Optimization design method and system for parametric modeling of centrifugal pump runner blade

Abstract

The invention belongs to the field of water pump runner blade design, and particularly relates to a centrifugal pump runner blade parameterization modeling and optimizing method and system.The method comprises the steps that firstly, a runner three-dimensional initial model is built according to parameters such as the inlet diameter and the outlet diameter of a runner in combination with a traditional unary theory design method; secondly, flow paths of an inlet, an outlet and front and rear cover plates of the section of a runner shaft are extracted, multiple streamlines on the runner blades are extracted at equal intervals, multiple control curves of the front and rear cover plates of the runner, blade bone lines, thickness and the like are expressed through a Bezier fitting line method, and a parameterized model of the runner blades is constructed; establishing a numerical model, and performing analog calculation to obtain multiple groups of runner external characteristics; and finally, performing iterative optimization in combination with a multi-target genetic algorithm to obtain an optimal solution. According to the method, the hydraulic optimization process of the rotating wheel is accelerated, and technical reference is provided for external characteristic optimization of the centrifugal pump.

Description

Technical Field

[0001] The invention belongs to the technical field of water pump impeller blade design, and specifically designs an optimization design method and a system for parametric modeling of centrifugal pump impeller blades. Background Art

[0002] Centrifugal pumps are a type of liquid conveying equipment widely used in various industries. They play an irreplaceable role in fluid transportation and pressure increase. With the continuous development of science and technology and the implementation of the "dual carbon goals", the requirements for pump efficiency and stability are gradually increasing. For small flow, large flow and optimal operating conditions, the impeller energy performance needs to have a wider operating range. Therefore, it is particularly important to optimize the structure of the centrifugal pump impeller by combining computational fluid dynamics (CFD) simulation technology, conduct in-depth research on the internal flow laws of the pump, and improve the design of the centrifugal pump impeller to improve the efficiency of the centrifugal pump and maximize economic benefits.

[0003] The blade is one of the most critical parts of the impeller. Its shape will directly affect the flow state of the fluid, thereby affecting the efficiency of the impeller. By reasonably selecting the design parameter range and matching it with a scientific optimization process, the performance of the optimized impeller can often be greatly improved. However, in the design of twisted blades of centrifugal pump impellers, the univariate theory is still widely used. There is a lot of tedious three-dimensional modeling work in the design process, which cannot achieve rapid development and optimization design of the product. In addition, there are many blade geometry variable parameters, which cannot meet the problem of obtaining the optimal model. Summary of the invention

[0004] The present invention provides an optimization design method for parametric modeling of centrifugal pump impeller blades. In the design process, there is a lot of tedious three-dimensional modeling work, which cannot achieve rapid development and optimization design of products, and the blades have many geometric variable parameters and cannot meet the problem of obtaining the optimal model.

[0005] The invention provides an optimization design system for parametric modeling of centrifugal pump runner blades, which is used to realize an optimization design method for parametric modeling of centrifugal pump runner blades.

[0006] The present invention is achieved through the following technical solutions:

[0007] An optimization design method for parametric modeling of centrifugal pump runner blades, the method comprising the following steps:

[0008] Step 1: Establish the centrifugal pump runner flow domain model;

[0009] Step 2: parameterize the centrifugal pump impeller model established in step 1;

[0010] Step 3: Mesh the centrifugal pump impeller model parameterized in step 2;

[0011] Step 4: Evaluate the external characteristic curves of head H and efficiency η based on the grid divided in step 3;

[0012] Step 5: Based on the evaluation results of step 4, a multi-objective genetic algorithm is used for iterative optimization to achieve parametric modeling and optimal design of centrifugal pump impeller blades.

[0013] Furthermore, the step 1 is specifically as follows: the inlet diameter D1, outlet diameter D2 and outlet width b of the centrifugal pump impeller are taken as initial design parameters, a 3D model of the impeller is drawn by 3D modeling software using a univariate theoretical design method, and the impeller flow domain model is obtained by Boolean operation.

