(Corrosion Division H. H. Uhlig Award) Application Of Electrochemistry in the Development of Performance Assessment Models for High Level Nuclear Waste Disposal
Bibliographic record
Abstract
The development of performance assessment models for high level nuclear waste repositories is a major environmental issue made challenging by the period of containment required which is many thousands of years. Within the multiple barrier repository designs proposed, the corrosion performance of both the waste container and the waste form are critical components of such models. The common practice of steadily improving engineering designs based on failure analyses is not an option. Consequently, the scientific and engineering credibility and social acceptance of the disposal solutions developed rely heavily on a fundamental and quantitative understanding of the science involved. This must include a realistic acknowledgement of the limitations encountered when attempting to transform such understanding into predictive models. Electrochemical, and associated microscopic and spectroscopic, techniques are proving essential in this challenge. They play a key role not only in the development of mechanistic understanding but also in the accumulation of the numerical database and the specification of the boundary conditions for computational models. This presentation will concentrate on the application of electrochemical, and associated microscopic and spectroscopic, methods to the study of nuclear fuel (predominantly uranium dioxide) and copper waste container corrosion processes, and will attempt to describe how the essential link between mechanistic understanding and model development can be achieved. While many nations contemplate the disposal of the waste processed and immobilized in the form of ceramics and glasses, the spent fuel discharged from reactors remains the primary wasteform. Prior to irradiation in a reactor it is a purified, close-to-stoichiometric (UO2.002) p-type semiconductor. However, on discharge from the reactor it is contaminated with heterogeneously-distributed nuclear fission products which have a significant influence on its subsequent corrosion properties. Of key importance from a corrosion perspective is the rare earth doping of the fuel matrix which increases its electrical conductivity and facilitates microgalvanic coupling to the noble metal particles segregated within the matrix. The influences of these heterogeneously distributed features on fuel corrosion have been studied on simulated fuels (non-radioactive surrogates for the actual wasteform) using a range of electrochemical techniques including scanning electrochemical microscopy and current-sensing atomic force microscopy and spectroscopic techniques such as microRaman and X-ray photoelectron spectroscopy, and time-of-flight secondary ion mass spectrometry. The use of these techniques to establish the mechanistic basis for radionuclide release models will be described with a particular emphasis on how fission product doping regulates the redox balance between potential oxidants (radiolytic hydrogen peroxide) and reductants (radiolytic hydrogen and the products of the container corrosion process). While a number of options exist for the choice of material for the fabrication of waste containers, carbon steel vessels with an outer corrosion-resistant copper shell or coating are strongly favoured in anoxic repository environments since copper should be thermodynamically stable. Despite such thermodynamic assurances, potential pathways for corrosion failure exist. The two primary routes are corrosion due to sulphides (possibly formed by remote microbial activity in the repository backfill materials) and galvanic corrosion at manufacturing defects in the copper shell/coating allowing groundwater contact with both the copper and the underlying steel vessel. This last mechanism is particularly important for steel vessels protected by a thin copper coating. X-ray tomography is being used to observe the progress of corrosion at the coating/steel interface, the properties of which (adherence being a key one) will be dictated by the coating process used. Particular emphasis will be given to how these measurements can be used to model and define the potential for failure by this process. Finally, a brief description will be given of how all these corrosion features can be integrated into a comprehensive model able to predict, at least semi-quantitatively, the evolution of redox conditions within a failed container and their impact on the critical radionuclide release process which determines the release of radioactivity to the environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.062 |
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".