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Record W4238113649 · doi:10.32920/ryerson.14665497.v1

Aerodynamic optimization of building augmented wind turbines

2021· preprint· en· W4238113649 on OpenAlexafffund
John Adam MacQuarrie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsToronto Metropolitan University
FundersCompute Canada
KeywordsWind powerRenewable energyTurbineAerodynamicsEnvironmental scienceWind speedMarine engineeringAutomotive engineeringComputer scienceMeteorologyEngineeringAerospace engineeringElectrical engineeringGeography

Abstract

fetched live from OpenAlex

The omnipresence of wind, low production cost and much advancement within the field, wind power provides a vast and promising renewable energy resource. With the current high prices of oil and pressure to reduce carbon emissions, wind energy has achieved great interest creating high demands for innovative wind technology. Additionally, producing energy at the door step of consumers, such as at consumer dwellings in urban areas, wind power provides a means of producing efficient and reliable energy. The use of architectural structures to provide an augmentation source for the wind has been pursued by some ambitious architects but the effects of building augmentation are still uncertain. This study used computational fluid dynamic models to analyze varying building geometries and their effects on power augmentation. A porous region was used to model a wind turbine back pressure across the gap between the buildings. Results show augmentation increases power production one to two times compared to equivalent size free-standing wind turbines. Results also show that certain wind incidence angles provide the best augmentation indicating that building geometry is optimal when design takes into consideration winds regularity azimuth.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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.

Study designSimulation or modeling
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

Citations1
Published2021
Admission routes2
Has abstractyes

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