  Computational methods for thermal radiative transfer problems exhibit high
computational costs and a prohibitive memory footprint when the spatial and
directional domains are finely resolved. A strategy to reduce such
computational costs is dynamical low-rank approximation (DLRA), which
represents and evolves the solution on a low-rank manifold, thereby
significantly decreasing computational and memory requirements. Efficient
discretizations for the DLRA evolution equations need to be carefully
constructed to guarantee stability while enabling mass conservation. In this
work, we focus on the Su-Olson closure leading to a linearized internal energy
model and derive a stable discretization through an implicit coupling of
internal energy and particle density. Moreover, we propose a rank-adaptive
strategy to preserve local mass conservation. Numerical results are presented
which showcase the accuracy and efficiency of the proposed low-rank method
compared to the solution of the full system.
