Modeling Zirconium Under Neutron Irradiation: Interatomic Potentials, Displacement Cascades, and Electron-Ion Coupling
Bibliographic record
Abstract
This thesis presents a comprehensive atomistic computational study on the effects of neutron irradiation on Zr, a material extensively used in CANada Deuterium Uranium (CANDU) reactors. Our goal is to improve the understanding of microstructural changes under irradiation, focusing on displacement cascades that produce defects. Point defects and their clusters influence the mechanical properties, dimensional stability, and service life of components in reactor environments. The key contributions are: 1. Reparameterization of Interatomic Potentials: Three embedded atom method (EAM) potentials were reparameterized to improve simulation accuracy. Using density functional theory (DFT) calculations, the two-body and embedding energy functions were refitted to better describe atomic interactions at short distances. The modified potentials maintained similar near-equilibrium properties such as defect formation energies and elastic constants but significantly improved threshold displacement energies (TDEs), particularly for [0001] direction. 2. Simulation of Displacement Cascades: Large-scale molecular dynamics (MD) simulations modeled high-energy collision cascades in hexagonal close-packed (HCP) α-Zr. The study examined defect generation, damage morphology and diffusion behaviour. For this purpose, a novel post-processing technique was developed. Self-interstitial atom (SIA) clusters displayed greater anisotropy than equilibrium clusters. 1D and 2D diffusion modes were observed. Findings were integrated into a rate-theory model that predicted radiation-induced growth strains. 3. Effect of Electron Stopping: MD simulations that incorporate ion-electron coupling were conducted using a calibrated version of the unified two-temperature model (UTTM). This approach allowed for a more accurate assessment of electronic effects on primary damage production in Zr, representing the first application of the UTTM to neutron irradiation in Zr. The results suggested that the electronic stopping and the assisted recovery altered the damage production. This thesis advances the atomic-scale understanding of radiation-induced damage in Zr. The reparameterized EAM potentials enhance the fidelity of future MD simulations. The insights into the anisotropy of defect diffusion and its role in radiationinduced growth, along with the novel UTTM-based simulations, contribute significantly to the field. These findings provide a stronger foundation for ongoing experimental, operational, and regulatory efforts to maintain the safety and performance of nuclear reactors.
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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 | 0.001 |
| 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 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".