CN109522665A - A kind of Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane - Google Patents
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Abstract
The invention discloses a kind of Multipurpose Optimal Methods of single flow gas-liquid cyclone separator guide vane, initially set up the Model for Multi-Objective Optimization of single flow gas-liquid cyclone separator guide vane, the Model for Multi-Objective Optimization established is to maximize separative efficiency and minimize pressure drop as optimization aim, using blade number, outlet blade angle, width of blade and subtended angle of blade this four blade construction parameters as design variable；The Model for Multi-Objective Optimization established is optimized using second generation non-dominated sorted genetic algorithm, obtains Pareto optimal solution set；According to the actual situation, selection maximizes separative efficiency and minimizes the scheme of pressure drop from the Pareto optimal solution set, and obtains corresponding guide vane structural parameters.This method can conveniently and efficiently obtain the Pareto optimal solution set of cyclone separating property, to select suitable guide vane parameter according to the actual situation for policymaker.
Description
Technical field
The present invention relates to petroleum gas technical field more particularly to a kind of single flow gas-liquid cyclone separator guide vanes
Multipurpose Optimal Method.
Background technique
China deep-sea natural gas resource is extremely abundant, and deep-sea natural gas extraction has important war to guarantee Chinese energy safety
Slightly meaning, but deep-sea natural gas extraction difficulty is very big, and harvesting scheme also has very big difference with shallow sea not only different from land.It is international
Mainstream recovery method is using underwater multiphase production system, and underwater gas-liquid separator is its key equipment.Single flow gas-liquid eddy flow
It is smaller, compact-sized and be easy to parallel connection that separator has many advantages, such as pressure drop, therefore has wide answer in gas-liquid separation under water
Use prospect.
Guide vane is the key structure of single flow gas-liquid cyclone separator, due to the presence of guide vane, gas-liquid mixed
After object enters axially into cyclone, it is changed into high speed rotation air-flow from axial movement, forms centrifugal field, in the effect of centrifugal force
Under, liquid is separated.The structural parameters of guide vane have significant impact, guide vane to the separating property of cyclone
Optimization design to improve cyclone separating property have great significance.The core of cyclone guide vane optimization design is thought
Want the best match sought between cyclone separating property and guide vane structure, that is, with certain guide vane structure
Parameter obtains optimal separating property, but the multi-objective optimization design of power about single flow gas-liquid cyclone separator in the prior art
It has not been reported.
Summary of the invention
The object of the present invention is to provide a kind of Multipurpose Optimal Methods of single flow gas-liquid cyclone separator guide vane, should
Method can conveniently and efficiently obtain the Pareto optimal solution set of cyclone separating property, to select according to the actual situation for policymaker
Select suitable guide vane parameter.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, which comprises
Step 1, the Model for Multi-Objective Optimization for establishing single flow gas-liquid cyclone separator guide vane, the multiple target established
Optimized model is to maximize separative efficiency and minimize pressure drop as optimization aim, with blade number, outlet blade angle, width of blade
It is design variable with this four blade construction parameters of subtended angle of blade；
Step 2 optimizes the Model for Multi-Objective Optimization established using second generation non-dominated sorted genetic algorithm and asks
Solution obtains Pareto optimal solution set；
Step 3, according to the actual situation, selection maximizes separative efficiency and minimizes pressure from the Pareto optimal solution set
The scheme of drop, and obtain corresponding guide vane structural parameters.
The mathematical model for the Model for Multi-Objective Optimization established in step 1 indicates are as follows:
Objective function:
Wherein, F1(X) separative efficiency is indicated；F2(X) pressure drop is indicated；X indicates that design variable, including blade number, blade go out
This four blade construction parameters of bicker, width of blade and subtended angle of blade；
Constraint condition are as follows:
Wherein, gi(X) nonlinear restriction of design variable, h are indicatedi(X) linear restriction of design variable is indicated.
