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Daniele Crocco

Publications and source records attributed to Daniele Crocco.

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A robust super-time-stepping scheme for Ohmic and ambipolar diffusion

Context. Non-ideal magnetohydrodynamics (MHD) is a key tool for modeling magnetic flux transport in astrophysical systems such as molecular clouds, protostellar cores, and protoplanetary disks. Conventional explicit methods for non-ideal MHD diffusion are severely limited by timestep constraints, while substepping approaches can be unstable due to truncation errors near boundaries and strong magnetic-field gradients. Aims. Our main goal is to address these limitations by developing robust super-time-stepping methods for Ohmic and ambipolar diffusion. Methods. We present a super-time-stepping method based on the stability of the Gegenbauer polynomials. The method is designed to enhance robustness in the presence of strongly anisotropic resistivity and to reduce sensitivity to truncation errors near boundaries. We implement the scheme in the PLUTO code and assess its performance through dedicated Ohmic and ambipolar diffusion tests. We also compare this novel numerical scheme against two common astrophysical problems, namely magnetic reconnection and the magnetorotational instability. Results. The novel Runge-Kutta-Gegenbauer scheme retains computational efficiency beyond purely explicit schemes while providing excellent stability compared with other traditional substepping methods. It remains stable in the presence of strongly anisotropic diffusion, enabling accurate magnetic-field evolution in regimes characteristic of protoplanetary disks and collapsing dense cores. Benchmark tests, including magnetic reconnection and magnetorotational-instability setups, confirm the method's accuracy, efficiency, and suitability for large-scale non-ideal MHD simulations.

astro-ph.IM

PyPLUTO: a data analysis Python package for the PLUTO code

In recent years, numerical simulations have become indispensable for addressing complex astrophysical problems. The MagnetoHydroDynamics (MHD) framework represents a key tool for investigating the dynamical evolution of astrophysical plasmas, which are described as a set of partial differential equations that enforce the conservation of mass, momentum, and energy, along with Maxwell's equations for the evolution of the electromagnetic fields. Due to the high nonlinearity of the MHD equations (regardless of their specifications, e.g., classical/relativistic or ideal/resistive), a general analytical solution is precluded, making the numerical approach crucial. Numerical simulations usually end up producing large sets of data files, and their scientific analysis leans on dedicated software designed for data visualization. However, in order to encompass all of the code output features, specialized tools focusing on the numerical code may represent a more versatile and built-in tool. Here, we present PyPLUTO, a Python package tailored for efficient loading, manipulation, and visualization of outputs produced with the PLUTO code (Mignone et al., 2007; Mignone et al., 2012). PyPLUTO uses memory mapping to optimize data loading and provides general routines for data manipulation and visualization. PyPLUTO also supports the particle modules of the PLUTO code, enabling users to load and visualize particles, such as cosmic rays (Mignone et al., 2018), Lagrangian (Vaidya et al., 2018), or dust (Mignone et al., 2019) particles, from hybrid simulations. A dedicated Graphical User Interface simplifies the generation of single-subplot figures, making PyPLUTO a powerful yet user-friendly toolkit for astrophysical data analysis.

astro-ph.IM