CN108182328A - A kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter - Google Patents
A kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter Download PDFInfo
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Abstract
A kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter of the invention designs sinusoidal multilevel signal as Nonlinear Aerodynamic identification signal first, and the amplitude of modelled signal need to meet substantially movement needs.Nonlinear Aerodynamic identification is carried out secondly based on the Recognition with Recurrent Neural Network model of deep learning, training output signal is calculated using CFD approach, dynamic Time-Domain Nonlinear Aerodynamic Model when substantially being vibrated with the Recognition with Recurrent Neural Network model foundation wing based on deep learning, and the system calculated with CFD under test signal responds, compared with the identification result of network model, the performance of model is verified.The present invention saves Flight Vehicle Design cost, improves stall flutter design efficiency, has developed the reduced-order model of Nonlinear Aerodynamic, so as to fast prediction stall flutter.
Description
Technical field
The invention belongs to aviation aircraft designs and System Discrimination field, are that one kind is quivered for fast prediction aircraft stall
It shakes characteristic, the non-linear reduced-order model for improving computational efficiency and developing.
Background technology
Stall flutter is aircraft aerofoil or when rudder face is in the big angle of attack, Nonlinear Aerodynamic and bullet caused by air-flow detaches
The self-excited vibration that property structure Coupling is occurred, stall flutter embody strong nonlinearity characteristic.The big angles-of-attack of aircraft (for example destroy
Hitting machine or guided missile has high maneuverability and agility, needs to fly in the range of High Angle of Attack) or when meeting with fitful wind, liter may be made
Power face is in stall conditions, aerodynamic stalling is easy to cause when the angle of attack reaches a certain critical value, and then stall flutter may occur,
Influence the structure safety of aircraft；Especially screw blade and helicopter blade, blade tip are easier to that stall and stall flutter occurs
Phenomenon.
There are experimental study, empirical model and the emulation of CFD-CSD fluid structurecouplings to the research method of wing stall flutter at present.
Test method takes time and effort, and cost is higher.Empirical model, which calculates, relies on model and test data, and as a result precision is not high.CFD-CSD
Calculating can obtain high-precision as a result, still calculation amount is huge, and time-consuming very long, increase calculates cost and aircraft development period.
Realize the prediction of aircraft stall flutter, the key technical problem for first having to solve is to the non-linear gas of the big angle of attack
The accurate recognition of power, is primarily present problems with：
1. traditional signal is poor in the Nonlinear Aerodynamic prediction effect of off-design point.Accurately to capture the big angle of attack limit
The characteristic of ring vibration, need to rationally design rational identification input signal, and the identification input signal of design need to accurately portray limit cycle
The rule of vibration, big motion amplitude and frequency requirement.
2. for designed identification input signal, need to develop the extensive reduced-order model of robust and algorithm is accurately distinguished
Know the mathematical model of the Nonlinear Aerodynamic of dynamic big angle of attack separation.
Based on the above situation, need to propose new model in input signal design and identification algorithm.
Invention content
In view of the above-mentioned problems, in order to save Flight Vehicle Design cost, stall flutter design efficiency is improved, it is proposed that Yi Zhongshi
For the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, so as to fast prediction stall flutter.
The present invention is suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, is obtained by following step：
Step 1：Amplitude, frequency and vibration regularity during according to wing limit-cycle oscillation are designing symmetrical multistage just
String signal, the input data as deep learning model system.
Step 2：The designed sinusoidal signal of step a kind is input in CFD software, the wing obtained under friction speed exists
Aerodynamic coefficient under signal excitation, the output data as deep learning model system.
Step 3：Recognition with Recurrent Neural Network model based on deep learning carries out Nonlinear Aerodynamic identification, and it is non-to obtain the big angle of attack
Linear aerodynamic reduced order model.
Above-mentioned big angle of attack Nonlinear Aerodynamic reduced-order model is brought into the gentle power coupling accounting equation of structure, profit
Time domain is carried out with 4 rank Runge-Kutta methods and promotes calculating, the response process of each mode at any time is predicted, so as to reach pre- dendrometry
The purpose of fast flutter.
