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Unsteady flows can help turbines surge ahead in power production

ORAL

Abstract

To augment the performance of energy-harvesting turbines in real flow conditions, the effects of temporal variations in the streamwise inflow must be considered. These unsteady dynamics can lead to enhancements or losses in the time-averaged power extraction of a turbine, depending on the characteristics of the flow perturbation and turbine aerodynamics, and alter the flow conditions downstream of the turbine. To quantify and predict these phenomena, an analytical framework for the time-varying and time-averaged power extraction of a turbine in a periodically varying streamwise inflow is derived and validated in wind-tunnel experiments. This framework can also be used to predict the time-varying flow properties upstream of the turbine. Extensions of this modeling paradigm to the wake region downstream of the turbine are then explored and compared with flow-field measurements of a periodically surging turbine in an optical towing tank using two-dimensional particle-image velocimetry. The phase-averaged wake measurements help parameterize the effects of unsteady inflow dynamics on the time-averaged wake profile and on the contributions of vortical structures to the evolution of the wake. These results inform control schemes for enhancing the power extraction of individual turbines in unsteady flow conditions, as well as minimizing unsteady wake losses on downstream turbines in an array.

Publication: N. J. Wei and J. O. Dabiri (2022). "Phase-averaged dynamics of a periodically surging wind turbine." Journal of Renewable and Sustainable Energy 14, 013305.<br>N. J. Wei and J. O. Dabiri (2023). "Power-generation enhancements and upstream flow properties of turbines in unsteady flow conditions." Journal of Fluid Mechanics 966: A30.

Presenters

  • Nathaniel J Wei

    California Institute of Technology, California Institute of Technology; Princeton University

Authors

  • Nathaniel J Wei

    California Institute of Technology, California Institute of Technology; Princeton University

  • Adnan El Makdah

    Queen's University

  • JiaCheng Hu

    Queen's University

  • Frieder Kaiser

    Queen's University

  • David E Rival

    Queen's University, Institute of Fluid Mechanics TU Braunschweig, Queen's University; Technische Universität Braunschweig

  • John O Dabiri

    Caltech, California Institute of Technology