Effects of high oil viscosity on oil‐gas downward flow in deviated pipes. Part 2: Holdup and pressure gradient
Bibliographic record
Abstract
Abstract In Part 1 of this work, new experimental data on high viscosity oil‐gas flow in inclined downward flow were presented. Flow patterns transitions were identified and analyzed, and the performance of some flow pattern prediction models was validated. This study uses the same experimental data set generated for a mixture of air‐oil (with a viscosity of 213 mPa · s) flowing in a 50.8 mm internal diameter pipe with inclination angles of −45°, −60°, −70°, −80°, −85° for a ranges 0.05 m/s‐0.7 m/s and 0.7 m/s‐7 m/s of superficial liquid and gas velocities, respectively, to investigate holdup and pressure gradient behaviour. The holdup and pressure recovery effects are explained in terms of the predominant transport mechanism through phase slippage and the mechanical energy balance. For a constant superficial liquid velocity, the average‐liquid holdup results show a discernible behaviour dependent on the relative velocity between phases (slip velocity). Consistently, results show a switch between the gravity or shear forces transport mechanism, that coincides with the switch of a sign of the total pressure gradient (pressure recovery effect). Performance analysis of the available mechanistic models has been presented. The results are not satisfactory, which justifies the need for a detailed study of the effects of viscosity and the inclination of the pipe in liquid‐gas mixtures.
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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 | 0.000 | 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 | 0.000 |
| 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".