Wake Dynamics of a Cylinder with a Flexible Splitter Plate

Abstract

As the world grapples with the challenges of global warming, there has been a significant push for the development of new methods of harnessing renewable energy. Energy harvesters have been a new avenue to generate electrical power locally for remote applications. An energy harvester is a device that taps into aeroelastic phenomena to convert vibrational energy into electrical energy. Additional benefits of harvesters include being lower in complexity, ease of maintenance, and the resulting cost-effectiveness.

The splitter plate represents a wake control device that alters the wake to make the energy capture more efficient and practical when attached to a cylinder. However, the flow mechanisms under different conditions have not been fully understood. The motivation of the present work is to bridge this gap by characterizing the mechanisms and wake dynamics of a cylinder with a flexible splitter plate. This will help advance the understanding of the wake behavior of these devices to make safe and efficient devices. To characterize the behavior of the wake, measurements are made at different flow speeds. A preliminary analysis of the splitter plate motion showed that increasing the Reynolds number produced four distinct regimes to be studied further: crossover, baseline, reduced amplitude, and chaotic regimes. The initial analysis also revealed a condition named burst, defined as a period of resonant, two-dimensional, high amplitude oscillations of the splitter plate. All regimes are compared to a bare cylinder with no splitter plate to ascertain differences in wake behavior.

The use Helium Filled Soap Bubbles (HFSB) in flow diagnostic tools such as Particle Image Velocity (PIV) experiments has permitted large-scale measurements. A Lagrangian Particle Tracking (LPT) experiment was performed in which the HFSB is tracked individually with the help of an algorithm. The present study employs Shake-The-Box (STB), a novel LPT tracking algorithm that uses time-resolved experimental data to predict future tracer particle locations. The benefit of such an algorithm includes reduced ghost particle detection and computational efficiency. The details of this experimental method have been covered extensively in this thesis. The raw experimental data was processed to perform statistical analysis, proper orthogonal decomposition, and spectral analysis to provide insights to meet the thesis objective.