CN115099165A - Aircraft engine modeling method considering performance degradation - Google Patents
Aircraft engine modeling method considering performance degradation Download PDFInfo
- Publication number
- CN115099165A CN115099165A CN202210386432.5A CN202210386432A CN115099165A CN 115099165 A CN115099165 A CN 115099165A CN 202210386432 A CN202210386432 A CN 202210386432A CN 115099165 A CN115099165 A CN 115099165A
- Authority
- CN
- China
- Prior art keywords
- degradation
- aircraft engine
- performance degradation
- module
- aeroengine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000015556 catabolic process Effects 0.000 title claims abstract description 149
- 238000006731 degradation reaction Methods 0.000 title claims abstract description 149
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000009826 distribution Methods 0.000 claims abstract description 69
- 238000012937 correction Methods 0.000 claims abstract description 36
- 238000005070 sampling Methods 0.000 claims description 26
- 230000007704 transition Effects 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 23
- 238000002485 combustion reaction Methods 0.000 claims description 20
- 239000011159 matrix material Substances 0.000 claims description 11
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 10
- 238000004088 simulation Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 5
- 230000004048 modification Effects 0.000 claims description 4
- 238000012986 modification Methods 0.000 claims description 4
- 239000003607 modifier Substances 0.000 claims description 3
- 239000002699 waste material Substances 0.000 claims description 3
- 238000002940 Newton-Raphson method Methods 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 abstract description 4
- 239000007789 gas Substances 0.000 description 32
- 230000006872 improvement Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000001816 cooling Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 239000000295 fuel oil Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 239000005437 stratosphere Substances 0.000 description 2
- 230000003746 surface roughness Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 108010014172 Factor V Proteins 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000567 combustion gas Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 239000002737 fuel gas Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000005436 troposphere Substances 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Images
Classifications
-
- 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
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Fluid Mechanics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses an aeroengine modeling method considering performance degradation, which comprises the steps of constructing an aeroengine baseline model module, and generating a maximum guess value of a target aeroengine when a gas circuit fault occurs; constructing a core degradation module, and initially obtaining a distribution rule of performance degradation factors of the rotating component; and constructing a random correction module, and correcting the performance degradation factor obtained by the core degradation module. The method is provided with a core degradation module, a core degradation interpolation table is established by scanning the engine blade which actually runs and then simulating through CFD software, the degradation rule of the engine is modeled on a physical mechanism, and the authenticity of the model is improved.
Description
Technical Field
The invention relates to the technical field of aero-engine performance monitoring, in particular to an aero-engine modeling method considering performance degradation.
Background
Existing aircraft engine modeling typically does not account for degradation issues. If the degradation problem is considered, it is also common to simply apply a fixed degradation value to the components in a given model.
For example, in a paper [ lufeng, fusion technology research of aircraft engine fault diagnosis [ D ]. nanjing aerospace university, 2009], when a fault diagnosis problem is researched, a degradation value fixed by a given model component is only artificially generated, and an aircraft engine model with degradation considered enough to be simulated is lacked.
However, in any mechanical device, the performance is inevitably degraded gradually in practical use. The aero-engine is required to work under various severe conditions, and performance of the aero-engine is ensured to meet index requirements, so that influence caused by degradation of the aero-engine is not negligible.
Moreover, the performance degradation of the engine has objective rules and certain randomness, and the degradation value can be simulated if the degradation value is not directly given.
Meanwhile, in the existing aircraft engine modeling technology, the degradation problem of the aircraft engine is less considered. These models do not work well in some aspects, such as fault-tolerant control validation of the controller, adaptive model building during the flight cycle, etc.
Disclosure of Invention
The invention aims to provide an aeroengine modeling method considering performance degradation, which can generate a large number of different degraded engine models by calculating and considering the difference between different engine individuals of the same model and adding a random correction link, so that a plurality of groups of random correction factors conforming to the distribution are simulated according to the joint probability distribution among different degradation factors, the individual difference among different engines of the same model is better reflected, and the problem in the prior art is solved. In order to achieve the purpose, the invention provides the following technical scheme:
an aircraft engine modeling method that accounts for performance degradation, comprising the steps of:
firstly, constructing an aeroengine baseline model module, carrying out gas path calculation along the sequence of a target aeroengine gas inlet flow, and establishing a common working equation of each rotating part of the target aeroengine according to the obtained gas path calculation data for simulating the internal operating gas path condition of the healthy engine so as to generate a maximum guess value of the target aeroengine when the gas path fault occurs;
secondly, constructing a core degradation module, and carrying out local simulation according to collected cloud data of a rotating part point of the target aircraft engine in the running process, so as to obtain an engine performance degradation difference table of the rotating part at different service times and different rotating speeds, so as to initially obtain a distribution rule of performance degradation factors of the rotating part;
thirdly, a random correction module is constructed, according to a given engine performance degradation distribution rule, a Monte Carlo method based on a Markov chain is adopted to obtain a random correction factor, the performance degradation factor obtained by the core degradation module is corrected, and therefore individual differences generated by degradation among different aero-engines are simulated, and the established model considering the performance degradation aero-engine is more simulated;
and fourthly, inputting the degradation factor corrected by the random correction module into the aeroengine baseline model module, and performing iterative operation to form a final aeroengine model considering performance degradation.
As an improvement to the method for modeling an aircraft engine in consideration of performance degradation of the present invention, the rotating parts of the target aircraft engine include an intake port, a compressor, a combustion chamber, a turbine, and a tail pipe,
in the first step, the step of establishing a common working equation of the components of the target aircraft engine is as follows:
firstly, according to the coaxial relationship between the compressor and the turbine, the power balance is required to be met, and a deviation equation is introduced:
(ηN T -N C )/N C ＝ε 1
where eta is the axial mechanical efficiency, N T Is turbine power, N C Is the power of the compressor;
according to the total pressure balance of the throat of the tail nozzle, a deviation equation is led out:
(p tC8 -p t8 )/p t8 ＝ε 2
in the formula, p tC8 For total jet pressure, p, continuously calculated from the flow t8 Back pressure of the tail nozzle;
according to the turbine inlet flow continuity, a deviation equation is introduced:
(W g4 -W g 41)/W g 41＝ε 3
in the formula, W g41 For turbine inlet flow calculated from the turbine flow characteristic curve, W g4 Is the mass flow of the combustion gas of the combustion chamber;
finally, three of the deviation equations are continuously solved based on a newton-raphson (N-R) method.
