Evaluation of Subspace Identification Techniques for the Analysis of Flight Test Data (original) (raw)
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Mechanical Systems and Signal Processing, 1990
The basic formula of a parametric maximum likelihood estimator (MLE) in the frequency domain for multi-input, multi-output (MIMO) systems is given. The proposed method takes into account the perturbation noise on all the measured input and output signals. It is an extension to MIMO systems of the single-input, single-output (SISO) estimation method called ELiS (Estimation of Linear Systems). The MIMO estimator will be shown to determine more accurately the natural frequencies and damping ratios of mechanical structures than the SISO estimator. The estimators are applied to very noisy flight flutter data. t Supported by the National Fund for Scientific Research (Belgium). 405 0888-3270/90/050405 + 12 %03.00/O
Modal parameter estimation and monitoring for on-line flight flutter analysis
Mechanical Systems and Signal Processing, 2004
The clearance of the flight envelope of a new airplane by means of flight flutter testing is time consuming and expensive. Most common approach is to track the modal damping ratios during a number of flight conditions, and hence the accuracy of the damping estimates plays a crucial role. However, aircraft manufacturers desire to decrease the flight flutter testing time for practical, safety and economical reasons by evolving from discrete flight test points to a more continuous flight test pattern. Therefore, this paper presents an approach that provides modal parameter estimation and monitoring for an aircraft with a slowly time-varying structural behaviour that will be observed during a faster and more continuous exploration of the flight envelope. The proposed identification approach estimates the modal parameters directly from input/output Fourier data. This avoids the need for an averaging-based pre-processing of the data, which becomes inapplicable in the case that only short data records are measured. Instead of using a Hanning window to reduce effects of leakage, these transient effects are modelled simultaneously with the dynamical behaviour of the airplane. The method is validated for the monitoring of the system poles during flight flutter testing. r
Mechanical Systems and Signal Processing, 2014
In this article a different approach to wind tunnel flutter testing is presented. This procedure can now be performed as one continuous test, resulting in a major time saving. Both analysis of the current behaviour of the structure, and prediction towards higher velocities, are important for flight flutter testing, and are dealt with in this article. The recently developed time-varying weighted non-linear least-squares estimator (TV-WNLS) [1] is applied to the aeroelastic flutter problem. Smooth variation of the transfer function coefficients is forced through the TV-WNLS estimator, and the obtained polynomials are used as basis for predicting the damping ratio towards higher velocities. Selection of the model order is based on linear variation of the airspeed and the evaluation of Theodorsen's unsteady aerodynamics for the frozen time-varying aeroelastic system at a certain constant velocity. Therefore, providing a physical justification for the extrapolation of the damping ratio towards higher velocities. The method is applied to wind-tunnel measurements on a cantilevered wing. It is shown that the proposed method outperforms flutter speed prediction by classic damping ratio extrapolation and a non-parametric analysis of the time-varying signal.
AIAA Atmospheric Flight Mechanics Conference and Exhibit, 2007
The aim of this paper is to determine the mathematical relationship (model) between control deflections and structural deflections of the F/A-18 modified aircraft in the Active Aeroelastic Wing AAW technology program. Five sets of signals from flight flutter tests corresponding to the excited sources were provided by NASA DFRC (Dryden Flight Research Center). These excitations are: differential ailerons, collective ailerons, collective stabilizers, differential stabilizers, and rudders. The signals are of 2 types: control deflections time history data and the corresponding structured deflections. We choose to use the subspace identification method in order to identify the MIMO (Multi Input, Multi Output) system. Nonlinear inputs are used to fit the outputs signals. We apply this method for sixteen flight conditions where Mach number varies from 0.85 to 1.30 and altitudes from 5,000 ft to 25,000 ft. We obtain good results with a fit between the estimated and the measured signals and a correlation factor higher than 90%.
