Investigation of Jeffrey fluid flow behavior in microchannels with slip under combined alternating current electromagnetic electroosmotic and pressure-driven conditions
Bibliographic record
Abstract
In this study, we investigate the combined alternating current electromagnetic electroosmotic and pressure-driven (EM-EO-PD) flow behavior of Jeffrey fluid in parallel plate microchannels, while considering slip boundary conditions. Analytical solutions for the velocity and volumetric flow rate of Jeffrey fluid are obtained using the method of separation of variables. The influences of various parameters on these solutions are thoroughly examined and analyzed in detail. Specifically, the velocity profiles of Jeffrey fluid under diverse flows conditions and those of different fluids in EM-EO-PD flow are compared separately, taking into account varying Reynolds numbers R e and slip lengths [Formula: see text]. The impacts of parameters such as slip length [Formula: see text], relaxation time [Formula: see text], and retardation time [Formula: see text] on the volumetric flow rate are evaluated across various conditions. The results reveal that compared to other flows, the velocity profiles of Jeffrey fluid in PD, EM, along with EM-PD flows show less sensitivity to variations in Reynolds number and slip length. For any given Reynolds number and slip length, the velocity amplitude of Maxwell fluid in the EM-EO-PD flow surpasses that of Jeffrey and Newtonian fluids. Elevated slip lengths [Formula: see text] and relaxation times [Formula: see text], paired with reduced retardation times [Formula: see text], yield higher volumetric flow rates, independent of other parameters. Significantly, the effect of slip length [Formula: see text] on volumetric flow rate is primarily constrained by the magnitude of the electrokinetic width K. As the electrokinetic width grows, the impact of slip length on volumetric flow rate gradually intensifies. More intriguingly, with larger Reynolds numbers R e and Hartmann numbers H a, as well as smaller slip lengths [Formula: see text] and electrokinetic width K, the effects of relaxation time [Formula: see text] and retardation time [Formula: see text] on volumetric flow rate are substantially attenuated. The resemblance between Jeffrey fluid and blood underscores the importance of this work in comprehending blood flow within microfluidic systems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".