Effect of high liquid viscosity on intermittent/annular flow pattern transition in two‐phase upward vertical flow
Bibliographic record
Abstract
Abstract Two‐phase flow pattern prediction is essential in predicting liquid holdup, pressure gradient, and flow assurance risks in various applications in the chemical, nuclear, and petroleum industries. Recent studies of flow pattern model evaluation in high liquid viscosity two‐phase flow in vertical upward pipe flow revealed discrepancies in all transition boundaries, specifically the intermittent (IN)/annular (AN) flow transition. Therefore, this study aims to investigate the effect of liquid viscosity on IN/AN flow pattern transition and to improve the existing models. Specifically, Taitel et al.'s IN/AN transition model is improved by incorporating the liquid viscosity effect on liquid droplet fallback and liquid film thickness. Furthermore, sensitivity analyses on Barnea's IN/AN flow pattern transition model revealed that the interfacial friction factor ( f i ) and liquid entrainment ( f E ) closure relationships are crucial in the film bridging and film instability mechanisms of the flow transition. Therefore, a comprehensive evaluation of the performance of the f i and f E closure relationships is carried out, revealing that Pan and Hanratty's f E correlation and Ishii and Grolmes' f i correlation is the best combination with the least prediction error over a wide range of liquid viscosity. A validation study against an extensive experimental high liquid viscosity flow pattern database with liquid viscosity ranging from 4 to 1600 mPa·s showed high prediction performance for the proposed improved Taitel et al. and Barnea IN/AN flow pattern transition models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".