An optimized design method for an electromagnetic drive plunger pump

Abstract

The present invention discloses an optimization design method for an electromagnetic-driven plunger pump, comprising the following steps: establishing a three-dimensional equivalent model of the electromagnetic-driven plunger pump; confirming optimization parameters and optimization objectives and performing normalization processing; conducting finite element simulation analysis to establish an experimental database; constructing a fitting function between input variables and output variables by using an artificial neural network; and performing multi-objective optimization on the structural parameters of the electromagnetic-driven plunger pump by using a cuckoo multi-objective search algorithm. Compared with the traditional design by empirical formula, the present invention has higher accuracy, lower cost and greatly improved efficiency, shortens the technology R & D cycle, and can more accurately solve the optimal parameter combination method. Compared with the commonly used 2D solution method, the present invention has the advantage of higher accuracy, and the problem of difficult solution for non-axisymmetric models in the 2D solver is also solved. The present invention adopts a cuckoo multi-objective search algorithm, which can better solve the continuous optimization problem of this type of structural parameters.

Description

Technical Field

[0001] The present invention belongs to the field of electromagnetic drive plunger pump design, and particularly relates to an optimized design method for an electromagnetic drive plunger pump. Background Art

[0002] An electromagnetic pump is a main actuator for driving the flow of fluid media (liquids or gases) in industrial systems. Electromagnetic pumps have the advantages of safe use, no external leakage and easy control of internal leakage, simple system structure, low price, fast action, small power, and light shape, and are widely used in industries such as home appliances, automobiles, and machinery. With the improvement of the industrial development level, the demand for low power consumption, high pressure, large flow rate, and reliability of electromagnetic pumps is becoming more and more urgent.

[0003] At present, the design and development of products mainly determine the performance parameters of products through empirical formulas and analytical calculations, and then improve them through repeatedly making prototypes for multiple test iterations. The cycle is long, the cost is high, and the accuracy is low. Introducing modern virtual simulation technology into the optimized design of solenoid valves can reduce the volume of electromagnetic mechanisms, save the use of copper and iron, optimize the action process, improve the service life of solenoid valves, reduce the working temperature rise in the stable state, and at the same time combine the specific situation of flow control to achieve energy saving, material saving, and stable operation without changing the electromagnetic force. Compared with traditional design methods, it has significant advantages such as low cost, high accuracy, and high efficiency.

[0004] Chinese Patent CN110263463A proposes a method for analyzing the electromagnetic induction characteristics based on Ansys Maxwell software. This method uses the eddy current field solver of the software to analyze the electromagnetic characteristics such as the magnetic induction intensity of a closely wound solenoid cylinder. This simulation method is too basic to consider the reciprocating motion of the pump core of the electromagnetic pump and the dynamic effects of multiple resultant forces.

[0005] Chinese Patent CN109308376A proposes a finite element analysis method for switched reluctance motors. The switched reluctance motors are optimized by using the finite element analysis method, which improves the energy utilization rate of the motors. However, due to the difference in the working principles between the motors and the electromagnetic drive plunger pumps, it is not applicable to the optimized design of electromagnetic pumps.

[0006] Chinese Patent CN 114048705 A proposes a simulation method for the dynamic working process of solenoid valves. However, it only considers a two-dimensional axisymmetric model, has high limitations, and only considers the single stroke of the valve core opening and closing, which does not meet the solution requirements of the high-frequency reciprocating motion of the pump core.

[0007] Aiming at the above problems, this patent aims to perform three-dimensional dynamic simulation and algorithm optimization on an electromagnetic drive plunger pump using numerical simulation, and provides an optimized design method applicable to the electromagnetic drive plunger pump. Summary of the Invention

[0008] To solve the above problems existing in the prior art, the present invention aims to design an optimized design method for an electromagnetic-driven plunger pump that can replace the traditional empirical formula design and is more efficient and accurate.

