CN109800519A - A kind of aerofoil fan multiple spot Aerodynamic optimization design method - Google Patents
A kind of aerofoil fan multiple spot Aerodynamic optimization design method Download PDFInfo
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Abstract
The present invention provides a kind of aerofoil fan multiple spot Aerodynamic optimization design methods, are related to aerofoil fan technical field.The present invention obtains all sample elements of sample space by the mapping of parameter space to sample space, for the same sample elements of sample space, the flow field calculation of the sample elements is carried out to obtain the adiabatic efficiency of peak efficiencies point and nearly stall point according to the peak efficiencies point boundary condition of prototype fan and nearly stall point boundary condition respectively, after the adiabatic efficiency for obtaining all sample elements optimization points, adiabatic efficiency built-up pattern is constructed in order to which subsequent development optimizing works to obtain optimal axial fan designs scheme.Not only two optimization point aeroperformances of peak efficiencies point and nearly stall point are obviously improved scheme after present invention optimization, unoptimizable point aeroperformance also be improved significantly, stall flow is obviously reduced compared with prototype solution, blocking flow obviously increases, i.e. under the conditions of full working scope, fan propeller aeroperformance gets a promotion, and stable operation range is effectively expanded.
Description
Technical field
The present invention relates to aerofoil fan technical field, in particular to a kind of aerofoil fan multiple spot Aerodynamic optimization design method.
Background technique
Aero-engine is known as modern industry " jewel on imperial crown ", indicates the comprehensive of a state aviation industrial development
Close technical level.Core Pneumatic component one of of the fan/compressor as aero-engine, aeroperformance usually decides boat
The working performance of empty engine entirety.The high thrust ratio of modern aeroengine, high efficiency, the application demand of low oil consumption, so that wind
Fan/compressor constantly horizontal, high throughflow and the feature growths such as compact-sized towards high grade airload, and need complete
Good level of efficiency and more wide stable operation range are maintained under operating condition operating condition, to meet aero-engine full working scope
The application demand of stable operation.
In fan/compressor Aerodynamic optimization design, single spot optimization design method is usually only capable of considering under optimization point operating condition
The variation of fan/compressor aeroperformance, can not effectively take into account the change of unoptimizable point aeroperformance, that is, be difficult to comprehensively consider wind
Fan/compressor pneumatic design scheme off design performance, can not effectively expand pneumatic design scheme stable operation range.Therefore,
Single spot optimization design method has certain limitation in terms of practical engineering application.
Summary of the invention
In view of this, the present invention is directed to propose a kind of aerofoil fan multiple spot Aerodynamic optimization design method, for single spot optimization
Design method improves, and is completed at the same time the Aerodynamic optimization design work of multiple optimization points.
In order to achieve the above objectives, the technical scheme of the present invention is realized as follows:
A kind of aerofoil fan multiple spot Aerodynamic optimization design method, comprising:
The three-dimensional geometry data of blade path in prototype solution are obtained, and carry out parameterized treatment；
Based on the rotor blade of blade path in prototype solution, control point and middle camber line are chosen, and establishes control point coordinates
Relationship between middle camber line curvilinear coordinate；
Under conditions of meeting design constraint, two different operating points of peak efficiencies point and nearly stall point are chosen as excellent
Change point；
Setting optimization constraint condition, the adiabatic efficiency of adiabatic efficiency and nearly stall point based on peak efficiencies point, building are exhausted
The built-up pattern of the thermal efficiency is optimized using the built-up pattern as objective function；
The variation range for setting control point coordinates is constructed using the coordinate information at control point as optimized variable about control
The parameter space of point；
All sample elements of sample space are obtained by the mapping of the parameter space to sample space, and are obtained each
The corresponding target function value of sample elements obtains adiabatic efficiency of each sample elements at the optimization point；
Objective function global optimizing is carried out, to obtain the target function value of optimization, and the target function value of optimization corresponds to
Control point parameter information；
According to the corresponding control point parameter information of the target function value of the optimization obtain in camber line curve coordinate information,
And then the axial fan designs side according to the Aerodynamic optimization design scheme of the middle camber line curve Reconstruction aerofoil fan, that is, after optimizing
Case.
