Flight Flutter Test Data Research Articles

Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.

Identification of reduced-order model for an aeroelastic system from flutter test data

Recently, flutter active control using linear parameter varying (LPV) framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI) models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF) algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML) estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.

Consistent multi-input modal parameter estimators in the frequency domain

In this paper, consistent multiple-input frequency-domain estimators will be presented based on a right matrix-fraction description of the frequency response function. This matrix-fraction description leads to a fast algorithm in the same way as the least-squares complex frequency-domain estimator (LSCF). The use of multiple inputs simultaneously in the estimation (so-called polyreference estimation) has the advantage that it allows to separate closely spaced modes.The main drawback of the LSCF estimator is that it is theoretically inconsistent, i.e.the estimates do not converge to the true values when the number of measurements increases to infinity. However, the noise information available from most measurements can be used to construct a maximum likelihood-like weighting for the LSCF estimators, giving consistent estimates. The results are fast, polyreference, and consistent weighted generalised total-least-squares (WGTLS) estimators. The iterative quadratic maximum likelihood (IQML) estimator is practically consistent for high signal-to-noise ratios, with the additional advantage that it yields clear stabilisation charts.The performance of the presented WGTLS and IQML estimators is evaluated by means of ground vibration test data and demonstrated on flight flutter test data.

Statistical evaluation of flutter boundaries from flight flutter test data

AbstractA methodology is described that determines the statistical confidence bounds on the results from flight flutter tests: modal parameter estimates, flutter margin values and flutter speed estimates, without the need for Monte-Carlo simulation. The approach is based on least squares statistics and eigenvalue perturbation theory applied to the various stages of the analysis process, starting with frequency and damping estimation through to the flutter margin calculations. The technique is demonstrated upon a number of data sets from aeroelastic simulations of flight flutter tests.

Flight Flutter Modal Parameters Identification with Atmospheric Turbulence Excitation Based on Wavelet Transformation

In view of the feature of flight flutter test data with atmospheric turbulence excitation, a method which combines wavelet transformation with random decrement technique for identifying flight flutter modal parameters is presented. This approach firstly uses random decrement technique to gain free decays corresponding to the acceleration response of the structure to some non-zero initial conditions. Then the continuous Morlet wavelet transformation of the free decays is performed; and the Parseval formula and residue theorem are used to simplify the transformation. The maximal wavelet transformation coefficients in different scales are searched out by means of band-filtering characteristic of Morlet wavelet, and then the modal parameters are identified according to the relationships with maximal modulus and angle of the wavelet transform. In addition, the condition of modal uncoupling is discussed according to variation trend of flight flutter modal parameters in the flight flutter state. The analysis results of simulation and flight flutter test data show that this approach is not only simple, effective and feasible, but also having good noise immunity.

Identification of structural surfaces' positions on an F/A-18 using the subspace identification method from flight flutter tests

In the current paper, a linear state-space mathematical model, identified from flight flutter tests is presented, to simulate the aeroelastic deflections of specific structural parts of the NASA F/A-18 Active Aeroelastic Wing research aircraft. The flight flutter tests were performed in steady-level flight with Schroeder frequency excitation induced on the aircraft ailerons by an on-board excitation system activated by the pilot. The results of the flight flutter tests were used to generate an aeroelastic model in which the deflections of the specific aircraft surfaces are functions of the control inputs combined with the deflections of other aircraft surfaces. The F/A-18 linear model is conceived as nine third-order multiple input-single output (MISO) models. Each model has nine inputs and one output. The nine inputs are the differential ailerons deflection and the deflections of all the other parts of the aircraft. The output of each model is the structural deflection of a given aircraft structure. The model's parameters are estimated with the subspace system identification algorithm, an efficient non-iterative algorithm that computes the system matrices directly from the input and output data. The model's quality is evaluated by calculating the fit and correlation coefficients between the model's outputs and the outputs from flight flutter test data. Although the fit coefficient results are very good - between 89 and 99 per cent - the correlation coefficient method gave the best results (nearly 100 per cent). Finally, resampled inputs were used to validate the F/A-18 model's robustness. The model's aircraft structure was validated for flutter flight tests at different Mach numbers and altitudes. The estimated linear model fits the flight flutter test data very well. The subspace method is therefore very convernvenient for model identification from flight flutter tests.

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Identification of Nonlinear Aeroelastic Systems Based on the Volterra Theory: Progress and Opportunities

The identification of nonlinear aeroelastic systems based on the Volterra theory of nonlinear systems is presented. Recent applications of the theory to problems in computational and experimental aeroelasticity are reviewed. Computational results include the development of computationally efficient reduced-order models (ROMs) using an Euler/Navier–Stokes flow solver and the analytical derivation of Volterra kernels for a nonlinear aeroelastic system. Experimental results include the identification of aerodynamic impulse responses, the application of higher-order spectra (HOS) to wind-tunnel flutter data, and the identification of nonlinear aeroelastic phenomena from flight flutter test data of the active aeroelastic wing (AAW) aircraft.

Wavelet Approach to Flutter Data Analysis

A wavelet-based method is presented to analyze flight flutter test data. The procedure utilizes the filter action of the continuous wavelet transform to enable damping estimation. An indication of the overall stability is obtained from changes in the shape of the wavelet transform amplitude cross sections. The method is illustrated using simple simulated examples and real flight-test data. A good agreement was achieved compared to classical parameter estimation techniques.

Flutter Prediction from Flight Flutter Test Data

The most common approach to flight flutter testing is to track estimated modal damping ratios of an aircraft over a number of flight conditions. These damping trends are then extrapolated to predict whether it is safe to move to the next test point and also to determine the flutter speed. In the quest for more reliable and efficient flight flutter testing procedures, a number of alternative data analysis methods have been proposed. Five of these approaches are compared on two simulated aeroelastic models. The comparison is based on both the accuracy of prediction and the efficiency of each method. It is found that, for simple aeroelastic systems, the Nissim and Gilyard method (Nissim, E., and Gilyard, G. B., Method for Experimental Determination of Flutter Speed by Parameter Identification, AIAA Paper 89-1324, 1989) yields the best flutter predictions and is also the least computationally expensive approach. However, for larger systems, simpler approaches such as the damping fit and envelope function methods are found to be most reliable.

Total least-squares criteria in parameter identification for flight flutter testing

An investigation of parameter identification of time series models for linear dynamic structural systems using the least squares and total least-squares criteria is presented. Excitation and response time-domain data are used to parameterize autoregressive, moving average models. The method, or criteria, which is used to solve a set of overdetermine d, linear algebraic equations developed from the time-domain data affects the solution. A commonly used criteria, least squares, introduces the possibility of significant bias error hi the system parameters and leads to bias errors in the modal parameter estimates when measurement errors are present. An alternative criteria, total least squares, provides an approach that appears to significantly reduce the bias error in the parameter estimates. These methods are applied to a simple, simulated system and then to flight flutter test data with particular emphasis on accurate modal damping estimates. [A] [A'] [4'.>'

The Analysis of Transient Vibration Data

A new proof is given of a method whereby records of the transient vibrations of linear systems can be analysed to give the damping factor and frequency of each mode of vibration associated with the motion. The method is applicable to acceleration, velocity or displacement records, and as the amplitude of each mode can be found, by using a suitable number of pickups, the normal modes of continuous systems may also be determined. Obvious applications lie in the analysis of flight flutter test data, and of the transient responses of servomechanisms.