Multiple tuned mass dampers for controlling coupled buffeting and flutter of long-span bridges
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Multiple tuned mass dampers are proposed to suppress the vertical and torsional buffeting and to increase the aerodynamic stability of long-span bridges. Each damper has vertical and torsional frequencies, which are tuned to the corresponding frequencies of the structural modes to suppress the resonant effects. These proposed dampers maintain the advantage of traditional multiple mass dampers, but have the added capability of simultaneously controlling vertical and torsional buffeting responses. The aerodynamic coupling is incorporated into the formulations, allowing this model to effectively increase the critical speed of a bridge for either single-degree-of-freedom flutter or coupled flutter. The reduction of dynamic response and the increase of the critical speed through the attachment of the proposed dampers to the bridge are also discussed. Through a parametric analysis, the characteristics of the multiple tuned mass dampers are studied and the design parameters - including mass, damping, frequency bandwidth, and total number of dampers - are proposed. The results indicate that the proposed dampers effectively suppress the vertical and the torsional buffeting and increase the structural stability. Moreover, these tuned mass dampers, designed within the recommended parameters, are not only more effective but also more robust than a single TMD against wind-induced vibration.Keywords:
Tuned mass damper
Aeroelasticity
Flutter
Aeroelasticity
Flutter
Energy method
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Abstract Flutter suppression is an important measure to improve fatigue life and enhance the performance of aircraft in modern aircraft design. In order to design more effective controllers for flutter suppression with high efficiency, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed. The traditional CFD-based ROMs have been successfully applied to active flutter suppression with high accuracy and efficiency. But, when a structure modification is made such as in aeroelastic tailoring and aeroelastic structural optimisation, the structural model should be updated, and the expensive, time-consuming CFD-based ROMs have to be reconstructed; such a process is impractical for passive flutter suppression. To overcome the realistic challenge, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed by extending an efficient aeroelastic CFD-based POD/ROM which we have developed. The proposed framework is demonstrated and evaluated using an improved AGARD 445.6 wing model. The results show that the proposed framework can accurately predict the aeroelastic response for active/passive hybrid flutter suppression with high efficiency. It provides a powerful tool for active/passive hybrid flutter suppression, and therefore, is ideally suited to design more effective controllers, and may have the potential to reduce the overall cost of aircraft design.
Aeroelasticity
Flutter
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A review and assessment of the state of the art in automated aeroelastic design is presented. Most of the aeroelastic design studies appearing in the literature deal with flutter, and, therefore, this paper also concentrates on flutter. The flutter design problem is divided into three cases: as isolated flutter mode, neighboring flutter modes, and a hump mode which can rise and cause a sudden, discontinuous change in the flutter velocity. Synthesis procedures are presented in terms of techniques that are appropriate for problems of various levels of difficulty. Current trends, which should result in more efficient, powerful and versatile design codes, are discussed. Approximate analysis procedures and the need for simultaneous consideration of multiple design requirements are emphasized.
Flutter
Aeroelasticity
Mode (computer interface)
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Purpose – To provide a general review of the flight flutter test techniques utilized in aeroelastic stability flight testing of aircraft, and to highlight the key items involved in flight flutter testing of aircraft, by emphasizing all the main information processed during the flutter stability verification based on flight test data.
Flutter
Aeroelasticity
Flight test
Longitudinal static stability
Aircraft flight mechanics
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Abstract Classical aeroelastic flutter instability historically has not been a driving issue in wind turbine design. In fact, rarely has this issue even been addressed in the past. Commensurately, among the wind turbines that have been built, rarely has classical flutter ever been observed. However, with the advent of larger turbines fitted with relatively softer blades, classical flutter may become a more important design consideration. In addition, innovative blade designs involving the use of aeroelastic tailoring, wherein the blade twists as it bends under the action of aerodynamic loads to shed load resulting from wind turbulence, may increase the blade's proclivity for flutter. With these considerations in mind it is prudent to revisit aeroelastic stability issues for a MW‐sized blade with and without aeroelastic tailoring. Focusing on aeroelastic stability associated with the shed wake from an individual blade turning in still air, the frequency domain technique developed by Theodorsen for predicting classical flutter in fixed wing aircraft has been adapted for use with a rotor blade. Results indicate that the predicted flutter speed of a MW‐sized blade is slightly greater than twice the operational speed of the rotor. When a moderate amount of aeroelastic tailoring is added to the blade, a modest decrease (12%) in the flutter speed is predicted. By comparison, for a smaller rotor with relatively stiff blades the predicted flutter speed is approximately six times the operating speed. When frequently used approximations to Theodorsen's method are implemented, drastic underpredictions result, which, while conservative, may adversely impact blade design. These underpredictions are also evident when this MW‐sized blade is analysed using time domain methods. Published in 2004 by John Wiley & Sons, Ltd.
