Heat Transfer Between Gas-Solid Phases Within Packed Particle Beds
Bibliographic record
Abstract
A new method is presented to determine the internal heat convection coefficients for air flowing steadily through a bed of spherical particles. In this technique, a series of step change experiments are carried out on beds of glass beads of different diameters that are subjected to a temperature step change in the inlet airflow at different flow rates. Thermocouples are arranged along the particle bed height to record the particle temperature distribution and inlet/outlet air temperatures at any time. A theoretical energy analysis of particle beds to determine the convective heat transfer coefficients is performed for the short time duration when the experimental data show that temperature distribution along the airflow direction at any time is almost spatially linear and the temperature at any specific position is almost temporally linear. The energy change of the particle beds within this time duration is shown to be a function of the heat convection coefficient. Therefore, a new correlation, in terms of Nusselt number versus Reynolds number (, with uncertainty limits at 95% confidence level, is developed and found to be in good agreement with most other correlations developed by other researchers for beds of similar spherical particles.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".