to generate a series of experimental parameter combinations.

[0008] As a further solution of the present invention: the proportional geometric model in step S4 includes a train carriage geometric model, a bogie geometric model, an induction heating coil model and an air domain model, and all proportional geometric models are divided into free tetrahedral meshes, and then the train carriage geometric model and the bogie geometric model are divided into boundary layer meshes, and the boundary layer mesh thickness is calculated according to the formula Setting, wherel is the boundary layer mesh thickness, α is the conductivity of the heated material, is the vacuum permeability, is the magnetic permeability of the induction heating coil material, f is the frequency of the current in the induction heating coil.

[0009] As a further solution of the present invention: Step S6 is specifically based on the temperature value sequence , according to the formula Calculate the sample standard deviation of each probe temperature; where, is the average value of each temperature value in the temperature value sequence, with the above sample standard deviation Indicates the uniformity of the surface temperature in the heated cabin, sample standard deviation The smaller it is, the better the temperature uniformity; the average surface temperature of the carriage and the sample standard deviation are taken as output variables, and a second-order polynomial response surface model is fitted with all input variables and output variables.

[0010] As a further solution of the present invention: Step S7 specifically tests the quality of the obtained second-order polynomial response surface model through variance analysis. In the variance analysis, items with p-values ​​less than 0.05 can be considered as significant items. The significant items are retained, and the non-significant items are removed to form the second-order polynomial response surface model after testing.

[0011] As a further solution of the present invention: Step S8 is specifically to correct the determination coefficient Evaluate the goodness of fit of the tested second-order polynomial response surface model and correct the coefficient of determination The closer the value is to 1, the better the fit of the second-order polynomial response surface model.

[0012] As a further solution of the present invention: Step S9 specifically converts the temperature value sequence The sample standard deviation is used as the evaluation index of temperature rise uniformity; the average temperature of the inner surface of the carriage reaches the minimum thawing temperature of the frozen coal as a constraint condition, and the standard deviation of each probe temperature is used as the optimization target. The sample standard deviation is required to be as small as possible while the average temperature of the inner surface of the carriage is as high as possible. The coil width, the spacing between coil 1 and coil 2 , current, and current frequency are optimized variables, and the multi-objective optimization algorithm parameters are configured to build a mathematical model. The NSGA-II optimization algorithm is used, and the second-order polynomial response surface model after inspection obtained in step S7 is used as the objective function. The Pareto solution set is obtained through the optimization algorithm.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention arranges and designs the coil spacing according to the first constant parameterized linear dynamic tolerance sequence, which effectively improves the temperature rise uniformity of induction heating and solves the problems of local overheating and local frozen coal unable to thaw in the prior art.

[0014] 2. The present invention adopts structural optimization design based on response surface method, which combines mathematical methods and statistical methods to model and analyze responses affected by multiple variables, thereby improving design efficiency and effect.

