Flight Flutter Research Articles

A data-driven approach for rapid detection of aeroelastic modes from flutter flight test based on limited sensor measurements

Flutter flight test involves the evaluation of the airframe’s aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor malfunction in one or more sensors make it time-consuming and corrupt the extraction process. In order to expedite the process of identifying and analyzing aeroelastic modes, this study implements a time-delay embedded Dynamic Mode Decomposition technique. This approach is complemented by Robust Principal Component Analysis methodology, and a sparsity promoting criterion which enables the automatic and optimal selection of sparse modes. The anonymized flutter flight test data, provided by the fifth author of this research paper, is utilized in this implementation. The methodology assumes no knowledge of the input excitation, only deals with the responses captured by accelerometer channels, and rapidly identifies the aeroelastic modes. By incorporating a compressed sensing algorithm, the methodology gains the ability to identify aeroelastic modes, even when the number of available sensors is limited. This augmentation greatly enhances the methodology’s robustness and effectiveness, making it an excellent choice for real-time implementation during flutter test campaigns.

Modal identification from turbulence response based on improved frequency domain decomposition

Turbulence excitation is an unavoidable form of excitation in flutter flight tests, and it is also a necessary and effective excitation method in high-speed flights, dives, other high-risk test, and high-frequency modal information mining. However, because the turbulence excitation signals are unmeasurable in the time domain, although the turbulence response contains rich and valuable flutter test information, the randomness and low quality of the data often cause difficulties in modal analysis. Therefore, an improved frequency domain decomposition algorithm for turbulence response processing is proposed in this paper. First, due to the irrelevancy of the atmospheric excitation, the power spectral density function matrix of the multi-channel turbulence response is subjected to singular value decomposition. There is a mathematical relationship between the maximum singular value curve and the system frequency response function. Second, the modal assurance criteria are used to calculate the maximum singular value of a single-degree-of-freedom system. Finally, an orthogonal polynomial method is applied to fit the maximum singular value curve, and the system identification is performed directly in the frequency domain. The simulated data and a certain type of aircraft flutter flight test data are used to verify the proposed method, and the results confirm the effectiveness and engineering applicability of the method developed in this work.

Application of a Modal Parameter Identification Method Based on Variational Mode Decomposition in Flight Flutter Testing

Signals of flight flutter testing exhibit non-stationary characteristics, closely spaced modes, and low signal-to-noise ratio, presenting challenges in data processing. In recent years, the variational mode decomposition (VMD) method has emerged as a promising approach to mitigate mode mixing and exhibit robust noise resistance. Therefore, a novel time-frequency domain modal parameter identification method based on VMD is proposed to process impulse response signals in flight flutter testing. The modal frequency and damping ratio are determined through a three-step process: VMD analysis, Hilbert transform, and least square fitting. The efficacy of the proposed method in identifying closely spaced modes and resisting noise is validated through a numerical example. Furthermore, this method is applied to analyze two types of pulse excitation signals in actual flight flutter testing: one induced by the pilot’s shaking stick and the other induced by small rocket excitation. The obtained modal parameters are compared with those from ground vibration tests and specialized software, respectively, to showcase the effectiveness and superiority of the proposed method.

Research on Simulation Technology of Excitation Response of Aircraft Structural Based on Finite Element Model in Flutter Flight Test

To simulate excitation response of the aircraft structural of flutter test aircraft under different excitation methods. A simulation method for excitation response of flutter flight tests based on finite element models has been proposed. This method is based on the finite element model of the aircraft structure. Establish aerodynamic and excitation force models for flutter flight tests. Obtain a simulation model for the excitation response of aircraft flutter flight tests. Solve the model and simulate various excitation methods for flutter flight tests. Firstly, a detailed introduction was given to the excitation modeling method for aircraft flutter flight tests, including two excitation models: external excitation and control surface excitation. Then, the finite element model of the structural dynamics of a single wing with ailerons is taken as the research object. Establish a single wing flutter flight test excitation response model. Implemented simulation of control surface sweep excitation, control surface pulse excitation, and small rocket excitation. The feasibility of this method has been verified. Finally, based on the finite element model of the entire aircraft, a flutter flight test excitation response simulation model was established. We have conducted analysis and research on sensor position optimization, excitation method optimization, excitation response amplitude prediction, and flutter boundary prediction. And the simulation optimization analysis results were compared with the flight test results. Analyzed the promoting effect of flutter flight test excitation response simulation on flight tests.

