Wind Farm Flow and Power Capture: Optimal Design of LiDAR Experiments, Flow Physics, and Mid-fidelity Modeling

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dc.contributor.advisorIungo, Giacomo Valerio
dc.contributor.advisorHamlen, Kevin
dc.contributor.committeeMemberGriffith, Todd
dc.contributor.committeeMemberJin, Yaqing
dc.contributor.committeeMemberLeonardi, Stefano
dc.creatorLetizia, Stefano
dc.date.accessioned2023-03-27T16:54:44Z
dc.date.available2023-03-27T16:54:44Z
dc.date.created2021-12
dc.date.issued2021-12-01T06:00:00.000Z
dc.date.submittedDecember 2021
dc.date.updated2023-03-27T16:54:45Z
dc.description.abstractNowadays there is an urgent need for wind farm flow models with increased accuracy and low computational costs for the prediction of turbine performances and wakes. An improvement of current standards of wind farm simulations can be achieved only through a better understanding and modeling of the complex physical mechanisms governing the wind farm aerodynamics. Low computational requirements are necessary to enable large amount of simulations needed for the optimal design, real-time monitoring and online control of wind power plants. To this aim, a holistic research project has been conceived and implemented, which is the focus of this Ph.D. thesis. The adopted research strategy includes three main tasks: i) optimal design and execution of field experiments for monitoring wind-farm operations through scanning LiDAR, meteorological and SCADA data; ii) statistical analysis of LiDAR field measurements for probing wake evolution, wake interactions, effects of atmospheric stability, and flow distortions due to topography; iii) development of a data-driven RANS model for accurate and low-computational-cost simulations of wind farm operations. This research project has enabled quantifying and modeling effects on wind farm operations connected with the turbine aerodynamics and the atmospheric stability regime, and detecting the occurrence of topography wakes, which are flow regions with reduced wind speed and enhanced turbulence intensity being detrimental for wind turbines installed on complex terrains. The main deliverables of this project are the LiDAR Statistical Barnes Objective Analysis (LiSBOA), a tool for the optimal collection and statistical characterization of LiDAR measurements, and the Pseudo-2D RANS (P2D-RANS) wind farm model, which has been recently distributed among several industrial partners and with the intended uses of simulating and monitoring the operations of several wind power plants.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9644
dc.language.isoen
dc.subjectEnergy
dc.subjectEngineering, Aerospace
dc.titleWind Farm Flow and Power Capture: Optimal Design of LiDAR Experiments, Flow Physics, and Mid-fidelity Modeling
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Engineering and Computer Science
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

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