CN106874542B - Multi-working-condition multi-target optimization design method for hydraulic turbine impeller - Google Patents
Multi-working-condition multi-target optimization design method for hydraulic turbine impeller Download PDFInfo
- Publication number
- CN106874542B CN106874542B CN201710003433.6A CN201710003433A CN106874542B CN 106874542 B CN106874542 B CN 106874542B CN 201710003433 A CN201710003433 A CN 201710003433A CN 106874542 B CN106874542 B CN 106874542B
- Authority
- CN
- China
- Prior art keywords
- hydraulic turbine
- turbine
- design
- optimization
- impeller
- 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
Links
- 238000013461 design Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000005457 optimization Methods 0.000 title claims abstract description 47
- 230000002068 genetic effect Effects 0.000 claims abstract description 29
- 238000013528 artificial neural network Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims abstract description 7
- 230000006870 function Effects 0.000 claims description 33
- 238000012360 testing method Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 11
- 238000011156 evaluation Methods 0.000 claims description 9
- 108090000623 proteins and genes Proteins 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000010998 test method Methods 0.000 claims description 4
- 125000000174 L-prolyl group Chemical group [H]N1C([H])([H])C([H])([H])C([H])([H])[C@@]1([H])C(*)=O 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 239000003795 chemical substances by application Substances 0.000 claims description 3
- 239000012530 fluid Substances 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000011084 recovery Methods 0.000 description 9
- 239000007788 liquid Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004517 catalytic hydrocracking Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010612 desalination reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000013535 sea water Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
Landscapes
- Physics & Mathematics (AREA)
- Geometry (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Structures Of Non-Positive Displacement Pumps (AREA)
Abstract
The invention relates to the field of optimization design of hydraulic machinery, in particular to a multi-working-condition and multi-target optimization design method for a hydraulic turbine impeller. The beneficial effects of the invention are as follows: and training a BP neural network optimization algorithm to establish and update an approximate prediction model, reducing the CFD calculation of a large load, and obtaining sufficient prediction precision by calculating for a few times. The BP neural network optimization algorithm and the NSGA-II multi-target genetic algorithm are organically combined, so that the diversity of the population is considered while the optimal solution set is continuously searched in the whole algorithm, and the precision is improved. The NSGA-II multi-target genetic algorithm is adopted to carry out optimization solution on the turbine impeller, the problems of difficulty in traditional design and type selection of the hydraulic turbine impeller, unstable vibration and power output, low efficiency and the like easily occurring when the hydraulic turbine is deviated from the optimal working condition in operation can be well solved, the turbine efficiency, the axial force and the radial force are considered, and the turbine operation capacity can be effectively improved.
Description
Technical Field
The invention relates to the field of optimization design of hydraulic machinery, in particular to a multi-working-condition and multi-target optimization design method for a hydraulic turbine impeller.
Background
The hydraulic turbine is a mechanical device for converting pressure energy in a liquid fluid working medium into mechanical energy, can recycle liquid excess pressure in a process flow by utilizing the hydraulic turbine, converts the liquid excess pressure into the mechanical energy to drive the mechanical device, is an energy recovery device, and is widely applied to the fields of petrochemical hydrocracking, large-scale ammonia synthesis, seawater desalination and the like at present. Technically, 20KW of recovered energy can be recycled by a hydraulic turbine. The energy recovery hydraulic turbine technology and the application have important significance for energy conservation and emission reduction. The hydraulic turbine mainly has a reverse pump mode, an impact mode, a guide vane mode and a special energy recovery hydraulic turbine which is developed in advanced countries at present. The basic arrangement mode of the turbine recovery device comprises a direct-drive type arrangement mode and an auxiliary type arrangement mode. The hydraulic turbine energy recovery device is widely applied, and research of the hydraulic turbine energy recovery device is developed towards specialization, specialization and diversification.
