An experimental investigation on debris bed formation from fuel coolant interactions of metallic and oxidic melts
Bibliographic record
Abstract
During postulated severe accidents in a light water reactor (LWR), the core melt (corium) may relocate to the lower head and fail the reactor pressure vessel (RPV). The corium is expected to undergo fuel coolant interactions (FCI) if the reactor cavity is flooded with water. Both FCI energetics and resulting debris bed coolability are of paramount importance to reactor safety, since the ex-vessel corium poses a threat to the containment integrity if steam explosion occurs or the debris bed is uncoolable, leading to release of radioactive fission products to the environment. The present study is intended to quantify the characteristics of a debris bed resulting from FCI, which are crucial to debris bed coolability. Different from the previous studies with only oxidic materials, various materials, including metallic ones of Sn, Sn-Bi and Zn as well as oxidic one of Bi2O3-WO3, were employed as the simulants of corium (mixture of UO2/ZrO2/Zr/Fe) in the present study to investigate the effects of melt materials, melt superheat and coolant subcooling on debris bed formation in a water pool. High-speed photography was applied to visualize melt jet breakup, droplets fragmentation, as well as fragments sedimentation on the pool floor. Other obtained data are debris bed shape (profile) and porosity, as well as morphology and size distribution of debris particles. The comparative results of various tests provided insights toward filling the knowledge gap on debris bed characteristics under different melt materials and compositions.
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 | 0.000 |
| 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".