Parametric Model-Order Reduction for Radiation Transport Simulations Based on an Affine Decomposition of the Operators

Abstract

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.