CN117150672A - Automatic optimizing method for thickness of centrifugal pump blade - Google Patents
Automatic optimizing method for thickness of centrifugal pump blade Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
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Abstract
The invention discloses a centrifugal pump blade thickness automatic optimizing method, which comprises the following steps: according to the flow and lift requirements under the design working condition, the impeller is initially designed and parameterized and modeled, and a thickness control variable t is defined 1 ～t n Creating constraint conditions, and generating a plurality of calculation samples by adopting a sampling method; carrying out leaf pattern design scheme corresponding to each calculation sampleThree-dimensional modeling, and then grid division, boundary condition setting and numerical simulation calculation are carried out on the three-dimensional modeling; inputting the numerical simulation calculation result into the optimization design software to obtain the lift and efficiency in the sample calculation result, and establishing a response surface model based on a radial basis function method; the response surface model is subjected to credibility analysis, optimization solution is carried out on the premise of meeting fitting precision, and simulation verification is carried out on an optimization result to obtain t 1 ～t n Is an optimal combination scheme of the (a). The invention has higher automation degree, shortens the optimization period, and has wider application range because of not depending on an empirical formula.
Description
Technical Field
The invention relates to the field of centrifugal pump flow-through component design, in particular to an automatic optimizing method for centrifugal pump blade thickness.
Background
Centrifugal pumps are widely used in various fields of national economy as a general fluid delivery device. As a complex fluid system, the centrifugal pump has the inherent properties of high three-dimensional, space uneven distribution, unsteady and the like, and the centrifugal pump inevitably generates a vortex in the impeller flow channel opposite to the rotation direction of the impeller due to the inertia action of fluid in the pump, the action of a plurality of factors such as limited number of blades and the like.
In the prior art, a design method for the thickness of impeller blades of a centrifugal pump is disclosed in the publication No.: CN112682350a describes a method for designing the thickness of impeller blades of a plastic centrifugal pump, which derives an empirical formula describing the thickness distribution of the blades by using a four-time spline curve from a set of initial blades, and is found to be helpful for improving the pump efficiency, but the method is too dependent on the empirical formula and has limited applicable range.
A compressor blade and cylinder structure and compressor, publication number: CN212508811U, adopts the mode of variable thickness to carry out the blade design to a rotor type compressor, through the mode of controlling blade thickness and cylinder body inner section diameter ratio value in a certain range to produce the blade of different thickness distributions, discovers through calculation can improve the compressor efficiency. A multi-wing centrifugal fan blade, an impeller and a multi-wing centrifugal fan, publication No.: CN109973427a, designs the pressure surface and the suction surface of a multi-wing centrifugal fan blade into an airfoil blade portion and an arc blade portion respectively, and generates the pressure surface and the suction surface with different shapes by controlling the ratio of the length of the arc blade portion to the length of the airfoil blade portion, so as to generate blades with different thickness distribution, and realize better aerodynamic performance. However, the optimization method of the blade has the problems of low automation degree and long optimization period.
Disclosure of Invention
Aiming at the defects of over-dependence on an empirical formula, low automation degree and long optimization period in the prior art, the invention provides an automatic optimization method for the thickness of a centrifugal pump blade.
The specific technical scheme is as follows:
a centrifugal pump blade thickness automatic optimizing method comprises the following steps:
s1: according to the flow and lift requirements under the design working condition and the geometric parameters of the matching volute, carrying out preliminary calculation on the geometric parameters of the impeller based on a speed coefficient method, realizing parametric modeling of the impeller through modeling software, and defining n thickness control variables t 1 ～t n ，n≥3；
S2: establishing n thickness control variables t 1 ～t n Is a constraint on (2);
s3: generating a plurality of calculation samples containing the n thickness control variables by adopting a sampling method based on the constraint condition;
s4: combining the impeller geometric parameters obtained in the step S1 and the calculation samples obtained in the step S3 to obtain a plurality of blade profile design schemes; carrying out three-dimensional modeling on each leaf pattern design scheme through modeling software, and carrying out grid division, boundary condition setting and numerical simulation calculation on the three-dimensional model through numerical simulation software;
s5: inputting the numerical simulation calculation result into an optimization design software to obtain the lift and efficiency in a sample calculation result, and establishing a lift, efficiency and thickness control variable t based on a radial basis function method 1 ～t n Between (a) and (b)A response surface model;
s6: performing credibility analysis on the response surface model, performing optimization solution if the fitting accuracy requirement is met, and performing simulation verification on the optimization result to obtain a thickness control variable t 1 ～t n The optimal combination scheme of the centrifugal pump blade thickness is completed.
