Aircraft Flutter Research Articles

Adjoint-Based Design Optimization of Stability Constrained Systems

A partial differential equation (PDE) constrained design optimization problem usually optimizes a characteristic of a dynamical system around an equilibrium point. However, a commonly omitted constraint is the linear stability constraint at the equilibrium point, which undermines the optimized solution’s applicability. To enforce the linear stability constraint in practical gradient-based optimization, the derivatives must be computed accurately, and their computational cost must scale favorably with the number of design variables. In this paper, we propose an algorithm based on the coupled adjoint method and the algorithmic differentiation method that can compute the derivative of such constraint accurately and efficiently. We verify the proposed method using several simple low-dimensional dynamical systems. The relative difference between the adjoint method and the finite differences is between 10−6 to 10−8. The proposed method is demonstrated through several optimizations, including a nonlinear aeroelastic optimization. The proposed algorithm has the potential to be applied to more complex problems involving large-scale nonlinear PDEs, such as aircraft flutter and buffet suppression.

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.

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.

A Detailed Physical Explanation of an Aircraft Flutter Mechanism

A Detailed Physical Explanation of an Aircraft Flutter Mechanism

Nonlinear aero-thermo-elastic flutter analysis of stiffened graphene platelets reinforced metal foams plates with initial geometric imperfection

Due to its lightweight design and high load-bearing capacity, the stiffened plate structure is extensively applied in the passive control of aircraft flutter. However, the evolution mechanism of the nonlinear response of stiffened plates within the unstable region remains unclear. In particular, there is a lack of research on the nonlinear aero-thermo-elastic properties of stiffened plates with initial geometric imperfection. Inspired by this, this article investigates the stability and post-instability behavior of stiffened geometrical imperfect graphene platelets reinforced metal foams (GPLRMF) plates under combined thermal load and aerodynamic pressure. Graphene platelets (GPLs) and foam are distributed evenly or unevenly in the plates, while the material properties are calculated by modified Halpin-Tsai model and rule of mixture. Considering initial geometric imperfection, the models of plates and stiffeners are established by first-order shear deformation (FOSD) plate and Timoshenko beam theories, respectively. Using the linear piston theory to simulate supersonic aerodynamic pressure. The stability boundary of stiffened plates is determined by eigenvalue analysis. The Newton-Raphson approach is used to simulate the aero-thermo-elastic static response of stiffened plates, and the dynamic post-flutter behavior of the stiffened plate is determined through the Runge-Kutta method. Finally, the effects of initial geometric imperfection, material parameters (foam distribution type, porosity coefficient, GPLs weight fraction and GPLs distribution type), and stiffeners (size, position, and stiffening scheme) on the thermal aeroelastic behavior of stiffened GPLRMF plates are revealed. It can be found that the presence of initial geometric imperfection will significantly change the nonlinear flutter behavior of the stiffened plates.

Set Membership Strategy for Closed Loop Aircraft Flutter Controller Design

Set Membership Strategy for Closed Loop Aircraft Flutter Controller Design

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.

Design Sensitivity Studies on the T-Tail Flutter of a Transport Aircraft

The studies of dynamic and aeroelastic characteristics of a high T-Tail transport aircraft at component and full level using MSC/NASTRAN code and correlation with the results obtained from ground vibration tests of the aircraft have thrown light on the complexities of the T-Tail dynamics and flutter characteristics. The critical flutter velocities of the complete aircraft have been evaluated by the PK and KE methods of NASTRAN. The effects of variability of the locations and magnitude of the non-structural masses in the fuselage on the T-Tail flutter velocities have been studied by a parametric analysis. Sensitivity study of the elevator mass balancing on its dynamics and the flutter velocities have also been addressed. The flutter velocities and margins have been examined from the certification aspects. Addressing design issues of the aircraft related to fuselage flexibility and T-Tail flutter from an aeroelastic point of view has been attempted in this paper.