In step 2, the Model for Multi-Objective Optimization established is carried out using second generation non-dominated sorted genetic algorithm excellent
Change the process solved specifically:
First using real coding method to this four optimizations of blade number, outlet blade angle, width of blade and subtended angle of blade
Variable is encoded, and generates initial population of the individual as second generation non-dominated sorted genetic algorithm at random, and the optimization becomes
The group matrix of amount composition is shown below:
Wherein, it is blade number, outlet blade angle, width of blade and leaf that four column of the group matrix are corresponding
Piece cornerite；
Then it will increase the corresponding fitness value of each individual in the matrix of above formula expression, finally obtain a such as following formula institute
The new matrix F shown:
Non-dominated ranking and crowding sequence are carried out to matrix F, realize the sequence of each individual in matrix F；
Genetic manipulation is carried out to matrix F again, the filial generation for generating parent with it using non-dominated ranking and crowding sequence
Merge, generate the group containing 2N individual, after being ranked up, only takes top n individual as hereditary filial generation；
Then judge whether the number of iterations has reached defined maximum number of iterations, terminate to optimize if reaching；If no
Reach defined maximum number of iterations, then check value is added to and continues to iterate to calculate in sample set, changes until reaching defined
Generation number, and obtain Pareto optimal solution set；
It obtains after obtaining Pareto optimal solution set, using reducing, ratio factor method obtains multiple groups separative efficiency and pressure drop is same
The solution of Shi Youhua.
The process that non-dominated ranking and crowding sequence are carried out to matrix F specifically:
Each individual i includes N in group matrix F "iAnd SiTwo parameters, wherein NiIt indicates that individual i can be dominated in population
Individual amount, SiIndicate the individual collections dominated by individual i；
During quick non-dominated ranking, all N in population are first foundi=0 individual is simultaneously stored in set Fi', then examine
Examine set Fi' each of individual j dominated individual collection Si, by set SiEach of individual k nkSubtract 1；
If nkIndividual k is then put into another set H by -1=0；
The F that will finally obtaini' the non-dominant individual collections of the first order are used as, the individual non-branch having the same in the set
With sequence；
It then proceedes to make H above-mentioned progressive operation, the classification until realizing all individuals, after obtaining a non-dominated ranking
Matrix F；
Finally, carrying out crowding sequence to matrix F, the sequence of each individual in matrix F is realized.
During carrying out genetic manipulation to matrix F:
Selecting operation uses the tournament method based on size relation between individual adaptation degree, and crossing operation is using uniformly friendship
Fork, mutation operator is using uniformly variation.
The corresponding fitness value of the individual includes separative efficiency and pressure drop, and numerical value is obtained by SVM prediction
It arrives.
As seen from the above technical solution provided by the invention, the above method can conveniently and efficiently obtain cyclone separation
The Pareto optimal solution set of performance, to select suitable guide vane parameter according to the actual situation for policymaker.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill in field, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is the Multipurpose Optimal Method stream of single flow gas-liquid cyclone separator guide vane provided in an embodiment of the present invention
Journey schematic diagram；
Fig. 2 is the schematic diagram of optimization gained Pareto optimal solution set in the embodiment of the present invention.
Specific embodiment
With reference to the attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on this
The embodiment of invention, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, belongs to protection scope of the present invention.
The embodiment of the present invention is described in further detail below in conjunction with attached drawing, is implemented as shown in Figure 1 for the present invention
The Multipurpose Optimal Method flow diagram for the single flow gas-liquid cyclone separator guide vane that example provides, which comprises
Step 1, the Model for Multi-Objective Optimization for establishing single flow gas-liquid cyclone separator guide vane, the multiple target established
Optimized model is to maximize separative efficiency and minimize pressure drop as optimization aim, with blade number, outlet blade angle, width of blade
It is design variable with this four blade construction parameters of subtended angle of blade；
In this step, the process for establishing the Model for Multi-Objective Optimization of single flow gas-liquid cyclone separator guide vane is specific
Are as follows:
According to the mathematical model of optimization design it is found that a problem is optimized need design variable, objective function with
And constraint condition, below according to cyclone guide vane the characteristics of, analyze the three elements of optimization design.
(1) design variable
Design variable refers to the correlated variables that objective function is influenced in process of optimization.For an optimization problem,
If the number of design variable is n, then design variable can be written as xi, i=1,2 ..., n.If by this n variable by certain rule into
Row sequence, can be obtained an array X, it may be assumed that
X=[x1,x2,…,xn]T (1)
The entirety of the array (n-dimensional vector) may make up a design space (n-dimensional space), and each point therein indicates one
Kind design scheme, commonly referred to as design point.It is exactly empty in design by certain method to the process that problem optimizes
Between in find the process of optimal design point, this optimum point is exactly final Optimum Design Results.To single flow gas-liquid eddy flow point
For optimization design from device guide vane, design variable mainly include blade number, outlet blade angle (blade exit tangentially with
The angle of cyclone axial direction), width of blade and subtended angle of blade.
