CN108710764B - Double-suction pump multi-objective optimization design method based on hybrid approximation model - Google Patents

Double-suction pump multi-objective optimization design method based on hybrid approximation model Download PDF

Info

Publication number
CN108710764B
CN108710764B CN201810499711.6A CN201810499711A CN108710764B CN 108710764 B CN108710764 B CN 108710764B CN 201810499711 A CN201810499711 A CN 201810499711A CN 108710764 B CN108710764 B CN 108710764B
Authority
CN
China
Prior art keywords
model
suction pump
double
adopting
design
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810499711.6A
Other languages
Chinese (zh)
Other versions
CN108710764A (en
Inventor
裴吉
王文杰
曹健
甘星城
顾延东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University
Original Assignee
Jiangsu University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu University filed Critical Jiangsu University
Priority to CN201810499711.6A priority Critical patent/CN108710764B/en
Publication of CN108710764A publication Critical patent/CN108710764A/en
Application granted granted Critical
Publication of CN108710764B publication Critical patent/CN108710764B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D29/00—Details, component parts, or accessories
    • F04D29/18—Rotors
    • F04D29/22—Rotors specially for centrifugal pumps

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Physics & Mathematics (AREA)
  • Mechanical Engineering (AREA)
  • Structures Of Non-Positive Displacement Pumps (AREA)

Abstract

The invention discloses a double-suction pump multi-objective optimization design method based on a mixed approximate model, which mainly comprises the following steps: firstly, the method comprises the following steps: establishing a data sample by taking main geometric parameters of an impeller as input values and pump efficiency as a target value; secondly, establishing an artificial neural network approximate model, and solving model coefficients by adopting a particle swarm algorithm; thirdly, establishing a second-order response surface approximate model, and solving model coefficients by adopting a particle swarm algorithm; fourthly, establishing a mixed approximate model of weighted superposition of the artificial neural network model and the second-order response surface model, and solving a weight coefficient by adopting a particle swarm algorithm; and fifthly, optimizing the mixed approximation model under three working conditions of 0.8Q, 1.0Q and 1.2Q by adopting a multi-target genetic algorithm, and searching for an optimal design point. The invention can establish a more accurate mixed approximate model, meet the design requirement of widening the high-efficiency area of the double-suction pump and reduce the design cost at the same time.

