Modelling grain-size distributions in C-type shocks using a discrete power-law model

TL;DR

This paper introduces a discrete power-law model for grain-size distributions in C-type shocks, demonstrating faster convergence and reduced computational costs compared to multispecies models in numerical MHD simulations.

Contribution

The paper implements and tests a discrete, piecewise power-law grain-size distribution in MHD shock models, showing improved efficiency over traditional multispecies approaches.

Findings

Discrete models converge faster than multispecies models.

A single discrete bin suffices for pure advection models.

Four bins are needed when including grain sputtering.

Abstract

In this paper we discuss the implementation of a discrete, piecewise power-law grain-size distribution method into a numerical multifluid MHD code as described in Sumpter (2020). Such a description allows to capture the full size range of dust grains and their dynamical effects. The only assumptions are that grains within a single discrete bin have the same velocity and charge. We test the implementation by modelling plane-parallel C-type shocks and compare the results with shock models of multispecies grain models. We find that both the discrete and multispecies grain models converge to the same shock profile. However, the convergence for the discrete models is faster than for the multispecies grain models. For the pure advection models a single discrete bin is sufficient, while the multispecies grain models need a minimum of 8 grain species. When including grain sputtering the necessary number of discrete bins increases to 4, as the grain distribution cannot be described by a single power-law as in the advection models. The multispecies grain models still need more grain species to model the distribution, but the number does not increase compared to the pure advection models. Our results show that modelling the grain distribution function using a discrete distribution reduces the computational cost needed to capture the grain physics significantly.