Atomistic Simulation of Radiation-induced Defects in Ni-based Systems
Bibliographic record
Abstract
Nickel (Ni)-based alloys show outstanding mechanical and physical properties at high temperatures and in corrosive environments. Therefore, Ni with measured additions of appropriate alloying elements would be a good structural material candidate for the next generation of nuclear reactors. One such example of particular interest in Canada deuterium uranium (CANDU) reactors is X-750 Ni-based spacer material, which is a modified 600 series alloy, strengthened by the addition of aluminum (Al) and titanium (Ti). However, such Ni and Ni-based alloys, when exposed to radiation under working conditions, form defects which ultimately degrade their mechanical properties. This dissertation addresses several questions with regards to point defect evolution in Ni and Ni-based alloys by employing atomistic scale modelling in order to understand and predict alloy performance in irradiated environments. Presented in this manuscript format, the dissertation can be outlined as follows: all utilized atomistic techniques in this thesis are explained in Chapter 2. Chapter 3 identifies the role of self interstitial atoms (SIAs) in the re-ordering of disordered Ni3Al, and moreover an applicable model for mean-field rate-theories is also presented. Chapter 4 indicates the complexity of energy landscape on point defect diffusion and localized traps in Ni(x)Fe(1−x) (0 ≤ x ≤ 1) using an in-home AkMC code. A modified version of this code is used to study point defect transport properties in Ni-Fe-Cr ternary alloy systems (appendix A). Helium (He) bubble evolution in pure Ni system as a model for Ni-based alloys is studied and the role of different objects on bubble growth are discussed in Chapter 5. In the final chapter, the primary conclusions of these studies and future steps are explored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".