Modal parameter estimation of turbulence response based on self-attention generative model

Modal parameter estimation of turbulence response is an important aspect of flutter test data processing. Using the modal parameter estimation results of turbulence response and the damping extrapolation curve, the flutter boundary of aircrafts can be predicted. However, owing to the randomness of atmospheric turbulence excitation, modal parameter estimation of the turbulence response has a challenge. This study analyses the turbulence response and calculates the corresponding impulse response using the seq-to-seq self-attention generative network. The encoder performs feature compression of the turbulence response, whereas the multi-head self-attention structure in the middle layer extracts the time series features. Up-sampling is performed based on the decoder to obtain the impulse response, and modal parameter estimation of the turbulence response is achieved. The self-attention generative model is validated using simulation data and the turbulence response is obtained by the wind tunnel test and the flutter flight test. The results demonstrate the feasibility and engineering applicability of the seq-to-seq generative model.

Flutter Qualification of a Composite Light Trainer Aircraft (Csir-Nal Hansa - 3 (Vt-Hoa))

This paper presents the aeroelastic analysis and flight flutter tests aimed at ascertaining the freedom from flutter instability in the design flight envelope of a composite light trainer aircraft. The critical flutter, divergence and control reversal velocities of the complete aircraft were computed using MSC/NASTRAN and the typical section method. The results thus obtained from the analyses have been correlated with the ground vibration and flight flutter test results. The dispersion in the damping and frequency results obtained from computations and the flight flutter tests were within acceptable limits showing a good correlation. The flutter margins and the total damping of the aircraft satisfy the JAR-VLA/ FAR 23 requirements, thus enabling the aircraft to be type certified for the required flight safety standards.

Ground Vibration Test Based Flutter Prediction for a Combat Aircraft

This paper compares the flutter characteristics of a combat aircraft predicted using experimental modal parameters from ground vibration tests with the results obtained by flight flutter tests. An equivalent finite element model representing the test points in ground vibration tests has been developed and further refined to obtain a finite element model with fine mesh encompassing the entire lifting surface areas. Infinite plate interpolation scheme has been used to extrapolate the eigenvectors for the refined model. A Direct Matrix Abstraction Program (DMAP) written in NASTRAN replaces the generated mass normalized eigenvalues and vectors of the refined model from a dynamic analysis in NASTRAN, with the experimentally obtained eigenvalues and extrapolated eigenvectors. Flutter analysis has been carried out with the replaced experimental modal parameters in subsonic, transonic and supersonic regimes using flutter modules in NASTRAN and ZAERO software. The results obtained using this procedure have been compared with the flight flutter test results. The dispersion in the damping and frequency results obtained from computations and the flight flutter tests were within acceptable limits showing a good correlation.

Model parameter estimations of the multi-channel turbulence response from flutter flight tests based on autoregressive spectra

Signal processing of flutter flight test data enables verification of aircraft flutter design, and the signal from a flutter flight test excited by atmospheric turbulence is a particularly important form of the flutter test. Owing to the randomness of atmospheric turbulence excitation, multi-channel analysis of turbulence responses at various positions in the same component can improve the analytical accuracy of flutter signal processing. The relationship between the maximum singular value of the multi-channel turbulence response power spectral density matrix and the system self-power spectral density function is elucidated herein using a frequency domain decomposition method. However, there is a contradiction in the power spectral density function between the spectral line density and the spectral line smoothing calculated based on the periodogram of the frequency domain decomposition. By applying an autoregressive spectral model, the power spectral density function of the turbulence response is calculated to achieve spectral line smoothing and sufficient spectral line density. Additionally, the power spectral density function is then used to construct the power spectral density function matrix of the multi-channel turbulence response, and the maximum singular value curve is calculated based on the singular value decomposition of each spectral pin. Finally, the modal parameters of the turbulence response signal are estimated via multi-modal frequency domain fitting. The developed approach is validated based on simulations and flutter flight test turbulence response signals.