Optionally, the relationship between the control point coordinates and the middle camber line curvilinear coordinate using quartic spline function into
Row association:
Wherein, Pi+rFor i-th section of curve S of controlling sectionsiControl point coordinates, Si(tu) it is i-th section of curve S of controlling sectionsi
Coordinate, Br(tu) it is spline base function, tuFor value of the parameter t at u ∈ [0,1] position, v (u) is that camber line curve is sat in blade profile
Mark.
Optionally, the spline base function Br(tu) form are as follows:
Optionally, the built-up pattern of the adiabatic efficiency are as follows: η=α ηd+(1-α)ηs, wherein α is weight coefficient, and η is sample
The whole adiabatic efficiency of the corresponding fan propeller of this Spatial elements, ηdAnd ηsThe respectively corresponding fan of elements of sample space turns
The adiabatic efficiency of the peak efficiencies point and nearly stall point of son.
Optionally, the optimization constraint condition includes mass flow constraint condition:
Wherein,WithThe peak value effect of blade path respectively in the corresponding blade path of sample elements and prototype solution
The mass flow of rate point,WithThe nearly mistake of blade path respectively in the corresponding blade path of sample elements and prototype solution
The mass flow of speed point.
Optionally, the optimization constraint condition further includes overall pressure tatio constraint condition:
Wherein, π and π0The peak efficiencies of blade path respectively in the corresponding blade path of sample elements and prototype solution
The overall pressure tatio of point, πsAnd πs0The nearly stall point of blade path respectively in the corresponding blade path of sample elements and prototype solution
Overall pressure tatio.
Optionally, the corresponding target function value of each sample elements that obtains includes:
The flow region for corresponding to scheme to sample elements each in sample space respectively carries out grid dividing；
Peak efficiencies point boundary condition and nearly stall point boundary condition are set based on the prototype solution, are respectively completed peak value
The flow field calculation of efficient point and nearly stall point, obtain sample elements correspond to scheme peak efficiencies point adiabatic efficiency and nearly stall point
Adiabatic efficiency；
The corresponding target function value of each sample elements is obtained according to the adiabatic efficiency built-up pattern of the optimization point.
Optionally, the parameterized treatment includes: and opens up direction along leaf to choose m controlling sections, and in each control
N control point is set on section.
Optionally, the control point of the controlling sections is wire looping point in the blade profile of the controlling sections.
Optionally, wire looping point includes that the curvature control point of camber line in blade profile and maximum defluxion control in the blade profile
Point.
Compared with the existing technology, a kind of aerofoil fan multiple spot Aerodynamic optimization design method of the present invention has following excellent
Gesture:
Not only two optimization point aeroperformances of peak efficiencies point and nearly stall point are obviously improved scheme after present invention optimization, non-
Optimization point aeroperformance also be improved significantly, stall flow is obviously reduced compared with prototype solution, blocking flow obviously increase, that is, exist
Under the conditions of full working scope, fan propeller aeroperformance gets a promotion, and stable operation range is effectively expanded.This hair is demonstrated simultaneously
The validity of the multiple spot Aerodynamic optimization design method of bright proposition.The multiple spot Aerodynamic optimization design method that the application proposes can be effective
Blade path geometry is adjusted, 67 fan propeller aeroperformance of NASA Rotor is promoted under the conditions of full working scope, and guarantee most
Excellent result scheme has more excellent channel mobility status.It is needed in addition, completing fan rotor blade channel multiple spot Aerodynamic optimization design
About 10 hours, the design cycle is short, high-efficient.
Detailed description of the invention
The attached drawing for constituting a part of the invention is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is the multiple spot Aerodynamic optimization design method flow diagram in the embodiment of the present invention two；
Fig. 2 is the controlling sections and effective control point schematic diagram in the embodiment of the present invention；
The comparative diagram of Fig. 3 relationship between the adiabatic efficiency and quality before and after the optimization in the embodiment of the present invention；
The comparative diagram of Fig. 4 relationship between the overall pressure tatio and quality before and after the optimization in the embodiment of the present invention；
Fig. 5 is the rotor blade structure schematic diagram before the optimization in the embodiment of the present invention；
Fig. 6 is the rotor blade structure schematic diagram after the optimization in the embodiment of the present invention；
Fig. 7 is the fan propeller array blade construction schematic diagram before the optimization in the embodiment of the present invention；
Fig. 8 is the fan propeller array blade construction schematic diagram after the optimization in the embodiment of the present invention；
Fig. 9 is the three-dimensional geometrical structure of the blade and wheel hub after the optimization in the embodiment of the present invention；
Figure 10 is the multiple spot Aerodynamic optimization design method flow diagram in the embodiment of the present invention one.
