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CONVECTIVE BOILING WITH ELECTROHYDRODYNAMIC ENHANCEMENT: THE INFLUENCE OF INLET QUALITY

2016· article· en· W2561782588 on OpenAlexaff
Gerard McGranaghan, A.J. Robinson

Bibliographic record

VenueInterfacial phenomena and heat transfer · 2016
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsMcMaster University
FundersIrish Research Council
KeywordsElectrohydrodynamicsMaterials scienceMechanicsBoilingHeat transferHeat fluxConvectionBaffleHeat transfer coefficientElectrodeChemistryThermodynamics

Abstract

fetched live from OpenAlex

This work investigates the influence of alternating current electric fields on the flow patterns, associated heat transfer, and power penalty during convective boiling of HFE7000. A single-pass counter-flow heat exchanger is employed, whereby heated water flowing in the shell side transfers heat to the two-phase HFE7000 fluid flowing within the tube side. In a novel design feature, optical transparency is achieved by using a sapphire central tube surrounded by a Perspex water jacket containing the heated water. A stainless steel rod running concentrically through the tube acts as an electrode while the outer surface of the sapphire tube is coated with a thin layer of indium tin oxide forming an electrically conductive and optically transparent ground to establish an electric field across the working fluid. This unique test setup facilitates visualization of the flow patterns caused by the electrohydrodynamic (EHD) forces and allows high-speed videography of the HFE7000 while boiling. Tests were performed at a low mass flux (100 kg/m2s) and fixed average heat flux (12 kW/m2)for inlet qualities of 2%, 15%, 30%, and 45% and applied voltages of V = 0, 4, and 8 kV. The results show that the average heat transfer coefficient improves with applied voltage over the entire range of qualities tested. However, as inlet quality increases the heat transfer enhancement tends to decrease, as does the electrical power required for EHD (EHD penalty). Conversely, pumping losses were seen to increase as inlet quality increases.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.271

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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