[0014] Furthermore, the step 2 is specifically to establish the flow paths of the inlet, outlet, front and rear cover plates of the runner shaft section, extract multiple streamlines on the runner blades at equal intervals, including the front cover plate line and the rear cover plate line, generate streamline data points, import the streamline data points into the three-dimensional modeling software, and use the Bezier fitting line method to express multiple control curves of the front and rear cover plates of the runner, the blade bone line and the thickness, so as to realize the automatic update of the parametric model of the runner blades.

[0015] Furthermore, the step 3 specifically includes generating a runner model sample library, performing structured grid division based on different runner flow field models, and obtaining and extracting the runner head curve Ht and efficiency curve η-t through numerical simulation calculation;

[0016] The wheel model design parameter sample is obtained by optimizing Latin hypercube sampling.

[0017] Furthermore, the step 4 is specifically as follows: setting the initial population size to 100 and stipulating the maximum number of iterations to 100, the crossover probability of independent individuals to 0.9, specifying the crossover distribution index to 10, retaining the default values ​​of the other parameters, and calculating the corresponding head H and efficiency η for each individual through CFD, considering the combination of head H and efficiency η to find the best balance between the two; the CFD calculation and analysis includes solving the RANS kw SST model, setting the convergence accuracy and the number of iterations, and numerically simulating the three-dimensional model through the expression expression.

[0018] Furthermore, the step 5 is specifically as follows: the inertia weight w is gradually reduced from 0.9 to 0.4, and the global search is switched to a more detailed local search. The learning factor c is dynamically adjusted according to the number of iterations to ensure a gradual transition from global exploration to local fine search; the fitness function is calculated based on H(t) and η(t) of each design, and non-dominated sorting and crowding distance d calculation are supported to promote the diversity and quality of solutions; a new generation is generated through selection, crossover and mutation operations, and it is continuously iterated until the termination condition is met.

[0019] Furthermore, the model is a Kriging proxy model, which is used to perform high-precision interpolation operation optimization design;

[0020] By giving the same weight coefficient to individual head and efficiency, the theoretical optimal solution is explored as the optimization scheme and verified. Finally, the single-channel runner igs format file can be obtained, and the complete runner 3D model can be obtained by rotating and copying it in the 3D modeling software.

[0021] An optimization design system for parametric modeling of centrifugal pump runner blades, the system uses the above-mentioned optimization design method for parametric modeling of centrifugal pump runner blades, and the system includes:

[0022] Model building module: building a centrifugal pump runner flow field model; parameterizing the built centrifugal pump runner model;

[0023] Meshing module: Meshing the parameterized centrifugal pump impeller model;

[0024] External characteristic curve evaluation module: external characteristic curve evaluation of head H and efficiency η based on divided grids;

[0025] Iterative optimization module: Based on the evaluation results, multi-objective genetic algorithm iterative optimization is performed to achieve parametric modeling and optimal design of centrifugal pump impeller blades.

[0026] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the above method is implemented.

[0027] A computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0028] The beneficial effects of the present invention are:

[0029] The invention improves the efficiency of centrifugal pump impeller design, reduces development costs, and shortens the development cycle.

[0030] The present invention can instantly adjust the three-dimensional hydraulic model of the centrifugal pump impeller according to a given input parameter range, thereby accelerating the simulation optimization rate.

[0031] The present invention adopts Bezier fitting curves to express control curves such as front and rear cover plates, blade skeleton, thickness, etc. in the twisted blades of the impeller, finely controls its three-dimensional shape, and obtains an optimized runner model by a multi-objective genetic algorithm.

[0032] The present invention realizes the parametric design of the impeller, the main flow-through component of the centrifugal pump, through curve parameterization, which will greatly improve the design efficiency, shorten the development cycle and reduce the design cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of the method of the present invention.

[0034] Figure 2 It is a schematic diagram of the shaft section and streamline distribution of the runner blade of the present invention.

