CN109376453B - Axial flow pump impeller optimization method based on bat algorithm and kriging model - Google Patents
Axial flow pump impeller optimization method based on bat algorithm and kriging model Download PDFInfo
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- CN109376453B CN109376453B CN201811336266.8A CN201811336266A CN109376453B CN 109376453 B CN109376453 B CN 109376453B CN 201811336266 A CN201811336266 A CN 201811336266A CN 109376453 B CN109376453 B CN 109376453B
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Abstract
The invention provides an axial flow pump impeller optimization method based on bat algorithm and Ke Li jin model combined CFD, relating to the field of pump optimization, comprising the following steps: determining optimization parameters and calculation fields: compiling bat algorithm by Matlab software, initializing bat position by adopting a random sampling mode, and determining fitness function; the impeller parameters are imported into UG NX10, and an impeller water body model is built; the impeller water body model obtained in the steps is guided into a model to carry out automatic grid division; introducing the impeller water body mesh obtained in the steps into ANSYS CFX for constant value simulation; using the results obtained by numerical simulation in the steps to evaluate the bat positions according to the bat algorithm evaluation method; judging whether an iteration stopping condition is met by utilizing the latest bat population, and stopping the iteration output result if the iteration stopping condition is met; if not, turning to the fourth step, and continuing iteration. The optimization of the axial flow pump by using the bat algorithm and the prediction of the kriging model can effectively improve the optimization efficiency and effect.
Description
Technical Field
The invention belongs to the field of pump optimization, and mainly relates to an optimization method of an axial flow pump.
Background
The axial flow pump belongs to a low-lift pump, has a large flow range, is simple in structure and convenient to overhaul, is widely applied to aspects such as water regulation engineering, municipal drainage, farm irrigation, power plant circulating water engineering, nuclear power and water jet propulsion, and is very important universal machinery, so that the performance improvement of the axial flow pump has very important significance. Currently, the design method of the axial flow pump mainly comprises similar design based on a model pump: lifting method and streamline method design. The design methods are all semi-empirical and semi-theoretical design methods and depend on the quality of the selected model pump seriously, and the designed pump is usually narrow in high-efficiency range and needs to be further optimized.
In the field of vane pump parts, related patents such as a multi-station hydraulic optimization method based on a loss centrifugal pump and the like provide references for optimizing an axial flow pump, but the method has the defects that an existing optimization platform is utilized, and the selectable optimization method is limited, so that the final optimization effect is influenced. Aiming at the problems, the invention provides an axial flow pump impeller optimization method based on a bat algorithm and a kriging model combined with CFD. The Bat Algorithm (Bat Algorithm), which is a meta-heuristic optimization Algorithm, was developed by Xin-She Yang in 2010. The bat algorithm is a global optimizing algorithm which simulates that the bat in nature utilizes echo positioning to detect and position the obstacle or prey most basically and relates the obstacle or prey to the optimized target function, and has faster convergence speed. The optimization of the axial flow pump by using the bat algorithm and the prediction of the kriging model can effectively improve the optimization efficiency and effect.
Disclosure of Invention
The invention provides an axial flow pump impeller optimization method based on a bat algorithm and a kriging model combined with CFD, which is based on an axial flow pump impeller designed by a traditional method, and optimizes the impeller with efficiency as a target by using the bat algorithm and the CFD technology, wherein the efficiency of the optimized impeller is 83.3%, and the efficiency of an original model is 76.2%. The efficiency is improved by 7.1 percent.
In order to achieve the above purpose, the following technical scheme is adopted:
the invention takes efficiency as an optimization target, so the fitness function value of the bat algorithm is taken as an efficiency value.