Model Parameter Estimation Method on Multichannel Structural Response from Turbulence Flutter Test

Flutter design is important for the design of new aircraft types, for which flutter tests are an important verification measure. Atmospheric turbulence excitation is a common form of excitation in flutter flight tests. The modal parameter estimation of the turbulence response is a key aspect to ensure accurate data processing of flutter test results. Atmospheric turbulence excitation acts on the structural system, and the turbulence response thus simultaneously contains both the randomness of the excitation signal and the determinism of the structure system. In view of the turbulence response characteristics, this paper addresses the incoherence of atmospheric turbulence excitation and the orthogonality of the frequency domain from a multichannel response. The turbulence response is used to perform modal parameter identification in the frequency domain. The power spectral density matrix can be calculated from the multichannel turbulence response using the periodogram method. Singular value decomposition is then performed on the power spectral density matrix at each spectral pin based on the orthogonality of the frequency domain. The maximum singular value of each spectral pin forms a curve over the entire frequency band, which is the autopower spectral density function of the system, the system is directly identified at the frequency domain using the polynomial fitting in the frequency domain, and the modal parameters (frequency, damping ratio) are calculated according to the fitted transfer function. This paper verifies the theoretical feasibility of the proposed method using simulation data. The engineering applicability is verified based on the turbulence response from the flutter flight test of a certain aircraft type.

Generative model: Impulse response generated from turbulence response in flutter signal

Impulse response facilitates ideal modal parameter estimation in flutter tests. However, impulse excitation is costly, difficult, and risky to execute in flutter engineering tests, so it is difficult to obtain an effective impulse response for modal parameter estimation. Limitations inherent to the physical testing conditions make atmospheric turbulence a common excitation in flutter testing signal processing. Modal analysis of the structural response excited by turbulence from the flutter testing signal is crucial. Existing methods for analyzing structural responses excited by atmospheric turbulence have certain shortcomings. This paper proposes a novel impulse response generative model corresponding turbulence response based on a deep neural network. Frequency and damping ratio calculations become a modal parameter estimation problem for natural excitation relevant to the turbulence response. The turbulence response can be used to calculate the corresponding impulse response through the generative model. The modal parameters of the generated impulse response are estimated by the generative model via Matrix Pencil (MP) method. The results can then be compared with the true model parameters from simulation data. The generative model is shown to accurately generate an impulse response as evidenced by comparison against physical testing data. The impulse response can indeed be generated by the turbulence response based on the generative model. The estimated model parameters of the Stochastic Subspace Identification (SSI) are compared with the generative model via MP in terms of accuracy and computation time, which indicate better on-line monitoring/warning flutter flight test effects. Flutter boundary predictions based on physical testing further validate the engineering applicability of the proposed method.

Multimodal Estimation of Sine Dwell Vibrational Responses from Aeroelastic Flutter Flight Tests

Aircraft envelope expansion during new underwing stores installation is a challenging problem, mainly related to the aeroelastic flutter phenomenon. Aeroelastic models are usually very hard to model, and therefore flight tests are usually required to validate the aeroelastic model predictions, which given the catastrophic consequences of reaching the flutter point pose an important problem. This constraint favors using short time excitations like Sine Dwell to perform the flight tests, so that the aircraft stays close to the flutter point as little time as possible, but short time data implies a poor spectrum resolution and therefore leads to inaccurate and non repetitive results. The present paper will address the problem related to processing Sine Dwell signals from aeroelastic Flutter Flight Tests, characterized by very short data length (less than 5 s) and low frequency (less than 10 Hz) and used to identify the natural modes associated with the structure. In particular, a new robust technique, the PRESTO algorithm, will be presented and compared to a Matching Pursuit estimation based on Laplace Wavelet. Both techniques have demonstrated to be very accurate and robust procedures on very short time (Sine Dwell) signals, with the particularity that the Laplace Wavelet estimation has already been validated over F-18 real Flutter Flight Test data as described in different papers. However, the PRESTO algorithm improves the performance and accuracy of the Laplace Wavelet processing while keeping its robustness, both on real and simulated data.

