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Record W1875224395

Numerical Assessments of Impingement Flow over Flat Surface due to Single and Twin Circular Long Water Jets

2013· article· en· W1875224395 on OpenAlexaff
Mohammad Mohsen Seraj, Elasadig Mahdi, Mohamed S. Gadala

Bibliographic record

VenueTransaction on control and mechanical systems · 2013
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTurbulenceMechanicsMaterials scienceHeat transferJet (fluid)Flow (mathematics)ThermodynamicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Hydrodynamics of impingement flow is a key partner of heat transfer analysis of run-out table (ROT) steel cooling. Velocity and pressure profiles before and after impingement of long circular free-surface industrial water jets (Re = 16,669-50,068) was numerically studied and also wetting front propagation and size of impingement zone were computed. The turbulent models represented impingement water flows over surface better in good agreement with the experimental data at the ROT facility. Higher velocity gradient was obtained for long turbulent jets indicating enhanced heat transfer at impingement zone. The effect of local pressure on saturation temperature changes predication of boiling heat transfer correlations up to 9% which is noticeable for ROT cooling. Impingement zone was found smaller respect to the estimation used in ROT modeling obtained from short jets experiments. For twin jets, simulation show calm interaction of low flow rate water jets with no splashing in accordance with the experiment. Water film thickness in interaction zone is elevated toward jet-jet axis.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.219
Teacher spread0.208 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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