Local scour around submerged spur dikes under ice-covered conditions: experimental and numerical investigation
Bibliographic record
Abstract
Local scour around submerged hydraulic structures under ice-covered conditions poses challenges to infrastructure stability and sediment management in cold-region waterways. This study investigates how the geometry of submerged spur dikes and the presence of ice cover influence local scour. Through flume experiments and numerical simulations, we analyze channel bed deformation and flow patterns around submerged spur dikes with varying slopes (both frontal and rear) in open and ice-covered flow conditions. The study analyzes how smooth and rough ice covers affect the scour profile, flow velocity fields, and turbulent kinetic energy (TKE) around the dikes. The results demonstrate that rough ice cover increases flow turbulence and local scour depth, with vertical wall dikes showing a greater maximum scour depth under rough ice-covered flow conditions. Notably, trapezoidal spur dikes, particularly those with a 30° slope, reduce maximum scour depths by dispersing turbulence over a broader area and minimizing near-bed shear stress, as evidenced by the reduction in peak turbulent kinetic energy (TKE) compared to vertical wall dikes. Numerical simulations closely replicate these dynamics and uniquely identify the absence of secondary scour holes around trapezoidal dikes under rough ice cover. The developed empirical equations incorporate parameters such as ice roughness ( n i /n b ) and dike geometry, improving the accuracy of scour depth estimation compared to existing models by accounting for ice cover effects and dike profile configurations, offering enhanced tools for designing scour-resistant structures in cold-region waterways. • Impact of ice cover and spur dike geometry on local scour through both experimental tests and numerical method. • Increase in turbulence and scour depth under rough ice cover. • Reduction in scour depth with decreased dike slope. • Development of equations for predicting the maximum scour depths under ice cover.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| 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".