[0009] To achieve the above object, the technical solution of the present invention is as follows: An optimized design method for an electromagnetic-driven plunger pump, comprising the following steps:

[0010] A. Establish a three-dimensional equivalent model of the electromagnetic-driven plunger pump

[0011] The electromagnetic-driven plunger pump includes a magnetic isolation tube, a magnetic conductive sleeve, a return spring, a pump core, a coil, a magnetic isolation ring, and a magnetic conductive plate; using three-dimensional modeling software, establish a three-dimensional equivalent model of the electromagnetic-driven plunger pump. The other components except the pump core in the three-dimensional equivalent model are modeled according to the actual size, and when modeling the pump core, a polygonal prism structure is used to replace the original cylindrical structure;

[0012] B. Confirm the optimization parameters and optimization objectives and perform normalization processing

[0013] According to the actual engineering needs, while keeping the original volume of the product unchanged, to obtain a higher service life and the maximum working pressure difference, select the position of the magnetic isolation ring, the length of the magnetic conductive sleeve, and the number of turns of the coil as the optimized structural parameters, and take the average electromagnetic force and the heating power as the optimization indicators.

[0014] Based on the response surface method, establish n groups of experimental parameters and perform normalization processing on them. The formula is as follows:

[0015]

[0016] Y′ = Y

[0017]

[0018] In the formula, X is the number of turns of the coil, Y is the position of the magnetic isolation ring, Z is the length of the magnetic conductive sleeve, X′ is the value of the number of turns of the coil after normalization processing, Y′ is the value of the position of the magnetic isolation ring after normalization processing, Z′ is the value of the length of the magnetic conductive sleeve after normalization processing, X max is the upper limit of the number of turns of the coil, X min is the lower limit of the number of turns of the coil, Y max is the upper limit of the position of the magnetic isolation ring, Y min is the lower limit of the position of the magnetic isolation ring, Z max is the upper limit of the length of the magnetic conductive sleeve, Z min is the lower limit of the length of the magnetic conductive sleeve;

[0019] C. Conduct finite element simulation analysis to establish an experimental database

[0020] Use Maxwell electromagnetic simulation software to perform finite element analysis on the three-dimensional equivalent model, and simulate and solve the dynamic performance and electromagnetic performance of the three-dimensional equivalent model. The dynamic performance and electromagnetic performance parameters include the electromagnetic force and heating power received by the pump core. After the solution is completed, establish a simulation database based on the simulation results:

[0021] C1. Import the three-dimensional equivalent model into the 3D transient solver, define the material parameters for each component. Set the pump core as a soft magnetic material, the magnetic conducting sleeve and magnetic conducting plate as pure iron, the magnetic isolation ring as copper, and the remaining components that do not affect the magnetic field distribution as non-magnetic materials. Click the create region option to create a cuboid-shaped solution space, and input the perpendicular distance x from the six faces of the cuboid to the nearest point of the three-dimensional equivalent model to complete the setting of the boundary conditions of the solution region;

[0022] C2. According to the Hooke's coefficients and effective elongation amounts of the two return springs, define the movement domain and movement direction of the spool valve, and define the load force curve of the spool valve. The formula is as follows:

[0023] force=(k1 + k1)x + f

[0024] In the formula, force is the magnitude of the load force received by the pump core, k1 is the Hooke's coefficient of return spring A, k2 is the Hooke's coefficient of return spring B, x is the displacement of the pump core, and f is the magnitude of the resultant force received at the initial position of the pump core;

[0025] C3. Perform finite element mesh division. Select the pump core, magnetic isolation ring, and magnetic conducting sleeve in the three-dimensional equivalent model, use the software mesh division tool for setting, click mesh operation→length based, and input the thickness a of the divided mesh to complete the finite element mesh division.

[0026] C4. Design the form of the excitation source as an external circuit drive, set the circuit system in the Maxwell circuit editor. The circuit system includes an excitation source, a resistor, and a coil, which is consistent with the actual drive circuit of the product. After the setting is completed, import it into the 3D transient solver.

[0027] C5. According to the experimental data groups designed in step B, after changing the structural parameters, turn to step C1 until the simulation analysis results of all experimental data groups are obtained by solving. Save and record the results as the simulation database in the subsequent steps.

[0028] D. Use an artificial neural network to construct a fitting function between the input variables and output variables

[0029] Based on the data model in the simulation database constructed in step C, 75%-80% of the data in the simulation database is selected as the training set, and the remaining 20%-25% of the data is used as the test set to train the network. The database is trained multiple times until the function correlation in the training result is greater than 0.9, and the construction of the fitting function between the input variables and the output target of the electromagnetic drive plunger pump is completed;

[0030] E. Use the cuckoo multi-objective search algorithm to perform multi-objective optimization on the structural parameters of the electromagnetic drive plunger pump

[0031] E1. Construct the fitness function of the genetic algorithm; use the fitting function obtained in step D to construct the fitness function of the genetic algorithm as follows:

[0032]

[0033] where index i is the fitness of the i-th individual in the population; H′ i and F′ i are the mean electromagnetic force and the mean heating power of the i-th individual in the population, respectively. The higher the individual fitness, the smaller the index i value, the greater the electromagnetic force, the lower the heating power, and the higher the performance of the electromagnetic drive plunger pump.

