Emil Løvbak

h-index3
2papers
17citations

2 Papers

6.9CEMar 24
2D implementation of Kinetic-diffusion Monte Carlo in Eiron

Oskar Lappi, Emil Løvbak, Thijs Steel et al.

Particle-based kinetic Monte Carlo simulations of neutral particles is one of the major computational bottlenecks in tokamak scrape-off layer simulations. This computational cost comes from the need to resolve individual collision events in high-collisional regimes. However, in such regimes, one can approximate the high-collisional kinetic dynamics with computationally cheaper diffusion. Asymptotic-preserving schemes make use of this limit to perform simulations in these regimes, without a blow-up in computational cost as incurred by standard kinetic approaches. One such scheme is Kinetic-diffusion Monte Carlo. In this paper, we present a first extension of this scheme to the two-dimensional setting and its implementation in the Eiron particle code. We then demonstrate that this implementation produces a significant speedup over kinetic simulations in high-collisional cases.

NAJun 26
A Dynamical Low-rank Multilevel Monte Carlo Estimator for High-Dimensional Kinetic Equations

Chinmay Patwardhan, Sebastian Krumscheid, Jonas Kusch et al.

Kinetic equations are used to model a wide range of phenomena important for real-world applications. Their applications span astrophysics, nuclear physics, engineering, and social sciences. Due to their high-dimensional phase space, modelling and quantifying uncertainties, relevant for applications, poses a significant challenge even for modern computing infrastructure. In recent years, dynamical low-rank approximation (DLRA) has gained popularity for making fine grid simulations of high-dimensional problems feasible by evolving the solution of a time-dependent PDE as a low-rank factorization. This reduces the computational and memory requirements significantly. In this work, we propose a low-rank multilevel Monte Carlo estimator for kinetic equations based on a probabilistic rank-adaptive DLRA time integrator. The level hierarchy of the low-rank multilevel estimator is constructed through spatial refinement and by ensuring that the low-rank error remains below the spatial discretization error. We demonstrate the efficacy of the estimator through several numerical experiments from radiation transport, radiation therapy, and shallow water flow.