Non-intrusive reduced-order modeling of radiation transport in the atmosphere

In this work, we investigate a non-intrusive parametric reduced-order model (ROM) for atmospheric scale neutral particle transport via an application to neutron transport. The spatial domain of interest consists of an 80-km tall cylinder with a 20-km radius. The system parameters are the elevation and emission spectrum of the radiation source, as well as the relative humidity, which alters the cross sections of the atmospheric air. The full-order model (FOM) employs a first-collision source approach to mitigate ray effects. The separate computation of the uncollided and collided flux components is exploited in the reduced-order model (ROM). The ROM utilizes various non-linear transformations of the FOM solution to address the many orders of magnitude present in the FOM data, which can vary by up to 26 orders of magnitude. Subspace discovery is performed on this transformed dataset and the parametric dependence in this latent space is learned via Gaussian process regression. Speed-up factors of 5, 100x to 9, 700x are observed, while maintaining less than 10% pointwise error throughout most of the spatial domain. Based on the choice of transformation, small regions of high error can be present as a result of the variable source position.