CN106227967A - Industrial steam turbine low-pressure stage group vane type line optimization method - Google Patents
Industrial steam turbine low-pressure stage group vane type line optimization method Download PDFInfo
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- CN106227967A CN106227967A CN201610618598.XA CN201610618598A CN106227967A CN 106227967 A CN106227967 A CN 106227967A CN 201610618598 A CN201610618598 A CN 201610618598A CN 106227967 A CN106227967 A CN 106227967A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
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- 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]
Abstract
The invention discloses a kind of industrial steam turbine low-pressure stage group vane type line optimization method, according to blade geometry moulding and TWO-DIMENSIONAL CASCADE Transform Type design parametrization and D remote sensing Automated Design parametric method, blade profile after parametrization is carried out CFD calculating, obtain the flow parameter of this blade profile, performance parameter relevant to leaf-level in flow parameter is done unified process, with can its performance of overall merit quality, with flow, exit Mach number is as constraints, it is added in aeroperformance index, as optimization aim, the Parallel multi-objective genetic using real coding carries out optimizing to the blade profile after parametrization, by Optimization Design, cascade parameter method and CFD method for solving combine, develop efficient low pressure twisted blade full three-dimensional pneumatic automatic optimal technology.Use the present invention to optimize the vane type line of design, make the low-pressure stage group of industry gas-turbine have the advantages that efficiency is high, stable.
Description
Technical field
The present invention relates to turbine blade technology, a kind of industrial steam turbine based on multiple target differential evolution algorithm
Low-pressure stage group vane type line optimization method.
Background technology
Along with the fast development of modern industry, the range of application of industrial steam turbine constantly expands, to the requirement of its performance also
More and more higher, apply high performance industrial steam turbine can improve efficiency of energy utilization, effectively reduce pollutant emission, to its people
Economy and social sustainable development have great importance.By industrial steam turbine vane type line, flow passage component structure are carried out
Multi-objective optimization design of power can significantly improve the parameter such as unit efficiency, power, is the effective of raising industrial steam turbine aeroperformance
One of means, thus become the key issue of industrial steam turbine design.
Owing to the working environment of industrial steam turbine flow passage component is extremely complex, it is that viscosity, three peacekeepings are time-dependent inside it
Three-dimensional Flow, under current measuring technology, it is unpractical for analyzing flow passage component flow field by laboratory facilities completely, and day by day
The CFD technology of development moves analog capability due to the three dimensional unsteady flow with complexity and the design cycle is short, R & D Cost is low
Advantage, obtains a wide range of applications in turbomachine field.
According to the feature of industrial steam turbine low-pressure stage group blade, how CFD technology is applied to the blade of industrial steam turbine
During molded line optimizes, it it is a problem with realistic meaning.
Summary of the invention
The invention aims to solve the prior art deficiency to vane type line design aspect, and a kind of industry is provided
Low-pressure stage of steam turbine group vane type line optimization method, it is possible to there is high efficiency stable operation, and be applicable to various design conditions
And variable working condition.
The above-mentioned technical problem of the present invention is mainly addressed by following technical proposals: a kind of industrial steam turbine low pressure
Level group vane type line optimization method, it is characterised in that:
1) optimization problem of three dimendional blade molded line is converted into the two-dimentional molded line optimization problem on different leaf height cross section: for given
The two-dimentional blade profile of design parameter, uses mean camber line to add the generation method of vane thickness distribution, and blade profile mean camber line is by 3 Bezier letters
Number definition, then gone out thickness distribution by Cubic Spline Functions Fitting, front trailing edge radius is determined by the design parameter given；For
The three-dimensional blade profile known, takes the two-dimensional section on different leaf height direction, by 3 Bezier functions matching pressure face and suction respectively
Face, completes the parametrization to two dimension blade profile.
