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Record W2915765638 · doi:10.1139/cjp-2018-0473

Influence of heat generation on magnetohydrodynamic (MHD) flow using a theory of kinetics for liquids

2019· article· en· W2915765638 on OpenAlexvenueno aff
Azad Hussain, Fouzia Javed, M.Y. Malik, Sumaira Ameer

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetohydrodynamic driveStreamlines, streaklines, and pathlinesMagnetohydrodynamicsPhysicsMechanicsWork (physics)Flow (mathematics)ThermodynamicsOrdinary differential equationClassical mechanicsDifferential equationMagnetic field

Abstract

fetched live from OpenAlex

In the present article, the influence of heat generation on the magnetohydrodynamic (MHD) flow of an Eyring–Powell fluid along a permeable plate has been explored. The influence of heat generation or absorption on a steady flow of non-Newtonian fluid over a surface is investigated. The governing equations obtained from Eyring–Powell fluid model are transubstantiated into ordinary differential equations using suitable transformations. Along with the Runge–Kutta method, we attained a numerical solution of the present problem by explicating the shooting technique. Influences of distinct parameters on temperature and the velocity field profiles are highlighted in graphs and tables. The acceleration in the value of γ, velocity profile shows decreasing behavior, but recovery occurs with an increase in M. The streamlines and three-dimensional results have been shown graphically for a selection of different parameters. Numerical results of the present work have been discussed with the support of tables.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.199
Teacher spread0.187 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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