Peristaltic Flow of Two-Layered Newtonian Fluids: Impact of Elasticity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".