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Record W2177148019 · doi:10.1139/cjp-2014-0388

Effect of heated wall position on heat transfer and entropy generation of Cu–water nanofluid flow in an open cavity

2015· article· en· W2177148019 on OpenAlexvenueno aff
Zouhaier Mehrez, Afif El Cafsi, Ali Belghith, Patrick Le Quéré

Bibliographic record

VenueCanadian Journal of Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidRichardson numberHeat transferThermodynamicsPhysicsMechanicsReynolds numberVolume fractionIsothermal processEntropy (arrow of time)Materials scienceTurbulence

Abstract

fetched live from OpenAlex

This paper reports the numerical results of the mixed convection and entropy generation of Cu–water nanofluid flow in an open cavity heated from different sides with non-uniform temperature distribution. The finite volume method is used to solve the governing equations. The analysis is carried out by a range of Richardson numbers, 0.01 ≤ Ri ≤ 10, at a nanoparticle volume fraction of 0 ≤ [Formula: see text] ≤ 0.1, and Reynolds number Re = 200, with a cavity aspect ratio of L/H = 2. Three heating modes are considered: (A) the left wall is heated (inflow side, assisting flow); (B) the horizontal bottom wall is heated; and (C) the right wall is heated (outflow side, opposing flow). The results show that the heat transfer and the entropy generation increase with increasing Richardson number and nanoparticle volume fraction. The highest heat transfer and entropy generation are obtained with heating mode C (opposing flow). The contribution of heat transfer and fluid friction irreversibilities in the entropy generation depends on Richardson number and the heater position. The present investigation shows that the configuration with non-isothermal heater located at the bottom wall (B) has the highest performance in terms of heat transfer enhancement with minimum entropy generation.

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 score0.429

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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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