Bibliographic record
Abstract
The research presented herein is a mix of meteorological and wind engineering disciplines. In many cases, there is a gap between these two fields and this thesis is an attempt to bridge that gap through multiscale wind modelling approaches. Data and methods used in this study cover a multitude of spatial and temporal scales. Applications are in the fields of sustainability and resilience. This relationship between multiscale wind modelling and sustainability and resilience is investigated examining several case studies of three different developments: urban, rural and coastal.\nAn urban wind modelling methodology is proposed and applied for a specific development in downtown Toronto, Canada. Micro-scale wind energy maps are created using computational fluid dynamics, analytical models, and the Canadian Wind Energy Atlas. It is demonstrated that urban wind energy projects are currently not feasible/sustainable due to decoupling between urban wind turbine power curves and wind speed histograms. Wind climatology for Toronto is calculated using the reanalysis data for the period 1948-2015. A trend analysis reveals statistically significant positive trends of the most frequent wind directions. Low-frequency wind spectra shows three distinguished peaks. The lowest frequency peak has the period of 11 year and it is linked to solar activity. The first methodology for microscale modelling of urban winds in changing climate is developed. Maximum wind speeds are more affected by climate change than the means.\nA wind sustainability study is performed for a unique development in South Central Kansas, United States. The analyses are conducted on the wind data obtained from the closest weather station and for the period 1984-2015. The WAsP package is used to calculate the wind atlas and wind resources at the site. Five locations suitable for installation of wind turbines are determined.\nA downburst that struck Livorno, Italy, on October 1, 2012 is analyzed from the wind resilience perspective. The analyses are conducted by gathering all available and relevant meteorological data. This research allowed for better understanding of downbursts, created a reference broad-band of information for the future calibration of analytical, physical and numerical models, and highlighted the potential of merging wind engineering and meteorology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".