An axial flow pump impeller optimization method based on bat algorithm and Ke Li jin model combined CFD comprises the following steps:
step one: determining optimization parameters and calculation fields: the axial flow pump impeller geometry is mainly composed of impeller diameter D, airfoil chord length L, impeller hub diameter D h Blade chord angle of incidence beta L The number Z of blades, the grid distance t and the attack angle delta alpha are determined; optimized on the existing impeller, thus keeping the impeller diameter D unchanged, the following parameters are optimized: airfoil chord length L and impeller hub diameter d h Angle of blade chord L The number Z of blades, the grid distance t and the attack angle delta alpha; the value range of the optimized parameter is a calculation domain;
step two: a bat algorithm is written by Matlab software, and the bat population quantity n, the emission frequency f and the emissivity r are determined;
step three: initializing bat positions by adopting a random sampling mode and determining a fitness function fitness;
step four: the impeller parameters are imported into UG NX10, and an impeller water body model is built;
step five: the impeller water body model obtained in the step four is guided into Gambit2.4.6 for automatic grid division;
step six: introducing the impeller water body mesh obtained in the fifth step into ANSYS CFX for constant value simulation;
step seven: and (3) evaluating the bat positions according to an evaluation method of a bat algorithm by utilizing the results obtained by the numerical simulation in the step six.
Step eight: judging whether the result is larger than 0.2 times of the set cycle times, if not, directly updating the bat position; if yes, establishing a Kriging model by using the obtained result, and updating the bat position by combining the position predicted by the Kriging model;
step nine: judging whether an iteration stopping condition is met by utilizing the latest bat population, and stopping the iteration output result if the iteration stopping condition is met; if not, turning to the fourth step, and continuing iteration.
Further, the bat algorithm parameters in the second step are specifically set as follows:
bat population: n=30;
maximum number of iterations: i.e max ＝1000；
Iterative tolerance: tor=10 -4 ；
The emission frequency is 0.2-0.8, wherein the maximum emission frequency and the minimum emission frequency are as follows: f (f) max ＝0.2，f min ＝0.8；
Emissivity:
crijin model predictive term correction coefficients: c=0.8;
loudness attenuation coefficient: α=0.8;
emissivity growth coefficient: γ=0.9.
Further, the calculation domain is L E [650,1200]、βL∈[20，40]、d h ∈[550，700]、Z∈[3，5]、t∈[850，1450]、Δα∈[0，20]The method comprises the steps of carrying out a first treatment on the surface of the Wherein L is the chord length of the airfoil, and the unit is mm; beta L The unit is a chord setting angle; d, d h The diameter of the impeller hub is in mm; z is the number of blades; t is the grid distance, and the unit is mm; Δα is the angle of attack in degrees.
The beneficial effects are that:
1. based on the axial flow pump impeller designed by the traditional method, the impeller is optimized by adopting bat algorithm and CFD technology with efficiency as a target, the efficiency of the optimized impeller is 83.3%, and the efficiency of the original model is 76.2%. The efficiency is improved by 7.1 percent.
2. Compared with the mainstream global optimization algorithm, namely the genetic algorithm and the PSO algorithm, the bat algorithm has higher convergence rate, so that the iteration times are fewer when the pump efficiency reaches the specified tolerance, the calculation period is shortened, and the calculation resources are saved.
3. The bat algorithm combines with the algorithm improvement of the Kerling model prediction, so that the efficiency optimization of the axial flow pump can reach higher precision, and an available scheme is provided for optimizing with higher requirements.
Drawings
FIG. 1 is a logic flow diagram of the present invention.
FIG. 2 is a schematic diagram of an axial flow pump optimization parameter.
FIG. 3 is a diagram of an impeller water automatically generated by UG.
Fig. 4 is a grid schematic of the impeller.
FIG. 5 is a pre-optimization efficiency;
fig. 6 is an optimized efficiency.
Detailed Description
The invention will be further illustrated by the accompanying drawings and examples.
Fig. 1 shows a flow chart of bat algorithm street and kriging model optimization, and a correction value based on the kriging model prediction optimal position is added on the basis of a basic bat algorithm, so that the optimization process is further accelerated and the accuracy is improved.
Fig. 2 is a main design parameter of the hydraulic design of the impeller of the axial flow pump, and is also an optimization parameter of an optimization algorithm.
