SPMHD with the geometric density force gradient operator
Magnetic fields are an essential part to a wide array of different astrophysical systems. While Eulerian codes with robust magnetohydrodynamic (MHD) algorithms have existed for a long time, Lagrangian codes with comparable sophistication have only more recently been developed. The benefit of Lagrangian codes comes when modeling system’s which are highly dynamic and have a wide range of spatial scales, such as galaxy formation. Smoothed particle hydrodynamics (SPH) is a popular meshless Lagrangian method, which have seen tremendous improvements in recent years. One major issue which has been tackled is the numerical surface tension effect, which suppresses the growth of fluid instabilities and effectively inhibits mixing in SPH. It was recently shown that this issue can be resolved with the use of a different gradient operator (GDSPH). During this talk, we will investigate if SPMHD could also benefit from the GDSPH formulation. We will show that while many of the standard tests show similar results to traditional SPMHD, significant improvements are seen in cases involving large density gradients. With the largest improvements being seen in the collapse of a magnetized cloud and in the development of the magneto rotational instability, showing comparable results to the meshless finite mass (MFM) method which employ more complex gradient estimates and fluxes from Riemann solvers.