[0035] Figure 3 It is a schematic diagram of Bezier curve control points of the front and rear cover plates of the present invention.

[0036] Figure 4 It is a schematic diagram of the control points of the Bezier curve of the runner blade thickness of the present invention.

[0037] Figure 5 This is a schematic diagram of the changing rules of the front and rear cover plates of the optimized centrifugal pump impeller of the present invention. DETAILED DESCRIPTION

[0038] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0039] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0040] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0041] The following is attached to this application specification Figure 1-5 , the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0043] This embodiment 1

[0044] This embodiment refers to Figure 1 Provided is a method for optimizing the design of parametric modeling of centrifugal pump runner blades, the method comprising the following steps:

[0045] Step 1: Establish the centrifugal pump runner flow domain model;

[0046] Step 2: parameterize the centrifugal pump impeller model established in step 1;

[0047] Step 3: Mesh the centrifugal pump impeller model parameterized in step 2;

[0048] Step 4: Evaluate the external characteristic curves of head H and efficiency η based on the grid divided in step 3;

[0049] Step 5: Based on the evaluation results of step 4, a multi-objective genetic algorithm is used for iterative optimization to achieve parametric modeling and optimal design of centrifugal pump impeller blades.

[0050] Furthermore, the step 1 is specifically to use the initial design parameters of the centrifugal pump impeller, such as the inlet diameter D1, the outlet diameter D2, and the outlet width b, to draw a three-dimensional model of the impeller using a univariate theoretical design method through three-dimensional modeling software, and obtain the impeller flow field model through Boolean operations.

[0051] Furthermore, the step 2 is specifically to establish the flow paths of the inlet, outlet, front and rear cover plates of the runner shaft section, extract multiple streamlines on the runner blades at equal intervals, including the front cover plate line (Span=1.0) and the rear cover plate line (Span=0.0), generate streamline data points, import the streamline data points into the three-dimensional modeling software, and use the Bezier fitting line method to express multiple control curves such as the front and rear cover plates of the runner, the blade bone line and the thickness, so as to realize the automatic update of the parametric model of the runner blades.

[0052] Furthermore, the step 3 specifically includes generating a runner model sample library, performing structured grid division based on different runner flow field models, and obtaining and extracting the runner head curve Ht and efficiency curve η-t through numerical simulation calculation;

[0053] The wheel model design parameter sample is obtained by optimized Latin hypercube sampling (OptimalLatinHypercube).

[0054] Furthermore, the step 4 is specifically as follows: setting the initial population size to 100 and stipulating the maximum number of iterations to 100, the crossover probability of independent individuals to 0.9, specifying the crossover distribution index to 10, retaining the default values ​​of the other parameters, and calculating the corresponding head H and efficiency η for each individual through CFD, considering the combination of head H and efficiency η to find the best balance between the two; the CFD calculation and analysis includes solving the RANS kw SST model, setting the convergence accuracy and the number of iterations, and numerically simulating the three-dimensional model through the expression expression.

[0055] Furthermore, the step 5 is specifically as follows: the inertia weight w is gradually reduced from 0.9 to 0.4, and the global search is switched to a more detailed local search. The learning factor c is dynamically adjusted according to the number of iterations to ensure a gradual transition from global exploration to local fine search; the fitness function is calculated based on H(t) and η(t) of each design, and non-dominated sorting and crowding distance d calculation are supported to promote the diversity and quality of solutions; a new generation is generated through selection, crossover and mutation operations, and it is continuously iterated until the termination condition is met.

[0056] Furthermore, the model is a Kriging proxy model, which is used to perform high-precision interpolation operation optimization design;

[0057] By giving the same weight coefficient to individual head and efficiency, the theoretical optimal solution is explored as the optimization scheme and verified. Finally, the single-channel runner igs format file can be obtained, and the complete runner 3D model can be obtained by rotating and copying it in the 3D modeling software.

