CPFD Simulation of Gas-Solid Flow in Dense Phase Zone of Pant-Leg Fluidized Bed with Secondary Air
Bibliographic record
Abstract
Aggregation of fluidization media may appear at the dense phase region of the pant-leg fluidized bed near the incline walls. When the particles flow along the inclined wall, the friction and drag force will cause the particles to accumulate on the inclined wall, resulting in an uneven distribution of particles. The stagnant zones can be minimized by correctly arranging secondary air. Computational particle fluid dynamics (CPFD) method was used to simulate the gas-solid two-phase flow pattern in the dense phase region of pant-leg fluidized bed. Cold tests were performed on a benchtop pant-leg fluidized bed. A high speed imaging technology was used to monitor the flow pattern in the dense phase area, whereas the bubble size and residence time were compared to verify the accuracy of the simulation. The gas-solid flow patterns under various models were simulated. The influence of different secondary air velocities on the reduction of stagnant zone in the dense phase zone of the fluidized bed were predicted. The results indicated that the introduction of secondary air could effectively promote the mixing of particles, and weaken the accumulation of particles on the inclined wall surface. Moreover, secondary air can effectively promote the flow between the gas-solid two-phases and improve the combustion characteristics in the furnace.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".