Influence of domain size on direct numerical simulation of turbulent flow in a moderately curved concentric annular pipe
Bibliographic record
Abstract
Direct numerical simulation (DNS) of turbulent flow in a concentric annular pipe was performed using a pseudo-spectral method computer code. In order to study the effects of computational domain size on the turbulence statistics, 12 test cases of different domain sizes are compared. The characteristics of the velocity field are examined at two different Reynolds numbers. It is observed that the predictive accuracy of the first- and second-order statistics is sensitive to the axial and azimuthal domain sizes. It is also found that the scales and dynamics of turbulence structures vary with the surface curvature of the concave and convex walls. The characteristic length scales of the turbulence structures are identified through a spectral analysis, and it is observed that a minimum computational domain is required in order to accurately capture the near-wall streaky and hairpin structures of a concentric annular pipe flow using DNS.
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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".