[0058] Specifically, based on the initial design parameters of the centrifugal pump impeller inlet diameter D1, outlet diameter D2, outlet width b, etc., a three-dimensional impeller model was drawn using the three-dimensional NX 12.0 modeling software through the traditional one-dimensional theoretical design method, and the impeller flow basin model was obtained through Boolean operation;

[0059] The shaft section is extracted from the created impeller 3D model and imported into the Ansys Workbench simulation software. The flow paths of the inlet, outlet, front cover and rear cover of the shaft section are identified. Five streamlines of the blade are extracted at equal intervals, including the front cover line (Span = 1.0) and the rear cover line (Span = 0.0). The streamlines are converted into scattered point format to complete the initial model import process. Figure 2 shown.

[0060] The curve line type of the front and rear cover profiles of the runner is formed by a five-point quartic Bezier curve, such as Figure 3 As shown, the front and rear cover control points are represented by P i (i=0,1,2,3,4) and P i'(i=0, 1, 2, 3, 4). (i=0) and (i=4) are regarded as fixed constraint control points, and (i=1, 2, 3) are regarded as optimization variable control points. The intersection of the runner axis and the inflow surface is taken as the spatial origin O. In the ZX section passing through the spatial origin, z is defined as the horizontal coordinate of each control point, and r is the vertical coordinate.

[0061] According to the above method, we get 3 control points of the front cover contour and 3 control points of the rear cover curve. There are 12 optimization parameters for the front and rear covers, and the horizontal and vertical coordinates are z i (i=1, 2, ..., 6) and r i (i=1, 2, ..., 6).

[0062] The parameter equation of the five-point quartic Bezier curve corresponding to the front and rear cover plates is expressed as follows:

[0063]

[0064] Where t represents the parameter variable and t∈[0,1]; when t is 0, it corresponds to the starting constraint point P0(z0,r0); when t is 1, it corresponds to the ending constraint point P4(z4,r4); i , r i (i=1, 2, ..., 6) are the horizontal and vertical coordinates of each constraint point respectively.

[0065] For blade bone line control, the streamlines at the front and rear covers of the runner (Span = 0, Span = 1.0) and the middle streamlines (Span = 0.5) are selected as control targets, and each streamline adopts a four-point cubic Bezier curve configuration. In order to ensure the good outflow characteristics of the original runner, the leading edge and trailing edge control points of the blade bone line are regarded as fixed constraint points, and the remaining two control points are optimized control variables. The positions of the streamline control points of Span = 0.25 and Span = 0.75 are determined by interpolation method, so that the pressure surface and suction surface of the blade are relatively smooth, preventing the influence of blade geometric distortion on subsequent calculations.

[0066] The blade bone line uses polar coordinates to describe the position of each control point of the curve. The control point angle in polar coordinates is defined as clockwise for positive values ​​and counterclockwise for negative values. The angle between the center origin O and the leading edge of the blade and the positive horizontal axis is θ0, and the angle between the center origin O and the trailing edge of the blade and the positive horizontal axis is θ3. The control point i in polar coordinates is expressed as (L i ,θ i ), where θ i Indicates the circular angle of the position, L i It represents the relative meridian length of the blade bone line after orthogonalization. The microelement length δM is solved by the cylindrical coordinates of the control point, and then the relative meridian length L after orthogonalization is obtained by integrating on the streamline s. i, as shown in formula (2) and formula (3):

[0067]

[0068] Where s is the relative length of the control point in the streamline.

[0069] The skeleton line is represented by a four-point cubic Bezier curve. By controlling the six control points corresponding to the three streamlines mentioned above, that is, 12 optimization parameters, the blade skeleton profile is finally determined. It is represented by L i (i=1, 2,...,6) and θ i (i=1, 2, ..., 6).

