Parameter optimization method of axial flow fan blades for charging piles based on genetic algorithm

Abstract

The present disclosure discloses a method for optimizing the parameters of axial flow fan blades for charging piles based on a genetic algorithm. In the method, the geometric shape of the existing blades is extracted, and the performance curve of the existing fan structure is calculated and verified by experiments; the hub and wheel cover lines are depicted in the meridian plane; the three-dimensional model of the moving blade is reconstructed; the constructed hub and axle profiles are kept unchanged, and the meridian plane blade leading and trailing edge profiles are widened so that their width can match the blade axial height, thereby constructing a single flow channel; in step five, three-dimensional calculations are performed using the obtained three-dimensional moving blade model to verify whether the data features match; in step six, a meta-model is established; and a genetic algorithm multi-objective optimization is performed; the present disclosure uses a genetic algorithm to quickly and accurately obtain an axial flow fan blade with the minimum fan power and the maximum static pressure efficiency; in addition, by studying the axial flow fan blades through parameterization, it is easier to verify the blades with changed geometric parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile axial flow fan blade optimization, and in particular to a charging pile axial flow fan blade parameter optimization method based on a genetic algorithm. Background Art

[0002] Axial fans within charging piles are characterized by their airflow aligning with the axis of the fan blades, resulting in high pressure and flow coefficients. They are also easy to install, making them ideal for cooling within charging piles. While the fan blades are a key factor in optimizing axial fan performance, research on their design optimization is currently insufficient.

[0003] The design optimization of axial fan blades is difficult and the workload is large due to the complexity of blade geometry. How to quickly carry out a large amount of blade geometry optimization work is the key to the development of axial fan blade optimization technology. There are two unreasonable aspects in the current axial fan blade parameterization method: First, due to the complexity of blade geometry, the current axial fan blade optimization is very tedious and the workload is large, making it difficult to quickly obtain the optimal axial fan blade design selection. Second, when using the current axial fan blade geometry feature method to perform blade optimization work, it is difficult to conveniently adjust the axial fan blades, which affects the work of verifying the effect of the changed axial fan blades.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The present invention provides a method for optimizing the parameters of axial flow fan blades for charging piles based on a genetic algorithm. The genetic algorithm is used to quickly and accurately obtain axial flow fan blades with minimum fan power and maximum static pressure efficiency. By studying the axial flow fan blades through parameterization, it is easier to verify blades with changed geometric parameters.

[0006] A method for optimizing blade parameters of an axial flow fan in a charging pile based on a genetic algorithm includes:

[0007] Step 1: Import the axial flow fan blade structure of the charging pile into the full 3D modeling software to extract the blade parameters. The fan rotation axis is the global Z axis in the 3D coordinate system. The end face of the blade close to the rotation axis and the end face close to the fan casing are extracted. The extracted end face is rotated and cut into four parts using the global YZ and ZX planes respectively. The hub and wheel cover rotation surface of the global XY positive half axis is retained. A new sketch is created facing the global ZX plane. The line mapped on the new sketch of the retained hub and wheel cover rotation surface is extracted as the hub and wheel cover line. At the same time, the performance curve of the existing fan structure is calculated and verified by experiments.

[0008] Step 2: Based on the blade structure parameters of the charging pile axial flow fan blade structure, use the blade modeling software and adopt the following blade shaping method: draw the hub line and wheel cover line in the meridian plane, and use Bezier curve to draw the hub line and wheel cover line in the meridian plane. Construct the stacking lines of the blade profile to generate the pressure and suction surface profiles at the blade hub and shroud sections, where P(t) is the coordinate of any point on the parameterized Bezier curve; n is the order of the parameterized Bezier curve; i is the current order, i=0, 1, 2, ...; P i is the position vector of each vertex; B i,n (t) is the Bernstein basis function, ; t is the curve parameter variable, ;

[0009] Step 3: Based on the extracted control points of the blade hub and wheel cover, re-import the model into the BladeEditor 3D software to automatically generate a 3D model of the rotor blade;

[0010] Step 4: Keep the constructed hub line and wheel cover line unchanged, widen the meridian blade leading and trailing edge profiles so that their width matches the blade axial height, and construct a single flow channel;

[0011] Step 5: Performing a three-dimensional calculation using the three-dimensional model of the moving blade. If the calculated flow distribution and pressure distribution data match the performance curve characteristics calculated in step 1, it proves that the blade parameter extraction is successful. If not, return to step 2 to step 5 until the flow distribution and pressure distribution data match the performance curve characteristics.

