Space-time adaptive ADER-DG schemes for dissipative flows: compressible   Navier-Stokes and resistive MHD equations
  This paper presents an arbitrary h.o. accurate ADER DG method on space-time adaptive meshes (AMR) for the solution of two important families of non-linear time dependent PDE for compr. dissipative flows: the compr. Navier-Stokes equations and the equations of visc. and res. MHD in 2 and 3 space-dimensions. The work continues a recent series of papers concerning the development and application of a proper a posteriori subcell FV limiting procedure suitable for DG methods. It is a well known fact that a major weakness of h.o. DG methods lies in the difficulty of limiting discontinuous solutions, which generate spurious oscillations, namely the so-called 'Gibbs phenomenon'. In the present work the main benefits of the MOOD paradigm, i.e. the computational robustness even in the presence of strong shocks, are preserved and the numerical diffusion is considerably reduced also for the limited cells by resorting to a proper sub-grid. An important feature of our new scheme is its ability to cure even floating point errors that may occur during a simulation, for example when taking real roots of negative numbers or after divisions by zero. We apply the whole approach for the first time to the equations of compr. gas dynamics and MHD in the presence of viscosity, thermal conductivity and magnetic resistivity, therefore extending our family of adaptive ADER-DG schemes to cases for which the numerical fluxes also depend on the gradient of the state vector. The distinguished high-resolution properties of the presented numerical scheme stands out against a wide number of non-trivial test cases both for the compr. Navier-Stokes and the viscous and resistive MHD equations. The present results show clearly that the shock-capturing capability of the news schemes are significantly enhanced within a cell-by-cell Adaptive Mesh Refinement implementation together with time accurate local time stepping (LTS). 
Francesco Fambri, Michael Dumbser and Olindo Zanotti
math.NA astro-ph.IM cs.NA