Downstream semi-circular obstacles' influence on floods arising from the failure of dams with different levels of reservoir silting
Bibliographic record
Abstract
Dam-break wave propagation in a debris flood event is strongly influenced by accumulated reservoir-bound sediment and downstream obstacles. For instance, the Brumadinho dam disaster in Brazil in 2019 released 12 × 106 m3 of mud and iron tailings and inflicted 270 casualties. The present work was motivated by the apparent lack of experimental or numerical studies on silted-up reservoir dam-breaks with downstream semi-circular obstacles. Accordingly, 24 dam-break scenarios with different reservoir sediment depths and with or without obstacles were observed experimentally and verified numerically. Multiphase flood waves were filmed, and sediment depths, water levels, and values of front wave celerity were measured to improve our scientific understanding of shock wave propagation over an abruptly changing topography. Original data generated in this study is available online in the public repository and may be used for practical purposes. The strength of OpenFOAM software in estimating such a complex phenomenon was assessed using two approaches: volume of fluid (VOF) and Eulerian. An acceptable agreement was attained between numerical and experimental records (errors ranged from 1 to 13.6%), with the Eulerian outperforming the VOF method in estimating both sediment depth and water level profiles. This difference was most notable when more than half of the reservoir depth was initially filled by sediment (≥0.15 m), particularly in bumpy bed scenarios.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".