[0012] Step 6: Using the extracted blade parameters as they change with the blade relative thickness M%, the advanced Latin hypercube method is used to establish a sample space within the restricted range. The output value of the established sample data is solved. Based on the extracted control points of the blade hub and wheel cover, the model is re-imported into the BladeEditor 3D software to automatically generate a 3D model of the rotor blade and establish a metamodel.

[0013] Step seven: Based on the meta-model, the optimal structural parameters are used as the initial values ​​for genetic algorithm optimization. The response surface model is applied and the optimization module is used to perform genetic algorithm multi-objective optimization. The fan static pressure efficiency is improved while reducing the fan power, thereby improving the aerodynamic performance of the fan and increasing the heat dissipation of the charging pile.

[0014] In the method for optimizing the blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, the full three-dimensional modeling software is Fluent, and the blade modeling software is BladeEditor.

[0015] In the method for optimizing the blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, the hub line and the wheel cover line are drawn in the meridian plane, one blade number is retained, the axial chord length remains unchanged, the leading edge is outlined using a square section, and the trailing edge is outlined using a section with an ellipse ratio of 1000.

[0016] In the method for optimizing the blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, in step 2, the hub and wheel cover lines formed by the stacked lines of the blade contours are the lines obtained by offsetting the blade root by 3% relative to the center axis of the hub and the blade tip by 3% relative to the center axis of the wheel cover.

[0017] In the method for optimizing the blade parameters of the charging pile axial flow fan based on genetic algorithm, in step 6, the relative thickness of the blade , which is the percentage of the blade thickness at a certain position relative to the chord length, where x is the distance from the leading edge to a specific position on the blade; t(x) is the thickness of the blade at position x; and C is the chord length of the blade.

[0018] In the method for optimizing blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, in step six, the extracted blade parameters include: blade angle at 0% of the blade hub, blade angle at 25% of the blade hub, blade angle at 50% of the blade hub, blade angle at 75% of the blade hub, blade angle at 100% of the blade hub, blade normal layer thickness at 0% of the blade hub, blade normal layer thickness at 25% of the blade hub, blade normal layer thickness at 50% of the blade hub, blade normal layer thickness at 75% of the blade hub 20 blade parameters: thickness, blade normal layer thickness at 100% of blade hub, blade angle at 0% of blade wheel cover, blade angle at 25% of blade wheel cover, blade angle at 50% of blade wheel cover, blade angle at 75% of blade wheel cover, blade angle at 100% of blade wheel cover, blade normal layer thickness at 0% of blade wheel cover, blade normal layer thickness at 25% of blade wheel cover, blade normal layer thickness at 50% of blade wheel cover, blade normal layer thickness at 75% of blade wheel cover, and blade normal layer thickness at 100% of blade wheel cover.

[0019] In the method for optimizing the blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, in step six, the sample output value to be solved refers to the static pressure efficiency and power of the fan.

[0020] In the above-mentioned method for optimizing the parameters of the axial flow fan blades of a charging pile based on a genetic algorithm, in step six, the meta-model evaluation criteria are: prognosis coefficient To evaluate the quality of the established response surface model; is the sum of squared prediction errors, which are estimated based on cross-validation; is the total change, .

[0021] In the method for optimizing the blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, in step seven, the objective function is to minimize the fan power and maximize the static pressure efficiency.

[0022] In the charging pile axial flow fan blade parameter optimization method based on genetic algorithm, the charging pile axial flow fan is arranged in the charging station.

[0023] Compared with the existing technology, the present invention has the following advantages: the charging pile axial flow fan blade parameter optimization method based on genetic algorithm described in the present invention uses genetic algorithm to quickly and accurately obtain axial flow fan blades with minimum fan power and maximum static pressure efficiency; using parameterization to study axial flow fan blades makes it easier to verify blades with changed geometric parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are intended only to illustrate preferred embodiments and are not to be construed as limiting the present invention. It should be understood that the drawings described below are merely examples of the present invention, and that those skilled in the art will be able to derive other drawings from these drawings without inventive effort. Throughout the drawings, identical reference numerals are used to denote identical components.