Further, in the step S1, the preliminary calculation of the geometric parameters of the impeller based on the velocity coefficient method is specifically as follows:
(1) Impeller inlet diameter D 1 The calculation expression is as follows:
wherein d h Diameter of the hub D 0 For the effective diameter of the impeller inlet, Q t For flow through the impeller, V 0 Is the pump inlet flow rate, Q is the pump flow rate, η v For pump volumetric efficiency, n s Is the specific rotation speed, n d The rotation speed of the pump is the rotation speed, and the lift is H;
(2) Impeller outer diameter D 2 The calculation expression is as follows:
wherein u is 2 For the peripheral speed of the impeller outlet,is the outlet peripheral velocity coefficient;
(3) Impeller outlet width b 2 The calculation expression is as follows:
in phi 2 The coefficient of displacement for the impeller outlet blades,for the impeller outlet shaft speed, < >>Is the velocity coefficient of the shaft surface of the outlet;
(4) The number of blades Z and the wrap angle ψ:
the number Z of the blades is adjusted according to the blade displacement coefficient at the inlet of the impeller; the wrap angle psi is adjusted according to the working rotation speed of the centrifugal pump;
(5) Blade inlet angle beta 1 Blade outlet angle beta 2 ：
The blade inlet angle beta 1 The flow angle of each streamline is added with the attack angle; the blade outlet angle beta 2 Is determined by the pump lift;
(6) Initial blade thickness profile:
according to the blade inlet angle beta 1 Blade outletAngle beta 2 Determining a camber line of the blade by the wrap angle psi, and changing the molded line of the blade by adopting a mode of thickening the camber line; the blade thickness distribution is gradually increased from the inlet to the outlet to obtain the initial blade thickness distribution, namely the thickness control variable t 1 ～t n Is set to be a constant value.
Further, the impeller outlet vane displacement coefficient phi 2 The value range of (2) is 0.85-0.95; the value range of the wrap angle phi is 85-120 degrees, and the blade inlet angle beta is as follows 1 The value range of the blade is 10-30 degrees, and the outlet angle beta of the blade is as follows 2 The range of the value of (2) is 22-30 degrees.
Further, in S2, the constraint condition is: the thickness of the blade increases from the inlet to the outlet, and the thickness control variable t 1 ～t n The up-down variation of (c) is within 20% of the initial value.
Further, in the step S3, the sampling method is a central combination design method, and the central combination design method is a multi-factor multi-level test design method, and is a horizontal full-factor method reinforced by taking a central point and two additional angular points located on the factor axis for each factor, and the two-level factorial design is based on adding extreme points and central points.
Further, in the step S6, the specific operations of performing the reliability analysis and the simulation verification are as follows:
by applying R 2 The error analysis method analyzes the credibility of the response surface model, returns to S2 if the fitting precision does not reach the standard, changes the constraint condition, and repeats S3-S6 until the fitting precision reaches the standard; carrying out optimization solution on the response surface model with the fitting precision reaching the standard, carrying out CFD simulation verification on the optimization result, returning to S5 if the CFD simulation verification is not passed, redefining the response surface model, and executing S6 until the CFD simulation verification is passed; the optimization result which can pass CFD simulation verification is the thickness control variable t 1 ～t n Is an optimal combination scheme of the (a).
Further, in the step S6, a secondary Lagrangian nonlinear programming algorithm is adopted to carry out optimization solution on the response surface model.
Further, CFturbo is selected as the modeling software, star-ccm+ is selected as the numerical simulation software, and Optimus is selected as the optimization design software.
The beneficial effects of the invention are as follows:
according to the invention, n thickness control variables are uniformly arranged on the flow direction position of the blade, a response surface model of pump lift, efficiency and n blade thickness control parameters is established, and under the condition that an accurate optimization objective function cannot be obtained, the thickness control variable t is controlled by means of an optimization algorithm 1 ～t n The optimization calculation is carried out, the automatic optimization design of the thickness of the centrifugal pump blade is realized, the hydraulic efficiency of the pump is improved, and compared with the prior art, the method has higher degree of automation, shortens the optimization period, and has wider application range because the method does not depend on an empirical formula.
