EXPERIMENTAL STUDY OF STAINLESS STEEL 304L INTERACTION WITH ZIRCALOY AT HIGH TEMPERATURES
Bibliographic record
Abstract
One of the critical factors in the analysis of the in-vessel retention of corium during a postulated severe accident in a nuclear power plant is the interaction of corium constituents with the reactor vessel wall material. In a CANDU© reactor, after fuel channel disassembly, corium constituents would come into contact with the calandria vessel wall. Ablation of the wall due to physicochemical interaction with corium at high temperature could cause vessel failure, increasing the likelihood of radioactive release to the environment. Therefore, the interaction of the calandria vessel wall material (stainless steel 304L) with each of the main corium constituents (Zircaloy-4, Zr-2.5%Nb, Zircaloy-2, and UO2) was studied experimentally. Arrhenius equations that can be used for analysis of in-vessel corium retention were derived from experimental results. For alloys of Zr, measured rates of interaction were comparable with those reported for similar light-water reactor materials. For UO2, the rate of interaction at 1200 °C was negligible. Experiments performed with a zirconium oxide layer of 10 μm on the Zircaloy samples showed that the oxide acts as a protective barrier against high-temperature interactions with the vessel material. In all cases, if the temperature of the corium–vessel interface remains below the lowest Fe–Zr eutectic temperature of 928 °C, no significant ablation is observed after 24 h.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".