Double‐slit model for partially wetted trickle flow hydrodynamics
Bibliographic record
Abstract
Abstract A double‐slit model developed can predict the frictional two‐phase pressure drop, external liquid holdup, pellet‐scale external wetting efficiency, and gas–liquid interfacial area in cocurrent downflow trickle‐bed reactors operated under partially wetted conditions in the trickle flow regime. The model, an extension of the Holub et al. (1992, 1993) mechanistic pore‐scale phenomenological approach, was designed to mimic the actual bed void by two inclined and interconnected slits: wet and dry slit. The external wetting efficiency is linked to both the pressure drop and external liquid holdup. The model also predicts gas–liquid interfacial areas in partially wetted conditions. An extensive trickle‐flow regime database including over 1,200 measurements of two‐phase pressure drop, liquid holdup, gas–liquid interfacial area and wetting efficiency, published in 1974–1998 on the partial‐wetted conditions, was used to validate the modeling approach. Two new improved slip‐factor functions were also developed using dimensional analysis and artificial neural networks. High‐pressure and ‐temperature wetting efficiency, liquid holdup, pressure drop, and gas–liquid interfacial area data from the literature on the trickle‐flow regime using conventional monosized beds and catalyst bed‐dilution conditions were successfully forecasted by the model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".