FeCrAl Alloys for Accident Tolerant Fuel Cladding in Light Water Reactors
Bibliographic record
Abstract
The goal of the U.S. Department of Energy (DOE) Accident Tolerant Fuel Program (ATF) for light water reactors (LWR) is to identify alternative fuel system technologies to further enhance the safety of commercial nuclear power plants. An ATF fuel system would endure loss of cooling in the reactor for a considerably longer period of time than the current systems. The General Electric (GE) and Oak Ridge National Laboratory (ORNL) ATF design concept utilizes an iron-chromium-aluminum (FeCrAl) alloy material as fuel rod cladding in combination with uranium dioxide (UO2) fuel pellets currently in use, resulting in a fuel assembly that leverages the performance of existing/current LWR fuel assembly designs and infrastructure with improved accident tolerance. Significant testing was performed in the last three years to characterize FeCrAl alloys for cladding applications, both under normal operation conditions of the reactor and under accident conditions. This article is a state of the art description of the concept.
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".