CN106021645A - An aero-engine compressor performance reliability design method - Google Patents
An aero-engine compressor performance reliability design method Download PDFInfo
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- CN106021645A CN106021645A CN201610297484.XA CN201610297484A CN106021645A CN 106021645 A CN106021645 A CN 106021645A CN 201610297484 A CN201610297484 A CN 201610297484A CN 106021645 A CN106021645 A CN 106021645A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Abstract
The invention provides an aero-engine compressor performance reliability design method. The method comprises the steps of firstly selecting compressor performance parameters and reliability requirements thereof; secondly, selecting the key parameters influencing the performance and the value ranges thereof; thirdly, determining the value distribution of the key parameters; fourthly, establishing a response surface model between the performance parameters and the key parameters; fifthly, performing performance reliability evaluation; sixthly, if the evaluation results meet the reliability requirements, outputting the design solution, and if the results fail to meet the requirements, performing the seventh step; seventhly, performing compressor performance reliability design by using Isight software; eighthly, if the design results meet the requirements, outputting the design solution as the final solution, and if the results do not meet the requirements, repeating the seventh step, and changing the value ranges of the key parameters and the standard deviations thereof according to conditions until the results meet the requirements. The method improves the performance reliability of a compressor, can perform quantitative evaluation and solves the problem that there is no performance reliability design method for the aero-engine compressor field at present in China.
Description
(1) technical field
The present invention provides a kind of aero-engine compressor performance reliability method for designing, and it is for aero-engine pressure
Mechanism of qi (hereinafter referred to as compressor), it is proposed that the design of its performance reliability and appraisal procedure.Here the performance of compressor can
By property be defined as compressor dispatch from the factory time, its performance meets the ability of minimum performance requirement (hereinafter referred to as minimum requirements).Pass through
The method, can make compressor have preferable performance reliability when dispatching from the factory, i.e. meet certain performance reliability requirement.
Owing to there is no the performance reliability definition generally acknowledged, when the performance reliability finger pressure mechanism of qi of compressor dispatches from the factory here, its
Performance meets the ability of minimum requirements；When the performance reliability finger pressure mechanism of qi of compressor dispatches from the factory, its performance meets minimum requirements
Probability.
(2) background technology
1. background technology source
The document that background technology relates to is as follows:
[1] JSSG-2007A aviation whirlpool spray turbofan whirlpool axle turbo oar engine is used in combination Practice guidelines " (DEPARTMENT
OF DEFENSE JOINT SERVICE SPECIFICATION GUIDE ENGINES, AIRCRAFT, TURBINE), 137-
Page 139；U.S. Department of Defense, issues, replaces the JSSG-2007 that on October 30th, 1998 is issued on January 29th, 2004.
[2]Brown H,Elgin J A.Aircraft engine control mode analysis[J].Journal
of Engineering for Gas Turbines and Power,1985,107(4):838-844.
2. background technology scheme
In U.S.'s " spray turbofan whirlpool, JSSG-2007A aviation whirlpool axle turbo oar engine is used in combination Practice guidelines ", in order to make
Aerocraft system can be satisfactory, and the performance requirement of electromotor should design requirement with other, such as reliability, usability, durable
Property etc. considers together, should obtain lowest performance in the case of being not above various limits value.
Electromotor lowest performance is normally defined-2 σ.Wherein, negative sign represents in electromotor sum, any given
Under duty, thrust may be had minimum or oil consumption rate is the highest；σ is the standard deviation of engine performance parameter distribution, it is assumed here that
Engine performance parameter Normal Distribution.Lowest performance may be defined as value (this in performance parameter probability distribution corresponding to-2 σ
In suppose performance parameter be distributed as normal distribution, σ be performance parameter distribution standard deviation, this standard deviation contains component capabilities ripple
The impacts such as dynamic, control tolerance, performance degradation), to ensure that the electromotor of at least 97.75% meets or exceeds performance in lifetime
Require and minimum safety stability requirement, for changing an angle, it is simply that require that motor power or oil consumption rate to meet the property of 0.9775
Can reliability requirement.Here, the performance reliability of electromotor refer to electromotor in lifetime, its performance indications meet minimum can
The probability of acceptance value.
