Influência do coeficiente de rugosidade de manning no estudo de ruptura de barragens : estudo de caso da Barragem do Salto
Bibliographic record
Abstract
In Brazil, Federal Law 14.066/2020 consolidated the mandatory Emergency Action Plan preparation criteria for a greater number of dams than Law 12.334/2010.To determine the risk classification associated with the dam and to delimit the map of the Self-Saving Zone, dam breach studies are required to estimate the extent of damage that a possible breach can cause downstream of the dam.Such studies need some input parameters, among them the specification of the roughness coefficient, being common the use of the Manning's coefficient.In this article, we sought to evaluate the influence of Manning's roughness coefficient in a case study with the hypothetical failure of the Salto dam.For this purpose, three different values for the roughness coefficient were used: 0.035, 0.06, and 0.11 sm -1/3 , and the respective rupture wave propagation results obtained using the hydrodynamic modeling software HEC-RAS version 6.1 in the two-dimensional module were compared.The results indicate that, in the range of values considered for the Manning roughness coefficient, the differences in the respectively obtained inundation areas are not very relevant.The analysis of other parameters characterizes the flood wave, meaning the maximum flow rates and depths of the runoff, also did not show significant differences among the three simulations.However, it is noted that changes in Manning's coefficients impact more significantly the maximum velocities of the flood wave and peak wave's arrival time.Resumo: No Brasil, a Lei Federal 14.066/2020 consolidou os critérios de obrigatoriedade da elaboração do Plano de Ação de Emergência para um número maior de barragens em relação à Lei 12.334/2010.Para determinar a classificação do risco associado à barragem e delimitar o mapa da Zona de Autossalvamento são necessários estudos de rompimento de barragens que estimem a extensão do dano que uma possível ruptura pode causar a jusante do barramento.Tais estudos necessitam de alguns parâmetros de entrada, dentre eles a determinação do coeficiente de rugosidade, sendo comum a consideração do coeficiente de Manning.Neste artigo, buscou-se avaliar a influência do coeficiente de rugosidade de Manning em um estudo de caso com a ruptura hipotética da barragem
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".