Drawings
Fig. 1 is a flowchart of a method for automatically optimizing the thickness of a centrifugal pump blade according to the present invention.
Fig. 2 is a schematic view of an impeller model used in the present invention, in which (a) is a top view and (b) is a side view.
FIG. 3 is a schematic diagram of an optimization platform constructed using Optimus software in accordance with the present invention.
FIG. 4 is a schematic view of the blade thickness distribution control of the present invention, wherein (a) is the blade thickness control curve and (b) is the blade thickness control point distribution.
FIG. 5 is a histogram of sample point calculations in accordance with an embodiment of the present invention.
FIG. 6 is a schematic representation of a response surface model of an embodiment of the present invention.
FIG. 7 is a graph of response surface fitting accuracy analysis of an embodiment of the present invention.
FIG. 8 is a graph comparing pump performance before and after optimization of an embodiment of the present invention.
Detailed Description
The objects and effects of the present invention will become more apparent from the following detailed description of the preferred embodiments and the accompanying drawings, in which the present invention is further described in detail. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Before describing embodiments of the present invention in further detail, the terms and terminology involved in the embodiments of the present invention will be described, and the terms and terminology involved in the embodiments of the present invention will be used in the following explanation.
(1) The determination coefficient (Coefficient Of Determination, hereinafter referred to as COD) is generally expressed as R 2 Is a statistical index for evaluating the goodness of fit of a regression model, and the closer the value is to 1, the higher the goodness of fit is.
(2) Computational fluid dynamics (Computational Fluid Dynamics, hereinafter CFD), which uses the wiener-stokes equation (including five partial differential equations) to simulate the flow of a fluid, which approximates the movement of the fluid in a virtual environment using computer resources; CFD simulation can use specific models to supplement the physical properties of the application and thereby predict realistic scenarios.
As shown in fig. 1, the invention provides a method for automatically optimizing the thickness of a centrifugal pump blade, which specifically comprises the following steps:
s1: according to the flow and lift requirements under the design working condition and the geometric parameters of the matching volute, carrying out preliminary calculation on the geometric parameters of the impeller based on a speed coefficient method, realizing parametric modeling of the impeller through modeling software, and defining n thickness control variables t 1 ～t n N is more than or equal to 3. The preliminary calculation of the geometric parameters of the impeller shown in fig. 2 based on the velocity coefficient method is specifically as follows:
(1) Impeller inlet diameter D 1 The calculation expression is as follows:
wherein d h Diameter of the hub D 0 For the effective diameter of the impeller inlet, Q t For flow through the impeller, V 0 Is the pump inlet flow rate, Q is the pump flow rate, η v For pump volumetric efficiency, n s Is the specific rotation speed, n d The rotation speed of the pump is shown, and the lift is shown as H.
(2) Impeller outer diameter D 2 The calculation expression is as follows:
wherein u is 2 For the peripheral speed of the impeller outlet,is the outlet peripheral velocity coefficient.
(3) Impeller outlet width b 2 The calculation expression is as follows:
in phi 2 To displace the impeller outlet bladesThe coefficient is generally 0.85-0.95;for the impeller outlet shaft speed, < >>And H is the pump lift.
(4) The number of blades Z and the wrap angle ψ.
In view of pump cavitation performance, the number of blades Z may be reduced appropriately in order to reduce the coefficient of blade displacement at the impeller inlet while leaving sufficient room for blade thickness adjustment. The blade wrap angle psi is generally 85-120 degrees, and the wrap angle can be properly increased in order to improve the hydraulic efficiency of the pump as much as possible in consideration of the higher working speed of the pump.
(5) Blade inlet angle beta 1 Blade outlet angle beta 2 。
Blade inlet angle beta 1 The blade inlet angle beta is obtained by adding an attack angle to each streamline liquid flow angle, wherein the attack angle is 3-10 degrees according to the traditional design experience 1 The inlet angle beta of the vane can be properly increased to improve the cavitation resistance of the pump by taking 10-30 degrees 1 。
Blade outlet angle beta 2 Mainly determined by the pump lift, and according to the traditional design experience, the blade outlet angle beta 2 Usually 22-30 DEG is adopted, and if the specific rotation speed is low, a large beta can be properly selected 2 The angle increases the lift.
(6) Initial blade thickness profile.
