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Record W2552293619

Multiscale Wind Modelling for Sustainability and Resilience

2016· article· en· W2552293619 on OpenAlexfundaboutno aff
Djordje Romanić

Bibliographic record

VenueScholarship@Western (Western University) · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
FundersEuropean Organization for the Exploitation of Meteorological SatellitesCompute Canada
KeywordsSustainabilityResilience (materials science)Environmental scienceEnvironmental resource managementComputer scienceBusinessPhysics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.288
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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