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Record W4390058182 · doi:10.18280/mmep.100620

Exploring Micromagnetorotation in Maxwell Viscous Fluid Flow Within a Porous Cylinder

2023· article· en· W4390058182 on OpenAlexvenueno aff
Yolanda Norasia, Mohammad Ghani

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
FundersUniversitas Airlangga
KeywordsMechanicsCylinderPorous mediumViscous liquidFlow (mathematics)PorosityFluid dynamicsPhysicsGeologyClassical mechanicsGeotechnical engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

The study of viscous fluids, ubiquitous in various industrial engineering applications, frequently reveals intriguing physical phenomena.Among these, micromagnetorotation, the rotation of a viscous fluid under the influence of a magnetic field, has recently garnered significant interest.This research aims to examine the behaviour of micromagnetorotational fluid flow within a porous cylinder.Fundamental equations constituting this analysis include the continuity equation, the momentum equation, and the energy equation, leading to a system of nonlinear ordinary differential equations.These equations are then dimensionally transformed and solved using the Gauss-Seidel numerical scheme under a suitable solution assumption.The investigation focuses on parameters influencing the velocity and temperature profiles of the micromagnetorotational fluid flow, namely viscosity, the Stuart number, the Prandtl number, the material parameter, and porosity.The study reveals that modifications in any of these parameters lead to a decrease in the velocity profile.Conversely, increases in the temperature profile are observed when influenced by the viscosity parameter, the Stuart number, the material parameter, and the porosity parameter.This research is anticipated to offer valuable insights for optimizing fluid flow velocity and temperature within engineering and industrial applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.408
Threshold uncertainty score1.000

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 head0.056
GPT teacher head0.205
Teacher spread0.148 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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