[0025] In the attached figure:

[0026] Figure 1 This is a schematic diagram of the parameter optimization process of the axial flow fan blades of a charging pile based on genetic algorithm;

[0027] Figure 2 It is a schematic diagram of the meridian plane of a single flow channel of the blade;

[0028] Figure 3 This is a schematic diagram of blade 3D model parameter extraction;

[0029] Figure 4 It is based on the NSGA-II multi-objective optimization Pareto chart;

[0030] Figure 5 This is a schematic diagram comparing blade angles before and after optimization;

[0031] Figure 6 This is a schematic diagram comparing the three-dimensional models of blades before and after optimization.

[0032] The present invention will be further explained below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0033] Specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0034] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the invention. The scope of protection of the present invention shall be as defined in the attached claims.

[0035] To facilitate understanding of the embodiments of the present invention, further explanation will be given below using specific embodiments as examples in conjunction with the accompanying drawings, and the accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0036] like Figures 1 to 6 As shown in FIG, the parameter optimization method of the axial flow fan blade of the charging pile based on the genetic algorithm includes the following steps:

[0037] Step 1: Import the axial flow fan blade structure of the charging pile into the full 3D modeling software to extract the blade parameters. The fan rotation axis is the global Z axis in the 3D coordinate system. The end face of the blade close to the rotation axis and the end face close to the fan casing are extracted. The extracted end face is rotated and cut into four parts using the global YZ and ZX planes respectively. The hub and wheel cover rotation surface of the global XY positive half axis is retained. A new sketch is created facing the global ZX plane. The line mapped on the new sketch of the retained hub and wheel cover rotation surface is extracted as the hub and wheel cover line. At the same time, the performance curve of the existing fan structure is calculated and verified by experiments.

[0038] Step 2: Based on the blade structure parameters of the charging pile axial flow fan blade structure, use the blade modeling software and adopt the following blade shaping method: draw the hub line and wheel cover line in the meridian plane, and use Bezier curve to draw the hub line and wheel cover line in the meridian plane. Construct the stacking lines of the blade profile to generate the pressure and suction surface profiles at the blade hub and shroud sections, where P(t) is the coordinate of any point on the parameterized Bezier curve; n is the order of the parameterized Bezier curve; i is the current order, i=0, 1, 2, ...; P i is the position vector of each vertex; B i,n(t) is the Bernstein basis function, ; t is the curve parameter variable, ;

[0039] Step 3: Based on the extracted control points of the blade hub and wheel cover, re-import the model into the BladeEditor 3D software to automatically generate a 3D model of the rotor blade;

[0040] Step 4: Keep the constructed hub line and wheel cover line unchanged, widen the meridian blade leading and trailing edge profiles so that their width matches the blade axial height, and construct a single flow channel;

[0041] Step 5: Performing a three-dimensional calculation using the three-dimensional model of the moving blade. If the calculated flow distribution and pressure distribution data match the performance curve characteristics calculated in step 1, it proves that the blade parameter extraction is successful. If not, return to step 2 to step 5 until the flow distribution and pressure distribution data match the performance curve characteristics.

[0042] Step 6: Using the extracted blade parameters as they change with the blade relative thickness M%, the advanced Latin hypercube method is used to establish a sample space within the restricted range. The output value of the established sample data is solved. Based on the extracted control points of the blade hub and wheel cover, the model is re-imported into the BladeEditor 3D software to automatically generate a 3D model of the rotor blade and establish a metamodel.

[0043] Step seven: Based on the meta-model, the optimal structural parameters are used as the initial values ​​for genetic algorithm optimization. The response surface model is applied and the optimization module is used to perform genetic algorithm multi-objective optimization. The fan static pressure efficiency is improved while reducing the fan power, thereby improving the aerodynamic performance of the fan and increasing the heat dissipation of the charging pile.

[0044] In a preferred embodiment of the method for optimizing blade parameters of an axial flow fan in a charging pile based on a genetic algorithm, the full three-dimensional modeling software is Fluent, and the blade modeling software is BladeEditor.