At present, a reverse pump is mainly used for a hydraulic turbine commonly used in China, so that the operation efficiency is low, the high-efficiency area is relatively narrow, the starting process is long in time consumption, the operation working condition is unstable, and the development of the field of energy recovery engineering is restricted. When the flow is higher than 10% of the optimal working condition, the energy recovery efficiency is reduced by 50%, and when the flow is lower than 40% of the optimal working condition, the hydraulic turbine has no recovery power and can generate the bad phenomena of vibration, rotating speed, unstable output power and the like when the hydraulic turbine deviates from the optimal working condition point, so that the hydraulic turbine has a series of problems of being sensitive to the change of the operating working condition and the like. Some researchers at home and abroad find that the hydraulic loss of an impeller accounts for more than 50% of the total hydraulic loss when a hydraulic turbine operates, which indicates that the main reason for poor hydraulic performance of the hydraulic turbine is that the impeller performance is poor. The reverse pump is used as a hydraulic turbine, and the operation of the reverse pump is often out of the standard under the working condition of the turbine, so that a series of problems of low efficiency, poor operation stability, short service life, serious noise and vibration and the like occur.
Based on a series of problems of the hydraulic turbine, the method starts from the geometric parameters of the impeller of the turbine, combines a BP neural network algorithm and a multi-target genetic algorithm NSGA-II to carry out multi-working-condition multi-target optimization solution on the turbine, gives consideration to the performance of a plurality of working condition points, improves the efficiency and ensures the operation stability of each working condition. Through search, the patents related to the invention patent include: the invention discloses a radial flow type hydraulic turbine optimization design method (publication number: CN 102608914A), which comprises a unitary thermodynamic optimization design and a complete machine optimization platform, and decomposes a complex multivariable optimization problem into a plurality of sub-problems which are relatively independent and interact, thereby not only keeping the characteristics of the original problem, but also effectively reducing the calculated amount, but only aiming at single-point optimization, and the phenomenon of volatile stability and stable operation of the turbine when the turbine deviates from the optimal working condition is not solved; the optimization design method (publication number: CN104331553A) for the impeller blade structure of the large-scale turboexpander considering the defects adds defect factors on the basis of the original impeller stress analysis, utilizes the generalized regression neural network and the multi-objective optimization algorithm based on the genetic algorithm to carry out genetic optimization operation on the impeller parameters, and finally obtains the optimal solution with uniform distribution as the optimization parameters of the impeller blade, but the optimization process is complex and fussy.
Disclosure of Invention
The invention provides a multi-working-condition multi-target optimization design method for a hydraulic turbine impeller, aiming at solving the problems of difficulty in traditional design and type selection of the hydraulic turbine impeller, unstable power output, greatly low efficiency and the like which are easy to occur when a hydraulic turbine is deviated from an optimal working condition in operation.
The technical scheme of the invention is as follows:
firstly, determining key geometric parameters of an impeller of a hydraulic turbine pump as an optimization design variable and a sample number, generating a sample by adopting a test method, screening the sample to obtain sample points meeting requirements, carrying out integral automatic modeling, grid division and CFD (computational fluid dynamics) calculation on the initial sample to obtain corresponding performance parameters, establishing an optimization sample database, importing a BP (back propagation) neural network module, carrying out learning training, establishing an approximate proxy model of an optimization algorithm, finally embedding the approximate proxy model into an NSGA-II multi-target genetic algorithm, carrying out optimization solution on the genetic algorithm by taking the optimal efficiency, radial force and axial force under three flow working condition points of 0.7Q, 1.0Q and 1.2Q as targets, and solving out the integral optimal solution set of the impeller.