Fig. 3 is a three-dimensional model of impeller water body of the axial flow pump, and fig. 4 is a grid division diagram of impeller water body.
Fig. 5 and 6 show that the efficiency is obviously improved by comparing the CFD calculation results before and after the optimization of the axial flow pump.
An axial flow pump impeller optimization method based on bat algorithm and Ke Li jin model combined CFD comprises the following steps:
step one: determining optimization parameters and calculation fields: the axial flow pump impeller geometry is mainly composed of impeller diameter D, airfoil chord length L, impeller hub diameter D h Blade chord angle of incidence beta L The number Z of blades, the grid distance t and the attack angle delta alpha are determined; optimized on the existing impeller, thus keeping the impeller diameter D unchanged, the following parameters are optimized: airfoil chord length L and impeller hub diameter d h Angle of blade chord L The number Z of blades, the grid distance t and the attack angle delta alpha; the value range of the optimized parameter is a calculation domain;
step two: a bat algorithm is written by Matlab software, and the bat population quantity n, the emission frequency f and the emissivity r are determined;
step three: initializing bat positions by adopting a random sampling mode and determining a fitness function fitness;
step four: the impeller parameters are imported into UG NX10, and an impeller water body model is built;
step five: the impeller water body model obtained in the step four is guided into Gambit2.4.6 for automatic grid division;
step six: introducing the impeller water body mesh obtained in the fifth step into ANSYS CFX for constant value simulation;
step seven: and (3) evaluating the bat positions according to an evaluation method of a bat algorithm by utilizing the results obtained by the numerical simulation in the step six.
Step eight: judging whether the result is larger than 0.2 times of the set cycle times, if not, directly updating the bat position; if yes, establishing a Kriging model by using the obtained result, and updating the bat position by combining the position predicted by the Kriging model;
step nine: judging whether an iteration stopping condition is met by utilizing the latest bat population, and stopping the iteration output result if the iteration stopping condition is met; if not, turning to the fourth step, and continuing iteration.
Further, the bat algorithm parameters in the second step are specifically set as follows:
bat population: n=30;
maximum number of iterations: i.e max ＝1000；
Iterative tolerance: tor=10 -4 ；
The emission frequency is 0.2-0.8, wherein the maximum emission frequency and the minimum emission frequency are as follows:
f max ＝0.2，f min ＝0.8；
emissivity:
crijin model predictive term correction coefficients: c=0.8,
Loudness attenuation coefficient: α=0.8;
emissivity growth coefficient: γ=0.9.
Further, the calculation domain is L E [650,1200]、βL∈[20，40]、d h ∈[550，700]、ZE[3，5]、t∈[850，1450]、Δα∈[0，20]The method comprises the steps of carrying out a first treatment on the surface of the Wherein L is the chord length of the airfoil, and the unit is mm; beta L The unit is a chord setting angle; d, d h The diameter of the impeller hub is in mm; z is the number of blades; t is the grid distance, and the unit is mm; Δα is the angle of attack in degrees.
In combination with the optimization flow shown in fig. 1, a design rotation speed n=200r/min and a flow rate q=13.35 m are adopted 3 Axial-flow pump with/s and head h=2.02m is specifically described as an exampleThis optimization method.
The diameter of the impeller designed initially is 2000mm, so that the basic external dimension is kept unchanged in the optimization process, namely the diameter D=2000 mm of the impeller is kept unchanged, and the diameter D of the impeller hub is obtained by the chord length L of the airfoil h Blade chord angle of incidence beta L The number z of blades, the grid distance t and the attack angle delta alpha are variables, and efficiency is taken as an optimization target. The specific implementation steps are as follows:
step one, determining an algorithm calculation domain: l epsilon [650,1200 (mm)]、β L ∈[20，40]、d h ∈[550，700]、Z∈[3，5]、t∈[850，1450(mm)]、Δα∈[0，20]；
Step two, using matlab programming to set bat algorithm parameters as follows: n=30, i max ＝1000、To＝10 -4 、f max ＝0.2，f min ＝0.8；
Step three: initializing bat population: generating 30 groups of data by adopting a random sampling method, wherein each group of data comprises 6 parameters, and each group of data is a bat individual and represents an impeller;
step four: the impeller parameters obtained in the third step are imported into UG NX10, and an impeller water body model is built;
step five: leading the impeller water body model obtained in the step four into Gambit2.4.6 to generate impeller grids;
step six: introducing the impeller water body mesh obtained in the fifth step into ANSYS CFX for constant value simulation;
step seven: and (3) evaluating the bat positions according to an evaluation method of a bat algorithm by utilizing the results obtained by the numerical simulation in the step six.