In JSSG-2007A, the method for designing of p-2 σ with reference to come from GE (GE) in 1985
The paper " Aircraft Engine Control Mode Analysis " of H.Brown and J.A.Elgin, we are to this paper
Relevant portion is summed up." Aircraft Engine Control Mode Analysis " describes for setting up closed loop
The control model controlling to require analyzes method, and the method can be used for Advanced Aircraft propulsion system.The side-product of this analysis method
It it is exactly the method for designing of the electromotor lowest performance establishing-2 σ.
First, aircraft propulsion control system uses closed loop control to set engine performance parameter and security parameters,
Such as thrust, stall margin, High Pressure Turbine Rotor inlet temperature etc., these parameters cannot directly be measured.Control model analysis makes
Engine performance is set up pre-with the maximum of security parameters with electromotor linear model and a series of estimated performance buggy model
Phase variable quantity.
For closed loop control, its plan demand for control (including rotation speed of the fan, internal actuator status requirement etc.) comes from
Power management system, the output signal that these requirements are measured with engine sensor compares, and obtains closed loop feedback deviation, and
It is converted into the input controlling rule that closed loop actuator requires.Then, requirements of plan compares to relevant actuator position
Relatively carry out divided ring actuator to be controlled.
And the main input planning demand for control is defined by variation model, model establish engine components Performance Characteristics and
The expection variable quantity of electromotor parameter sensing actual value.Variation model mainly includes component capabilities model, degradation model and control
Tolerance model three part, is derived from by the historical data with front engine, and is adjusted being reflected in design concept, material
And the known variant in manufacture and assembly.
Below engine mockup mentioned above, component capabilities model, Performance Degradation Model and control tolerance model are entered
Row brief explanation.
(1) engine mockup
Described in this paper, engine mockup is linear motor model, it defines component capabilities change and exports electromotor
The impact of characteristic, it represents the matrix of a partial derivative, it is thus achieved that the continuous disturbance around a comfortable selected operating point.Linearly
Engine mockup is derived from a stable state, non-linear engine mockup, is shown below:
Wherein, Δ P represents performance parameter variation value, and Δ S represents security parameters changing value, and Δ C representative sensor changes
Value, Δ M1 represents closed loop controlled parameter changing value, and Δ M2 represents open loop controlled parameter changing value, and A is matrix element, is a series of
Partial derivative.
Δ P and Δ S depends on engine quality rate of change and control tolerance, they and selected closed-loop measuring parameter, open loop
Control variable and engine mockup are relevant.
(2) part quality model
Quality model represents real engine component capabilities characteristic and the standard analyzed for electromotor design and performance
Mean engine steady-state model compares the expection variable quantity of gained or uncertainty.After it represents installation on board the aircraft
Performance.Mainly include that critical piece (fan, turbine) flow and efficiency parameters and engine air flow are revealed.
Quality model is an asymmetrical distribution, by selecting minimum and peak to produce a prestige on intermediate value both sides
Boolean is distributed.If symmetrical during distribution, then minimum and maximum is equivalent to ± deviation of 2 σ.Such as following table:
(3) degradation model
Degradation model represent newly installed electromotor with final retired time compared with intended component capabilities amount of degradation, use
Definitiveness degradation model, its represent retired before intended maximum degradation values, such as table, these values come from what GE cooperated with NASA
" Engine Diagnostics Program Cf6-50 Engine Performance Deterioration ", this project
Carry out going deep into detailed research in a large number to the performance degradation of Cf6-50.
(4) control tolerance model
Control tolerance model represents electromotor and the actuator sensor anticipation error compared with actual parameter value.Real
Border parameter value can affect closed loop feedback signal and order.Actuator error it is assumed to be calibration removed all deviations after random
Residual value.
Control tolerance model includes pedestal sensor noise, Acquisition Error (sample error in Non-uniform gas flow), signal
Actuator linkage error in conditional error, A/D or D/A transformed error, wire noise, engine variable structure, and suppose these
Error is separate, and Normal Distribution, therefore can be obtained the total error of each sensor by root-mean-square, and errors table is shown as
Actual value can exceed the percentage ratio of total electromotor limiting value for operation.
Under closed loop control, electromotor is affected by the engine sensor error of selected feedback parameter, and not by actuator
Site error affects.
