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DOE OSTI · 1801262

Reduced-order modeling of parameterized multi-group diffusion k-eigenvalue problems

German, Péter·Ragusa, Jean C.
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Abstract

In this paper, a group-wise Reduced-Order Model, based on a Proper Orthogonal Decomposition (POD) technique, is presented for parameterized multi-group diffusion k-eigenvalue problems. The group-wise approach is shown to be more robust than model-order reduction based on monolithic multi-group POD modes. In addition, fewer POD modes are required. The method of snapshots is employed to generate the offline training set. Two Naïve and a Greedy snapshot-generation strategies are compared. The input parameter space is surveyed using Latin Hypercube Sampling techniques. The effectiveness of the group-wise model-order reduction method is demonstrated on two reactor-physics benchmarks, a two-group 3D Pressurized Water Reactor problem with 26 uncertain input parameters and a seven-group UO 2 -MOX fuel 3D mini-core with 287 uncertain input parameters. In both examples the Reduced-Order Models built using the snapshots generated by the Greedy algorithm proved to be slightly better. Model-order reduction yields results that are within 1 pcm of the full-order model, with speed-up factors of about 1000–3000, depending upon the test case.

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73 NUCLEAR PHYSICS AND RADIATION PHYSICSModel-Order Reduction (MOR)Proper Orthogonal Decomposition (POD)Reduced-Order Model (ROM)multi-group diffusionreduced eigenvalue problem

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BibTeXRIS

German, Péter, Ragusa, Jean C.. 2019-06-13. Reduced-order modeling of parameterized multi-group diffusion k-eigenvalue problems. https://doi.org/10.1016/j.anucene.2019.05.049

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