Generating Automatic Network Reduction Module for Chemical Hydrodynamics Simulations

Abstract

Since molecules exist in the interstellar cloud and they affect the hydrodynamic evolution through their formation and destruction, physical states of the actual interstellar cloud are different from calculations of conventional hydrodynamics simulations. Furthermore, in the case of running star-forming simulations, conventional hydrodynamics models are not enough to explain molecular lines emitted from clouds such as those detected from the ALMA observatory. In order to simulate the chemical evolution of a hydrodynamic cloud, building an efficient chemical network that contains relevant chemical reactions is crucial for cutting down the computation cost. A key factor for generating an efficient chemical network is to avoid using an abnormally small network that contains only a few reactions because using too small a network does not simulate the effects of molecules accurately. There already exist a few chemical hydrodynamics simulation codes, which provide pre-built reduced networks. Although those prebuilt networks make the simulations simple and light, they cannot be used universally for various clouds under diverse initial chemical compositions and environmental conditions. Therefore, it is necessary to build a more flexible network according to the conditions of the model. In this study, we propose to make an automatic network reduction module which builds an optimized closed network corresponding to the specific simulation conditions. As a preliminary result, we test our module with simple primordial test clouds by comparing our results with those obtained with the full network. In the future study, we will validate our reduction module for primordial cloud models and expand its usage to various physicochemical cases such as AGB stars.