MétaCan
Menu
Back to cohort
Record W2158262239 · doi:10.1115/1.2213271

New Spatio-Temporal Wind Exergy Maps

2006· article· en· W2158262239 on OpenAlexafffundabout
Ahmet Duran Şahin, İbrahim Dinçer, Marc A. Rosen

Bibliographic record

VenueJournal of Energy Resources Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaIstanbul Teknik ÜniversitesiUniversity of Ontario Institute of Technology
KeywordsExergyWind powerEnvironmental scienceWind speedTurbineMeteorologyEnergy (signal processing)Atmospheric sciencesMarine engineeringEngineeringGeologyGeographyMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, energy and exergy characteristics of wind energy are investigated. The effects of wind speed and air temperature and pressure at the inlet of a wind turbine on windchill temperature are examined. We also investigate energy and exergy efficiencies of the wind energy generating system and verify the models through a case study on a 100 kW wind generating system for 21 climatic stations in the province of Ontario, Canada. New energy and exergy efficiency maps of the wind energy generating system are introduced to provide a common basis for regional assessments and interpretations. These efficiency maps are plotted for 4 months of the year (January, April, July, October), which are taken to be representative months of the seasons. The results show that aerial differences between energy and exergy efficiencies are approximately 20%–24% at low wind speeds and approximately 10%–15% at high wind speeds.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.004
GPT teacher head0.188
Teacher spread0.183 · 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 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

Citations31
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Energy Resources TechnologySame topicWind Energy Research and DevelopmentFrench-language works237,207