Synchronization transition of limit-cycle system with homogeneous phase shifts

The behaviors of coupled oscillators, each of which has periodic motion with random natural frequency in the absence of coupling, are investigated when phase shifts are considered. In the system of coupled oscillators, phase shifts are the same between different oscillators. Synchronization and synchronization transition are revealed with different phase shifts. Phase shifts play an important role for this kind of system. When the phase shift α〈 0.5π, the synchronization state can be attained by increasing the coupling, and the system cannot reach the synchronization state while α≥ 0.5π. A clear scaling between complete synchronization critical coupling strength Kpc and α - 0.5π is found.

Stall flutter prediction based on multi-layer GRU neural network

The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model(ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit(GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation(LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.

Nonlinear Aeroelastic Response of High-aspect-ratio Flexible Wings

The aeroelastic analysis of high-altitude, long-endurance （HALE） aircraft that features high-aspect-ratio flexible wings needs take into account structural geometrical nonlinearities and dynamic stall. For a generic nonlinear aeroelastic system, besides the stability boundary, the characteristics of the limit-cycle oscillation （LCO） should also be accurately predicted. In order to conduct nonlinear aeroelastic analysis of high-aspect-ratio flexible wings, a first-order, state-space model is developed by combining a geometrically exact, nonlinear anisotropic beam model with nonlinear ONERA （Edlin） dynamic stall model. The present investigations focus on the initiation and sustaining mechanism of the LCO and the effects of flight speed and drag on aeroelastic behaviors. Numerical results indicate that structural geometrical nonlinearities could lead to the LCO without stall occurring. As flight speed increases, dynamic stall becomes dominant and the LCO increasingly complicated. Drag could be negligible for LCO type, but should be considered to exactly predict the onset speed of flutter or LCO of high-aspect-ratio flexible wings.