Research Projects

The increasing demand for flexible structures in various applications, such as wind turbine blades, helicopter wings, fighter aircraft, unmanned aerial vehicles (UAVs), and micro aerial vehicles (MAVs), presents opportunities for instabilities. These structures develop elastic couplings and are prone to nonlinear characteristics, which can lead to abrupt bifurcation scenarios. Aerodynamic nonlinearity, particularly dynamic stall-induced bifurcations, can accumulate enormous fatigue damage, which can lead to structural failure. In-field wind turbulence exacerbates these instabilities and jeopardizes the one-one relationship between instabilities and fatigue damage. The low structure-to-fluid added mass ratio also makes aerodynamic nonlinearities (vortex-induced) prominent, resulting in multiple bifurcations that can be abrupt and potentially catastrophic to structural safety. Using artificial intelligence tools, a multi-parameter bifurcation study for instability and fatigue prediction is possible. Lightweight flying devices, such as UAVs, MAVs, and drones, are high in demand for reconnaissance, surveillance, and counter-terrorism purposes. This study proposes investigating stall-induced dynamic transitions for lightweight aeroelastic structures under nonlinear aerodynamic conditions, using both deterministic and stochastic on-flow conditions. The theory of synchronization is used to investigate the underlying physical mechanism leading to catastrophic dynamical transitions. The study uses machine learning algorithms to predict instability and catastrophic transitions, making this the only study to characterize the physical mechanisms behind dangerous dynamical stall-induced signatures and fatigue damage accumulation.

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