Oxygenation of flow at an elbow deflector: characterisation of air pocket and bubble plume formation and subsequent mass transfer
Bibliographic record
Abstract
Hydropower, an important renewable energy source for climate change mitigation, presents downstream environmental impacts, particularly regarding low dissolved oxygen levels. Draft tube aeration using an elbow deflector as a retrofit solution to increase dissolved oxygen levels downstream is investigated. An experimental rig was built to investigate air pocket and bubble dynamics, oxygenation rates, aspiration potential and energy loss in a vertical conduit, in which the buoyancy force and the water flow are co-directional, for a range of relative flow velocities ( U/U 0 = 0.54 – 1, Re O ( 10 5 ) ) and air-water void ratios ( Φ= 0 – 4.4%). Increasing air flow rates results in larger bubbles, while increasing the water flow velocity results in a decrease in the bubble sizes shed from the air pocket. The oxygen transfer coefficient ( k L a ) represented by the Sherwood number increases linearly with relative water velocity and void ratio, due to smaller bubbles and increased surface area. Dimensional analysis is used to develop an equation expressing the Sherwood number as a function of Reynolds number (flow velocity) and air-water void ratio. Self-aspiration potential increases with velocity. Energy loss across the deflector increases with velocity and with air pocket formation. An increased understanding of the oxygenation processes in draft tube aeration techniques will lead to environmental benefits.
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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) | 0.001 | 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".