An integral criterion for turbulence suppression in swirling flows
Bibliographic record
Abstract
Abstract A numerical study of flow in rotating pipes was conducted to elucidate the relative importance of convection and turbulence. CFD (Computational Fluid Dynamics) simulations of flow inside a rotating pipe (D = 2 cm and L/D = 20) were carried out, using the Reynolds Stress Model, for four different Reynolds numbers and a range of rotation numbers. The objective was to gain a deeper understanding of the interaction between fluid forces in swirling flows. This widely‐studied model problem was used to ascertain the conditions under which computationally cheaper turbulence models such as the k‐ϵ model should be accurate. We identified a dimensionless rotation parameter that delineates the condition at which decreasing turbulence force equals increasing convective force as rotational speed increases. This dimensionless number establishes a criterion for knowing which forces are dominant, and thereby a rational basis for choosing turbulence models that are both cost‐effective and accurate. We found a universal, critical threshold that determines when convective forces dominate over turbulence forces. This threshold determination is based on an ‘integral measure criterion’ of local forces in the radial direction. The threshold itself is defined by a dimensionless rotation number, N, based on the ratio of the circumferential and axial flow velocities. The critical value was found to be Ncr = 0.45. Above this, convection dominates; below it, turbulence dominates. This finding will facilitate selection of CFD models to optimize cost and accuracy for modelling swirling flows. For example, k‐ϵ models suffice when Ncr < 0.45, but more complex models are required for higher values.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 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".