Effects of aspect ratio on turbulent elliptical pipe flow and structures
Bibliographic record
Abstract
Turbulent flows within elliptical pipes of varying aspect ratios (AR) have been studied using direct numerical simulations. To understand the AR effects on turbulence statistics and structures, three elliptical pipe flow cases (of AR=1.5:1, 2:1, and 3:1) have been investigated and compared with a circular pipe flow case (of AR=1:1). As a result of the varying wall curvature of elliptical pipes, large-scale secondary flows appear in the cross-stream direction as two pairs of counter-rotating vortices at relatively small AR values, which significantly impact the turbulence statistics of the flow. As the AR value increases, it is interesting to observe that the mean cross-stream vortical structures become increasingly stretched along the direction of the semi-major axis, eventually splitting into four pairs of smaller vortices at the largest AR value tested. Furthermore, as the AR value increases, a significant trend toward turbulence suppression is observed at the ends of the major axis, where the formation and strength of hairpin structures reduce monotonically. Along the semi-major axis, it is discovered that the turbulence kinetic energy levels (as indicated by the three components of the premultiplied velocity spectra) reduce dramatically across all wavelengths, while their modes migrate monotonically toward larger wavelengths as the AR value increases. The characteristics of the flow field under the influence of a varying AR are investigated in both physical and spectral spaces in terms of the mean and instantaneous flows, turbulence intensities, Reynolds stress budgets, and coherent flow structures.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".