[0015] 3. The present invention adopts a multi-objective optimization algorithm to optimize the temperature rise uniformity. This method can optimize the Pareto solution set with the average surface temperature of the car interior and the sample standard deviation as the target, and then make a decision with the TOPSIS method to obtain the optimal parameter combination under the weight of balancing the average temperature and the sample standard deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without creative work. Among them: Figure 1 is a flow chart of the present invention; Figure 2 is a structural diagram of a train model in an embodiment of the present invention; Figure 3 This is a coil arrangement structure diagram in an embodiment of the present invention; Figure 4 is a diagram of the arrangement of temperature probes in a quarter model in an embodiment of the present invention; Figure 5 is a Pareto solution set diagram obtained in an embodiment of the present invention; Figure 6 3 is a comparison diagram of temperature uniformity before and after optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example See also Figure 1As shown, the present invention provides a method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization. The optimization method comprises the following steps: Step 1: Determine the parameters required for modeling and the required material parameters based on the train carrying frozen coal and the induction heating coil; Step 2: The induction heating coils are divided into 2n-1 groups. In this embodiment, the induction heating coils have a total of 500 turns, each with 20 turns in a group, divided into 25 groups, and n is 13; a longitudinal magnetic flux layout is adopted, with the central coil as the central axis, and the two sides are axially symmetrical, and the spacing between each group of coils is wide in the middle and narrow on both sides; each group of coils is numbered, the central coil is numbered 1, and the numbering is from 1 to 13 outwards, the symmetrical coils on the left and right sides are the same, and the spacing between each group of induction heating coils is in accordance with the first constant parameterized linear dynamic tolerance sequence: set up, d is the distance between each set of induction heating coils, in m, i Number the coils, is the distance between coil 1 and coil 2, in m. Fixed, in this embodiment 0.4m; d is the decreasing distance each time, i.e. the tolerance, in m. d The value of can be changed; in this embodiment, the coil arrangement structure is as follows Figure 2 As shown; select the response surface method to coil width, tolerance d , the coil current and current frequency are taken as input variables, their numerical ranges are determined, a central composite experimental design is performed, and a series of experimental parameter combinations are generated; the variable ranges of the response surface experimental design in this embodiment are shown in Table 1 below: The response surface experimental design is shown in Table 2 below: ; Step 3: Establish a proportional geometric model based on the parameters determined in step 1 and step 2 and the test parameter combination. The proportional geometric model includes a train carriage geometric model, a bogie geometric model, an induction heating coil model and an air domain model. In this embodiment, the train carriage geometric model and the bogie geometric model are as follows: Figure 3 As shown; Step 4: Divide the free tetrahedron meshes for all proportional geometric models, and then divide the boundary layer meshes for the train carriage geometric models and bogie geometric models. The boundary layer mesh thickness is based on the formula Setting; where, l is the boundary layer mesh thickness, α is the conductivity of the heated material, is the vacuum permeability, is the magnetic permeability of the induction heating coil material,f is the frequency of the current in the induction heating coil. In this embodiment, α for , is 1, is 1; Step 5: Add the corresponding materials of each part of the model according to the material parameters obtained in step 1, set the magnetic field boundary conditions and temperature field boundary conditions, set temperature probes at equal distances on the inner surface of the carriage, perform magnetic thermal coupling calculation on the entire model, and obtain the temperature value sequence after heating In this embodiment, the temperature probe is arranged as follows Figure 4 As shown, the simulation results of magnetic thermal coupling calculation are shown in Table 2 above; Step 6: Based on the temperature value sequence , according to the formula Calculate the sample standard deviation of each probe temperature; where, is the average value of each temperature value in the temperature value sequence, with the above sample standard deviation Indicates the uniformity of the surface temperature in the heated cabin, sample standard deviation The smaller it is, the better the temperature uniformity is; the average temperature of the inner surface of the carriage and the sample standard deviation are taken as output variables, and a second-order polynomial response surface model is fitted with all input variables and output variables; Step 7: The quality of the obtained second-order polynomial response surface model is tested by variance analysis. In the variance analysis, the items with a p value less than 0.05 can be considered as significant items, and the significant items are retained, and the non-significant items are removed to form the second-order polynomial response surface model after the test; in this embodiment The items with p-values ​​less than 0.05 in the variance analysis results are shown in Table 3 below, and the items with p-values ​​less than 0.05 in the σ variance analysis results are shown in Table 4 below: According to Table 3 and Table 4, in the variance analysis results of the average temperature and sample standard deviation, l , d , I , f , If , and The p-value is less than 0.05. The obtained second-order polynomial response surface model is composed of these terms and the constant term. The specific second-order polynomial response surface model is shown in Table 5 below: ; Step 8: According to the correction coefficient of determination Evaluate the goodness of fit of the tested second-order polynomial response surface model and correct the coefficient of determination The closer the value is to 1, the better the fit of the second-order polynomial response surface model is. The evaluation results are shown in Table 6 below: According to Table 6, the correction determination coefficient of the average temperature and the sample standard deviation is They are 0.9758 and 0.9666, respectively, which are close to 1, indicating that the fitting accuracy of the obtained second-order polynomial response surface model is high; Step 9: Sequence the temperature values The sample standard deviation is used as the evaluation index of temperature rise uniformity; the average temperature of the inner surface of the carriage reaches the minimum thawing temperature of the frozen coal as a constraint condition, and the standard deviation of each probe temperature is used as the optimization target. The sample standard deviation is required to be as small as possible while the average temperature of the inner surface of the carriage is as high as possible. The coil width, the spacing between coil 1 and coil 2 , current, and current frequency are optimized variables, and the multi-objective optimization algorithm parameters are configured to build a mathematical model. The NSGA-II optimization algorithm is used, and the second-order polynomial response surface model after inspection obtained in step S7 is used as the objective function. The Pareto solution set is obtained through the optimization algorithm; in this embodiment, the Pareto solution set is as follows Figure 5 As shown, the parameter values ​​of the multi-objective optimization algorithm are set as shown in Table 7 below: ; Step 10: Based on the TOPSIS method, the optimal decision solution is obtained. In this embodiment, the weight ratio of the average surface temperature of the compartment to the sample standard deviation is set to 0.1:0.9; Step 11: The obtained optimal decision solution is used as an input variable, and the fitting accuracy of the second-order polynomial response surface model is verified by simulation calculation to obtain the optimized heating effect. The design results of the optimal decision solution in this embodiment are shown in Table 8 below: According to Table 8, the difference between the response surface value and the simulation result is small, the error of the average temperature is 1.64%, and the error of the sample standard deviation is 2.59%; compared with the original model without coil design under the same current value and current frequency conditions, the relative standard deviation σ after optimization is reduced to 39.43% of the original, and the temperature uniformity is significantly improved; Specifically, the present invention establishes a refined electromagnetic-thermal coupling model including a car, a bogie, an induction coil and an air domain, and adopts a central composite experimental design to construct a second-order polynomial response surface model with the width of each coil group, the decreasing tolerance of the coil spacing, the current frequency and the current as inputs, and the temperature uniformity and the average temperature of the inner surface of the car as outputs; an axisymmetric coil layout based on the first constant parameterized linear dynamic tolerance sequence is designed, and the boundary layer grid adaptive division technology is combined to improve the calculation accuracy, and the NSGA-II multi-objective optimization algorithm is used to represent the temperature uniformity with the sample standard deviation under the constraint of the thawing temperature, and the temperature sample standard deviation is minimized and the average temperature is maximized as the goal to optimize, and the Pareto solution set is obtained, and finally the optimal parameter combination is determined by the TOPSIS decision method. After simulation verification, the comparison of the temperature uniformity of the induction heating simulation before and after optimization is as follows. Figure 6 As shown in the figure, this method can effectively improve the uniformity of the electromagnetic heating temperature field on the inner surface of the frozen coal compartment, providing a theoretical basis for engineering applications.

