CN104443427A - Aircraft flutter prediction system and method - Google Patents
Aircraft flutter prediction system and method Download PDFInfo
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Abstract
The invention discloses an aircraft flutter prediction system and method which are used for solving the technical problem that an existing flutter prediction system is poor in prediction accuracy. According to the technical scheme, the system comprises an excitation signal generator, an excitation executing mechanism, a signal acquiring module and a data processing and flutter prediction module, wherein excitation signals generated by the excitation signal generator are input into a steering engine, the steering engine drives a control plane to deflect through a power booster according to a predetermined instruction so that gas power can be generated and excitation can be formed, and the control plane deflects at different speeds according to different input signals so that different external excitation forces can be generated and a test part can be fully excited. The signal acquiring module is used for acquiring acceleration responses and external excitation force response signals. According to each mach number, the data processing and flutter prediction module can predict a flutter critical point of the test mach number just through a subcritical speed testing point, prediction accuracy can also meet the requirement when the speed testing point is far away from the flutter critical point, and the prediction accuracy of the flutter prediction system is improved.
Description
Technical Field
The invention relates to a flutter prediction system, in particular to an aircraft flutter prediction system. And also relates to a flutter prediction method based on the aircraft flutter prediction system.
Background
The most striking physical phenomenon of the aeroelastic dynamics problem is flutter, and this dynamic instability often leads to catastrophic failure of the structure. The novel airplane or the airplane with the significant structural change must carry out flutter test flight so as to confirm that the airplane does not generate flutter in a flight envelope.
The flutter prediction system is an application of an excitation technology, a measurement technology, a data processing technology and the like in flutter flight tests. Flutter test flight has the characteristics of high risk, high consumption and long period, and is internationally recognized as a class I risk subject. The design and manufacture of a set of efficient, low-cost and low-risk flutter prediction system has great significance for reducing the flutter test flight period and cost and reducing the flutter test flight risk.
The flutter test flight mainly comprises three parts of structure excitation, response measurement, data processing and flutter boundary prediction, and the flow is generally as follows: the method comprises the steps of enabling an airplane to fly at a series of subcritical speed points under the same Mach number, applying excitation in a proper form to a structure, analyzing measured signals by monitoring responses of a certain number of points on the structure, utilizing curve extrapolation and predicting safety of the next flight speed point according to a proper stability criterion, further obtaining a flutter critical point under the Mach number, repeating the process under other Mach numbers, and finally obtaining a flight envelope determined by a flutter boundary through a large number of flight tests.
Refer to fig. 15. The document "Ring line and Marty Brenner" Flutter: An on-line tool predict robust flusher markers ", Journal of Aircraft, vol.37, No.6, pp.1105-1112,1998" discloses a flutter meter for online prediction of flutter boundaries. The flutter instrument is based on a robust flutter analysis method, integrates the functions of signal acquisition and processing, model confirmation, robust flutter margin calculation and the like, and is a highly integrated flutter test flight test tool. The flutter meter firstly needs to establish a nominal model of a system by utilizing theoretical aeroelasticity data, then establishes an uncertainty model, estimates the uncertainty of the model by combining test flight data, and finally carries out robust stability analysis to obtain a robust flutter critical point. The method combines a theoretical model and test data, and can effectively reduce flutter test flight risk and consumption on the basis of meeting the prediction accuracy. However, the model confirmation process of the flutter meter has large influence on robust flutter analysis, the prediction result has certain conservatism, and the predicted flutter critical speed is generally lower than that of the actual flutter critical speed.
Disclosure of Invention
The invention provides an aircraft flutter prediction system, aiming at overcoming the defect of poor prediction accuracy of the existing flutter prediction system. The system comprises an excitation signal generator, an excitation executing mechanism, a signal acquisition module and a data processing and flutter prediction module. The excitation signal generator generates an excitation signal based on natural frequency information obtained by a ground mode test. The excitation signal is input into the steering engine, the steering engine drives the control surface to deflect according to a preset instruction through the booster to generate aerodynamic force to form excitation, and according to the difference of the input signal, the control surface deflects at different rates to generate different external excitation force, so that the test part is fully excited. The signal acquisition module is used for acquiring acceleration response signals and external excitation force response signals. When the wing vibrates at a subcritical flutter flight test point, an acceleration/displacement response signal is collected by an acceleration sensor and transmitted to a storage element and displayed on a screen, and the change of the wing vibration is tracked in real time; the external excitation force response signal is collected by the force sensor and transmitted to the storage element and displayed on a screen, so that the excitation signal can be adjusted to obtain more proper external excitation. The data processing and flutter prediction module can predict the flutter critical point under the test Mach number only by one subcritical speed test point under each Mach number, and the prediction precision can meet the required requirement when the speed test point is far away from the flutter critical point, so that the flutter test flight safety can be improved, the test flight consumption and period can be reduced, the conservatism of the prediction result can be reduced, and the prediction precision of the flutter prediction system can be improved.