[0034] E2. Use the cuckoo multi-objective search algorithm to find the optimal solution set; within the value range of the structural parameters, initialize the population and set the maximum number of iterations T. Calculate the fitness value of the current bird's nest. Update the position of the bird's nest in the way of Wright flight, calculate the index i values of all the updated bird's nests, compare the index i values of the new bird's nest and the old bird's nest, and retain the bird's nest with the smaller index i value. Loop through this process until the number of loops is equal to the maximum number of iterations T and output the finally generated population as the optimal solution set.

[0035] E3. Construct the decision function; make a decision on the optimal solution in the optimal solution set. The decision function constructed according to the optimization objective is:

[0036]

[0037] where: a and b are decision parameters, and by changing the values of a and b, the weights of the electromagnetic force and the response time in the final decision are adjusted; F is the sum of the mean electromagnetic force values of the population individuals, H is the sum of the mean heating power values of the population individuals, and N is the population size. Calculate the σ value of each group of parameters in the optimal solution set, and select the parameter group with the largest σ value as the final result of the multi-objective optimization.

[0038] Furthermore, the number n in step B is selected according to the different numbers of experimental parameters.

[0039] Further, the 3D modeling software described in step A includes Solidworks, AutoCAD, Proe, UG, and Catia.

[0040] The beneficial effects of the present invention are as follows:

[0041] 1. The present invention provides a new optimization method for the design of electromagnetic drive plunger pumps. Compared with the traditional empirical formula design, it has higher accuracy, lower cost, and significantly improves efficiency, shortens the technology R & D cycle, and can more accurately solve the optimal parameter combination method.

[0042] 2. The present invention adopts a simulation analysis method of 3D dynamic solution, which has the advantage of higher accuracy compared with the commonly used 2D solution method before. The problem of difficult solution for non-axisymmetric models in 2D solvers is also solved.

[0043] 3. The present invention adopts the cuckoo multi-objective search algorithm, which has better global search ability compared with the commonly used genetic algorithm and can better solve the continuous optimization problem of this type of structural parameters. Description of the Drawings

[0044] Figure 1 is a flow chart of the present invention.

[0045] Figure 2 is a three-dimensional equivalent model diagram of the electromagnetic drive plunger pump constructed by the present invention.

[0046] Figure 3 is a comparison of the average electromagnetic force of the electromagnetic drive plunger pump before and after optimization of the present invention.

[0047] Figure 4 is a comparison of the average heat generation power of the electromagnetic drive plunger pump before and after optimization of the present invention.

[0048] In the figure: 1 is the magnetic isolation tube, 2 is the magnetic conductive sleeve, 3 is the return spring, 4 is the pump core, 5 is the coil, 6 is the magnetic isolation ring, and 7 is the magnetic conductive plate. Detailed Embodiments

[0049] Next, the present application will be described completely and in detail with reference to the drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the detailed content of the present invention, and are part of the embodiments of the present invention, rather than all embodiments.

[0050] Figure 1 is a flow chart of the multi-objective optimization method for the structural parameters of the electromagnetic drive plunger pump described in the present application, including the following steps:

[0051] A. Establish a three-dimensional equivalent model of the electromagnetic drive plunger pump

[0052] The electromagnetic drive plunger pump includes a magnetic isolation tube 1, a magnetic conduction sleeve 2, a return spring 3, a pump core 4, a coil 5, a magnetic isolation ring 6 and a magnetic conduction plate 7; Using Solidworks modeling software, a three-dimensional equivalent model of the electromagnetic drive plunger pump is established. The other components except the pump core 4 in the three-dimensional equivalent model are modeled according to the actual size. When modeling the pump core 4, a polygonal prism structure is used to replace the original cylindrical structure;

[0053] B. Confirm the optimization parameters and optimization objectives and perform normalization processing

[0054] As the present invention needs to obtain a higher service life and the maximum working pressure difference while keeping the original volume of the product unchanged, therefore, the position of the magnetic isolation ring 6, the length of the magnetic conduction sleeve 2 and the number of turns of the coil 5 are selected as the structural parameters to be optimized, and the average electromagnetic force and the heating power are used as the optimization indexes.