2) optimization aim is optimum with the synthetic aerodynamic performance of low-pressure stage group, and the index thinking poorly of group aeroperformance of arbitrarily downgrading has
Some items of entropic efficiency, pitot loss, specific power, and aeroperformance is also had an impact by flow, blade exit Mach number, with flow,
Blade exit Mach number is as the constraints of aeroperformance, as synthetic aerodynamic performance in the aeroperformance index that is added to
Index, chooses one or more targets as overall optimization aim.
3) use the Parallel multi-objective genetic of real coding that the blade profile after parametrization is carried out optimizing.
4) blade parameter method, CFD method for solving are combined with Parallel multi-objective genetic, pass through programming realization
The Automatic Optimal of blade.
As preferably, the technical program is further comprising the steps of:
The first step, the parametrization of vane type line
For the parametrization of the blade of given design parameter, the basic geometric parameters of given blade design, pass through cubic Bezier
Function is calculated the mean camber line of blade profile, according to given maximum gauge and throat dimension, uses cubic spline interpolation to go out to close
The thickness distribution curve of reason, front trailing edge radius is determined by the design parameter given, and completes the blade type to given design parameter
The parametrization of line.
For known three dimendional blade, along a series of two-dimensional section bigger to effect of aerodynamic performance of leaf height set direction,
Use cubic Bezier matching each two-dimensional section curve, complete the parametrization to vane type line.
Second step, stress and strain model and Flow Field Calculation
To the vane type line after parametrization, carry out stress and strain model, then grid file is carried out CFD and solves and calculate its flow field and divide
Cloth, entrance gives stagnation pressure, stagnation temperature, mass dryness fraction, exports to constant static-pressure, and fluid uses IF97 steam model, arrangesRNGк-εTurbulent flow
Model, fluid governing equation uses Finite Volume Method to solve, selects second order spatial discrete scheme.
3rd step, Performance Evaluation
The isentropic efficiency of low-pressure stage group, pitot loss, specific power, flow, exit Mach number etc. are read in Flow Field Calculation result
Flow parameter, is added to flow and exit Mach number in isentropic efficiency, pitot loss, specific power, respectively as comprehensive constant entropy
Efficiency, comprehensive isobaric loss, comprehensive specific power, isobaric loss reduction maximum in comprehensive isentropic efficiency, comprehensive, comprehensive specific power are
Big three optimization aim choose 1～3, as object function to be optimized.
4th step, optimizing algorithm
Use the Parallel multi-objective genetic of real coding as the optimizing algorithm of object function, carry out the optimum molded line to blade
Carry out optimizing.
5th step, blade profile optimizes automatic optimal
Using the control variable of the blade profile after parametrization as design variable, use Parallel multi-objective genetic at design variable model
Enclose interior optimizing, the blade profile that each parameter combination generating multi-objective genetic algorithm is formed, it is carried out auto grid plot, right
Its flow field calculates, and transfers the numerical value required for object function from Flow Field Calculation result, calculates the mesh of each parameter combination
Offer of tender numerical value, by the continuous optimizing of Parallel multi-objective genetic, obtains the vane type line that synthetic aerodynamic performance is best.
First the technical program according to blade geometry formative method and TWO-DIMENSIONAL CASCADE Transform Type design parametric method, is developed
D remote sensing Automated Design parametric method, to the blade profile after parametrization, carries out CFD calculating, obtains the flow parameter of this blade profile,
Choose one or more performance parameters as optimization aim, after using the Parallel multi-objective genetic of real coding to come parametrization
Blade profile carry out optimizing, this algorithm has outstanding multi-objective optimization ability and global convergence performance.By Optimization Design, leaf
Grid parametric method and CFD method for solving combine, and develop efficient low pressure twisted blade full three-dimensional pneumatic automatic optimal technology.