As shown in FIG. 2, according to the blade inlet angle beta 1 Blade outlet angle beta 2 And the wrap angle psi can determine the camber line of the blade, and the mode of thickening the camber line is adopted to change the molded line of the blade on the basis. According to the research results of the variable-thickness blades, the blade thickness distribution adopts a gradually increasing form from the inlet to the outlet, so that secondary flow in the flow channel can be furthest inhibited, and the initial blade thickness distribution, namely the thickness control variable t, can be obtained 1 ～t n Is set to be a constant value.
S2: construction is carried out in consideration of the strength of the blade and the requirements of the processing technologyVertical n thickness control variables t 1 ～t n The constraint condition is determined by the strength of the blade and the requirements of the processing technology.
After the initial blade thickness profile is obtained, the thickness control variable t is calculated to explore as much as possible the effect of the thickness profile variation on efficiency 1 ～t n The up-down variation of (2) is controlled to be 20% of the initial value; however, considering that a smaller vane inlet thickness is advantageous for improving cavitation resistance of the pump, t can be suitably reduced 1 The amount of float change in (i.e., the amount of float change is less than 20%).
S3: based on n thickness control variables t 1 ～t n A sampling method is used to generate a plurality of calculation samples containing the n thickness control variables, and the number of the calculation samples is not less than 27. The sampling method used is preferably a design of experiments method (Design OfExperiment, hereinafter referred to as DOE).
S4: combining the impeller geometric parameters obtained in the step S1 and the calculation samples obtained in the step S3 to obtain a plurality of blade profile design schemes; and carrying out three-dimensional modeling on each leaf pattern design scheme through modeling software, and then carrying out grid division, boundary condition setting and numerical simulation calculation on the three-dimensional model through numerical simulation software.
S5: inputting the numerical simulation calculation result into the optimization design software to obtain the lift and efficiency in the sample calculation result, and establishing the control variables t of the lift, the efficiency and the thickness based on the radial basis function method 1 ～t n A response surface model between. The radial basis function method is a common response surface construction method, a response surface is constructed by taking sample data as interpolation nodes, the construction algorithm is simple, and the problem of high-dimensional and high-order nonlinearity caused by optimization of the thickness of the centrifugal pump blade can be well solved.
S6: by applying R 2 The error analysis method analyzes the credibility of the response surface model, and if the fitting precision does not reach the standard, the method returns to S2, and t is changed 1 ～t n And repeating the steps S3 to S6 until the fitting precision reaches the standard. Carrying out optimization solution on a response surface model meeting the fitting precision requirement, and carrying out CFD simulation verification on an optimization result if the optimization result does not passAnd returning to the step S5, redefining the response surface model, and executing the step S6 until the CFD simulation verification is passed. The optimization result which can pass CFD simulation verification is n thickness control variables t 1 ～t n The optimal combination scheme of the centrifugal pump blade thickness is completed.
In this embodiment, a quadratic lagrangian nonlinear programming (Non-Linear Programming Quadratic Programming Lagrange, hereinafter referred to as NLPQL) algorithm is used to perform optimization solution on the obtained response surface model. NLPQL algorithm is a sequence quadratic programming method, which expands the objective function and constraint condition according to Taylor series, the objective function takes the first second order, the constraint condition takes the first order, so as to construct a quadratic programming sub-problem, the solution of the sub-problem is used as iterative searching direction and one-dimensional searching is carried out along the direction, and finally the approximate constraint optimal point of the original problem is approximated. The NLPQL algorithm not only utilizes the function value information and the first derivative information of the objective function and the constraint function in the iterative process, but also utilizes the second derivative information of the objective function and the constraint function, so that the NLPQL algorithm has high convergence rate and high efficiency, and is suitable for nonlinear constraint optimization.
Based on the impeller geometric parameter obtained in S1 and n thickness control variables t obtained in S6 1 ～t n And (3) carrying out three-dimensional modeling on the blade to obtain an optimized blade design result.
The invention is illustrated in more detail below by way of an example.
Examples
In this embodiment, CFturbo is selected as the parameterized modeling software, star-ccm+ is selected as the numerical simulation software, optimus is selected as the optimization design software, and the thickness control variables are 5, i.e., t 1 ～t 5 。
S1: selecting the rotation speed n d 7000r/min, flow q=2.1m 3 The centrifugal pump with the head h=20m was calculated based on the velocity coefficient method to obtain the impeller geometry parameters as shown in table 1.
