Review of the Modelling of Corrosion Processes and Lifetime Prediction for HLW/SF Containers—Part 2: Performance Assessment Models
Bibliographic record
Abstract
The disposal of high-level radioactive waste (HLW) and spent nuclear fuel (SF) presents a unique challenge for the prediction of the long-term performance of corrodible structures since the HLW/SF canisters are expected, in some cases, to have lifetimes of one million years or longer. Various empirical and deterministic models have been developed over the past 45 years for making predictions of the long-term corrosion behaviour, including models for uniform and localized corrosion, environmentally assisted cracking and microbiologically influenced corrosion. As well as process models focused on specific corrosion mechanisms (described in Part 1 of this review), there is also a need for performance assessment models as part of the overall analysis of the safety of a deep geological repository (DGR). Performance assessment models are often based on simplified or abstracted process models. The manner in which various international waste management programs have predicted the long-term performance of HLW/SF containers with copper, steel, Ni and Ti alloy corrosion barriers is discussed. Performance assessments are repeated periodically during the development and implementation of a DGR, and the corrosion models are constantly updated in light of new mechanistic understanding and/or more information about the deep geological environment. Two examples of how the container performance assessment models evolve over time are also described. Performance assessment models cannot easily be validated, so it is important to build confidence in the long-term predictions using other methods, including natural analogues and large-scale in situ tests and the use of complementary models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".