Specific embodiment
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.
The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
Embodiment one
The present embodiment provides a kind of aerofoil fan multiple spot Aerodynamic optimization design methods, as shown in Figure 10, comprising:
Step S1: the three-dimensional geometry data of blade path in prototype solution are obtained, and carry out parameterized treatment.
Here, the parameterized treatment includes: to choose m controlling sections along leaf exhibition direction, and cut in each control
N control point is set on face.Preferably, the control point of the controlling sections is wire looping point in the blade profile of the controlling sections,
And wire looping point includes curvature control point and the maximum defluxion control point of camber line in blade profile in the blade profile.That is, this
In, multiple control points are set, and all control points form a control point combination, and corresponding prototype solution is combined at control point here
In rotor blade.
Step S2: the rotor blade based on blade path in prototype solution chooses control point and middle camber line, and establishes control
Relationship between point coordinate and middle camber line curvilinear coordinate.Wherein, between the control point coordinates and the middle camber line curvilinear coordinate
Relationship be associated using quartic spline function:
Wherein, Pi+rFor i-th section of curve S of controlling sectionsiControl point coordinates, Si(tu) it is i-th section of curve S of controlling sectionsi
Coordinate, Br(tu) it is spline base function, tuFor value of the parameter t at u ∈ [0,1] position, v (u) is that camber line curve is sat in blade profile
Mark.Here, i-th section of curve refers in blade profile that curve where control point can be divided into multistage, can be between every section of curve
There is overlapped part.
Spline base function Br(tu) form are as follows:
Step S3: under conditions of meeting design constraint, two different operating conditions of peak efficiencies point and nearly stall point are chosen
Point is as optimization point.
Step S4: setting optimization constraint condition, the adiabatic efficiency of adiabatic efficiency and nearly stall point based on peak efficiencies point,
The built-up pattern for constructing adiabatic efficiency, optimizes using the built-up pattern as objective function.
Wherein, the optimization constraint condition includes mass flow constraint condition and overall pressure tatio constraint condition；Mass flow is about
Beam condition are as follows:
Wherein,WithThe peak value effect of blade path respectively in the corresponding blade path of sample elements and prototype solution
The mass flow of rate point,WithThe nearly mistake of blade path respectively in the corresponding blade path of sample elements and prototype solution
The mass flow of speed point.
Overall pressure tatio constraint condition are as follows:
Wherein, π and π0The peak efficiencies of blade path respectively in the corresponding blade path of sample elements and prototype solution
The overall pressure tatio of point, πsAnd πs0The nearly stall point of blade path respectively in the corresponding blade path of sample elements and prototype solution
Overall pressure tatio.
The built-up pattern of the adiabatic efficiency are as follows: η=α ηd+(1-α)ηs, wherein α is weight coefficient, and η is each sample
Adiabatic efficiency of the element at optimization point, ηdAnd ηsThe respectively peak efficiencies point of the corresponding fan propeller of elements of sample space and close
The adiabatic efficiency of stall point.
Step S5: setting the variation range of control point coordinates, and using the coordinate information at control point as optimized variable, building is closed
Parameter space in control point.Here, the coordinate information at control point is changed in a certain range, so as to generate multiple controls
A corresponding rotor blade is combined at system point combination, a control point, and each control point, which is combined, to form a parameter space element, institute
Some control combination forms parameter space.Sample space refers to that corresponding different rotor leaf is combined at all different control points
Piece combination, sample elements refer to that corresponding rotor blade is combined at a control point.
Step S6: obtaining all sample elements of sample space by the mapping of the parameter space to sample space, and
The corresponding target function value of each sample elements is obtained, that is, obtains adiabatic efficiency of each sample elements at the optimization point.
Specifically, the corresponding target function value of each sample elements that obtains includes:
Step S61: the flow region for corresponding to scheme to sample elements each in sample space respectively carries out grid dividing；
Step S62: based on prototype solution setting peak efficiencies point boundary condition and nearly stall point boundary condition, respectively
Complete the flow field calculation of peak efficiencies point and nearly stall point, obtain sample elements correspond to the peak efficiencies point adiabatic efficiency of scheme with
Nearly stall point adiabatic efficiency.
