Study on gas–solid two‐phase fluidization and heat transfer in carbon fibre porous media
Bibliographic record
Abstract
Abstract In the study, solid phase fluidization in porous media was investigated based on the pore network model method. Micro computed tomography (Micro‐CT) technology was adopted to reconstruct the three‐dimensional structure of porous media. A computational fluid dynamics (CFD) model was established based on the Euler–Euler two‐fluid model, which was verified by experiments. The effect of air inlet velocity and solid content on the solid phase fluidization and heat transfer in porous media was systematically studied. It is found that when the solid content is constant, with the enhancement of solid phase fluidization, the particle velocity and solid concentration standard deviation gradually decreases, and at the same time, when the air inlet velocity is large, the value is small, the bed height increases faster, and the particle temperature decreases more. When the air inlet velocity is constant, the solid velocity gradually decreases and approaches stability with the fluidization process in the flow field with different solid content. At the same time, the higher the solid content, the higher the bed height, the smaller the , and the better the particle distribution uniformity. The research results can assist in the reaction design of multiphase media in porous media reactors, provide good guidance for the uniformity of temperature distribution and the withdrawal of reaction heat in the reactor.
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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 | 0.000 |
| 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 | 0.000 | 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".