(2) objective function
Objective function is the index for being evaluated the result that optimization design obtains.As previously mentioned, optimization design is just
It is that one or more design schemes are found out in design space under the constraint of design variable value range, so that objective function
Obtain the process of optimal solution.According to the number of objective function in optimization problem, single goal and multiple-objection optimization two can be classified as
Class.For separator, if only optimized to separative efficiency, other characteristics such as pressure drop are not considered, as single goal is excellent
Change design problem.It for cyclone, needs to consider simultaneously separative efficiency and pressure drop, establishes the objective function of the two respectively.
In addition, it is conflicting between separative efficiency and pressure drop, it can not simply evaluate the superiority and inferiority of a design scheme.Therefore, direct current
The optimization problem of formula gas-liquid cyclone separator is a typical multi-objective optimization design of power problem.
(3) constraint condition
In general, the value of design variable needs in allowed limits, and is also possible to meet between each variable certain
Relationship.Each specific optimization problem has certain limit to the relationship between the value range and variable of design variable
System, these limitations are known as constraint condition.
By being analyzed above it is found that the mathematical model for the Model for Multi-Objective Optimization established indicates are as follows:
Objective function:
Wherein, F1(X) separative efficiency is indicated；F2(X) pressure drop is indicated；X indicates that design variable, including blade number, blade go out
This four blade construction parameters of bicker, width of blade and subtended angle of blade；
Constraint condition are as follows:
Wherein, gi(X) nonlinear restriction of design variable, h are indicatedi(X) linear restriction of design variable is indicated.
Step 2 optimizes the Model for Multi-Objective Optimization established using second generation non-dominated sorted genetic algorithm and asks
Solution obtains Pareto optimal solution set；
In step 2, the Model for Multi-Objective Optimization established is carried out using second generation non-dominated sorted genetic algorithm excellent
Change the process solved specifically:
First using real coding method to this four optimizations of blade number, outlet blade angle, width of blade and subtended angle of blade
Variable is encoded, and generates initial population of the individual as second generation non-dominated sorted genetic algorithm at random, and the optimization becomes
Shown in the group matrix such as following formula (4) for measuring composition:
Wherein, it is blade number, outlet blade angle, width of blade and leaf that four column of the group matrix are corresponding
Piece cornerite；
Then it will increase the corresponding fitness value of each individual in the matrix of formula (4) expression, individual corresponding adaptation here
Angle value includes separative efficiency and pressure drop, and numerical value is obtained by SVM prediction, finally obtains one as shown in formula (5)
New matrix F:
Non-dominated ranking and crowding sequence are carried out to matrix F, realize the sequence of each individual in matrix F；
Genetic manipulation is carried out to matrix F again；
Then expand sample space by introducing elitism strategy, progeny population and parent population are combined, jointly
Next-generation population is generated after competition, is specifically merged parent with the filial generation that it is generated using non-dominated ranking and crowding sequence,
The group containing 2N individual is generated, after being ranked up, only takes top n individual as hereditary filial generation；
Judge whether the number of iterations has reached defined maximum number of iterations, terminates to optimize if reaching；If not reaching
Check value is then added to and continues to iterate to calculate in sample set by defined maximum number of iterations, until reaching defined iteration time
Number obtains Pareto optimal solution set；
It obtains after obtaining Pareto optimal solution set, using ratio factor method is reduced, obtains multiple groups separative efficiency and pressure drop
Simultaneously all than preferably solving.
In the specific implementation, the above-mentioned process for carrying out non-dominated ranking and crowding sequence to matrix F specifically:
Each individual i includes N in group matrix F "iAnd SiTwo parameters, wherein NiIt indicates that individual i can be dominated in population
Individual amount, SiIndicate the individual collections dominated by individual i；
During quick non-dominated ranking, all N in population are first foundi=0 individual is simultaneously stored in set Fi', then examine
Examine set Fi' each of individual j dominated individual collection Si, by set SiEach of individual k nkSubtract 1；
If nkIndividual k is then put into another set H by -1=0；
The F that will finally obtaini' the non-dominant individual collections of the first order are used as, the individual non-branch having the same in the set
With sequence；
It then proceedes to make H above-mentioned progressive operation, the classification until realizing all individuals, after obtaining a non-dominated ranking
Matrix F；
Finally, carrying out crowding sequence to matrix F, the sequence of each individual in matrix F is realized.