The control model analysis obtaining research uses Monte Carlo analysis to be estimated, to obtain electromotor output spy
Property influential engine quality and control tolerance statistical data.Engine output characteristics is available from closed loop engine model, knot
Fruit saves as the data of new engine, and the data adding degradation effects save as old engine data.The parameter distribution calculated is used
Instruct in providing, to ensure that the electromotor of at least 97.5% meets or exceeds performance requirement and minimum safety stability requirement, to exceed
Service life of aeroengine.
Due to therefore each the closed loop control of engine working mode more than one such as VTOL electromotor
Molding formula will carry out the control model described in paper and analyze program, to meet performance requirement.
To sum up, this paper studies component capabilities quality, performance degradation on engine performance and the impact of security parameters,
And by the method during using at electromotor, relevant actuator being controlled, these impacts are minimized.In this process
In, it is also contemplated that control the impact of the tolerance produced during actuator, to ensure electromotor the meeting or exceeding property of at least 97.5%
Can require and minimum safety stability requirement.
3. the problem that background technology exists
1) as described in background technology scheme, in actual use, engine components (fan, compressor, high pressure
Turbine, low-pressure turbine etc.) performance inconsistency can make the performance off-design requirement of electromotor, fluctuate the biggest so electromotor
It is the most that performance deviates.Document [2] is by electromotor use during improve engine components (fan, compressor,
High-pressure turbine, low-pressure turbine etc.) workload make up performance inconsistency produce deviation, such as improve rotation speed of the fan.But mesh
Front engine working environment is the harshest, and component working load is bigger, if being continuing with this mode may reduce parts
Service life, cause other adverse effect.If such as increasing rotation speed of the fan, fan wheel disc stress can be made to increase, increase it
The probability of fracture；
2) background technology scheme is to carry out statistical analysis by the line data conventional to electromotor to obtain and start
The performance inconsistency situation of machine parts, it means that need to carry out substantial amounts of actual loading test, relatively costly；
3) additionally, there is no similar performance requirement and method for designing at home so that performance reliability when electromotor dispatches from the factory
Relatively low, such as certain h type engine h, produce 20 only 5 performances and meet requirement.
(3) summary of the invention
1. purpose
The present invention proposes the design evaluation method of the performance reliability of a kind of aero-engine compressor, at compressor
Design phase, it is determined by compressor key design parameter, and is its design tolerance, Capability of Compressor can be made to meet certain
Reliability requirement.Such as, if the performance parameter of compressor is set to efficiency, minimum requirements is 0.8, and its reliability is set to 0.97, i.e.
When compressor dispatches from the factory, its efficiency probability more than or equal to 0.97 is 97%, then after design, calms the anger if producing 100
Machine, the efficiency having 97 can be more than or equal to 0.97.
The present invention can effectively reduce the performance inconsistency of compressor, and by design production carry out ensureing rather than
Work increases its workload.Additionally, the present invention need not the field data of conventional compressor, it is adaptable to newly grind compressor.
2. technical scheme
The preliminary preparation that the present invention needs is as follows:
● preliminary design scheme；
● getting out Fluid Mechanics Computation (CFD) software, conventional work station (hereinafter referred to as work station), Minitab is soft
Part, response surface model modeling software, Isight software.
