COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > DAMTP Astrophysics Seminars > Comoving magnetohydrodynamic waves and a new suite of cosmological galaxy cluster simulations
Comoving magnetohydrodynamic waves and a new suite of cosmological galaxy cluster simulationsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Loren E. Held. Galaxy clusters form at the densest knots in the cosmic web and can contain thousands of individual galaxies. The space between the galaxies contains a very hot and dilute plasma, known as the intracluster medium, which can be observed with X-ray telescopes. Galaxy clusters continuously grow via accretion and mergers, which excite motions and turbulence in the intracluster medium. In this talk, I will first describe how analytic solutions for linear hydromagnetic waves can be used for testing cosmological magnetohydrodynamic (MHD) codes. I will then present PICO -Clusters, a new suite of cosmological zoom-in simulations of 25 massive (M_200,c > 10^14.9 M_sun) galaxy clusters. These simulations use the moving mesh code Arepo, the IllustrisTNG galaxy formation model and start with a weak seed magnetic field initialised at z=127, which is then evolved using comoving ideal magnetohydrodynamics. I will discuss the driving of turbulence and amplification of magnetic fields in the intracluster medium and the cluster mass dependence of the resulting amplitudes. Finally, I will present the current status of performing cosmological cluster simulations using Braginskii viscosity to model a weakly collisional intracluster medium. This talk is part of the DAMTP Astrophysics Seminars series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsChris Davis' list C.P. Snow Lectures CAPEOther talksMaximising the impact of CAR T cell therapy for acute leukaemia Carbon Clarity in the Global Petrochemical Supply Chain Nanomanufacturing Paradigms for Sustainable Large-Area Electronics The role of sulphur from human emissions in driving climate change Identifying the nature, causes and consequences of youth depression trajectories in population cohorts Identification of novel antibiotic resistance mechanisms in Klebsiella pneumoniae using machine learning |