As an improvement of the aircraft engine modeling method considering performance degradation, the specific way of continuously solving the three deviation equations based on the Newton-Raphson method is as follows:
firstly, according to each steady-state point in the flight envelope of the target aircraft engine, all rotating parts should meet the conditions of pressure balance, continuous mass flow and rotor power balance, and the solution of the three deviation equations is attributed to the solution of a steady-state equation residual error epsilon i |<ε min The solution of i 1,2,3,
and finally, correcting the solution of the equation in the direction of reducing the residual error of the steady state equation through iterative operation until the iterative operation is quitted after the error requirement is met:
f i (n,π C ,π T )＝ε i (ε i →0)i＝1,2,3
wherein n is the rotation speed of the aeroengine, pi C For the pressure ratio of the compressor, pi T The turbine pressure drop ratio.
As an improvement of the aircraft engine modeling method considering performance degradation of the present invention, in the second step, a specific implementation manner of constructing a core degradation module to initially obtain a distribution rule of performance degradation factors of a rotating member is as follows:
s2-1, blade point cloud data of each rotating component at a plurality of time points in the life cycle of the target aircraft engine are acquired;
s2-2, importing the acquired blade point cloud data of each rotating part of the target aircraft engine into CFD software, and reconstructing the blade point cloud data until the entity of the rotating part forms a blade model;
s2-3, carrying out simulation experiment on the obtained blade model to obtain a degradation proportion omega of the circulation capacity coefficient and the efficiency of the rotating component under different degradation time points t and different rotating speeds n compared with the efficiency of the rotating component under the healthy condition ij (t,n),η ij (t,n)；
S2-4, calculating the degradation ratio omega ij (t,n),η ij Mean value ω of (t, n) i (t,n),η i (t, n) and the resulting mean value ω i (t,n),η i (t, n) carrying out interpolation by taking the t, n as an axis to form a target aeroengine core degradation difference table;
s2-5, based on the obtained mean value omega i (t,n),η i (t, n) and calculating the degradation ratio omega of each rotating part ij (t,n),η ij (t, n) relative to the mean value ω i (t,n),η i The ratio of (t, n) to obtain the degradation factor delta ω1 ,δ η1 ,...,δ ωm ,δ ηm Then, the distribution rule pi (delta) of the degradation factor is obtained ω1 ,δ η1 ,...,δ ωm ,δ ηm )。
As an improvement of the aircraft engine modeling method considering performance degradation of the present invention, in the third step, when the stochastic correction module is constructed to obtain the stochastic correction factor:
it is necessary to construct a Markov chain of discrete random variable sets satisfying the stationary distribution condition and having Markov properties:
P(X t |X 0 ,X 1 ,...,X t-1 )＝P(X t |X t-1 ),t＝1,2....
in the formula, X t Representing random variables at the moment t, wherein the value sets of all the random variables are the same; conditional probability distribution P (X) t |X t-1 ) Transition probability distribution called Markov chain(ii) a Wherein,
the specific steps of constructing the Markov chain meeting the stable distribution condition are as follows:
obtaining the transition probability of the random variable:
wherein S is a state space;
if the state space equation S consists of n sets of states, then a state transition matrix P is formed:
which satisfies the following conditions:
when the following conditions are satisfied: the markov chain reaches convergence when pi P is pi.
As an improvement to the aircraft engine modeling method considering performance degradation of the present invention, after the markov chain is constructed, a careful balance condition of the constructed markov chain needs to be found to reduce the influence of the state transition matrix P on the convergence property of the markov chain:
π i P ij ＝π j P ji 。
as an improvement to the aircraft engine modeling method considering performance degradation, the method for finding the careful balance condition of the Markov chain adopts a Gibbs sampling method to reduce the waste of calculation power caused by rejecting sampling in other Monte Carlo methods.
As an improvement of the aircraft engine modeling method considering performance degradation, the specific way for constructing the random correction module to obtain the random correction factor is as follows:
firstly, the distribution rule pi (delta) of the degradation factor with smooth distribution is input ω1 ,δ η1 ,...,δ ωm ,δ ηm ) Setting a state transition time threshold k and the number n of the required random correction factors;
secondly, an initial state value is randomly given
Again, the loop executes for i:
let the sample at the end of the (i-1) th iteration be
The ith iteration proceeds with the following steps:
(1) from conditional probability distributions
Sampling to obtain a sample
(2) From conditional probability distributions
Sampling to obtain a sample
...；
(2j-1) from the conditional probability distribution
Sampling to obtain a sample
(2j) From conditional probability distributions
Sampling to obtain a sample
...；
(2m-1) from the conditional probability distribution
Sampling to obtain a sample
(2m) from the conditional probability distribution
Sampling to obtain samples
Obtained from the ith iteration
Finally, abandoning the samples generated by the previous k iterations when the Markov chain has not reached the stability, and obtaining the residual sample set
I.e. the set of random modifiers corresponding to the desired smooth distribution.
As an improvement of the aircraft engine modeling method considering the performance degradation of the present invention, in the fourth step, a specific implementation manner of forming an aircraft engine model considering the performance degradation is:
firstly, inputting the service time t and the rotating speed omega into the core degradation module, and obtaining the degradation proportion of each rotating component according to a target aircraft engine core degradation difference table;
then, inputting a state transition time threshold k and the number n of models required to be generated into the random correction module, and correcting the degradation proportion generated by the core degradation module by using the obtained n groups of random correction factors to finally obtain n groups of corrected degradation proportions;
and finally, inputting the height H, the Mach number Ma and the corrected n groups of degradation ratios into the aero-engine baseline model module, and performing iterative operation to form an aero-engine model considering performance degradation.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the core degradation module is set and constructed, the engine blade which actually runs is scanned, and then a CFD software simulation mode is used for establishing the core degradation interpolation table, so that the degradation rule of the engine is modeled on a physical mechanism, and the authenticity of the model is improved;
2. according to the invention, a random correction module is set and constructed, and a plurality of groups of random correction factors conforming to the distribution are simulated according to the joint probability distribution (typically high-dimensional Gaussian distribution) among different degradation factors based on the Monte Carlo Method (MCMC) of the Markov chain, so that the individual difference among different engines of the same model can be better simulated, a plurality of different degradation engine models can be generated, and meaningful data can be provided for relevant research.
