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Record W2073733223 · doi:10.1115/fedsm-icnmm2010-30778

Analytical Treatment of Heat Transfer in Electrokinetic Flows

2010· article· en· W2073733223 on OpenAlexafffund
Jafar Jamaati, Hamid Niazmand, Metin Renksizbulut

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaFerdowsi University of Mashhad
KeywordsElectrokinetic phenomenaZeta potentialSlip ratioMechanicsMicrochannelSlip (aerodynamics)Electric fieldElectric potentialHeat transferElectro-osmosisElectrohydrodynamicsFlow velocityVoltageThermodynamicsChemistryFlow (mathematics)PhysicsElectrophoresisQuantum mechanicsShear stress

Abstract

fetched live from OpenAlex

This paper investigates the effects of velocity slip in the presence of an electric double-layer on fluid flow and heat transfer in a parallel plate hydrophobic microchannel. The electric potential filed is determined through the Poisson-Boltzmann equation together with the Debye-Hu¨ckel (D-H) approximation, while the velocity field is obtained by solving the Navier-Stokes equations under fully developed conditions. In most previous studies, zeta-potential has been considered as an independent variable for the analysis of induced voltage. However, experimental findings show that in electrokinetic slip flows with constant wall potential, the zeta potential is related to the slip coefficient and the D-H parameter. Therefore, in the present study, the wall potential is considered as an independent variable and the zeta potential is determined from an available experimental correlation. The effects of velocity slip, the D-H parameter, the wall potential and the Brinkman number on the induced voltage and the velocity and temperature fields are examined in detail. Results indicate that the slip effects on the zeta potential dramatically affect the flow and temperature fields.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.220

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 head0.213
Teacher spread0.207 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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