Science based integrated approach to advanced nuclear fuel development - vision, approach, and overview
Bibliographic record
Abstract
Advancing the performance of Light Water Reactors, Advanced Nuclear Fuel Cycles, and Advanced Rcactors, such as the Next Generation Nuclear Power Plants, requires enhancing our fundamental understanding of fuel and materials behavior under irradiation. The capability to accurately model the nuclear fuel systems is critical. In order to understand specific aspects of the nuclear fuel, fully coupled fuel simulation codes are required to achieve licensing of specific nuclear fuel designs for operation. The backbone of these codes, models, and simulations is a fundamental understanding and predictive capability for simulating the phase and microstructural behavior of the nuclear fuel system materials and matrices. The purpose of this paper is to identify the modeling and simulation approach in order to deliver predictive tools for advanced fuels development. The coordination between experimental nuclear fuel design, development technical experts, and computational fuel modeling and simulation technical experts is a critical aspect of the approach and naturally leads to an integrated, goal-oriented science-based R & D approach and strengthens both the experimental and computational efforts. The Advanced Fuels Campaign (AFC) and Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Integrated Performance and Safety Code (IPSC) are working together to determine experimental data and modeling needs. The primary objective of the NEAMS fuels IPSC project is to deliver a coupled, three-dimensional, predictive computational platform for modeling the fabrication and both normal and abnormal operation of nuclear fuel pins and assemblies, applicable to both existing and future reactor fuel designs. The science based program is pursuing the development of an integrated multi-scale and multi-physics modeling and simulation platform for nuclear fuels. This overview paper discusses the vision, goals and approaches how to develop and implement the new approach.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".