Numerical Study on Gas-Yield Power-Law Fluid in T-Junction Minichannel
Bibliographic record
Abstract
Abstract In this study, a computational examination of Taylor bubbles was performed for gas/non-Newtonian fluid two-phase flows developed in a minichannel T-junction mixer with a hydraulic diameter of 1 mm. The investigations employed three separate aqueous xanthan gum solutions at concentrations of 0.05, 0.1 and 0.15 w/w, which are referred to as non-Newtonian (yield power-law) fluids. The effective concentration of the xanthan gum solutions and superficial velocity of the inlet liquid phase on the length, velocity, and shape of the Taylor bubbles was studied using the ANSYS FLUENT 19 software package. The simulation results show an increase in bubble velocity with increasing film thickness, particularly in solutions of higher viscosity XG-0.15%. Furthermore, bubble lengths decreased as the xanthan gum concentrations increased, but bubble shapes underwent alterations when the concentrations increased. Another interesting result of the tests shows that when the liquid inlet velocity increases, bubble lengths decrease during lower liquid superficial velocity, whereas during higher velocities, they change only slightly after increases in concentration. Finally, with increasing XG concentration, the liquid film thickness around the bubble increased. The results show good agreement with correlations after modifying a capillary number (Ca*) for non-Newtonian liquids in all cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".