Abstract
Cascade flutter driven by aerodynamic instability leads to severe structural destruction of turbine blades in aircraft engines. The development of a sophisticated methodology for detecting a precursor of cascade flutter is one of the most important topics in current aircraft engineering and related branches of nonlinear physics. A novel detection methodology combining symbolic dynamics, dynamical systems, and machine learning is proposed in this experimental study to detect a precursor of cascade flutter in a low-pressure turbine. Two important measures, the weighted permutation entropy in terms of symbolic dynamics and the determinism in recurrence plots in terms of dynamical systems theory, are estimated for the strain fluctuations on turbine blades to capture the significant changes in the dynamical state during a transition to cascade flutter. A feature space consisting of the two measures obtained by a support vector machine, can appropriately be classified into three dynamical states: a stable state, a transition state, and a cascade flutter state. The proposed methodology is valid for detecting a precursor of cascade flutter.
Highlights
Nonlinear time series analyses based on the theories of chaos and fractals have created broad platforms of quantitative ways of evaluating the complexity in nonlinear systems, leading to a review of standard and conventional linear time series analysis such as power spectra.[1]
Gotoda and co-workers have recently shown the potential utility of nonlinear time series analyses incorporating symbolic dynamics, statistical complexity, and complex networks for the characterization of highly nonlinear dynamic behaviors in various physical settings: flame front instability driven by the interaction of buoyancy and centrifugal force,[3] radiative heat loss,[4] and medium-scale turbulent fire,[5,6,7] including the proposal of early detection methodologies for thermoacoustic combustion oscillations and lean blowout.[8–10]
Our primary interest in this study is to explore the applicability of a methodology combining symbolic dynamics, dynamical systems, and machine learning for the early detection of cascade flutter
Summary
Nonlinear time series analyses based on the theories of chaos and fractals have created broad platforms of quantitative ways of evaluating the complexity in nonlinear systems, leading to a review of standard and conventional linear time series analysis such as power spectra.[1]. The onset of cascade flutter imposes a restriction on the development of advanced jet engines, and its early detection is a long-standing and challenging subject in current aerospace propulsion engineering. The early detection of cascade flutter by nonlinear time series analyses has not been fully examined except in two recent experimental studies.[14,15]. These studies revealed the importance of recurrence quantification analysis and the Hurst exponent for the early detection of flutter on NACA0012 in a wind channel, including the presence of a multifractal structure during the dynamical state prior to the onset of flutter. Our primary interest in this study is to explore the applicability of a methodology combining symbolic dynamics, dynamical systems, and machine learning for the early detection of cascade flutter
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