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Experimental Investigation on Heat Transfer and Pressure Drop of CNT-Base Oil Nano-Fluid Flow in Rectangular Channels under Constant Wall Temperature

2012· article· en· W1998579231 on OpenAlexaff
Mohammad Reza Naghavi, M.A. Akhavan-Behabadi, Mohammad Pakdaman

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPressure dropMaterials scienceHeat transferHeat transfer coefficientThermodynamicsMechanicsLaminar flowIsothermal processConvective heat transferFluid dynamicsHeat transfer enhancementDrop (telecommunication)Composite materialMechanical engineering

Abstract

fetched live from OpenAlex

An experimental investigation has been carried out to study the heat transfer and pressure drop characteristics of MWCNT-Base oil nano-fluid flow inside horizontal rectangular channels under constant wall temperature. The temperature of the tube wall was kept constant at around 95 °C to have isothermal boundary condition. The required data were acquired for laminar fully developed flow inside round and rectangular channels. The effect of different parameters such as mass velocity, aspect ratio of rectangular channels and nano-particles concentration on heat transfer coefficient and pressure drop of the flow is studied. Observations show that the heat transfer performance is improved as the aspect ratio is increased. Also, increasing the aspect ratio will result in the pressure drop increasing. In addition, the heat transfer coefficient as well as pressure drop is increased by using nano-fluid instead of base fluid. Furthermore, the performance evaluation of the two enhanced heat transfer techniques studied in this investigation showed that applying rectangular channels instead of the round tube is a more effective way to enhance the convective heat transfer compared to the second method which is using nano-fluids instead of the base fluid.

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)0.001
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 score0.006
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.035
GPT teacher head0.286
Teacher spread0.252 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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