| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Mechanical Behavior of Nuclear Reactor Materials and Components IV
|
| Presentation Title |
Simulation of Spark Plasma Sintering of Uranium Mononitride: Finite Element and Machine Learning Approaches |
| Author(s) |
Faris B. Sweidan, Amit Hassan Arpon, Justin Kermarrec, Yi Meng Chan, Pär Olsson |
| On-Site Speaker (Planned) |
Faris B. Sweidan |
| Abstract Scope |
The sintering behavior of uranium mononitride (UN) during spark plasma sintering (SPS) was simulated using COMSOL Multiphysics utilizing experimentally obtained shrinkage data produced by SPS. At KTH, UN has been sintered in collaboration with the Spark Plasma Sintering Centre at Stockholm University for almost a decade, resulting in data for 85 pellets. These pellets' sintering data was used to simulate the behavior of UN during sintering. In addition, the data profiles of the 85 UN pellets were collected in a single dataset with their corresponding properties reported in the literature. This dataset was used in statistical analyses in addition to generating a sintering model for UN using machine learning. The resulting sintering models were compared and implemented in COMSOL Multiphysics. This study contributes to understanding the behavior of UN during SPS and provides predictive capabilities of the resulting UN pellet properties when certain sintering parameters are adjusted. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Nuclear Materials, Solidification |