Generation of Supersaturated Total Dissolved Gases from Low-Level Outlets at Hydropower Facilities
Bibliographic record
Abstract
Supersaturated total dissolved gases (TDGs) generated by dams can cause gas bubble trauma and mortality in fish in downstream waters. In this study, we evaluated air entrainment and generation of supersaturated TDG at two submerged low-level outlets (LLOs) at Hugh L. Keenleyside Dam, British Columbia, Canada. Specifically, we determined how air entrainment (less than 1% volume fraction) through the gate well, discharge level through the outlets, and geometry of the stilling basins at the south and north ends of the dam influenced supersaturated TDG generation. A mathematical formulation was developed, incorporating physical processes including air entrainment, bubble breakup, and gas transfer. Numerical modeling was also adopted to validate turbulence and flowfield downstream of the submerged low-level outlets. Despite being 8-m shallower, significantly higher TDG levels were measured in the stilling basin of the south LLO (about 120%) compared to that of the north LLO (≤110%). Results show that turbulence in the stilling basin can produce smaller bubbles and increase the mass transfer coefficient across bubbles, which will substantially enhance gas transfer and TDG generation. Higher TDG generated in the shallower LLO was therefore attributed to the water depth generating stronger turbulence flow, with more efficient gas transfer. This study improves TDG prediction and helps inform the development of operational alternatives during periods of high TDG generation to mitigate impacts on the aquatic environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".