CN109783957A - A kind of multivariable multiple target parallel optimization method for high-temperature pump design - Google Patents
A kind of multivariable multiple target parallel optimization method for high-temperature pump design Download PDFInfo
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Abstract
The present invention is a kind of multivariable multiple target parallel optimization method for high-temperature pump design, the present invention is based on multiple target multidisciplinary optimization bases, this high-temperature pump operates mainly under > 300 degrees centigrade limiting conditions, by the Optimizing Flow modularization of high-temperature pump, module one is experimental design module, module two is that multiple target parallel optimization calculates module, and module three is optimum analysis and redesign module.Present invention combination CFD is calculated and finite element analysis, and establish the database of high-temperature pump, hydraulic performance, cavitation erosion three indexs of performance and security reliability are assessed, and multiple design variables, obtain the optimal design and worst design to three optimizing index of high-temperature pump, and go constrained optimum to design using the result of worst design, to establish a kind of design optimization method of high-temperature pump that multivariable multiple target is parallel.
Description
Technical field
The present invention relates to the optimum design method of high-temperature pump, specifically a kind of multivariable multiple target for high-temperature pump design
Parallel optimization method.
Background technique
High-temperature pump is widely used in the various fields such as power generation, petroleum, chemical industry, pharmacy.With the raising of national standard, to height
The requirement of the performance and security reliability of temperature pump is higher and higher, and more and more producers, requires nothing to the medium that it is conveyed
The process environments of leakage, there is an urgent need to select ideal pump-type.The demand of high-temperature pump high-end at present is increasing, however state
The interior development to large scale industry pump is difficult to make a breakthrough, and mostly relies on import.The design of high temperature and pressure centrifugal pump is related to
To multiple subjects such as hydrodynamics, the mechanics of materials, structural mechanics and thermodynamics, even family's mark such as fire pump, super boiler are given
Pump is filled on water pump, nuclear power station belongs to high temperature and pressure centrifugal multistage pump multiple centrifugal pump.
Traditional design method is rule of thumb improved to the pump model that medium is water, so based on velocity-coefficient method
And the factor that this design considers is excessively single, only the hydraulic performance of high-temperature pump is taken into account, the design of high-temperature pump is also
Two critically important indexs, i.e. cavitation erosion performance and security reliability need to guarantee that high-temperature pump passes through and use prolonged productivity
Continuous operation test.
Summary of the invention
It is a primary object of the present invention to overcome drawbacks described above in the prior art, a kind of water suitable for high-temperature pump is proposed
Traditional the Hydraulic Design is applied to high-temperature pump, and by way of multivariable multiple target parallel optimization, is based on by power optimization method
Four hydrodynamics, the mechanics of materials, structural mechanics and thermodynamics subjects carry out the Hydraulic Design, main optimizing index packet to high-temperature pump
Efficiency, cavitation erosion three directions of performance and structural reliability are included, while meeting high-temperature pump in the index of the Hydraulic Design and security performance.
