Conference Tools for 2024 TMS Annual Meeting & Exhibition

Help

About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Deep Neural Network for Porosity and Microstructure Analytics of a High Burnup U-10Zr Metallic Solid Fuel
Author(s)	Fei Xu, Elijah Danquah Darko , Lu Cai, Daniele Salvatoa, Fidelma Giulia Di Lemmaa, Luca Capriottib, Tiankai Yao, Min Xian
On-Site Speaker (Planned)	Fei Xu
Abstract Scope	U-10Zr metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity and fuel constitutional redistribution are two important factors on changing the thermal conductivity, fuel composition etc. Therefore, it is crucial to accurately segment and analyze porosity and microstructure distributions to understand the U-10Zr fuel system and design future fast reactors. To address the above issues, we developed deep fully convolutional networks on Scanning Electron Microscopy (SEM) data to segment pores accurately and classify multiple phases, including pure Zr, Nd, pores, and two matrices α-U and (U, Zr). Sufficient comparison results demonstrate that our method quantitatively outperforms other models on multiple lens SEM images. Finally, quantitative porosity and microstructure analysis results of whole cross-sectional images are discussed. Our findings will provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Nuclear Materials, Mechanical Properties

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Characterization of Radiation Driven Processes using Machine Learning Tools

Characterizing Microstructures in Aluminide Coatings Captured in SEM Image with Convolutional Neural Networks

Deep Neural Network for Porosity and Microstructure Analytics of a High Burnup U-10Zr Metallic Solid Fuel

Defect Evolution in Multi-principal Chemically Disordered Alloys from Multiscale Simulations

E-25: Feature Engineering for Construction of High-accuracy Thermal Conductivity Prediction Model for Uranium Compounds

E-26: Materials Genomics Search for Possible Helium-absorbing Nano-phases in Fusion Structural Materials and Experimental Validation

Emergent Molecular Structure and Dynamics of Tetrahedral Liquids Revealed by Neural Network Forcefield Simulations and Neutron Spin Echo Experiments

Few-shot Machine Learning for Automated Analysis of TEM Images of Nuclear Materials

Influence of Empirical Potentials on Data Quality in Computational Studies of Zr Alloys

Inverse Uncertainty Quantification of Dispersion Analysis Research Tool (DART) Parameters Necessary for the Calculation of Fission Gas Swelling in U-Mo Fuel

Machine Learning Enhanced Kinetic Monte Carlo Modeling of Molten Salt Corrosion of Ni-Cr Alloys

Machine Learning for Predicting Reactor Pressure Vessel Embrittlement

Materials Genomics Search for Helium-absorbing Nano-phases in Fusion Structural Materials

Modeling Cascade Damage in Tungsten Using Machine Learning SNAP Interatomic Potential: Electron-Phonon Interaction Model

Neural Networks of Defect Kinetics in Refractory Alloys

Optimizing Thermal Conductivity Prediction of Uranium Compounds using Balanced Multiclass Classification

Probing Radiation Induced Interface Metastability Using Deep Learning Object Detection

Putting Artificial Intelligence into Action to Quantify Radiation Effects in Materials

Quantitative Insight to Fission Gas Bubble Distribution and Lanthanide Movement in Irradiated Annular U-10Zr Metallic Fuel Using Deep Learning

Revealing the Story of Defects from Coupled Extreme Environments with Autoencoders and Dense Neural Networks

Scaling Ductility from Microscale to Bulk by Coupling Crystal Plasticity Simulations with 3D Convolutional Neural Networks

Scanning-TEM (STEM) 3D Tomography for Quantification of Radiation Damage in Neutron Irradiated 316L Stainless Steel

Synthetic Data Driven Materials Informatics Methods for Nuclear Materials Characterization

Utilizing Mechanistic Modeling and Uncertainty Analysis to Support Nuclear Fuel Qualification

Questions about ProgramMaster? Contact programming@programmaster.org