Mode classification for vortex shedding from an oscillating wind turbine using machine learning

This study presents an effective strategy that applies machine learning methods to classify vortex shedding modes produced by the oscillating cylinder of a bladeless wind turbine. A 2-dimensional computational fluid dynamic (CFD) simulation using OpenFOAMv2006 was developed to simulate a bladeless wind turbines vortex shedding behavior. The simulations were conducted at two wake modes (2S, 2P) and a transition mode (2PO). The local flow measurements were recorded using four sensors: vorticity, flow speed, stream-wise and transverse stream-wise velocity components. The time-series data was transformed into the frequency domain to generate a reduced feature vector. A variety of supervised machine learning models were quantitatively compared based on classification accuracy. The best performing models were then reevaluated based on the effects of artificial noisy experimental data on the models’ performance. The velocity sensors orientated transverse to the pre-dominant flow (u y ) achieved improved testing accuracy of 15% compared to the next best sensor. The random forest and k-nearest neighbor models, using u y , achieved 99.3% and 99.8% classification accuracy, respectively. The feature noise analysis conducted reduced classification accuracy by 11.7% and 21.2% at the highest noise level for the respective models. The random forest algorithm trained using the transverse stream-wise component of the velocity vector provided the best balance of testing accuracy and robustness to data corruption. The results highlight the proposed methods’ ability to accurately identify vortex structures in the wake of an oscillating cylinder using feature extraction.

Flow past a rotationally oscillating cylinder with an attached flexible filament

Experimental studies are conducted on a rotationally oscillating cylinder with an attached flexible filament at a Reynolds number of 150. Parametric studies are carried out to investigate the effect of cylinder forcing parameters and filament stiffness on the resultant wake structure. The diagnostics are flow visualization using the laser-induced fluorescence technique, frequency measurement using a hot film, and characterization of the velocity and vorticity field using planar particle image velocimetry. The streamwise force and power are estimated through control volume analysis, using a modified formulation, which considers the streamwise and transverse velocity fluctuations in the wake. These terms become important in a flow field where asymmetric wakes are observed. An attached filament significantly modifies the flow past a rotationally oscillating cylinder from a Bénard–Kármán vortex street to a reverse Bénard–Kármán vortex street, albeit over a certain range of Strouhal number, $St_{A} \sim 0.25\text {--}0.5$ , encountered in nature in flapping flight/fish locomotion and in the flow past pitching airfoils. The transition from a Kármán vortex street to a reverse Kármán vortex street precedes the drag-to-thrust transition. The mechanism of unsteady thrust generation is discussed. Maximum thrust is generated at the instants when vortices are shed in the wake from the filament tip. At $St_{A} > 0.4$ , a deflected wake associated with the shedding of an asymmetric vortex street is observed. Filament flexibility delays the formation of an asymmetric wake. Wake symmetry is governed by the time instant at which a vortex pair is shed in the wake from the filament tip.

Fluid forces and vortex patterns of an oscillating cylinder pair in still water with both fixed side-by-side and tandem configurations

Models of cylinders in the oscillatory flow can be found virtually everywhere in the marine industry, such as pump towers experiencing sloshing load in a LNG ship liquid tank. However, compared to the problem of a cylinder in the uniform flow, a cylinder in the oscillatory flow is less studied, let alone multiple cylinders. Therefore, we experimentally and numerically studied two identical circular cylinders oscillating in the still water with either a side-by-side or a tandem configuration for a wide range of Keulegan-Carpenter number and Stokes number β. The experiment result shows that the hydrodynamic performance of an oscillating cylinder pair in the still water is greatly altered due to the interference between the multiple structures with different configurations. In specific, compared to the single-cylinder case, the drag coefficient is greatly enhanced when two cylinders are placed side-by-side at a small gap ratio, while dual cylinders in a tandem configuration obtain a smaller drag coefficient and oscillating lift coefficient. In order to reveal the detailed flow physics that results in significant fluid forces alternations, the detailed flow visualization is provided by the numerical simulation: the small gap between two cylinders in a side-by-side configuration will result in a strong gap jet that enhances the energy dissipation and increase the drag, while due to the flow blocking effect for two cylinders in a tandem configuration, the drag coefficient decreases.