Aeroelasticity
Flutter
Aerodynamic force
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Citations (119)
The aeroelastic properties of modern aircraft are such that necessary calculations, of critical flutter speeds for instance, can be remarkably complicated. A flutter analyst requires an inordinate degree of skill and experience if he is to acquire a “sense of feel” in calculations. The difficulty of deciding how to adjust a critical flutter speed predictably by some structural modification is a typical outcome of this complication.
Flutter
Aeroelasticity
Critical speed
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Abstract Flutter is a self‐excited vibration under the interaction of the inertial force, aerodynamic force, and elastic force of the structure. After the flutter occurs, the aircraft structures will exhibit limit cycle oscillation, which will cause catastrophic accidents or fatigue damage to the structures. Therefore, it is of great theoretical and practical significance to study the aeroelastic characteristics and flutter control for improving the aeroelastic stability of aircraft structures. This paper reviews the recent advances in aeroelastic analysis and flutter control of wings and panel structures. The mechanism of aeroelastic flutter of wings and panels is presented. The research methods of aeroelastic flutter for different structures developed in recent years are briefly summarized. Various control strategies including the linear and nonlinear control algorithms as well as the active flutter control results of wings and panels are presented. Finally, the paper ends with conclusions, which highlight challenges of the development in aeroelastic analysis and flutter control, and provide a brief outlook on the future investigations. This study aims to present a comprehensive understanding of aeroelastic analysis and flutter control. It can also provide guidance on the design of new wings and panel structures for improving their aeroelastic stability.
Aeroelasticity
Flutter
Aerodynamic force
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Citations (71)
In this chapter, a simple binary flutter model is developed, making use of strip theory with simplified unsteady aerodynamic terms; the model is then used to illustrate the dynamic characteristics of aeroelastic systems, considering the effect of varying the position of the elastic axis, the mass distribution and the frequency spacing between the two modes. It considers the phenomenon of control surface flutter and the effect of rigid body modes. The chapter also briefly explores flutter in the transonic flight regime, and introduces some effects of non-linearities. It shows that it is important to include unsteady aerodynamic effects in the dynamic models that are used to predict the sub critical aeroelastic behaviour and the onset of flutter. A number of MATLAB codes related to the chapter are included in the companion website.
Aeroelasticity
Flutter
Aerodynamic force
Position (finance)
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Uncertainty quantification of aeroelastic wings flutter using an optimized machine learning approach
This study outlines the flutter characteristics of aeroelastic wings under unsteady aerodynamic loading based on an efficient support vector machine assisted k-method. First, the aeroelastic wing flutter speed and flutter frequency are obtained using k-method. Then, the uncertain input parameters distribution is modeled by probability density functions. These parameters are propagated to the aeroelastic wing equations. The Monte Carlo simulation using 12 parallel logical threads is carried out to obtain the flutter speed and the flutter frequency distribution. An optimal robust surrogate model is trained by limited numbers of input and output using support vector machine. Monte Carlo simulation is also carried out in conjunction with the machine learning based k-method computational framework for obtaining the complete probabilistic description of flutter speed and flutter frequency. The coupled support vector regression based k-method is a novel approach that is first used in the aeroelastic wings flutter. The present method is found to reduce the computational time and cost significantly without compromising the accuracy of results.
Flutter
Aeroelasticity
Aerodynamic force
Uncertainty Quantification
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Aeroelasticity
Flutter
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