Bibliographic record
Abstract
Introduction A great number of processes carried out in spouted beds require the application of different modes of heat and/or mass transfer. We may distinguish among the following modes: Heat transfer, mass transfer, simultaneous heat and mass transfer – between fluid and particles, Heat transfer between wall and bed, and Heat transfer between submerged object and bed. For each mode, transfer mechanisms are examined; then experimental findings and, in some cases, theoretical studies are discussed. Between fluid and particles Transfer mechanisms and models Quite often, the basic assumption for analysis of heat or mass exchanged between fluid and particles is that heat is transferred to the particles under conditions of external control, neglecting heat transmission within the particles. For heat transfer in the absence of mass transfer, this is justified when the particle heat transfer Biot number is sufficiently small (e.g., <0.1) and the corresponding Fourier number exceeds 0.2 2 . For simultaneous heat and mass transfer, such as when the particles are well wetted at the surface, the average temperature at the particle surfaces is substantially uniform, and external control again prevails. Assuming plug flow conditions through the bed, the axial fluid temperature distribution can be described by a dimensionless function. Because the spouted bed consists of two distinct regions, with the average gas velocity in the spout being one or two orders of magnitude greater than that in the annulus, the decline of gas temperature in these two zones is quite different; it is slight in the spout and considerable in the annular zone.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".