Assessment of two‐phase slug frequency correlations in horizontal pipes under different operational conditions
Bibliographic record
Abstract
Abstract Accurate prediction of the slug frequency in horizontal pipe flow is essential for appropriate design and operation in various industrial processes. This study provides a comprehensive review of existing empirical correlations for slug frequency in horizontal pipes, highlighting their limitations and applicability. A total of 36 correlations were examined, and 1083 data points were collected from experiments using pipes with inner diameters ranging from 3.7 to 150 mm. The correlations were categorized based on pipe diameter and gas–liquid working fluids. The correlations based on the Froude number had the best performance for superficial liquid velocities within the range of v SL = 0.502–1.505 m/s, with a maximum mean relative difference (MRD) of ±30% and a mean absolute relative difference (MARD) of 40% for superficial gas velocities v SG greater than 1 m/s. The Strouhal number produced the most effective correlations for most of the datasets examined in slug frequency testing. In contrast, the air–oil slug frequency correlations were unable to accurately predict the air‐water experimental data, except for that of Al‐Safran (2016), which was limited to small‐diameter pipes. 909 data points were used to evaluate slug frequency for high viscous fluids; the results showed that the correlation of Baba et al. (2017) provided the best prediction performance, with a maximum MRD of 30%.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".