Step S63: the corresponding target letter of each sample elements is obtained according to the adiabatic efficiency built-up pattern of the optimization point
Numerical value.
Step S7: carrying out objective function global optimizing, to obtain the target function value of optimization, and the objective function of optimization
It is worth corresponding control point parameter information.For example, genetic algorithm, which can be used, carries out global optimizing, the major parameter of genetic algorithm is chosen
As follows: Population in Genetic Algorithms size is 30, greatest iteration step number 150, crossover probability 0.9, and variation or mutation probability are
0.15, procreation algebra is 20.It is noted that other existing optimization algorithms can be used for realizing the complete of the objective function
Office's optimizing, the disclosure are not limited in this respect.
Step S8: according to the seat of camber line curve in the corresponding control point parameter information acquisition of the target function value of the optimization
Mark information, and then the axis stream wind according to the Aerodynamic optimization design scheme of the middle camber line curve Reconstruction aerofoil fan, that is, after optimizing
Fan design scheme.
Comparison obtains before and after Multi point optimization of the present invention, the scheme after optimization not only peak efficiencies point and nearly stall point two
A optimization point aeroperformance is obviously improved, unoptimizable point aeroperformance also be improved significantly, stall flow is bright compared with prototype solution
Aobvious to reduce, blocking flow obviously increases, i.e., under the conditions of full working scope, fan propeller aeroperformance gets a promotion, steady operation model
It encloses and is effectively expanded.
Embodiment two
The present embodiment uses 67 transonic speed aerofoil fan rotor of NASA Rotor as prototype solution, and is based on its geometry
Data carry out parameterized treatment, and main design parameters are as shown in table 1.
1 NASA Rotor of table, 67 main design parameters
Multiple spot Aerodynamic optimization design method is worked by being completed at the same time the Aerodynamic optimization design of multiple optimization points, can be taken into account
Design conditions and off-design behaviour fan/compressor aeroperformance change, and acquisition has good Study on Variable Condition Features and has higher
The fan/compressor Aerodynamic optimization design scheme of steady operation nargin.Therefore, research and development have optimization design under multiple working conditions ability, energy
Enough fan/compressor Aerodynamic optimization design methods for automatically and efficiently completing the work of multi-state Aerodynamic optimization design, first to development
Into fan/compressor pneumatic design technology, expansion fan/compressor Aerodynamic Design System there is highly important Practical Project to answer
With value.A kind of aerofoil fan multiple spot Aerodynamic optimization design method proposed by the present invention is completed pneumatic to the multiple spot of the fan propeller
Optimization design, and obtain ideal result of study.Specific implementation step is as follows, as shown in Figure 1:
Step S1 ': 67 rotor blade passage three-dimensional geometry data of NASA Rotor are obtained, simultaneously as prototype solution
Carry out parameterized treatment.Specific parameterized treatment process is as follows:
When parameterizing to 67 rotor blade passage of NASA Rotor, m controlling sections are chosen along leaf exhibition direction, often
The parametric control points of a controlling sections are n.In the present embodiment, the control point of each controlling sections is the controlling sections leaf
Wire looping point in type, during actual optimization, (including the 5 effectively controls that are 7 of the numbers of control points of each controlling sections
Point and 2 ends control point, end control point are the intersection point of leading edge or trailing edge and controlling sections), each controlling sections have
Effect control points are 5, and (wherein, 4 control points are for controlling blade profile meanline curvature, and 1 control point is for controlling in blade profile
The maximum defluxion position of camber line), the actually active control point sum of each three dimendional blade is 25.Fig. 2 gives controlling sections
With effective control point schematic diagram, leaf exhibition direction refers to the direction along leading edge or trailing edge, sets respectively on 5 controlling sections here
5 control points are equipped with, control point is numbered respectively, is successively named as cp1,cp2... cpi…,cp25, wherein bottom is
Blade root section, the control point on the blade root section are followed successively by cp1,cp2, cp3,cp4, cp5。
Step S2 ': based on camber line parametric data in prototype solution blade profile, the selection of control point parameter information, and benefit are carried out
Control point coordinates and middle camber line curvilinear coordinate are associated with quartic spline function.