The invention also provides a flutter prediction method based on the aircraft flutter prediction system.
The technical scheme adopted by the invention for solving the technical problems is as follows: the aircraft flutter prediction system is characterized by comprising an excitation signal generator, an excitation execution mechanism, a signal acquisition module and a data processing and flutter prediction module. The excitation signal generator generates an excitation signal based on natural frequency information obtained by a ground mode test. The excitation actuator utilizes a steering engine 2, a booster 3 and a control surface 5 in an electric/electronic-hydraulic autopilot of the airplane. The excitation signal is input into the steering engine 2, the steering engine 2 drives the control surface 5 to deflect according to a preset instruction through the booster 3 to generate aerodynamic force to form excitation, and according to the difference of the input signal, the control surface 5 deflects at different rates to generate different external excitation force, so that a test part is fully excited. The signal acquisition module is used for acquiring acceleration response signals and external excitation force response signals. When the wing 1 vibrates at a subcritical flutter flight test point, an acceleration/displacement response signal is collected by the acceleration sensor 6 and transmitted to the storage element and displayed on a screen, and the change of the vibration of the wing 1 is tracked in real time; the external excitation force response signal is collected by the force sensor 4 and transmitted to the storage element and displayed on the screen, so that the excitation signal can be adjusted to obtain more proper external excitation.
A flutter prediction method based on the aircraft flutter prediction system is characterized by comprising the following steps:
reading generalized mass matrix M, rigidity matrix K, and transformation matrix phi of physical coordinate and modal coordinate obtained by ground modal test1Conversion matrix phi of physical force and modal force2. Flight Mach number Ma, incoming flow density ru, incoming flow velocity v, incoming flow dynamic pressure Q of flutter test flight state, and monitored physical acceleration responseExternal force response Fb *；
Responding to monitored physical acceleration by using band-pass filtering or wavelet de-noising methodAnd external force response Fb *Carrying out denoising treatment;
according to the relationship between the physical coordinates and the generalized coordinates:
Fb＝Φ2Fb * (2)
solving modal acceleration responseAnd the external force response F in generalized coordinatesb；
Solving generalized displacement response xi by utilizing discrete frequency domain/frequency domain quadratic integral;
neglecting the structural damping, utilizing the structural equation of motion under the modal coordinates:
calculate Q.fa+FbSubtract FbObtaining a generalized aerodynamic coefficient response fa；
Establishing an arx model of multiple input and multiple output:
performing aerodynamic modeling, e (k) is random noise, xi is input signal, faFor output signals, na and nb are the delay orders of the output and input, respectively, and the process performs automatic optimization according to the requirement of identifying the error range. After the input and output parameters are cleared in a steady state, system identification is adopted to select a proper delay order to obtain A in the modeliAnd Bi；
Introducing a state vector xa(t)，xa(t)＝[fa(t-1),…,fa(t-na),ξ(t-1),…,ξ(t-nb+1)]TThe arx model is converted into a pneumatic equation of state:
wherein D isa＝[B0]，Ca＝[A1,A2 … Ana-1 Ana B1 B2 … Bnb-2 Bnb-1]，Ba＝[B0 0 0 … 0 I 0 0 … 0]，
Introducing a structural state variable xs，Converting the structural motion equation into a structural state equation:
wherein, Ds＝[I 0]；
coupling the pneumatic state equation and the structural state equation to obtain a stability analysis state equation of the aeroelastic system:
on the basis of dynamic pressure of a flight test, gradually increasing dynamic pressure q, and calculating a state matrix under different dynamic pressures <math>
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</math> When the real part of the eigenvalue of a certain order is changed from negative to positive for the first time, the flutter critical point is obtained. Or another test at another incoming flow pressure at Mach number to verify that obtained firstCritical point of flutter. And changing the flight Mach number, repeating the process, and determining the flight envelope determined by the flutter boundary.
Solving modal acceleration responseAnd selecting a method for acquiring the physical displacement response signal x of the test part and solving the physical displacement response signal x after coordinate conversion and secondary differentiation.