The advantage of the invention is that：
1st, the present invention is suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, by being vibrated to stall flutter
The analysis of feature devises multistage sinusoidal identification signal, and this signal covers the amplitude of vibration, frequency range, can be fine
Prediction vibration processes Nonlinear Aerodynamic.
2nd, the present invention is suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, is following based on deep learning
The Nonlinear Aerodynamic identification model of ring neural network model, in terms of the modeling of kinematic nonlinearity flow field have big advantage and
Application prospect.
3rd, the present invention is suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, adapts to strong, identification precision height,
Operating method is fairly simple, can be realized using MATLAB programmings.
4th, the present invention is suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, with the development of CFD technologies,
Complicated experimental study is eliminated, while computational efficiency calculates than CFD and improves one to two orders of magnitude again, discrimination method also may be used
In other Nonlinear Aerodynamic identification systems, there is certain versatility.
Description of the drawings
Fig. 1 is the big angle of attack Nonlinear Aerodynamic reduced-order model design flow diagram that the present invention is suitable for stall flutter；
Fig. 2 is the identification signal signal for the big angle of attack Nonlinear Aerodynamic reduced-order model that the present invention is suitable for stall flutter
Figure；
Fig. 3 be the present invention used suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter based on depth
The Recognition with Recurrent Neural Network model structure of study；
Fig. 4 is neuron elements unfolding calculation structure chart in the Recognition with Recurrent Neural Network model based on deep learning.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
The present invention is suitable for the big angle of attack Nonlinear Aerodynamic reduced-order model of stall flutter, as shown in Figure 1, passing through following steps
Suddenly it obtains：
Step 1：Design input signal.
Input signal is designed for prediction wing stall flutter, each when big angle of attack limit-cycle oscillation occurs for wing
The vibration mode in a period is sinusoidal vibration.Design method of the present invention is, for a kind of wing model, according to its limit-cycle oscillation
When amplitude, frequency and vibration regularity, design symmetrical multistage sinusoidal signal, these sines contain different amplitudes and
Frequency information, and cover required amplitude and frequency information；Wherein, frequency is generally low-frequency vibration, therefore signal frequency model
Enclose the range of covering stall flutter vibration frequency.As shown in Figure 2.When for specific model, need according to vibration width
The needs of value and frequency design.
Step 2：CFD solvers are developed, calculate aerodynamic coefficient.
The designed sinusoidal signal of step a kind is input in CFD software, it is contemplated that under the conditions of the big angle of attack, different comes
Flow velocity degree can have an impact the aerodynamic parameter calculated, therefore by the wing under CFD software acquisition friction speed in the letter
Number excitation under aerodynamic coefficient, including lift coefficient, torque coefficient, resistance coefficient etc..Above-mentioned aerodynamic coefficient is depth
The output data of habit model system, and the input data that the sinusoidal signal designed is deep learning model system.
Step 3：Nonlinear Aerodynamic recognizes.
In the present invention, Recognition with Recurrent Neural Network model of the Nonlinear Aerodynamic identification based on deep learning, i.e. depth in step 2
Learning model is spent, as shown in Figure 3.Due to the complexity in non-linear flow field, conventional model is very high to skill requirement, it is difficult to provide one
A suitable explicit expression, and neural network (Neural Network, NN) model based on deep learning have do not need to
The advantages of providing the display mathematic(al) representation between identification system input/output.The neural network model passes through learning training type
The feature of input/output and input influence the mode of output in number, so as to obtain the feature with identification system, and with learning
" experience " predict output under new input.
Recognition with Recurrent Neural Network model based on deep learning is divided into input layer, hidden layer and output layer.Wherein, the number of plies is implied
More than 4 layers, the specific number of plies can be adjusted according to realistic model.From input layer to output layer, every layer of neuron elements are with before
The neuron elements of layer connect and transmit data afterwards.Each neuron elements of hidden layer can be fed back as the defeated of itself
Enter.
The internal structure expanded view of above-mentioned each neuron elements is as shown in Figure 4.
Neuron elements are to be unfolded in temporal sequence, and following explanation is done to the expanded view：
1)x(t)For the input of t moment training sample, likewise, x(t-1)And x(t+1)The respectively t-1 moment and t+1 moment instructs
Practice the input of sample.
2)h(t)For the hidden state of t moment model, h(t)By x(t)And h(t-1)It codetermines.