In addition, during carrying out genetic manipulation to matrix F:
Selecting operation uses the tournament method based on size relation between individual adaptation degree, and crossing operation is using uniformly friendship
Fork, mutation operator is using uniformly variation.
Step 3, according to the actual situation, selection maximizes separative efficiency and minimizes pressure from the Pareto optimal solution set
The scheme of drop, and obtain corresponding guide vane structural parameters.
Above-mentioned optimization method is described in detail with specific example below:
Step 1 establishes the Model for Multi-Objective Optimization of single flow gas-liquid cyclone separator guide vane, to maximize separation
Efficiency and minimum pressure drop are optimization aim, with this four blades of blade number, outlet blade angle, width of blade and subtended angle of blade
Structural parameters are optimized variable, and the value range of optimized variable is as shown in table 1.
The value range of optimized variable selected by table 1
Note: N indicates blade number
Step 2, using the multiple-objection optimization for the guide vane that second generation non-dominated sorted genetic algorithm obtains step 1
Model is solved, and one group of Pareto optimal solution set is obtained, the specific steps are as follows:
(1) using real number coding method to this four optimizations of blade number, outlet blade angle, width of blade and subtended angle of blade
Variable is encoded, and generates initial population of the individual as NSGA-II algorithm at random.The group matrix of optimized variable composition
As shown in formula (6), it is blade number, outlet blade angle, width of blade and blade packet that wherein four column of matrix are corresponding
Angle.
(2) the corresponding fitness value of each individual (separative efficiency and pressure drop) will be increased in the matrix of formula (6) expression, counted
Value is predicted to obtain by support vector machine method, finally obtains a new matrix F as shown in formula (7).
(3) non-dominated ranking.Each individual i includes N in F "iAnd SiTwo parameters, wherein NiIt indicates to dominate in population
The individual amount of individual i, SiIndicate the individual collections dominated by individual i.During quick non-dominated ranking, population is first found
In all Ni=0 individual is simultaneously stored in set Fi', then investigate set Fi' each of individual j dominated individual collection Si, will
Set SiEach of individual k nkSubtract 1.If nkIndividual k is then put into another set H by -1=0.It will finally obtain
Fi' the non-dominant individual collections of the first order are used as, the individual non-dominated ranking having the same in the set.It then proceedes to make H
Above-mentioned progressive operation, the classification until realizing all individuals, the F matrix after obtaining a non-dominated ranking.Finally, being carried out to F
Crowding sequence, realizes the sequence of each individual in matrix F.
(4) genetic manipulation is carried out to matrix F.Wherein, Selecting operation is used based on size relation between individual adaptation degree
Tournament method, crossing operation use uniform crossover, and mutation operator is using uniformly variation.The control parameter setting such as table of genetic manipulation
Shown in 2.
Parameter setting when 2 genetic algorithm optimization cyclone guide vane of table
(5) expand sample space by introducing elitism strategy, progeny population and parent population are combined, it is common competing
Next-generation population is generated after striving.Parent is merged with the filial generation that it is generated using non-dominated ranking and crowding sequence, generation contains
There is the group of 2N individual, after being ranked up, only takes top n individual as hereditary filial generation.
(6) judge whether the number of iterations has reached defined maximum number of iterations, if reaching, terminate to optimize；If no
Reach defined maximum number of iterations, then check value is added to and continues to iterate to calculate in sample set, changes until reaching defined
Generation number.
After obtaining optimal solution set, uses and reduce ratio factor method (in the present embodiment scale factor value for 0.3), obtain
Several groups of separative efficiencies and pressure drop are simultaneously all than preferably solving.