Drawings
FIG. 1 is a block diagram of a proposed architecture for forming a degraded performance aircraft engine model in one embodiment of the present invention;
FIG. 2 is a block diagram of a baseline model module for an aircraft engine according to an embodiment of the invention;
FIG. 3 is a detailed operational schematic diagram for constructing a degraded performance aero-engine model in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, which indicate orientations or positional relationships, are used based on the orientations or positional relationships shown in the drawings, only for the convenience of describing the present invention and for the simplicity of description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, are not to be construed as limiting the present invention.
The present invention will be described in further detail with reference to the accompanying drawings, but the present invention is not limited thereto.
As shown in fig. 1, as an embodiment of the present invention, an aircraft engine modeling method considering performance degradation includes the steps of:
the method comprises the steps of firstly, constructing an aeroengine baseline model module, carrying out gas path calculation along the sequence of a target aeroengine gas inlet flow, and establishing a common working equation of each component of the target aeroengine according to obtained gas path calculation data for simulating the internal operation gas path condition of a healthy engine so as to generate a maximum guess value of the target aeroengine when a gas path fault occurs.
As shown in fig. 2, based on the above technical concept, when a single-shaft turbojet engine is used as an example, and an aero-engine baseline model module is constructed to simulate the gas path condition of the single-shaft turbojet engine, the components such as the gas inlet channel, the gas compressor, the combustion chamber, the turbine, the tail nozzle and the like of the single-shaft turbojet engine are sequentially subjected to pneumatic calculation along the gas inlet flow path of the single-shaft turbojet engine, and it should be noted that, during the specific calculation, the known flight altitude H, the flight mach number Ma and the ambient pressure p are assumed s0 Fuel oil flow W f And the sectional area A of the throat of the tail nozzle 8 And it is also known that, in the case of the characteristics of the various components,
because the outlet of the air inlet channel is the inlet of the air compressor, the pressure, the temperature, the flow and the like of the inlet of the air compressor are related, and the performance of the inlet of the air compressor has a large influence on the overall performance of the engine, when a baseline model module of the aero-engine is established, the air inlet channel is required to be taken as a first component, namely, the total temperature and the total pressure of the air at the outlet of the air inlet channel (the total temperature and the total pressure of the inlet of the air compressor) are required to be calculated, and the specific calculation mode is as follows:
firstly, obtaining the static temperature T of the atmosphere s0 And static pressure p of atmosphere s0 ：
When H is less than 11.0km, it should be noted that 11.0km is the boundary between the atmospheric troposphere and the stratosphere, the calculation methods of different stratosphere are different,
T s0 ＝273.15+T sl -6.5H1 (1)
p s0 ＝p sl ×(1.0-0.0225577H) 5.25588 (2)
in the formula, p sl And T sl Atmospheric pressure and temperature at local sea level;
when the H is more than 11.0km,
T s0 ＝201.65+T sl (3)
p s0 ＝p sl ×0.22336/e [0.1576932×(H-11.0)] (4)。
secondly, acquiring the total temperature T of the inlet air of the air inlet passage t1 And total pressure p t1 (subscript 1 denotes the inlet cross section):
T t1 ＝T s0 (1+0.2Ma 2 ) (5)
p t1 ＝p s0 (1+0.2Ma 2 ) 3.5 (6)。
finally, calculating the total temperature T of the air at the outlet of the air inlet t2 And total pressure p t2 ：
T t2 ＝T t1 (6)
p t2 ＝σ I p t2 (7)。
When the total temperature and the total pressure of the outlet air of the air inlet channel are obtained, the total temperature T of the outlet air of the air compressor needs to be calculated next t3 Total pressure p t3 And compressor power N C 。
It should be noted that the power N to the compressor is C In the process of the calculation, the calculation is carried out,
first, the enthalpy H of the air needs to be calculated n And entropy S n The value of which is calculated from an empirical formula using temperature, thus obtaining the enthalpy H of each section ni With the temperature T at the cross-section ti The relation between:
entropy of each section S ni With the temperature T at the cross-section ti The relation between:
in the above formulae (10) and (11), c i ,c si (i ═ 0,1, 2.., 5) is an empirical coefficient.
Based on the entropy S, the ideal state entropy S of the outlet of the compressor is obtained n Comprises the following steps: s n3I ＝S n2 +lg(π C ) (12); enthalpy H at the compressor outlet n3 Comprises the following steps: h n3 ＝H n2 +(H n3I -H n2 )/η C (13) In the formula, H n3I The ideal state enthalpy at the compressor outlet.
Secondly, calculating and acquiring total outlet air temperature T of the air compressor t3 It can be understood that the total outlet air temperature T of the compressor t3 From the enthalpy H at the compressor outlet n3 Substituting into the formula (10) to complete the calculation; and, the total outlet pressure p of the compressor t3 Then the compressor pressure ratio is calculated to obtain: p is a radical of t3 ＝π C p t2 (14) (ii) a Compressor power N C Calculated by mass air flow and enthalpy drop, the following results are obtained: n is a radical of C ＝W a2 (H n3 -H n2 ) (15)。
Finally, it can be understood that the bleed air exists in the air compressor, so that the pressure ratio pi of the air compressor at the current rotating speed can be calculated through three-dimensional interpolation from the characteristics of the pressure ratio, the efficiency, the flow and the like of the air compressor according to the rotating speed of the air compressor C Compressor efficiency eta C Mass flow W of air at outlet of air compressor a3 I.e. the compressor outlet flow W a3 Is composed of
W a3 ＝W a2 -W cin -W cout (16) In the formula, W cin ,W cout The inner flow and the outer flow of the middle stage of the compressor are used for air entraining respectively.
