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The height of maximum speed-up in the atmospheric boundary layer flow over low hills

2004· article· en· W2026812727 on OpenAlexfundno aff
C. Pellegrini, Gustavo C. R. Bodstein

Bibliographic record

VenueJournal of the Brazilian Society of Mechanical Sciences and Engineering · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoYork University
KeywordsWind speedPlanetary boundary layerBoundary layerAtmosphere (unit)Momentum (technical analysis)InertiaFlow (mathematics)TurbulenceLog wind profileMathematicsMechanicsField (mathematics)MeteorologyMathematical analysisWind gradientPhysicsWind profile power lawClassical mechanics

Abstract

fetched live from OpenAlex

In this paper, we study the height of the maximum speed-up, l, for atmospheric boundary layer flows over low hills in a neutral atmosphere. A recent analytically-derived expression for l is compared to the results of several other expressions available in the literature. A critical analysis of all these equations is presented and a new constant obtained from field data is proposed for one of them. We find that the new expression describes observational data better than the others. Through an order of magnitude analysis, we also show that the inner layer depth, calculated as the height where inertia and turbulent forces dominate the other terms and balance each other in the x-momentum equation, can also be used to estimate the height of maximum speed-up. Starting from four analytical speed-up profiles available in the literature, we calculate l by searching for the critical points of these speed-up functions, resulting in new equations for l. All these equations are analysed and our results suggest which one of them performs better when compared to field and wind tunnel data.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations7
Published2004
Admission routes1
Has abstractyes

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