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Record W2747785560 · doi:10.11159/ffhmt17.185

Heat Transfer Characteristics of Submerged Two Phase Impinging Jets

2017· article· en· W2747785560 on OpenAlexvenueno aff
Abdullah M. Kuraan, Victoria J. Centofanti, Yunus Ulus, Kyosung Choo

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferMechanicsMaterials sciencePhase (matter)Physics

Abstract

fetched live from OpenAlex

Impinging jets are widely applied in many engineering applications for the cooling, heating, and drying of surfaces due to higher rates of cooling, heating, and drying.Many researchers have investigated the heat transfer and fluid flow characteristics of single phase impinging jets in the past decades [1][2][3].Recently, several researchers have examined heat transfer and fluid flow characteristics of single phase impinging jets at low nozzle-to-plate spacings and high nozzle-toplate spacings [4][5].However, the understanding of heat transfer and fluid flow characteristics for submerged two-phase impinging jets in the wide range of nozzle-to-plate spacings is still limited.The purpose of this study is to determine the effect of nozzle-to-plate spacing on heat transfer and fluid flow characteristics of submerged two-phase impinging jets on a flat surface using water and air as the working fluids.The effects of the nozzle-to-plate spacings (H/d = 0.08 -30) on the Nusselt number and pressure are considered under a fixed water flow rate condition.Stagnation pressure of the submerged two-phase impinging jets was measured to understand a relationship with heat transfer characteristics.The nozzle-to-plate spacing was controlled with an x-y-z stage with a 10 µm resolution, (Thorlabs, Inc, PT3A/M).Compressed air was supplied from a mass flow controller (Omega FMA5520A) having an accuracy level of ±1% and a repeatability of ±0.15%.A grear pump (Micropump) was used to control water flow rate.A stainless steel foil was used as a heated surface, which was connected with a high voltage DC power supply (Agilent 6651A #J03) in series with a shunt, rated 0-6 V and 0-60 A. Four K-type thermocouples of diameter 0.08 mm were used to measure temperature using OMEGA (OM-CP-QuadTemp2000) digital data acquisition system.The results show that the Nusselt number and stagnation pressure are divided into three regions; Region I) jet deflection region (H/d ≤ 0.5), Region II) inertia dominant region (0.5 < H/d ≤ 7), and Region III) buoyancy region (7 < H/d ≤ 30).In region I, the Nusselt number drastically increases with decreasing the nozzle-to-plate spacing due to the increase of the stagnation pressure with bubble collisions and bursts.In region II, the effect of the nozzle-to-plate spacing is negligible on the Nusselt number due to the inertia dominant effect in the potential core region with bubble collisions.In region III, the Nusselt number monotonically decrease with increasing the nozzle-to-plate spacing due to the decrease of bubble collisions on the surface by buoyancy force.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.263
Teacher spread0.235 · 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".

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Citations0
Published2017
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