Experimental investigation on the synchronization characteristics of a pitch-plunge aeroelastic system exhibiting stall flutter

Abstract

This study focuses on characterizing the bifurcation scenario and the underlying synchrony behavior in a nonlinear aeroelastic system under deterministic as well as stochastic inflow conditions. Wind tunnel experiments are carried out for a canonical pitch-plunge aeroelastic system subjected to dynamic stall conditions. The system is observed to undergo a subcritical Hopf bifurcation, giving way to large-amplitude limit cycle oscillations (LCOs) in the stall flutter regime under the deterministic flow conditions. At this condition, we observe intermittent phase synchronization between pitch and plunge modes near the fold point, whereas synchronization via phase trapping is observed near the Hopf point. Repeating the experiments under stochastic inflow conditions, we observe two different aeroelastic responses: low amplitude noise-induced random oscillations (NIROs) and high-amplitude random LCOs (RLCOs) during stall flutter. The present study shows asynchrony between pitch and plunge modes in the NIRO regime. At the onset of RLCOs, asynchrony persists even though the relative phase distribution changes. With further increase in the flow velocity, we observe intermittent phase synchronization in the flutter regime. To the best of the authors’ knowledge, this is the first study reporting the experimental evidence of phase synchronization between pitch and plunge modes of an aeroelastic system, which is of great interest to the nonlinear dynamics community. Furthermore, given the ubiquitous presence of stall behavior and stochasticity in a variety of engineering systems, such as wind turbine blades, helicopter blades, and unmanned aerial vehicles, the present findings will be directly beneficial for the efficient design of futuristic aeroelastic systems.

Fluid–structure–surface interaction of a flexibly mounted pitching and plunging flat plate in proximity to the free surface

“…At this point, it is important to note that typically, in a system with two DoFs such as a foil or flat plate with pitching and plunging modes, the start of LCOs is explained by the synchronisation of the modal frequencies (Tripathi et al. 2022). In this context, the modal frequencies refer to the frequencies of the pitching and plunging oscillations.…”

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

“…Bifurcation theory is used to study these transitions. There are several types of bifurcations, such as fold bifurcation in a pitchplunge aeroelastic system exhibiting stall flutter [3], [4], Hopf bifurcation in aeroelastic systems in the presence of cubic hardening nonlinearity [5], and transcritical bifurcation in an asymmetric 2D pitching wing section with cubic stiffness [6]. Each bifurcation type corresponds to distinct dynamical behavior or state.…”

“…The interaction of fluid flow with flexible structures or acoustic-heat sources can be best described by the paradigms of nonlinear dynamical systems. Aeroelasticity, which refers to the interaction of fluid flow with structures, and thermoacoustic behavior, which involves the interaction of flows with heat-acoustic sources, are prone to unpredictable and often catastrophic bifurcations [3], [4], [10], [11], [13]. Interestingly, these two fluid dynamical systems can exhibit similar bifurcation scenarios under specific conditions.…”

“…Structural freeplay nonlinearity, which arises from loose hinges or worn parts, leads to a period-doubling route to chaos and subsequent divergent oscillations [23]. Aerodynamic nonlinearity induced by dynamic stall can lead to both subcritical [3], [4], [24] and supercritical Hopf bifurcation routes [25], as well as a period-doubling route to chaos [23], [26].…”

“…Additionally, both the aeroelastic and thermoacoustic problems are highly complex multiphysics problems, wherein the role of nonlinearity, fluid characteristics, and the sensitivity to parameter degradation is not fully understood [3], [5], [10] and, in turn, compelling the community to formulate alternate metrics to foretell oscillatory instabilities. In other words, the suite of measures, such as, say, characterizing multifractality [12], [30] or using a complexity measure [5], is tailored for specific bifurcation scenarios.…”