MétaCan
Menu
Back to cohort
Record W2946075135 · doi:10.1139/cjp-2018-0547

Implementation of Darcy–Forchheimer effect on magnetohydrodynamic Carreau–Yasuda nanofluid flow: Application of Von Kármán

2019· article· en· W2946075135 on OpenAlexvenueno aff
Mair Khan, T. Salahuddin, M.Y. Malik

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
FundersKing Khalid University
KeywordsNanofluidPhysicsWeissenberg numberMechanicsMagnetohydrodynamic driveMagnetohydrodynamicsDiffusionFlow (mathematics)ThermodynamicsHeat transferClassical mechanicsMagnetic field

Abstract

fetched live from OpenAlex

Transient MHD flow in Carreau–Yasuda nanofluid produced by impulsively started rotating disk is examined in the occurrence of Darcy–Forchheimer and chemical reactive species considering conventional Fourier’s and Fick’s laws. Appropriate transformations are used to transform the constitutive equations into nonlinear ordinary differential equations and then solve by using improved form of RK-4 Fehlberg scheme (Cash and Karp). The deviation in flow field due to velocity, friction factor, temperature, heat diffusion rate, nanoparticle concentration, and mass transfer rate is analyzed subsequent to various ambient parameters appearing in the problem. The results of the study reveal that Weissenberg number appearing in the equations leads to deceleration of the radial and tangential velocities while the power law index tends to accelerate the radial and tangential velocities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2019
Admission routes1
Has abstractyes

Explore more

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