A multi-working-condition and multi-target optimization design method for a hydraulic turbine impeller specifically comprises the following implementation steps:
step 1, determining design variables, objective functions and constraints of a hydraulic turbine impeller according to the outer diameter D of the impeller2Diameter of outlet D1Two angles of inclination of the front and rear cover platesα1、α2Two arc radii R on the front and rear cover plate streamlines1、R2Width of inlet b2Angle of blade exit beta1The wrap angle phi of the blades and the number Z of the blades are design variables; secondly, generating a test sample of the design variable in the space of the design variable by adopting a test design method; finally, Pro/E software is adopted to carry out batch parameterization design on variables in the hydraulic turbine initial model;
step 2, manufacturing batch processing files of grids and CFD software, realizing automatic grid division and performance CFD calculation of all test samples of the hydraulic turbine, and finally obtaining performance values of all models;
step 3, after acquiring the performance parameters corresponding to all the sample models, training a BP neural network according to the geometric parameters of the test sample and the performance parameters corresponding to the hydraulic turbine, and establishing an approximate proxy model by taking the design variables as the input parameters of the approximate model and the performance parameters corresponding to the design variables as the output parameters;
and 4, performing performance evaluation and genetic evolution operation on each individual in the population, wherein the performance evaluation function of the individual is realized by a trained agent model embedded in a genetic algorithm. When the geometric parameters of the impeller are optimized by adopting an NSGA-II multi-target genetic algorithm, firstly, an initial population is randomly generated, and individual codes in the initial population are coded; secondly, performing performance evaluation on each individual in the population, wherein the performance parameters are related to the target function; finally, after the genetic gene and the objective function of each individual exist, carrying out genetic operation on the population according to the principle of the genetic gene and the objective function until the algorithm termination criterion is met, and obtaining a Pareto optimization solution set of multi-objective optimization;
and 5, selecting the optimal Pareto optimal solution set by adopting a combined weighting-based method, and selecting an optimal solution which is most suitable for the multi-working-condition efficient and stable operation of the hydraulic turbine from the Pareto optimal solution set, so as to obtain the optimal hydraulic model of the final optimal design.
The design variables, parameter constraints and objective functions in the above steps are as follows:
(1) designing variables: x ═ D1,D2,α1,α2,R1,R2,b2,β1,Φ,Z]T,
(2) The design variable constraint conditions are respectively as follows:
0.8Φ0≤Φ≤1.2Φ0；
INT(0.6Z0)≤Z≤INT(1.4Z0)；
initial values of geometric parameters in the formula
The speed coefficient is obtained by designing or model selection conversion through a traditional design method such as a speed coefficient method of a pump or turbine product.
(3) An objective function:
1) the maximum efficiency under the three working condition points of flow of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
wherein X-is a design variable vector with dimension 10;
f (X) -an optimized objective function;
o-initial value;
i-represents the working points of the flow rates of 0.7Q, 1.0Q and 1.3Q, 1,2 and 3;
Wi-the weighting factors for the operating points are determined by means of a hyper-transport approximation;
p-penalty factor;
h, a turbine pressure head;
2) the radial force minimum of three flow working condition points of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
3) the axial force minimum of three flow working condition points of 0.7Q, 1.0Q and 1.3Q is taken as an objective function:
the test method in the step 1 uses an optimized Latin hypercube design method, the uniformity of random Latin hypercube design is improved, the fitting of factors and responses is more accurate, and the design method has better space filling property and equilibrium.
The combined weighting method in the step 5 comprises the following steps:
(1) pareto optimization obtained by NSGA-II algorithmThe solution set is used as a scheme set, and is further converted into a decision matrix, the decision matrix is normalized by vector normalization, and a normalized decision matrix B is obtained as (B)ij)n×mN is the number of schemes, and m is the number of attributes;
(2) subjective preference weight vector omega (omega) of m attributes is determined by using expert deviation adjustment method1,ω2,…,ωm)T；
(3) Determining objective weight mu (mu) of attribute based on normalized decision matrix by using information entropy method1,μ2,…,μm)T；
(4) Combining the subjective weight and the objective weight by adopting a least square method to obtain a combination w ═ of each attribute of the comprehensive subjective decision and the objective decision1,w2,…,wm)T。
The beneficial effects of the invention are as follows:
1) and training a BP neural network optimization algorithm to establish and update an approximate prediction model, reducing the CFD calculation of a large load, and obtaining sufficient prediction precision by calculating for a few times.
2) The BP neural network optimization algorithm and the NSGA-II multi-target genetic algorithm are organically combined, so that the diversity of the population is considered while the optimal solution set is continuously searched in the whole algorithm, and the precision is improved.
3) The NSGA-II multi-target genetic algorithm is adopted to carry out optimization solution on the turbine impeller, the problems of difficulty in traditional design and type selection of the hydraulic turbine impeller, unstable vibration and power output, low efficiency and the like easily occurring when the hydraulic turbine is deviated from the optimal working condition in operation can be well solved, the turbine efficiency, the axial force and the radial force are considered, and the operation capacity of the turbine under multiple working conditions can be effectively improved.
