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Record W3005346602 · doi:10.1139/cjp-2018-0582

Numerical investigation of MHD Prandtl melted fluid flow towards a cylindrical surface: comprehensive outcomes

2020· article· en· W3005346602 on OpenAlexvenueno aff
Muhammad Awais, S. Bilal, Khalil Ur Rehman, M.Y. Malik

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPrandtl numberPhysicsMechanicsClassical mechanicsMagnetohydrodynamicsFluid dynamicsTurbulent Prandtl numberLorentz forceCurvatureMagnetic fieldReynolds numberTurbulenceNusselt numberGeometryHeat transferMathematics

Abstract

fetched live from OpenAlex

In this work, we explore the mathematical structuring of Prandtl fluid flow towards an inclined stretched cylinder. Modelling of this newly proposed model is manifested in three different ways. A momentum equation for the concerned model is established under the role of an induced Lorentz field, whereas the thermal and mass transport mechanisms account for the role of the heat generation–absorption process and chemical reaction phenomenon. For a more realistic and schematic analyses of the present problem, the field expressions are expressed mathematically in terms of PDEs. The obtained intricate differential system is converted into ODEs by means of transformations. Improved numerical simulation is conducted using the Cash and Carp method to foresee the pattern of thermophysical distribution towards existing involved constraints. It is noticed that Prandtl fluid velocity declines for increases in both the Prandtl fluid parameter and elastic parameter. Further, the fluid temperature increases the effects of the magnetic field, curvature, and the melting parameters while the concentration profile shows decline curves for both the Schmidt and chemical reaction parameters. It is important to note that this nature is preserved for both the magnetized and non-magnetized cases.

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 score0.252
Threshold uncertainty score0.595

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 head0.030
GPT teacher head0.221
Teacher spread0.191 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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