Engine Placement Effects on the Flutter of a Medium-Range Box-Wing Aircraft

Engine Placement Effects on the Flutter of a Medium-Range Box-Wing Aircraft

Direct data–driven strategy for closed-loop aircraft flutter test

PurposeThis new paper aims to extend the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data–driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors’ own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data–driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data–driven strategy. Generally, during our new information age, direct data–driven strategy is widely applied around our living life.Design/methodology/approachFirst, consider one more complex closed loop stochastic aircraft flutter model, whose input–output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data–driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data–driven control is proposed to design the feedback controller, which suppresses the flutter response actively.FindingsA novel direct data–driven strategy is proposed to achieve the dual missions, i.e. identification and control for closed-loop aircraft flutter test. First, direct data–driven identification is applied to identify that unknown aircraft flutter model being related with aircraft flutter model parameters identification. Second, direct data–driven control is proposed to design that feedback controller.Originality/valueTo the best of the authors’ knowledge, this new paper extends the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. Consider the identification problem of aircraft flutter model parameters within the presented closed loop environment, direct data–driven identification algorithm is proposed to achieve the identification goal. Direct data–driven control is proposed to design the feedback controller, i.e. only using the observed data to design the feedback controller.

Whirl Flutter Suppression of Tiltrotor Aircraft Using Actively Controlled Aileron

Whirl flutter of a tiltrotor aircraft is a complex aeroelastic phenomenon and it can result in catastrophic consequences. The deflection of an aileron mounted on a wing has the potential to solve this fatal problem. Whirl flutter suppression using an actively controlled aileron is studied in this study. Firstly, a semi-span aeroelastic model is established for the whirl flutter problem using the Hamilton principle. This model is composed of three parts: a rigid rotor, a rigid nacelle and a flexible wing, and the effect of the aileron deflection on the aeroelastic responses is also taken into consideration through a quasi-steady aerodynamic model. In addition, the accuracy of this aeroelastic model is validated with the results of two different wind-tunnel tests. Then, an LQR controller is developed to control the dynamic deflection of the aileron, and a full-dimensional state observer is built to estimate the state of the time-invariant system of a tiltrotor aircraft. Finally, simulations are carried out using the aeroelastic model and the LQR controller at different flight conditions to study the influence of the aileron deflection on whirl flutter. The simulation results demonstrate that the flutter boundary speed can be improved by 18.1% with the active deflection of the aileron, compared with the uncontrolled condition.

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.

Aircraft Flutter and Aerodynamic Work

This paper is about an application of an energy approach to computational aeroelasticity. A frequency-domain calculation of aerodynamic work is presented in a form that has not been previously discussed. In large aeroelastic systems such as aircraft flutter models, the obtained expression allows us to quantify the roles played in flutter by the generalized coordinates, the phases between them, and the generalized aerodynamic forces. This is exemplified in a body-freedom flutter analysis of a flying-wing aircraft: the X-56A. Another interesting feature that is proposed is a diagram of the aerodynamic work as a function of airspeed. Such functions allow an observance of an evolution with the airspeed of the terms that are most reflective of the aeroelastic stability changes: for example, a phase between the two dominant modes. These diagrams can complement typical flutter trends in analysis documentation and aid in flutter suppression. The approach also permits a perspective on flutter as an interaction of aircraft surfaces rather than vibrational modes. Once a sensitivity of an aeroelastic eigenvalue to a surface area is measured with the presented approach, it can be used in the aircraft design to mitigate flutter and other undesirable aeroelastic responses associated with lightly damped eigenvalues. An illustration of this idea is provided. Finally, energy-based computations allow posing energy-efficient active flutter suppression problems. This has been presented before in the literature. Examples of this aspect are made here with aircraft models (for the first time, as far as the author knows).

Closed-loop identification for aircraft flutter model parameters

PurposeThe purpose of this paper is to extend the authors’ previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today’s practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input–output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.Design/methodology/approachFirst, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.FindingsThis aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model.Originality/valueTo the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.

Application of Structured Robust Synthesis for Flexible Aircraft Flutter Suppression

This article presents a method for structured robust control design for systems with a mixture of parametric and dynamic uncertainty. The proposed method alternates between an analysis step and a synthesis step. Samples of the parametric uncertainty are computed during the analysis steps, thus yielding an array of uncertain systems containing only dynamic uncertainty. The controller is then synthesized on this array of uncertain models. This synthesis step itself involves an alternation between constructing a D-scale for each of the uncertain systems and tuning a single controller for the entire collection of scaled plants. The controller tuning is performed using structured control design techniques. The proposed method is utilized to design a flutter suppression controller for a flexible aircraft. The aircraft dynamics are described by both a high-fidelity and a reduced-order model. The design objectives for flutter suppression are to achieve robust stabilization in the presence of mixed uncertainty. The proposed structured design method yields a single, low-order, linear time-invariant (LTI) controller, which increases the flutter speed by 15%. Additional robustness analyses and high-fidelity simulations are provided to assess the controller performance.