When acquiring the total outlet air temperature T of the compressor t3 Total pressure p t3 And compressor power N C When the temperature and the total pressure of the outlet of the combustion chamber are required to be calculated, the total temperature and the total pressure of the outlet of the combustion chamber are subjected to the outlet air mass flow W of the air compressor a3 Fuel flow W f Combustion chamber efficiency eta B And total pressure recovery coefficient sigma B The specific calculation method of the influence of (2) is as follows:
first, the mass flow W of air in the combustion chamber is obtained a31 ：
W a31 ＝W a3 -W ac3 (17)
In the formula, W a3 Is the outlet air mass flow, W, of the compressor ac3 Flow W of cooling air flow led out from outlet of compressor ac3 。
Secondly, calculating and acquiring the gas mass flow W of the combustion chamber g4 ：
W g4 ＝W a31 +W f (18)，
The fuel flow rate W in the formula (18) f Calculating according to the energy balance of the combustion chamber:
H n4 ＝(W a3 ×H n3 +η B ×H VF ×W f )/W g4 (19)
in the formula, H n4 Is the enthalpy at the outlet of the combustion chamber, H n3 Enthalpy at the compressor outlet; h VF The fuel oil has low heat value.
Finally, by enthalpy H at the outlet of the combustion chamber n4 Calculating the outlet gas temperature T of the combustion chamber based on the gas-oil ratio f based on an empirical formula t4 ：
In the formula (d) i (i ═ 1, 2.., 5) are empirical coefficients; the calculation mode of the total pressure of the gas at the outlet of the combustion chamber is as follows:
p t4 ＝σ B p t3 (21)。
when the total temperature T of the outlet of the combustion chamber is obtained t4 Total pressure p t4 In time, the total temperature T of the gas at the outlet of the turbine needs to be calculated t5 Total pressure p t5 And turbine power N T While it is to be understood that the turbine outlet gas total temperature T t5 Total pressure p t5 And turbine power N T The calculation method is similar to the calculation method of the gas compressor, and the turbine pressure drop ratio pi is calculated according to the characteristics of the turbine through the current rotor rotating speed n T Flow rate W g41 And efficiency η T And then the total temperature T of the gas at the inlet of the turbine t41 And total pressure p t41 (Total temperature T at the outlet of the Combustion Chamber t4 Total pressure p t4 ) And (6) performing calculation. The specific calculation steps are as follows:
first, the enthalpy at the turbine inlet is calculated:
H n41 ＝(W g4 ×H n4 +W ac3 ×H n3 )/W g41 (22)
in the formula, W g4 Is the gas mass flow of the combustion chamber, W ac3 Flow W of cooling air flow led out from outlet of compressor ac3 ，H n3 The enthalpy at the compressor outlet.
Secondly, the temperature and the entropy of the outlet of the turbine are calculated through an empirical formula based on the enthalpy at the inlet of the turbine, then the entropy of the outlet of the turbine under an ideal state can be calculated through the pressure drop ratio of the turbine, and similarly, the enthalpy of the turbine under the ideal state is calculated based on the entropy of the outlet of the turbine under the ideal state, and then the efficiency eta is utilized T Calculating the actual enthalpy H at the turbine outlet n5 。
Finally, calculating the total temperature T of the gas at the outlet of the turbine t5 Total pressure p t5 And turbine power N T ：
N T ＝W g41 (H n41 -H n5 ) (24)
p t5 ＝p t41 /π T (25)。
When the total temperature T of the gas at the outlet of the turbine is obtained t5 Total pressure p t5 And turbine power N T During the process, the total pressure of the throat of the jet pipe and the air flow velocity at the outlet of the jet pipe need to be calculated, and it can be understood that in the actual calculation, the air flow velocity at the outlet of the jet pipe also needs to be considered, and the specific calculation mode of the total pressure of the throat of the jet pipe is as follows:
first, it is necessary to obtain the jet nozzle back pressure p t8 ：
p t8 ＝σ e p t5 (26)
In the formula, σ e Recovering coefficient for total pressure of tail nozzle, total pressure p t5 The total temperature of the gas at the outlet of the turbine;
the air flow speed at the outlet of the tail nozzle is calculated in the following mode:
wherein λ is 8 Is the throat flow coefficient, k 8 Is the adiabatic coefficient.
Secondly, calculating the total pressure p at the nozzle throat of the tail nozzle according to the mass flow of the fuel gas in the nozzle throat and the sectional area of the nozzle throat of the tail nozzle tC8 ：
In the formula, A 8 Is the area of the throat cross-section of the exhaust nozzle, C 8 Is a parameter related to pressure and aerodynamic thermal coefficient, and it is required to be noted that the gas mass flow of the throat of the nozzle is the same as the mass flow of the outlet of the turbine, namely W g8 ＝W g5 ＝W g41 。
Based on the technical concept, after the gas circuit parameters of the target aircraft engine are obtained through simulation, a common working equation of all parts of the target aircraft engine needs to be established, so that all parts can meet the conditions of pressure balance, mass flow continuity and rotor power balance at each steady-state point in the flight envelope of the target aircraft engine.
Then, the step of establishing the common working equation of each component of the target aircraft engine is as follows:
firstly, according to the coaxial relationship between the compressor and the turbine, the power balance is required to be met, and a deviation equation is introduced:
(ηN T -N C )/N C ＝ε 1
where eta is the axial mechanical efficiency, N T Is turbine power, N C Is the compressor power;
according to the total pressure balance of the throat of the tail nozzle, a deviation equation is led out:
(p tC8 -p t8 )/p t8 ＝ε 2
in the formula, p tC8 For total jet pressure, p, continuously calculated from the flow t8 Back pressure of the tail nozzle;
according to the turbine inlet flow continuity, a deviation equation is introduced:
(W g4 -W g41 )/W g41 ＝ε 3
in the formula, W g41 For turbine inlet flow calculated from the turbine flow characteristic curve, W g4 Is the gas mass flow of the combustion chamber.
Finally, the three deviation equations are continuously solved based on a Newton-Raffson (N-R) method, so that the 3 deviation equations meet certain precision requirements, and it can be understood that at each steady-state point in the flight envelope of the target aircraft engine, each part should meet the conditions of pressure balance, mass flow continuity and rotor power balance, so that the solution can be summarized in solving the steady-state equation residual error epsilon i |<ε min I is the solution of 1,2 and 3, but since the three deviation equations are implicit nonlinear equations, the change of each variable is subjected to complicated nonlinear operation to influence the equation residual, and cannot be analyzedSolutions for these variables are obtained, so that only numerical solutions that meet the error requirements can be found by means of numerical solutions.