One aero-engine compressor performance reliability method for designing of the present invention, its General Implementing step is as follows:
Step one: select Capability of Compressor parameter and determine the reliability requirement of performance parameter；
Step 2: select to affect key parameter and the span thereof of Capability of Compressor；
Step 3: determine the value distribution of key parameter；
Step 4: on a workstation, uses response surface model modeling software to set up Capability of Compressor parameter and key parameter
Between response surface model, its process is as follows:
1) the Latin hypercube method design experiment side in the span of key parameter in EXPERIMENTAL DESIGN software is used
Case；
2) CFD software is used to carry out simulation calculation according to testing program and preserve result of calculation；
3) response surface model modeling software is used to set up response surface model according to result of calculation；
Step 5: on a workstation, uses Isight software to carry out performance reliability assessment, and its process is as follows:
1) in Isight software, " Six Sigma (6sigma) " task is set up；
2) under " 6sigma " task, " computer " module is set up；
3) response surface model built up in " computer " module input step four is opened；
4) open " 6sigma " task choosing " 6sigma analysis ", and select " Monte Carlo " in " analysis type "；
5) the value distribution of the key parameter determined in input step three in " stochastic variable "；
6) minimum requirements of input performance parameter in " response "；
7) performance reliability assessment is proceeded by；
8) result is checked at result output interface；
Step 6: if assessment result meet reliability require; output design as final design result, if
It is unsatisfactory for reliability to require then to carry out step 7；
Step 7: use Isight software to carry out the performance reliability design of compressor on a workstation.Its process is as follows:
1) in Isight software, " optimization " task is set up；
2) nested " 6sigma " task under " optimization " task；
3) nested " computer " module under " 6sigma " task；
4) response surface model built up in " computer " module input step four is opened；
5) " optimization " task of opening selects " the non-bad Sorting Genetic Algorithm of the second filial generation (NSGA-II) " in " algorithm setting ",
Arranging span and the span of key parameter standard deviation of key parameter in " variable ", in " constraint ", input is calmed the anger
Machine performance parameter minimum requirements and reliability requirement.The standard deviation design object of performance parameter is set to by " optimization aim "
" minimum "；
6) open " 6sigma " task choosing " 6sigma optimization ", and select " Monte Carlo " in " analysis type ".?
The value distribution of the key parameter determined in input step three in " stochastic variable ", in " response ", input performance parameter is minimum
Requirement；
7) performance reliability design is proceeded by；
8) result is checked at result output interface.
Step 8: if design result meet reliability require; output design as final design scheme, if
It is unsatisfactory for reliability to require then to repeat step 7, and according to circumstances changes span and the key parameter mark of key parameter
The span of quasi-difference, until result meets requirement；
Wherein, CFD described in preliminary preparation is that one solves hydromechanical by computer and numerical method
Governing equation, the method that Fluid Mechanics problem is simulated and analyzes；Isight software refers to that Dassault Systemes is public
The computer aided optimum software of department；Work station is purchased according to CFD software used, can be with properly functioning software used；
Minitab software is a kind of modern quality control statistical software of Minitab company, can be used for design experiment scheme；
Wherein, include pressure ratio, efficiency, flow, rotating speed etc. in " the Capability of Compressor parameter " described in step one, " select
Capability of Compressor parameter " refer to select voluntarily in these parameters according to needs；It is one that the reliability of performance parameter requires
Individual probability, it is possible to according to needing to select voluntarily, such as, if user requires that pressure ratio reliability is 0.8, it is possible to by its reliability
Requirement is set to 0.8.
Wherein, the key parameter of Capability of Compressor " select " described in step 2 to follow following principle:
(1) parameter influence degree to Capability of Compressor；
(2) processing, install, use during the probability of Parameters variation；
(3) Parameters variation can be descriptive；
The span of key parameter to determine according to practical situation.
Wherein, " value of key parameter is distributed " described in step 3 to investigate really according to practical situation
Fixed, and the value of institute's containing parameter in distribution to be determined.Such as, according to actual working ability, the value of certain key parameter is divided
Cloth is set to normal distribution, comprises average and two parameters of standard deviation in normal distribution, then, the actual of this key parameter can be taken
The meansigma methods of value is set to average, and the bound of actual value is defined to standard deviation.
Wherein, in step 4 1) described in " Latin hypercube method " be at present conventional a kind of sampling approach, the party
Method has good orthogonality and uniformity, and available less test number (TN) obtains the comprehensive sample that is evenly distributed.With Minitab
As a example by software, open Minitab software, a newly-built EXPERIMENTAL DESIGN project, select " Latin hypercube " method, Selection parameter number
Amount (determining several key parameter in step 2, quantity here just selects several), then inputs test number (TN) (test number (TN)
Oneself determine) and parameter area (span of the key parameter determined in step 2), determine that rear software just can be given birth to automatically
Become testing program.
