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Record W1994239837 · doi:10.1115/fedsm2014-21868

Numerical Simulations of Wind Effects on an Array of Ground Mounted Solar Panels

2014· article· en· W1994239837 on OpenAlexafffundabout
Chowdhury Jubayer, Horia Hangan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWind Energy Institute of Canada
FundersOntario Centres of Excellence
KeywordsWind gradientWind tunnelPlanetary boundary layerLog wind profileReynolds-averaged Navier–Stokes equationsRoughness lengthParticle image velocimetryWind profile power lawTurbulenceBoundary layerWakeWind speedWind shearMeteorologyAerodynamicsMechanicsPhysicsGeology

Abstract

fetched live from OpenAlex

In this study, numerical simulations using unsteady Reynolds-Averaged Navier-Stokes (RANS) approach with Shear Stress Transport (SST) k-ω turbulence closure are employed to investigate the wind loads and wind flow field of a ground mounted solar panel array. Atmospheric boundary layer wind profiles for open terrain roughness with Reynolds number of 2.2×106, based on the wind speed at the lower edge and the chord length of a stand-alone system, are employed. Four different wind directions (0°, 45°, 135° and 180°) are considered. The numerical modeling approach employed in this study is validated for a stand-alone solar panel system by comparing the surface pressures with the study by [1] and the velocity field with a Particle Image Velocimetry (PIV) measurement carried out in the Boundary Layer Wind Tunnel I at the Western University, Canada. Analyzing the wind flow field for the array configuration shows that for 0° and 180° wind directions, all trailing rows are in the complete wake of the first windward row. It is also shown that in terms of maximum drag and lift, 0° and 180° wind directions are the critical wind directions with the first windward row being the critical row. On the other hand, in terms of overturning moment, 45° and 135° are the critical wind directions, with similar overturning moment coefficients for each row.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.407

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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 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

Citations1
Published2014
Admission routes3
Has abstractyes

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