The expression formula of used quartic spline function are as follows:
Wherein, Pi+rFor i-th section of curve S of controlling sectionsiControl point coordinates, Si(tu) it is i-th section of curve S of controlling sectionsi
Coordinate, Br(tu) it is spline base function, tuFor value of the parameter t at u ∈ [0,1] position, v (u) is that camber line curve is sat in blade profile
Mark.Here, i-th section of curve refers in blade profile that curve where control point can be divided into multistage, can be between every section of curve
There is overlapped part, i and previously described cp hereiIn i meaning it is inconsistent.
Spline base function Br(tu) mathematical definition are as follows:
Step S3 ': the same sample elements of sample space are directed to, under conditions of meeting design constraint, with peak efficiencies point
The operating point different with nearly stall point two is as optimization point.Here sample space refers to all different control point combinations
Corresponding different rotor blade combination, sample elements refer to that corresponding rotor blade is combined at a control point.The present embodiment
In, it is equivalent to 25 dominating pair of vertices and answers a rotor blade.
Step S4 ': guarantee mass flow and overall pressure tatio constraint condition, pass through peak efficiencies point adiabatic efficiency and nearly stall point
The target function model of the adiabatic efficiency combination of adiabatic efficiency building optimization point, using the target function model as optimization aim letter
Number.
The NASA Rotor67 fan rotor blade CHANNEL OPTIMIZATION design mathematic model that the present embodiment is established is as follows:
Objective function: optimization point adiabatic efficiency built-up pattern: η=α ηd+(1-α)ηs
Wherein, α is weight coefficient, in the present embodiment, chooses α=0.8.η is each sample elements at optimization point
Adiabatic efficiency, ηdAnd ηsThe respectively adiabatic efficiency of elements of sample space corresponding fan propeller peak efficiencies point and nearly stall point,
They are specifically defined are as follows: ηd=f1(cp1,cp2,……,cp25), ηs=f2(cp1,cp2,……,cp25)。cp1,……,cp25
For effective control point of camber line in the blade profile of fan rotor blade section, the i.e. optimized variable of objective function.That is, with 25
When the coordinate information at a control point changes, peak efficiencies point is different with the adiabatic efficiency of nearly stall point.
In the present invention, in conjunction with genetic algorithm, global optimizing is carried out to objective function η, obtains the maximum insulation effect of optimization point
Rate ηmax, complete the multiple spot Aerodynamic optimization design of aerofoil fan.
Optimize constraint condition: mass flow and overall pressure tatio constraint condition.
(1) mass flow constraint condition are as follows:
Wherein,WithThe peak value effect of blade path respectively in the corresponding blade path of sample elements and prototype solution
The mass flow of rate point,WithThe nearly mistake of blade path respectively in the corresponding blade path of sample elements and prototype solution
The mass flow of speed point.
(2) overall pressure tatio constraint condition are as follows:
Wherein, π and π0The peak efficiencies of blade path respectively in the corresponding blade path of sample elements and prototype solution
The overall pressure tatio of point, πsAnd πs0The nearly stall point of blade path respectively in the corresponding blade path of sample elements and prototype solution
Overall pressure tatio.
Optimized variable: it extends to 5 controlling sections are chosen, (wherein, 4 control at each effective 5, control point of controlling sections
Point is used to control the maximum defluxion position of camber line in blade profile for controlling blade profile meanline curvature, 1 control point), 25 in total
Optimized variable of effective control point as objective function.Controlling sections and control point are as shown in Figure 2.
In the present embodiment, the parameter that optimizing design scheme needs is less, chooses 5 controls along leaf exhibition direction in the present embodiment
Section processed, each controlling sections choose 7 control point parameters, each section 5 control points of practical selection as effective control point,
Actually active control point sum is 25, and computing resource occupies small, resource utilization height, with good practical implementation meaning
Justice.
Step S5 ': using the coordinate information of control point parameter as optimized variable, according to the geometrical characteristic and reality of research object
Border engineering limits given control point coordinates value range.I.e. by giving the value range of optimized variable, parameter space is constructed.
