Date of Award
Master of Science
Dr. Daniel Foti
Wind turbine wakes are dominated by several energetic turbulent coherent structures that oscillate at specific Strouhal numbers. Implications on wind power harvesting of these dynamic, induced features require accurate unsteady modeling. Dynamic mode decomposition (DMD), a data-driven modal analysis, has demonstrated the ability to identify flow features based on specific frequencies. In this work, the selection of modes and data-driven DMD models pertaining to wakes with constant Strouhal number coherent structures are investigated using physically-informed criteria and sparse sampling. Both criteria are validated with a low Reynolds number flow behind a square cylinder. Next, the techniques are applied to data derived from the large-eddy simulation of a wind turbine wake. Modes related to tip vortices and hub vortex system are identified. Sparse identification shows remarkable ability to select the optimal modes for reduced-order modeling. Error becomes nearly independent of the number of modes when using fewer than 10% of the modes.
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Qatramez, Ala' Eyad, "Reduced-order Model Predictions of Wind Turbines via Mode Decomposition and Sparse Sampling" (2020). Electronic Theses and Dissertations. 2130.