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Record W2801879350 · doi:10.7508/jmerd.2017.04.001

Analysis of Laminar Flow in a Porous Pipe with Slip Velocity

2017· article· en· W2801879350 on OpenAlexvenueno aff
Ashwini Bhat, Nagaraj N. Katagi, N. M. Bujurke

Bibliographic record

VenueMechanical Engineering Research · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLaminar flowHomotopy analysis methodSlip (aerodynamics)MechanicsShear stressVector fieldReynolds numberMathematicsSeries (stratigraphy)PorosityGeometryMaterials scienceHomotopyGeotechnical engineeringGeologyPhysicsThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

In this manuscript, we present the semi-numerical solution for laminar flow in a porous pipe with velocity slip. The flow is analyzed by employing Computer extended series method(CES) and Homotopy analysis method(HAM). The primary objective is to study the influence of non-zero tangential slip velocity on the velocity field and shear stress. The convergence region of the obtained solutions are examined by Domb-Sykes plot and h-curve. The validity of the series solution is further extended to a larger value of Reynolds number for different slip coefficients. Finally, we compared the results obtained by the proposed methods, and are presented in the form of graphs. The above methods admits desired accuracy and are validated with the available numerical results.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 head0.026
GPT teacher head0.291
Teacher spread0.266 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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