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Title: ROM-Based Surrogate Systems Modeling of EBR-II

Journal Article · · Nuclear Science and Engineering

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), Argonne, 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, Vol. 195, Issue 5; ISSN 0029-5639
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English

References (10)

Application and Evaluation of Surrogate Models for Radiation Source Search journal December 2019
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems journal January 2015
Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics journal January 2002
A survey of model reduction methods for large-scale systems book January 2001
A reduced-order approach for optimal control of fluids using proper orthogonal decomposition journal January 2000
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions journal January 2011
Dimensionality reducibility for multi-physics reduced order modeling journal December 2017
Cosmic calibration: Constraints from the matter power spectrum and the cosmic microwave background journal October 2007
A subspace approach to balanced truncation for model reduction of nonlinear control systems
  • Lall, Sanjay; Marsden, Jerrold E.; Glava?ki, Sonja
  • International Journal of Robust and Nonlinear Control, Vol. 12, Issue 6 https://doi.org/10.1002/rnc.657
journal January 2002
Estimating Extremal Eigenvalues and Condition Numbers of Matrices journal August 1983

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