[0045] In a preferred embodiment of the method for optimizing the blade parameters of an axial flow fan for a charging pile based on a genetic algorithm, one blade number is retained in the hub line and the wheel cover line drawn in the meridian plane, the axial chord length remains unchanged, the leading edge is outlined using a square section, and the trailing edge is outlined using a section with an ellipse ratio of 1000.

[0046] In a preferred embodiment of the method for optimizing the blade parameters of a charging pile axial flow fan based on a genetic algorithm, in step 2, the hub and wheel cover lines formed by the stacked lines of the blade contours are lines obtained by offsetting the blade root by 3% relative to the center axis of the hub and the blade tip by 3% relative to the center axis of the wheel cover.

[0047] In the preferred embodiment of the method for optimizing the parameters of the axial flow fan blades of the charging pile based on the genetic algorithm, in step six, the relative thickness of the blades , which is the percentage of the blade thickness at a certain position relative to the chord length, where x is the distance from the leading edge to a specific position on the blade; t(x) is the thickness of the blade at position x; and C is the chord length of the blade.

[0048] In a preferred embodiment of the method for optimizing the blade parameters of the charging pile axial flow fan based on a genetic algorithm, in step six, the extracted blade parameters include: blade angle at 0% of the blade hub, blade angle at 25% of the blade hub, blade angle at 50% of the blade hub, blade angle at 75% of the blade hub, blade angle at 100% of the blade hub, blade normal layer thickness at 0% of the blade hub, blade normal layer thickness at 25% of the blade hub, blade normal layer thickness at 50% of the blade hub, blade normal layer thickness at 75% of the blade hub There are 20 blade parameters including normal layer thickness, blade normal layer thickness at 100% of blade hub, blade angle at 0% of blade wheel shroud, blade angle at 25% of blade wheel shroud, blade angle at 50% of blade wheel shroud, blade angle at 75% of blade wheel shroud, blade angle at 100% of blade wheel shroud, blade normal layer thickness at 0% of blade wheel shroud, blade normal layer thickness at 25% of blade wheel shroud, blade normal layer thickness at 50% of blade wheel shroud, blade normal layer thickness at 75% of blade wheel shroud and blade normal layer thickness at 100% of blade wheel shroud.

[0049] In a preferred embodiment of the method for optimizing the blade parameters of an axial flow fan in a charging pile based on a genetic algorithm, in step six, the sample output value to be solved refers to the static pressure efficiency and power of the fan.

[0050] In the preferred embodiment of the method for optimizing the parameters of the axial flow fan blades of the charging pile based on the genetic algorithm, in step 6, the meta-model evaluation criteria are: prognosis coefficient To evaluate the quality of the established response surface model; is the sum of squared prediction errors, which are estimated based on cross-validation; is the total change, .

[0051] In a preferred embodiment of the method for optimizing the parameters of the axial flow fan blades of a charging pile based on a genetic algorithm, in step seven, the objective function is to minimize the fan power and maximize the static pressure efficiency.

[0052] In a preferred embodiment of the method for optimizing blade parameters of an axial flow fan of a charging pile based on a genetic algorithm, the axial flow fan of the charging pile is arranged in a charging station.

[0053] In one embodiment, Figure 1As shown in the figure, a parameter optimization process of the axial flow fan blade of a charging pile based on a genetic algorithm includes: based on the Bezier curve, the three-dimensional blade shape, chord length, and installation angle of the fan blade are parametrically defined, and the overall structural parameters of the fan such as the tip and root diameter and the number of blades are added to obtain 20 blade parameters. After obtaining the three-dimensional coordinate points, they are imported into BladeEditor for surface and solid modeling, and finally the hub, wheel cover and blade solids are merged to complete the modeling; within the established sample space, the optimal Latin hypercube test method is used to collect sample points to provide sufficient learning samples for establishing a response surface model, and parameter sensitivity analysis is performed to filter out parameters with little influence on the target parameters to narrow the subsequent optimization space and improve the calculation speed; a genetic algorithm is used to perform multi-objective optimization with fan power and fan static pressure efficiency as target parameters to obtain the Pareto optimal solution set;

