MétaCan
Menu
Back to cohort
Record W2956512455 · doi:10.1139/cjp-2019-0208

Rotational impact on nanoscale particles Fe<sub>2</sub>O<sub>4</sub>, NiZnFe<sub>2</sub>O<sub>4</sub>, MnZnFe<sub>2</sub>O<sub>4</sub> suspended in C<sub>2</sub>H<sub>6</sub>O<sub>2</sub> confined between two stretchable disks: a computational study

2019· article· en· W2956512455 on OpenAlexvenueno aff
E.N. Maraj, Shakil Shaiq

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidPhysicsMagnetic fieldPrandtl numberDragMagnetic Prandtl numberMechanicsHeat transferFerrofluidReynolds numberTurbulenceNusselt number

Abstract

fetched live from OpenAlex

This communication addresses the fluid and heat transfer flanked by two stretchable rotating disks influenced by the induced magnetic field. The fluid flows due to the rotation of the stretchable disks enclosing a nanofluid having nanoparticles of three distinct ferrite compounds. The mathematical formulation of the problem is performed in a cylindrical coordinate system. Similarity analysis is applied for the sake of simplification. The velocity, pressure, induced magnetic field, and temperature distributions accompanied by surface drag force and heat flux are computed numerically by employing implicit finite difference algorithm. Effects of important emerging parameters are addressed through graphs and tables. Some pivotal findings include that magnetic parameter and reciprocal magnetic Prandtl number contribute to increasing fluid pressure near the lower disk and the opposite trend is reported in the vicinity of the upper disk. The present investigation is a benchmark problem that has a promising future in geophysics, oil refinery, and the chemical processing industry.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
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 score0.058
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0080.009
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0060.008
Science and technology studies0.0030.002
Scholarly communication0.0030.006
Open science0.0050.001
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0000.003

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicNanofluid Flow and Heat TransferFrench-language works237,207