Turning the knobs on dust evolution: Comparing codes, parameters and their effects on planet formation and disc observables

Abstract

Protoplanetary discs contain a wide range of dust sizes that strongly influence their thermal structure and planet formation processes such as planetesimal formation and pebble accretion. Dust evolution models are therefore essential for both planet formation simulations and the interpretation of disc observations. Several open-source dust evolution codes are available, each adopting different methods and assumptions. We present a systematic comparison of 1D radial simulations using DustPy, TriPoD, and two-pop-py, and 2D radial-vertical simulations with TriPoD, mcdust, and cuDisc. The comparison includes dust size distributions, dust disc masses, planetary gap structures, millimetre fluxes and disc sizes from synthetic observations, planetesimal formation regions, and planetary growth via pebble accretion. We also perform a parameter study to assess how key dust-evolution parameters influence disc evolution, planet formation, and code agreement. In 1D, two-pop-py depletes dust masses faster and produces higher dust concentrations outside planetary gaps than DustPy or TriPoD. The latter two generally agree well, except when size distributions deviate strongly from a power law. While the calculated millimetre fluxes and disc radii typically agree well, planetesimal formation locations and pebble accretion rates vary significantly between codes. In 2D, we compare cuDisc, mcdust, and TriPoD in simulations of turbulence- and sedimentation-driven coagulation. The dust size distributions agree well, despite the completely different numerical approaches used to model dust coagulation. The largest differences arise in the upper atmosphere, where mcdust suffers from low mass resolution and TriPoD fails to reproduce the exact shape of size distributions that deviate from a power-law.

Figures (20)

• Figure 1: Comparison of the particle size distribution at $3\, \textrm{Myr}$ from the three different 1D codes, at three different semimajor axis and for two different simulations. The bottom axis shows the particle size and the top axis shows the corresponding Stokes number. The dashed lines show the density-weighted average for TriPoD and DustPy.
• Figure 2: Particle size distribution for a subset of simulations from the parameter study, produced using DustPy and shown for three different semimajor axes and two different times. The simulation name indicated in the top left corner indicate how the varied parameter compares with the Nominal simulation; e.g., simulation $0.01Z$ has $Z = 0.01 \times Z_{\rm Nom}$.
• Figure 3: Comparison of the total dust mass - calculated by integrating the dust surface density - at three different times for two-pop-py and TriPoD, shown relative to DustPy. The gray shaded region indicates where the total dust mass differs by less than a factor of 2. The agreement between DustPy and TriPoD is generally good, whereas discs simulated with two-pop-py typically deplete their dust mass too quickly.
• Figure 4: Total dust mass versus time for all simulations in the parameter study, and some simulations including a planetary gap, produced with data from DustPy. The top row contains simulations where the varied parameter affects not only the evolution of the dust disc but also its initial mass. Conversely, the three leftmost panels in the bottom row contains simulations where the varied parameter only influences the disc’s evolution after initialization.
• Figure 5: Comparison of the dust surface density (left) and maximum particle size (right) as a function of semimajor axis for the nominal simulation with a planet of $0.3\, \textrm{M}_{\rm jup}$ (top) and $2\, \textrm{M}_{\rm jup}$ (bottom) located at $10\, \rm{au}$. The snapshot is taken at the end of the simulation.