Genetic algorithm is as a kind of efficient search and the method seeking optimal problem, the biggest to having solved practical problems
Help, its structure-oriented object operates, and takes to choose, intersects, the basic operation such as variation, carries out the solution of problem.And
Mode, the real-valued restructuring of heavy constituent, the discrete recombination etc. such as have probabilistic manner to choose under selection operation, greediness is chosen, variation has single-point
Variation, discrete variation etc..In addition, specific algorithm also has the genetic manipulation of response, also for seeking a problem
Excellent solution provides important solution.Optimization aim uses the mode that aeroperformance index combines with penalty function, by all kinds of
Constraints, such as flow, outlet flow angle, exit Mach number etc., given penalty factor is processed into penalty function, and penalty function will have about
Bundle optimization problem is converted into and solves Unconstrained Optimization Problem；Aeroperformance index to blade, such as isentropic efficiency, total crushing
Mistake, acting ability etc., by with penalty function be multiplied or be divided by be used as entirety optimization aim.
The invention has the beneficial effects as follows: either give the two-dimentional blade profile of design parameter or known three-dimensional blade profile, logical
Cross the blade of this method optimization design, vane type line can be carried out parametrization, make entirety reach optimization aim, it is achieved blade
Automatic Optimal, and by the continuous optimizing of Parallel multi-objective genetic, obtain the vane type line that synthetic aerodynamic performance is optimal, make
The low-pressure stage group of industry gas-turbine has high efficiency, stable operation, and is applicable to various design conditions and variable working condition.
Accompanying drawing explanation
Fig. 1 is a kind of technology path flow chart of the present invention.
Fig. 2 to Fig. 5 is that a kind of low-tension unit grade stator blade of the present invention uses difference leaf height cross sections before and after this method optimization
Molded line contrast, wherein: Fig. 2 is root section schematic diagram；Fig. 3 is 1/3 times of leaf height schematic cross-section；Fig. 4 is that 2/3 times of leaf height cuts
Face schematic diagram；Fig. 5 is top cross-section schematic diagram.
Note: label 1 is original stator blade state, piece number 2 is to optimize stator blade state.
Fig. 6 is the total aggregate efficiency contrast before and after a kind of low-tension unit of the present invention uses this method to optimize under different expansion ratios
Figure.
Detailed description of the invention
Below by embodiment, and combine accompanying drawing, technical scheme is described in further detail.
The present embodiment one industrial steam turbine low-pressure stage group vane type line optimization method, its technology path flow chart such as Fig. 1
Shown in, its process is as follows:
1) optimization problem of three dimendional blade molded line is converted into the two-dimentional molded line optimization problem on different leaf height cross section:
The first situation: for the two-dimentional blade profile of given design parameter, uses mean camber line to add the generation method of vane thickness distribution,
Blade profile mean camber line is defined by 3 Bezier functions, then is gone out thickness distribution by Cubic Spline Functions Fitting, and front trailing edge radius is by giving
Design parameter determine, generate parameterized three-dimensional blade profile by given each cross section height in the radial direction.
The second situation: for known three-dimensional blade profile, take the two-dimensional section on different leaf height direction, by 3 times
The pressure face of Bezier function matching two-dimensional section molded line respectively and suction surface, complete the parametrization to two dimension blade profile, then superposition
Cross section height in the radial direction, completes the parametrization of three-dimensional blade profile.
2) optimization aim is optimum with the synthetic aerodynamic performance of low-pressure stage group.The index thinking poorly of group aeroperformance of arbitrarily downgrading has very
Many, such as isentropic efficiency, pitot loss, specific power etc., and flow, blade exit Mach number etc. also have certain shadow to aeroperformance
Ring, using flow, blade exit Mach number as the constraints of aeroperformance, as combining in the aeroperformance index that is added to
Close aeroperformance index.Choose one or more targets as overall optimization aim.
3) use the Parallel multi-objective genetic of real coding that the blade profile after parametrization is carried out optimizing.Real coding
Parallel multi-objective genetic be a kind of adaptive global optimization searching algorithm, there is the effect of the survival of the fittest.