Step eight: judging whether the result is larger than 0.2 times of the set cycle times, if not, directly updating the bat position; if yes, establishing a Kriging model by using the obtained result, and updating the bat position by combining the position predicted by the Kriging model;
step nine: judging whether an iteration stopping condition is met by utilizing the latest bat population, and stopping the iteration output result if the iteration stopping condition is met; if not, turning to the fourth step, and continuing iteration.
After the circulation is finished, the optimized impeller is obtained, the efficiency is 83.3%, and the efficiency of the original model is 76.2%. The efficiency is improved by 7.1 percent. As shown in connection with fig. 5 and 6. The convergence rate of the bat algorithm is greatly improved by combining the kriging model.
The core algorithm for updating bat position by combining bat algorithm with Ke Li jin model includes
Speed update mode:
position updating mode:
wherein:
the speed of the bat numbered i at the t-th and t+1-th cycles, respectively; />
The positions of the bat numbered i at the t-th and t+1-th cycles, respectively; x is x best Is the current optimal position in the bat algorithm; x is x kb The most position predicted for the kriging model; f (f) i The frequency of sound wave emitted for bat is updated as follows (f min 、f max Maximum and minimum frequency of sound wave respectively, rand is [0,1]Random number within):
f i ＝f min +(f max -f min )*rand
during the course of searching for a prey, the bat emits sound waves with relatively high loudness (a) and very low emissivity (r) before no prey is found, which is convenient for large-scale searching. After the hunting object is found, the sound wave loudness emitted by the bat is gradually reduced in the process of approaching the hunting object, and the emissivity is higher and higher so as to accurately position the hunting object. To simulate this process, the following steps were added to the algorithm:
wherein:
emissivity of the bat numbered i at the t-th and t+1-th cycles, respectively; />
The loudness of the sound wave at the t-th and t+1-th cycles of the bat numbered i, respectively; alpha and gamma are loudness attenuation coefficients and frequency increase coefficients respectively, and are constants of large and 0; f in the process of initializing algorithm max 、f min 、/>
C. Alpha, gamma, bat number n, maximum iteration step number i max The tolerance Tor needs to be set, and others are updated automatically through iteration.
The examples are preferred embodiments of the present invention, but the present invention is not limited to the above-described embodiments, and any obvious modifications, substitutions or variations that can be made by one skilled in the art without departing from the spirit of the present invention are within the scope of the present invention.
Claims (3)
1. An axial flow pump impeller optimization method based on bat algorithm and Ke Li jin model combined with computational fluid dynamics CFD, comprising the following steps:
step one: determining optimization parameters and calculation fields: the key design parameters of the impeller of the axial flow pump comprise the diameter D of the impeller, the chord length L of the airfoil profile and the diameter D of the hub of the impeller h Blade chord angle of incidence beta L Blade number Z, grid pitcht, determining an attack angle delta alpha; optimized on existing impellers, thus keeping the impeller diameter D unchanged, the following parameters are optimized: airfoil chord length L and impeller hub diameter d h Angle of blade chord L The number Z of blades, the grid distance t and the attack angle delta alpha; the value range of the optimized parameter is a calculation domain;
step two: adopting Matlab software to write bat algorithm, and determining bat population quantity, transmitting frequency, transmitting rate and bat optimizing maximum iteration number i max ；
Step three: initializing bat positions by adopting a random sampling mode, wherein the bat positions are the combination of the optimization parameters in the first step, the positions of one bat represent the design scheme of one impeller, and the fitness function fitness is determined;
step four: starting iterative optimization, importing the latest bat positions of impeller parameters into three-dimensional modeling software UGNX10, and establishing an impeller water body model;
step five: the impeller water body model obtained in the step four is imported into three-dimensional grid division software Gambit2.4.6 for automatic grid division;
step six: introducing the impeller water body mesh obtained in the fifth step into a computational fluid dynamics solver ANSYSCFX to perform constant value simulation;
step seven: utilizing the result obtained by the numerical simulation in the step six, and evaluating the bat position according to an evaluation method of a bat algorithm;
step eight: judging whether the result is larger than 0.2 times of the set cycle times, if not, directly updating the bat position; if yes, establishing a Kriging model by using the obtained result, and updating the bat position by combining the position predicted by the Kriging model;
step nine: judging whether an iteration stopping condition is met by utilizing the latest bat population, and stopping the iteration output result if the iteration stopping condition is met; if not, turning to the fourth step, and continuing iteration.
