Measurements of the size, rise velocity, and specific interfacial area of bubbles in an impinging-jet bubble column
Bibliographic record
Abstract
A 2-D laser particle dynamics analyzer was used to simultaneously measure the bubble size, bubble rise velocity, bubble size distribution, and gas-phase turbulence intensities in an impinging-jet bubble column that utilized two venturi injectors to create turbulent gasliquid jets in the ambient fluid. These measurements were conducted in clean deionized water and in a Kraft pulp mill effluent that was ozonated using a wide range of utilized ozone doses. The intersecting of the gasliquid jets caused an increase in the turbulence produced in the ambient fluid and as a result, the count mean bubble diameter (d B ) and the Sauter mean bubble diameter (d S ) were smaller than those obtained in conventional bubble columns. This has led to a significant increase in the specific gas bubble interfacial area (a) compared to that in conventional bubble columns. The count mean bubble and Sauter mean bubble diameters were found to be dependent on the superficial gas and liquid velocities (u G and u L , respectively). The count and Sauter mean bubble diameters were smaller in the raw Kraft pulp mill effluent compared to those in the deionized water. This has led to a significant increase in the specific gas bubble interfacial area and gas hold-up (εε G ) compared to those in the deionized water. As the raw Kraft pulp mill effluent was ozonated and the amount of utilized ozone increased, the count mean and Sauter mean bubble diameters increased. The bubble size distributions exhibited different trends depending on the type of the test liquid and on the operating conditions in terms of u G and u L . Generally, those distributions could be well described using normal, gamma, or log-normal density functions. Key words: laser, phase, Doppler, impinging-jet, bubble column, bubble, size, gas hold-up, interfacial area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".