Table 1 impeller geometry parameters
Parametric modeling was performed in CFturbo software using the impeller geometry parameters in table 1, and five thickness control variables t were defined using the Batch mode/Optimization module within the software 1 ～t 5 And outputting a cft-batch file, wherein the file contains a complete blade profile parameterized modeling script, and a blade thickness distribution control schematic diagram is shown in fig. 4. The initial blade thickness control variables are respectively set as t 1 ＝1.5mm，t 2 ＝2.6mm，t 3 ＝5.5mm，t 4 ＝7.8mm，t 5 ＝8.7mm。
S2: establishing constraint conditions of each parameter: t is t 1 The variation range is controlled to be 1.5-1.8mm, and the up-down variation of the rest thickness control parameters is controlled to be 20% of the initial value.
S3: and calling the cft-batch file generated in the step S1 by utilizing Optimus software through script command, wherein in the embodiment, a sampling method adopts a central combination design method, and finally 27 calculation samples are generated. The central combined design method is a multi-factor multi-level test design method, is a horizontal full factor method enhanced by taking a central point and two additional angular points positioned on the axes of factors for each factor, and is formed by adding extreme points and central points on the basis of two-level factorial design, so that the design space is expanded, high-order information can be obtained, primary and secondary terms are effectively estimated, and the fitting precision is higher. The 27 calculation samples are specifically shown in table 2.
Table 2 (Unit: mm)
| Sample of | t 1 | t 2 | t 3 | t 4 | t 5 |
| PRE | 1.5 | 2.6 | 5.5 | 7.8 | 8.7 |
| 1 | 1.65 | 2.6 | 5.5 | 7.75 | 8.65 |
| 2 | 1.725 | 2.85 | 4.95 | 8.525 | 7.775 |
| 3 | 1.575 | 2.35 | 6.05 | 8.525 | 9.525 |
| 4 | 1.725 | 2.35 | 6.05 | 8.525 | 7.775 |
| 5 | 1.65 | 3.1 | 5.5 | 7.75 | 8.65 |
| 6 | 1.65 | 2.6 | 4.4 | 7.75 | 8.65 |
| 7 | 1.65 | 2.6 | 6.6 | 7.75 | 8.65 |
| 8 | 1.65 | 2.6 | 5.5 | 9.3 | 8.65 |
| 9 | 1.725 | 2.85 | 6.05 | 6.975 | 7.775 |
| 10 | 1.575 | 2.35 | 4.95 | 8.525 | 7.775 |
| 11 | 1.65 | 2.6 | 5.5 | 7.75 | 10.4 |
| 12 | 1.575 | 2.35 | 4.95 | 6.975 | 9.525 |
| 13 | 1.725 | 2.35 | 4.95 | 6.975 | 7.775 |
| 14 | 1.575 | 2.85 | 4.95 | 6.975 | 7.775 |
| 15 | 1.725 | 2.85 | 4.95 | 6.975 | 9.525 |
| 16 | 1.575 | 2.35 | 6.05 | 6.975 | 7.775 |
| 17 | 1.725 | 2.35 | 6.05 | 6.975 | 9.525 |
| 18 | 1.65 | 2.6 | 5.5 | 6.2 | 8.65 |
| 19 | 1.8 | 2.6 | 5.5 | 7.75 | 8.65 |
| 20 | 1.65 | 2.6 | 5.5 | 7.75 | 6.9 |
| 21 | 1.725 | 2.35 | 4.95 | 8.525 | 9.525 |
| 22 | 1.575 | 2.85 | 4.95 | 8.525 | 9.525 |
| 23 | 1.5 | 2.6 | 5.5 | 7.75 | 8.65 |
| 24 | 1.575 | 2.85 | 6.05 | 6.975 | 9.525 |
| 25 | 1.65 | 2.1 | 5.5 | 7.75 | 8.65 |
| 26 | 1.575 | 2.85 | 6.05 | 8.525 | 7.775 |
| 27 | 1.725 | 2.85 | 6.05 | 8.525 | 9.525 |
In this step, the script command for the Optimus software to call the cft-batch file is: call "-batch opt.cft-batch, wherein the reference numbers are the installation catalogues of the CFturbo software executable application programs, and the installation catalogues are correspondingly adjusted according to different installation catalogues.
