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Record W4387456302 · doi:10.1142/s0217979224503703

Squeezing flow in the existence of carbon nanotubes past a Riga plate

2023· article· en· W4387456302 on OpenAlexaff
Islam Zari, Farooq H. Ali, Taza Gul, Chinwendu E. Madubueze, Ishtiaq Ali

Bibliographic record

VenueInternational Journal of Modern Physics B · 2023
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsYork University
Fundersnot available
KeywordsNusselt numberMaterials scienceCarbon nanotubeDimensionless quantityHomotopy analysis methodBoundary value problemBoundary layerMechanicsNanofluidFlow (mathematics)Thermal radiationHeat transferNanoparticleComposite materialThermodynamicsNanotechnologyHomotopyPhysicsReynolds numberMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Squeezing or squeeze flows have tremendous applications in applied fields, like engineering, biomedical sciences and rheological studies. This paper demonstrates the squeezing flow of kerosene-based nanoliquids between parallelly aligned plates, with a Riga-type fixed lower boundary. The effects of dispersing two types of copper-functionalized carbon nanotubes (CNTs), single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) are examined. Using appropriate transformations, a self-similar ordinary differential system is derived from the governing model of partial differential equations and substantial boundary conditions. Using the homotopy analysis method (HAM) and the [Formula: see text] package, analytical and numerical estimates are obtained, respectively. For higher values of the squeezing parameter, dimensionless temperature increases, while velocity patterns are upside-down. Moreover, increments in dimensionless parameters representing Riga constituent width, modified Hartmann number, nanoparticle concentration and radiation parameter improve heat transfer rates and thermal boundary layer thickness, however, adversely affect velocity. Despite enhanced friction effects, numerical results show that the Nusselt number increases as nanoparticle loads and radiation parameters increase. This suggests that convection rates are improved over conduction rates. Excellent agreement is found between analytical and numerical evaluations. Apparently, it is noticed that MWCNTs perform better than SWCNTs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.256

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 head0.243
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations3
Published2023
Admission routes1
Has abstractyes

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