Understanding two‐phase flow behaviour: <scp>CFD</scp> Assessment of silicone oil–air and water–air in an intermediate vertical pipe
Bibliographic record
Abstract
Abstract The study utilized computational fluid dynamics (CFD) simulations employing the volume of fluid (VOF) model to analyze silicone oil–air flows in a vertical pipe with a diameter of 67 mm. Various structured meshes ensured grid independence, and the k ‐ ε realizable model addressed turbulence. The analysis characterized bubbly to annular flows, involving the evaluation of flow patterns, radial void fractions, void fraction time series, probability density functions (PDFs), power spectral densities (PSDs), and mean void fractions. Results indicated a transition from bubbly to annular flow with increasing gas velocities and notable changes in radial void fraction profiles. Void fraction exhibited significant variations with distinct flow patterns at constant liquid velocity. PDFs identified flow regimes, and PSDs revealed frequency patterns. CFD results were validated against experiments, demonstrating good agreement. The validated CFD model was utilized to investigate radial gas velocities and pressure drops, revealing a shift from uniform velocity distributions to irregular patterns and a decrease in total pressure drop with an increase in gas superficial velocity. The model was also applied to a water‐air system to explore two‐phase flow behaviour. The impact of superficial gas velocity on flow patterns and radial void fractions was studied through numerical analysis and compared with the silicone oil‐air system. Results showed that at extreme gas velocities (0.06 and 5.53 m/s), silicone oil exhibited bubbly and annular flows, while water displayed cap bubbly and churn flows. The significant variation in radial void fraction at these velocities emphasized the impact of fluid properties.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".