Step S6 ': obtaining all sample elements of sample space by the mapping of the parameter space to sample space, and
The corresponding target function value of each sample elements is obtained, that is, obtains adiabatic efficiency of each sample elements at optimization point.Wherein,
Obtaining the corresponding target function value of each sample elements includes:
S61 ': the flow region for corresponding to scheme to sample elements each in sample space respectively carries out grid dividing；
S62 ': based on prototype solution setting peak efficiencies point boundary condition and nearly stall point boundary condition, distinguished
At the flow field calculation of peak efficiencies point and nearly stall point, the peak efficiencies point adiabatic efficiency and closely that sample elements correspond to scheme is obtained
Stall point adiabatic efficiency；
S63 ': the corresponding objective function of each sample elements is obtained according to the adiabatic efficiency built-up pattern of the optimization point
Value.
Step S7 ': in conjunction with genetic algorithm, objective function global optimizing is carried out to control point parameter, to obtain optimal objective
Functional value ηmaxAnd the corresponding control point parameter information of optimal objective function value.The major parameter of genetic algorithm in the present embodiment
Choose as follows: Population in Genetic Algorithms size is 30, greatest iteration step number 150, crossover probability 0.9, variation or mutation probability
It is 0.15, procreation algebra is 20.It should be noted that other existing optimization algorithms can be used for realizing the objective function
Global optimizing, the disclosure is not limited in this respect.
Step S8 ': according to the seat of camber line curve in the corresponding control point parameter information acquisition of the optimal objective function value
Mark information, and then the axis stream wind according to the Aerodynamic optimization design scheme of the middle camber line curve Reconstruction aerofoil fan, that is, after optimizing
Fan design scheme, that is, NASA Rotor67 fan propeller geometry new departure of optimal design.
Under two operating conditions of peak efficiencies point and nearly stall point, the aerofoil fan multiple spot aerodynamic optimization provided according to Fig. 1 is set
Method flow is counted, using optimization point adiabatic efficiency built-up pattern as optimization object function, with 67 fan rotor blade of NASA Rotor
Camber line is optimal control variable (being namely control variable with the coordinate information at effective control point) in blade profile, while guaranteeing quality
Flow and overall pressure tatio meet corresponding constraint condition, thus a large amount of optimizing design schemes of quick obtaining, and finally obtain optimal
67 fan propeller multiple spot Aerodynamic optimization design result of NASA Rotor.
Specific optimum results analysis is expressed as follows: table 2 gives gas before and after 67 multiple spot Aerodynamic optimization design of NASA Rotor
Dynamic performance comparison.It is bright that result of study shows that two optimization point aeroperformances of peak efficiencies point and nearly stall point have after optimization
It is aobvious to be promoted.Specifically, peak efficiencies point adiabatic efficiency and overall pressure tatio have increased separately 1.08% and 7.04%, respectively reach
91.36% and 1.794, although mass flow has declines to a certain degree, but still meets constraint condition, is maintained at 33.25kg/s；Closely
Stall point adiabatic efficiency and overall pressure tatio have increased separately 0.37% and 6.58%, respectively reach 88.58% and 1.831, stalled flow
Amount reduces 3% after optimization, is reduced to 30.37kg/s.In conjunction with shown in Fig. 3 and Fig. 4, A point is nearly stall point in figure, and B point is exhausted
Thermal efficiency point, the front and back comparison of 67 Multi point optimization of NASA Rotor obtain, not only peak efficiencies point and the nearly stall of optimal result scheme
Point two optimization point aeroperformances be obviously improved, unoptimizable point aeroperformance also be improved significantly, stall flow is compared with prototype side
Case is obviously reduced, and blocking flow obviously increases, i.e., under the conditions of full working scope, fan propeller aeroperformance gets a promotion, and stablizes work
It is effectively expanded as range.The validity of multiple spot Aerodynamic optimization design method proposed by the present invention is demonstrated simultaneously.Multiple spot gas
The comparison of fan propeller three dimendional blade is as shown in Figure 5 and Figure 6 before and after dynamic optimization design, wherein and the lower edge of blade is blade root in figure,
Top edge is leaf top, and left edge is trailing edge, and right hand edge is leading edge, it is described here all around based on shown in Fig. 5 or Fig. 6 and
Speech.
2 multiple spot Aerodynamic optimization design result of table
67 fan propeller array blade comparison of NASA Rotor is as shown in Figure 7 and Figure 8 before and after multi-point Optimal Design.Fig. 9 gives
The three-dimensional geometrical structure of optimal result scheme different views blade and wheel hub is gone out.Result of study shows that the application proposes more
Point Aerodynamic optimization design method can effectively adjust blade path geometry, and NASA Rotor is promoted under the conditions of full working scope
67 fan propeller aeroperformances, and guarantee that optimal result scheme has more excellent channel mobility status.
