Abstract.  The Center for Radiative Shock Hydrodynamics (CRASH) was established at the University of Michigan by the United States Department of Energy under the Predictive Science Academic Alliances Program (PSAAP) to study the physics of radiative shocks propagating through plasma. This work is part of the laser astrophysics program at the University of Michigan, and the study is relevant to shocks emerging from supernova explosions. For a shock propagating at a sufficiently high velocity, radiative effects become important, and the shock structure becomes quite different from that of a simple hydrodynamic shock, producing much greater compression. We typically achieve compression ratios of a factor of 25 or more. Experiments have been conducted using the Omega laser facility at the University of Rochester. The laser beams strike a thin disk of beryllium, driving a shock into a tube filled with xenon with an initial speed of 200 km/s, well above the limit for radiative effects to be important. We are also attempting to perform numerical simulations of these experiments using an Eulerian radiation-hydrodynamics code called CRASH. Since CRASH does not currently contain the physics necessary to compute the laser-plasma interaction, initial conditions are computed using the twodimensional Lagrangian code Hyades. Hyades can only be used for the initial evolution of the system due to grid tangling problems frequently encountered with Lagrangian codes. After the laser turns off, the results are passed to CRASH for the remainder of the evolution. CRASH uses a high-order Godunov solver for the Euler equations and uses multigroup flux-limited diffusion for the radiation transfer. The ions, electrons, and radiation are all allowed to have different temperatures. Electron heat conduction is also included, and the equations of state and opacities of the various materials are computed self consistently. Adaptive mesh refinement is used to reduce the computational cost of the simulations. The experiments and simulations will be described in detail and the results will be compared.

This research was supported by the DOE NNSA/ASC under the Predictive Science Academic Alliance Program by grant number DEFC52- 08NA28616.