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Feedback Driven Wind Simulations - AREPO

AREPO version that was developed for the purpose of building stellar feedback driven wind simulations. The code was developed for a Master Thesis at the Heidelberg Institute of Theoretical Astrophysics under the supervision of Dr. Dylan Nelson. The original code was developed by Volker Springel et al. and can be found here. This modified version is distributed under the GNU GPL license.

Click here to view a copy of the Master Thesis.

The code contains several new configuration options to simulate the wind physics. The core wind physics are configured using INJECT_WITHIN_RADIUS, which models the dynamics of a starburst-driven wind based on parameterizations of the analytic wind model of Chevalier and Clegg 1985, or CC85. At each time-step in the simulation, mass and energy is deposited in a spherical starburst region with $R_{\rm inject}$. The distribution of mass and energy can be using either:

  1. (Default) An even distribution of mass and energy across each cell contained inside a radius of $r \leq R_{\rm inject}$.
  2. (VOLUME_BASED_INJECTION), where cells are weighted by their volume and normalized by the total volume of all cells where $r \leq R_{\rm inject}$.

The injection process can be controlled by several key parameters, such as injection radius $R_{\rm inject}$, mass load $\beta$, energy load $\alpha$, and burst duration $t_{\rm burst}$.

Gravitational effects of the disk on the wind flow is represented in the form of a Miyamoto-Nagai potential, which can be controlled using by configuring stellar disk mass M_stars, stellar scale radius R_stars, and stellar scale height z_stars

Additionally, the code also implements a simple metallic radiative cooling scheme that reads off of publically available lookup tables of Wiersma et al. 2009. Two options for mettalic cooling are included: (i) CIE_PIE_COOLING, which is dependent on both density and temperature, and (ii) CIE_COOLING, which is solely temperature dependent.

Avenues for future development include implementing more complex stellar feedback routines (For example, a cluster feedback model similar to the one implemented by Schneider et al. 2018), as a well a more extensive radiative cooling model.

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