Numerical characteristics of a coupled river ice and hydrodynamic model
Bibliographic record
Abstract
Prediction of dam break surges using numerical tools has been the subject of tremendous research efforts in the past four decades. Powerful numerical tools that can model surge phenomena are readily available on the market. However, for winter conditions, when a river is covered by ice, it becomes difficult or even impossible to predict wave propagation dynamics using these traditional tools. It is therefore important to know what will happen should a dam break or an ice jam release in an ice-covered river. In this study, fully conservative form of the one-dimensional St. Venant equations are derived for water hydrodynamics in variable width trapezoidal channels having a stiff floating ice cover (that is not frozen to the river banks) in addition to the cover’s one-dimensional flexural response (as a beam on an elastic foundation) to incoming waves. A coupled numerical model (HYDROBEAM) using the Galerkin finite element method (FEM) is then developed. The coupling technique in the model uses an iterative computation process to find a simultaneous solution to both flow and ice cover models at each time step. The FEM schemes are evaluated by the simulation of progressive and regressive wave propagations and by the simulation of Stocker’s hypothetical dam break problem. For these simplified cases, analytical solutions exist and are used as references to evaluate the numerical attenuation of the model. The results of the hypothetical dam break simulations are in agreement with the theory. The effects of the spatial and temporal discretization on numerical attenuation of flow dynamics and ice cover peak stresses are evaluated and presented. When used within the recommended guidelines, HYDROBEAM’s performance is more than adequate to simulate (a) open channel flow, (b) rivers with passive (flexible) ice covers or (c) rivers with stiff ice covers that respond as a beam on an elastic foundation.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".