[0019] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the uniformity of temperature rise of frozen coal induction heating on trains based on response surface methodology and multi-parameter optimization, characterized in that: The optimization method includes the following steps: S1. Determine the parameters required for modeling and the required material parameters according to the train carrying frozen coal and the induction heating coil; S2, using central composite experimental design to establish a second-order polynomial response surface model and generate a series of experimental parameter combinations; S3, establishing a proportional geometric model according to the parameters determined in step S1 and step S2 and the test parameter combination; S4, meshing all proportional geometric models; S5. Add the corresponding materials of each part of the model to the material parameters obtained in step 1, set the magnetic field boundary conditions and temperature field boundary conditions, set temperature probes at equal distances on the inner surface of the carriage, perform magnetic-thermal coupling calculations on the models under all test parameter combinations, and obtain the temperature value sequence after heating. ; S6, calculating the average temperature and the sample standard deviation according to the temperature value sequence, and fitting a second-order polynomial response surface model on this basis; S7. The quality of the obtained second-order polynomial response surface model was tested by variance analysis; S8. Evaluate the goodness of fit of the second-order polynomial response surface model after testing based on the corrected determination coefficient; S9, using the second-order polynomial response surface model obtained in step S7 as the objective function, and obtaining a Pareto solution set through an optimization algorithm; S10. Based on the TOPSIS method, the optimal decision solution is obtained; S11. The optimal decision solution is used as an input variable, and the fitting accuracy of the second-order polynomial response surface model is verified through simulation calculation to obtain the optimized heating effect.

