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Record W2000316236 · doi:10.1115/ajtec2011-44652

Two-Phase Cooling Characteristics of Mono-Dispersed Droplets Impacted on a Heated Surface

2011· article· en· W2000316236 on OpenAlexaff
Seyed Reza Mahmoudi, Kazimierz Adamiak, G.S.P. Castle

Bibliographic record

VenueASME/JSME 2011 8th Thermal Engineering Joint Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceHeat fluxStrouhal numberCoolantVolumetric flow rateHeat transferMechanicsSuperheatingFlux (metallurgy)Analytical Chemistry (journal)ThermodynamicsChemistryPhysicsChromatography

Abstract

fetched live from OpenAlex

Droplet impact cooling has been shown to be a promising method for high heat flux removal applications. Recent experimental studies have revealed that even higher heat transfer at low mass fluxes and low Weber number can be achieved with only few degrees of superheat. In the present work, mono-dispersed droplet cooling of a horizontal upward facing heated surface was investigated at low Weber numbers. The impact velocity and frequency of free falling stream of droplets were varied dependently through changing the gap between the heated surface and tip of different capillaries and variation of volumetric flow rate (0.5–4.7 cc/min).The range of impact velocity and droplet frequency was ranged between 0.28 to 1.3 m/s and 0.5 Hz to 5 Hz, respectively using different capillaries size between 17g to 22g. The coolant was 25°C deionized water and all the experiments were performed at atmospheric pressure. The time-averaged two-phase characteristic curves were obtained up to Critical Heat Flux (CHF)-regime. Through the extensive set of experiments, two separate correlations are proposed to predict the average CHFs based on the Weber between 3

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.028
GPT teacher head0.216
Teacher spread0.188 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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