S4: performing three-dimensional modeling on the leaf pattern design scheme corresponding to each calculation sample by using CFturbo software; and performing grid division, boundary condition setting and numerical simulation calculation on the established three-dimensional model by utilizing a Star-ccm+ intra-software command macro function. The command macro recording can generate a java file, and can be directly called through a script command in Optimus software, wherein the script command is as follows:
@echo off
set starinput_file＝％1
“*”-power-np 8 001.sim-batch％starinput_file％
and the index number is Star-ccm+the installation catalog of the software executable application program, and the installation catalog is correspondingly adjusted according to different installation catalogs.
S5: inputting the numerical simulation calculation result into optimizing design software Optimus, setting up an optimizing platform schematic diagram based on the optimizing design software Optimus as shown in figure 3, and finally obtaining the lift and efficiency of each sample calculation result as shown in figure 5. And then a response surface model is established based on a radial basis function method, wherein the response surface model is shown in figure 6.
S6: by applying R 2 The error analysis method analyzes the credibility of the response surface model, the analysis result is shown in fig. 7, and as can be seen from fig. 7, R 2 =1, the fitting accuracy is better, satisfies the fitting accuracy requirement. Then, the NLPQL algorithm is utilized to carry out optimization solution on the obtained response surface model, CFD simulation verification is carried out, and the thickness control variable t is obtained through multiple iterations 1 ～t 5 The optimal combination scheme of (a) is as follows: t is t 1 ＝1.65mm，t 2 ＝2.2mm，t 3 ＝6.1mm，t 4 ＝9.3mm，t 5 =8.1 mm, and performing three-dimensional modeling on the impeller according to the optimal combination scheme, an optimized impeller design result can be obtained, as shown in fig. 2.
Centrifugal pumps before and after optimization were tested to obtain a performance comparison graph as shown in fig. 8. It can be clearly seen that the efficiency of each working point of the impeller (i.e. the impeller obtained by modeling based on the initial thickness control variable) in the original scheme is improved to different degrees after optimization, the highest efficiency is improved by 3.16% compared with that before optimization, and meanwhile, the highest lift is almost unchanged, so that the optimization of the hydraulic performance of the centrifugal pump is realized.
The invention provides an automatic optimizing method for the thickness of a centrifugal pump blade, which realizes parameterization design for the thickness distribution of the blade and evenly sets n thickness variables t at different flow positions 1 ～t n The thickness of the blade is controlled, a plurality of calculation samples are generated by adopting a central combination design method under constraint conditions, a response surface model is constructed, the response surface model is optimally solved, and after fitting precision is met and CFD simulation verification is passed, a variable t is obtained 1 ～t n Is an optimal combination scheme of the (a). The automatic optimizing method for the thickness of the centrifugal pump blade does not depend on an empirical formula, has wider application range and higher degree of automation, can shorten the optimizing period and realize the maximization of optimizing income.
It will be appreciated by persons skilled in the art that the foregoing description is a preferred embodiment of the invention, and is not intended to limit the invention, but rather to limit the invention to the specific embodiments described, and that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for elements thereof, for the purposes of those skilled in the art. Modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The automatic optimizing method for the thickness of the centrifugal pump blade is characterized by comprising the following steps of:
s1: according to the flow and lift requirements under the design working condition and the geometric parameters of the matching volute, carrying out preliminary calculation on the geometric parameters of the impeller based on a speed coefficient method, realizing parametric modeling of the impeller through modeling software, and defining n thickness control variables t 1 ～t n ，n≥3；
S2: establishing n thickness control variables t 1 ～t n Is a constraint on (2);
s3: generating a plurality of calculation samples containing the n thickness control variables by adopting a sampling method based on the constraint condition;
s4: combining the impeller geometric parameters obtained in the step S1 and the calculation samples obtained in the step S3 to obtain a plurality of blade profile design schemes; carrying out three-dimensional modeling on each leaf pattern design scheme through modeling software, and carrying out grid division, boundary condition setting and numerical simulation calculation on the three-dimensional model through numerical simulation software;
s5: inputting the numerical simulation calculation result into an optimization design software to obtain the lift and efficiency in a sample calculation result, and establishing a lift, efficiency and thickness control variable t based on a radial basis function method 1 ～t n A response surface model between;
s6: performing credibility analysis on the response surface model, performing optimization solution if the fitting accuracy requirement is met, and performing simulation verification on the optimization result to obtain a thickness control variable t 1 ～t n The optimal combination scheme of the centrifugal pump blade thickness is completed.
