About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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Transmutation Effects in Fusion Reactor Materials: Critical Challenges & Path Forward
|
Presentation Title |
Machine-learned Interatomic Potential Development for H Trapping in ZrC Strengthened W |
Author(s) |
Ember Sikorski, Mary Alice Cusentino, Megan McCarthy, Julien Tranchida, Mitchell Wood, Aidan Thompson |
On-Site Speaker (Planned) |
Ember Sikorski |
Abstract Scope |
While tungsten is a leading candidate material for plasma-facing components, it has a high ductile-to-brittle transition temperature. Additionally, W may undergo recrystallization and grain growth at fusion reactor temperatures (≥1000K). Strengthening W with ZrC dispersoids can improve ductility and limit grain growth. However, the effects of ZrC dispersoids on thermomechanical properties and interaction with plasma species are not well understood. We have developed machine-learned interatomic potentials to leverage the predictive capability of first-principles methods for simulations of millions of atoms on the nanosecond scale. We will discuss the development of a Spectral Neighbor Analysis Potential (SNAP) for W-ZrC-H and its performance at fusion reactor temperatures. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Nuclear Materials, High-Temperature Materials |