Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. This thesis describes the creation of a new model which can predict the wind speed, turbulence intensity, and wind power density at any point in an obstacle’s region of influence, relative to unsheltered conditions. Artificial neural networks were used to learn the relationship between an obstacle’s characteristics and its effects on the local wind. The neural network was trained using measurements collected in the wakes of scale models exposed to a simulated atmospheric boundary layer in a wind tunnel. A field experiment was conducted to validate the wind tunnel measurements. Model predictions are most accurate in the far wake region. The estimated mean uncertainties associated with model predictions of velocity deficit, power density deficit, and turbulence intensity excess are 5.0%, 15%, and 12.8%, respectively. / Industrial collaborators: Weather INnovations Inc., Wenvor Technologies Inc. / Ontario Centre of Excellence for Energy
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/2181 |
| Date | 03 June 2010 |
| Creators | Brunskill, Andrew |
| Contributors | Lubitz, William David |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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