Description

Double-suction pump multi-objective optimization design method based on hybrid approximation model
Technical Field
The invention relates to an optimal design method of a double-suction pump.
Background
The double-suction centrifugal pump is widely applied to many national economic fields such as agricultural irrigation and drainage, large-scale pump stations, chemical engineering and the like. The design of the double-suction pump is still based on semi-empirical semi-theoretical design at present, performance characteristics with narrow high-efficiency area range are easy to appear, and further optimization is needed.
Optimization of centrifugal pumps has been one of the long-standing research hotspots in the pump field. The patent number 201010520561.6 provides a CFD-based centrifugal pump multi-working-condition hydraulic optimization method, and the efficient area of the centrifugal pump is enlarged by constructing a response surface model and carrying out global optimization, so that the method has referential significance. However, the fitting accuracy of the data samples in the method depends on a single response surface approximate model, and the design requirement may not be met.
The existing double-suction centrifugal pump is designed mainly by means of design experience and a numerical simulation design method. At present, a method for improving the high-efficiency area range of the double-suction pump by adopting a mixed approximate model does not exist.
Disclosure of Invention
The invention aims to provide a double-suction pump multi-objective optimization design method based on a hybrid approximate model, which designs a double-suction centrifugal pump by integrating Latin hypercube test design, numerical simulation, an artificial neural network model, a second-order response surface model, a particle swarm optimization algorithm and a multi-objective genetic algorithm, thereby obtaining an optimal set of centrifugal pump impeller geometric parameter combinations of the double-suction pump and widening the high-efficiency area of the double-suction pump.
In order to achieve the purpose, the invention adopts the following technical scheme: a double-suction pump multi-objective optimization design method based on a hybrid approximation model comprises the following steps: the method comprises the following steps: selecting parameters which have great influence on the efficiency of the double-suction pump according to design experience, and carrying out numerical scheme design on the parameters by adopting a Latin hypercube test design method; step two: adopting CFturbo software to carry out impeller three-dimensional modeling based on the numerical scheme selected in the step one, storing the impeller three-dimensional modeling as an STP file, introducing the STP file into ICEM software to carry out non-structural grid division, wherein the grid is an STX 5 file, introducing the CFX5 into CFX to carry out the steady numerical simulation calculation of the design working condition, and obtaining the efficiency values of the pump under the working conditions of 0.8, 1.0 and 1.2 respectively to obtain 60 groups of efficiency values; step three: the method comprises the steps of taking a parameter which has a large influence on the efficiency of the double-suction pump as an input value, taking the efficiency of the pump under 0.8, 1.0 and 1.2 times of design conditions as an output value, establishing a data sample, adopting an artificial neural network model to establish an approximate model between the input value and the output value, and solving all coefficients in the model by using a particle swarm algorithm. Step four: establishing an approximate model between an input value and an output value by using a data sample in the third step and adopting a second-order response surface model, and solving a model coefficient by using a particle swarm algorithm; step five: weighting and superposing the artificial neural network model in the third step and the second-order response surface model in the fourth step, solving the weight by adopting a particle swarm optimization algorithm, and respectively obtaining mixed approximate models of efficiency values and parameters under the working conditions of 0.8 times, 1.0 time