Therefore, in an embodiment of the present invention, it is proposed to continuously solve the above three deviation equations by using a newton-raphson (N-R) method, that is, in specific implementation, the solution of the equation is corrected in a direction of reducing the equation residual by iterative operation, and the iterative operation is not exited until the error requirement is met:
f i (n,π C ,π T )＝ε i (ε i →0)i＝1,2,3
wherein n is the rotation speed of the aeroengine, pi C For the pressure ratio of the compressor, pi T The turbine pressure drop ratio.
In addition, it should be noted that, in the existing product, it is inevitable that performance of any mechanical device is gradually degraded in actual use, and an aircraft engine needs to operate under various temperature, speed, power and various environmental conditions, and at the same time, performance of the aircraft engine is guaranteed to meet requirements, so that the degradation of the aircraft engine is inevitable, it is understood that gas path degradation is the most common degradation of the aircraft engine, and since the aircraft engine, as a complex mechanical device, has a plurality of modules (such as an air inlet, a tail nozzle, a rotating part, a combustion chamber, and the like) which are composed of parts such as a vane, a seal, a housing, and the like, each part (especially, the rotating part) can undergo gas path degradation during operation, and thus, the degradation of the overall aircraft engine can be defined as the degradation of the performance of each module of the aircraft engine And (4) accumulating.
Meanwhile, pollution, erosion, corrosion, thermal deformation, foreign matter damage and the like of the rotating parts are common causes of gas path degradation, and the faults (degradation) are mainly reflected on the change of parameters (such as surface roughness, blade tip clearance and the like) of the engine blade.
Therefore, in one embodiment of the present invention, it is proposed
And secondly, constructing a core degradation module, and performing local simulation according to collected cloud data of the rotating part point of the target aircraft engine in the operation process, so as to obtain an engine performance degradation difference table of the rotating part at different service times and different rotating speeds, and initially obtain a distribution rule of performance degradation factors of the rotating part. It can be understood that by setting the core degradation module, scanning the engine blade which actually runs, and then establishing the core degradation interpolation table in a simulation mode through CFD software, the degradation rule of the engine is modeled on a physical mechanism, so that the authenticity of the model is improved, and the specific implementation steps are as follows:
s2-1, acquiring blade point cloud data of rotating parts such as a gas compressor, a turbine and the like at multiple time points in the life cycle of a target aircraft engine based on a laser scanning method, wherein the blade point cloud data comprises the surface roughness of the blades of the rotating parts and the size of blade tip clearances, the point cloud data of the blades is obtained by a laser scanning technology, a degradation objective rule of the aircraft engine is summarized in a physical mechanism, and in the concrete implementation:
firstly, taking x aircraft engines which are not degraded and have m rotating parts as sampling objects;
secondly, carrying out laser scanning on each rotating component blade once every p cycles (generally taking p as 50) to obtain a plurality of groups of point cloud data;
s2-2, importing the acquired blade point cloud data of the target aircraft engine rotating part into CFD software, and reconstructing the blade point cloud data until the rotating part is formed into a blade model;
s2-3, carrying out simulation experiment on the obtained blade model to obtain the circulation capacity coefficient of the rotating component at different degradation time points t and different rotating speeds n and the degradation coefficient omega, eta of the rotating component efficiency in comparison with the degradation coefficient under the healthy condition, wherein in the concrete implementation:
firstly, setting the rotating speed of a rotating part blade model at each different degradation time point t to be between 60% and 105% of the rotating speed, and changing by 5% every time so as to simulate the working state of the rotating part during rotation;
second, groupThe obtained degradation proportion omega of the i-th rotating component of the j-th engine in the degradation time point t, the rotation speed n, the flow capacity coefficient and the component efficiency compared with the healthy engine running condition ij (t,n),η ij (t,n)；
S2-4, calculating the degradation ratio omega ij (t,n),η ij Mean value ω of (t, n) i (t,n),η i (t, n) and the resulting mean value ω i (t,n),η i (t, n) interpolating with the t, n as an axis to form an aircraft engine core degradation difference table;
s2-5, based on the obtained mean value omega i (t,n),η i (t, n) and calculating the degradation ratio omega of each rotating part ij (t,n),η ij (t, n) relative to the mean value ω i (t,n),η i The ratio of (t, n) to obtain the degradation factor delta ω1 ,δ η1 ,...,δ ωm ,δ ηm Then, the distribution of the degradation factor pi (delta) is obtained ω1 ,δ η1 ,...,δ ωm ,δ ηm ) It will be appreciated that, in particular implementations,
taking high-dimensional Gaussian distribution as an example, the invention respectively calculates the degradation factor delta based on the technical conception: delta is [ delta ] ω1 ,δ η1 ,...,δ ωm ,δ ηm ] T The mean value μ and the covariance matrix Σ, then, the high-dimensional gaussian distribution rule of the obtained degradation factor is as follows:
it should be noted that, considering that even the same model of aircraft engine has individual differences, the differences between the engines are random, and therefore, the core degradation module needs to be modified randomly in construction.
In one embodiment of the present invention, therefore,
and thirdly, constructing a random correction module, obtaining a random correction factor by adopting a Monte Carlo method based on a Markov chain according to a given engine performance degradation distribution rule, correcting the performance degradation factor obtained by the core degradation module, and simulating the individual difference generated by degradation among different aero-engines so that the built aero-engine model considering the performance degradation is more true, wherein after the random correction module is adopted, the individual difference between different engines of the same model can be simulated, and a plurality of different degraded engine models can be generated at the same time, thereby providing meaningful data for related researches.
It should be noted that, when constructing the stochastic modification module, it is first necessary to construct a markov chain satisfying a smooth distribution condition, and it is understood that the markov chain is a set of discrete random variables having a markov property, that is,
sequence X ═ X considering a random variable 0 ,X 1 ,...,X t ,.., it will be appreciated that X t Representing the random variables at the time t, wherein the value sets of all the variables are the same and become a state space S;
assume a random variable X at time 0 0 Following a probability distribution P (X) 0 )＝π 0 It is called initial state distribution, and obtains random variable X with t ≧ 1 at a certain time t Random variable X from previous time t-1 Conditional distribution P (X) between t |X t-1 )。
Then, if X t Dependent only on X t-1 Without relying on past random variables { X } 0 ,X 1 ,...,X t-2 This property is called markoff, i.e. markoff
P(X t |X 0 ,X 1 ,...,X t-1 )＝P(X t |X t-1 ),t＝1,2....(29)
And, conditional probability distribution P (X) t |X t-1 ) A transition probability distribution called markov chain.