The present invention adopts the following technical scheme:
A kind of multivariable multiple target parallel optimization method for high-temperature pump design, which comprises the steps of:
The optimization aim model set of efficiency, the performance and structural reliability of cavitating is established respectively
Firstly, being based on orthogonal optimization, 52 groups of mathematical model collection of the first optimization aim efficiency are established respectively, to every group of data
Corresponding pump model carries out waterpower modeling, and calculates its hydraulic efficiency Models Sets based on CFD analogue technique, and every group includes 12 factors
With 5 variables, 12 factors include 11 structural parameters and 1 hydraulic efficiency value；
Secondly, carrying out random variation to hydraulic efficiency Models Sets, establish with the performance that cavitates as 52 groups of the second optimization aim
Mathematical model set successively carries out waterpower modeling to the corresponding pump model of every group of data, and calculates its gas based on CFD analogue technique
Corrosion energy Models Sets, every group includes 12 factors and 5 variables, and 12 factors include 11 structural parameters and 1 net positive suction head value；
Again, random variation is carried out to cavitation erosion performance model collection, established using structural reliability as the 52 of third optimization aim
Group mathematical model set is successively carried out waterpower modeling to the corresponding pump model of every group of data, and is calculated based on CFD analogue technique
The interface fluid pressure data intersected to impeller and guide vane with fluid, is then based on the list that workbench builds fluid machinery
To the mathematics computing model of coupling, interface fluid pressure data is imported into the static(al) point in workbench to impeller and guide vane
Module is analysed, the maximum deformation quantity and maximum strain power for calculating impeller and guide vane obtain Model of Structural Reliability collection, and every group includes 13
Factor and 5 variables, 13 factors include 11 structural parameters, 1 maximum strain force value and 1 minimum strain force value；
Multiple target parallel optimization sampling Combination Design
Firstly, hydraulic efficiency Models Sets, cavitation erosion performance model collection and Model of Structural Reliability are concentrated evaluation index respectively
Worst 18 models removal；
Secondly, concentrating remaining 34 to hydraulic efficiency Models Sets, cavitation erosion performance model collection and Model of Structural Reliability respectively
A model is resequenced according to corresponding evaluation index, and corresponding number is successively filled Model of Structural Reliability collection
Middle composition compares combination table；
The optimum analysis of structural parameters variable
By comparing preceding 10 groups of data of combination table, analyze while making efficiency, cavitation erosion performance and structural reliability three excellent
Change the optimal optimized parameter of target, the frequency occurred to optimized parameter counts, according to the waterpower of wherein three groups of optimum combinations
Model provides the corresponding value range of each structural parameters；By compare combination table rear 8 groups of data, analysis at the same make efficiency,
The performance that cavitates and the worst worst parameter of three optimization aims of structural reliability, the frequency occurred to worst parameter count,
The parameter overlapped with optimum combination is found out, motionless factor is regarded as.
The structural parameters include impeller outlet diameter D2, vane inlet laying angle β 1, blade exit laying angle β 2, impeller
Outlet-inclined impeller vane angle γ, impeller blade corneriteImpeller blade exit width b2, impeller vane import and export gap L, guide vane blade
Number Z2, guide vane subtended angle of bladeGuide vane vane inlet laying angle β 3, guide vane blade exit width b3；5 variable includes 5
Kind design scheme.
The primary condition of 11 structural parameters is determined by following constraint:
ns≤ 300, n are revolution speed, and Q is pump discharge；
β1=20~35；
β2=(1.2~1.3) β1；
γ=60~75；
D1For impeller inlet
Diameter；
L=2~6mm；
Z2=4~7；
2=50~80 Φ；
β3=(0.85~0.98) β2；
B3=(0.35~0.45) D1。
It is described that random variation is carried out for respectively by every group of data of the hydraulic efficiency Models Sets to hydraulic efficiency Models Sets
Wherein 2-4 structural parameters carry out random variation.
It includes every group of data for respectively concentrating the performance model that cavitates that described pair of cavitation erosion performance model collection, which carries out random variation,
Wherein 2-4 structural parameters carry out random variation.
It is described to be resequenced according to corresponding evaluation index, and corresponding number is successively filled into structural reliability mould
Type is concentrated, specifically: it resequences according to optimal to the minimum sequence of evaluation index, and corresponding number is successively filled
Model of Structural Reliability, which is concentrated, constitutes comparison combination table.
The upper limit value of the value range is the maximum value in three groups of optimum combinations, and lower limit value is in three groups of optimum combinations
Minimum value.
By the above-mentioned description of this invention it is found that compared with prior art, the invention has the following beneficial effects:
The present invention is based on multiple target multidisciplinary optimization basis, including structure design and the Hydraulic Design, be related to hydrodynamics,
Multiple subjects such as the mechanics of materials, structural mechanics and thermodynamics in conjunction with CFD calculating and finite element analysis, and establish the number of high-temperature pump
According to library, hydraulic performance, cavitation erosion three indexs of performance and security reliability are assessed, and design variable is 11, is obtained pair
The database of the optimal design of three optimizing index of high-temperature pump and the database of worst design, and gone using the result of worst design
Constrained optimum database, to establish a kind of design optimization method of high-temperature pump that multivariable multiple target is parallel.
