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Record W2559499924 · doi:10.1139/cjp-2016-0570

Analysis of entropy generation and natural convection in an inclined partially porous layered cavity filled with a nanofluid

2016· article· en· W2559499924 on OpenAlex
Taher Armaghani, Muneer A. Ismael, Ali J. Chamkha

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidRayleigh numberNatural convectionPhysicsLaminar flowMechanicsHeat transferThermalVolume fractionPorous mediumPorosityThermodynamicsRayleigh scatteringFinite volume methodConvectionMaterials scienceComposite materialOptics

Abstract

fetched live from OpenAlex

The present numerical study investigates the analysis of thermodynamic irreversibility generation and the natural convection in inclined partially porous layered cavity filled with a Cu–water nanofluid. The finite difference method with up-wind scheme is used to solve the governing equations. The study is achieved by examining the effects of nanoparticle volume fraction, inclination angle, and the porous layer thickness. Besides, the computations are achieved within the laminar range of the Rayleigh number. The results show that at Ra = 10 4 , a reduction of total entropy generation is recorded with increasing nanoparticle volume fraction when the porous layer thickness is greater than 0.2. Moreover, when Ra is less than 10 5 , the nanoparticle volume fraction increases the heat transfer irreversibility, and improves the overall thermal performance. It is found also that for a low Rayleigh number, the largest porous layer thickness and the highest cavity orientation improve the thermal performance. On the contrary, at high Rayleigh numbers, these parameter ranges give the worst thermal performance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.912

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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