2. The axial flow pump impeller optimization method based on the bat algorithm and the kriging model combined with computational fluid dynamics CFD according to claim 1, wherein the bat algorithm parameters in the step two are specifically set as follows:
bat population number: n=30;
bat optimizing maximum iteration number: i.e max ＝1000；
Iterative tolerance: tor=10 -4 ；
The emission frequency is 0.2-0.8, wherein, the maximum emission frequency and the minimum emission frequency are as follows: f (f) max ＝0.2，f min ＝0.8；
Emissivity:
crijin model predictive term correction coefficients: c=0.8;
loudness attenuation coefficient: α=0.8;
emissivity growth coefficient: γ=0.9.
3. The axial flow pump impeller optimization method based on bat algorithm and kriging model combined with computational fluid dynamics CFD of claim 1, wherein in step one, the computational domain is L e [650,1200 ]]、β L ∈[20,40]、d h ∈[550,700]、Z∈[3,5]、t∈[850,1450]、Δα∈[0,20]The method comprises the steps of carrying out a first treatment on the surface of the Wherein L is the chord length of the airfoil, and the unit is mm; beta L Is the chord setting angle, unit degree; d, d h The diameter of the impeller hub is in mm; z is the number of blades; t is the grid distance, and the unit is mm; Δα is the angle of attack in degrees.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107800570A (en) * | 2017-10-23 | 2018-03-13 | 天津大学 | SDN controller dispositions methods based on bat algorithm |
| CN107886158A (en) * | 2017-10-30 | 2018-04-06 | 中国地质大学（武汉） | A kind of bat optimized algorithm based on Iterated Local Search and Stochastic inertia weight |
| CN107886157A (en) * | 2017-10-30 | 2018-04-06 | 中国地质大学（武汉） | A kind of new bat optimized algorithm system |
| CN108694438A (en) * | 2018-04-25 | 2018-10-23 | 武汉大学 | A kind of single-object problem method and system of combination explosion strategy, backward learning and bat algorithm |
| CN108765951A (en) * | 2018-06-11 | 2018-11-06 | 广东工业大学 | Method for identifying traffic status of express way based on bat algorithm support vector machines |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107800570A (en) * | 2017-10-23 | 2018-03-13 | 天津大学 | SDN controller dispositions methods based on bat algorithm |
| CN107886158A (en) * | 2017-10-30 | 2018-04-06 | 中国地质大学（武汉） | A kind of bat optimized algorithm based on Iterated Local Search and Stochastic inertia weight |
| CN107886157A (en) * | 2017-10-30 | 2018-04-06 | 中国地质大学（武汉） | A kind of new bat optimized algorithm system |
| CN108694438A (en) * | 2018-04-25 | 2018-10-23 | 武汉大学 | A kind of single-object problem method and system of combination explosion strategy, backward learning and bat algorithm |
| CN108765951A (en) * | 2018-06-11 | 2018-11-06 | 广东工业大学 | Method for identifying traffic status of express way based on bat algorithm support vector machines |
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