A Comparative Study of Closure Relations for CFD Modelling of Bubbly Flow in a Vertical Pipe
Bibliographic record
Abstract
Commercial code CFX was used to examine the performance of a two-fluid model to predict the details of upward isothermal bubbly flow of air and water in a vertical pipe. The model equations are volume-averaged Navier-Stokes equations that require closure models for interfacial forces and bubble-induced turbulence effects. Two-equation SST and k-epsilon RANS turbulence models were also used. A parametric study of closure models included both standard options in CFX and previously published novel closure models that were implemented with user-defined functions. The CFD simulations were compared with two cases from the MTLoop experiments by Lucas et al. at the Helmholtz-Zentrum Dresden Rossendorf: one with wall-peak void fraction profile (MT039), and another with a core-peak void fraction profile (MT118). The effect of changing the drag force closures was not significant for the set examined. Poor predictions were found when the lift force and wall lubrication models were incompatible in magnitude. There was no significant effect of changing the liquid phase turbulence model. Changing the bubble-induced turbulence models, however, had a significant impact on the radial void fraction profile. The novel wall force from Lubchenko et al. at the Massachusetts Institute of Technology significantly improved the prediction of the near wall void fraction in the wall peak profile.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".