Two-Phase Flow Mass Transfer Analysis of Airlift Pump for Aquaculture Applications
Bibliographic record
Abstract
Airlift pumps can be used in the aquaculture industry to provide aeration while concurrently moving water utilizing the dynamics of two-phase flow in the pump riser. The oxygen mass transfer that occurs from the injected compressed air to the water in the aquaculture systems can be experimentally investigated to determine the pump aeration capabilities. The objective of this study is to evaluate the effects of various airflow rates as well as the injection methods on the oxygen transfer rate within a dual injector airlift pump system. Experiments were conducted using an airlift pump connected to a vertical pump riser within a recirculating system. Both two-phase flow patterns and the void fraction measurements were used to evaluate the dissolved oxygen mass transfer mechanism through the airlift pump. A dissolved oxygen (DO) sensor was used to determine the DO levels within the airlift pumping system at different operating conditions required by the pump. Flow visualization imaging and particle image velocimetry (PIV) measurements were performed in order to better understand the effects of the two-phase flow patterns on the aeration performance. It was found that the radial injection method reached the saturation point faster at lower airflow rates, whereas the axial method performed better as the airflow rates were increased. The standard oxygen transfer rate (SOTR) and standard aeration efficiency (SAE) were calculated and were found to strongly depend on the injection method as well as the two-phase flow patterns in the pump riser.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".