Inducer efficient anti-cavitation numerical optimization method based on blade reverse parameterization

Abstract

The invention discloses an inducer efficient anti-cavitation numerical optimization method based on blade reverse parameterization, and relates to the technical field of optimization design of inducers for pumps, the inducer is designed and machined through five-axis linkage numerical control, and meanwhile, an inducer visual cavitation test testbed is built; hydraulic performance and cavitation erosion tests are carried out through a test bed, and curves of lift, efficiency, net positive suction head and cavitation erosion area changing along with time are obtained; finding an inducer turbulence model, an inducer cavitation model and a cavitation erosion calculation model with the minimum calculation error; carrying out reverse parametric modeling on the inducer blade; building an inducer parameterized intelligent computing platform based on modeling; screening main geometric parameters of the inducer; optimizing the inducer based on the turbulence model, the cavitation model, the cavitation erosion model and the intelligent computing platform; and performing hydraulic performance test and cavitation erosion test verification on the optimized inducer. The anti-cavitation performance of the inducer can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of pump inducer optimization design, and more particularly to an inducer high-efficiency anti-cavitation numerical optimization method based on blade inverse parameterization. Background Art

[0002] Cavitation is an inevitable hydrodynamic phenomenon during the operation of pump machinery. When cavitation develops to a certain extent, the bubbles in the pump will continue to collapse, causing erosion and damage to pump components. In order to reduce and suppress the adverse effects of cavitation, adding an inducer before the pump inlet has become an important means to improve the anti-cavitation performance of the pump. The inducer accelerates and pressurizes the fluid in advance at the pump inlet, increases the fluid pressure, thereby reducing the necessary cavitation margin of the pump and alleviating the cavitation phenomenon.

[0003] Although the inducer can effectively improve the cavitation in the pump, as a cavitation compensation component, it is difficult for it to completely avoid cavitation and cavitation, and the degree of cavitation directly affects the service life of the inducer. However, the current optimization design of the inducer is mostly focused on the cavitation problem, and there are few studies involving cavitation optimization. Moreover, most of the optimizations are to optimize the structural parameters of the inducer by permutation and combination, which greatly increases the optimization research cycle and cost. Therefore, it is of great significance to carry out automatic joint parameterization of the anti-cavitation performance optimization design of the inducer for the long-term stable operation of the pump.

[0004] Therefore, how to develop an efficient anti-cavitation numerical optimization method for inducer based on blade inverse parameterization, effectively shorten the test and optimization cycle, deal with the inducer cavitation problem, and improve the anti-cavitation performance of inducer is a key technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, the present invention provides a highly efficient anti-cavitation numerical optimization method for an inducer based on blade inverse parameterization to solve the problems existing in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A highly efficient anti-cavitation numerical optimization method for an inducer based on blade inverse parameterization, comprising:

[0008] Step 1: Design an inducer, and process the inducer by five-axis CNC machining, and build a visual cavitation test bench for the inducer;

[0009] Step 2: Conduct hydraulic performance and cavitation tests on the inducer by using the inducer visualization cavitation test bench to obtain the variation curves of the head, efficiency, NPSH and cavitation area of ​​the inducer over time under rated conditions;

[0010] Step 3: Based on the head and efficiency, determine the inducer turbulence model with the smallest calculation error; based on the NPSH, determine the inducer cavitation model with the smallest calculation error; based on the change curve, determine the cavitation calculation model with the smallest calculation error;

[0011] Step 4: performing reverse parameterized modeling of the inducer blades of the inducer;

[0012] Step 5: Building an inducer parameterized intelligent computing platform based on the reverse parameterized modeling;

[0013] Step 6: Perform sensitivity analysis on the geometric parameters of the inducer to obtain the main geometric parameters whose influence weights on the cavitation area and hydraulic performance are greater than a preset influence weight threshold;

[0014] Step 7: Based on the turbulence model, cavitation model and cavitation erosion model, with the lift as a constraint and the main geometric parameters as design variables, the inducer is optimized based on the inducer parameterized intelligent computing platform;

[0015] Step 8: The optimized inducer is processed by five-axis CNC machining, and the hydraulic performance test and cavitation test are carried out to verify the optimized inducer.

[0016] Preferably, the cavitation calculation model with the smallest calculation error is determined, specifically comprising: based on the change curve, respectively using the intensity function method, the time-averaged cavitation erosion estimation method, the grayscale method, and the energy method to calculate the cavitation area of ​​the inducer under the rated working condition, and obtaining the cavitation model with the smallest cavitation calculation error; locally encrypting the near wall surface of the inducer blade so that the blade surface y + The value is between 1 and 10, analyzing the blade surface y + The influence of the value on the cavitation area of ​​the inducer is used to obtain the inducer blade surface y with the minimum cavitation calculation error. + value.

[0017] Preferably, the reverse parametric modeling specifically includes: identifying the meridian contour and blade profile of the inducer based on CFturbo or BladeEditor, adjusting the blade angle, center line, contour, leading edge and trailing edge of the inducer, and preliminarily reverse modeling the inducer blade; performing numerical calculations on the inducer obtained by the preliminary reverse modeling, and comparing the simulation results of the inducer head and hydraulic efficiency with the calculation results of the inducer in step 1; if the errors are all less than the calculation error threshold, the inducer model currently obtained by reverse engineering meets the requirements; if the errors are greater than the calculation error threshold, adjusting the inducer blade angle, center line, contour, leading edge and trailing edge until the errors are all less than the preset calculation error threshold, thereby realizing the reverse parametric modeling of the inducer.

[0018] Preferably, building an inducer parametric intelligent computing platform based on the reverse parametric modeling specifically includes: obtaining basic parameters of the inducer according to the reverse parametric modeling; meshing the inducer model according to the basic parameters, and encrypting the inducer blades and blade clearances; performing numerical calculations on the inducer obtained by the reverse parametric modeling based on the turbulence model and the cavitation model; modifying the inducer parameters by the calculated values ​​and updating the mesh and calculation results, so as to realize automatic parametric calculation of the inducer.

[0019] Preferably, the sensitivity analysis specifically includes: any one of principal component analysis, FAST method, Latin hypercube sampling method or response surface analysis method.

[0020] Preferably, the main geometric parameters include: rim inlet blade angle, rim outlet blade angle, blade sweep angle, blade wrap angle, and number of blades.

[0021] Preferably, the induction wheel optimization specifically includes: sampling the variable design space by Latin hypercube sampling method or orthogonal test sampling method to obtain sample points; performing three-dimensional modeling, fluid domain meshing, flow field calculation and extracting calculation results according to the sample points; establishing a proxy model for the sample points and the calculation results, and optimizing the induction wheel parameters by intelligent optimization algorithm until the highest hydraulic efficiency and the smallest cavitation area are met; or the highest hydraulic efficiency, the lowest cavitation margin and the smallest cavitation area are met, and the optimization ends.

[0022] Preferably, the head constraint specifically includes: the inducer head is within ±1%.

[0023] Preferably, the intelligent optimization algorithm specifically includes any one of a particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm, a neural network algorithm or a differential evolution algorithm.

[0024] Preferably, in the variable design space, the preset value ranges of the geometric parameters are specifically: rim inlet blade angle 5-12°; rim outlet blade angle 5-12°; blade sweep angle 70-150°; blade wrap angle 300-370°; and the number of blades is 2-4.

[0025] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a highly efficient numerical optimization method for inducer wheel anti-cavitation based on blade inverse parameterization, which greatly shortens the test and optimization cycle and greatly improves the optimization efficiency and design level of the inducer wheel. It can effectively target the cavitation phenomenon of the inducer wheel, improve the anti-cavitation performance of the inducer wheel, and take into account the hydraulic performance on the basis of optimizing the anti-cavitation performance of the inducer wheel, which is of great significance for the long-term stable operation of the pump. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0027] Figure 1 A schematic diagram of the method provided by the present invention;

[0028] Figure 2 Comparison of numerical calculation results of cavitation area between the original model and the optimized model in the specific embodiment provided by the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] The embodiment of the present invention discloses a method for numerically optimizing an inducer wheel for high efficiency anti-cavitation based on blade inverse parameterization. Figure 1 As shown, including:

[0031] Step 1: Design the inducer based on the traditional inducer design method, and process the inducer through five-axis CNC machining, and build a visual cavitation test bench for the inducer;

[0032] Step 2: Conduct hydraulic performance and cavitation tests on the inducer using the inducer visualization cavitation test bench to obtain the curves of the head, efficiency, NPSH and cavitation area of ​​the inducer over time under rated conditions;

[0033] Step 3: Based on the test values ​​of inducer head and efficiency under rated conditions, find the inducer turbulence model with the smallest calculation error; based on the test values ​​of inducer NPSH under rated conditions, find the inducer cavitation model with the smallest calculation error; based on the experimentally measured curve of the change of the inducer cavitation area over time under rated conditions, determine the cavitation calculation model with the smallest calculation error;

[0034] Step 4: Import the inducer structure designed in step 1 into CFturbo or BladeEditor to realize reverse parametric modeling of inducer blades;

[0035] Step 5: Integrate CFturbo or BladeEditor, Mesh or TurboGrid, CFX or Fluent, and optiSLang based on the Workbench platform, and build an inducer parametric intelligent computing platform based on reverse parametric modeling;

[0036] Step 6: Conduct sensitivity analysis on the geometric parameters of the inducer to obtain the main geometric parameters whose influence weights on the cavitation area and hydraulic performance are greater than the preset influence weight threshold (the influence weight threshold is 5%-10%);

[0037] Step 7: Based on the turbulence model, cavitation model and cavitation model, with the head as the constraint, the main geometric parameters as the design variables, and the highest hydraulic efficiency and the smallest cavitation area, or the highest hydraulic efficiency, the lowest NPSH and the smallest cavitation area as the goal, the inducer is optimized based on the inducer parameterized intelligent computing platform;

[0038] Step 8: The optimized inducer is processed by five-axis CNC machining, and the hydraulic performance test and cavitation test are carried out to verify the optimized inducer.

[0039] In a specific embodiment, the cavitation calculation model with the smallest calculation error is determined, specifically including: based on the change curve, using the intensity function method, the time-averaged cavitation erosion estimation method, the grayscale method, and the energy method to calculate the cavitation area of ​​the inducer under the rated working condition, and obtaining the cavitation model with the smallest cavitation calculation error; locally encrypting the near wall surface of the inducer blade so that the blade surface y + The value is between 1 and 10, analyzing the leaf surface y + The influence of the value on the cavitation area of ​​the inducer is used to obtain the inducer blade surface y with the minimum cavitation calculation error. + value.

[0040] In a specific embodiment, the reverse parametric modeling specifically includes: based on CFturbo or BladeEditor, identifying the meridian contour and blade profile of the inducer, adjusting the blade angle, center line, contour, leading edge and trailing edge of the inducer, and preliminarily reverse modeling the inducer blade; performing numerical calculations on the inducer obtained by the preliminary reverse modeling, and comparing the simulation results of the inducer head and hydraulic efficiency with the calculation results of the inducer in step 1. If the errors are all less than the calculation error threshold (the calculation error threshold is ±0.5%), the inducer model currently obtained by reverse engineering meets the requirements. If the errors are greater than the calculation error threshold, the inducer blade angle, center line, contour, leading edge and trailing edge are adjusted until the errors are all less than the preset calculation error threshold, thereby realizing the reverse parametric modeling of the inducer.

[0041] In a specific embodiment, building an inducer parametric intelligent computing platform based on reverse parametric modeling specifically includes: obtaining the inducer model by reverse engineering according to CFturbo or BladeEditor to obtain the basic parameter sizes of the inducer; meshing the inducer model according to the basic parameters, and focusing on encrypting the inducer blades and blade gaps; performing numerical calculations on the inducer obtained by reverse parametric modeling based on the turbulence model and the cavitation model; and modifying the inducer parameters in Workbench according to the calculated values ​​and updating the mesh and calculation results to achieve automatic parametric calculation of the inducer.

[0042] In a specific embodiment, the sensitivity analysis specifically includes: any one of principal component analysis, FAST method, Latin hypercube sampling method or response surface analysis method.

[0043] In a specific embodiment, the main geometric parameters include: rim inlet blade angle, rim outlet blade angle, blade sweep angle, blade wrap angle, and number of blades.

[0044] In a specific embodiment, optimizing the inducer specifically includes: sampling the variable design space through the Latin hypercube sampling method or the orthogonal test sampling method to obtain sample points; performing three-dimensional modeling, fluid domain meshing, flow field calculation and extracting calculation results according to the sample points; the calculation process calls the joint simulation platform under the Workbench environment to realize automatic parameterized simulation of the inducer; establishing an agent model for the sample points and their three-dimensional modeling, fluid domain meshing, flow field calculation, and extraction of calculation results, and optimizing the inducer parameters through the intelligent optimization algorithm until the hydraulic efficiency is the highest and the cavitation area is the smallest; or the hydraulic efficiency is the highest, the cavitation margin is the lowest, and the cavitation area is the smallest, then the optimization ends.

[0045] In a specific embodiment, taking the lift as a constraint specifically includes: the inducer lift is within ±1%.

[0046] In a specific embodiment, the intelligent optimization algorithm specifically includes any one of a particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm, a neural network algorithm or a differential evolution algorithm.

[0047] In a specific embodiment, in the variable design space, the preset value range of the geometric parameters is specifically: rim inlet blade angle 5~12°; rim outlet blade angle 5~12°; blade sweep angle 70~150°; blade wrap angle 300~370°; number of blades is 2~4.

[0048] Embodiment 1:

[0049] Embodiment 1 of the present invention discloses a specific application of a numerical optimization method for high-efficiency cavitation resistance of an inducer based on inverse parameterization of blades, as follows:

[0050] Application: A three-blade equal pitch inducer with a design flow rate of 45m 3 / h, the lift is 10m and the speed is 5000r / min.

[0051] Step 1: Design a three-blade equal-pitch inducer based on the traditional inducer design method, process the inducer through five-axis CNC machining, and build an inducer visualization cavitation test bench;

[0052] Step 2: Conduct hydraulic performance test and cavitation test on the inducer to obtain the head, efficiency, cavitation margin of the inducer under rated conditions, and the curve of the change of cavitation area over time.

[0053] The experimental results of the inducer under rated conditions are: head is 11.95m, efficiency is 43.15%, and critical cavitation margin is 0.37m.

[0054] Step 3: Based on the test results of the inducer under rated conditions in step 2, it was found that the turbulence model with the smallest experimental error in the numerical calculation of the inducer is the k-ω model; the cavitation model with the smallest experimental error in the numerical calculation of the inducer is the ZGB model; the cavitation model with the smallest experimental error in the numerical calculation of the inducer is the cavitation calculation model based on the grayscale method. The lift of the original inducer calculated based on the k-ω turbulence model and the ZGB cavitation model is 12.08m, the hydraulic efficiency is 43.86%, and the critical cavitation margin is 0.37m. The near-wall surface of the inducer blade was locally encrypted, and it was observed that the blade surface y + When the value is less than 10, the occurrence and development of cavitation can be accurately predicted. + The smaller the value, the closer the calculated inducer cavitation area is to the experimental value. However, considering the computing resources and speed, the blade surface y + The value is 8.

[0055] Step 4: Select to import the inducer structure into CFturbo to implement reverse parametric modeling of the inducer blades and obtain the specific geometric parameters of the inducer, as shown in Table 1:

[0056] Table 1 Geometric parameters

[0057]

[0058] Numerical calculation of the inducer obtained by reverse parametric modeling showed that the inducer head was 11.53m and the hydraulic efficiency was 44.37%. The calculation errors of the inducer model were within 0.5%. Therefore, it was determined that the inducer obtained by reverse engineering met the requirements.

[0059] Step 5: Based on the Workbench platform, CFturbo, Mesh and CFX are integrated to build an inducer parameterized intelligent computing platform. Mesh is used to mesh the inducer, and the number of meshes in the inducer is 7638704, with a mesh quality of more than 0.4. CFX is selected to perform numerical calculations on the inducer, the k-ω model is selected as the turbulence model, the ZGB cavitation model is used as the cavitation model, the total pressure inlet is 0.023MPa, and the outlet is the flow outlet, which is 45m 3 / h, the cavitation model adopts the cavitation calculation model based on the grayscale method. In Workbench, the parameters of the inducer are modified and the grid and calculation results are updated to realize the automatic parameterization calculation of the inducer.

[0060] Step 6: The Latin hypercube sampling method is used to conduct sensitivity analysis on the inducer parameters, and the number of samples is 80. The main geometric parameters with an influence weight greater than 10% on the inducer cavitation area and hydraulic performance are obtained, as shown in Table 2.

[0061] Table 2 Main geometric parameters

[0062]

[0063] Step 7: Taking the main geometric parameters as optimization variables, minimizing the cavitation area and maximizing the hydraulic efficiency of the inducer as the optimization objectives, numerically optimizing the inducer using a global optimization algorithm based on the preset value range of the geometric parameters and the optimization constraints. In Example 1 of the present invention, the optimization constraint limits the head to not less than 10m, and the selected global optimization algorithm is a genetic algorithm.

[0064] The parameters of the original model and the optimized model are shown in Table 3. The cavitation area of ​​the original model and the optimized model are compared. Figure 2 As shown, the cavitation area of ​​the optimized model is reduced by 18.64% compared with the original model, which proves the superiority of the present invention.

[0065] Table 3 Parameters of the original model and the optimized model

[0066]

[0067] Step 8: The optimized inducer is subjected to five-axis CNC machining, and the hydraulic performance test and cavitation test are performed on the optimized inducer. The test results show that the lift of the inducer is 11.87m and the efficiency is 45.36%. The cavitation area of ​​the optimized model is reduced by 13.81% compared with the original model, and the cavitation areas of the three blades are all smaller than the cavitation areas of the original model, which proves the excellence of the present invention.

[0068] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0069] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A highly efficient anti-cavitation numerical optimization method for inducer based on blade inverse parameterization, characterized in that: include: Step 1: Design an inducer, and process the inducer by five-axis CNC machining, and build a visual cavitation test bench for the inducer; Step 2: Conduct hydraulic performance and cavitation tests on the inducer by using the inducer visualization cavitation test bench to obtain the variation curves of the head, efficiency, NPSH and cavitation area of ​​the inducer over time under rated conditions; Step 3: Based on the head and efficiency, determine the inducer turbulence model with the smallest calculation error; based on the NPSH, determine the inducer cavitation model with the smallest calculation error; based on the change curve, determine the cavitation calculation model with the smallest calculation error; Step 4: performing reverse parameterized modeling of the inducer blades of the inducer; Step 5: Building an inducer parameterized intelligent computing platform based on the reverse parameterized modeling; Step 6: Perform sensitivity analysis on the geometric parameters of the inducer to obtain the main geometric parameters whose influence weights on the cavitation area and hydraulic performance are greater than a preset influence weight threshold; Step 7: Based on the turbulence model, cavitation model and cavitation erosion model, with the lift as a constraint and the main geometric parameters as design variables, the inducer is optimized based on the inducer parameterized intelligent computing platform; Step 8: The optimized inducer is processed by five-axis CNC machining, and the hydraulic performance test and cavitation test are carried out to verify the optimized inducer.

2. According to claim 1, a method for numerical optimization of inducer high-efficiency anti-cavitation based on blade inverse parameterization is characterized in that: The method of determining the cavitation calculation model with the smallest calculation error specifically includes: based on the change curve, respectively using the intensity function method, the time-averaged cavitation erosion estimation method, the grayscale method, and the energy method to calculate the cavitation area of ​​the inducer under the rated working condition, and obtaining the cavitation model with the smallest cavitation calculation error; locally encrypting the near wall surface of the inducer blade so that the blade surface y + The value is between 1 and 10, analyzing the blade surface y + The influence of the value on the cavitation area of ​​the inducer is used to obtain the inducer blade surface y with the minimum cavitation calculation error. + value.

3. The method for numerical optimization of inducer wheel high efficiency anti-cavitation based on blade inverse parameterization according to claim 1 is characterized in that: The reverse parametric modeling specifically includes: based on CFturbo or BladeEditor, identifying the meridian contour and blade profile of the inducer, adjusting the blade angle, center line, contour, leading edge and trailing edge of the inducer, and preliminarily reverse modeling the inducer blade; performing numerical calculation on the inducer obtained by the preliminary reverse modeling, and comparing the simulation results of the inducer head and hydraulic efficiency with the calculation results of the inducer in step 1. If the errors are all less than the calculation error threshold, the inducer model currently obtained by reverse engineering meets the requirements. If the errors are greater than the calculation error threshold, adjusting the inducer blade angle, center line, contour, leading edge and trailing edge until the errors are all less than the preset calculation error threshold, thereby realizing the reverse parametric modeling of the inducer.

4. The method for numerical optimization of inducer wheel high efficiency anti-cavitation based on blade inverse parameterization according to claim 1 is characterized in that: The method of building an inducer parametric intelligent computing platform based on the reverse parametric modeling specifically includes: obtaining basic parameters of the inducer according to the reverse parametric modeling; meshing the inducer model according to the basic parameters, and encrypting the inducer blades and blade gaps; performing numerical calculations on the inducer obtained by the reverse parametric modeling based on the turbulence model and the cavitation model; modifying the inducer parameters by the calculated values ​​and updating the mesh and calculation results, so as to realize automatic parametric calculation of the inducer.

5. The method for numerical optimization of inducer high-efficiency anti-cavitation based on blade inverse parameterization according to claim 1 is characterized in that: The sensitivity analysis specifically includes: any one of principal component analysis, FAST, Latin hypercube sampling or response surface analysis.

6. The method for numerical optimization of inducer wheel high efficiency anti-cavitation based on blade inverse parameterization according to claim 1 is characterized in that: The main geometric parameters include: rim inlet blade angle, rim outlet blade angle, blade sweep angle, blade wrap angle, and blade number.

7. The method for numerical optimization of inducer high-efficiency anti-cavitation based on blade inverse parameterization according to claim 1 is characterized in that: The induction wheel optimization specifically includes: sampling the variable design space by Latin hypercube sampling method or orthogonal test sampling method to obtain sample points; performing three-dimensional modeling, fluid domain meshing, flow field calculation and extracting calculation results according to the sample points; establishing a proxy model for the sample points and the calculation results, and optimizing the induction wheel parameters by intelligent optimization algorithm until the highest hydraulic efficiency and the smallest cavitation area are met; or the highest hydraulic efficiency, the lowest cavitation margin and the smallest cavitation area are met, then the optimization ends.

8. The method for numerical optimization of inducer high-efficiency anti-cavitation based on blade inverse parameterization according to claim 1 is characterized in that: The head constraint specifically includes: the inducer head is within ±1%.

9. The method for numerical optimization of inducer wheel high efficiency anti-cavitation based on blade inverse parameterization according to claim 7 is characterized in that: The intelligent optimization algorithm specifically includes any one of a particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm, a neural network algorithm or a differential evolution algorithm.

10. The method for numerical optimization of inducer high-efficiency anti-cavitation based on blade inverse parameterization according to claim 7, characterized in that: In the variable design space, the preset value ranges of the geometric parameters are specifically: rim inlet blade angle 5-12°; rim outlet blade angle 5-12°; blade sweep angle 70-150°; blade wrap angle 300-370°; and the number of blades is 2-4.

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