|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||NOW ON-DEMAND ONLY – M-8: A Data-driven Surrogate Model for Fast Predicting the U-10Mo Fuel Grain Structures during the Hot Rolling and Annealing Processes
||Yucheng Fu, William E. Frazier, Kyoo Sil Choi, Lei Li, Zhijie Xu, Vineet Joshi, Ayoub Soulami
|On-Site Speaker (Planned)
The U-10Mo fuel foil fabrication process steps of hot rolling and annealing play an important role in determining the foil grain structure. The grain structure then impacts the foil’s performance during irradiation. To enable fast prediction and optimization of U-10Mo grain structure during these processing steps, a data-driven surrogate model has been developed using simulation data, produced from a coupled Kinetic Monte Carlo (KMC) Potts model and finite element method (FEM) model. The microstructures simulated using these techniques cover a wide range of initial grain size, uranium carbide volume fraction and rolling reductions. With the acquired high fidelity data, the deep learning, stack ensemble-based surrogate model has been trained on the acquired dataset. This data-driven surrogate model demonstrates good accuracy in predicting the U-10Mo fuel microstructures and guides the selection of proper processing parameters for U-10Mo foil production.
||Nuclear Materials, Machine Learning, Modeling and Simulation