[0055] According to the response surface method, 17 groups of experimental parameters are established and normalized, and the formula is as follows:

[0056]

[0057] Y′ = Y

[0058]

[0059] In the formula, X is the number of turns of the coil 5, Y is the position of the magnetic isolation ring 6, Z is the length of the magnetic conduction sleeve 2, X′ is the value of the number of turns of the coil 5 after normalization processing, Y′ is the value of the position of the magnetic isolation ring 6 after normalization processing, Z′ is the value of the length of the magnetic conduction sleeve 2 after normalization processing, X max is the upper limit of the value of the number of turns of the coil 5, X min is the lower limit of the value of the number of turns of the coil 5, Y max is the upper limit of the value of the position of the magnetic isolation ring 6, Y min is the lower limit of the value of the position of the magnetic isolation ring 6, Z max is the upper limit of the value of the length of the magnetic conduction sleeve 2, Z min is the lower limit of the value of the length of the magnetic conduction sleeve 2.

[0060] The comparison before and after the data normalization processing is shown in the following table:

[0061]

[0062]

[0063] C. Perform finite element simulation analysis to establish an experimental database

[0064] C1. Import the 3D equivalent model into the 3D transient solver, define the material parameters for each component. Set the pump core 4 as steel-1008, the magnetic conductive sleeve 2 and the magnetic conductive plate 7 as pure iron, the magnetic isolation ring 6 as copper, and the other components that do not affect the magnetic field distribution as polyester. Click the create region option, input 5 mm, and complete the setting of the boundary conditions of the solution region.

[0065] C2. According to the Hooke's coefficients and effective elongation amounts of the two return springs 3, define the motion domain and motion direction of the spool valve, and define the load force curve of the spool valve. The formula is as follows:

[0066] force=(k1 + k1)x + f

[0067] In the formula, force is the magnitude of the load force received by the pump core 4, k1 is the Hooke's coefficient of the return spring A, k2 is the Hooke's coefficient of the return spring B, x is the displacement amount of the pump core 4, and f is the magnitude of the resultant force received at the initial position of the pump core 4.

[0068] The constructed load force curve is:

[0069] force=-1.2x + 0.2

[0070] C3. Perform finite element mesh division. Select the pump core 4, the magnetic isolation ring 6, and the magnetic conductive sleeve 2 in the 3D equivalent model, use the software mesh division tool for setting, click mesh operation→length based, input 0.1 mm, and complete the finite element mesh division.

[0071] C4. Use the Maxwell circuit editor to set the form of the excitation source as external circuit drive, set the circuit system in the Maxwell circuit editor. The excitation source type of the target model electromagnetic drive plunger pump is 220V, 50Hz, the parameters of the coil 5 are 10000 turns, and the internal resistance is 336Ω. The circuit type after setting is an AC voltage source in series with an equivalent internal resistance, and import the external circuit drive into the 3D transient solver.

[0072] C5. Perform simulation analysis to obtain the solution results. According to the experimental data group design table, repeat the above steps after changing the structural parameters until the simulation analysis results of 17 experimental data groups are obtained by solving. Save and record the results for use as the simulation database in the subsequent steps.

[0073] D. Use an artificial neural network to construct a fitting function between the input variables and the output variables

[0074] Based on the data model in the simulation database constructed in step C, 75% of the data in the simulation database is selected as the training set, and the remaining 25% of the data is used as the test set to train the network. The database is trained multiple times until the function correlation in the training result is greater than 0.9, and the construction of the fitting function between the input variables and output targets of the electromagnetic drive plunger pump is completed.

[0075] E. Use the cuckoo multi-objective search algorithm to perform multi-objective optimization on the structural parameters of the electromagnetic drive plunger pump

[0076] E1. Construct the fitness function of the genetic algorithm. Use the relationship model obtained in the third step to construct the fitness function of the genetic algorithm as follows:

[0077]

[0078] where index i is the fitness of the i-th individual in the population; H′ i and F′ i are the average electromagnetic force and average heating power of the i-th individual in the population. The higher the individual fitness, the smaller the index i value, the greater the electromagnetic force, the lower the heating power, and the higher the performance of the electromagnetic drive plunger pump.

