Vortex dynamics and Reynolds number effects of an oscillating hydrofoil in energy harvesting mode

Abstract

The energy extraction and vortex dynamics from the sinusoidal heaving and pitching motion of an elliptical hydrofoil is explored through large-eddy simulations (LES) at a Reynolds number of $50,000$. The LES is able to capture the time-dependent vortex shedding and dynamic stall properties of the foil as it undergoes high relative angles of attack. Results of the computations are validated against experimental flume data in terms of power extraction and leading edge vortex (LEV) position and trajectory. The kinematics for optimal efficiency are found in the range of heave amplitude $h_o/c=0.5-1$ and pitch amplitude ${\theta }_o=60^{\circ }-65^{\circ }$ for $fc/U_{\infty }=0.1$ and of $h_o/c=1-1.5$ and ${\theta }_o=75^{\circ }-85^{\circ }$ for $fc/U_{\infty }=0.15$. Direct comparison with low Reynolds number simulations and experiments demonstrate strong agreement in energy harvesting performance between Reynolds numbers of $1000$ to $50,000$, with the high Reynolds number flows demonstrating a moderate $0.8-6.7\%$ increase in power compared to the low Reynolds number flow. In the high Reynolds number flows, the coherent LEV, which is critical for high-efficiency energy conversion, forms earlier and is slightly stronger, resulting in more power extraction. After the LEV is shed from the foil, the LEV trajectory is demonstrated to be relatively independent of Reynolds number, but has a very strong nonlinear dependence with kinematics. It is shown that the LEV trajectories are highly influenced by the heave and pitch amplitudes as well as the oscillation frequency. This has strong implications for arrays of oscillating foils since the coherent LEVs can influence the energy extraction efficiency and performance of downstream foils.

arXiv comment32 pages, 14 figures

Cited by in corpus (8)

• Wake-foil interactions and energy harvesting efficiency in tandem oscillating foils
• Simulations of Intracycle Angular Velocity Control for a Cross-Flow Turbine
• Triglobal resolvent-analysis-based control of separated flows around low-aspect-ratio wings
• Leading edge vortex formation and wake trajectory: Synthesizing measurements, analysis, and machine learning
• A Machine Learning Approach to Classify Vortex Wakes of Energy Harvesting Oscillating Foils
• Unsupervised Clustering and Performance Prediction of Vortex Wakes from Bio-inspired Propulsors
• Analysis of dynamic stall development on a cross-flow turbine blade
• A Machine Learning Approach to Classify Kinematics and Vortex Wake Modes of Oscillating Foils