The present invention can guarantee pump hydraulic performance, cavitation erosion performance, can multi-state operation in the case where, make pump Special safety
Property is best, more reliable.
Detailed description of the invention
Fig. 1 is the structure diagram of high-temperature pump of the present invention；
Description of symbols:
1- impeller, 2- impeller blade, 3- guide vane, 4- guide vane blade.
Specific embodiment
Below by way of specific embodiment, the invention will be further described.
Fig. 1 is the impeller of one embodiment of the invention and the structure diagram of guide vane, is based on multivariable and multiple target is excellent parallel
Change method optimizes high temperature impeller of pump and guide vane, this high-temperature pump operates mainly under > 300 degrees centigrade limiting conditions,
By the Optimizing Flow modularization of high-temperature pump, module one is experimental design module, and module two is that multiple target parallel optimization calculates module, mould
Block three is optimum analysis and redesign module；The specific method is as follows:
I impeller outlet diameter D2 of design parameter, II vane inlet laying angle β 1 of design parameter, design parameter III blade go out
Mouth laying angle β 2, design parameter IV impeller outlet inclination angle γ, design parameter V impeller blade corneriteDesign parameter VI impeller
Blade exit width b2, design parameter VII impeller vane inlet and outlet gap L, design parameter VIII guide vane number of blade Z2, design ginseng
Number IX guide vane subtended angle of bladeDesign parameter X guide vane vane inlet laying angle β 3, design parameter XI guide vane blade exit width
The primary condition of b3,11 structural parameters are determined by following constraint:
x1:
x2:β1=20~35；
x3:β2=(1.2~1.3) β1；
X4: γ=60~75, the normal of impeller outlet plane and the angle of axis；
x5:
x6:
X7:L=2~6mm, impeller outlet plane is at a distance from plane where vane inlet；
X8:Z2=4~7；
2=50~80 x9: Φ；
x10:β3=(0.85~0.98) β2；
X11:b3=(0.35~0.45) D1；
By I impeller outlet diameter D2 of design parameter, II vane inlet laying angle β 1 of design parameter, design parameter III blade
Export laying angle β 2, design parameter IV impeller outlet inclination angle γ, design parameter V impeller outlet inclination angleDesign parameter VI
Impeller blade exit width b2, it design parameter VII impeller vane inlet and outlet gap L, design parameter VIII guide vane number of blade Z2, sets
Count parameter IX guide vane subtended angle of bladeDesign parameter X guide vane vane inlet laying angle β 1, design parameter XI guide vane blade exit
Width b3 is indicated with 11 system variables x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, each system variable respectively
Corresponding 5 kinds of design schemes；
Each parameter level table of table 1
| X1 | 1 | 2 | 3 | 4 | 5 |
| X2 | 1 | 2 | 3 | 4 | 5 |
| X3 | 1 | 2 | 3 | 4 | 5 |
| …… | |||||
| X11 | 1 | 2 | 3 | 4 | 5 |
Module one, experimental design establish the model set of efficiency, the performance and structural reliability of cavitating respectively.
