Abstract
Optimal wind power plant design requires understanding of wind turbine wake physics and validation of engineering wake models under wake-controlled operating conditions. In this work, we have developed and investigated several different wake identification and characterization methods for analyzing wake evolution and dynamics. The accuracy and robustness of these methods, based on Gaussian function fitting and adaptive contour identification, have been assessed by application to a large-eddy simulation data set. A new contour-based method based on downstream momentum deficit has been considered. Uncertainties arising from wake-identification errors result in characterizations of the wake expansion, recovery, and meandering motion that differ by 19% of the rotor area, 4% of the freestream, and 15% rotor diameter, respectively.
Highlights
Modern wind plant design leverages understanding of wake physics to increase energy capture and reduce structural loads, thereby reducing the cost of energy
Because the transformation to the MFoR requires knowledge of the wake center, the expansion and recovery characteristics may be used to assess the goodness of the wake identification provided by SAMWICH Box
To assess the degree to which upstream and downstream turbines interact within a wind power plant, and the extent to which wake control strategies may be utilized, we have analyzed the evolution and dynamics of large-eddy simulation (LES)-simulated wakes
Summary
Modern wind plant design leverages understanding of wake physics to increase energy capture and reduce structural loads, thereby reducing the cost of energy. Over the years, researchers have independently developed various distinct approaches to tracking wakes that are based on their individual needs and resources This lack of coordination in wake analyses can lead to higher uncertainty in results and engender confusion among researchers regarding which methodology should be applied under which conditions. We address these issues by presenting a new open-source, Python-based library of wake tracking algorithms called the Simulated And Measured Wake Identification and CHaracterization ToolBox (SAMWICH Box) [17]. The algorithms implemented within SAMWICH Box are based on previously proposed methods described in published literature
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