No Arabic abstract
We present MULTIGRAIN, an algorithm for simulating multiple phases of small dust grains embedded in a gas, building on our earlier work in simulating two-phase mixtures of gas and dust in SPH (Laibe & Price 2012a,b; Price & Laibe 2015). The MULTIGRAIN method (Hutchison, Price & Laibe 2018) is more accurate than single-phase simulations because the gas experiences a backreaction from each dust phase and communicates this change to the other phases, thereby indirectly coupling the dust phases together. The MULTIGRAIN method is fast, explicit and low storage, requiring only an array of dust fractions and their derivatives defined for each resolution element. We demonstrate the MULTIGRAIN algorithm on test problems related to the settling of dust in the discs of gas around young stars, where solar systems are born. Finally I will discuss possible extensions of the method to incorporate both large and small grains, together with recent improvements in our numerical techniques for gas and dust mixtures. In particular, I will show how the overdamping problem identified by Laibe & Price (2012a) can be solved.
We present a fix to the overdamping problem found by Laibe & Price (2012) when simulating strongly coupled dust-gas mixtures using two different sets of particles using smoothed particle hydrodynamics. Our solution is to compute the drag at the barycentre between gas and dust particle pairs when computing the drag force by reconstructing the velocity field, similar to the procedure in Godunov-type solvers. This fixes the overdamping problem at negligible computational cost, but with additional memory required to store velocity derivatives. We employ slope limiters to avoid spurious oscillations at shocks, finding the van Leer Monotonized Central limiter most effective.
We describe a simple method for simulating the dynamics of small grains in a dusty gas, relevant to micron-sized grains in the interstellar medium and grains of centimetre size and smaller in protoplanetary discs. The method involves solving one extra diffusion equation for the dust fraction in addition to the usual equations of hydrodynamics. This diffusion approximation for dust is valid when the dust stopping time is smaller than the computational timestep. We present a numerical implementation using Smoothed Particle Hydrodynamics (SPH) that is conservative, accurate and fast. It does not require any implicit timestepping and can be straightforwardly ported into existing 3D codes.
We present a new approach to simulating mixtures of gas and dust in smoothed particle hydrodynamics (SPH). We show how the two-fluid equations can be rewritten to describe a single-fluid mixture moving with the barycentric velocity, with each particle carrying a dust fraction. We show how this formulation can be implemented in SPH while preserving the conservation properties (i.e. conservation of mass of each phase, momentum and energy). We also show that the method solves two key issues with the two fluid approach: it avoids over-damping of the mixture when the drag is strong and prevents a problem with dust particles becoming trapped below the resolution of the gas. We also show how the general one-fluid formulation can be simplified in the limit of strong drag (i.e. small grains) to the usual SPH equations plus a diffusion equation for the evolution of the dust fraction that can be evolved explicitly and does not require any implicit timestepping. We present tests of the simplified formulation showing that it is accurate in the small grain/strong drag limit. We discuss some of the issues we have had to solve while developing this method and finally present a preliminary application to dust settling in protoplanetary discs.
Recently Squire & Hopkins showed that charged dust grains moving through magnetized gas under the influence of any external force (e.g. radiation pressure, gravity) are subject to a spectrum of instabilities. Qualitatively distinct instability families are associated with different Alfvenic or magnetosonic waves and drift or gyro motion. We present a suite of simulations exploring these instabilities, for grains in a homogeneous medium subject to an external acceleration. We vary parameters such as the ratio of Lorentz-to-drag forces on dust, plasma $beta$, size scale, and acceleration. All regimes studied drive turbulent motions and dust-to-gas fluctuations in the saturated state, can rapidly amplify magnetic fields into equipartition with velocity fluctuations, and produce instabilities that persist indefinitely (despite random grain motions). Different parameters produce diverse morphologies and qualitatively different features in dust, but the saturated gas state can be broadly characterized as anisotropic magnetosonic or Alfvenic turbulence. Quasi-linear theory can qualitatively predict the gas turbulent properties. Turbulence grows from small to large scales, and larger-scale modes usually drive more vigorous gas turbulence, but dust velocity and density fluctuations are more complicated. In many regimes, dust forms structures (clumps, filaments, sheets) that reach extreme over-densities (up to $gg 10^{9}$ times mean), and exhibit substantial sub-structure even in nearly-incompressible gas. These can be even more prominent at lower dust-to-gas ratios. In other regimes, dust self-excites scattering via magnetic fluctuations that isotropize and amplify dust velocities, producing fast, diffusive dust motions.
We present a method for simulating the dynamics of a mixture of gas and multiple species of large Stokes number dust grains, typical of evolved protoplanetary discs and debris discs. The method improves upon earlier methods, in which only a single grain size could be represented, by capturing the differential backreaction of multiple dust species on the gas. This effect is greater for large dust-to-gas ratios that may be expected in the later stages of the protoplanetary disc life. We benchmark the method against analytic solutions for linear waves, drag and shocks in dust-gas mixtures, and radial drift in a protoplanetary disc showing that the method is robust and accurate.