Cross-Section Calculation and Comparative Assessment of Al and Zr as Cladding for NIRR-1
Bibliographic record
Abstract
The NIRR-1 went through conversion from highly enrich uranium HEU to low enriched Uranium LEU fuel.The design of the fuel core is such that the cladding materials have been changed from aluminum to zirconium.The cladding materials may likely experience neutron dose which is susceptible to degradation of the materials.Hence, the needs to ascertain the level of degradation of the materials are crucial.Therefore, we calculate the reaction cross section of Al and Zr target with EMPIRE 3.2.3modular nuclear reaction code.The calculated results were compared with measured data from EXFOR and the Evaluated Nuclear Data (ENDF).Comparative assessment of neutronic impact of Al and Zr used in the high and low enrich uranium fuel in NIRR-1 were carry out by compared cross section of Al with Zr results in the reaction channel relevance to the cladding materials.The results show that 90 𝑍𝑟(𝑛.𝑒𝑙) have high mean cross section of 1720.30mb and 90 𝑍𝑟(𝑛.𝛾) with lower mean cross section of 0.54 mb while 27 𝐴𝑙(𝑛.𝑝) and 27 𝐴𝑙(𝑛.𝛾) high and low mean cross section is 482.5 mb and 0.022 mb respectively.It was observed that Zr target absorption cross section is better compared to Al target.This indicates that Zr has proven higher resistance to corrosion and longevity in terms of degradation as cladding materials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".