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

German, Péter; Ragusa, Jean C.

doi:10.1016/j.anucene.2019.05.049

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

Journal Article · Thu Jun 13 00:00:00 EDT 2019 · Annals of Nuclear Energy (Oxford)

DOI:https://doi.org/10.1016/j.anucene.2019.05.049· OSTI ID:1801262

German, Péter ^[1]; Ragusa, Jean C. ^[2]

Texas A & M Univ., College Station, TX (United States); OSTI
Texas A & M Univ., College Station, TX (United States)

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 UO₂-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.

View Accepted Manuscript (DOE)

Research Organization:: Texas A & M Univ., College Station, TX (United States)

Sponsoring Organization:: USDOE; USDOE Office of Nuclear Energy (NE), Nuclear Energy University Program (NEUP)

Grant/Contract Number:: NE0008651

OSTI ID:: 1801262

Alternate ID(s):: OSTI ID: 1781702
OSTI ID: 22846310

Journal Information:: Annals of Nuclear Energy (Oxford), Journal Name: Annals of Nuclear Energy (Oxford) Vol. 134; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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

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