Drawings
FIG. 1 is a general flow chart of a multi-condition multi-objective optimization design method for a hydraulic turbine impeller according to the present invention;
Detailed Description
The invention is further elucidated below with reference to the accompanying drawings:
fig. 1 is a general flow chart of a hydraulic turbine impeller multi-condition multi-target optimization design method, which mainly includes the following steps:
step 1, determining design variables, objective functions and constraints of a hydraulic turbine impeller according to the outer diameter D of the impeller2Diameter of outlet D1Two angles of inclination alpha of the front and rear cover plates1、α2Two arc radii R on the front and rear cover plate streamlines1、R2Width of inlet b2Angle of blade exit beta1The wrap angle phi of the blades and the number Z of the blades are design variables; secondly, generating a test sample of the design variable in the space of the design variable by adopting a test design method; finally, Pro/E software is adopted to carry out batch parameterization design on variables in the hydraulic turbine initial model;
step 2, manufacturing batch processing files of grids and CFD software, realizing automatic grid division and performance CFD calculation of all test samples of the hydraulic turbine, and finally obtaining performance values of all models;
step 3, after acquiring the performance parameters corresponding to all the sample models, training a BP neural network according to the geometric parameters of the test sample and the performance parameters corresponding to the hydraulic turbine, and establishing an approximate proxy model by taking the design variables as the input parameters of the approximate model and the performance parameters corresponding to the design variables as the output parameters;
and 4, performing performance evaluation and genetic evolution operation on each individual in the population, wherein the performance evaluation function of the individual is realized by a trained agent model embedded in a genetic algorithm. When the geometric parameters of the impeller are optimized by adopting an NSGA-II multi-target genetic algorithm, firstly, an initial population is randomly generated, and individual codes in the initial population are coded; secondly, performing performance evaluation on each individual in the population, wherein the performance parameters are related to the target function; finally, after the genetic gene and the objective function of each individual exist, carrying out genetic operation on the population according to the principle of the genetic gene and the objective function until the algorithm termination criterion is met, and obtaining a Pareto optimization solution set of multi-objective optimization;
and 5, selecting the optimal Pareto optimal solution set by adopting a combined weighting-based method, and selecting an optimal solution which is most suitable for the multi-working-condition efficient and stable operation of the hydraulic turbine from the Pareto optimal solution set, so as to obtain the optimal hydraulic model of the final optimal design.
The design variables, parameter constraints and objective functions in the above steps are as follows:
(1) designing variables: x ═ D1,D2,α1,α2,R1,R2,b2,β1,Φ,Z]T,
(2) The design variable constraint conditions are respectively as follows:
0.8Φ0≤Φ≤1.2Φ0；
INT(0.6Z0)≤Z≤INT(1.4Z0)；
initial values of geometric parameters in the formula
The speed coefficient is obtained by designing or model selection conversion through a traditional design method such as a speed coefficient method of a pump or turbine product.
(3) An objective function:
1) the maximum efficiency under the three working condition points of flow of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
wherein X-is a design variable vector with dimension 10;
f (X) -an optimized objective function;
o-initial value;
i-represents the working points of the flow rates of 0.7Q, 1.0Q and 1.3Q, 1,2 and 3;
Wi-the weighting factors for the operating points are determined by means of a hyper-transport approximation;
p-penalty factor;
h, a turbine pressure head;
2) the radial force minimum of three flow working condition points of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
3) the axial force minimum of three flow working condition points of 0.7Q, 1.0Q and 1.3Q is taken as an objective function:
the test method in the step 1 uses an optimized Latin hypercube design method, the uniformity of random Latin hypercube design is improved, the fitting of factors and responses is more accurate, and the design method has better space filling property and equilibrium.
