Identification of the regime boundaries in bubble columns based on the degree of randomness in the signals<sup>†</sup>
Bibliographic record
Abstract
Abstract A new parameter (degree of randomness (DR)) was defined for the identification of the main transition velocities, U trans. The new method reconstructs the time series into multiple state vectors, thus generating non‐overlapping vector pairs and then compares the distance between them with a pre‐selected cut‐off length. The DR values were extracted from gauge and differential pressure fluctuations as well as x‐ray tomographic scans. At every U trans value, the DR index exhibited a well‐pronounced local minimum. Three cylindrical bubble columns (BCs) with various diameters (0.1, 0.14, and 0.45 m in ID) and one rectangular BC (width = 0.2 m, depth = 0.04 m) were used. They were aerated by means of different perforated plate gas distributors. It was found that in the cylindrical BCs the disintegration of the bubbly flow regime took place always at U trans = 0.04 m/s. In the case of the rectangular BC the first critical velocity appeared at U trans = 0.012 m/s. The lower boundary of the churn‐turbulent regime was identified at U trans = 0.11 m/s in the smallest cylindrical BC and at about U trans = 0.095 m/s in the other two cylindrical BCs. In the case of the rectangular BC, the second critical velocity was identified at U trans = 0.039 m/s. The low U trans in the rectangular BC imply that the hydrodynamic regimes are less stable in this particular column due to higher degree of liquid turbulence. The calculated DR values from the gauge pressure fluctuations successfully distinguished the upper boundary of the gas maldistribution and the first transition sub‐regime.
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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 | 0.002 |
| 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 | 0.000 |
| 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".