In step 4 2) described in the method for " carrying out simulation calculation " be in CFD software, input the work of compressor
(, with reference to actually used CFD software, different software input modes may not for specifically used method for ambient parameter and use parameter
With), the most just can start to calculate, obtain the Capability of Compressor parameter described in step one, preserve result of calculation and namely protect
Deposit the value of Capability of Compressor parameter；
In step 4 3) described in " setting up response surface model " be exactly to utilize above-mentioned result of calculation, set up Capability of Compressor
Mathematical relationship between key parameter in parameter and step 2, here as a example by design specialist (Design Expert), at software
Middle input 1) in the testing program and 2 that determines) in the simulation result that obtains, it is possible to automatically calculate, it is thus achieved that response surface model.
Wherein, " 6sigma " described in step 5 is a kind of angle from statistical analysis, in the design of product
Stage just uses probabilistic model to consider the uncertainty of design factor, analyzes its impact bringing product quality and performance,
The uncertainty of control design case factor is carried out, it is thus achieved that meet each side such as performance, reliability, robustness by probability statistical analysis method
The method for designing of the product of the requirement in face；NSGA-II is the non-bad Sorting Genetic Algorithm of the second filial generation, for the multiple-objection optimization of product
Design.Set up 6sigma task and refer in the toolbar of software, 6sigma icon is dragged to working region, set up " computer " mould
Block is to be dragged to below the 6sigma icon of working region by computer icon.
Wherein, " " under " optimization " task nested " 6sigma " task " described in step 7 " method be by
" 6sigma " icon drag is under optimization task；The method of nested " computer " module is " computer " icon drag to be arrived
Under " 6sigma " icon, as shown in Figure 4.
By above step, improve the performance reliability of compressor, it is possible to carry out qualitative assessment, provide design side
Case.Additionally can effectively reduce the performance inconsistency of compressor.Solve domestic current aero-engine compressor field not have
The problem of performance reliability method for designing.
3. advantage
1) present invention can improve the performance reliability of compressor, the performance reliability of qualitative assessment compressor, and is given
Design.Additionally can effectively reduce the performance inconsistency of compressor, and then reduce the performance off-design requirement of electromotor
Degree, it is not necessary to make up by use increasing the workload of parts, it is ensured that the life-span of parts, safety
Property；
2) present invention can be suitably applied to newly grind compressor, required data all can be obtained by CFD, it is not necessary to substantial amounts of material object
Test, can reduce cost.
3) problem that domestic current aero-engine compressor field does not has performance reliability method for designing is solved.
(4) accompanying drawing explanation
Fig. 1: certain type aero-engine compressor.
Fig. 2: aero-engine compressor performance reliability method for designing flow process.
Fig. 3: the value distribution of key parameter is set.
Fig. 4: 6sigma task nested calculator modules are set the most in software.
Fig. 5: Monte Carlo simulation method is set the most in software.
Fig. 6: optimization task nested 6sigma task and calculator modules are set the most in software.
In figure, symbol, code name are described as follows:
Wherein, in Fig. 3 " General " refer to " common option ", it is used for arranging parameter；" Random Variables " refers to
" stochastic variable ", is used for arranging each independent variable in model；" Response " refers to " response ", be used for arrange in model because of
Variable；" Optimization " refers to " optimization ", the parameters needed during being used for arranging optimization；" Parameters " is
Refer to " parameter ", refer to each independent variable being currently included；" Random Variable " is " stochastic variable ", refers to
The variable being currently being operated in " Parameters "；" Distribution " is " distribution ", refers to as current stochastic variable choosing
Select a distribution；" Normal " is normal distribution；" Mean " refers to the average of the distribution of current stochastic variable；“Standard
Deviation " refer to the standard deviation of the distribution of current stochastic variable.
Wherein, " General " in Fig. 5, " Random Variables ", " Response's " and " Optimization "
Implication is identical with Fig. 3." Run Mode " refers to operational mode, is current ongoing task；“Six Sigma
Optimization " it is Six Sigma optimization, stain represents and refers to that current ongoing task is Six Sigma optimization；
" Analysis Type " is analysis type, refers to the analysis method used in currently carrying out of task；“Monte Carlo
Sampling " it is Monte Carlo sampling, black box refers to that the analysis method used in currently carrying out of task is that Monte Carlo is taken out
Sample.
Wherein, optimize in Fig. 6,6sigma designs identical with the definition of the design procedure in embodiment with computer.
