Mechanical Testing and Characterization of Proposed Copper Materials for the Disposal of Used Nuclear Fuel Waste
Bibliographic record
Abstract
For permanent and safe storage of Canada’s used nuclear fuel, it is proposed to contain and emplace the used fuel bundles in a deep underground repository where they will remain for long term storage. The used fuel containers (UFCs), made of copper-plated steel, must be designed and manufactured to be robust under diverse environmental conditions, including applied loads. In the analysis of proposed copper coatings of UFCs reported here, five types of material manufacturing and processing methods for the copper were examined within two main categories: electrodeposited (ED) or cold-sprayed (CS). Testing included scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) imaging, tensile tests, and micro-computed tomography (MicroCT).\n\nThe findings presented in this thesis highlight the significant impact of heat treatment on the mechanical properties of CS material, markedly reducing porosity and internal defects compared to the as-sprayed (AS) state. While electrodeposition yields a minimally porous copper structure, the composition of the electrodeposition bath influences mechanical behavior during straining. EBSD analysis of CS-AS material was constrained by high grain boundary proportions and heavily deformed grains, with annealing enhancing grain size uniformity. The material retained a mix of brittle and ductile fracture characteristics post-heat treatment at either 350 or 600°C. Porosity distribution was heterogeneous across CS samples, with annealing notably reducing identifiable porosity. SEM imaging revealed sub-micrometer pores on fracture surfaces, particularly at pore-particle interfaces. In uniaxial tensile tests on an interfacial sample, no delamination or significant localized strain occurred along the interface, demonstrating uniform behavior with necking observed through SEM imaging. This suggests the ductility of copper enables it to deform to match the steel under uniaxial strain, reducing delamination risk and maintaining protection during deformation. Moreover, the study highlights the influence of hydrogen content on material elongation tolerance, with ED-Acid samples exhibiting earlier failure compared to ED-Pyrophosphate tests. Fracture surface analysis revealed small-scale porosity in all failed samples, with digital image correlation mapping providing valuable insights into bulk behavior and fracture localization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".