Based on this, when the random variable is transferred from one time to the next, i.e. from t k To t k+1 The state of which is represented by s i Transfer to s j Is called transition probability and can be expressed as
If the state space equation S consists of n sets of states, then there is a transition probability:
it satisfies the following conditions:
when the following conditions are met:
πP＝π (33)
the markov chain reaches convergence.
It should be noted that, when the random correction module is constructed, the initial state of the proposed markov chain may be arbitrary, but the stable distribution of the markov chain is just the target distribution f (x) to which the random correction factor obeys, and after the markov chain reaches the stable convergence state, the subsequent sampling results may be considered to satisfy the target distribution condition, so that it is necessary to complete sample sampling by using the monte carlo method to obtain the random correction factor satisfying the target distribution f (x).
Based on the technical concept, after the degradation factor δ is obtained by the core degradation module constructed in the second step provided by the invention, taking a single-shaft turbojet engine as an example, the assumption is that two rotating components are shared, so that the random variable δ is a quaternary quantity including δ ω1 ,δ η1 ,δ ω2 ,δ η2 ，(δ ωi Coefficient of flow capacity, δ, for each rotating member ηi I represents the ith rotating part) which obeys a given distribution pi, the probability density function of which is pi (δ), a random correction factor for the efficiency of the part.
Therefore, it can be found that the transition matrix directly influences the convergence property of the markov chain, and to obtain stable target distribution, a corresponding transition matrix P needs to be found to satisfy the detailed balance condition of the markov chain:
π i P ij ＝π j P ji (34).
therefore, in an embodiment of the present invention, when a plurality of sets of random correction factors are generated by using an MCMC method (monte carlo method of markov chain), a gibbs sampling method is used to find the transition probability matrix P, so as to reduce the computation power waste caused by rejecting sampling in other monte carlo methods, and in the implementation:
using a two-dimensional distribution as an example, assume π (x) 1 ,x 2 ) Is a two-dimensional joint probability distribution, and two points with the same first characteristic dimension are observed
And
the following two equations are readily found to hold:
since the right parts of the two formulas are equal, the method is obtained
based on the above, it can be understood that
On the straight line, if used, the condition ruleRate distribution
As the state transition probability of the markov chain, since the transition between any two points satisfies the fine balance condition, the state transition matrix P corresponding to the markov chain of the reconstructed distribution pi (x1, x2) is:
P (a → D) ═ 0 otherwise.
Based on this, it should be noted that the state transition matrix P corresponding to the markov chain of the reconstructed distribution pi (x1, x2) is exemplified by the two-dimensional distribution described above, but is still true when generalized to multidimensional, i.e., it is moved on any one of the coordinate axes every time n-1 coordinate axes are fixed.
In order to better understand the technical idea of finding the transition probability matrix P by using the gibbs sampling method when generating multiple sets of random correction factors by using the MCMC method (monte carlo method of markov chain) provided by the present invention, taking a single-shaft turbojet engine as an example: the step of obtaining the random correction factor comprises
First, the smooth distribution of π (δ) is entered ω1 ,δ η1 ,δ ω2 ,δ η2 ) Setting a state transition time threshold k and the number n of required random correction factors;
secondly, an initial state value is randomly given
Again, a sample was taken:
Fort＝0to n+m-1
from conditional probability distributions
Sampling to obtain a sample
From conditional probability distributions
Sampling to obtain samples
From conditional probability distributions
Sampling to obtain a sample
From conditional probability distributions
Sampling to obtain samples
Finally, abandoning the samples of which the first m Markov chains have not reached the stability, and obtaining a sample set
A set of random modifiers corresponding to the desired smooth distribution.
As shown in fig. 3, as an embodiment of the present invention, when the aeroengine baseline model module, the core degradation module, and the stochastic modification module are all constructed, the aeroengine model considering the performance degradation is specifically constructed as follows:
firstly, inputting the service time t and the rotating speed omega into a core degradation module, and obtaining the degradation proportion of each rotating component according to an interpolation table;
then, inputting a state transition time threshold k and the number n of models required to be generated into a random correction module, correcting the degradation proportion generated by a core degradation module by using n groups of obtained random correction factors, and finally obtaining n groups of corrected degradation proportions;
and finally, inputting the height H, the Mach number Ma and the corrected n groups of degradation ratios into an aircraft engine baseline model module, and performing iterative operation to form a final performance degradation-considered aircraft engine model.
While there have been shown and described what are at present considered to be the basic principles and essential features of the invention and advantages thereof, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, but is capable of other embodiments without departing from the spirit or essential characteristics thereof; the present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (9)
1. An aircraft engine modeling method that accounts for performance degradation, comprising the steps of:
firstly, constructing an aeroengine baseline model module, carrying out gas path calculation along the sequence of a target aeroengine gas inlet flow, and establishing a common working equation of each rotating part of the target aeroengine according to the obtained gas path calculation data for simulating the internal operating gas path condition of the healthy engine so as to generate a maximum guess value of the target aeroengine when a gas path fault occurs;
secondly, constructing a core degradation module, and performing local simulation according to collected point cloud data of a rotating part of the target aircraft engine in the operation process so as to obtain an engine performance degradation difference table of the rotating part at different service times and different rotating speeds, so as to initially obtain a distribution rule of performance degradation factors of the rotating part;
thirdly, a random correction module is constructed, according to a given engine performance degradation distribution rule, a Monte Carlo method based on a Markov chain is adopted to obtain a random correction factor, the performance degradation factor obtained by the core degradation module is corrected, and therefore individual differences generated by degradation among different aeroengines are simulated, and the established aeroengine model considering the performance degradation is more simulated;
and fourthly, inputting the degradation factor corrected by the random correction module into the aeroengine baseline model module, and performing iterative operation to form a final aeroengine model considering performance degradation.