[0079] E2. Use the cuckoo multi-objective search algorithm to find the optimal solution set. Initialize the population within the value range of the structural parameters. The population size is 50, and the maximum number of iterations is set to 200. Calculate the fitness value of the current bird's nest. Update the position of the bird's nest in the way of Wright flight, calculate the index i values of all the updated bird's nests, compare the index i values of the new bird's nest and the old bird's nest, and retain the bird's nest with the smaller index i value. Loop through this process until the number of loops is equal to 200, and output the finally generated population as the optimal solution set.

[0080] E3. Construct the decision function and decide the optimal solution in the optimal solution set. The decision function constructed according to the optimization objective is:

[0081]

[0082] where: a and b are decision parameters, and the weights of the electromagnetic force and response time in the final decision can be adjusted by changing the values of a and b; F is the total sum of the average electromagnetic force values of the population individuals, H is the total sum of the average heating power values of the population individuals, and N is the population size. Calculate the σ value of each group of parameters in the optimal solution set, and select the parameter group with the largest σ value as the final result of the multi-objective optimization.

[0083] The average electromagnetic force under the final optimal structural parameters is 6.06 N, and the average heating power is 12.693 W. Compared with the results before optimization, as Figure 3 shown, the electromagnetic force only decreases by 13.77%, as Figure 4 shown, the heating power decreases by 63.33%, and the optimization effect is remarkable.

Claims

1. An optimized design method for an electromagnetic drive plunger pump, characterized in that: It includes the following steps: A. Establish a three-dimensional equivalent model of the electromagnetic-driven plunger pump B. Confirm the optimization parameters and optimization objectives and perform normalization processing According to the actual engineering requirements, without changing the original volume of the product, in order to obtain a higher service life and the maximum working pressure difference, select the position of the magnetic isolation ring (6), the length of the magnetic conduction sleeve (2), and the number of turns of the coil (5) as the optimized structural parameters, and use the average electromagnetic force and the heating power as the optimization indicators; Based on the response surface method, establish n groups of experimental parameters and perform normalization processing on them; C. Conduct finite element simulation analysis to establish an experimental database Use Maxwell electromagnetic simulation software to perform finite element analysis on the three-dimensional equivalent model, simulate and solve the dynamic performance and electromagnetic performance of the three-dimensional equivalent model. The dynamic performance and electromagnetic performance parameters include the electromagnetic force and heating power received by the pump core (4). After the solution is completed, establish a simulation database according to the simulation results; D. Use an artificial neural network to construct a fitting function between the input variables and the output variables Based on the data model in the simulation database constructed in step C, select 75%-80% of the data in the simulation database as the training set, and the remaining 20%-25% of the data as the test set to train the network. Train the database multiple times until the function correlation in the training result is greater than 0.9, and complete the construction of the fitting function between the input variables and the output target of the electromagnetic-driven plunger pump; E. Use the cuckoo multi-objective search algorithm to perform multi-objective optimization on the structural parameters of the electromagnetic-driven plunger pump E1. Construct the fitness function of the genetic algorithm; E2. Use the cuckoo multi-objective search algorithm to find the optimal solution set; within the value range of the structural parameters, initialize the population and set the maximum number of iterations T; calculate the fitness value of the current bird's nest; update the position of the bird's nest in the way of Wright flight, calculate the values of all updated bird's nests, compare the sizes of the new bird's nest and the old bird's nest, and retain the bird's nest with a smaller value. Loop through this process until the number of loops is equal to the maximum number of iterations T and output the finally generated population as the optimal solution set; E3. Construct the decision function; make a decision on the optimal solution in the optimal solution set. The decision function constructed according to the optimization objective is: Wherein: a and b are decision parameters, and the weights of electromagnetic force and response time in the final decision are adjusted by changing the values of a and b ; F is the total mean value of electromagnetic force of population individual values, H is the total mean value of heating power of population individual values, N is the population size; calculate the value of each group of parameters in the optimal solution set, and select the parameter group with the largest value as the final result of multi-objective optimization.

