A computational fluid dynamics study on rimming flow in a rotating cylinder
Bibliographic record
Abstract
Extensive computational fluid dynamics (CFD) simulations were conducted to study “rimming” flow in a partially filled horizontally rotating cylinder. These flows are encountered in aero-engine bearing chambers, which often exhibit complex two-phase flow scenarios as well as in multiple other engineering applications. In this study, a robust numerical scheme to model two-phase rimming flow has been adopted and validated against analytical expression and experimental data obtained from the literature. Additionally, a vast parametric study of the flow conditions has been performed. We used the volume of fluid method to solve the system of multi-phase flow governing equations and track the interface of rimming flow. The time-dependent gas–liquid interface was resolved, and the liquid-film thickness was determined. First, we performed our simulations within small to moderate ranges of Reynolds and Bond numbers and compared our results with previously reported analytical and experimental investigations. The present CFD results were found to be in very good agreement with previously reported data, both in identifying different regimes reported in the literature for rimming flow and in liquid-film thickness predictions. We also performed several additional simulations at much larger and practical ranges of Reynolds and Bond numbers, beyond the limitations imposed in previous analytical and experimental investigations on thin-film flows. We showed that three different flow regimes—shear-dominated, transitional, and gravitational-dominated—are attainable for the rimming flow for different combinations of Reynolds, Bond, and gravitational numbers. The present numerical results led us to propose a new map of rimming flow regimes by introducing functions of the Froude number and capillary number, which successfully identify and separate these regimes for a significant number of flow conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".