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[Seminar] Modeling of Advanced Reactors Assisted by Physics-Informed Machine Learning: an “Open-Box” Approach
September 10 @ 4:00 pm - 5:00 pm
Dr. Yang Liu
Argonne National Laboratory
Advanced reactors are expected to fulfill a key role in next-generation nuclear power plants due to their increased safety performance and reliability. The System Analysis Module (SAM), an advanced system code primarily developed in Argonne, is currently developing a reduced-order three-dimensional module to accurately model complex thermal-fluid phenomena in advanced reactor systems. This module adopts a coarse mesh setup to be consistent with the one-dimensional system modeling framework, which ensures computational efficiency. A major difficulty for the coarse mesh 3D module in SAM is to accurately capture turbulence.
This seminar will discuss some recent efforts to develop a coarse mesh turbulence model with a physics-informed machine learning approach. This approach leverages deep learning technologies and utilizes fine mesh CFD data for the turbulence model development. The performance of different architectures of deep neural networks, including Densely Connected Convolutional Networks and Long-Short-Term-Memory Networks, are evaluated. The optimized neural network model will be implemented into SAM as a data-driven turbulence model. Such an “open-box” approach puts the machine learning model within the solver so that the major physics constraints of the system can be preserved. Furthermore, a Bayesian approach that relies on the iterative Kalman Filtering method is developed to quantify the uncertainty of the machine learning model when it is implemented into SAM.
Dr. Yang Liu is a Nuclear Engineer at the Argonne National Laboratory. He obtained his bachelor’s degree in Nuclear Engineering from Tsinghua University, China. He obtained his Ph.D. degree in Nuclear Engineering from North Carolina State University in 2018. Prior to joining Argonne, he was a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor.
Dr. Liu’s research interests include: physics-informed machine learning, data assimilation, uncertainty and sensitivity analysis, modeling and simulation of advanced reactor systems.
Thursday, September 10. 2020
4:00 pm seminar