The invention has the beneficial effects that: the system comprises an excitation signal generator, an excitation executing mechanism, a signal acquisition module and a data processing and flutter prediction module. The excitation signal generator generates an excitation signal based on natural frequency information obtained by a ground mode test. The excitation signal is input into the steering engine, the steering engine drives the control surface to deflect according to a preset instruction through the booster to generate aerodynamic force to form excitation, and according to the difference of the input signal, the control surface deflects at different rates to generate different external excitation force, so that the test part is fully excited. The signal acquisition module is used for acquiring acceleration response signals and external excitation force response signals. When the wing vibrates at a subcritical flutter flight test point, an acceleration/displacement response signal is collected by an acceleration sensor and transmitted to a storage element and displayed on a screen, and the change of the wing vibration is tracked in real time; the external excitation force response signal is collected by the force sensor and transmitted to the storage element and displayed on a screen, so that the excitation signal can be adjusted to obtain more proper external excitation. The data processing and flutter prediction module can predict the flutter critical point under the test Mach number only by one subcritical speed test point under each Mach number, and the prediction precision can meet the required requirement when the speed test point is far away from the flutter critical point, so that the flutter test flight safety can be improved, the test flight consumption and period can be reduced, the conservatism of the prediction result can be reduced, and the prediction precision of the flutter prediction system can be improved.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a block diagram of an aircraft flutter prediction system of the present invention.
FIG. 2 is an excitation module implementation strategy of the aircraft flutter prediction system and method of the present invention.
FIG. 3 is a signal acquisition module implementation of the aircraft flutter prediction system and method of the present invention.
FIG. 4 is a flow chart of the data analysis and flutter prediction module operation of the aircraft flutter prediction system and method of the present invention.
FIG. 5 is a numerical simulation noisy physical displacement signal for the aircraft flutter prediction system and method of the present invention.
FIG. 6 is a signal after denoising of numerical simulation physical displacement for the system and method for predicting aircraft flutter of the present invention.
FIG. 7 is a generalized displacement signal obtained by numerical simulation calculations of the aircraft flutter prediction system and method of the present invention.
FIG. 6 is a generalized acceleration signal obtained by numerical simulation calculation of the aircraft flutter prediction system and method of the present invention
FIG. 9 is a numerical simulation external force sampling signal of the aircraft flutter prediction system and method of the present invention.
FIG. 10 shows the external excitation force in the numerical simulation modal coordinates of the system and method for predicting aircraft flutter according to the present invention.
FIG. 11 is a numerical simulation calculated modal aerodynamic coefficient for the aircraft flutter prediction system and method of the present invention.
FIG. 12 is a diagram of the numerical simulation system identification results of the aircraft flutter prediction system and method of the present invention.
FIG. 13 is a numerical simulation root trace diagram of the aircraft flutter prediction system and method of the present invention.
FIG. 14 is a numerical simulated velocity-damping diagram of the aircraft flutter prediction system and method of the present invention.
Fig. 15 is a flowchart of a method for predicting flutter based on a vibrator according to the related art.
In the figure, 1-wing, 2-steering engine, 3-booster, 4-force sensor, 5-control surface and 6-acceleration sensor.
Detailed Description
The following examples refer to fig. 1-14.
The aircraft flutter prediction system comprises an excitation signal generator, an excitation execution mechanism, a signal acquisition module and a data processing and flutter prediction module.
The excitation module consists of an excitation signal generator and an excitation actuating mechanism. The excitation signal generator generates an excitation signal based on natural frequency information obtained by a ground mode test. The control plane 5 deflects at different rates to generate different external exciting forces according to different input signals, so that a test part is fully excited. The excitation mode utilizes the inherent mechanism and equipment on the airplane, and does not add extra equipment to the wing 1, so the inherent mode of the wing 1 structure is not influenced, the excitation mode is simple, convenient and easy to operate, and meanwhile, enough energy and frequency band range can be provided, the main mode of the test structure can be fully excited, and high-quality response data can be obtained.
The signal acquisition module is used for acquiring acceleration response signals and external excitation force response signals. When the wing 1 vibrates at a subcritical flutter flight test point, an acceleration/displacement response signal is collected by the acceleration sensor 6 and transmitted to the storage element and displayed on a screen, and the change of the vibration of the wing 1 is tracked in real time; the external excitation force response signal is collected by the force sensor 4 and transmitted to the storage element and displayed on the screen, so that the excitation signal can be adjusted to obtain more proper external excitation.