and 1.2 times; step six: and solving the mixed approximate model by adopting a multi-target genetic algorithm to obtain the optimal combination of parameters which have larger influence on the efficiency of the double-suction pump when the efficiency is higher under three working conditions. Step seven: and (3) performing three-dimensional modeling on the optimal combination of parameters with large influence on the efficiency of the double-suction pump, performing numerical simulation by adopting the same CFX setting, judging whether the design requirement can be met, completing the design if the design requirement is met, and returning to the step three to reselect the parameters if the design requirement is not met.
In the scheme, the parameters which have great influence on the efficiency of the double-suction pump and are selected in the first step and the sixth step are as follows: diameter d of blade inletiDiameter d of hubhDiameter d of the blade outletoBlade exit setting angle beta2Vane wrap angle phi and vane outlet width b2(ii) a In the first step, 20 sets of scheme designs are carried out on the six parameters.
In the above scheme, in the second step, the impeller is three-dimensionally shaped based on the 20 selected sets of scheme designs.
In the above scheme, in the second step, when CFX5 is introduced into CFX to perform steady numerical simulation calculation of the design working condition and obtain the efficiency values of the pump under the working conditions of 0.8, 1.0 and 1.2, respectively, 60 groups of efficiency values are obtained.
In the above scheme, in step three, the formula for solving all coefficients in the approximate model is as follows:
Figure BDA0001669980610000021
wherein the content of the first and second substances,
Figure BDA0001669980610000022
in order to be the weight coefficient,
Figure BDA0001669980610000023
b2is a threshold value, i is a designer of the pumpAnd (4) obtaining the condition multiple.
In the above scheme, in step four, the formula for solving the model coefficients is:
Figure BDA0001669980610000024
wherein, w0、wj、w′j、wjkIs a coefficient of a quadratic function;
in the above scheme, in step five, the formula of the hybrid approximation model is:
Ti(x)＝w1iAi(x)+w2iBi(x)
wherein w1i、w2iAre respectively a function Ai(x) And Bi (x).
The invention has the beneficial effects that: the method can establish a mixed approximate model with high accuracy and strong adaptability, and effectively broadens the range of the high-efficiency area of the double-suction pump.
Drawings
FIG. 1 is a flow chart of a double-suction pump multi-objective optimization design method based on a hybrid approximation model.
FIG. 2 is a three-dimensional schematic view of a double suction pump impeller
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
FIG. 1 is the idea of the invention, and the design method for widening the range of the high-efficiency area of the double-suction pump mainly comprises the following steps: firstly, selecting parameters which have great influence on the efficiency of the double-suction pump according to design experience, and carrying out 60 groups of test design by adopting a Latin hypercube; secondly, three-dimensional modeling is carried out on the impeller by adopting CFturbo software, the model is divided into grids by adopting ICEM software, numerical simulation is carried out on the scheme by adopting CFX, and the efficiency of the double-suction pump under the working conditions of 0.8 time, 1.0 time and 1.2 times is calculated; and thirdly, establishing a data sample by taking main geometric parameters of the impeller as input values and pump efficiency as output values, respectively establishing a mathematical model between the input values and the output values by adopting an artificial neural network model, a second-order response surface approximate model and a mixed approximate model of weighted superposition of the second-order response surface approximate model and the artificial neural network model, and solving model parameters by combining a particle swarm algorithm. And fourthly, optimizing the hybrid approximate model under three working conditions by adopting a multi-target genetic algorithm.
