TY - JOUR
T1 - Parametric Model-Order Reduction for Radiation Transport Simulations Based on an Affine Decomposition of the Operators
AU - Behne, Patrick
AU - Vermaak, Jan
AU - Ragusa, Jean
N1 - Funding Information:
This work was made possible through a grant by the U.S. Department of Defense, Defense Threat Reduction Agency under award no. HDTRA1-18-1-0020. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.This material is based on work supported under an Integrated University Program Graduate Fellowship. Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the U.S. Department of Energy, Office of Nuclear Energy. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing High Performance Computing resources that contributed to the research results reported within this paper (http://www.tacc.utexas.edu).
Publisher Copyright:
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2022/12/15
Y1 - 2022/12/15
N2 - This work presents a data-driven, projection-based parametric reduced-order model (ROM) for the neutral particle radiation transport (linear Boltzmann transport) equation. The ROM utilizes the method of snapshots with proper orthogonal decomposition. The novelty of the work is in the detailed proposal to exploit the parametrically affine transport operators to intrusively, yet efficiently, build the reduced transport operators in real time in a matrix-free manner compatible with sweep-based transport solvers. This affine-based ROM is applied to one-dimensional (1-D), two-dimensional (2-D), and 2-D multigroup transport benchmarks and is found to significantly outperform less intrusive ROMs in terms of speed for a desired accuracy level. The ROM has an 18.2 to 89.4 speedup with an error range of 0.0002% to 0.01% for the 1-D benchmark, a 1120× to 4870× speedup with an error range of 0.0009% to 0.01% for the 2-D benchmark, and a 54 600× to 399 800× speedup with an error range of 0.00022% to 0.01% for the multigroup 2-D benchmark. Even higher speedups are expected for three-dimensional multigroup transport problems.
AB - This work presents a data-driven, projection-based parametric reduced-order model (ROM) for the neutral particle radiation transport (linear Boltzmann transport) equation. The ROM utilizes the method of snapshots with proper orthogonal decomposition. The novelty of the work is in the detailed proposal to exploit the parametrically affine transport operators to intrusively, yet efficiently, build the reduced transport operators in real time in a matrix-free manner compatible with sweep-based transport solvers. This affine-based ROM is applied to one-dimensional (1-D), two-dimensional (2-D), and 2-D multigroup transport benchmarks and is found to significantly outperform less intrusive ROMs in terms of speed for a desired accuracy level. The ROM has an 18.2 to 89.4 speedup with an error range of 0.0002% to 0.01% for the 1-D benchmark, a 1120× to 4870× speedup with an error range of 0.0009% to 0.01% for the 2-D benchmark, and a 54 600× to 399 800× speedup with an error range of 0.00022% to 0.01% for the multigroup 2-D benchmark. Even higher speedups are expected for three-dimensional multigroup transport problems.
KW - Proper orthogonal decomposition
KW - affine decomposition
KW - model-order reduction
KW - radiation transport
KW - reduced-order models
UR - http://www.scopus.com/inward/record.url?scp=85138419782&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/285814de-e381-31c2-9490-f3b92fa2766f/
U2 - 10.1080/00295639.2022.2112901
DO - 10.1080/00295639.2022.2112901
M3 - Article
AN - SCOPUS:85138419782
SN - 0029-5639
VL - 197
SP - 233
EP - 261
JO - Nuclear Science and Engineering
JF - Nuclear Science and Engineering
IS - 2
ER -