Spanning the flow regimes: Generic fluidized‐bed reactor model
Bibliographic record
Abstract
Abstract Probabilistic averaging is used to model fluidized‐bed reactors across the three fluidlization flow regimes most commonly encountered in industry (bubbling, turbulent, and fast fluidization), extending earlier work, which introduced this approach to bridge the bubbling and turbulent regimes of fluidization. In extending this concept to the fast fluidization regime, the probabilities of being in each of the three regimes are represented as probability density functions derived from regime boundary transition data. The three regime‐specific models—a generalized version of a two‐phase bubbling bed model at low gas velocities, a dispersed flow model for turbulent beds at intermediate velocities, and a generalized version of a core‐annulus model at higher velocities—are employed, leading to improved predictions compared with any of the individual models, while avoiding discontinuities at the regime boundaries. Predictions from the new integrated model are in good agreement with available ozone decomposition data over the full range of applicability covered elsewhere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".