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Record W4283587244 · doi:10.11159/ffhmt22.125

Influence of Porous Oxide Layer on Water Spray Cooling

2022· article· en· W4283587244 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2022
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
FundersMinisterstvo Školství, Mládeže a Tělovýchovy
KeywordsMaterials scienceLayer (electronics)PorosityOxideEnvironmental scienceComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Steel production and processing are connected with the formation of an oxide layer on a hot surface of steel. The oxide layer influences cooling and the final quality of the steel. Spray cooling is mainly influenced by water impingement density and by surface temperature, but the influence of the oxide layer is not negligible. The oxide layer can be considerably porous. The porosity of the oxide layer significantly influences its thermal conductivity, because air pores have much lower thermal conductivity compared to pure oxides. In this paper, the influence of the oxide layer on water spray cooling is experimentally and numerically investigated. The heat transfer coefficient of an oxidized steel surface and a clean steel surface are compared and the effect of the oxide layer on the Leidenfrost temperature is studied. Also, the porosity of the oxide layer and the average thermal conductivity of the porous oxide layer are determined for different oxidation regimes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.385

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 head0.015
GPT teacher head0.218
Teacher spread0.204 · 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