Numerical Modeling of Tsunami-Induced Scouring around a Square Column: Performance Assessment of FLOW-3D and Delft3D
Bibliographic record
Abstract
April Le Quéré, P.; Nistor, I., and Mohammadian, A., 2020. Numerical modeling of tsunami-induced scouring around a square column: Performance assessment of FLOW-3D and Delft3D. Journal of Coastal Research, 36(6), 1278–1291. Coconut Creek (Florida), ISSN 0749-0208.In recent years, tsunamis have caused considerable damage to coastal infrastructures and inflicted numerous casualties in coastal communities in the impacted regions. The information, which the design requirements for tsunami-resistant infrastructures is based on, is still in its preliminary stages. The focus of the study was to investigate, by means of a numerical model, the scouring occurring around a single, square column subjected to tsunami floods. A three-dimensional (3D) hydrostatic numerical model (Delft3D) and a 3D nonhydrostatic model (FLOW-3D) were used to replicate a series of physical tests conducted at the University of Ottawa, which consisted of a dam-break wave impacting onto a single square column installed over a movable sediment bed. These experimental tests were conducted in the Dambreak Flume at the University of Ottawa. Four different turbulence models and two different sediment-transport models were tested to find the most appropriate combination, which could model the complex flow characteristics associated with a dam-break–type bore. An extensive review of the hydrodynamic and scouring performance of various numerical models was also included in this study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".