Andres Galindo-Olarte

arXiv2026

A Nodal Discontinuous Galerkin Method with Rank-Adaptive Velocity Space Representation for the Multiscale BGK Model

A novel hybrid algorithm is presented for the Boltzmann-BGK equation, in which a rank-adaptive decomposition is applied solely in the velocity subspace, while a full-rank representation is maintained in the physical (position) space. This approach establishes a foundation for extending modern rank-adaptive techniques to solve the Boltzmann equation in realistic settings, particularly where structured representations, such as conformal geometries, may not be feasible in practical engineering applications. A nodal discontinuous Galerkin method is employed for spatial discretization, coupled with a rank-adaptive decomposition over the velocity grid, as well as implicit-explicit Runge-Kutta methods for time integration. To handle the limit of vanishing collision time, a multiscale implicit integrator based on an auxiliary moment equation is utilized. The algorithm's order of accuracy, reduced computational complexity, and robustness are demonstrated on a suite of canonical gas kinetics problems with increasing complexity.

math.NA↗

arXiv2025

Superconvergence Extraction of Upwind Discontinuous Galerkin Method Solving the Radiative Transfer Equation

We theoretically analyze the superconvergence of the upwind discontinuous Galerkin (DG) method for both the steady-state and time-dependent radiative transfer equation (RTE), and apply the Smooth-Increasing Accuracy-Conserving (SIAC) filters to enhance the accuracy order. Direct application of SIAC filters on low-dimensional macroscopic moments, often the quantities of practical interest, can effectively improve the approximation accuracy with marginal computational overhead. Using piecewise $k$-th order polynomials for the approximation and assuming constant cross sections, we prove $(2k+2)$-th order superconvergence for the steady-state problem at Radau points on each element and $(2k+1/2)$-th order superconvergence for the global $L^2$ and negative-order Sobolev norms for the time-dependent problem. Numerical experiments confirm the efficacy of the filtering, demonstrating post-filter convergence orders of $2k+2$ for steady-state and $2k+1$ for time-dependent problems. More significantly, the SIAC filter delivers substantial gains in computational efficiency. For a time-dependent problem, we observed an approximately $2.22 \times$ accuracy improvement and a $19.94 \times$ reduction in computational time. For the steady-state problems, the filter achieved a $4$--$9 \times$ acceleration without any loss of accuracy.

math.NA↗