Exploring miscible jet dynamics in complex fluids
Bibliographic record
Abstract
This study explores the fluid dynamics of a Newtonian jet injected horizontally into a viscoplastic (yield stress) ambient fluid, a process relevant to various industrial applications, including jet cleaning process in oil and gas wells. To replicate industrial scenario, a perforated wall is considered between the nozzle and the tank wall, dividing the flow domain into two zones. We focus on the effects of key parameters, including the perforation diameter and the rheological properties of the ambient fluid, on jet behavior. The jet radius is used as a main metric to quantify mixing, while self-similarity is examined across varying conditions to provide insights into jet dynamics. The results reveal that a reduction in the perforation diameter leads to an increase in jet radius in the first zone, while causing a decrease in the second zone. In addition, the jet maintains its self-similar behavior, regardless of the perforation size. Furthermore, the presence of a viscoplastic fluid contributes to a decrease in the jet radius in both zones and deviate from self-similarity behavior, highlighting the significant impact of yield stress on the jet flow dynamics. These findings provide valuable insights into jet flow and can be leveraged to optimize jet cleaning processes, with the dimensionless results allowing for their extension to a range of relevant applications involving jets and non-Newtonian fluids, including liquid-in-liquid printing, cleaning-in-place systems, and industrial mixing operations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".