Clustered SNe

Riemann solver with cooling, for evolving a superbubble produced by 1-1000 SNe from a single cluster

Author: Eric Gentry (gentry.e@gmail.com; egentry@ucsc.edu)

Licensed under the GPLv3.

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Main Objectives

• Evolve a spherical symmetric blast wave, incorporating hydrodynamics and radiative cooling
  • Achieve accurate and stable results through Sedov and (thin shell) radiative phases
• Incorporate multiple supernovae, with realistic delay times and energies
  • Include pre-SNe winds as a constant wind through a star's life
• Measure the energy and momentum injected into the surrounding medium, to be used as feedback prescriptions in low resolution galaxy/cosmology simulations

Getting started

• Install dependencies listed below
• Adapt src/makefile to reflect location of required libraries (INC_* and LIB_* variables)
• In the src directory, run make all install clean

That should get you a working executable. Once you have data to process, this repo's wiki has instructions for using the python analysis code.

Requires

• c++ compiler (assumes clang for OS X, gcc for Linux)
  • should (must?) be c++11 capable
  • you must use the same compiler as you used for installing Boost. If you're unsure, then use the default I set.
• slug2
  • slug2 must be built as a shared library (change into the src directory and call make lib; if you enabled FITS at compile time, then keep it enabled at link time. For simplicity, use make all && make lib in the src directory.)
  • A fork frozen to the version used in my simulations can be found at: https://bitbucket.org/egentry/slug2
  • requires the GSL, Boost
• libuuid
• grackle cooling (v3)
  • requires HDF5
• For visualization:
  • Python (tested for v3.5, mostly backwards compatible)
  • Jupyter notebook (tested for v4)
    • ipywidgets (tested for v4, v5 -- mildly broken on v6)
  • Matplotlib
  • Bokeh
  • Numpy
  • Scipy
  • Astropy
  • Pandas
  • Seaborn
  • Numba
  • SQLAlchemy
  • corner
    • Only needed if you call BayesianFit.create_corner_plots

Acknowledgements

This project built upon RT1D, an open-source riemann solver from Paul Duffell.

This project was also includes the sedov3.f code of http://cococubed.asu.edu/research_pages/sedov.shtml in order to generate the analytic sedov solution for the purpose of visualization. While it's not difficult to generate a basic sedov solution, it's difficult to do well. Using sedov3.f allows us to avoid worrying about the details of quad precision math ourselves.

Ben Wibking was a big help in proposing efficiency improvements and identifying bugs.