A new streamlined bubble‐cap distributor for high gas–liquid ratio and the liquid distribution mechanisms
Bibliographic record
Abstract
Abstract The bubble‐cap distributor is the most commonly used and critical internal for trickle bed reactors but holds the inherited disadvantage of liquid central aggregation when operating at a high gas–liquid ratio. A new bubble‐cap distributor with a streamlined downcomer was developed in this paper to counter back the liquid aggregation and improve its comprehensive performance. The effect of the streamline parameters of the new distributor on liquid distribution was systematically explored by the coupled Euler–Euler‐population balance equation (PBE) model. Compared with the classical and polyline converging–diverging structures, the results showed that the streamlined downcomer was a key to generating radial velocity of two‐phase flow and reducing the liquid central aggregation for the bubble‐cap distributor. The mechanism of well‐distribution was explored for the new distributor. Lower converging and diverging angles enhance the distribution performance. A downcomer with a small converging angle and a 30° diverging angle was recommended for dispersing the central aggregated liquid column and acquiring the high distribution uniformity and spray covering circle. These data would be helpful to the optimal design and scale‐up of the bubble‐cap distributor in further industrial applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".