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Record W4317401774 · doi:10.18280/mmep.090606

Maxwell Dusty Fluid Flow Past a Variable Thickness Porous Stretching Surface

2022· article· en· W4317401774 on OpenAlexvenueno aff
Mohamed Fathy, Emad A. Sayed

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsSurface (topology)MechanicsPorosityFlow (mathematics)Porous mediumMaterials scienceVariable (mathematics)Fluid dynamicsGeologyComposite materialPhysicsGeometryMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

An efficient Legendre-Galerkin method was applied to obtain a solution of the Maxwell dusty fluid flow past a changeable thickness stretching surface in porous medium.It is common to reduce a governing system of partial differential equations to a collection of ordinary differential equations using similarity transformation to obtain the asymptotic solutions using the Legendre-Galerkin technique.Comparison of the numerical results is made with previously published results under the special cases.The results are found to be in a good agreement.Numerical results were obtained for the skin-friction, velocity profiles of the fluid as well as velocity profiles of dusty particles for selected values of the governing parameters, such as velocity power index 𝑛, wall thickness α, elasticity β, fluid particle interaction βv, porosity γ, and mass concentration ℓ parameters.Also, the impacts of these parameter on the physical (surface heat flux and surface shear stress) and mechanical properties (surface cracking, strength, hardness, stiffness, etc.) are presented and investigated.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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 studies0.0000.001
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 head0.015
GPT teacher head0.177
Teacher spread0.162 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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