The combined weighting method in the step 5 comprises the following steps:
(1) taking a Pareto optimal solution set obtained by an NSGA-II algorithm as a scheme set, further converting the scheme set into a decision matrix, and carrying out normalization processing on the decision matrix by using vector normalization to obtain a normalized decision matrix B ═ (B)ij)n×mN is the number of schemes, and m is the number of attributes;
(2) subjective preference weight vector omega (omega) of m attributes is determined by using expert deviation adjustment method1,ω2,…,ωm)T；
(3) Determining objective weight mu (mu) of attribute based on normalized decision matrix by using information entropy method1,μ2,…,μm)T；
(4) Combining the subjective weight and the objective weight by adopting a least square method to obtain a combination w ═ of each attribute of the comprehensive subjective decision and the objective decision1,w2,…,wm)T。
The present invention is not limited to the above-described embodiments, and includes other embodiments and modifications within the scope of the inventive concept.
Claims (4)
1. A multi-working-condition multi-target optimization design method for a hydraulic turbine impeller is characterized by comprising the following steps: firstly, determining key geometric parameters of an impeller of a hydraulic turbine pump as an optimization design variable and the number of samples, generating samples by adopting a test method, screening the samples to obtain sample points meeting requirements, carrying out integral automatic modeling, grid division and CFD (computational fluid dynamics) calculation on the initial samples to obtain corresponding performance parameters, establishing an optimization sample database, importing a BP (back propagation) neural network module, carrying out learning training, establishing an approximate proxy model of an optimization algorithm, finally embedding the approximate proxy model into an NSGA-II multi-target genetic algorithm, carrying out optimization solution on the genetic algorithm by taking the optimal efficiency, radial force and axial force under three flow working condition points of 0.7Q, 1.0Q and 1.2Q as targets, and solving out the integral optimal solution set of the impeller;
the hydraulic turbine impeller multi-working-condition multi-target optimization design method specifically comprises the following implementation steps:
step 1, determining design variables, objective functions and parameter constraint conditions of a hydraulic turbine impeller; secondly, generating a test sample of the design variable in the space of the design variable by adopting a test design method; finally, Pro/E software is adopted to carry out batch parameterization design on variables in the hydraulic turbine initial model;
step 2, manufacturing batch processing files of grids and CFD software, realizing automatic grid division and performance CFD calculation of all test samples of the hydraulic turbine, and finally obtaining performance values of all models;
step 3, after acquiring the performance parameters corresponding to all the sample models, training a BP neural network according to the geometric parameters of the test sample and the performance parameters corresponding to the hydraulic turbine, and establishing an approximate proxy model by taking the design variables as the input parameters of the approximate model and the performance parameters corresponding to the design variables as the output parameters;
performing performance evaluation and genetic evolution operation on each individual in the population, wherein the performance evaluation function of the individual is realized by a trained agent model embedded in a genetic algorithm, and when the geometric parameters of the impeller are optimized by adopting an NSGA-II multi-target genetic algorithm, firstly randomly generating an initial population, and encoding the individual in the initial population; secondly, performing performance evaluation on each individual in the population, wherein the performance parameters are related to the target function; finally, after the genetic gene and the objective function of each individual exist, carrying out genetic operation on the population according to the principle of the genetic gene and the objective function until the algorithm termination criterion is met, and obtaining a Pareto optimization solution set of multi-objective optimization;
and 5, selecting the optimal Pareto optimal solution set by adopting a combined weighting-based method, and selecting an optimal solution which is most suitable for the multi-working-condition efficient and stable operation of the hydraulic turbine from the Pareto optimal solution set, so as to obtain the optimal hydraulic model of the final optimal design.
