Numerical Modeling of Flow Pattern with Different Spillway Locations
Bibliographic record
Abstract
A spillway is a significant structure that releases the exceeded water during extreme flood events.It is considered as a safety valve during dams operating.Spillway location at dam body is one of the most important requirements of dam design.Different locations may influence the flow patterns which lead to change in the hydraulic performance of the spillway.In this paper, a numerical model was presented utilizing the Ansys fluent software using the realizable k-ɛ turbulence model to investigate the flow patterns of Mandali Dam's spillway at different locations.The interaction between air and water phases was simulated by using volume of fluid (VOF) model.Moreover, cavitation damage was studied for different flow discharges values.the validation of the numerical model was executed in compression with the measurement of the physical model.A discharge of 1800 m 3 /s was utilized for validation, while five different discharges; 1800, 1300, 1040, 440, and 67 m 3 /s were employed to investigate the hydraulic properties.The results show an agreement between numerical model and the physical model.According to the hydraulic properties, the spillway location at the center of the dam is better than its location at the edge of the dam.The discharge coefficient value in scenario one was found to be closer to the physical model value compared to the second scenario.At discharge 1800 and 1300, the discharge coefficient was 2.02 and 1.72, while in scenario two it was 1.46 and 1.21 in comparison with physical model which was 2.06 and 1.99.For cavitation investigations, the numerical model shows that the spillway is safe with no cavitation effects for the whole applied discharge values.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".