Investigation of the liquid carryovers in the branching T-junction, based on historical data
Bibliographic record
Abstract
High liquid content is obtained in the T-junction side arm, which affects the operational performance of downstream equipment installed in offshore petroleum industries. Diameter ratio, velocity ratio, and side arm angle of T-junction play dynamic role to control phase redistribution. The effect of these parameters on liquid take-offs is unclear from literature, without any agreement among researchers. Literature only provides empirical correlations valid for one specific application for which the correlations were developed. There is a dire need of correlations with high accuracies to conclude the effect of a certain variable on liquid carryovers based on available data. Objectives of this study are to collect data from multiple research publications, develop easy to use correlations, and to study parameters response surfaces. Plot digitization and Design Expert software are utilized for data collection and statistical analysis, respectively. Results depicted that phase separation improves by reducing diameter ratio to 0.33 and upward inclined side arm. Furthermore, liquid carryovers escalate by increasing velocity ratio and decrease at higher velocity ratios. This research study is first attempt in last few decades which is based on collection of data from different experimentation facilities and eliminating confusions among researchers about effect of parameters on phase separation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".