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Meeting MS&T23: Materials Science & Technology
Symposium Leveraging Integrated Computational Materials Engineering for High-fidelity Physics-based and Machine Learning Models
Presentation Title Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
Author(s) Peter Toma, Md Ali Muntaha, Joel B. Harley, Michael R. Tonks
On-Site Speaker (Planned) Peter Toma
Abstract Scope The mechanism of fission gas release (FGR) in UO2 nuclear fuel significantly impacts the longevity of nuclear fuel rods. Gaining insight into the fundamental mechanism and forecasting the behavior of fission gas transport and release is of paramount importance. Traditionally, the phase field method has been employed to compute FGR from a UO2 microstructure, but this method is highly computationally intensive and time-consuming. Neural networks, a machine learning architecture, provide an alternative means of determining both the FGR of a microstructure and its evolution over time. In this research, a hybrid convolutional-recurrent neural network was designed to forecast the development of a simulated microstructure and the subsequent FGR over time, utilizing training data from a multiphysics code that couples the phase field and cluster dynamics methods. This framework offers a novel way of simulating time-dependent physical processes with reduced computational expense.


Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Hybrid Simulation Method Based on Molecular Dynamics and Machine Learning to Improve Property Prediction with Lower Computational Cost in Complex System
New Refractory High Entropy Alloys Discovery by Physics Discovery
Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
Robotic Bending of Craniomaxillofacial Graft Fixation Plates
Simulating Macroscale Microstructures Using Advanced Programming and Numerical Methods

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