Computational fluid dynamics study of kaolin–water flow in a <scp>T</scp>‐junction using a novel shear‐thinning fluid model
Bibliographic record
Abstract
Abstract A recently proposed shear‐thinning fluid model that mimics the response of seemingly viscoplastic materials is evaluated in computational fluid dynamics simulations by studying the steady flow of a kaolin–water suspension in a 2D T‐junction. The velocity profiles for the kaolin–water suspension are reported at the mid‐length of the main channel and the root of the bifurcation (where recirculation is expected to appear). The velocity profiles of the proposed model are compared with those from conventional viscoplastic models (Bingham plastic model and the Herschel–Bulkley model) at low (=100) and high Reynolds number (=2000). The new model predicts a recirculation zone (at the inner edge of the bifurcation arm) that conventional models do not. The effect of the variation in the model parameters (α1 and α2) on velocity profiles at low (=100) and high Reynolds numbers (=2000) is also documented. These indicate the disappearance of the recirculation zone at low Reynolds number as α1 (equivalently, viscosity) increases, whereas the recirculation zone persists even for higher values of α1 at high Reynolds number. Further, at low Reynolds number, the skewing of maximum velocity towards the outer edge of the bifurcation arm disappears as α2 increases, whereas the skewing persists even at the highest value of α2 used at the high Reynolds number.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".