Progress Toward Instrumented Nuclear Fuel Pellets Using Additive Manufacturing
Bibliographic record
Abstract
Instrumented fuel pellets offer the potential to be used for the real-time measurement of fuel properties within emerging nuclear reactor designs. The use of three-dimensional (3D) printed nuclear fuel pellets is one approach to accommodate instrumentation. The 3D printing of nuclear materials requires that a printable feedstock material be developed for use with a specific additive manufacturing technology. In the present work, an iterative design process was used to formulate a filament containing yttria-stabilized zirconia, as a surrogate for uranium dioxide, that is suitable for use with fused filament fabrication 3D printers.The components of the filament and their amounts, the printing parameters, and the debinding process were varied to produce an optimized printing procedure. A final five-component formulation containing 50.0 ± 0.1 vol % organic material was developed. With this formulation, the requirement to print to a 16-mm wall thickness, consistent with CANDU pellet dimensions rather than the maximum of 4 mm reported previously, resulted in numerous production failures. Ultimately, the manipulation of specific printer parameters to form microchannels within the pellet during printing resulted in pellets consistent with the target criteria. In the final set of eight pellets, seven pellets met the density criterion of 95% theoretical density, with an average density of 96.2 ± 1.0% of theoretical density.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".