Deoxygenation of Water for Small-Scale Laboratory Studies in Hydraulic Engineering
Bibliographic record
Abstract
Achieving low dissolved oxygen levels in water is required for rigorous investigation of aeration processes in laboratory experiments in hydraulic engineering. The effectiveness of ultrasonic irradiation, vacuum degassing, water spraying techniques, and combinations thereof for deoxygenating tap water is evaluated in a closed-loop laboratory setting. A parametric study assessed the impact of ultrasonic irradiation (20 kHz, 2 kW), vacuum surface pressure (16–101 kPa), initial water temperature (11°C–39°C), and reservoir residence time (68–250 s) for flow rates in the range of 2.0–7.3 L/s. Results demonstrate that ultrasonic irradiation at 20 kHz did not significantly enhance the deoxygenation rate compared to vacuum degassing alone, suggesting that higher frequencies may be required to induce effective acoustic cavitation. Vacuum degassing effectively reduced dissolved oxygen levels, achieving a reduction from 10 down to 2.4 mg/L within 30 min at 16 kPa surface pressure and 33°C. Water spraying in a vacuum marginally improved deoxygenation at higher surface pressures by increasing the air–water interfacial area but became less effective at lower pressures due to increased surface mixing. Higher initial water temperatures and lower residence times had a minor influence on the deoxygenation rate. The results enable the design of deoxygenation systems suited for small-scale closed-loop hydraulic laboratories to establish well-controlled initial dissolved oxygen conditions.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".