Atmospheric wind characterisation for power generation
In this dissertation, two exploratory studies on the statistical characterisation of atmospheric winds in the deep tropics are presented. The first is an investigation on the variance of the upper-air winds in a near-equator monsoon region using radiosonde data. An unconventional vector-tensor metho...
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sg-ntu-dr.10356-1389012020-11-01T04:51:44Z Atmospheric wind characterisation for power generation Tieo, Jing Jin Alessandro Romagnoli Interdisciplinary Graduate School (IGS) Energy Research Institute @NTU Narasimalu Srikanth a.romagnoli@ntu.edu.sg; nsrikanth@ntu.edu.sg Science::Physics::Meteorology and climatology Engineering::Mechanical engineering::Alternative, renewable energy sources In this dissertation, two exploratory studies on the statistical characterisation of atmospheric winds in the deep tropics are presented. The first is an investigation on the variance of the upper-air winds in a near-equator monsoon region using radiosonde data. An unconventional vector-tensor method was used that allows the directional characteristics of the variance to be quantified. It was found that the size and the anisotropy of the variance vary with height whereas the preferred axis of the variance tends to align with the direction of the mean wind. In the second study, the boundary-layer wind profile on an offshore island of Singapore was investigated. It was found that the observed wind profiles deviated characteristically from what can be predicted by using the traditional power-law wind profile model. Two alternative wind profile models were then proposed that gave better predictions. It was thus concluded that the tropical wind characteristics can deviate considerably from well-known established models. Doctor of Philosophy 2020-05-13T12:20:04Z 2020-05-13T12:20:04Z 2019 Thesis-Doctor of Philosophy Tieo, J. J. (2019). Atmospheric wind characterisation for power generation. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/138901 10.32657/10356/138901 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
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Science::Physics::Meteorology and climatology Engineering::Mechanical engineering::Alternative, renewable energy sources Tieo, Jing Jin Atmospheric wind characterisation for power generation |
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In this dissertation, two exploratory studies on the statistical characterisation of atmospheric winds in the deep tropics are presented. The first is an investigation on the variance of the upper-air winds in a near-equator monsoon region using radiosonde data. An unconventional vector-tensor method was used that allows the directional characteristics of the variance to be quantified. It was found that the size and the anisotropy of the variance vary with height whereas the preferred axis of the variance tends to align with the direction of the mean wind. In the second study, the boundary-layer wind profile on an offshore island of Singapore was investigated. It was found that the observed wind profiles deviated characteristically from what can be predicted by using the traditional power-law wind profile model. Two alternative wind profile models were then proposed that gave better predictions. It was thus concluded that the tropical wind characteristics can deviate considerably from well-known established models. |
author2 |
Alessandro Romagnoli |
author_facet |
Alessandro Romagnoli Tieo, Jing Jin |
format |
Thesis-Doctor of Philosophy |
author |
Tieo, Jing Jin |
author_sort |
Tieo, Jing Jin |
title |
Atmospheric wind characterisation for power generation |
title_short |
Atmospheric wind characterisation for power generation |
title_full |
Atmospheric wind characterisation for power generation |
title_fullStr |
Atmospheric wind characterisation for power generation |
title_full_unstemmed |
Atmospheric wind characterisation for power generation |
title_sort |
atmospheric wind characterisation for power generation |
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Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/138901 |
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1683493463774986240 |