[0054] In one embodiment, Figure 2 As shown, constructing a single flow channel of a blade includes: extracting a three-dimensional model of blade 2, outlining the meridian flow channel hub profile 3 and wheel cover profile 4 of blade 2 on the global ZX plane, closing the meridian flow channel with inlet 1 and outlet 5 in the same ZX plane, fitting the blade hub and wheel cover profiles with a 5th-order Bezier curve, and fitting the blade installation angle distribution curve with a 3rd-order Bezier curve for blade parameterized control;

[0055] In one embodiment, Figure 3 As shown in the figure, the space control points of the hub and wheel cover profile of the three-dimensional blade model are extracted. The profiles of the wheel cover and the axle are generated along the stacking line to generate a three-dimensional blade. The leading and trailing edges of the blade are depicted by an ellipse with a curvature of 1000.

[0056] In one embodiment, Figure 4 As shown in the figure, for multi-objective optimization problems, the various objectives are generally conflicting. While improving one sub-objective, another may be reduced. The Pareto optimal solution set established based on NSGA-II multi-objective optimization requires a coordinated trade-off and compromise between the two optimization objectives for these design points based on actual needs. The Pareto frontier non-inferior solution B that meets the requirements is selected. Table 1 below compares the selected optimal solution with the initial value, showing a 5.9% decrease in static pressure efficiency and a 25.8% reduction in required power. This demonstrates that the proposed approximate model optimization method and process have important engineering application value.

[0057] Table 1: Pareto optimal solution set

[0058] Fan power (P / W) Static pressure efficiency Original Model 55.585888 0.34050566 Optimized model 41.2308 0.320366

[0059] In one embodiment, Figure 5 As shown in the figure, by comparing the blade angles of the optimized model and the original model, it can be clearly seen that the hub part close to the trailing edge of the blade and the wheel cover part close to the leading edge have a great influence on the aerodynamic performance of the blade;

[0060] In one embodiment, Figure 6 The figure shows a comparison of the three-dimensional models before and after fan optimization. By changing the blade center arc line, blade thickness and blade angle distribution at the blade hub and wheel cover, the fan power can be significantly reduced, laying the foundation for the rapid optimization design of the fan.

[0061] Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields. The above-mentioned specific embodiments are merely illustrative and instructive, and are not restrictive. A person skilled in the art, guided by this specification and without departing from the scope of protection of the claims of the present invention, may also devise various forms, all of which fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the blade parameters of an axial flow fan in a charging pile based on a genetic algorithm, characterized in that: The steps include: Step 1: Import the axial flow fan blade structure of the charging pile into the full 3D modeling software to extract the blade parameters. The fan rotation axis is the global Z axis in the 3D coordinate system. The end face of the blade close to the rotation axis and the end face close to the fan casing are extracted. The extracted end face is rotated and cut into four parts using the global YZ and ZX planes respectively. The hub and wheel cover rotation surface of the global XY positive half axis is retained. A new sketch is created facing the global ZX plane. The line mapped on the new sketch of the retained hub and wheel cover rotation surface is extracted as the hub and wheel cover line. At the same time, the performance curve of the existing fan structure is calculated and verified by experiments. Step 2: Based on the blade structure parameters of the charging pile axial flow fan blade structure, use the blade modeling software and adopt the following blade shaping method: draw the hub line and wheel cover line in the meridian plane, and use Bezier curve to draw the hub line and wheel cover line in the meridian plane. Construct the stacking line of the blade profile to generate the pressure surface and suction surface profiles at the blade hub and wheel cover sections, where P(t) is the coordinate of any point of the parameterized Bezier curve; n is the order of the parameterized Bezier curve; i is the current order, i=0, 1, 2, ...; P i is the position vector of each vertex; B i,n (t) is the Bernstein basis function, ; t is the curve parameter variable, ; Step 3: Based on the extracted control points of the blade hub and wheel cover, re-import the model into the BladeEditor 3D software to automatically generate a 3D model of the rotor blade; Step 4: Keep the constructed hub line and wheel cover line unchanged, widen the meridian blade leading and trailing edge profiles so that their width matches the blade axial height, and construct a single flow channel; Step 5: Performing a three-dimensional calculation using the three-dimensional model of the moving blade. If the calculated flow distribution and pressure distribution data match the performance curve characteristics calculated in step 1, it proves that the blade parameter extraction is successful. If not, return to step 2 to step 5 until the flow distribution and pressure distribution data match the performance curve characteristics. Step 6: Using the extracted blade parameters as they change with the blade relative thickness M%, the advanced Latin hypercube method is used to establish a sample space within the restricted range. The output value of the established sample data is solved. Based on the extracted control points of the blade hub and wheel cover, the model is re-imported into the BladeEditor 3D software to automatically generate a 3D model of the rotor blade and establish a metamodel. Step seven: Based on the meta-model, the optimal structural parameters are used as the initial values ​​for genetic algorithm optimization. The response surface model is applied and the optimization module is used to perform genetic algorithm multi-objective optimization. The fan static pressure efficiency is improved while reducing the fan power, thereby improving the aerodynamic performance of the fan and increasing the heat dissipation of the charging pile.

