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During the past few decades, several significant progresses have been made in exploring complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil models. Additionally, some new challenges have arisen. To the best of the author’s knowledge, most studies are concerned with the deterministic case; however, the effects of stochasticity encountered in practical flight environments on the nonlinear dynamical behaviors of the airfoil systems are neglected. Crucially, coupling interaction of the structure nonlinearities and uncertainty fluctuations can lead to some difficulties on the airfoil models, including accurate modeling, response solving, and vibration suppression. At the same time, most of the existing studies depend mainly on a mathematical model established by physical mechanisms. Unfortunately, it is challenging and even impossible to obtain an accurate physical model of the complex wing structure in engineering practice. The emergence of data science and machine learning provides new opportunities for understanding the aeroelastic airfoil systems from the data-driven point of view, such as data-driven modeling, prediction, and control from the recorded data. Nevertheless, relevant data-driven problems of the aeroelastic airfoil systems are not addressed well up to now. This survey contributes to conducting a comprehensive overview of recent developments toward understanding complex dynamical behaviors and vibration suppression, especially for stochastic dynamics, early warning, and data-driven problems, of the conceptual two-dimensional airfoil models with different structural nonlinearities. The results on the airfoil models are summarized and discussed. Besides, several potential development directions that are worth further exploration are also highlighted.
Aeroelastic flutter, as a dynamic instability caused by the fluid–structure interaction of inertial, elastic, and aerodynamic forces, has been a significant and fascinating research topic in the aeroelastic scientific community. However, the presence of both nonlinearities and stochasticities, such as atmospheric turbulence, gust, etc., poses a challenge in discerning the underlying mechanisms, which can lead to more complex dynamical behaviors than the deterministic airfoil systems and even induce the occurrence of extreme events. Such vibrations are extremely dangerous and unexpected for engineering practice and can lead to damage or fatigue of the wing structure and even bring catastrophic consequences to an aircraft. Consequently, it is of great significance, but difficulty, to accurately understand, predict, and suppress the complex dynamical behaviors of airfoil models. In recent years, there have been distinguished developments in machine learning, in particular, deep learning, as its powerful capabilities of modeling and characterization. Data-driven techniques for relevant aeroelastic analysis of airfoil models with a complicated nonlinear structure are becoming increasingly fashionable. In the present paper, we give a state-of-the-art overview on complex dynamics and vibration suppression of conceptual airfoil models, which complements the previous results and promotes the rapid development of related fields.