Step 3 has been carried out more using guide vane of the optimization method being established above to single flow gas-liquid cyclone separator
Objective optimization has obtained including 60 individual Pareto optimal solution sets, has been illustrated in figure 2 in the embodiment of the present invention and optimizes institute
The schematic diagram of Pareto optimal solution set is obtained, as can be seen from Figure 2: not dominating mutually between these design points, that is, from one
Design point is moved to another design point, and one of objective function improves, then another objective function is necessarily deteriorated.It is deposited in figure
In five special optimal design points；Separative efficiency is minimum at point A, pressure drop is also minimum, separative efficiency highest, pressure drop at point E
Maximum, when point A is mobile to point B, separative efficiency can be increased substantially by increasing pressure drop；And when point D is mobile to point E, it improves and divides
Pressure drop can be dramatically increased from efficiency；Point C is separative efficiency and pressure drop preferably design points.
It is worth noting that, the content being not described in detail in the embodiment of the present invention belongs to professional and technical personnel in the field's public affairs
The prior art known.
In conclusion the method for the embodiment of the present invention using maximize separative efficiency and minimize pressure drop as optimization aim into
The optimization of row single flow gas-liquid cyclone separator guide vane can find one group of Pareto optimal solution to cut both ways simultaneously
Collection, meets the needs of oil and gas gas-liquid separation field difference operating condition, facilitates policymaker according to the actual situation, to select
Suitable guide vane structural parameters.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Within the technical scope of the present disclosure, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the protection model of claims
Subject to enclosing.
Claims (6)
1. a kind of Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, which is characterized in that the method packet
It includes:
Step 1, the Model for Multi-Objective Optimization for establishing single flow gas-liquid cyclone separator guide vane, the multiple-objection optimization established
Model is to maximize separative efficiency and minimize pressure drop as optimization aim, with blade number, outlet blade angle, width of blade and leaf
This four blade construction parameters of piece cornerite are optimized variable；
Step 2 optimizes the Model for Multi-Objective Optimization established using second generation non-dominated sorted genetic algorithm, obtains
Obtain Pareto optimal solution set；
Step 3, according to the actual situation, selection maximizes separative efficiency and minimizes pressure drop from the Pareto optimal solution set
Scheme, and obtain corresponding guide vane structural parameters.
2. the Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, feature exist according to claim 1
In the mathematical model for the Model for Multi-Objective Optimization established in step 1 indicates are as follows:
Objective function:
Wherein, F1(X) separative efficiency is indicated；F2(X) pressure drop is indicated；X indicates design variable, including blade number, blade exit
This four blade construction parameters of angle, width of blade and subtended angle of blade；
Constraint condition are as follows:
Wherein, gi(X) nonlinear restriction of design variable, h are indicatedi(X) linear restriction of design variable is indicated.
3. the Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, feature exist according to claim 1
In in step 2, being optimized using second generation non-dominated sorted genetic algorithm to the Model for Multi-Objective Optimization established
Process specifically:
First using real coding method to this four optimized variables of blade number, outlet blade angle, width of blade and subtended angle of blade
It is encoded, generates initial population of the individual as second generation non-dominated sorted genetic algorithm, the optimized variable group at random
At group matrix be shown below:
Wherein, it is blade number, outlet blade angle, width of blade and blade packet that four column of the group matrix are corresponding
Angle；
Then will above formula indicate matrix in increase the corresponding fitness value of each individual, finally obtain one it is shown in following formula newly
Matrix F:
Non-dominated ranking and crowding sequence are carried out to matrix F, realize the sequence of each individual in matrix F；
Genetic manipulation is carried out to matrix F again, is merged parent with the filial generation that it is generated using non-dominated ranking and crowding sequence,
The group containing 2N individual is generated, after being ranked up, only takes top n individual as hereditary filial generation；
Then judge whether the number of iterations has reached defined maximum number of iterations, terminate to optimize if reaching；If not reaching
Check value is then added to and continues to iterate to calculate in sample set by defined maximum number of iterations, until reaching defined iteration time
Number, and obtain Pareto optimal solution set；
It obtains after obtaining Pareto optimal solution set, using reducing, ratio factor method obtains multiple groups separative efficiency and pressure drop is excellent simultaneously
The solution of change.