2. The automatic optimizing method for vane thickness of centrifugal pump according to claim 1, wherein in S1, the preliminary calculation of the geometric parameters of the impeller based on the velocity coefficient method is specifically as follows:
(1) Impeller inlet diameter D 1 The calculation expression is as follows:
wherein d h Grain diameter of wheel, D 0 For the effective diameter of the impeller inlet, Q t For flow through the impeller, V 0 Is the pump inlet flow rate, Q is the pump flow rate, η v For pump volumetric efficiency, n s Is the specific rotation speed, n d The rotation speed of the pump is the rotation speed, and the lift is H;
(2) Impeller outer diameter D 2 The calculation expression is as follows:
wherein u is 2 For the peripheral speed of the impeller outlet,is the outlet peripheral velocity coefficient;
(3) Impeller outlet width b 2 The calculation expression is as follows:
in phi 2 The coefficient of displacement for the impeller outlet blades,for the impeller outlet shaft speed, < >>Is the velocity coefficient of the shaft surface of the outlet;
(4) The number of blades Z and the wrap angle ψ:
the number Z of the blades is adjusted according to the blade displacement coefficient at the inlet of the impeller; the wrap angle psi is adjusted according to the working rotation speed of the centrifugal pump;
(5) Blade inlet angle beta 1 Blade outlet angle beta 2 ：
The blade inlet angle beta 1 The flow angle of each streamline is added with the attack angle; the blade outlet angle beta 2 Is determined by the pump lift;
(6) Initial blade thickness profile:
according to the blade inlet angle beta 1 Blade outlet angle beta 2 Determining a camber line of the blade by the wrap angle psi, and changing the molded line of the blade by adopting a mode of thickening the camber line; blade thickness profile from inlet to outletThe ports being in a progressively increasing form to obtain an initial blade thickness profile, i.e. the thickness control variable t 1 ～t n Is set to be a constant value.
3. The method for automatically optimizing the thickness of a centrifugal pump blade according to claim 2, wherein the impeller outlet blade displacement coefficient phi 2 The value range of (2) is 0.85-0.95; the value range of the wrap angle phi is 85-120 degrees, and the blade inlet angle beta is as follows 1 The value range of the blade is 10-30 degrees, and the outlet angle beta of the blade is as follows 2 The range of the value of (2) is 22-30 degrees.
4. The automatic optimizing method for centrifugal pump blade thickness according to claim 1, wherein in S2, the constraint condition is: the thickness of the blade increases from the inlet to the outlet, and the thickness control variable t 1 ～t n The up-down variation of (c) is within 20% of the initial value.
5. The automatic optimizing method for vane thickness of centrifugal pump according to claim 1, wherein in S3, the sampling method is a central combination design method, the central combination design method is a multi-factor multi-level test design method, and is a horizontal full-factor method reinforced by taking a central point and two additional angular points on the factor axis for each factor, and extreme points and central points are added on the basis of two-level factorial design.
6. The automatic optimizing method for centrifugal pump blade thickness according to claim 1, wherein in S6, the specific operations of performing the reliability analysis and the simulation verification are as follows:
by applying R 2 The error analysis method analyzes the credibility of the response surface model, returns to S2 if the fitting precision does not reach the standard, changes the constraint condition, and repeats S3-S6 until the fitting precision reaches the standard; carrying out optimization solution on the response surface model with the fitting precision reaching the standard, carrying out CFD simulation verification on the optimization result, and returning to S5 if the optimization result does not pass the CFD simulation verificationRedefining a response surface model, and executing S6 until the CFD simulation verification is passed; the optimization result which can pass CFD simulation verification is the thickness control variable t 1 ～t n Is an optimal combination scheme of the (a).
7. The automatic optimizing method for the thickness of the centrifugal pump blade according to claim 1, wherein in the step S6, a quadratic lagrangian nonlinear programming algorithm is adopted to perform optimization solution on the response surface model.
8. The automatic optimizing method for the thickness of the centrifugal pump blade according to claim 1, wherein the modeling software is CFturbo, the numerical simulation software is Star-ccm+, and the optimizing design software is Optimus.
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