2. The method for modeling an aircraft engine in consideration of performance degradation as claimed in claim 1, wherein the rotating parts of the target aircraft engine include an intake duct, a compressor, a combustion chamber, a turbine, and an exhaust nozzle, and in the first step, the step of establishing the common working equation of the respective parts of the target aircraft engine is:
firstly, according to the coaxial relationship between the compressor and the turbine, the power balance is required to be met, and a deviation equation is introduced:
(ηN T -N C )/N C ＝ε 1
where eta is the axial mechanical efficiency, N T To turbine power, N C Is the compressor power;
according to the total pressure balance of the throat of the tail nozzle, a deviation equation is led out:
(p tC8 -p t8 )/p t8 ＝ε 2
in the formula, p tC8 For total nozzle pressure, p, calculated continuously from the flow t8 Back pressure of the tail nozzle;
according to the turbine inlet flow continuity, a deviation equation is introduced:
(W g4 -W g41 )/W g41 ＝ε 3
in the formula, W g41 For turbine inlet flow calculated from the turbine flow characteristic curve, W g4 Is the gas mass flow of the combustion chamber;
finally, three of the deviation equations are continuously solved based on a newton-raphson (N-R) method.
3. The aircraft engine modeling method considering performance degradation as claimed in claim 2, wherein the specific way to continuously solve the three deviation equations based on the newton-raphson method is:
firstly, according to each steady-state point in the flight envelope of the target aircraft engine, all rotating parts should meet the conditions of pressure balance, continuous mass flow and rotor power balance, and the solution of the three deviation equations is attributed to the solution of a steady-state equation residual error epsilon i |<ε min The solution of i 1,2,3,
and finally, correcting the solution of the equation in the direction of reducing the residual error of the steady state equation through iterative operation until the iterative operation is exited after the error requirement is met:
f i (n,π C ,π T )＝ε i (ε i →0)i＝1,2,3
wherein n is the rotation speed of the aeroengine, pi C For the pressure ratio of the compressor, pi T The turbine pressure drop ratio.
4. The aircraft engine modeling method taking performance degradation into consideration as claimed in claim 1, wherein in the second step, the specific implementation of constructing the core degradation module to initially obtain the distribution rule of the performance degradation factor of the rotating component is:
s2-1, blade point cloud data of each rotating part at a plurality of time points in the life cycle of the target aircraft engine are obtained;
s2-2, importing the acquired blade point cloud data of each rotating part of the target aircraft engine into CFD software, and reconstructing the blade point cloud data until the entity of the rotating part forms a blade model;
s2-3, carrying out simulation experiment on the obtained blade model to obtain the flow capacity coefficient and the degradation ratio omega of the rotating component under the conditions that the efficiency of the rotating component is compared with the efficiency of the rotating component under the healthy condition at different degradation time points t and different rotating speeds n ij (t,n),η ij (t,n)；
S2-4, calculating the degradation ratio omega ij (t,n),η ij Mean value ω of (t, n) i (t,n),η i (t, n) and the resulting mean value ω i (t,n),η i (t, n) carrying out interpolation by taking the t, n as an axis to form a target aeroengine core degradation difference table;
s2-5, based on the obtained mean value omega i (t,n),η i (t, n) and calculating the degradation ratio omega of each rotating part ij (t,n),η ij (t, n) relative to the mean value ω i (t,n),η i The ratio of (t, n) to obtain the degradation factor delta ω1 ,δ η1 ,…,δ ωm ,δ ηm Then, the distribution rule pi (delta) of the degradation factor is obtained ω1 ,δ η1 ,…,δ ωm ,δ ηm )。
5. The method for modeling an aircraft engine taking into account performance degradation according to claim 1, wherein in the third step, when the stochastic correction module is constructed to obtain the stochastic correction factor:
it is necessary to construct a markov chain of discrete random variable sets satisfying the condition of smooth distribution and having markov properties:
PX t X 0 ,X 1 ,…,X t-1 )＝PX t X t-1 ),t＝1,2…
in the formula, X t Representing random variables at the moment t, wherein the value sets of all the random variables are the same; conditional probability distribution P (X) t |X t-1 ) A transition probability distribution called a Markov chain; wherein,
the specific steps of constructing the Markov chain meeting the stable distribution condition are as follows:
obtaining the transition probability of the random variable:
in the formula, S is a state space;
if the state space equation S consists of n sets of states, then a state transition matrix P is formed:
which satisfies the following conditions:
when the following conditions are met: the markov chain reaches convergence when pi P is pi.
6. An aircraft engine modeling method taking into account performance degradation according to claim 5,
after the markov chain is constructed, a detailed balance condition of the constructed markov chain needs to be searched to reduce the influence of the state transition matrix P on the convergence property of the markov chain:
π i P ij ＝π j P ji 。
7. the method of modeling an aircraft engine in consideration of performance degradation according to claim 6,
the method for finding the detailed balance condition of the Markov chain adopts a Gibbs sampling method to reduce the waste of calculation power caused by refusing sampling in other Monte Carlo methods.
8. The aircraft engine modeling method considering performance degradation according to claim 4 or 5, wherein the stochastic modification module is constructed in a manner that the stochastic modification factor is obtained by:
firstly, the distribution rule pi (delta) of the degradation factors of the smooth distribution is input ω1 ,δ η1 ,…,δ ωm ,δ ηm ) Setting a state transition time threshold k and the number n of required random correction factors;
secondly, an initial state value is randomly given
Again, for i, loop execution:
let the sample at the end of the (i-1) th iteration be
The ith iteration proceeds with the following steps:
(1) from conditional probability distributions
Sampling to obtain samples
(2) From conditional probability distributions
Sampling to obtain a sample
…；
(2j-1) from the conditional probability distribution
Sampling to obtain samples
(2j) From conditional probability distributions
Sampling to obtain samples
…；
(2m-1) from the conditional probability distribution
Sampling to obtain a sample
(2m) from the conditional probability distribution
Sampling to obtain a sample
Obtained from the ith iteration
Finally, abandoning the samples generated by the previous k iterations when the Markov chain has not reached the stability, and obtaining the residual sample set
I.e. the set of random modifiers corresponding to the desired smooth distribution.
