MétaCan
Menu
Back to cohort
Record W2007487412 · doi:10.1115/ht2012-58016

Experimental Investigation of Nanofluid Heat Transfer in a Plate Heat Exchanger

2012· article· en· W2007487412 on OpenAlexafffund
Matthew Taws, Cong Tam Nguyen, Nicolas Galanis, Iulian Gherasim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversité de SherbrookeUniversité de Moncton
FundersHydro-Québec
KeywordsNanofluidNusselt numberReynolds numberMaterials scienceHeat transferLaminar flowThermodynamicsHeat transfer enhancementHeat exchangerTurbulenceHeat transfer coefficientMechanicsConvective heat transferComposite materialPhysics

Abstract

fetched live from OpenAlex

An experimental study was carried out to determine the forced convective heat transfer and hydraulic characteristics of a chevron-type two-channel industrial PHE when used with a nanofluid. The PHE is composed of two fluid passages formed by three corrugated plates, which have a herringbone pattern and the corrugations are of a trapezoidal shape. Heated water is used on the hot side. On the cold side a mixture of 29nm-diameter CuO nanoparticles in suspension in water is forced. Collected data for the nanofluid side covers two particle volume fractions, 2% and 4.65%, and the range of Reynolds number up to 1000. Results have shown that, for a given Reynolds number, CuO-water nanofluid clearly exhibits a higher friction factor compared to that of water. Calculated Nusselt numbers have shown no significant heat transfer enhancement when using the 2% nanofluid. A decrease of heat transfer was even observed with the 4.65% nanofluid. The laminar-turbulent transition was also observed for the nanofluids studied.

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 score0.010
Threshold uncertainty score0.716

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)0.0010.000

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations6
Published2012
Admission routes2
Has abstractyes

Explore more

Same topicNanofluid Flow and Heat TransferFrench-language works237,207