Pressure gradients, voidage and gas flow in the annulus of spouted beds
Bibliographic record
Abstract
Abstract Parallel measurements of pressure gradients with a differential pressure probe and voidage profiles with a fibre optic system have been carried out to study gas flow distributions in the annulus of spouted beds. The observation of Grbavcic et al. (1976) that for a given fluid‐solid combination and column geometry the annulus pressure gradient at any bed level is independent of bed depth was corroborated again. Calibration curves of pressure drops versus superficial gas velocities for beds of voidage higher than the loose‐packed voidage were obtained by applying the Ergun (1952) equation, making it possible to estimate superficial gas velocities in the annulus using the static pressure gradient method. The local superficial gas velocity in the annulus was found to be higher in a deep bed than in a shallow bed of the same material, contrary to the conclusion (Grbavcic et al., 1976) that, for a given fluid‐solid combination and column geometry, the annulus fluid velocity at any level is independent of bed depth. Theoretical models and equations which do not account for the conical geometry near the bottom were found to underpredict superficial gas velocities in the annulus. Increasing the spouting gas flow was found to increase the net gas flow through the annulus.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".