9. The method for modeling an aircraft engine taking performance degradation into account as claimed in claim 1, wherein in the fourth step, the concrete implementation form of the model of the aircraft engine taking performance degradation into account is:
firstly, inputting the service time t and the rotating speed omega into the core degradation module, and obtaining the degradation proportion of each rotating component according to a target aircraft engine core degradation difference table;
then, inputting a state transition time threshold k and the number n of models required to be generated into the random correction module, and correcting the degradation proportion generated by the core degradation module by using the obtained n groups of random correction factors to finally obtain n groups of corrected degradation proportions;
and finally, inputting the height H, the Mach number Ma and the corrected n groups of degradation ratios into the aeroengine baseline model module, and performing iterative operation to form an aeroengine model considering performance degradation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210386432.5A CN115099165B (en) | 2022-04-13 | 2022-04-13 | Aeroengine modeling method considering performance degradation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210386432.5A CN115099165B (en) | 2022-04-13 | 2022-04-13 | Aeroengine modeling method considering performance degradation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115099165A true CN115099165A (en) | 2022-09-23 |
| CN115099165B CN115099165B (en) | 2023-05-02 |
Family
ID=83287473
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210386432.5A Active CN115099165B (en) | 2022-04-13 | 2022-04-13 | Aeroengine modeling method considering performance degradation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115099165B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116702337A (en) * | 2023-08-07 | 2023-09-05 | 南京航空航天大学 | Global sampling method based on differential evolution |
| CN116702380A (en) * | 2023-08-03 | 2023-09-05 | 南京航空航天大学 | Aeroengine performance degradation monitoring and model correction method based on digital twin |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105404750A (en) * | 2015-12-07 | 2016-03-16 | 南京航空航天大学 | Turboshaft engine self-adaptive model building method based on modified Broyden algorithm to solve equation set |
| CN106021826A (en) * | 2016-07-11 | 2016-10-12 | 北京航空航天大学 | Method for predicting complete residual life of aero-engine under variable working conditions based on working condition identification and similarity matching |
| CN107357994A (en) * | 2017-07-14 | 2017-11-17 | 哈尔滨工业大学（威海） | A kind of aero-engine performance decline mode excavation method stage by stage |
| CN107545112A (en) * | 2017-09-07 | 2018-01-05 | 西安交通大学 | Complex equipment Performance Evaluation and Forecasting Methodology of the multi-source without label data machine learning |
| CN110222401A (en) * | 2019-05-30 | 2019-09-10 | 复旦大学 | Aero-engine nonlinear model modeling method |
| CN111914367A (en) * | 2020-08-05 | 2020-11-10 | 南京航空航天大学 | Aeroengine part level model |
-
2022
- 2022-04-13 CN CN202210386432.5A patent/CN115099165B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105404750A (en) * | 2015-12-07 | 2016-03-16 | 南京航空航天大学 | Turboshaft engine self-adaptive model building method based on modified Broyden algorithm to solve equation set |
| CN106021826A (en) * | 2016-07-11 | 2016-10-12 | 北京航空航天大学 | Method for predicting complete residual life of aero-engine under variable working conditions based on working condition identification and similarity matching |
| CN107357994A (en) * | 2017-07-14 | 2017-11-17 | 哈尔滨工业大学（威海） | A kind of aero-engine performance decline mode excavation method stage by stage |
| CN107545112A (en) * | 2017-09-07 | 2018-01-05 | 西安交通大学 | Complex equipment Performance Evaluation and Forecasting Methodology of the multi-source without label data machine learning |
| CN110222401A (en) * | 2019-05-30 | 2019-09-10 | 复旦大学 | Aero-engine nonlinear model modeling method |
| CN111914367A (en) * | 2020-08-05 | 2020-11-10 | 南京航空航天大学 | Aeroengine part level model |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116702380A (en) * | 2023-08-03 | 2023-09-05 | 南京航空航天大学 | Aeroengine performance degradation monitoring and model correction method based on digital twin |
| CN116702337A (en) * | 2023-08-07 | 2023-09-05 | 南京航空航天大学 | Global sampling method based on differential evolution |
| CN116702337B (en) * | 2023-08-07 | 2023-11-07 | 南京航空航天大学 | Global sampling method based on differential evolution |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115099165B (en) | 2023-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115099165B (en) | Aeroengine modeling method considering performance degradation | |
| Tsoutsanis et al. | Dynamic performance simulation of an aeroderivative gas turbine using the matlab simulink environment | |
| Chi et al. | Multi-dimensional platform for cooling design of air-cooled turbine blades | |
| Teixeira et al. | A methodology for fully-coupled CFD engine simulations, applied to a micro gas turbine engine | |
| Wang et al. | The Off‐Design Performance Simulation of Marine Gas Turbine Based on Optimum Scheduling of Variable Stator Vanes | |
| Yang et al. | Gas turbine engine transient performance and heat transfer effect modelling: A comprehensive review, research challenges, and exploring the future | |
| Cheng et al. | Uncertainty quantification and sensitivity analysis on the aerodynamic performance of a micro transonic compressor | |
| Shuang et al. | An adaptive compressor characteristic map method based on the Bezier curve | |
| CN116842861A (en) | Coupling analysis method for aeroengine performance and fuel/lubricating oil system | |
| Goulos et al. | Design optimisation of separate-jet exhausts for the next generation of civil aero-engines | |
| Song et al. | Aerodynamic and structural multidisciplinary optimization design method of fan rotors based on blade curvature constraints | |
| Bufi et al. | Robust optimization of supersonic ORC nozzle guide vanes | |
| Krishnababu et al. | Ai assisted high fidelity multi-physics digital twin of industrial gas turbines | |
| CN114154234A (en) | Modeling method, system and storage medium for aircraft engine | |
| Jia et al. | Research on the volume-based fully coupled method of the multi-fidelity engine simulation | |
| Medic et al. | Integrated RANS/LES computations of turbulent flow through a turbofan jet engine | |
| Yuan et al. | A CFD approach to fluid dynamic optimum design of steam turbine stages with stator and rotor blades | |
| Zhang et al. | An integrated modeling approach for variable cycle engine performance analysis | |
| Yarlagadda | Performance analysis of J85 turbojet engine matching thrust with reduced inlet pressure to the compressor | |
| Göing et al. | Performance simulation of roughness induced module variations of a jet propulsion by using pseudo bond graph theory | |
| Blanco-Patiño et al. | Performance prediction of a centrifugal compressor for a cogeneration microturbine | |
| Carcasci et al. | Heavy duty gas turbine simulation: a compressor IGV airfoil off-design characterization | |
| Turner | Full 3D Analysis of the GE90 Turbofan Primary Flowpath | |
| Tomita et al. | Numerical tools for high performance axial compressor design for teaching purpose | |
| Turner et al. | High fidelity 3d turbofan engine simulation with emphasis on turbomachinery-combustor coupling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |