Impact of slug length in gas–liquid two‐phase flow on the structural stress characteristics of horizontal rigid pipelines
Bibliographic record
Abstract
Abstract The intermittent passage of liquid slugs and gas pockets in slug flow generates substantial cyclic stress damage to piping systems and their supports. This issue poses significant challenges to the various industries in which this flow pattern is present. Despite their critical implications, the structural response to slug‐induced forces has not yet been thoroughly elucidated. This study addresses this gap through a comprehensive experimental investigation of the influence of slug length on the structural integrity of pipes. A non‐invasive image‐processing technique was employed to measure the slug length, while biaxial strain gauges captured the pipe wall strain, accounting for Poisson and friction fluid–structure interaction (FSI) coupling mechanisms. The findings revealed a reduction in the induced stresses with increasing superficial liquid velocity and slug length. Furthermore, a semi‐empirical model was developed by integrating slug length with the superficial gas and liquid velocities based on the slug unit concept to predict the structural stresses. The model provides a robust predictive framework for elucidating the relationship between slug length and induced stresses. However, its accuracy is influenced by the slug formation mechanism and various slug flow sub‐regimes. The model demonstrated exceptional predictive capability, achieving a mean error of 6.2% and coefficient of determination of 93%.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".