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Record W7125112383 · doi:10.18280/mmep.121234

Peristaltic Flow of Two-Layered Newtonian Fluids: Impact of Elasticity

2025· article· W7125112383 on OpenAlexvenueno aff
Vijaya Kumar Sankranthi, Naga Satya Srinivas Akkiraju, S. Sreenadh, Selvi Chittoor Kuppaswamy

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2025
Typearticle
Language
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsElasticity (physics)Non-Newtonian fluidFlow (mathematics)Newtonian fluidRheology

Abstract

fetched live from OpenAlex

This paper investigates the impact of elasticity on two-layered peristaltic flow of Newtonian fluids in a channel.The two-dimensional flow is considered with two regions: the peripheral and core regions.A Newtonian fluid model is applied in both regions to understand the characteristics of peristaltic transport in a channel with elastic properties.The problem is solved analytically, and expressions for axial velocity and flux are obtained.The variation in flux is studied under the influence of the channel wall's elasticity.Expressions for the stream function in both peripheral and core regions are presented.The interface, a key phenomenon in multi-phase flows, is analyzed, and the corresponding equation is derived and explained through graphs.Elastic parameters significantly affect the volume flow rate.As the elasticity of the channel wall increases, the channel expands, leading to an increase in flow rate.The observed flow characteristics suggest interesting behaviors that warrant further study of physiological fluids in multi-phase flows with elasticity.The present work includes the elasticity of the channel, which allows for a better understanding of physiological structures.This inclusion opens up the possibility for further investigation into various biological structures and physiological processes, where the elasticity of the channel plays a crucial role.

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: none
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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