Lateral migration of cylindrical particle in a constricted microchannel—A numerical study
Bibliographic record
Abstract
Abstract Inertial migration of a single cylindrical particle in a constricted microchannel is addressed in this work. A computational model (two‐dimensional) has been constructed with the assistance of the immersed boundary finite volume method. The feedback forcing strategy is utilized for the simulation of lateral migration. The parameters like equilibrium position, migration time, and shortest equilibrium distance are computed to analyze the inertial migration characteristics of the particle. Also, a comprehensive parametric study has been performed on the migration behaviour of particles inside the constricted channel by addressing the effects of Reynolds number, diameter, initial release position, and constriction clearance. The parametric study shows that the equilibrium position changes with variations in the initial release position and particle diameter. On the other hand, it stays unaffected by changes in Reynolds number and constriction clearance. The parameters like the shortest equilibrium distance and migration time increase with a rise in Reynolds number and particle diameter. On the other hand, it reduces with the reduction in constriction clearance. Inspired by the parametric study results, in the following stage, a prediction model is created with an artificial neural network algorithm. This is used for an effective forecast of equilibrium position, migration time, and shortest equilibrium distance. Further, the computational model is utilized to check for the existence of a critical Reynolds number for the particle movement in a constricted microchannel. It is observed that the critical Reynolds number remains unchanged with a change in particle diameter. However, it increases linearly with an increase in constriction clearance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".