Discussion on the pressure drop calculation for oil‐water separated flow using a one dimensional two‐fluid model
Bibliographic record
Abstract
The purpose of this work is to seek the key factors influencing the pressure drop calculation for oil‐water separated flow using a one dimensional two‐fluid model. Closure relations published for the two‐fluid model such as interface configuration, wall, and interfacial shear stress correlations are summarized. Interface configurations are established by numerically solving the Young‐Laplace equation, correlated with the Bond number, contact angle, and water holdup. Results show that the interface transforms from concave to convex with the enlargement of the contact angle and becomes flat as the Bond number increases. For the pressure drop calculation, a limited difference of predicted accuracy between the curve and flat interface is found. Discussions of both the wall and interfacial friction factor correlation on the pressure drop calculation are performed. In contrast to the effect of the interfacial friction factor, the correlation of the wall friction factor is found to have more contributions. We validate the prediction accuracy of different wall frictions factors using eight groups of published experiment results, and one correlation is recommended and being further extended.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".