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Record W3160837970 · doi:10.18280/acsm.450201

Enhanced Heat Transfer by Oil/Multi-Walled Carbon Nano-Tubes Nanofluid

2021· article· en· W3160837970 on OpenAlexvenueno aff
Abdelhakim Boursas, Mohamed Salmi, Giulio Lorenzini, Hijaz Ahmad, Younes Menni, Djamal Fridja

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidMaterials scienceHeat exchangerHeat transferTurbulenceHeat transfer enhancementMechanicsFlow (mathematics)ThermalFluid dynamicsHeat transfer coefficientComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The subject of the study is mainly based on thermal reinforcement by an oily fluid containing nanometer particles of carbon. The study is carried out by the presence of discontinuous bars in two different shapes, i.e. flat and V, inside a horizontal heat exchanger. The study relies on simulations in thermal and dynamic terms from the literature. The turbulence effect is diagnosed by applying the k-ε model, while the flow hydrothermal transport relationships are modeled based on the finite volume technique. Both the flow and heat-transfer aspects of all channel regions are studied and analyzed. The new heat-exchanger structure has been enhanced in the presence of these discontinuous bars by reducing the friction coefficient and eliminating stations with poor transfer of heat behind these deflectors.

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 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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.236
Teacher spread0.217 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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