The transition of an in-line vortex to slug flow: correlating pressure and reaction force measurements with high-speed video
Bibliographic record
Abstract
In this work, the performance of a Slug Flow Expander (SFE) was investigated. Multiphase flow consisting of air and water was injected into the SFE. Critical flow rates of air and water were identified that caused transition to the slug flow regime. It was observed that at water flow rates of 1.00 L/s and air flow rates above 3.00 L/s, the flow regime in the SFE transitioned to slug flow. A high-speed digital camera was used to visualize the flow inside the SFE. Wall pressure and reaction force measurements were synchronized with the high-speed camera. Autocorrelation, cross-correlation, and power spectral density functions were performed on reaction force, wall pressure, and air-core diameter. Power spectra of reaction force and wall pressure revealed strong energy peaks at low, middle, and high frequencies. Dominant low frequency energy (2.9 Hz and 4.4 Hz) was found to be attributed to the rotation rate of the liquid component in the SFE. The dominant middle frequency spikes (39.5 Hz, 31.3 Hz, 24.4 Hz for reaction force; 20 Hz, 15.6 Hz for wall pressure) are believed to be attributed to the formation, convection, and exiting of the liquid slugs from the SFE. It is speculated that the dominant high-frequency spikes are attributed to both mechanical vibrations and pressure fluctuations caused by the flow facility pump.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".