Two-Phase Flow-Induced Forces on Piping in Vertical Upward Flow: Excitation Mechanisms and Correlation Models
Bibliographic record
Abstract
Momentum variation in two-phase flow generates significant low frequency forces, capable of producing unwanted and destructive vibrations in nuclear or petroleum industries. Two-phase flow-induced forces in piping were previously studied over a range of diameters from 6 mm to 70 mm in different piping element geometries, such as elbows, U-bends, and tees. Dimensionless models were then developed to estimate the rms forces and generate vibration excitation force spectra. It was found that slug flow generates the largest forces due to the large momentum variation between Taylor bubbles and slugs. The present study was conducted with a 52 mm diameter U-bend tube carrying a vertical upward flow. Two-phase flow-induced forces were measured. In addition, two-phase flow parameters, such as the local void fraction, bubble size and velocity, and slug frequency were studied to understand the relationship between the force spectra and the two-phase flow patterns. A new two-phase flow pattern map, based on existing transition models and validated using our own local void fraction measurements and force spectra, is proposed. This paper also presents a comparison of the present dimensionless forces with those of previous studies, thus covers a wide range of geometries and Weber numbers. Finally, a dimensionless spectrum is proposed to correlate forces with large momentum variations observed for certain flow patterns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".