Kinetic Modules

A suite of codes for astrophysical hydrochemistry

Introduction to Kinetic Modules:

Kinetic Modules (KM) is a suite of codes for astrophysical hydrochemical simulations including large chemical networks, radiation, fluid dynamics, and self-gravity. No existing code yet has its wide multiphysics coupled capabilities. Recently optimized by using sparse-matrix techniques, and benchmarked for photon-dominated regions (PDRs), codes in this suite have also been used to study FUV and magnetic effects in bright-rimmed clouds, and radiation-driven implosion models.

Current abilities of this code include astrochemical simulation of PDRs, cold cores, and hot corinos, in addition to axisymmetric hydrodynamic simulations with self-gravity in cylindrical coordinates.

Summary of KM’s Approach:
  • Hybrid hydrodynamic + chemical algorithm with radiation
  • Hydrodynamic module:
    • Conservative Godunov-type finite volume scheme, with heating and cooling. Optionally isothermal. Self-gravity.
    • Axisymmetric 2D Eulerian grid
    • Chemical species advected as scalar quantities
  • Chemical module:
    • Adopts databases involving gas-gas and gas-grain reactions, such as UMIST and KIDA, including photoreactions.
    • Implicitly solves the simultaneous equations of nonequilibrium chemistry with an ODE solver, and finds the energy changes due to thermal processes with transfer of UV radiation.
    • Sparse-matrix techniques for efficient treatment of the large but sparse stoichiometry matrix used to compute reaction rates
Application of the suite:
Benchmark results of KM compared with four other codes
Column density map of a pseudodisk model with magnetic field lines.

Hydrochemical examples. FUV-illuminated slabs. Benchmark results of KM compared with four other codes, taken from Model F1 in Motoyama et al. (2015). (left). A benchmark chemical network of 31 species undergoing 287 reactions was used, with the plot showing five species. On the right is a dynamic movie showing time evolution.

KM shock tube test
KM shock tube test
KM Sedov explosion test
KM Sedov explosion test
Improving the performance of KM
Improving the performance of KM. The large chemical network is implemented with four sparse-matrix methods, which are then compared in OpenMP domain decomposition.