Structural organization of large and very large scales in turbulent pipe flow simulation
Bibliographic record
Abstract
Abstract The physical structures of velocity are examined from a recent direct numerical simulation of fully developed incompressible turbulent pipe flow (Wu, Baltzer & Adrian, J. Fluid Mech., vol. 698, 2012, pp. 235–281) at a Reynolds number of ${\mathit{Re}}_{D} = 24\hspace{0.167em} 580$ (based on bulk velocity) and a Kármán number of ${R}^{+ } = 685$ . In that work, the periodic domain length of $30$ pipe radii $R$ was found to be sufficient to examine long motions of negative streamwise velocity fluctuation that are commonly observed in wall-bounded turbulent flows and correspond to the large fractions of energy present at very long streamwise wavelengths ( ${\geq }3R$ ). In this paper we study how long motions are composed of smaller motions. We characterize the spatial arrangements of very large-scale motions (VLSMs) extending through the logarithmic layer and above, and we find that they possess dominant helix angles (azimuthal inclinations relative to streamwise) that are revealed by two- and three-dimensional two-point spatial correlations of velocity. The correlations also reveal that the shorter, large-scale motions (LSMs) that concatenate to comprise the VLSMs are themselves more streamwise aligned. We show that the largest VLSMs possess a form similar to roll cells centred above the logarithmic layer and that they appear to play an important role in organizing the flow, while themselves contributing only a minor fraction of the flow turbulent kinetic energy. The roll cell motions play an important role with the smaller scales of motion that are necessary to create the strong streamwise streaks of low-velocity fluctuation that characterize the flow.
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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 | 0.001 |
| 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".