Heat Transfer Intensification Mechanism of a Baffled Fluidized Bed Heat Exchanger
Bibliographic record
Abstract
Gas–solid fluidized bed heat exchangers are commonly applied to remove heat in highly exothermic processes. The heat transfer performance is related to hydrodynamics. Baffles, such as ring-type baffles, louver baffles, and packings, are widely applied to improve the hydrodynamics in a fluidized bed. In this study, a baffled fluidized bed heat exchanger was proposed to enhance the bed-to-surface heat transfer coefficient. The baffles are specially designed to intensify the contact characteristics between heat transfer tubes and fluidized bed medium. The hydrodynamics and heat transfer characteristics were compared between the conventional and the newly designed baffled heat exchanger to explore the intensification mechanism in dilute and dense fluidization regions. Proper arrangement of the baffles in the fluidized bed heat exchanger increases the time fraction of the particle packet as well as the solid holdup, which enhances the heat transfer coefficient in dilute region. For the dense region, the contact time between the packet and heat transfer surface is decreased in the baffled fluidized bed heat exchanger due to the reduced bubble size, increased bubble frequency, and enhanced radial movement of bubbles and particles. Therefore, the bed-to-surface heat transfer in the dense region is significantly intensified.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".