4. the Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, feature exist according to claim 3
In the process for carrying out non-dominated ranking and crowding sequence to matrix F specifically:
Each individual i includes N in group matrix F "iAnd SiTwo parameters, wherein NiIndicate that individual i can be dominated in population
Body quantity, SiIndicate the individual collections dominated by individual i；
During quick non-dominated ranking, all N in population are first foundi=0 individual is simultaneously stored in set Fi', then investigate collection
Close Fi' each of individual j dominated individual collection Si, by set SiEach of individual k nkSubtract 1；
If nkIndividual k is then put into another set H by -1=0；
The F that will finally obtaini' the non-dominant individual collections of the first order are used as, the individual non-dominant row having the same in the set
Sequence；
It then proceedes to make H above-mentioned progressive operation, the classification until realizing all individuals, the square after obtaining a non-dominated ranking
Battle array F；
Finally, carrying out crowding sequence to matrix F, the sequence of each individual in matrix F is realized.
5. the Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, feature exist according to claim 3
During carrying out genetic manipulation to matrix F:
Selecting operation uses the tournament method based on size relation between individual adaptation degree, and crossing operation uses uniform crossover, becomes
Xor is using uniformly variation.
6. the Multipurpose Optimal Method of single flow gas-liquid cyclone separator guide vane, feature exist according to claim 3
In the corresponding fitness value of the individual includes separative efficiency and pressure drop, and numerical value is obtained by SVM prediction.
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Cited By (4)
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| CN111125904A (en) * | 2019-12-20 | 2020-05-08 | 哈尔滨工业大学 | Large-scale high-speed rotation equipment blade sorting method based on multi-target regulation |
| CN111339610A (en) * | 2020-02-04 | 2020-06-26 | 中国人民解放军空军工程大学 | Impeller mechanical rotor blade assembly optimizing and sequencing method |
| CN112901698A (en) * | 2021-03-10 | 2021-06-04 | 北京航空航天大学 | Isothermal air spring |
| CN114218688A (en) * | 2021-10-28 | 2022-03-22 | 北京建筑大学 | Method for optimizing characteristic parameters of blades of sectional inclined grooves of ventilated brake disc |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003106298A (en) * | 2001-09-28 | 2003-04-09 | Hitachi Ltd | Method and system for designing turbo type fluid machinery |
| CN107368920A (en) * | 2017-07-01 | 2017-11-21 | 南京理工大学 | A kind of off-peak period multi-train movement energy conservation optimizing method |
| CN107844835A (en) * | 2017-11-03 | 2018-03-27 | 南京理工大学 | Multiple-objection optimization improved adaptive GA-IAGA based on changeable weight M TOPSIS multiple attribute decision making (MADM)s |
-
2018
- 2018-11-27 CN CN201811426191.2A patent/CN109522665A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003106298A (en) * | 2001-09-28 | 2003-04-09 | Hitachi Ltd | Method and system for designing turbo type fluid machinery |
| CN107368920A (en) * | 2017-07-01 | 2017-11-21 | 南京理工大学 | A kind of off-peak period multi-train movement energy conservation optimizing method |
| CN107844835A (en) * | 2017-11-03 | 2018-03-27 | 南京理工大学 | Multiple-objection optimization improved adaptive GA-IAGA based on changeable weight M TOPSIS multiple attribute decision making (MADM)s |
Non-Patent Citations (2)
| Title |
|---|
| DEB K,ET AL: "A fast and elitist multi-objective genetic algorithm:NSGA-II", 《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》 * |
| 卫德强等: "内联式脱液器导叶几何参数优化的数值研究", 《水动力学研究与进展》 * |
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| CN111125904A (en) * | 2019-12-20 | 2020-05-08 | 哈尔滨工业大学 | Large-scale high-speed rotation equipment blade sorting method based on multi-target regulation |
| CN111125904B (en) * | 2019-12-20 | 2024-01-16 | 哈尔滨工业大学 | Large-scale high-speed rotation equipment blade sequencing method based on multi-target regulation and control |
| CN111339610A (en) * | 2020-02-04 | 2020-06-26 | 中国人民解放军空军工程大学 | Impeller mechanical rotor blade assembly optimizing and sequencing method |
| CN111339610B (en) * | 2020-02-04 | 2023-04-07 | 中国人民解放军空军工程大学 | Impeller mechanical rotor blade assembly optimizing and sequencing method |
| CN112901698A (en) * | 2021-03-10 | 2021-06-04 | 北京航空航天大学 | Isothermal air spring |
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