Example (c): design working condition Q of double-suction centrifugal pump is 500m3H is 40m, and the rotating speed n is 1480 r/min;
Figure BDA0001669980610000031
in the formula: n is the rotation speed, unit r/min; q is flow rate, unit m 3/h; h is the lift, unit m; specific number of revolutions 127.
Referring to modern pump theory and design, a double-suction pump is designed, and according to design experience, the initial blade inlet diameter di is 192mm, the hub diameter dh is 64mm, the blade outlet diameter do is 365mm, the blade outlet setting angle β 2 is 30 °, the blade wrap angle phi is 150 °, and the blade outlet width b2 is 8mm, which are main geometric parameters influencing the pump efficiency. Latin hypercube was used for 20 sets of experimental design.
Test method Vane inlet diameter di Hub diameter dh Impeller exit diameter do Blade outlet setting angle beta 2 Blade wrap angle phi Vane outlet width b2
1 194.8 69.8 383.9 28.0 148.2 8.2
2 179.4 63.7 392.2 29.1 148.8 7.8
3 173.2 60.4 384.7 32.4 158.4 8.1
4 206.1 58.4 372.8 28.6 162.5 8.6
5 209.1 66.5 331.8 32.2 141.1 8.5
6 201.3 62.7 343.1 29.4 137.4 7.9
7 208.5 62.9 359.6 27.1 150.6 8.1
8 189.2 64.4 379.4 29.1 156.0 7.3
9 178.5 65.3 348.6 27.8 164.5 8.6
10 187.8 61.1 336.6 32.9 144.5 7.7
11 197.4 65.1 395.3 30.2 155.5 7.2
12 192.6 58.6 344.7 30.6 160.7 7.6
13 204.7 60.2 401.3 28.5 146.6 7.6
14 198.1 61.7 376.6 31.7 139.1 7.1
15 191.0 67.7 353.2 27.5 151.6 7.3
16 182.1 66.1 366.0 31.1 135.8 8.9
17 183.1 67.1 388.2 29.8 160.2 8.3
18 203.3 68.7 355.5 32.0 141.0 8.8
19 185.2 69.3 338.5 30.5 143.7 7.4
20 174.9 59.3 368.7 31.5 154.0 9.0
Adopting CFturbo software to carry out three-dimensional modeling on each 20 groups of impellers, storing the three-dimensional modeling as an star. stp file, importing the star. stp file into ICEM software to carry out non-structural grid division, wherein the grid is an star. CFX5 file, importing the star. CFX5 into CFX to carry out the steady numerical simulation calculation of the design working condition, and obtaining the efficiency of the pump under the design working conditions of 0.8, 1.0 and 1.2 times; the efficiencies obtained under the standard operating conditions were as follows:
test protocol Efficiency (0.8Q) Efficiency (1.0Q) Efficiency (1.2Q)
1 75.6 85.0 79.7
2 87.1 79.8 87.9
3 79.5 82.2 87.2
4 76.2 80.3 85.5
5 84.0 75.8 80.5
6 77.5 75.1 84.3
7 79.6 76.4 86.3
8 82.0 84.1 79.4
9 84.6 78.3 75.8
10 78.6 79.0 81.6
11 74.1 74.6 78.4
12 77.5 86.5 85.1
13 83.7 83.6 76.2
14 85.4 82.7 77.4
15 80.7 87.0 81.9
16 86.3 87.4 74.1
17 81.3 81.7 77.9
18 88.0 76.8 83.4
19 82.8 77.7 75.2
20 75.4 85.5 82.6
The diameter d of the inlet of the blade is the main geometric parameter of the impelleriDiameter d of hubhDiameter d of the blade outletoBlade exit setting angle beta2Vane wrap angle phi and vane outlet width b2As an input value, the efficiency of the pump under 0.8, 1.0 and 1.2 times of the design condition is used as an output value, a data sample is established, and an approximate model between the efficiency and the main geometric parameters is established by respectively adopting an artificial neural network model, a second-order response surface model and a mixed approximate model of weighted superposition of the artificial neural network model and the second-order response surface model;
Figure BDA0001669980610000041
wherein the content of the first and second substances,
Figure BDA0001669980610000042
in order to be a weight coefficient of the image,
Figure BDA0001669980610000043
b2and i is a design working condition multiple of the pump.
Figure BDA0001669980610000044
Wherein, w0、wj、w′j、wjkIs a coefficient of a quadratic function;
Ti(x)＝w1i Ai(x)+w2i Bi(x)
wherein w1i、w2iAre respectively a function Ai(x) And Bi (x).
Solving all the formula coefficients by adopting a particle swarm optimization algorithm; optimizing the mixed approximation model under three working conditions by adopting a multi-target genetic algorithm; finally, obtaining the optimal combination of impeller parameters: diameter d of blade inleti191mm, hub diameter dh67.7mm, blade exit diameter do353.2mm blade outlet setting angle beta227.5 DEG, 151.6 DEG for the blade wrap angle phi and the blade outlet width b27.3 mm; the efficiency of the impeller after optimization under three working conditions of 0.8Q, 1.0Q and 1.2Q is more than 80%.

