Prediction of water aeration efficiency in high turbulent flow
Bibliographic record
Abstract
ABSTRACT This paper presents the study of a complex turbulent flow with disperse gas–liquid flow with adverse pressure gradient, where the mass transfer through the interface is a dynamic process associated with the interface’ dynamics, and the interface’s aria varies along the flow. The experimental setup is equipped with a disperse aeration device, fitted with interchangeable perforated plate. The air flow is injected as disperse bubbles of different sizes at different air flow rates through the performed plates. This paper presents the aeration performances of four disperse aeration devices, mounted non-invasive on the wall of a pipeline. The water flow corresponds to Reynolds number in the range 1 × 10 5 to 5 × 10 5 . The objective is to find the optimal aeration device to increase the transfer of the dissolved oxygen content in water, with a minimum power and volume of injected air. The following parameters are considered: the dissolved oxygen deficit from the water, the air–water interface area, pressure losses of the aerator, aerator design and the contact time of the two phases. The aeration devices are tested for different void fraction and the following parameters are obtained: volumetric mass transfer, standard oxygen transfer rate, standard oxygen transfer efficiency, power consumption for air injection and standard aeration efficiency. Finally, a comparative study on the kLa performance of several types of aerators is presented.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".