Operational Modal Analysis for Simulated Flight Flutter Test of an Unconventional Aircraft
2019
Abstract: The framework of this research paper concerns a phenomenon called ”flutter” which is a well-known dynamic aeroelastic instability caused by an interaction between structural vibrations and unsteady aerodynamic forces, whereby the level of vibration may trigger large amplitudes, eventually leading to catastrophic failure of the aircraft within a couple of seconds. Flutter prediction and flutter clearance are major issues in the design, development and certification process of an aircraft. Hence, it is mandatory for certification to guarantee that the aircraft is free from flutter throughout the entire flight envelope. Within the framework of the FLEXOP (Flutter Free FLight Envelope eXpansion for ecOnomic Performance improvement) project, an output-only operational modal parameter estimation algorithm in frequency domain has been implemented for monitoring the evolution of the aeroelastic modes for the nearly real-time surveillance of flutter. Therefore an integrated aeroela...
New time-domain technique for flutter boundary identification
Dynamics Specialists Conference, 1992
A new methodology for flutter boundary identification in the time domain is presented. This technique is based on a single-input single-output deterministic ARMA model and an on-line parameter estimation procedure. It is capable of simultaneous identification of the aeroelastic modal parameters as well as the static offset term which represents the static deformation or state of the aeroelastic system. The capabilities of the method are illustrated by applying it to several examples, such as: damped free oscillations, a two degree of freedom NACA 64A010 airfoil in transonic flight, and a cantilevered rectangular wing in subsonic flow. Numerical implementations of the new methodology developed in this study demonstrates that it is a cost effective time-domain technique for flutter boundary identification. Nomenclature Denotes value at continuous time t. Denotes { x) at discrete time k. Nondimensional elastic axis location, measured from midchord AR coefficient Airfoil semichord (c 12) MA coefficient Estimated static aeroelastic deflection Estimated static aeroelastic deflection due to a d RMS estimation error for C due to random noise Young's modulus The i-th undamped natural frequency (= oi12n) The i-th damped natural frequency (= 0di12~)
Unveiling modal parameters with forced response using SVD and QR during flutter flight testing
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2016
This article presents an algorithm for the identification of modal parameters during flutter flight testing when forced excitation is employed and the aircraft possesses several sensors for structural response acquisition. The main novelty of the method, when compared with other classical modal analysis methods, is that the analysis is carried out in intervals of time instead of in the whole duration of the excitation. It means that, even when the response signal is only partially available, some modal parameters may be still identified. Application to analytic signals as well as structural response of modern fighter aircraft using frequency-swept excitation is provided in order to demonstrate the effectiveness, robustness and noise immunity of the proposed method.
Recursive output-only subspace identification for in-flight flutter monitoring
2004
In this paper a new recursive output-only identification algorithm is proposed based on stochastic realization, a classical covariance driven subspace identification technique. The recursive algorithm yields results that are in close agreement with those of its non-recursive counterpart. Furthermore, the relatively low complexity of the algorithm makes it an excellent kandidate for use during in-flight flutter tests.
Identification of flutter parameters for a wing model
Journal of The Brazilian Society of Mechanical Sciences and Engineering, 2006
A flexible mounting system has been developed for flutter tests with rigid wings in wind tunnel. The two-degree-of-freedom flutter obtained with this experimental system can be described as the combination of structural bending and torsion vibration modes. Active control schemes for flutter suppression, using a trailing edge flap as actuator, can be tested using this experimental setup. Previously to the development of the control scheme, dynamic and aeroelastic characteristics of the system must be investigated. Experimental modal analysis is performed and modes shape and frequencies are determined. Then, wind tunnel tests are performed to characterize the flutter phenomenon, determining critical flutter speed and frequency. Frequency response functions are also obtained for the range of velocities below the critical one showing the evolution of pitch and plunge modes and the coupling tendency with increasing velocity. Pitch and plunge data obtained in the time domain during these tests are used to evaluate the ability of the Extended Eigensystem Realization Algorithm to identify flutter parameter with increasing velocity. The results of the identification process are demonstrated in terms of the evolution of frequency and damping of the modes involved in flutter.