Claims (6)

1. A double-suction pump multi-objective optimization design method based on a hybrid approximation model is characterized by comprising the following steps:
the method comprises the following steps: selecting parameters which have great influence on the efficiency of the double-suction pump according to design experience, and carrying out numerical scheme design on the parameters by adopting a Latin hypercube test design method;
step two: adopting CFturbo software to carry out impeller three-dimensional modeling based on the numerical scheme selected in the step one, storing the impeller three-dimensional modeling as an STP file, introducing the STP file into ICEM software to carry out non-structural grid division, wherein the grid is an STX 5 file, introducing the CFX5 into CFX to carry out the steady numerical simulation calculation of the design working condition, and obtaining the efficiency values of the pump under the working conditions of 0.8, 1.0 and 1.2 respectively to obtain 60 groups of efficiency values;
step three: taking a parameter which has a large influence on the efficiency of the double-suction pump as an input value, taking the efficiency of the pump under 0.8, 1.0 and 1.2 times of design working conditions as an output value, establishing a data sample, establishing an approximate model between the input value and the output value by adopting an artificial neural network model, and solving all coefficients in the model by using a particle swarm algorithm;
step four: establishing an approximate model between an input value and an output value by using a data sample in the third step and adopting a second-order response surface model, and solving a model coefficient by using a particle swarm algorithm;
step five: weighting and superposing the artificial neural network model in the third step and the second-order response surface model in the fourth step, solving the weight by adopting a particle swarm optimization algorithm, and respectively obtaining mixed approximate models of efficiency values and parameters under the working conditions of 0.8 times, 1.0 time and 1.2 times;
step six: solving the mixed approximate model by adopting a multi-target genetic algorithm to obtain an optimal combination of parameters which have larger influence on the efficiency of the double suction pump when the efficiency is higher under three working conditions;
step seven: and (3) performing three-dimensional modeling on the optimal combination of parameters with large influence on the efficiency of the double-suction pump, performing numerical simulation by adopting the same CFX setting, judging whether the design requirement can be met, completing the design if the design requirement is met, and returning to the step three to reselect the parameters if the design requirement is not met.
2. The double suction pump multi-objective optimization design method based on the hybrid approximation model as claimed in claim 1, wherein the parameters selected in the first step and the sixth step, which have a large influence on the efficiency of the double suction pump, are: diameter d of blade inletiDiameter d of hubhDiameter d of the blade outletoBlade exit setting angle beta2Blade wrap angle
Figure FDA0003577799230000011
Width b of blade outlet2(ii) a In the first step, 20 sets of scheme designs are carried out on the six parameters.
3. The method for multi-objective optimization design of double suction pumps based on the hybrid approximation model as claimed in claim 2, wherein in step two, the impeller is three-dimensionally shaped based on the 20 sets of scheme designs.
4. The double suction pump multi-objective optimization design method based on the hybrid approximation model as claimed in claim 1, wherein in step three, the formula for solving all coefficients in the approximation model is:
Figure FDA0003577799230000021
wherein the content of the first and second substances,
Figure FDA0003577799230000022
in order to be the weight coefficient,
Figure FDA0003577799230000023
b2and i is a design working condition multiple of the pump.
5. The double suction pump multi-objective optimization design method based on the hybrid approximation model as claimed in claim 4, wherein in step four, the formula for solving the model coefficients is as follows:
Figure FDA0003577799230000024
wherein w0、wj、w′j、wjkIs a coefficient of a quadratic function.
6. The double suction pump multi-objective optimization design method based on the hybrid approximation model as claimed in claim 5, wherein in the fifth step, the formula of the hybrid approximation model is as follows:
Ti(x)＝w1iAi(x)+w2iBi(x)
wherein w1i、w2iAre respectively a function Ai(x)、Bi(x) The weighting coefficient of (2).
CN201810499711.6A 2018-05-23 2018-05-23 Double-suction pump multi-objective optimization design method based on hybrid approximation model Active CN108710764B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810499711.6A CN108710764B (en) 2018-05-23 2018-05-23 Double-suction pump multi-objective optimization design method based on hybrid approximation model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810499711.6A CN108710764B (en) 2018-05-23 2018-05-23 Double-suction pump multi-objective optimization design method based on hybrid approximation model

Publications (2)

Publication Number Publication Date
CN108710764A CN108710764A (en) 2018-10-26
CN108710764B true CN108710764B (en) 2022-06-21

Family

ID=63868480

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810499711.6A Active CN108710764B (en) 2018-05-23 2018-05-23 Double-suction pump multi-objective optimization design method based on hybrid approximation model

Country Status (1)

Country Link
CN (1) CN108710764B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109783857A (en) * 2018-12-12 2019-05-21 珠海博雅科技有限公司 A kind of quick charge pump design method and device
CN112861277B (en) * 2021-01-13 2024-03-22 江苏大学 Centrifugal pump impeller main size design method and device
CN112836409A (en) * 2021-02-03 2021-05-25 浙江工业大学 Optimal design method of bistable composite shell
CN114297793B (en) * 2021-12-24 2023-07-25 山东双轮股份有限公司 Multidisciplinary optimization design method for impeller structure of seawater desalination pump
CN117973266A (en) * 2024-03-21 2024-05-03 四川省机械研究设计院(集团)有限公司 Method, device, equipment and medium for optimizing design parameters of high-speed centrifugal pump

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103020345A (en) * 2012-12-05 2013-04-03 华南理工大学 Centrifugal pump design method based on matrix laboratory (MATLAB) genetic algorithm
CN104573282A (en) * 2015-01-29 2015-04-29 河海大学 Aerodynamic optimum design method of airfoil profile of vertical axis wind turbine
CN107180148A (en) * 2017-07-05 2017-09-19 中南大学 A kind of reliability analyzing method based on generalized regression nerve networks and response phase method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170030359A1 (en) * 2015-07-31 2017-02-02 Siemens Aktiencesellschaft Batch change control for variable speed driven centrifugal pumps and pump systems