[0070] The thickness law of the runner blade is divided into radial and axial changes. The radial direction refers to the direction from the leading edge to the trailing edge of the blade, while the axial direction refers to the direction from the front cover to the rear cover. Since the thickness change of the blade in the middle position of the streamline direction has the greatest impact on the flow performance, the streamline at the position of Span=0.5 is selected to control the thickness of the runner blade. The thickness is represented by a four-point cubic Bezier curve. The control points at the beginning and end of the curve represent the thickness of the leading edge and trailing edge of the blade, respectively. The thickness change at this position has little effect on the flow performance and can be regarded as a fixed constraint point. The control point α (L′1, T1) and the control point β (L2′, T2) close to the middle position are used as blade thickness control parameters, that is, 4 optimization parameters.

[0071] Based on this, the Bezier fitting line method was used to preliminarily obtain 28 candidate parameters consisting of the front and rear covers of the runner, the blade bone line and the thickness, and the parametric modeling was completed.

[0072] The wheel design sample library is obtained through optimized Latin hypercube sampling, and the wheel structured grid division is completed; Turbogrid is used for automatic structured grid division, and the SSTk-w model is used for solution, and the convergence accuracy is set to 10 -4 and the number of iterations is 1000. The three-dimensional model is numerically simulated by expression to obtain the extracted runner head curve Ht and efficiency curve η-t.

[0073] The model is established as a Kriging proxy model. The Kriging proxy model needs to determine the weight coefficients of each objective function ω=[ω (1) ,ω (2) , ..., ω (n) ] T , the interpolation result of the function It is expressed as the following formula:

[0074]

[0075] Cov[Z(x1),Z(x2)]=σ 2 R(x1,x2) (5)

[0076] Where Z(x1) and Z(x2) represent the local deviations of the associated variables x1 and x2.

[0077]

[0078] Where, Y(x) is the global trend. The global trends of all interpolation results in the random process are the same.

[0079] The initial population size is set to 100, the maximum number of iterations is set to 100, the crossover probability of independent individuals is set to 0.9, the crossover distribution index is set to 10, and the other parameters are left as default. Considering the combination of head H and efficiency η, the optimal balance between the two is sought, as shown in formula (7).

[0080] The multi-objective genetic algorithm is iteratively optimized, and the inertia weight w is gradually reduced from 0.9 to 0.4. The learning factor c is dynamically adjusted according to the number of iterations to ensure a gradual transition from global exploration to local fine search. The fitness function is calculated to support non-dominated sorting and crowding distance d calculation, as shown in formula (8), to promote the diversity and quality of solutions. A new generation is generated through selection, crossover and mutation operations, and it is iterated continuously until the termination condition is met.

[0081] f(x i )=(H(x i ),η(x i )) (7)

[0082] In the formula, H(x i ) is the lift, η(x i ) is efficiency, x i Represents the set of control points that make up a Bezier curve.

[0083]

[0084] Among them, d is the crowding distance and m represents the index of the objective function.

[0085]

[0086] In the formula, is the position of the ith individual at time t, is the speed of the ith individual at time t, c is the learning factor, where c1 = 2.0, c2 = 2.0, and r is a random factor in the range of [0, 1].

[0087]

[0088] Where w is the inertia weight, w max=0.9, w min =0.4.

[0089]

[0090] In the formula, c 1，max =2.5, c 1，min =1.5, c 2，max =2.5, c 2，min =1.5.

[0091]

[0092] In the formula, μ is the mutation rate, where μ start =0.1,μ end =0.01.

[0093] According to different weight coefficients, an optimization solution is given on the Pareto frontier, which improves efficiency by 1.98% and lift by 15.73m. Figure 5 The diagram shows the change rules of the front and rear covers of the centrifugal pump impeller before and after optimization. Finally, the single-channel igs format file can be exported and the complete impeller 3D model can be obtained by rotating and copying it in the 3D modeling software.

