|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Analysis of AA6061 Cladding Diffusion Bonding Quality for the U-10Mo Monolithic Fuel Using Multi-fidelity Machine Learning Surrogate
||Yucheng Fu, Rajib Kalsar, Taylor Mason, Zhijie Xu, Kriston P Brooks, Ayoub Soulami, Vineet Joshi
|On-Site Speaker (Planned)
To reduce nuclear proliferation, low-enriched U-10Mo alloy has been identified as a promising fuel candidate for United States high-performance research reactors. During fabrication, the fuel will be encapsulated in the aluminum alloy 6061 (AA6061) cladding, to prevent fuel corrosion and fission product release. The cladding was diffusion bonded using the hot isostatic pressing (HIP), which promotes a homogeneous AA6061/AA6061 bonding interface. To reduce the high experimental cost and efficiently optimize the diffusion bonding process, a multi-fidelity Gaussian process surrogate was developed to predict the aluminum cladding bond strength. This machine learning surrogate leverages the high-fidelity experimental data with the low-fidelity numerical model to maximize the bond strength prediction accuracy. Sensitivity analysis was followed to identify the influential HIP parameters. It was found that the interface Mg2Al2O5 particles were closely related to the bond strength and the temperature was suggested as the most dominant factor in determining the bonding quality.