2. A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization according to claim 1, characterized in that: Step S2 specifically includes dividing the induction heating coils into 2n-1 groups, adopting a longitudinal magnetic flux layout, taking the central coil as the central axis, and the two sides are axially symmetrical, and the spacing between each group of coils is wide in the middle and narrow on both sides; each group of coils is numbered, the central coil is numbered 1, and the numbering is sequentially from 1 to n outward, the symmetrical coils on the left and right sides have the same number, and the spacing between each group of induction heating coils is in accordance with the first constant parameterized linear dynamic tolerance sequence: set up, d is the distance between each set of induction heating coils, in m, i Number the coils, is the distance between coil 1 and coil 2, in m. fixed, d is the decreasing distance each time, i.e. the tolerance, in m. d The value of is variable; Select response surface methodology to use coil width, tolerance d , the coil current and current frequency are taken as input variables, their numerical ranges are determined, and a second-order polynomial response surface model is established using central composite experimental design to generate a series of experimental parameter combinations.

3. A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization according to claim 1, characterized in that: The proportional geometric model in step S4 includes a train carriage geometric model, a bogie geometric model, an induction heating coil model and an air domain model. Free tetrahedral meshes are divided for all proportional geometric models, and boundary layer meshes are divided for the train carriage geometric model and the bogie geometric model. The boundary layer mesh thickness is calculated according to the formula Setting, where l is the boundary layer mesh thickness, is the conductivity of the heated material, is the vacuum permeability, is the magnetic permeability of the induction heating coil material, f is the frequency of the current in the induction heating coil.

4. A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization according to claim 1, characterized in that: Step S6 is specifically based on the temperature value sequence , according to the formula Calculate the sample standard deviation of each probe temperature; where, is the average value of each temperature value in the temperature value sequence, with the above sample standard deviation Indicates the uniformity of the surface temperature in the heated cabin, sample standard deviation The smaller it is, the better the temperature uniformity; the average surface temperature of the carriage and the sample standard deviation are taken as output variables, and a second-order polynomial response surface model is fitted with all input variables and output variables.

5. A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization according to claim 1, characterized in that: Step S7 specifically tests the quality of the obtained second-order polynomial response surface model through variance analysis. In the variance analysis, items with p-values ​​less than 0.05 can be considered as significant items. The significant items are retained, and the non-significant items are removed to form the second-order polynomial response surface model after the test.

6. A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization according to claim 1, characterized in that: Step S8 is specifically to correct the determination coefficient Evaluate the goodness of fit of the tested second-order polynomial response surface model and correct the coefficient of determination The closer the value is to 1, the better the fit of the second-order polynomial response surface model.

7. A method for optimizing the uniformity of temperature rise of frozen coal induction heating in trains based on response surface methodology and multi-parameter optimization according to claim 1, characterized in that: Step S9 specifically includes converting the temperature value sequence The sample standard deviation is used as the evaluation index of temperature rise uniformity; the average temperature of the inner surface of the carriage reaches the minimum thawing temperature of the frozen coal as a constraint condition, and the standard deviation of each probe temperature is used as the optimization target. The sample standard deviation is required to be as small as possible while the average temperature of the inner surface of the carriage is as high as possible. The coil width, the spacing between coil 1 and coil 2 , current, and current frequency are optimized variables, and the multi-objective optimization algorithm parameters are configured to build a mathematical model. The NSGA-II optimization algorithm is used, and the second-order polynomial response surface model after inspection obtained in step S7 is used as the objective function. The Pareto solution set is obtained through the optimization algorithm.

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