52 groups of mathematical model collection (12 factors 5 change of the first optimization aim efficiency need to be established respectively by being primarily based on orthogonal optimization
Amount):
Waterpower modeling is carried out to the corresponding pump model of every group of data, and its hydraulic efficiency is calculated based on CFD analogue technique and is obtained
To efficiency Model collection, such as table 2；
2 efficiency Model collection of table
| X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | η | |
| 1 | 1 | 1 | 2 | 4 | 3 | 2 | 5 | 4 | 3 | 1 | 4 | A0 |
| 2 | 1 | 2 | 5 | 5 | 5 | 4 | 3 | 3 | 1 | 2 | 5 | A1 |
| 3 | 1 | 3 | 4 | 1 | 4 | 1 | 4 | 2 | 4 | 3 | 1 | A2 |
| …… | ||||||||||||
| 52 | 5 | 5 | 1 | 4 | 3 | 3 | 3 | 2 | 4 | 2 | 3 | A52 |
It is established secondly based on random variation with the 52 groups of mathematical model set that performance is the second optimization aim that cavitate:
The wherein 2-4 factor of every group of data in table 2 is subjected to random variation respectively:
Such as first group of data: (1,1,2,4,2,2,5,4,3, Isosorbide-5-Nitrae) → (1,2,2,4,5,2,5,4,3,3,4), then according to
Secondary is the 2nd group, the 3rd group ... the 52nd group.
Waterpower modeling successively is carried out to the corresponding pump model of every group of data, and is calculated more than its cavitation based on CFD analogue technique
Corrosion energy Models Sets are measured, such as table 3；
The cavitation erosion performance model collection of table 3
| X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | NPSH | |
| 1 | 1 | 2 | 2 | 4 | 5 | 2 | 5 | 4 | 3 | 3 | 4 | B1 |
| 2 | 3 | 2 | 5 | 5 | 5 | 2 | 3 | 3 | 1 | 2 | 5 | B2 |
| 3 | 1 | 3 | 4 | 5 | 4 | 1 | 1 | 2 | 4 | 2 | 4 | B3 |
| …… | ||||||||||||
| 52 | 5 | 5 | 2 | 4 | 2 | 3 | 3 | 2 | 1 | 2 | 3 | B52 |
It establishes secondly based on random variation using structural reliability as 52 groups of mathematical model set of third optimization aim:
The wherein 2-4 factor of every group of data in table 3 is subjected to random variation respectively:
Such as first group of data: (1,2,2,4,5,2,5,4,3,3,4) → (3,2,2,1,5,2,5,4,4, Isosorbide-5-Nitrae), then according to
Secondary is the 2nd group, the 3rd group ... the 52nd group.
Waterpower modeling successively is carried out to the corresponding pump model of every group of data, and impeller is calculated based on CFD analogue technique
The interface fluid pressure data intersected with guide vane with fluid is then based on the unidirectional couplings that workbench builds fluid machinery
Mathematics computing model, the pressure data of CFD is imported in workbench to the static analysis module of impeller and guide vane, is calculated
The maximum deformation quantity and maximum strain power of impeller and guide vane obtain Model of Structural Reliability collection, such as table 4；
4 Model of Structural Reliability collection of table
| X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | δ | τ | |
| 1 | 3 | 2 | 2 | 1 | 5 | 2 | 5 | 4 | 4 | 1 | 4 | C1 | D1 |
| 2 | 3 | 3 | 5 | 5 | 2 | 2 | 1 | 3 | 1 | 2 | 5 | C2 | D2 |
| 3 | 1 | 3 | 4 | 5 | 4 | 1 | 1 | 5 | 4 | 2 | 4 | C3 | D3 |
| …… | |||||||||||||
| 52 | 5 | 2 | 2 | 4 | 2 | 1 | 3 | 2 | 1 | 4 | 3 | C52 | D52 |
Module two, multiple target parallel optimization sampling Combination Design:
18 worst models of evaluation index in table 2, table 3 and table 4 are removed respectively first；
Secondly respectively to 34 models remaining in table 2, table 3 and table 4, sequence again is carried out according to corresponding evaluation index
(sequence optimal to minimum from evaluation index), and corresponding number is successively filled in table 4, i.e., combination number 1 is effect in table 4
The optimal number of rate, cavitation erosion best performance number and the optimal number of structural behaviour, combination number 34 are the worst number of efficiency, cavitation erosion property
It worst can number and the worst number of structural behaviour, composition compare combination table 4；
4 efficiency of table-cavitation erosion-structural reliability comparison combination
| Combination number | Efficiency number | Cavitation erosion number | Structure number | Combination number | Efficiency number | Cavitation erosion number | Structure number |
| 1 | 15 | 3 | 20 | 2 | 20 | 13 | 32 |
| 3 | 5 | 25 | 30 | 4 | 9 | 18 | 11 |
| …… | …… | …… | …… | …… | …… | …… | …… |
| 33 | 23 | 27 | 2 | 34 | 18 | 22 | 17 |
Module three, the optimum analysis of structural parameters variable
By preceding 10 groups of data of table 4, efficiency, cavitation erosion three optimization aims of performance and structural reliability are analyzed while made most
Optimized parameter in excellent x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, the frequency occurred to optimized parameter are united
Meter, such as table 5；By rear 8 groups of data of table 4, efficiency, cavitation erosion three optimization aims of performance and structural reliability are analyzed while made most
Difference x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11 in corresponding worst parameter, to worst parameter occur frequency into
Row statistics, such as table 6.
