MétaCan
Menu
Back to cohort
Record W2306713076 · doi:10.2298/tsci151130016x

A computational study of heat transfer under twin turbulent slot jets impinging on planar smooth and rough surfaces

2016· article· en· W2306713076 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThermal Science · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsMcGill University
FundersChina Scholarship CouncilNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsJet (fluid)TurbulenceHeat transferMechanicsPlanarSurface roughnessMaterials sciencePhysicsHeat transfer enhancementHeat transfer coefficientThermodynamicsComputer science

Abstract

fetched live from OpenAlex

The flow and heat transfer characteristics of twin turbulent slot jets impinging on planar smooth and rough surfaces are examined using a computational fluid dynamics model. The interaction between jets lowers the heat transfer performance of each jet in the zone where the wall jets collide. A single jet performs better than the equivalent twin jet. The average heat transfer under twin jets which are injected alternately so that each one of the pair of jets behaves like a single jet, is found to be better than twin jets issuing simultaneously. It is proposed that alternating jet flows in the twin jet arrangement is a simple novel way to enhance thermal performance of jet pairs. Along with parametric studies of the key flow and geometric parameters, effects of large temperature differences between the jet air and the target surface being heated, and model roughness of the target surface are also evaluated. Interestingly, roughness can lower the heat transfer performance in the impingement zone as the fluid can get trapped in the valleys in the rough surface.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.316

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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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