This thesis proposes a sensor approach for quantifying the hydrodynamic performance of Ocean Current Turbines (OCT), and investigates the influence of sensor-specific noise and sampling rates on calculated turbine performance. Numerical models of the selected sensors are developed, and then utilized to add stochastic measurement error to numerically-generated, non-stochastic OCT data. Numerically-generated current velocity and turbine performance measurements are used to quantify the relative influence of sensor-specific error and sampling limitations on sensor measurements and calculated OCT performance results. The study shows that the addition of sensor error alters the variance and mean of OCT performance metric data by roughly 7.1% and 0.24%, respectively, for four evaluated operating conditions. It is shown that sensor error results in a mean, maximum and minimum performance metric to Signal to Noise Ration (SNR) of 48.6% and 6.2%, respectively. / by Matthew T. Young. / Thesis (M.S.C.S.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_4062 |
| Contributors | Young, Matthew T., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Text, Electronic Thesis or Dissertation |
| Format | xi, 108 p. : ill. (some col.), electronic |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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