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Record W2115002581 · doi:10.1155/s1023621x03000034

Abnormally High Power Output of Wind Turbine in Cold Weather: A Preliminary Study

2003· article· en· W2115002581 on OpenAlexafffundabout
Christophe Leclerc, Christian Masson

Bibliographic record

VenueInternational Journal of Rotating Machinery · 2003
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDensity of airStall (fluid mechanics)TurbineEnvironmental scienceWind powerMeteorologyWind speedWind profile power lawAtmospheric sciencesMechanicsPhysicsElectrical engineeringThermodynamicsEngineering

Abstract

fetched live from OpenAlex

According to popular belief, air temperature effects on wind turbine power output are produced solely by air density variations, and power is proportional to air density. However, some cases have been reported, all involving stall‐controlled wind turbines, in which unexpected high power output was observed at very low temperatures. As a preliminary study, this article intends to quantify the influence of air temperature on the power production of the Tacke TW600 wind turbine installed in Tiverton, Ontario, Canada. Increases in power output due to air temperature variation are stratified by wind velocity, showing that these increases are below the theoretical limits of air density variations during operation in low winds and are comparable to and beyond those theoretical limits at higher wind velocities. At – 9°C and 0°C, narrow bands of power at distinct levels are observed in the stall regime of the turbine; they are typical of many stall phenomena observed on stall‐controlled rotors, but these levels have been found to be independent of any parameters recorded.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2003
Admission routes3
Has abstractyes

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