2. The optimized design method of an electromagnetic-driven plunger pump according to claim 1, characterized in that: The method for establishing the three-dimensional equivalent model of the electromagnetic-driven plunger pump described in step A is as follows: The electromagnetic-driven plunger pump includes a magnetic isolation tube (1), a magnetic conduction sleeve (2), a return spring (3), a pump core (4), a coil (5), a magnetic isolation ring (6), and a magnetic conduction plate (7); use three-dimensional modeling software to establish a three-dimensional equivalent model of the electromagnetic-driven plunger pump. The other components of the three-dimensional equivalent model except the pump core (4) are modeled according to the actual size. When modeling the pump core (4), use a polygonal prism structure to replace the original cylindrical structure.

3. The optimized design method of an electromagnetic-driven plunger pump according to claim 1, characterized in that: The formula for the normalization processing described in step B is as follows: In the formula, is the number of turns of the coil (5), is the position of the magnetic isolation ring (6), is the length of the magnetic conduction sleeve (2), is the value of the number of turns of the coil (5) after normalization processing, is the value of the position of the magnetic isolation ring (6) after normalization processing, is the value of the length of the magnetic conduction sleeve (2) after normalization processing, is the upper limit of the value range of the number of turns of the coil (5), is the lower limit of the value range of the number of turns of the coil (5), is the upper limit of the value range of the position of the magnetic isolation ring (6), is the lower limit of the value range of the position of the magnetic isolation ring (6), is the upper limit of the value range of the length of the magnetic conduction sleeve (2), is the lower limit of the value range of the length of the magnetic conduction sleeve (2).

4. The optimized design method of an electromagnetic drive plunger pump according to claim 1, characterized in that: The method for conducting finite element simulation analysis to establish an experimental database described in step C includes the following steps: C1. Import the three-dimensional equivalent model into the 3D transient solver, define the material parameters of each component. The pump core (4) is set as a soft magnetic material, the magnetic conduction sleeve (2) and the magnetic conduction plate (7) are set as pure iron, the magnetic isolation ring (6) is set as copper, and the other components that do not affect the magnetic field distribution are set as non-magnetic materials. Click the create region option to create a solution space in the shape of a cuboid, and input the perpendicular distance x from the six faces of the cuboid to the nearest point of the three-dimensional equivalent model to complete the setting of the boundary conditions of the solution region; C2. According to the Hooke's coefficients and effective elongation amounts of the two return springs (3), define the movement domain and movement direction of the valve core, and define the load force curve of the valve core. The formula is as follows: In the formula, is the magnitude of the load force on the pump core (4), is the Hooke's coefficient of the return spring A, is the Hooke's coefficient of the return spring B, is the displacement of the pump core (4), is the magnitude of the resultant force on the pump core (4) at the initial position; C3. Perform finite element mesh generation. Select the pump core (4), magnetic isolation ring (6), and magnetic conduction sleeve (2) in the three-dimensional equivalent model, and use the software mesh generation tool for settings. Click mesh operation → length based, input the thickness a of the divided mesh, and complete the finite element mesh generation; C4. Design the excitation source form as external circuit drive, and set the circuit system in the Maxwell circuit editor. The circuit system includes an excitation source, a resistor, and a coil (5), which is consistent with the actual drive circuit of the product. After the settings are completed, import it into the 3D transient solver; C5. According to the experimental data groups designed in step B, after changing the structural parameters, go to step C1 until the simulation analysis results of all experimental data groups are obtained by solving. Save and record the results as the simulation database in the subsequent steps.

5. The optimized design method of an electromagnetic drive plunger pump according to claim 1, characterized in that: The method for constructing the fitness function of the genetic algorithm in step E1 is as follows: Use the fitting function obtained in step D to construct the fitness function of the genetic algorithm as follows: Among them is the fitness of the i th individual in the population; and are respectively the mean electromagnetic force and the mean heat generation power of the i th individual in the population. The higher the individual fitness, the smaller the value, the greater the electromagnetic force, the lower the heat generation power, and the higher the performance of the electromagnetic drive plunger pump.

6. The optimized design method of an electromagnetic drive plunger pump according to claim 1, characterized in that: The number of groups in step B n Take values according to the different numbers of experimental parameters.

7. The optimized design method of an electromagnetic drive plunger pump according to claim 1, characterized in that: The three-dimensional modeling software described in step A includes Solidworks, AutoCAD, Proe, UG, and Catia.

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