20 citations · 43 across the 7 of their papers we have counts for
7 papers
A model of grain growth in UN integrating molecular dynamics, phase-field modeling, and uncertainty quantification
Mohamed AbdulHameed, Fadel M. Nasr, Wen Jiang +2
Grain growth kinetics and grain-boundary (GB) properties in uranium mononitride (UN) are investigated through an integrated multiscale framework combining molecular dynamics (MD),…
Super-Resolution Initialization of High-Fidelity CFD Simulations for Pebble-Bed Reactors
Guilherme Gottems, Vasil Ivakimov, Luiz Aldeia Machado +13
High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the in…
Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification
Mohamed AbdulHameed, Khadija Mahbuba, Mahmoud Yaseen +3
Silver released from TRISO fuel particles can migrate through the SiC layer and deposit on reactor components, posing radiation hazards and operational challenges. Despite numerous…
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation
Farah Alsafadi, Mahmoud Yaseen, Xu Wu
The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering field…
Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model with Operator Learning
Mahmoud Yaseen, Dewen Yushu, Peter German +1
Advanced Manufacturing (AM) has gained significant interest in the nuclear community for its potential application on nuclear materials. One challenge is to obtain desired material…
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning
Mahmoud Yaseen, Dewen Yushu, Peter German +1
One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipu…
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data
Ziyu Xie, Mahmoud Yaseen, Xu Wu
Inverse UQ is the process to inversely quantify the model input uncertainties based on experimental data. This work focuses on developing an inverse UQ process for time-dependent r…