A new automatic, very efficient method for the analysis of flight flutter testing data

This article presents a new data processing method, based on higher order dynamic mode decomposition, to analyze actual flight test experimental data. The new methodology (i) is fast and efficient, (ii) is automatic, (iii) provides a clean spectrum in terms of the frequencies and damping rates of the flutter modes, and (iv) reconstructs the spatial distribution of the modes over the aircraft surface. The method gives useful and interesting information for predicting flutter and is suitable to analyze flight test data in real time.

A comparative assessment of flutter prediction techniques

ABSTRACTTo establish flutter onset boundaries on the flight envelope, it is required to determine the flutter onset dynamic pressure. Proper selection of a flight flutter prediction technique is vital to flutter onset speed prediction. Several methods are available in literature, starting with those based on velocity damping, envelope functions, flutter margin, discrete-time Autoregressive Moving Average (ARMA) modelling, flutterometer and the Houbolt–Rainey algorithm. Each approach has its capabilities and limitations. To choose a robust and efficient flutter prediction technique from among the velocity damping, envelope function, Houbolt–Rainey, flutter margin and auto-regressive techniques, an example problem is chosen for their evaluation. Hence, in this paper, a three-degree-of-freedom model representing the aerodynamics, stiffness and inertia of a typical wing section is used(1). The aerodynamic, stiffness and inertia properties in the example problem are kept the same when each of the above techniques is used to predict the flutter speed of this aeroelastic system. This three-degree-of-freedom model is used to generate data at speeds before initiation of flutter, during flutter and after occurrence of flutter. Using these data, the above-mentioned flutter prediction methods are evaluated and the results are presented.

Wind-Tunnel Study of the Autoregressive Moving-Average Flutter Prediction Method

This paper presents an experimental study of flutter prediction via autoregressive moving-average (ARMA) system identification and the use of a linear stability parameter. System identification at subcritical (preflutter) conditions is based on measured structural responses to excitation by natural air turbulence. The current study investigates the application aspects of the methodology in a dedicated wind-tunnel experiment. An elastic wing was designed and manufactured using rapid prototyping and tested in a subsonic wind tunnel all the way to flutter. Structural responses were recorded by accelerometers, strain gauges, and fiber-optic sensors (measuring strains). The data were filtered, averaged, and used for system identification and flutter prediction. The study focuses on the prediction characteristics and accuracy, method applicability with various dynamic data, and signal processing techniques. It was shown that both accelerations and strains provide good data for system identification and accurate flutter prediction, even when sparse and short records are used. Averaging significantly improves the flutter onset prediction and its confidence bounds. The study results support the feasibility of using the ARMA method for flutter flight test.

Minimization of Ground Vibration Test Configurations for F‐16 Aircraft by Subtractive Modification

The certification process of external loads designed for aircraft needs to satisfy various criteria where compatibility with existing systems is one of the essential requirements. Flight flutter testing is a critical part of a certification process that requires many preliminary studies. Computational flutter analysis must precede actual flutter test to determine an approximately safe flight envelope to ensure the safety of the personnel and aircraft. To be able to perform flutter analysis of an aircraft, an accurate structural model such as finite element (FE) model is required. An accurate FE model can be obtained from a coarse model using ground vibration test (GVT) which is also the primary test campaign for certification of a new external load, new aircraft design, or modification on existing aircraft. On the other hand, performing GVT for each configuration of an aircraft is both time consuming and costly. It would be more practical to determine the critical configurations for an aircraft using computational tools and perform actual GVT for those configurations. The objective of this study is to simulate GVT characteristics for downloading and fuel configurations of F‐16 aircraft. A novel methodology is proposed where various loading configurations can be simulated by subtractive modification from loaded GVT data so that joint stiffnesses between stores and aircraft need not be identified. The proposed technique decreases the number of necessary physical GVT testing campaigns.