In conclusion using a kind of aerofoil fan multiple spot Aerodynamic optimization design method proposed by the present invention, for sample sky
Between same sample elements, meet optimization constraint under conditions of, respectively be directed to prototype aerofoil fan peak efficiencies point and nearly stall
Point two different operating conditions optimize point adiabatic efficiency built-up pattern as objective function by building, complete multiple spot aerodynamic optimization and set
Meter.Optimal design result is not only promoted 67 fan propeller peak efficiencies point operating condition aeroperformance of NASA Rotor, non-design work
Condition aeroperformance is also effectively improved, and the synthetic aerodynamic performance boundary of 67 fan propeller variable working condition of NASA Rotor has been expanded.
This demonstrates the validity of multiple spot Aerodynamic optimization design method proposed by the present invention, shows that the Aerodynamic optimization design method can open up
Open up transonic fan stage Aerodynamic optimization design system, practical engineering application value with higher.In addition, completing NASA Rotor67
Fan rotor blade channel multiple spot Aerodynamic optimization design needs about 10 hours, and the design cycle is short, high-efficient.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of aerofoil fan multiple spot Aerodynamic optimization design method characterized by comprising
The three-dimensional geometry data of blade path in prototype solution are obtained, and carry out parameterized treatment；
Based on the rotor blade of blade path in prototype solution, control point and middle camber line are chosen, and establishes control point coordinates in
Relationship between camber line curvilinear coordinate；
Under conditions of meeting design constraint, peak efficiencies point and nearly stall point two different operating points are chosen as optimization
Point；
Setting optimization constraint condition, the adiabatic efficiency of adiabatic efficiency and nearly stall point based on peak efficiencies point, building insulation effect
The built-up pattern of rate is optimized using the built-up pattern as objective function；
The variation range for setting control point coordinates is constructed using the coordinate information at control point as optimized variable about control point
Parameter space；
All sample elements of sample space are obtained by the mapping of the parameter space to sample space, and obtain each sample
The corresponding target function value of element obtains adiabatic efficiency of each sample elements at the optimization point；
Objective function global optimizing is carried out, to obtain the target function value of optimization, and the corresponding control of target function value of optimization
System point parameter information；
According to the coordinate information of camber line curve in the corresponding control point parameter information acquisition of the target function value of the optimization, in turn
Axial fan designs scheme according to the Aerodynamic optimization design scheme of the middle camber line curve Reconstruction aerofoil fan, that is, after optimizing.
2. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 1, which is characterized in that the control point is sat
Relationship between mark and the middle camber line curvilinear coordinate is associated using quartic spline function:
Wherein, Pi+rFor i-th section of curve S of controlling sectionsiControl point coordinates, Si(tu) it is i-th section of curve S of controlling sectionsiSeat
Mark, Br(tu) it is spline base function, tuFor value of the parameter t at u ∈ [0,1] position, v (u) is camber line curvilinear coordinate in blade profile.
3. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 2, which is characterized in that the spline Basis letter
Number Br(tu) form are as follows:
4. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 1, which is characterized in that
The built-up pattern of the adiabatic efficiency are as follows: η=α ηd+(1-α)ηs, wherein α is weight coefficient, and η is that each sample elements exist
Adiabatic efficiency at optimization point, ηdAnd ηsRespectively the peak efficiencies point of the corresponding fan propeller of elements of sample space and nearly stall
The adiabatic efficiency of point.
5. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 4, which is characterized in that the optimization constraint
Condition includes mass flow constraint condition:
Wherein,WithThe peak efficiencies point of blade path respectively in the corresponding blade path of sample elements and prototype solution
Mass flow,WithThe nearly stall point of blade path respectively in the corresponding blade path of sample elements and prototype solution
Mass flow.
6. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 5, which is characterized in that
The optimization constraint condition further includes overall pressure tatio constraint condition:
Wherein, π and π0The peak efficiencies point of blade path is total respectively in the corresponding blade path of sample elements and prototype solution
Pressure ratio, πsAnd πs0The stagnation pressure of the nearly stall point of blade path respectively in the corresponding blade path of sample elements and prototype solution
Than.
7. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 1, which is characterized in that
The corresponding target function value of each sample elements that obtains includes:
The flow region for corresponding to scheme to sample elements each in sample space respectively carries out grid dividing；
Peak efficiencies point boundary condition and nearly stall point boundary condition are set based on the prototype solution, are respectively completed peak efficiencies
The flow field calculation of point and nearly stall point obtains peak efficiencies point adiabatic efficiency and the insulation of nearly stall point that sample elements correspond to scheme
Efficiency；
The corresponding target function value of each sample elements is obtained according to the adiabatic efficiency built-up pattern of the optimization point.
8. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 1, which is characterized in that
The parameterized treatment includes: to choose m controlling sections along leaf exhibition direction, and n is arranged on each controlling sections
A control point.
9. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 8, which is characterized in that the controlling sections
Control point be the controlling sections blade profile in wire looping point.
10. aerofoil fan multiple spot Aerodynamic optimization design method according to claim 9, which is characterized in that
Wire looping point includes curvature control point and the maximum defluxion control point of camber line in blade profile in the blade profile.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113297706A (en) * | 2021-04-22 | 2021-08-24 | 西北工业大学 | Method for predicting efficiency point of each rotating speed peak value of gas compressor according to design index |
| CN117669269B (en) * | 2024-01-30 | 2024-05-03 | 苏州元脑智能科技有限公司 | Method and device for optimizing cooling fan, electronic equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105697420A (en) * | 2016-01-18 | 2016-06-22 | 北京航空航天大学 | Model for estimating performance of partial treatment casing |
| CN107563074A (en) * | 2017-09-08 | 2018-01-09 | 哈尔滨工业大学 | A kind of Optimization Design and Optimization Platform of fan/compressor end wall profile |
| CN108153998A (en) * | 2018-01-25 | 2018-06-12 | 哈尔滨工业大学 | The full Aerodynamic 3 D optimization design method of centrifugal blower impeller |
| CN108229015A (en) * | 2017-12-30 | 2018-06-29 | 中国科学院工程热物理研究所 | A kind of high-altitude two-stage turbocharger variable working condition adaptation design method |
| CN108536922A (en) * | 2018-03-20 | 2018-09-14 | 北京航空航天大学 | The internal and external flow field integration method for numerical simulation of aircraft and engine |
| CN108829970A (en) * | 2018-06-12 | 2018-11-16 | 深圳森蓝忠信科技有限公司 | Axial fan blade optimum design method and optimization system based on vortex dynamics |
-
2019
- 2019-01-25 CN CN201910074877.8A patent/CN109800519A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105697420A (en) * | 2016-01-18 | 2016-06-22 | 北京航空航天大学 | Model for estimating performance of partial treatment casing |
| CN107563074A (en) * | 2017-09-08 | 2018-01-09 | 哈尔滨工业大学 | A kind of Optimization Design and Optimization Platform of fan/compressor end wall profile |
| CN108229015A (en) * | 2017-12-30 | 2018-06-29 | 中国科学院工程热物理研究所 | A kind of high-altitude two-stage turbocharger variable working condition adaptation design method |
| CN108153998A (en) * | 2018-01-25 | 2018-06-12 | 哈尔滨工业大学 | The full Aerodynamic 3 D optimization design method of centrifugal blower impeller |
| CN108536922A (en) * | 2018-03-20 | 2018-09-14 | 北京航空航天大学 | The internal and external flow field integration method for numerical simulation of aircraft and engine |
| CN108829970A (en) * | 2018-06-12 | 2018-11-16 | 深圳森蓝忠信科技有限公司 | Axial fan blade optimum design method and optimization system based on vortex dynamics |
Non-Patent Citations (1)
| Title |
|---|
| 柳薇: "跨声速压气机转子流场数值模拟及优化设计研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113297706A (en) * | 2021-04-22 | 2021-08-24 | 西北工业大学 | Method for predicting efficiency point of each rotating speed peak value of gas compressor according to design index |
| CN113297706B (en) * | 2021-04-22 | 2023-02-21 | 西北工业大学 | Method for predicting efficiency point of each rotating speed peak value of gas compressor according to design index |
| CN117669269B (en) * | 2024-01-30 | 2024-05-03 | 苏州元脑智能科技有限公司 | Method and device for optimizing cooling fan, electronic equipment and storage medium |
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