4) blade parametric method, CFD method for solving are combined with Parallel multi-objective genetic, real by programming
The Automatic Optimal of existing blade.CFD calculates can complete required task by business software, it is possible to controls oneself and directly writes calculating
Program, to flowing, heat transfer problem, stable state, transient problem all can be showed by Fig. 1.
The detailed process of computational methods is as follows:
The first step, the parametrization of vane type line:
Embodiment one, for the two-dimentional blade profile of given design parameter, uses mean camber line to add the generation method of vane thickness distribution, leaf
Type mean camber line is defined by cubic Bezier, then is gone out thickness distribution by Cubic Spline Functions Fitting, and front trailing edge radius is by giving
Design parameter determine, generate parameterized three-dimensional blade profile by given each cross section height in the radial direction.
Embodiment two, i.e. the second situation, it is known that three-dimensional blade profile, choose the two-dimensional section on different leaf height direction, use
Pressure face and the suction surface in different leaf height cross sections are fitted by cubic Bezier.
Rule of thumb each control point being given excursion, optimization method is by optimizing in this range.See Fig. 2 to Fig. 5,
Being the molded line contrast in different leaf height cross sections before and after a kind of low-tension unit grade vane optimization, wherein label 1 is original stator blade state,
Piece number 2 is for optimizing stator blade state.
Second step, stress and strain model and Flow Field Calculation:
Parallel multi-objective genetic, by the initial blade profile of stochastic generation n group in the excursion at control point, enters often organizing blade profile
Row grid divides and Flow Field Calculation automatically.
To the vane type line after parametrization, carry out stress and strain model, then grid file is carried out CFD solve calculate its stream
Field distribution, entrance gives stagnation pressure, stagnation temperature, mass dryness fraction, exports to constant static-pressure, and fluid uses IF97 steam model, arrangesRNGк-ε
Turbulence model, fluid governing equation uses Finite Volume Method to solve, selects second order spatial discrete scheme.
3rd step, Performance Evaluation:
The isentropic efficiency of low-pressure stage group, pitot loss, specific power, flow, exit Mach number etc. are read in Flow Field Calculation result
Flow parameter, is added to flow and exit Mach number in isentropic efficiency, pitot loss, specific power, respectively as comprehensive constant entropy
Efficiency, comprehensive isobaric loss, comprehensive specific power, isobaric loss reduction maximum in comprehensive isentropic efficiency, comprehensive, comprehensive specific power are
Big three optimization aim choose 1～3, as object function to be optimized.
4th step, optimizing algorithm
Use the Parallel multi-objective genetic of real coding as the optimizing algorithm of object function, carry out the optimum molded line to blade
Carry out optimizing.The object function often organizing blade profile will be calculated by Parallel multi-objective genetic, it is judged that whether it reaches convergence
Requirement, if not reaching convergent requirement, is constantly carried out optimizing, after reaching convergent requirement, to its best parameter group by its algorithm
Output molded line data, as the final result optimized.
6th step, target optimizing
Using the control variable of the blade profile after parametrization as design variable, Parallel multi-objective genetic program is used to become in design
Optimizing in weight range, the blade profile that each parameter combination generating multi-objective genetic algorithm is formed, it is carried out automatic mesh and draws
Divide and Flow Field Calculation, from Flow Field Calculation result, transfer the numerical value required for object function, pass through Parallel multi-objective genetic
The object function often organizing blade profile being calculated, it is judged that whether it reaches convergent requirement, if not reaching convergent requirement, calculating by it
Method constantly carries out optimizing, after reaching convergent requirement, its best parameter group is exported molded line data, as the termination optimized
Really.Fig. 6 is the total aggregate efficiency comparison diagram before and after a kind of low-tension unit optimizes under different expansion ratios.
Above-described embodiment is the description of the invention, is not limitation of the invention, any simple transformation to the present invention
After structure, method etc. belong to protection scope of the present invention.
