Abstract
This article focuses on parametric reduced-order models to decrease the computational cost of large-scale neutron transport problems in the atmosphere. The atmospheric humidity content and neutron source spectrum are considered as uncertain parameters throughout the problem. We first present our full-order model, including an axisymmetric SN transport methodology, a geopotential model for computing the thermophysical properties of the atmosphere, and a support vector regression method for rapidly generating one-group cross sections at every point. Then, reduction of the full-order problem using the POD-Galerkin and least-squares POD-Petrov-Galerkin methods is presented. The reduced-order models implemented introduce mean relative errors between 0.06% and 0.4% with acceleration factors between 3.6×103-8.4×104, respectively.
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