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New Viscosity Data for CuO-Water Nanofluid – The Hysteresis Phenomenon Revisited

2012· article· en· W2045362949 on OpenAlex
Cong Tam Nguyen, Nicolas Galanis, Thierry Maré, Erwan Eveillard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in science and technology · 2012
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversité de SherbrookeUniversité de Moncton
Fundersnot available
KeywordsNanofluidHysteresisMaterials scienceViscosityThermodynamicsSuspension (topology)Particle (ecology)Work (physics)Atmospheric temperature rangePhase (matter)Volume (thermodynamics)Composite materialNanoparticleNanotechnologyCondensed matter physicsChemistry

Abstract

fetched live from OpenAlex

In the present work, we have experimentally investigated the stability and hysteresis behaviors of CuO-water nanofluid when submitted to a repeated heating and cooling process. Data has shown that for a low particle volume concentration, 1.6% in particular, the hysteresis phenomenon did not occur for the temperature range considered. For a higher particle concentration, 5% in particular, the hysteresis behaviour was clearly observed when fluid temperature exceeded 52°C approximately. Beyond this critical temperature, the nanofluid viscosity has increased, and such an increase even continued with a decrease of temperature during the cooling phase. Subsequent measured viscosity and observations in laboratory after the first occurrence of the hysteresis phenomenon have confirmed that the alterations on the particle suspension and on the nanofluid stability appear indeed permanent. Such alterations were found to worsen with further heating/cooling cycles.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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