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Pressure Drop and Heat Transfer Characteristics of MWCNT/Heat Transfer Oil Nanofluid Flow inside Microfinned Helical Tubes with Constant Wall Temperature

2012· article· en· W1992388896 on OpenAlexaff
M. Kazemi, 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
KeywordsNanofluidLaminar flowMaterials sciencePressure dropHeat transferHeat transfer enhancementReynolds numberThermodynamicsMechanicsDimensionless quantityHeat transfer coefficientTurbulence

Abstract

fetched live from OpenAlex

Experiments are performed to investigate the single-phase flow heat transfer augmentation of MWCNT/HT-B oil in both smooth and microfinned helical tubes with constant wall temperature. The tests in laminar regime were carried out in helical tubes with three curvature ratios of 2R/d=25, 30 and 35. Flow Reynolds number varied from 170 to 1800 resulting in laminar flow regime. The effect of some parameters such as the nanoparticles concentration, the dimensionless curvature radius (2R/d) and the Reynolds number on heat transfer was investigated for the laminar flow regime. The weight fraction of nanoparticles in base fluid was less than 0.4%. within the applied range of Reynolds number; results indicated that for smooth helical tube the addition of nanoparticles to the base fluid enhanced heat transfer remarkably. However, compared to the smooth helical tube, the average heat transfer augmentation ratio due to nanoparticle addition for finned tube was small, about 17%. Also, by increasing the weight fraction of nanoparticles in microfinned helical tubes, no substantial changes were observed in the rate of heat transfer enhancement. For the pressure drop, the results show that the pressure drop of nanofluids was slightly higher than the base fluid and increase as the volume concentrations go up.

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 categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.256
Teacher spread0.240 · 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.

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

Citations2
Published2012
Admission routes1
Has abstractyes

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