3)o(t)Represent the output of t moment model, o(t)Only by the current hidden state h of model(t)It determines.
4)L(t)Represent the loss function of t moment model.
5)y(t)Represent the true output of t moment training sample.
6) these three matrixes of U, W, V are the linear relationship parameters of model, it is shared in entire recirculating network, is embodied
The thought of " the cycle feedback " of Cyclic Operation Network.
Using the above-mentioned Recognition with Recurrent Neural Network model based on deep learning, arbitrary kinematic nonlinearity mapping system can be approached
System, can recognize kinematic nonlinearity unsteady aerodynamic model well.The propagated forward algorithm of entire Recognition with Recurrent Neural Network and general
Logical feedforward neural network is identical, and wherein hidden layer activation primitive selects continuous hyperbolic tangent function, and output layer activation primitive is selected
Softmax functions.The gradient descent method in error backpropagation algorithm is selected during Recognition with Recurrent Neural Network parameter training, by repeatedly
The connection weight and threshold values inside Recognition with Recurrent Neural Network are adjusted, network output is finally made to approach practical output.
Step 4：Using trained network model, the stall flutter of wing is predicted.By network model be brought into structure and
In aerodynamic force coupling accounting equation, carry out time domain using 4 rank Runge-Kutta methods and promote to calculate, predict each mode at any time
Response process, so as to achieve the purpose that predict stall flutter.
Claims (5)
1. a kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter, it is characterised in that：Pass through following step
It obtains：
Step 1：Amplitude, frequency and vibration regularity during according to wing limit-cycle oscillation design symmetrical multistage sinusoidal letter
Number, the input data as deep learning model system；
Step 2：The designed sinusoidal signal of step a kind is input in CFD software, obtains the wing under friction speed in the letter
Number excitation under aerodynamic coefficient, the output data as deep learning model system；
Step 3：Recognition with Recurrent Neural Network model based on deep learning carries out Nonlinear Aerodynamic identification, and it is non-linear to obtain the big angle of attack
Aerodynamic reduced order model.
2. a kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter as described in claim 1, feature exist
In：The inner multistage sinusoidal signal of step 1 includes different amplitude and frequency information.
3. a kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter as described in claim 1, feature exist
In：In Recognition with Recurrent Neural Network model based on deep learning, hidden layer activation primitive selects continuous hyperbolic tangent function, output layer
Activation primitive selects softmax functions.
4. a kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter as described in claim 1, feature exist
In：Recognition with Recurrent Neural Network model based on deep learning selected under the gradient in error backpropagation algorithm during parameter training
Drop method.
5. a kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter as described in claim 1, feature exist
In：Big angle of attack Nonlinear Aerodynamic reduced-order model is brought into the gentle power coupling accounting equation of structure, using 4 rank Long Ge-
Library tower method carries out time domain and promotes calculating, predicts the response process of each mode at any time, so as to reach the mesh of prediction stall flutter
's.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109063290A (en) * | 2018-07-20 | 2018-12-21 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of flutter prediction technique based on nerual network technique |
| CN109086501A (en) * | 2018-07-20 | 2018-12-25 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of flutter prediction technique |
| CN110826600A (en) * | 2019-10-18 | 2020-02-21 | 北京航空航天大学 | Engine surge prediction method based on adaptive resonance network online incremental learning |
| CN111898327A (en) * | 2020-06-30 | 2020-11-06 | 西北工业大学 | Flutter signal abnormal data expansion method for aeroelastic system |
