Andrea Beck

TL;DR: It is shown that a low-order approximation exhibits unacceptable numerical discretization errors, whereas a naive application of high-order discretizations in those situations is often unstable due to aliasing, so proper stabilization is necessary for a successful computation of underresolved turbulence.

TL;DR: In this paper, a data-based approach to turbulence modeling by artificial neural networks is presented, which can generalize from the data and learn approximations with a cross correlation of up to 47% and even 73% for the inner elements, demonstrating that the perfect closure can indeed be learned from provided coarse grid data.

TL;DR: The DGSEM scheme is described and the efficiency of element-wise operations is highly improved compared to standard DG implementations, and the applicability of the scheme is shown with a direct numerical simulation of a weak turbulent flow past a sphere at Reynolds number 1000.

TL;DR: In this paper, the authors investigated the accuracy and efficiency of discontinuous Galerkin spectral method simulations of under-resolved transitional and turbulent flows at moderate Reynolds numbers, where the accurate prediction of closely coupled laminar regions, transition and developed turbulence presents a great challenge to large eddy simulation modelling.

TL;DR: This work presents a novel spatially and temporally adaptive dealiasing strategy by projection filtering that is more efficient for underresolved turbulence than the classical overintegration strategy and compared to a state of the art variational multi-scale eddy viscosity formulation.