(5) specific embodiments
The present invention applies performance reliability method for designing on certain type aero-engine compressor, this compressor schematic diagram
See that accompanying drawing 2 is shown in by accompanying drawing 1, overall design approach flow process, following (due to Isight software according to the specific embodiments of method flow
Not having Chinese edition, in implementing step, each function of software all represents with English):
One aero-engine compressor performance reliability method for designing of the present invention, implementation step is as follows:
Step one: pressure ratio, efficiency, flow are set to Capability of Compressor parameter, its reliability requires such as following table:
| Performance indications | Minimum performance requirement | Performance reliability requirement |
| Efficiency (y1) | 0.8 | 0.9775 |
| Pressure ratio (y2) | 8.1 | 0.9775 |
| Flow (y3) | 1.76(kg/s) | 0.9775 |
Step 2: by inlet flow support plate established angle, active wheel external diameter, tip clearance, radial diffuser established angle, axially
Diffuser established angle is set to the key parameter affecting Capability of Compressor, its span such as following table:
| Key parameter | Span |
| Rectification support plate established angle | -2～2 (°) |
| Tip clearance | 0.2～0.4 (mm) |
| Active wheel external diameter | -2～2 (mm) |
| Radial diffuser established angle | -2～2 (°) |
| Axially diffuser established angle | -2～2 (°) |
Signal in software is shown in Table 1, for convenience, and rectification support plate established angle, tip clearance, active wheel external diameter, radially expansion
Depressor established angle and axial diffuser established angle corresponding x respectively1～x5。
Table 1
Step 3: determine that the value of 5 key parameters is distributed as normal distribution, corresponding average (representing with μ below) and
Standard deviation (representing with σ below) is such as following table:
Wherein, in order to calculate simplicity, outside disleaf intercuspal space, the average of other four parameters is relative to preliminary design scheme
Changing value.It is inputted in software, such as Fig. 3.With x1As a example by, " Distribution " is the value of key parameter and divides
Cloth, selects " Normal ", i.e. normal distribution at this；" Mean " is average, and " Standard Deviation " is standard deviation, as above
Shown in table, respectively 0 and 0.5.Other parameter is arranged like this.
Step 4: on a workstation, uses response surface model modeling software to set up Capability of Compressor parameter and key parameter
Between response surface model, its process is as follows:
1) the Latin hypercube method in Minitab software is used to devise 20 examinations in the span of key parameter
Proved recipe case, such as following table:
Wherein, in order to calculate simplicity, outside disleaf intercuspal space, the average of other four parameters is relative to preliminary design scheme
Changing value.
2) use NUMECA software according to 1) in testing program carry out simulation calculation and preserve result of calculation, such as following table:
3) Design-Expert software is used to set up response surface model according to result of calculation as follows:
y2=8.20807-0.00961813x3+0.18128x5
Wherein, each English alphabet implication such as following table:
Wherein, in modeling process, find that radial diffuser established angle is not notable factor, to property when i.e. it changes
Can parameter have no significant effect.
