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Record W2889068730 · doi:10.1139/tcsme-2017-0096

Performance of a Darrieus turbine on the roof of a building

2018· article· en· W2889068730 on OpenAlexaffvenue
Samson Victor, Marius Paraschivoiu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsTurbineRoofMarine engineeringComputational fluid dynamicsEnvironmental scienceWind powerMeteorologyAerospace engineeringEngineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

Various efforts have been made to introduce micro wind turbines in urban areas. Their onsite wind generation can be beneficial, but general rules about their ideal placement in terms of energy extraction need to be identified. This paper investigates the performance of a Darrieus turbine when installed on the roof of a building and offers a rare analysis of the synergy between the turbine and the building. This study focuses on computational fluid dynamic (CFD) analysis of a vertical-axis wind turbine mounted on the upstream edge of a building. The CFD methodology is validated by comparing the calculated performance with experimental data. Three different turbine positions at different heights are investigated to capture the C p –λ curve sensitivity. Positions 1 and 2 are at the edge of the building, whereas position 3 is a few meters away from the edge, directed towards the geometric center of the building. To simulate realistic atmospheric wind conditions, an atmospheric boundary layer is imposed at the inlet. The results show that the power coefficient is higher compared with a standalone turbine and that the location of the turbine on the building clearly affects the value of the tip-speed ratio at maximum power coefficient.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.266

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 science0.0000.000
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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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 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

Citations12
Published2018
Admission routes2
Has abstractyes

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