| CN112380794A (en) * | 2020-12-08 | 2021-02-19 | 中北大学 | Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade |
| CN112800543A (en) * | 2021-01-27 | 2021-05-14 | 中国空气动力研究与发展中心计算空气动力研究所 | Nonlinear unsteady aerodynamic modeling method based on improved Goman model |
| CN112948973A (en) * | 2021-03-04 | 2021-06-11 | 北京航空航天大学 | Wing stall flutter closed-loop control method for continuously variable camber trailing edge |
| CN113673031A (en) * | 2021-08-11 | 2021-11-19 | 中国科学院力学研究所 | Flexible airship service attack angle identification method integrating strain response and deep learning |
| CN115034152A (en) * | 2022-05-17 | 2022-09-09 | 浙江大学 | Data-driven fluid-solid coupling system nonlinear order reduction prediction method and device |
| CN115343012A (en) * | 2022-07-07 | 2022-11-15 | 中国航空工业集团公司哈尔滨空气动力研究所 | Unsteady-state large-amplitude oscillation test method |
| CN115408931A (en) * | 2022-08-16 | 2022-11-29 | 哈尔滨工业大学 | Vortex vibration response prediction method based on deep learning |
| CN115422497A (en) * | 2022-08-16 | 2022-12-02 | 哈尔滨工业大学 | Ordinary differential equation identification method based on convolution differential operator and symbol network |
| CN117992761A (en) * | 2024-04-07 | 2024-05-07 | 西北工业大学 | Intelligent prediction method for aerodynamic force of dynamic stall of wind turbine blade |
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109063290A (en) * | 2018-07-20 | 2018-12-21 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of flutter prediction technique based on nerual network technique |
| CN109086501A (en) * | 2018-07-20 | 2018-12-25 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of flutter prediction technique |
| CN110826600A (en) * | 2019-10-18 | 2020-02-21 | 北京航空航天大学 | Engine surge prediction method based on adaptive resonance network online incremental learning |
| CN111898327B (en) * | 2020-06-30 | 2022-06-07 | 西北工业大学 | Flutter signal abnormal data expansion method for aeroelastic system |
| CN111898327A (en) * | 2020-06-30 | 2020-11-06 | 西北工业大学 | Flutter signal abnormal data expansion method for aeroelastic system |
| CN112380794A (en) * | 2020-12-08 | 2021-02-19 | 中北大学 | Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade |
| CN112380794B (en) * | 2020-12-08 | 2022-11-08 | 中北大学 | Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade |
| CN112800543A (en) * | 2021-01-27 | 2021-05-14 | 中国空气动力研究与发展中心计算空气动力研究所 | Nonlinear unsteady aerodynamic modeling method based on improved Goman model |
| CN112800543B (en) * | 2021-01-27 | 2022-09-13 | 中国空气动力研究与发展中心计算空气动力研究所 | Nonlinear unsteady aerodynamic modeling method based on improved Goman model |
| CN112948973A (en) * | 2021-03-04 | 2021-06-11 | 北京航空航天大学 | Wing stall flutter closed-loop control method for continuously variable camber trailing edge |
| CN112948973B (en) * | 2021-03-04 | 2022-05-03 | 北京航空航天大学 | Wing stall flutter closed-loop control method for continuously variable camber trailing edge |
| CN113673031A (en) * | 2021-08-11 | 2021-11-19 | 中国科学院力学研究所 | Flexible airship service attack angle identification method integrating strain response and deep learning |
| CN113673031B (en) * | 2021-08-11 | 2024-04-12 | 中国科学院力学研究所 | Flexible airship service attack angle identification method integrating strain response and deep learning |
| CN115034152A (en) * | 2022-05-17 | 2022-09-09 | 浙江大学 | Data-driven fluid-solid coupling system nonlinear order reduction prediction method and device |
| CN115343012B (en) * | 2022-07-07 | 2023-04-07 | 中国航空工业集团公司哈尔滨空气动力研究所 | Unsteady-state large-amplitude oscillation test method |
| CN115343012A (en) * | 2022-07-07 | 2022-11-15 | 中国航空工业集团公司哈尔滨空气动力研究所 | Unsteady-state large-amplitude oscillation test method |
| CN115408931A (en) * | 2022-08-16 | 2022-11-29 | 哈尔滨工业大学 | Vortex vibration response prediction method based on deep learning |
| CN115422497A (en) * | 2022-08-16 | 2022-12-02 | 哈尔滨工业大学 | Ordinary differential equation identification method based on convolution differential operator and symbol network |
| CN117992761A (en) * | 2024-04-07 | 2024-05-07 | 西北工业大学 | Intelligent prediction method for aerodynamic force of dynamic stall of wind turbine blade |
| CN117992761B (en) * | 2024-04-07 | 2024-06-11 | 西北工业大学 | Intelligent prediction method for aerodynamic force of dynamic stall of wind turbine blade |
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