Numerical Tracking of Shallow Water Waves by the Unstructured Finite Volume WAF Approximation
Bibliographic record
Abstract
The depth averaged shallow water equations (s.w.e.) are useful and reliable for dam break flow and flood modeling. For their approximation, the finite volume method (FVM) in conjunction with Riemann solvers permits shock capturing. This work provides an overview of the FV weighted average flux (WAF) method applied to s.w.e., and its implementation on unstructured triangular or quadrilateral meshes. The inviscid fluxes are given by the HLLC solver, and stabilization is ensured by the proper WAF limiters inherited from the total variation diminishing (T.V.D.) theory. Additional numerical improvements are incorporated to the model, such as enhancing the calculation of bed slopes, using an optional semi-implicit discretization of the friction source term, and affecting a depth tolerance to dry areas. The model performance is displayed through the simulation of well known synthetic and experimental examples including CADAM test 1 and test 2, which all show that the predictions are accurate and that the triangular mesh seems more efficient than quadrilateral. The applicability to real cases is assessed by simulating the flooding flow in a breakwater on “rivière des Prairies” having an irregular bathymetry.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".