The data processing and flutter predicting module consists of a microcomputer and a data processing and flutter predicting program. Acceleration/displacement signals and external excitation signals obtained by sampling are combined with generalized stiffness information obtained by a ground modal test, and flutter critical characteristics under the test Mach number can be obtained by a flutter prediction program after data processing and calculation. The calculation method and the implementation process of the data processing and flutter prediction module comprise the following steps:
reading generalized mass matrix M, rigidity matrix K, and transformation matrix phi of physical coordinate and modal coordinate obtained by ground modal test1Conversion matrix phi of physical force and modal force2. Flight Mach number Ma, incoming flow density ru, incoming flow velocity v, incoming flow dynamic pressure Q of flutter test flight state, and monitored physical acceleration responseExternal force response Fb *；
Acquired by band-pass filtering or wavelet de-noisingAnd Fb *Carrying out denoising treatment;
according to the relationship between the physical coordinates and the generalized coordinates:
Fb＝Φ2Fb * (2)
solving modal acceleration responseAnd the external force response F in generalized coordinatesb；
Solving generalized displacement response xi by utilizing discrete frequency domain/frequency domain quadratic integral;
in order to improve the applicability of the flutter prediction system, according to the test environment and conditions, a physical displacement response signal x of a test component can be selected and collected, and after coordinate conversion and secondary differentiation, modal acceleration response is solvedThe subsequent steps are the same.
Neglecting the structural damping, utilizing the structural equation of motion under the modal coordinates:
calculate Q.fa+FbSubtract FbObtaining a generalized aerodynamic coefficient response fa；
Establishing an arx model of multiple input and multiple output:
performing aerodynamic modeling, e (k) is random noise, xi is input signal, faFor output signals, na and nb are the delay orders of the output and input, respectively, and the process performs automatic optimization according to the requirement of identifying the error range. After the input and output parameters are cleared in a steady state, system identification is adopted, a proper delay order is selected, and A in the model can be obtainediAnd Bi；
Introducing a state vector xa(t)，xa(t)＝[fa(t-1),…,fa(t-na),ξ(t-1),…,ξ(t-nb+1)]TThe arx model is converted into a pneumatic equation of state:
wherein: da＝[B0]，Ca＝[A1,A2 … Ana-1 Ana B1 B2 … Bnb-2 Bnb-1]，Ba＝[B0 0 0 … 0 I 0 0 … 0]，
Introducing a structural state variable xs，Converting the structural motion equation into a structural state equation:
wherein Ds＝[I 0]；
Coupling the pneumatic state equation and the structural state equation to obtain a stability analysis state equation of the aeroelastic system:
on the basis of dynamic pressure of a flight test, gradually increasing dynamic pressure q, and calculating a state matrix under different dynamic pressures <math>
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</math> Of the eigenvalue of a certain orderThe real part is changed from negative to positive for the first time, and then the flutter critical point can be obtained. Another test at another incoming flow pressure at this mach number was also performed to verify the first obtained critical point for flutter. And changing the flight Mach number, and repeating the process to determine the flight envelope determined by the flutter boundary.
The invention relates to a data analysis and flutter prediction module numerical simulation process of an aircraft flutter prediction system and method.
In the numerical simulation calculation example, the first three-order mode of the wing 1 is the main mode, and the flutter critical speed under the test Mach number is about 194 m/s. In the simulation process, acquiring physical displacement response signals with noise at 12 points on the wing 1, filtering and converting the physical displacement response signals into a modal space to obtain modal displacement response signals, and obtaining modal acceleration response signals through secondary differentiation; acquiring an external excitation force signal received by the wing 1, and filtering and converting coordinates to obtain an external excitation force of a modal space; combining generalized mass and generalized stiffness matrix obtained by a ground modal test, solving a resultant external force at the right end of an equation by using a structural motion equation, subtracting an external excitation of a modal space and dividing the external excitation by an incoming flow pressure to obtain a modal aerodynamic coefficient; establishing a discrete input and output differential model, taking modal displacement as an input signal and a modal aerodynamic coefficient as an output signal, and performing system identification to establish an aerodynamic model; coupling the aerodynamic model and the structural model to obtain a reduced-order aeroelasticity stability analysis state matrix, gradually increasing incoming flow pressure on the basis of testing the dynamic pressure, solving a characteristic value of the state matrix to obtain a root locus diagram of the system, wherein a point where the characteristic value passes through a virtual axis for the first time is a flutter critical point; the root locus diagram is converted into a v-g diagram, namely a speed-damping diagram, so that the flutter critical speed under the Mach number can be more conveniently and visually seen.
The model in the numerical simulation calculation example has a flutter critical speed of 193m/s at the test Mach number. The incoming flow speed in the simulation calculation example is 150m/s, the predicted flutter critical speed under the Mach number is 190m/s, namely the flutter critical speed under the Mach number can be accurately predicted by utilizing a subcritical test point far away from the flutter critical speed, and the flutter prediction technology has the characteristics of safety, high efficiency and accuracy requirement satisfaction.
