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Record W2037424734 · doi:10.1115/ht2007-32438

Influence of Wall Heating on Turbulent Structure

2007· article· en· W2037424734 on OpenAlexaff
Shivani T. Gajusingh, Kamran Siddiqui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsConcordia University
Fundersnot available
KeywordsLaminar flowTurbulenceMechanicsBuoyancyOpen-channel flowFlow (mathematics)Chézy formulaMean flowMaterials sciencePhysics

Abstract

fetched live from OpenAlex

An experimental study was conducted to investigate the impact of wall heating on the flow structure in the near-wall region inside a square channel. PIV was used to measure the two-dimensional velocity fields. The measurements were conducted for a range of mass flow rates that cover laminar and turbulent regimes. The results have shown that when a flow is unstably stratified via heating through a bottom wall, both the mean and turbulent characteristics are affected. The results have shown that the impact of wall heating on the flow behavior is significantly different for laminar and turbulent flow regimes. It was found that when a flow that is originally laminar is heated, the mean streamwise velocity in the near-wall region is significantly increased and turbulence is generated in the flow predominantly due to buoyancy. When the flow is in the turbulent regime, addition of heat reduces the magnitudes of mean streamwise velocity and turbulent properties. The reduction in the magnitudes of turbulent properties in this flow regime is due to the working of turbulence against the buoyancy forces.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.199
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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
Published2007
Admission routes1
Has abstractyes

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