Use of CFD Modeling for Creating Recreational Opportunities at the Calgary Bow River Weir
Bibliographic record
Abstract
The Calgary Bow River Weir Project is intended to remove an existing safety hazard created by an ogee weir, while maintaining the weir's ability to divert water for irrigation and not increasing upstream flood levels. The river reach downstream of the weir will be transformed into a high water channel and a low water channel, each comprised of multiple pool-and-drop features to provide recreational opportunities for boaters and improve fish passage at the site. Computational fluid dynamics models were developed to evaluate hydraulic conditions of design modifications to HWC Drop #1. In this study, a volume-of-fluid (VOF) model was employed to predict the water surface profile and to assess whether a hydraulic jump would form downstream of the drop structure. The CFD models were validated by comparing CFD results with qualitative and quantitative data collected in the physical models. The comparisons indicated that the CFD models were able to correctly predict hydraulic jump formation immediately downstream of the weir for the existing design, and demonstrated satisfactory hydraulic conditions for the proposed design at flows at which boat passage is expected to occur. This study demonstrated that CFD modeling is a viable tool for predicting flows involving highly deformed water surfaces, such as those associated with hydraulic jumps.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".