Visual three-dimensional measurement of aircraft flutter in hypersonic wind tunnel tests

Visual three-dimensional measurement of aircraft flutter in hypersonic wind tunnel tests

Engine Modeling for Small-Disturbance-CFD Related to Aircraft Flutter Investigations

Aeroelastic behavior of aircraft is significantly affected by the presence of engines mounted under the wings. Powered engines influence the unsteady aerodynamics on the one hand and lead to additional unsteady forces due to thrust vector oscillations on the other hand. This work focuses on the incorporation of aerodynamic engine effects into a small disturbance CFD framework to enhance the modeling accuracy of unsteady aerodynamics of aircraft. The effects are numerically modeled by mimicking physically reasonable flow conditions at the intake and nozzle planes of the engine nacelle. Subsequently, the influence of the engine effects on the flutter behavior of an aircraft is studied employing the small-disturbance-CFD-based flutter analysis. The basis for the investigation is the Common Research Model, which represents a modern transonic commercial airliner with a cruise Mach number of 0.85. Two configurations are considered: aircraft with passive engines represented by flow-through nacelles and aircraft with powered engines, where the novel small disturbance engine model is applied. The results are compared in terms of the flutter trends and the predicted flutter boundary. Furthermore, the impact of the modal induced thrust oscillations on the aeroelastic behavior of the aircraft is discussed.

СОВРЕМЕННЫЙ МЕТОД СОЗДАНИЯ ДИНАМИЧЕСКИ ПОДОБНЫХ МОДЕЛЕЙ ДЛЯ ИССЛЕДОВАНИЯ ФЛАТТЕРНЫХ ХАРАКТЕРИСТИК ЛЕТАТЕЛЬНЫХ АППАРАТОВ

СОВРЕМЕННЫЙ МЕТОД СОЗДАНИЯ ДИНАМИЧЕСКИ ПОДОБНЫХ МОДЕЛЕЙ ДЛЯ ИССЛЕДОВАНИЯ ФЛАТТЕРНЫХ ХАРАКТЕРИСТИК ЛЕТАТЕЛЬНЫХ АППАРАТОВ

Combing Instrumental Variable and Variance Matching for Aircraft Flutter Model Parameters Identification

When the observed input‐output data are corrupted by the observed noises in the aircraft flutter stochastic model, we need to obtain the more exact aircraft flutter model parameters to predict the flutter boundary accuracy and assure flight safety. So, here we combine the instrumental variable method in system identification theory and variance matching in modern spectrum theory to propose a new identification strategy: instrumental variable variance method. In the aircraft flutter stochastic model, after introducing instrumental variable to develop a covariance function, a new criterion function, composed by a difference between the theory value and actual estimation value of the covariance function, is established. Now, the new criterion function based on the covariance function can be used to identify the unknown parameter vector in the transfer function form. Finally, we apply this new instrumental variable variance method to identify the transfer function in one electrical current loop of flight simulator and aircraft flutter model parameters. Several simulation experiments have been performed to demonstrate the effectiveness of the algorithm proposed in this paper.

Application of LQG and H∞ Gain Scheduling Techniques to Active Suppression of Flutter

Abstract Aircraft flutter behaviour is highly dependent on flight conditions, such as airspeed, altitude and Mach number. Thus when closed-loop control is applied to suppress flutter, if these variations are taken into account then improved performance can be obtained if the controller has a certain degree of adaptivity to the variations. In this paper, a Linear Parameter-Varying (LPV) model of a rectangular wing including a trailing-edge control surface is considered. Two different gain scheduling controllers based on LQG and H∞ techniques are designed to suppress flutter and reject perturbations. Simulations of the closed-loop system show that gain scheduling techniques are capable of fully stabilizing the system over the full range of the considered air velocity, and they increase flutter speed by more than 90%.