2. The method for optimizing the blade parameters of an axial flow fan of a charging pile based on a genetic algorithm according to claim 1, characterized in that: The full 3D modeling software is Fluent, and the blade modeling software is BladeEditor.

3. The method for optimizing blade parameters of an axial flow fan for a charging pile based on a genetic algorithm according to claim 1, characterized in that: When drawing the hub line and wheel cover line in the meridian plane, the number of blades is retained, the axial chord length remains unchanged, the leading edge is outlined using a square section, and the trailing edge is outlined using a section with an ellipse ratio of 1000.

4. The method for optimizing blade parameters of an axial flow fan for a charging pile based on a genetic algorithm according to claim 1, characterized in that: In step 2, the hub shroud lines formed by the blade contour stacking lines are lines obtained by offsetting the blade root by 3% relative to the hub center axis and the blade tip by 3% relative to the shroud center axis.

5. The method for optimizing blade parameters of an axial flow fan for a charging pile based on a genetic algorithm according to claim 1, characterized in that: In step 6, the relative thickness of the blade , which is the percentage of the blade thickness at a certain position relative to the chord length, where x is the distance from the leading edge to a specific position on the blade; t(x) is the thickness of the blade at position x; and C is the chord length of the blade.

6. The method for optimizing blade parameters of an axial flow fan for a charging pile based on a genetic algorithm according to claim 1, characterized in that: In step six, the extracted blade parameters include: blade angle at 0% of the blade hub, blade angle at 25% of the blade hub, blade angle at 50% of the blade hub, blade angle at 75% of the blade hub, blade angle at 100% of the blade hub, blade normal layer thickness at 0% of the blade hub, blade normal layer thickness at 25% of the blade hub, blade normal layer thickness at 50% of the blade hub, blade normal layer thickness at 75% of the blade hub, blade normal layer thickness at 100% of the blade hub, blade angle at 0% of the blade shroud, blade angle at 25% of the blade shroud, blade angle at 50% of the blade shroud, blade angle at 75% of the blade shroud, blade angle at 100% of the blade hub, blade normal layer thickness at 0% of the blade shroud, blade normal layer thickness at 25% of the blade shroud, blade normal layer thickness at 50% of the blade shroud, blade normal layer thickness at 75% of the blade shroud, blade angle at 100% of the blade hub, blade normal layer thickness at 0% of the blade shroud, blade normal layer thickness at 25% of the blade shroud, blade normal layer thickness at 50% of the blade shroud, blade normal layer thickness at 75% of the blade shroud, and blade normal layer thickness at 100% of the blade shroud.

7. The method for optimizing the blade parameters of an axial flow fan of a charging pile based on a genetic algorithm according to claim 1 is characterized in that ,In step six, the sample output value solved refers to the fan static pressure efficiency and power.

8. The method for optimizing the blade parameters of an axial flow fan of a charging pile based on a genetic algorithm according to claim 1 is characterized in that ,In step six, the meta-model evaluation criteria are: prognostic coefficient To evaluate the quality of the established response surface model; is the sum of squared prediction errors, which are estimated based on cross-validation; is the total change, .

9. The method for optimizing the blade parameters of an axial flow fan of a charging pile based on a genetic algorithm according to claim 1 is characterized in that ,In step seven, the objective function is to minimize the fan power and maximize the static pressure efficiency.

10. The method for optimizing blade parameters of an axial flow fan of a charging pile based on a genetic algorithm according to claim 1, characterized in that: The charging pile axial flow fan is installed in the charging station.

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