Step 5: on a workstation, uses Isight software to carry out performance reliability assessment, and its process is as follows:
1) in Isight software, " 6sigma " task is set up；
2) nested " computer " module under " 6sigma " task.1)～2) as shown in Figure 4；
3) response surface model built up in " computer " module input step four is opened；
4) open " 6sigma " task choosing " 6sigma analysis ", and select " Monte Carlo " in " analysis type ", as
Shown in Fig. 5；
5) the value distribution of the key parameter determined in input step three in " stochastic variable ", as shown in Figure 3；
6) minimum requirements of input performance parameter in " response ", i.e. pressure ratio 8.1, efficiency 0.8, flow 1.76, such as table 2 institute
Showing, " Lower Bound " is minimum requirements；
Table 2
| Parameter | Lower Bound | Value | Upper Bound |
| y1 | 8.1 | ||
| y2 | 0.8 | ||
| y3 | 1.76 |
7) performance reliability assessment is proceeded by；
8) result is checked at result output interface, such as following table:
Step 6: from the assessment result of step 5 it will be seen that efficiency, pressure ratio, the reliability of flow are respectively 0.2245,
0.512 and 0.301, it is unsatisfactory for the performance reliability design requirement of 0.9775, it is therefore desirable to carry out step 7, to initial designs
Scheme carries out performance reliability design
Step 7: use Isight software to carry out the performance reliability design of compressor on a workstation.Its process is as follows:
1) in Isight software, " optimization " task is set up；
2) nested " 6sigma " task under " optimization " task；
3) nested " computer " module under " 6sigma " task.1)～3) step result is as shown in Figure 4；
4) response surface model built up in " computer " module input step four is opened；
5) " optimization " task of opening selects " NSGA-II ", in " variable " in input step two in " algorithm setting "
The span of key parameter, and the span of key parameter standard deviation, input Capability of Compressor parameter is in " constraint "
Low requirement: efficiency is 0.8, pressure ratio is 8.1, and flow is 1.76, and reliability requires: efficiency, pressure ratio, flow are
0.9775, as shown in table 2.In " optimization aim ", the standard deviation design object of performance parameter is set to " minimum ", such as table 3 institute
Showing, " Std Deviations " is standard deviation, and " Direction " is design object；
Table 3
| Parameter | Direction |
| Std Deviations | |
| y1 | minimize |
| y2 | minimize |
| y3 | minimize |
6) open " 6sigma " task choosing " 6sigma optimization ", and select " Monte Carlo " in " analysis type ".?
The value distribution of the key parameter determined in input step three in " stochastic variable ", in " response ", input performance parameter is minimum
Requirement, as shown in table 2；
7) performance reliability design is proceeded by；
8) result is checked at result output interface, as shown in the table:
By above step, the efficiency average of this engine compressor has been brought up to 0.8036 by 0.7903, reliability by
0.2245 has brought up to 0.99333；Pressure ratio has been brought up to 8.7485 by 8.129, and reliability has been brought up to 1.0 by 0.512,；Stream
Amount has been brought up to 1.8194 by 1.742, and reliability has been brought up to 0.9776 by 0.301.The standard deviation of key parameter can be elected as
Tolerance, to instruct processing.
Claims (7)
1. an aero-engine compressor performance reliability method for designing, it is characterised in that: its General Implementing step is as follows:
Step one: select Capability of Compressor parameter and determine the reliability requirement of performance parameter；
Step 2: select to affect key parameter and the span thereof of Capability of Compressor；
Step 3: determine the value distribution of key parameter；
Step 4: on a workstation, uses response surface model modeling software to set up between Capability of Compressor parameter and key parameter
Response surface model, its process is as follows:
1) the Latin hypercube method design experiment scheme in the span of key parameter in EXPERIMENTAL DESIGN software is used；
2) CFD software is used to carry out simulation calculation according to testing program and preserve result of calculation；
3) response surface model modeling software is used to set up response surface model according to result of calculation；
Step 5: on a workstation, uses Isight software to carry out performance reliability assessment, and its process is as follows:
1) in Isight software, " 6sigma " task is set up；
2) under " 6sigma " task, " computer " module is set up；
3) response surface model built up in " computer " module input step four is opened；
4) open " 6sigma " task choosing " 6sigma analysis ", and select " Monte Carlo " in " analysis type "；
5) the value distribution of the key parameter determined in input step three in " stochastic variable "；
6) minimum requirements of input performance parameter in " response "；
7) performance reliability assessment is proceeded by；
8) result is checked at result output interface；
Step 6: if assessment result meets reliability and requires, output design is as final design result, if be discontented with
Foot reliability requires then to carry out step 7；
Step 7: use Isight software to carry out the performance reliability design of compressor on a workstation；Its process is as follows:
1) in Isight software, " optimization " task is set up；
2) nested " 6sigma " task under " optimization " task；
3) nested " computer " module under " 6sigma " task；
4) response surface model built up in " computer " module input step four is opened；
5) " optimization " task of opening selects " the non-bad Sorting Genetic Algorithm of the second filial generation (NSGA-II) " in " algorithm setting ", " is becoming
Amount " in span and the span of key parameter standard deviation of key parameter are set, " constraint " inputs compressor
Can parameter minimum requirements and reliability requirement；The standard deviation design object of performance parameter is set to by " optimization aim " "
Little "；
6) open " 6sigma " task choosing " 6sigma optimization ", and select " Monte Carlo " in " analysis type "；" random
Variable " in the value distribution of key parameter that determines in input step three, " response " inputs the minimum requirements of performance parameter；
7) performance reliability design is proceeded by；
8) result is checked at result output interface；
Step 8: if design result meets reliability and requires, output design is as final design scheme, if be discontented with
Foot reliability requires then to repeat step 7, and according to circumstances changes span and the key parameter standard deviation of key parameter
Span, until result meets requirement；
By above step, improve the performance reliability of compressor, and qualitative assessment can be carried out, provide design, in addition
Can also effectively reduce the performance inconsistency of compressor, solving domestic current aero-engine compressor field does not has performance reliability
The problem of method for designing.
