Multi‐scale study of fluidized bed‐chemical vapour deposition process in nuclear fuel coated particle fabrication for high‐temperature gas‐cooled reactor: A review
Bibliographic record
Abstract
Abstract Fluidized bed‐chemical vapour deposition (FB‐CVD) is a kind of key technology used widely in many application fields, such as semiconductors, nuclear energy, energy storage, and catalysts. In recent years, it has drawn much attention in the preparation of nuclear fuel coated particles (CP). It also has long played a crucial role in the preparation of high‐temperature gas‐cooled reactor (HTGR) fuel pebbles. The multi‐scale study of FB‐CVD technology has paid attention to the industrial fabrication of nuclear fuel particles at a large scale. In this paper, the recent FB‐CVD studies of different application fields are summarized first. Then, the recent works of our group in the field of FB‐CVD process in nuclear fuel particle fabrication are summarized. The FB‐CVD process in nuclear fuel particle fabrication and the multi‐scale study of the FB‐CVD process are overviewed in detail. Molecular dynamics (MD) simulation is used to study the CVD process of preparing the coating layer at the micro‐scale. Computational fluid dynamics–discrete element model (CFD‐DEM) simulation is used to study the high‐density particle fluidization, mixing particle fluidization, and particle coating process at the particle scale. Process simulation is used to study the entire FB‐CVD production line at the macro scale. Finally, the great application potential of the multi‐scale coupling study of the FB‐CVD process in the industrial fabrication of nuclear fuel particles is revealed. This paper is helpful to develop the academic research field of fluidized beds. It also has inspiration and reference significance for the expansion of other industrial applications of FB‐CVD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".