Year: 2015
Type: preprint
Abstract: Please see the current version (v2) on arXiv (https://arxiv.org/abs/1604.06071) This version is as submitted to the university for my master's degree. I might have worded my critique of Arepo a bit too harshly, and a significant amount of issues were already addressed since the original submission. I removed section 3.4 and most of the discussion in 4.1, as these sections may need rework and further objective underpinning. Supernova (SN) explosions play an important role in the development of galactic structures. The energy and momentum imparted on the interstellar medium (ISM) in so called ”supernova feedback” drives turbulence, heats the gas, enriches it with heavy elements, can lead to the formation of new stars or even suppress star formation by disrupting stellar nurseries. In the numerical simulation at the sub-galactic level, not including the energy and momentum of supernovas in the physical description of the problem can also lead to several problems that might partially be resolved by including a description of supernovas. In this thesis such an implementation is attempted for the combined numerical hydrodynamics and N-body simulation software Arepo (Springel, 2010). In a stochastic process a large amount of thermal energy is imparted on a number of neighbouring cells, mimicking the effect of a supernova explosions. We test this approach by modelling the explosion of a single supernova in a uniform density medium and comparing the evolution of the resulting supernova remnant to the theoretically-predicted behaviour. We also run a simulation with our feedback code and a fixed supernova rate derived from the Kennicutt-Schmidt relation (Kennicutt, 1998) for a duration of about 20 Myrs. We describe our method in detail in this text and discuss the properties of our implementation.
Source: arXiv (Cornell University)
DOI: 10.5281/zenodo.49764
Author: Bubel, André-Patrick
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2336623672