Claims (3)
1. An aircraft flutter prediction system characterized by: the device comprises an excitation signal generator, an excitation executing mechanism, a signal acquisition module and a data processing and flutter prediction module; the excitation signal generator generates an excitation signal on the basis of natural frequency information obtained by a ground mode test; the excitation actuating mechanism utilizes a steering engine (2), a booster (3) and a control surface (5) in an electric/electronic-hydraulic automatic pilot of the airplane; an excitation signal is input into the steering engine (2), the steering engine (2) drives the control surface (5) to deflect according to a preset instruction through the booster (3) to generate aerodynamic force to form excitation, and the control surface (5) deflects at different rates to generate different external excitation force according to different input signals, so that a test part is fully excited; the signal acquisition module is used for acquiring acceleration response signals and external stress response signals; when the wing (1) vibrates at a subcritical flutter test flying point, an acceleration/displacement response signal is collected by an acceleration sensor (6) and transmitted to a storage element and displayed on a screen, and the change of the vibration of the wing (1) is tracked in real time; the external excitation force response signal is collected by the force sensor (4) and transmitted to the storage element and displayed on a screen, so that the excitation signal can be adjusted to obtain more proper external excitation.
2. A flutter prediction method using the aircraft flutter prediction system according to claim 1, characterized by comprising the steps of:
reading generalized mass matrix M, rigidity matrix K, and transformation matrix phi of physical coordinate and modal coordinate obtained by ground modal test1Conversion matrix phi of physical force and modal force2(ii) a Flight Mach number Ma, incoming flow density ru, incoming flow velocity v, incoming flow dynamic pressure Q of flutter test flight state, and monitored physical acceleration responseExternal force response Fb *；
Responding to monitored physical acceleration by using band-pass filtering or wavelet de-noising methodAnd external force response Fb *Carrying out denoising treatment;
according to the relationship between the physical coordinates and the generalized coordinates:
solving modal acceleration responseAnd the external force response F in generalized coordinatesb；
Solving generalized displacement response xi by utilizing discrete frequency domain/frequency domain quadratic integral;
neglecting the structural damping, utilizing the structural equation of motion under the modal coordinates:
calculate Q.fa+FbSubtract FbObtaining a generalized aerodynamic coefficient response fa；
Establishing an arx model of multiple input and multiple output:
Introducing a state vector xa(t)，xa(t)＝[fa(t-1),…,fa(t-na),ξ(t-1),…,ξ(t-nb+1)]TThe arx model is converted into a pneumatic equation of state:
wherein D isa＝[B0]，Ca＝[A1,A2 … Ana-1Ana B1B2 … Bnb-2Bnb-1]，Ba＝[B0 0 0 … 0 I 0 0 … 0]，
Introducing a structural state variable xs，Converting the structural motion equation into a structural state equation:
coupling the pneumatic state equation and the structural state equation to obtain a stability analysis state equation of the aeroelastic system:
on the basis of dynamic pressure of a flight test, gradually increasing dynamic pressure q, and calculating a state matrix under different dynamic pressures <math>
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</math> When the real part of the eigenvalue of a certain order is changed from negative to positive for the first time, the flutter critical point is obtained; or another test is carried out under another incoming flow pressure under the Mach number to verify the first obtained flutter critical point; and changing the flight Mach number, repeating the process, and determining the flight envelope determined by the flutter boundary.
3. The method of claim 2, wherein: solving modal acceleration responseAnd selecting a method for acquiring the physical displacement response signal x of the test part and solving the physical displacement response signal x after coordinate conversion and secondary differentiation.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105083584A (en) * | 2015-06-23 | 2015-11-25 | 中国航空工业集团公司西安飞机设计研究所 | Flutter model of plane missile system |
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| CN105843073A (en) * | 2016-03-23 | 2016-08-10 | 北京航空航天大学 | Method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction |
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| CN110929336A (en) * | 2019-11-22 | 2020-03-27 | 扬州大学 | Method for solving linear flutter speed of three-dimensional wing based on multi-body system transfer matrix method |
| CN110929336B (en) * | 2019-11-22 | 2023-04-28 | 扬州大学 | Method for solving linear flutter speed of three-dimensional wing based on multi-body system transfer matrix method |
| CN110949689B (en) * | 2019-12-06 | 2022-07-22 | 江西洪都航空工业集团有限责任公司 | Method for measuring full-motion horizontal tail rotation mode of airplane |
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