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Record W1868576201 · doi:10.1139/cjp-2013-0324

Numerical simulation of magnetohydrodynamic micropolar fluid flow and heat transfer in a channel with shrinking walls

2013· article· en· W1868576201 on OpenAlexvenueno aff
Kashif Ali, Muhammad Ashraf, Nimra Jameel

Bibliographic record

VenueCanadian Journal of Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsMechanicsMagnetohydrodynamic driveDiscretizationHeat transferFluid dynamicsNonlinear systemShooting methodCompressibilityFlow (mathematics)Nusselt numberFinite difference methodClassical mechanicsBoundary value problemMagnetohydrodynamicsMathematical analysisThermodynamicsReynolds numberTurbulenceMathematics

Abstract

fetched live from OpenAlex

We numerically study the steady hydromagnetic (magnetohydrodynamic) flow and heat transfer characteristics of a viscous incompressible electrically conducting micropolar fluid in a channel with shrinking walls. Unlike the classical shooting methodology, two distinct numerical techniques are employed to solve the transformed self-similar nonlinear ordinary differential equations (ODEs). One is the combination of a direct and an iterative method (successive over-relaxation with optimal relaxation parameter) for solving the sparse system of linear algebraic equations arising from the finite difference discretization of the linearised ODEs. For the second one, a pseudotransient method is used where time plays the role of an iteration parameter until the steady state is reached. The two approaches may be easily extended to other geometries (for example, sheets, disks, and cylinders) with possible wall conditions like slip, stretching, rotation, suction, and injection. Effects of some physical parameters on the flow and heat transfer are discussed and presented through tables and graphs. Detailed description of the computational procedure and the results of the study may be beneficial for the researchers in the flow and thermal control of polymeric processing.

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 score0.065
Threshold uncertainty score0.427

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 head0.006
GPT teacher head0.172
Teacher spread0.167 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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