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Record W3114315511 · doi:10.18280/ijdne.150615

3D Transient Spray Cooling Heat Transfer Simulation for Metallic Slabs of Various Alloys

2020· article· en· W3114315511 on OpenAlexvenueno aff
Mohamed Elguerri, Farid Belfodil, Yassine El Guerri, Abdelilah Bouragba

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceHeat transferHeat fluxTransient (computer programming)Water coolingMechanicsHeat transfer coefficientMetallurgyMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Water spray cooling is widely used in many industrial processes to control the surface dissipation of a material ported at high temperatures. To predict heat transfer and obtain the rate of required temperature distributions of the surface, it is necessary to understand the basic spray cooling dynamics and a more precise estimation of the heat transfer rate. This paper is about a three-dimensional simulation to estimate the transient heat transfer obtained locally by water spray to reduce the temperature of heated metal. The use of water spraying is a practical and flexible process. It is possible to vary, in space, time, and in large proportions the flux of extracted heat and controls the density of the flow of water which is a key element and very simple to achieve. Globally, the aim of this study is to simulate the spray cooling of different metal slabs for various alloys (steel, cast iron, titanium, nickel) by mainly comparing cooling in maps of iso-surfaces and in curves (at starts and globally) obtained after estimation of the heat flux.

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: none
Teacher disagreement score0.780
Threshold uncertainty score0.572

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 head0.014
GPT teacher head0.240
Teacher spread0.225 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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