5 optimized parameter statistical form of table
| 1 | 2 | 3 | 4 | 5 | |
| X1 | n11 | n21 | n31 | n41 | n51 |
| X2 | n12 | n22 | n32 | n42 | n52 |
| …… | |||||
| X11 | n13 | n21 | n33 | n43 | n52 |
The worst parametric statistics table of table 6
| 1 | 2 | 3 | 4 | 5 | |
| X1 | n11 | n21 | n31 | n41 | n51 |
| X2 | n12 | n22 | n32 | n42 | n52 |
| …… | |||||
| X11 | n13 | n21 | n33 | n43 | n52 |
In table, n11 to n52 respectively represents the corresponding horizontal number occurred of each parameter.
It is analyzed according to the corresponding optimized parameter of the above table 5, the hydraulic model of three groups of optimum combinations is obtained, according to three group models
In 11 structural factors x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11 value, respectively provide each structural parameters
Corresponding value range:
The bound value range of x1 is the maximum and minimum value of the x1 in three groups of optimal models；X2, x3, x4 ..., x11
Similarly；
According to the corresponding worst Parameter analysis of the above table 6, find out the parameter overlapped with optimum combination, for example, x2 certain
One horizontal high-frequency simultaneously appears in table 5 and table 6, then is regarded as motionless factor, i.e. x2 is in 11 design parameters for not
Key factor then only needs to optimize other 10 design parameters.
The above is only a specific embodiment of the present invention, but the design concept of the present invention is not limited to this, all to utilize this
Design makes a non-material change to the present invention, and should all belong to behavior that violates the scope of protection of the present invention.
Claims (7)
1. a kind of multivariable multiple target parallel optimization method for high-temperature pump design, which comprises the steps of:
The optimization aim model set of efficiency, the performance and structural reliability of cavitating is established respectively
Firstly, being based on orthogonal optimization, 52 groups of mathematical model collection of the first optimization aim efficiency are established respectively, it is corresponding to every group of data
Pump model carry out waterpower modeling, and its hydraulic efficiency Models Sets is calculated based on CFD analogue technique, every group includes 12 factors and 5
Variable, 12 factors include 11 structural parameters and 1 hydraulic efficiency value；
Secondly, carrying out random variation to hydraulic efficiency Models Sets, establish with the 52 groups of mathematics that performance is the second optimization aim that cavitate
Model set successively carries out waterpower modeling to the corresponding pump model of every group of data, and calculates its cavitation erosion property based on CFD analogue technique
Energy Models Sets, every group includes 12 factors and 5 variables, and 12 factors include 11 structural parameters and 1 net positive suction head value；
Again, random variation is carried out to cavitation erosion performance model collection, established using structural reliability as 52 groups of numbers of third optimization aim
Model set is learned, waterpower modeling successively is carried out to the corresponding pump model of every group of data, and leaf is calculated based on CFD analogue technique
The interface fluid pressure data that wheel and guide vane intersect with fluid, is then based on the unidirectional coupling that workbench builds fluid machinery
Interface fluid pressure data is imported the static analysis mould in workbench to impeller and guide vane by the mathematics computing model of conjunction
Block, the maximum deformation quantity and maximum strain power for calculating impeller and guide vane obtain Model of Structural Reliability collection, and every group includes 13 factors
With 5 variables, 13 factors include 11 structural parameters, 1 maximum strain force value and 1 minimum strain force value；
Multiple target parallel optimization sampling Combination Design
Firstly, concentrating evaluation index worst hydraulic efficiency Models Sets, cavitation erosion performance model collection and Model of Structural Reliability respectively
