ROM-Based Surrogate Systems Modeling of EBR-II

Journal Article · Mon Dec 21 00:00:00 EST 2020 · Nuclear Science and Engineering

DOI:https://doi.org/10.1080/00295639.2020.1840238· OSTI ID:1864164

Li, Yeni ^[1]; Abdel-Khalik, Hany S. ^[1]; Brunett, Acacia J. ^[2]; Jennings, Elise ^[2]; Mui, Travis ^[2]; Hu, Rui ^[2]

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1. Purdue Univ., West Lafayette, IN (United States)
2. Argonne National Lab. (ANL), Lemont, IL (United States)

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

• Research Organization: Argonne National Laboratory (ANL), Lemont, IL (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1864164
• Report Number(s): APT-160734; 160734
• Journal Information: Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering Journal Issue: 5 Vol. 195; ISSN 0029-5639
• Publisher: Taylor & Francis
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
SAM
range finding algorithm
reduced order modeling
shutdown heat removal tests
surrogate systems modeling