Effects of aspect ratio on higher-order moments, conditional statistics, TKE budget and anisotropy in narrow open channel flow
Bibliographic record
Abstract
The purpose of the present study is to investigate the influence of aspect ratio on the higher- order statistics of velocity fluctuations, conditional statistics, turbulent kinetic energy (TKE) fluxes, TKE budget, and Reynolds stress anisotropy in hydraulically rough narrow open channel flow (OCF). In the experiments, the aspect ratios were maintained low and varied between 2.5 and 4. The velocities were measured with an acoustic Doppler velocimeter (ADV). The higher-order moments, quadrant analysis, and TKE budget from the present experiments were compared with available literature data in wide and narrow OCFs to elucidate the effect of the aspect ratio. In this analysis, the third-order moments of velocity fluctuations were found to be sensitive to the aspect ratio in the outer region. The fractional contributions of all quadrant events are approximately equal in magnitude in lower aspect ratio flows, whereas ejections and sweeps are the dominant events as the aspect ratio increases. The upward transfer of TKE flux increases in the outer layer with increase in aspect ratio. In the inner layer, the TKE production and rate of dissipation are found to be increasing with decreasing aspect ratio. Therefore, the production and dissipation of TKE are dependent on the aspect ratio of the flow. The analysis of Reynolds stress AIM reveals that for low aspect ratio flows turbulence tends to attain rod-like axisymmetric turbulence only in the intermediate layer whereas for higher aspect ratio, turbulence attains rod-like axisymmetric turbulence throughout the depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".