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Record W2970967319 · doi:10.1615/thmt-18.540

Comparison of Different Heat Transfer Mechanisms Governing the Growth of a Bubble on a Heated Surface and in a Subcooled Flow

2018· article· en· W2970967319 on OpenAlexaff
Maryam Medghalchi, Nasser Ashgriz

Bibliographic record

VenueProceeding of THMT-18. Turbulence Heat and Mass Transfer 9 Proceedings of the Ninth International Symposium On Turbulence Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBubbleSubcoolingHeat transferThermodynamicsMaterials scienceMechanicsIsothermal processThermal conductionHeat fluxEvaporationBubble pointReynolds numberNucleate boilingPhysicsTurbulence

Abstract

fetched live from OpenAlex

Growth of a non isothermal bubble on a heated horizontal surface in a subcooled flow is studied to determine the significance of different heat transfer mechanism on the bubble growth. The heat transfer mechanisms that are considered are: (i) the microlayer evaporation, (ii) transient thermal boundary layer conduction, and (iii) bubble surface evaporation and condensation. The results indicate that a different heat transfer mechanism dominates the bubble growth at different stages of the bubble growth. And the temperature gradient inside the bubble decreases after bubble lift off in high Reynolds numbers. The results also show an oscillatory heat flux during the initial stages of the bubble growth.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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.

Study designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

Explore more

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