About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Thermodynamic and diffusion properties of oxide nuclear fuels: results of electronic structure calculations and machine learning potentials |
| Author(s) |
Marjorie Bertolus, Unai Aizpurua Tome, Giovanni Nicodemo, Luca Messina |
| On-Site Speaker (Planned) |
Marjorie Bertolus |
| Abstract Scope |
The safety and efficiency of nuclear reactors depend on a comprehensive knowledge of nuclear fuel behaviour under both normal and off-normal operational conditions. Fuel performance codes, such as the PLEIADES multiphysics platform, developed by CEA and its industrial partners EDF and Framatome, are essential tools in this effort.
Key aspects are to know the physico-chemical properties of fuel materials accurately and develop more physically-based behavioural models. To advance this understanding, basic research approaches, such as multiscale modelling starting at the atomic scale and separate effect experiments, provide valuable insights into material properties and the key phenomena governing fuel evolution in reactor.
We combine advanced electronic structure simulations and interatomic potentials, including machine learning ones, to investigate the structural, thermal and diffusion properties of actinide oxides. We will present the results of these investigations, which contribute to a deeper understanding of nuclear fuels.
This research is co-funded by the European Union. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Nuclear Materials, Machine Learning |