2. The multi-condition multi-target optimization design method for the hydraulic turbine impeller as claimed in claim 1, characterized in that: the design variables, parameter constraints and objective function are as follows:
(1) designing variables: x ═ D1,D2,α1,α2,R1,R2,b2,β2,Φ,Z]T,
X-design variable vector with dimension 10;
D1-turbine wheel exit diameter, m;
D2-turbine wheel inlet diameter, m;
α1-turbine wheel front shroud inclination, °;
α2-turbine wheel back shroud angle, °;
R1-the radius of the arc of the front shroud of the turbine wheel, m;
R2-turbine wheel back shroud arc radius, m;
b2-turbine wheel inlet axial width, m;
β2-turbine impeller blade inlet setting angle, °;
phi-turbine impeller blade wrap angle, °;
z is the number of blades of the turbine impeller, one blade;
(2) the design variable constraint conditions are respectively as follows:
0.8Φ0≤Φ≤1.2Φ0；
INT(0.6Z0)≤Z≤INT(1.4Z0)；
initial values of geometric parameters in the formula
Designing or type selection conversion by a speed coefficient method of a pump or turbine product;
(3) an objective function:
1) the maximum efficiency under the three working condition points of flow of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
wherein X-is a design variable vector with dimension 10;
f (X) -an optimized objective function;
o-initial value;
i-represents the working points of the flow rates of 0.7Q, 1.0Q and 1.3Q, 1,2 and 3;
Wi-the weighting factors for the operating points are determined by means of a hyper-transport approximation;
p-penalty factor;
h, a turbine pressure head;
2) the radial force minimum of three flow working condition points of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
3) the axial force minimum of three flow working condition points of 0.7Q, 1.0Q and 1.2Q is taken as an objective function:
3. the multi-condition multi-target optimization design method for the hydraulic turbine impeller as claimed in claim 1, characterized in that: initial values of the geometric parameter constraints
The speed coefficient is designed or converted by model selection through a speed coefficient method of a pump or turbine product.
4. The multi-condition multi-target optimization design method for the hydraulic turbine impeller as claimed in claim 1, characterized in that: the combined weighting method in the step 5 comprises the following steps:
(1) taking a Pareto optimal solution set obtained by an NSGA-II algorithm as a scheme set, further converting the scheme set into a decision matrix, and carrying out normalization processing on the decision matrix by using vector normalization to obtain a normalized decision matrix B ═ (B)ij)n×mN is the number of schemes, and m is the number of attributes;
(2) subjective preference weight vector omega (omega) of m attributes is determined by using expert deviation adjustment method1,ω2,…,ωm)T；
(3) Based on normalized decision matrixDetermining objective weight mu (mu) of attribute by using information entropy method1,μ2,…,μm)T；
(4) Combining the subjective weight and the objective weight by adopting a least square method to obtain a combination w ═ of each attribute of the comprehensive subjective decision and the objective decision1,w2,…,wm)T。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710003433.6A CN106874542B (en) | 2017-01-04 | 2017-01-04 | Multi-working-condition multi-target optimization design method for hydraulic turbine impeller |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710003433.6A CN106874542B (en) | 2017-01-04 | 2017-01-04 | Multi-working-condition multi-target optimization design method for hydraulic turbine impeller |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106874542A CN106874542A (en) | 2017-06-20 |
| CN106874542B true CN106874542B (en) | 2020-11-13 |
Family
ID=59164535
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710003433.6A Active CN106874542B (en) | 2017-01-04 | 2017-01-04 | Multi-working-condition multi-target optimization design method for hydraulic turbine impeller |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106874542B (en) |
Families Citing this family (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146112B (en) * | 2018-02-06 | 2021-06-08 | 中国科学院沈阳应用生态研究所 | Forest-oriented multi-objective optimization decision-making operation system |
| CN109472389B (en) * | 2018-09-10 | 2021-10-08 | 江苏大学 | Multi-working-condition intelligent optimization method for impeller with medium and low specific speed |
| CN110378016B (en) * | 2019-07-17 | 2022-07-05 | 哈尔滨工业大学 | Multi-objective optimization design method for pump impeller adjustable hydraulic torque converter |
