Modelling turbulence and star formation

Abstract:

We use large-scale numerical simulations to model the physical processes relevant for the formation of stars: turbulence, magnetic fields, radiation, gravity, etc. These simulations are typically performed on uniform grids or using adaptive mesh refinement (AMR), often run on thousands of compute cores, e.g., on the supercomputer Gadi (NCI). I will explain some of the models we implement for star formation and feedback (mass, momentum, energy injection from proto-stars), how we generally model some of the key physical process mentioned above, and how they are relevant for star formation and our general understanding of gas dynamics (in astrophysics and beyond).

Bio:

Federrath's research focuses on understanding the formation of stars in the Universe. Almost everything we know about the Universe comes from looking at the stars. The formation of stars controls galaxy evolution and sets the initial conditions for planet formation. To advance our understanding of star formation, Federrath develops theoretical models and supercomputer simulations of molecular clouds, the birthplaces of stars. This involves the physics of turbulence, gravity, magnetic fields, radiation, stellar feedback and chemical evolution. An essential part of this work is to compare supercomputer simulations performed on thousands of computer cores to real observations in galactic clouds. The ultimate goal of Federrath's research is to establish a model that combines all the relevant physics and chemistry to enable self-consistent comparisons with observations and to provide powerful predictions for how galaxies, stars and planets form and evolve in the Universe.

Zoom Details:

Topic: Mathematics and Computational Sciences Seminar Series 
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