Estimate of Neutron Fluence Using Isotope Ratio Method for ZR-2.5NB Materials Irradiated in the National Research Universal Reactor
Bibliographic record
Abstract
Abstract The isotope ratio method (IRM) is a technique used for estimating the energy production in a fission reactor by measuring isotope ratios in nonfuel reactor components. This method has been successfully demonstrated on the estimation of cumulative energy production as well as plutonium production in graphite-moderated and light-water-moderated reactors for nonproliferation purposes. In this paper, IRM was used to estimate neutron fluence in Zr-2.5Nb materials irradiated in fast neutron (FN) irradiation facilities in the National Research Universal (NRU) reactor. Neutron fluence has been shown to be an important parameter for studying irradiation effects on the performance and properties of critical reactor components. Selected isotope ratios of hafnium and iron were used as indicators of the neutron fluence in Zr-2.5Nb sample materials. Correlations between neutron fluence and the indicator element isotope ratios were generated using the reactor physics simulation codes Winfrith improved multigroup scheme (WIMS)-AECL and SCALE/Oak Ridge Isotope GENeration code (ORIGEN). Inductively coupled plasma-mass spectrometry (ICP-MS) was used to obtain accurate measurements of the isotope ratios. Neutron fluence values estimated using IRM, were in good agreement with the values based on measured irradiation power histories of the NRU reactor. This study proposes a potential application of IRM to the estimation of neutron fluence for critical reactor components in heavy-water-moderated reactors such as pressure and calandria tubes in CANDU® reactors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".