Novel Nonstationarity Assessment Method for Hypersonic Flutter Flight Tests

Hypersonic aircraft have been rapidly developed in recent years both theoretically and experimentally. Aerothermoelastic simulation is very challenging due to its inherent complexity, but physical tests are a workable approach. Flutter tests with variable speed are a popular alternative to hypersonic tests which provide nonstationary structural response data. This paper proposes a nonstationarity assessment method based on energy distribution in the time-frequency domain. The proposed method reveals the nonstationarity level corresponding to the appropriate modal identification algorithm or flutter boundary prediction (FBP) method. Several classic flutter criteria are utilized to build a hypersonic aircraft FBP framework. Numerical simulation and experimental applications demonstrate the effectiveness and feasibility of the proposed method, which facilitates accurate flutter predictions for the subcritical turbulence response during hypersonic flutter flight.

New Robust Method to Study Flight Flutter Testing

This paper presents the application of a new method, called higher-order dynamic mode decomposition (HODMD), to extract aircraft frequencies and damping in experimental data obtained from a flight flutter test. The method is an extension of standard dynamic mode decomposition, which is a method typically used to extract flow patterns and frequencies from unsteady fluid dynamics measurements or numerical simulations. In the fluid dynamic field, HODMD has proven to be a very efficient, robust, and accurate method to extract modes, frequencies, and damping from very noisy and large signals, with reduced manual interaction. In a similar way, this paper shows the good performance of this method when it is applied to the noisy data of this complex experiment. This fact sheds light on the new possibilities for the aerospace industry to incorporate HODMD to perform robust analyses.

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.

Recent progress of immersed boundary method and its applications in compressible fluid flow

Fluid-structure interaction problems are ubiquitous in nature and engineering applications. Typical examples include leaves waving, flag flapping, insect flight and tail flutter of high speed aircrafts. Accompanied with the development of high performance computers, numerical simulation has been applied to more and more areas and taken as an effective method for fluid–structure interaction problems. The immersed boundary method, first developed by Peskin and based on Cartesian mesh, is one of the methods for handling fluid-structure interaction problems involving complex geometries and large deformations. In this method, the Lagrangian force is spread onto the fluid nodes around the fluid-solid interface, as a forcing source of the Navier-Stokes equations to achieve the boundary conditions at the interface. In the immersed boundary method, it is not necessary to generate body conformal mesh. In addition, mesh movement and regeneration are avoided. Therefore, the efficiency could be higher compared to those methods based on body conformal mesh such as the arbitrary Lagrangian-Eulerian method and the deforming-spatial-domain/stabilized space-time method. Due to its advantages associated mesh generation, the immersed boundary method has recently been extended into compressible fluid-structure interaction problems, and is expected to have more and more significant impact in aeroelasticity and fluid-structure-sound interaction problems especially those involving complex geometries and large deformations. In this review article, we review the basic principle of the immersed boundary method and its recent progress. The applications of the immersed boundary method in compressible flows will be introduced by presenting a few benchmark cases and typical applications, including a flexible plate interacting with shock waves in a shock tube, a flexible plate in a hypersonic flow, acoustics in human phonation, acoustics scattered by a stationary cylinder, and sound generated by a rotating cylinder and a flapping wing in a uniform flow.

Airplane wing deformation and flight flutter detection method by using three-dimensional speckle image correlation technology.

Airplane wing deformation is an important element of aerodynamic characteristics, structure design, and fatigue analysis for aircraft manufacturing, as well as a main test content of certification regarding flutter for airplanes. This paper presents a novel real-time detection method for wing deformation and flight flutter detection by using three-dimensional speckle image correlation technology. Speckle patterns whose positions are determined through the vibration characteristic of the aircraft are coated on the wing; then the speckle patterns are imaged by CCD cameras which are mounted inside the aircraft cabin. In order to reduce the computation, a matching technique based on Geodetic Systems Incorporated coded points combined with the classical epipolar constraint is proposed, and a displacement vector map for the aircraft wing can be obtained through comparing the coordinates of speckle points before and after deformation. Finally, verification experiments containing static and dynamic tests by using an aircraft wing model demonstrate the accuracy and effectiveness of the proposed method.