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103020345A (en) * 2012-12-05 2013-04-03 华南理工大学 Centrifugal pump design method based on matrix laboratory (MATLAB) genetic algorithm
CN104573282A (en) * 2015-01-29 2015-04-29 河海大学 Aerodynamic optimum design method of airfoil profile of vertical axis wind turbine
CN107180148A (en) * 2017-07-05 2017-09-19 中南大学 A kind of reliability analyzing method based on generalized regression nerve networks and response phase method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Optimization of the diffuser in a centrifugal pump by combining response surface method with multi-island genetic algorithm;Wenjie Wang 等;《ARCHIVE Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering》;20150531;第1989-1996页 *
混合神经网络匹配响应面的多学科设计方法;刘道华 等;《西安电子科技大学学报》;20121031;第39卷(第5期);第161-167页 *

Also Published As

Publication number Publication date
CN108710764A (en) 2018-10-26

Similar Documents

Publication Publication Date Title
CN108710764B (en) Double-suction pump multi-objective optimization design method based on hybrid approximation model
CN106650105A (en) Design method for mixed-flow pump impeller
CN102608914B (en) Optimization design method of radial-flow-type hydraulic turbine
CN109325264B (en) High-efficiency high-altitude chemical performance double-suction pump hydraulic design method
CN103020345A (en) Centrifugal pump design method based on matrix laboratory (MATLAB) genetic algorithm
CN105465037B (en) The hydraulic optimization method and device of a kind of impeller for double suction centrifugal pump
CN109344444B (en) Energy loss evaluation method for blade angle optimization effect of hydraulic torque converter
CN113177271A (en) Matching optimization method for improving performance and hump instability of large vertical centrifugal pump
CN111027148B (en) Automatic calibration and industrial axial flow compressor performance calculation method for loss lag angle model
CN108984983B (en) Blade angle optimization method for improving comprehensive efficiency of adjustable hydraulic torque converter
CN113032920A (en) Optimum design method of aviation fuel centrifugal pump based on orthogonal test
CN109145321B (en) Centrifugal pump energy-saving optimization design method based on multi-objective genetic algorithm
CN114547987A (en) Centrifugal pump turbine performance prediction method based on improved artificial neural network
CN104675713B (en) A kind of centrifugal pump No-mistake Principle method for designing based on data sample
CN111832132B (en) Design method of hydraulic model of low-specific-speed high-speed centrifugal pump
CN107066686B (en) Axial flow pump impeller hydraulic optimization design method based on genetic algorithm
CN108446452A (en) A kind of mixed-flow pump impeller Robust Optimal Design
CN106934177A (en) A kind of Optimization Design of double volute mixed-flow pump dividing plate
CN116717503A (en) Design method of second impeller molded line of water lifting pump for multistage well
CN107515996B (en) Optimal design method for molded line of flow guide cover of Dalie turbine
CN115455821A (en) Centrifugal pump turbine performance prediction method based on improved PSO-GA algorithm
CN109472389A (en) A kind of middle low specific speed impeller multi-state intelligent optimization method
CN109145461B (en) Matching optimization design method for overflow parts of high-pressure seawater desalination pump
CN111079367B (en) Axial flow pump spoke optimization design method suitable for different water inlet taper pipes
CN112761969A (en) Optimization method for structural parameters of centrifugal pump

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB03 Change of inventor or designer information
CB03 Change of inventor or designer information

Inventor after: Pei Ji

Inventor after: Wang Wenjie

Inventor after: Cao Jian

Inventor after: Gan Xingcheng

Inventor after: Gu Yandong

Inventor before: Pei Ji

Inventor before: Cao Jian

Inventor before: Wang Wenjie

Inventor before: Gan Xingcheng

Inventor before: Gu Yandong

GR01 Patent grant
GR01 Patent grant