Litcius/Paper detail

Minimally-invasive parametric model-order reduction for sweep-based radiation transport

Patrick Behne, Jan I.C. Vermaak, Jean C. Ragusa

2022Journal of Computational Physics20 citationsDOIOpen Access PDF

Abstract

We present a parametric reduced-order model for the neutral particle radiation transport equation. The approach devised is a minimally-intrusive, projection-based reduced-order model using global modes obtained via Proper Orthogonal Decomposition. The reduced-order model is specifically designed to work in a matrix-free fashion with radiation transport solvers relying on transport sweeps. The advantages and disadvantages of this model-order reduction approach are discussed and tested on two fixed-source radiation-transport benchmark problems, as well as a k-eigenvalue benchmark. The performance of the reduced-order model in terms of speedup and accuracy are found to be problem-dependent with speedup factors of around 2 for the fixed-source benchmarks and 43 for the k-eigenvalue benchmark. The corresponding reduced solution relative error for these speedups is 1% for the fixed-source benchmarks and 4 pcm for the k-eigenvalue benchmark.

Topics & Concepts

SpeedupBenchmark (surveying)Eigenvalues and eigenvectorsReduction (mathematics)Applied mathematicsModel order reductionParametric statisticsProjection (relational algebra)Mathematical optimizationComputer scienceAlgorithmMathematicsPhysicsParallel computingGeometryStatisticsGeodesyGeographyQuantum mechanicsModel Reduction and Neural NetworksNuclear reactor physics and engineeringProbabilistic and Robust Engineering Design