Turbulent structures in smooth and rough open channel flows: effect of depth
Bibliographic record
Abstract
In this thesis, detailed experiments are performed to study the effect of the flow depth on turbulent structures in smooth and rough bed open channel flow. When the rough bed is introduced in the shallow flow, the local turbulence near the roughness element intensifies and becomes highly heterogeneous. The model roughness under study consists of a train of two dimensional square ribs spanning the whole length of the channel. The height of the ribs (k) occupy 10-15% of the depth of flow (d) and falls in the category of large roughness. Velocity measurements were conducted using laser Doppler velocimetry (LDV) and particle image velocimetry (PIV) systems. While on the smooth bed, mean velocity scaling in the classical logarithmic format was confirmed from the present experiments, for the deep-flow cases, turbulence quantities were found to be influenced by the free surface. A modified length scale based on a region of constant turbulence intensity is proposed to account for the effect of the free surface. Two-dimensional PIV measurements were made in the streamwise-wall normal plane of the smooth open channel flow at d = 0.10 m and Red = 21,000 to further study the influence of the free surface on the turbulent structures. Proper orthogonal decomposition (POD) and swirling strength analysis were employed to investigate the structures present in the flow. Analysis of the POD reconstructed velocity fields reveals the presence of large-scale energetic structures near the free surface.
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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 | 0.001 |
| 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".