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Record W2969949056 · doi:10.1063/1.5109797

Wall turbulence response to surface cooling and formation of strongly stable stratified boundary layers

2019· article· en· W2969949056 on OpenAlexafffund
Amir Atoufi, K. Andrea Scott, Michael L. Waite

Bibliographic record

VenuePhysics of Fluids · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaShared Hierarchical Academic Research Computing NetworkCompute Canada
KeywordsTurbulencePhysicsK-epsilon turbulence modelK-omega turbulence modelMechanicsBoundary layerStratified flowsLaminar flowTurbulence kinetic energyDirect numerical simulationReynolds numberStratification (seeds)Turbulence modelingStratified flow

Abstract

fetched live from OpenAlex

This paper investigates the processes by which stable boundary layers are formed through strong surface cooling imposed on neutrally stratified wall-bounded turbulence using high-resolution direct numerical simulation at a moderate Reynolds number. The adjustment of the flow to the imposed strong surface cooling is investigated. We further focus on a strongly stable case where turbulence partially collapses. We show that, due to a significant reduction in turbulence production, turbulence becomes patchy, with a band of turbulence coexisting with quiet regions. The nature of the quiet regions, which are often characterized as laminar, is investigated and shown to be consistent with viscously coupled stratified turbulence. The one-dimensional longitudinal streamwise velocity spectrum exhibits kx−5 and kx−3 behavior in the buffer and logarithmic layers, respectively, adjacent to an active region of three-dimensional turbulence with a kx−5/3 spectrum. Scenarios for turbulence recovery from such a patchy state are also discussed. We show that the presence of outer layer turbulence above z+ ≈ 300 is a key requirement for recovery. For higher values of stratification, it is shown that inner layer turbulence is damped entirely and outer layer turbulence is damped subsequently.

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 score0.398
Threshold uncertainty score0.500

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 head0.008
GPT teacher head0.208
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

Citations11
Published2019
Admission routes2
Has abstractyes

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