Electronic Theses and Dissertations

Identifier

6639

Date

2020

Date of Award

11-16-2020

Document Type

Thesis

Degree Name

Master of Science

Major

Mechanical Engineering

Concentration

Power Systems

Committee Chair

Dr. Daniel Foti

Abstract

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.

Comments

Data is provided by the student.

Library Comment

dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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