| CN110488754B (en) * | 2019-08-09 | 2020-07-14 | 大连理工大学 | Machine tool self-adaptive control method based on GA-BP neural network algorithm |
| CN110955940B (en) * | 2019-11-18 | 2022-04-15 | 中国农业大学 | AHP-based mechanical device design model selection method |
| CN111079367B (en) * | 2019-11-28 | 2022-02-01 | 河海大学 | Axial flow pump spoke optimization design method suitable for different water inlet taper pipes |
| CN111046540B (en) * | 2019-11-28 | 2021-09-07 | 河海大学 | Axial flow pump spoke optimization design method based on NSGA-II |
| CN111832119A (en) * | 2020-06-23 | 2020-10-27 | 恒大新能源汽车投资控股集团有限公司 | Optimization method and device for vehicle suspension system |
| CN112287484B (en) * | 2020-10-29 | 2021-12-07 | 复旦大学 | Complex engineering system reliability design method based on vector agent model |
| CN112464478B (en) * | 2020-11-30 | 2023-06-30 | 中国长江电力股份有限公司 | Control law optimization method and device for water turbine speed regulation system |
| CN112632728B (en) * | 2020-12-30 | 2022-10-25 | 西安交通大学 | Turbine mechanical blade profile design and performance prediction method based on deep learning |
| CN112733351B (en) * | 2020-12-31 | 2024-03-01 | 沈阳透平机械股份有限公司 | Method and device for processing axial force data of impeller of centrifugal compressor |
| CN112784379B (en) * | 2021-03-09 | 2023-05-23 | 重庆邮电大学 | Optimal design method of impeller rotor system |
| CN113255211B (en) * | 2021-05-14 | 2022-04-26 | 湘潭大学 | ORC working medium screening method based on BP neural network and multi-objective optimization |
| CN113361047B (en) * | 2021-08-10 | 2021-11-05 | 浙江大学 | Impeller high-dimensional optimization and model selection method for multilayer sensing structure |
| CN113657033B (en) * | 2021-08-13 | 2023-07-28 | 哈尔滨工程大学 | Model-based gas turbine multi-objective optimization method |
| 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 |
| CN115114750B (en) * | 2022-07-19 | 2024-07-02 | 北京航空航天大学 | Multi-working-point optimization method for wide-working-condition power turbine |
| CN115481510B (en) * | 2022-09-30 | 2023-05-02 | 北京科技大学 | Radial compressor blade multi-working-condition optimization method and device based on improved NSGA-II algorithm |
| CN115481511B (en) * | 2022-09-30 | 2023-08-29 | 北京科技大学 | FFD-based multi-working-condition local configuration pneumatic optimization method and device for centrifugal impeller |
| CN117852403B (en) * | 2024-01-08 | 2024-09-27 | 扬州大学 | Centrifugal pump impeller hydraulic design method, centrifugal pump impeller and centrifugal pump |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101956711A (en) * | 2010-10-27 | 2011-01-26 | 江苏大学 | Computational fluid dynamics (CFD)-based centrifugal pump multi-working condition hydraulic power optimization method |
| CN102608914A (en) * | 2011-12-22 | 2012-07-25 | 西安交通大学 | Optimization design method of radial-flow-type hydraulic turbine |
| CN104331553A (en) * | 2014-10-29 | 2015-02-04 | 浙江大学 | Optimal design method of large turbo expander impeller blade structure with defect consideration |
| CN105022871A (en) * | 2015-07-07 | 2015-11-04 | 同济大学 | Blade number optimization method of hydraulic torque converter on the basis of neural network and complete machine |
| CN105275711A (en) * | 2015-10-16 | 2016-01-27 | 江苏大学 | Hydraulic design method for bent tail water pipe of hydraulic turbine device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012151447A2 (en) * | 2011-05-03 | 2012-11-08 | The Administrators Of The Tulane Educational Fund | Underground reactor system |
-
2017
- 2017-01-04 CN CN201710003433.6A patent/CN106874542B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101956711A (en) * | 2010-10-27 | 2011-01-26 | 江苏大学 | Computational fluid dynamics (CFD)-based centrifugal pump multi-working condition hydraulic power optimization method |
| CN102608914A (en) * | 2011-12-22 | 2012-07-25 | 西安交通大学 | Optimization design method of radial-flow-type hydraulic turbine |
| CN104331553A (en) * | 2014-10-29 | 2015-02-04 | 浙江大学 | Optimal design method of large turbo expander impeller blade structure with defect consideration |
| CN105022871A (en) * | 2015-07-07 | 2015-11-04 | 同济大学 | Blade number optimization method of hydraulic torque converter on the basis of neural network and complete machine |
| CN105275711A (en) * | 2015-10-16 | 2016-01-27 | 江苏大学 | Hydraulic design method for bent tail water pipe of hydraulic turbine device |
Non-Patent Citations (1)
| Title |
|---|
