Numerical simulation of flow through a spillway and diversion structure
Bibliographic record
Abstract
Flow through the Keeyask Generating Station's diversion and spillway (rollway), to be constructed 730km north of Winnipeg, Canada on the Nelson River, is investigated by a comparison of a numerical model and a 1:50 scaled sectional physical model. The physical model was constructed at the LaSalle Consulting Group's laboratory in a permanent flume. A commercially available computational fluid dynamics (CFD) program, Flow3D, was used to model the physical model by using the Reynolds-averaged Navier-Stokes equations in combination with the standard K-e eddy-viscosity closure model. In order to determine the required mesh size to obtain a mesh independent solution tests were conducted on the diversion structure in 2D for four partial gate openings and in 3D for a fully open gate condition, as 2D did not model the contraction of the physical model adequately. Mesh independence was only obtained for the fully open condition and was nearly obtained for a 6m gate opening while other gate openings diverged with refinement. Further refinement was not realistic due to excessive computing times. The two meshes that adequately modeled the flow were used for all subsequent investigations. Sensitivity tests were performed for the diversion structure for the fully open case in 3D and the 6m gate opening in 2D in order to observe the effects of the numerical model scale (prototype, 1:50 scale) and the effects of two turbulence models (K-e, RNG). For both tests, similar results were obtained. Finally, numerical modeling was performed in 3D for the fully open gate condition for both the diversion and rollway structures in order to compare discharges/discharge coefficients, water surfaces and pressures to those of the physical model. Modeling of partial gate openings in 3D was not possible due to large calculation times. Results for the fully open case showed that the numerical model and physical model are in reasonably good agreement with one another.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".