[0094] Implementation Method 2

[0095] This embodiment provides an optimization design system for parametric modeling of centrifugal pump runner blades. The system uses the optimization design method for parametric modeling of centrifugal pump runner blades as described in Embodiment 1. The system includes:

[0096] Model building module: building a centrifugal pump runner flow field model; parameterizing the built centrifugal pump runner model;

[0097] Meshing module: Meshing the parameterized centrifugal pump impeller model;

[0098] External characteristic curve evaluation module: external characteristic curve evaluation of head H and efficiency η based on divided grids;

[0099] Iterative optimization module: Based on the evaluation results, multi-objective genetic algorithm iterative optimization is performed to achieve parametric modeling and optimal design of centrifugal pump impeller blades.

[0100] Furthermore, the working principle of the model building module is as follows: the initial design parameters of the centrifugal pump impeller, such as the inlet diameter D1, the outlet diameter D2, and the outlet width b, use the univariate theoretical design method to draw the three-dimensional model of the impeller through three-dimensional modeling software, and obtain the impeller flow field model through Boolean operation.

[0101] Furthermore, the working principle of the model building module is specifically as follows: establishing the flow paths of the inlet, outlet, front and rear cover plates of the impeller shaft section, extracting multiple streamlines on the impeller blades at equal intervals, including the front cover plate line (Span=1.0) and the rear cover plate line (Span=0.0), generating streamline data points and importing the streamline data points into the three-dimensional modeling software, and using the Bezier fitting line method to express multiple control curves such as the impeller front and rear cover plates, blade bone lines and thickness, so as to realize automatic updating of the parametric model of the impeller blades.

[0102] Furthermore, the working principle of the grid division module is specifically as follows: generating a runner model sample library, performing structured grid division based on different runner flow basin models, and obtaining and extracting the runner head curve Ht and efficiency curve η-t through numerical simulation calculation;

[0103] The wheel model design parameter sample is obtained by optimized Latin hypercube sampling (OptimalLatinHypercube).

[0104] Furthermore, the working principle of the external characteristic curve evaluation module is as follows: the initial population size is set to 100 and the maximum number of iterations is specified to be 100, the crossover probability of independent individuals is 0.9, the crossover distribution index is specified to be 10, and the other parameters retain the default values. The corresponding head H and efficiency η are calculated for each individual through CFD, and the combination of head H and efficiency η is considered to find the best balance between the two; the CFD calculation and analysis includes solving the RANS kw SST model, setting the convergence accuracy and the number of iterations, and numerically simulating the three-dimensional model through expression.

[0105] Furthermore, the working principle of the iterative optimization module is as follows: the inertia weight w is gradually reduced from 0.9 to 0.4, and the global search is turned to a more detailed local search. The learning factor c is dynamically adjusted according to the number of iterations to ensure a gradual transition from global exploration to local fine search; the fitness function is calculated based on H(t) and η(t) of each design, and non-dominated sorting and crowding distance d calculation are supported to promote the diversity and quality of solutions; a new generation is generated through selection, crossover and mutation operations, and it is continuously iterated until the termination condition is met.

[0106] Implementation Method 3

[0107] An embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the memory is used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and the processor are connected via a bus. Specifically, the processor implements any step in the first embodiment above by running the computer program stored in the memory.

[0108] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0109] The memory may include a read-only memory, a flash memory, and a random access memory, and provides instructions and data to the processor. A part or all of the memory may also include a nonvolatile random access memory.

[0110] It can be seen from the above that the electronic device provided in the embodiment of the present invention can implement the parametric optimization design method of the centrifugal pump impeller blades as described in the first embodiment by running a computer program, which is conducive to accelerating the hydraulic optimization process of the impeller and provides a technical reference for optimizing the external characteristics of the centrifugal pump.

[0111] It should be understood that if the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method implementations when executed by the processor. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0112] 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 range consistent with the principles and novel features disclosed herein.

[0113] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0114] It should be noted that the methods and detailed examples provided in the above embodiments can be combined with the devices and equipment provided in the embodiments, and references can be made to each other, and no further details will be given.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0116] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal equipment and method can be implemented in other ways. For example, the device / equipment implementation described above is only illustrative, for example, the division of the above modules or units is only a logical function division, and in actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0117] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above-mentioned embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An optimization design method for parametric modeling of centrifugal pump runner blades, characterized in that: The method comprises the following steps: Step 1: Establish the centrifugal pump runner flow domain model; Step 2: Parameterize the centrifugal pump impeller model established in step 1; Step 3: Mesh the centrifugal pump impeller model parameterized in step 2; Step 4: Evaluate the external characteristic curves of head H and efficiency η based on the grid divided in step 3; Step 5: Based on the evaluation results of step 4, a multi-objective genetic algorithm is used for iterative optimization to achieve parametric modeling and optimal design of centrifugal pump impeller blades.

2. The optimization design method according to claim 1, characterized in that: Specifically, the inlet diameter D1, outlet diameter D2 and outlet width b of the centrifugal pump impeller are used as initial design parameters, a 3D model of the impeller is drawn by 3D modeling software using a univariate theoretical design method, and a flow field model of the impeller is obtained by Boolean operation.

3. The optimization design method according to claim 1, characterized in that: Specifically, step 2 is to establish the flow paths of the inlet, outlet, front and rear cover plates of the runner shaft section, extract multiple streamlines on the runner blades at equal intervals, including the front cover plate line and the rear cover plate line, generate streamline data points, import the streamline data points into the three-dimensional modeling software, and use the Bezier fitting line method to express multiple control curves of the front and rear cover plates of the runner, the blade bone line and the thickness, so as to realize the automatic update of the parametric model of the runner blades.

4. The optimization design method according to claim 1, characterized in that: The step 3 specifically includes generating a runner model sample library, performing structured grid division based on different runner flow field models, and extracting the runner head curve Ht and efficiency curve η-t through numerical simulation calculation; The wheel model design parameter sample is obtained by optimizing Latin hypercube sampling.

5. The optimization design method according to claim 1, characterized in that: Specifically, step 4 includes setting the initial population size to 100 and the maximum number of iterations to 100, the crossover probability of independent individuals to 0.9, the crossover distribution index to 10, retaining the default values ​​of other parameters, and calculating the corresponding head H and efficiency η for each individual through CFD, considering the combination of head H and efficiency η to find the best balance between the two; the CFD calculation and analysis includes solving the RANS kw SST model, setting the convergence accuracy and the number of iterations, and numerically simulating the three-dimensional model through expression.

6. The optimization design method according to claim 2, characterized in that: Specifically, step 5 is as follows: the inertia weight w is gradually reduced from 0.9 to 0.4, and the global search is switched to a more detailed local search. The learning factor c is dynamically adjusted according to the number of iterations to ensure a gradual transition from global exploration to local fine search; the fitness function is calculated based on H(t) and η(t) of each design, and non-dominated sorting and crowding distance d calculation are supported to promote the diversity and quality of solutions; a new generation is generated through selection, crossover and mutation operations, and it is continuously iterated until the termination condition is met.

7. The optimization design method according to claim 2, characterized in that: The model is a Kriging proxy model, which is used to perform high-precision interpolation optimization design; The same weight coefficient is given to individual head and efficiency to explore the theoretical optimal solution as the optimization plan, and then verify it, and finally obtain the single-channel runner igs format file, and obtain the complete runner 3D model by rotating and copying it in the 3D modeling software.

8. An optimization design system for parametric modeling of centrifugal pump runner blades, characterized in that: The system uses the optimization design method of parametric modeling of centrifugal pump impeller blades as claimed in any one of claims 1 to 7, and the system includes: Model building module: building a centrifugal pump runner flow field model; parameterizing the built centrifugal pump runner model; Meshing module: Meshing the parameterized centrifugal pump impeller model; External characteristic curve evaluation module: external characteristic curve evaluation of head H and efficiency η based on divided grids; Iterative optimization module: Based on the evaluation results, multi-objective genetic algorithm iterative optimization is performed to achieve parametric modeling and optimal design of centrifugal pump impeller blades.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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