| 基于神经网络－遗传算法的液力透平叶片型线优化;苗森春;《航空动力学报》;20150831;第1918-1925页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106874542A (en) | 2017-06-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106874542B (en) | Multi-working-condition multi-target optimization design method for hydraulic turbine impeller | |
| CN110110349B (en) | Multi-working-condition optimization design method for space guide vane of rotary vane type mixed flow pump | |
| CN109872012A (en) | Based on the determination method for thermal power plant's operation multiple-objection optimization that operating condition divides | |
| CN112615387B (en) | Energy storage capacity configuration method, device, computer equipment and readable storage medium | |
| CN106777526B (en) | Genetic algorithm-based high-temperature high-pressure centrifugal impeller multidisciplinary optimization method | |
| CN103020345A (en) | Centrifugal pump design method based on matrix laboratory (MATLAB) genetic algorithm | |
| CN103761385A (en) | Optimized design method for mult-heat-source annular pipe network | |
| Shang et al. | Performance of genetic algorithms with different selection operators for solving short-term optimized reservoir scheduling problem | |
| CN116702611A (en) | Pump unit optimization method based on genetic algorithm | |
| CN106503401B (en) | Blades For Horizontal Axis Wind adaptability design method | |
| CN113361047B (en) | Impeller high-dimensional optimization and model selection method for multilayer sensing structure | |
| CN111859807B (en) | Method, device, equipment and storage medium for optimizing initial pressure of steam turbine | |
| CN106844905B (en) | Hydraulic turbine impeller optimization design method | |
| CN109408861A (en) | A kind of pipeline pump inlet bend pipe Multiobjective Intelligent optimization method | |
| CN117109345B (en) | Optimal configuration method and device for high-temperature molten salt heat storage device of coupling thermal power generating unit | |
| CN115642655B (en) | Multi-unit load distribution optimization method and system for hydraulic power plant | |
| CN118211502A (en) | Multi-objective optimization design method for aviation fuel centrifugal pump based on neural network | |
| CN114201926B (en) | Centrifugal pump performance curve sample acquisition method and application thereof in machine learning | |
| CN114781685B (en) | Large user electricity load prediction method and system based on big data mining technology | |
| CN114398838A (en) | Centrifugal pump performance intelligent prediction method considering performance constraint | |
| CN114065621A (en) | Gas compressor characteristic equation coefficient fitting method based on genetic algorithm | |
| CN111428325A (en) | Method and apparatus for customizing a wind turbine generator system | |
| CN118333242B (en) | Cascade reservoir group joint scheduling rule optimization method based on big data samples | |
| Niu et al. | Optimizing Pump Scheduling for Water Supply through Improved Multiple Population Genetic Algorithm | |
| CN114896699B (en) | Multidisciplinary optimization design method for centripetal turbine impeller in aero-engine |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210602 Address after: 256600 west of Gaoqi road and north of Xinliu Road, high tech Zone, Binzhou City, Shandong Province Patentee after: Shandong Binrui Precision Machinery Co.,Ltd. Address before: Caowang Town, Boxing County, Binzhou City, Shandong Province Patentee before: BINZHOU DONGRUI MACHINERY Co.,Ltd. |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A multi-objective optimization design method for hydraulic turbine impeller under multiple operating conditions Effective date of registration: 20230324 Granted publication date: 20201113 Pledgee: Agricultural Bank of China, Boxing County branch, Limited by Share Ltd. Pledgor: Shandong Binrui Precision Machinery Co.,Ltd. Registration number: Y2023980035928 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Granted publication date: 20201113 Pledgee: Agricultural Bank of China, Boxing County branch, Limited by Share Ltd. Pledgor: Shandong Binrui Precision Machinery Co.,Ltd. Registration number: Y2023980035928 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A multi-objective optimization design method for hydraulic turbine impeller under multiple operating conditions Granted publication date: 20201113 Pledgee: Agricultural Bank of China, Boxing County branch, Limited by Share Ltd. Pledgor: Shandong Binrui Precision Machinery Co.,Ltd. Registration number: Y2024980009195 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |