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Record W2771090623 · doi:10.2495/cmem-v6-n2-291-302

Ferrofluidic plug flow heat transfer enhancement

2017· article· en· W2771090623 on OpenAlexfundno aff
Nicolette Gan Jia Gui, Cameron Stanley, Nam‐Trung Nguyen, Gary Rosengarten

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2017
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsnot available
FundersRMIT UniversityCommonwealth Scientific and Industrial Research OrganisationAustralian Research CouncilOntario Ministry of Natural Resources and Forestry
KeywordsFerrofluidMaterials scienceMicrochannelHeat transferMiniaturizationHeat sinkMechanicsHeat transfer enhancementNanofluidTwo-phase flowMicrofluidicsMagnetic fieldFlow (mathematics)Heat transfer coefficientThermodynamicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Overheating of power electronic devices has become a significant issue due to their continued miniaturization and increased heat flux that needs to be dissipated. Microchannel heat sinks utilising two-phase flow are capable of very high heat transfer rates and represent a possible means of cooling such devices. In this paper, we focus on two-phase liquid-liquid plug flow using water-based ferrofluid (magnetic nanofluid) plugs as the dispersed phase and silicone oil as the continuous phase. An external magnetic field was applied to generate enhanced mixing of the microfluidic flow. We show that material properties of the ferrofluid plug influence heat transfer properties of the microfluidic flow, and demonstrate that cooling performance is further enhanced by the application of an external magnetic field, which induces mixing. We also show that microchannel heat transfer using a ferrofluid is superior to that using de-ionized water as the dispersed phase for two-phase liquid-liquid plug flow.

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 score0.245
Threshold uncertainty score0.437

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 head0.056
GPT teacher head0.376
Teacher spread0.320 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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