Combined Effect of Irradiation, Temperature, and Water Coolant Flow on Corrosion of Zr-, Ni–Cr-, and Fe–Cr-Based Alloys
Bibliographic record
Abstract
Abstract Investigation of the role of irradiation on the corrosion resistance of structural alloys is of vital importance for selection of supercritical water-cooled reactor (SCWR) materials. Gamma heating under SCWR conditions, which induces enhancement of radiolysis and corrosion kinetics at interfaces, can be efficiently simulated by electron beam irradiation over a wide range of deposited dose and temperature. The NSC KIPT-sited Canada–Ukraine Electron Irradiation Test Facility (CU-EITF) still remains the only operating facility capable of in situ irradiation of specimens in a supercritical water (SCW) natural circulation loop. This paper reports the results of postirradiation studies of Zr–1%Nb and Ni–Cr Inconel 690/52MSS alloys after a ~500-h-long exposure in the CU-EITF in the near-critical (23.5 MPa/360–385°C) regime. Results of scanning electron microscopy (SEM) studies of the sample microstructure are presented along with those of the electron-irradiated loop piping, SS X18H10T. The results of corrosion tests under electron-irradiation are correlated to the calculated three-dimensional (3D) fields of absorbed dose and temperature and to the reference data obtained in-pile for topical materials. The paper also discusses the prospects for the use of the CU-EITF facility within a cooperative SCWR program and presents an outlook of the facility development.
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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 | 0.000 |
| 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".