18 models removal；
Secondly, concentrating remaining 34 moulds to hydraulic efficiency Models Sets, cavitation erosion performance model collection and Model of Structural Reliability respectively
Type is resequenced according to corresponding evaluation index, and corresponding number is successively filled Model of Structural Reliability and concentrates structure
In contrast with combination table；
The optimum analysis of structural parameters variable
By comparing preceding 10 groups of data of combination table, efficiency, cavitation erosion three optimization mesh of performance and structural reliability are analyzed while made
Optimal optimized parameter is marked, the frequency occurred to optimized parameter counts, according to the hydraulic model of wherein three groups of optimum combinations
Provide the corresponding value range of each structural parameters；By comparing rear 8 groups of data of combination table, efficiency, cavitation erosion are analyzed while made
Performance and the worst worst parameter of three optimization aims of structural reliability, the frequency occurred to worst parameter are counted, are found out
The overlapped parameter with optimum combination, is regarded as motionless factor.
2. a kind of multivariable multiple target parallel optimization method for high-temperature pump design as described in claim 1, feature exist
In the structural parameters include impeller outlet diameter D2, vane inlet laying angle β 1, blade exit laying angle β 2, impeller outlet
Inclination angle γ, impeller blade corneriteImpeller blade exit width b2, impeller vane inlet and outlet gap L, guide vane number of blade Z2,
Guide vane subtended angle of bladeGuide vane vane inlet laying angle β 3, guide vane blade exit width b3；5 variable includes 5 kinds and sets
Meter scheme.
3. a kind of multivariable multiple target parallel optimization method for high-temperature pump design as claimed in claim 2, feature exist
In the primary condition of 11 structural parameters is determined by following constraint:
ns≤ 300, n are revolution speed, and Q is pump discharge；
β1=20~35；
β2=(1.2~1.3) β1；
γ=60~75；
D1=(0.51~0.87) D2,D1For impeller inlet diameter；
L=2~6mm；
Z2=4~7；
2=50~80 Φ；
β3=(0.85~0.98) β2；
B3=(0.35~0.45) D1。
4. a kind of multivariable multiple target parallel optimization method for high-temperature pump design as described in claim 1, feature exist
In, it is described to hydraulic efficiency Models Sets carry out random variation be respectively by every group of data of the hydraulic efficiency Models Sets wherein
2-4 structural parameters carry out random variation.
5. a kind of multivariable multiple target parallel optimization method for high-temperature pump design as described in claim 1, feature exist
In, described pair cavitation erosion performance model collection carry out random variation include respectively by cavitate performance model concentrate every group of data wherein
2-4 structural parameters carry out random variation.
6. a kind of multivariable multiple target parallel optimization method for high-temperature pump design as described in claim 1, feature exist
In, it is described to be resequenced according to corresponding evaluation index, and corresponding number is successively filled into Model of Structural Reliability collection
In, specifically: it resequences according to optimal to the minimum sequence of evaluation index, and corresponding number is successively filled into structure
Reliability model, which is concentrated, constitutes comparison combination table.
7. a kind of multivariable multiple target parallel optimization method for high-temperature pump design as described in claim 1, feature exist
In the upper limit value of the value range is the maximum value in three groups of optimum combinations, and lower limit value is the minimum in three groups of optimum combinations
Value.
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| CN114692338B (en) * | 2022-04-12 | 2024-04-26 | 浙江理工大学 | Comprehensive optimization design method for cavitation and efficiency of low-temperature centrifugal pump |
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