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Record W3084320174 · doi:10.32393/csme.2020.1186

RANS Simulation of HAWT Immersed on ABL Flow under Neutral and Stratified Conditions Using OpenFOAM

2020· article· en· W3084320174 on OpenAlexaff
Mohammed Nadjib Hamlaoui, Hachimi Fellouah, Arezki Smaili

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsReynolds-averaged Navier–Stokes equationsFlow (mathematics)MechanicsComputational fluid dynamicsComputer scienceEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

This paper proposes numerical method for aerodynamic performance predictions of Horizontal Axis Wind Turbines (HAWT) immersed on Atmospheric Boundary Layer (ABL) flows under unstable, stable and neutral conditions. The flow field has been described using the three-dimensional RANS equations complemented by the k turbulence model with modified constants corresponding to ABL flows. The density variation has been introduced into the momentum equation using the Boussinesq approximation and appropriate buoyancy term has been included. The equation of energy based on potential temperature has been implemented and solved to take into account the effect of temperature on the flow field (turbulence). The ABL profiles ( ,

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 score0.065
Threshold uncertainty score1.000

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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueProgress in Canadian Mechanical Engineering. Volume 3Same topicLattice Boltzmann Simulation StudiesFrench-language works237,207