Turbulence Kinetic Energy and High-Order Moments of Velocity Fluctuations of Flows in the Presence of Submerged Vegetation in Pools
Bibliographic record
Abstract
The flow in arid and semi-arid regions changes significantly during seasons, letting many vegetation patches develop in different parts of rivers. In the presence of aquatic plants in streams, different flow structures have resulted. When the water level increases in these rivers, the presence of vegetation patches influences the turbulent flow structures, which may considerably change the estimation of key hydraulic parameters. The results of earlier investigations indicated that a wide range of submerged and non-submerged vegetation influences the hydrodynamic features of flows in rivers and streams. In the present investigation, two pools with various slopes of entry and exit sections were used to conduct eight independent experiment runs. In addition, a vegetation patch over the entire pool section has been set up to investigate the effects of the vegetation patch on flow structures in pools. The effect of two slopes of 5 and 10 degrees for both entrance and exit of the pools on flow structure has been investigated. Considering two aspect ratios of 2.0 and 2.7, the distributions of flow velocity, Reynolds normal and shear stresses, turbulence intensities, turbulent kinetic energy (TKE), quadrant analysis, and spectral analysis have been studied at the trailing edge of the vegetation patch along an artificial pool. Results show that, for large entrance and exit slopes (10 degrees), the TKE distribution profiles have no specific form. However, the TKE values have a convex-shaped distribution pattern with the maximum TKE value near the bed when the slopes of the entrance and exit sections of the pool are small (5 degrees). Results showed that both ejections and sweeps govern the turbulence structures and coherent motions at the trailing edge of the vegetation patch along the pool. The geometry, entrance, and exit slopes of the pool have no effect on the validation of power spectral function compared to the presence of a vegetation patch in a flatbed.
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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 | 0.000 |
| 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 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".