Claims (2)
1. an industrial steam turbine low-pressure stage group vane type line optimization method, it is characterised in that:
1) optimization problem of three dimendional blade molded line is converted into the two-dimentional molded line optimization problem on different leaf height cross section: for given
The two-dimentional blade profile of design parameter, uses mean camber line to add the generation method of vane thickness distribution, and blade profile mean camber line is by 3 Bezier letters
Number definition, then gone out thickness distribution by Cubic Spline Functions Fitting, front trailing edge radius is determined by the design parameter given；For
The three-dimensional blade profile known, takes the two-dimensional section on different leaf height direction, by 3 Bezier functions matching pressure face and suction respectively
Face, completes the parametrization to two dimension blade profile；
2) optimization aim is optimum with the synthetic aerodynamic performance of low-pressure stage group, and the index thinking poorly of group aeroperformance of arbitrarily downgrading has constant entropy to imitate
The some items of rate, pitot loss, specific power, and aeroperformance is also had an impact, with flow, leaf by flow, blade exit Mach number etc.
Sheet exit Mach number, as the constraints of aeroperformance, the aeroperformance index that is added to refers to as synthetic aerodynamic performance
Mark, chooses one or more targets as overall optimization aim；
3) use the Parallel multi-objective genetic of real coding that the blade profile after parametrization is carried out optimizing；
4) blade parameter method, CFD method for solving are combined with Parallel multi-objective genetic, by programming realization blade
Automatic Optimal.
Industrial steam turbine low-pressure stage group vane type line optimization method the most according to claim 1, it is characterised in that include with
Lower step:
The first step, the parametrization of vane type line
For the parametrization of the blade of given design parameter, the basic geometric parameters of given blade design, pass through cubic Bezier
Function is calculated the mean camber line of blade profile, according to given maximum gauge and throat dimension, uses cubic spline interpolation to go out to close
The thickness distribution curve of reason, front trailing edge radius is determined by the design parameter given, and completes the blade type to given design parameter
The parametrization of line；
For known three dimendional blade, along a series of two-dimensional section bigger to effect of aerodynamic performance of leaf height set direction, use
Cubic Bezier matching each two-dimensional section curve, completes the parametrization to vane type line；
Second step, stress and strain model and Flow Field Calculation
To the vane type line after parametrization, carry out stress and strain model, then grid file is carried out CFD and solves and calculate its flow field and divide
Cloth, entrance gives stagnation pressure, stagnation temperature, mass dryness fraction, exports to constant static-pressure, and fluid uses IF97 steam model, arrangesRNGк-εTurbulent flow
Model, fluid governing equation uses Finite Volume Method to solve, selects second order spatial discrete scheme；
3rd step, Performance Evaluation
The isentropic efficiency of low-pressure stage group, pitot loss, specific power, flow, exit Mach number etc. are read in Flow Field Calculation result
Flow parameter, is added to flow and exit Mach number in isentropic efficiency, pitot loss, specific power, respectively as comprehensive constant entropy
Efficiency, comprehensive isobaric loss, comprehensive specific power, isobaric loss reduction maximum in comprehensive isentropic efficiency, comprehensive, comprehensive specific power are
Big three optimization aim choose 1～3, as object function to be optimized；
4th step, optimizing algorithm
Use the Parallel multi-objective genetic of real coding as the optimizing algorithm of object function, carry out the optimum molded line to blade
Carry out optimizing；
5th step, blade profile optimizes automatic optimal
Using the control variable of the blade profile after parametrization as design variable, use Parallel multi-objective genetic at design variable model
Enclose interior optimizing, the blade profile that each parameter combination generating multi-objective genetic algorithm is formed, it is carried out auto grid plot, right
Its flow field calculates, and transfers the numerical value required for object function from Flow Field Calculation result, calculates the mesh of each parameter combination
Offer of tender numerical value, by the continuous optimizing of Parallel multi-objective genetic, obtains the vane type line that synthetic aerodynamic performance is best.
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