A kind of aero-engine compressor performance reliability method for designing the most according to claim 1, it is characterised in that:
Pressure ratio, efficiency, flow and rotating speed is included in " the Capability of Compressor parameter " described in step one；" select Capability of Compressor
Parameter " refer to select voluntarily in these parameters according to needs；It is a probability that the reliability of performance parameter requires, also can depend on
Select voluntarily according to needs, such as, if user requires that pressure ratio reliability is 0.8, just require to be set to 0.8 by its reliability.
A kind of aero-engine compressor performance reliability method for designing the most according to claim 1, it is characterised in that:
The key parameter of Capability of Compressor " select " described in step 2 to follow following principle:
(1) parameter influence degree to Capability of Compressor；
(2) processing, install, use during the probability of Parameters variation；
(3) Parameters variation can be descriptive；
The span of key parameter to determine according to practical situation.
A kind of aero-engine compressor performance reliability method for designing the most according to claim 1, it is characterised in that:
" determining the value distribution of key parameter " described in step 3, will investigate according to practical situation and determine, and
And the value of institute's containing parameter in distribution to be determined；Such as, according to actual working ability, the value of certain key parameter is distributed and is set to
Normal distribution, comprises average and two parameters of standard deviation in normal distribution, then, average by the actual value of this key parameter
Value is set to average, and the bound of actual value is defined to standard deviation.
A kind of aero-engine compressor performance reliability method for designing the most according to claim 1, it is characterised in that:
In step 4 1) described in " Latin hypercube method ", the method can obtain with less test number (TN) be evenly distributed complete
The sample in face；As a example by Minitab software, opening Minitab software, a newly-built EXPERIMENTAL DESIGN project, " Latin is super vertical in selection
Side " method, Selection parameter quantity, then input test number (TN), determine that rear software just can automatically generate testing program；
In step 4 2) described in the method for " carrying out simulation calculation " be in CFD software, input the working environment of compressor
Parameter and use parameter, then begin to calculate, obtain the Capability of Compressor parameter described in step one, preserve result of calculation also
It it is exactly the value preserving Capability of Compressor parameter；
In step 4 3) described in " setting up response surface model " be exactly to utilize above-mentioned result of calculation, set up Capability of Compressor parameter
And mathematical relationship between key parameter in step 2, here as a example by design specialist Design Expert, input in software
1) testing program determined in and 2) the middle simulation result obtained, just can automatically calculate, it is thus achieved that response surface model.
A kind of aero-engine compressor performance reliability method for designing the most according to claim 1, it is characterised in that:
" 6sigma " described in step 5 is a kind of angle from statistical analysis, just uses in the design phase of product
Probabilistic model considers the uncertainty of design factor, analyzes its impact bringing product quality and performance, unites by probability
Score analysis method carrys out the uncertainty of control design case factor, it is thus achieved that meet the product of the requirement of performance, reliability, robustness each side
The method for designing of product；NSGA-II is the non-bad Sorting Genetic Algorithm of the second filial generation, for the multi-objective optimization design of power of product；Set up
6sigma task refers in the toolbar of software, and 6sigma icon is dragged to working region, and setting up " computer " module is to count
Calculate device icon to be dragged to below the 6sigma icon of working region.
A kind of aero-engine compressor performance reliability method for designing the most according to claim 1, it is characterised in that:
The method of " " nested " 6sigma " task under " optimization " task " " described in step 7 is " 6sigma " icon drag to be arrived
Under optimization task；The method of nested " computer " module is by under " computer " icon drag to " 6sigma " icon.
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