Effects of Submerged Vegetation Arrangement Patterns and Density on Flow Structure
Bibliographic record
Abstract
Aquatic vegetation appears very often in rivers and floodplains, which significantly affects the flow structure. In this study, experiments have been conducted to investigate the effects of submerged vegetation arrangement patterns and density on flow structure. Deflected and non-bending vegetation is arranged in square and staggered configurations in the channel bed of a large-scale flume. Results showed that the staggered configuration leads to intensified streamwise velocity, turbulence kinetic energy (TKE), and Reynolds shear stress (RSS) compared to the square configuration. When vegetation density is low (λ = 0.04 and λ = 0.07), the produced wake in the rear of the vegetation is more expansive than that with high vegetation density (λ = 0.09 and λ = 0.17) because the velocity in the center of four vegetation elements is lower than that in the middle of two vegetation elements with low vegetation density. Results of TKE in the wake zone of the deflected vegetation indicate that the maximum root-mean-square velocity fluctuations of flow occur at the sheath section (z/H = 0.1) and the top of the vegetation (z/H = 0.4). In the wake zone behind the vegetation elements, the maximum value of the RSS occurred slightly above the interface between deflected vegetation and the non-vegetation layer, showing the Kelvin–Helmholtz instability that is associated with inflectional points of the longitudinal velocity. Within the range of vegetation density in this study (0.04 < λ ≈< 0.23), as the vegetation density increases, the negative and positive values of RSS throughout the flow depth increase.
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 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".