A stochastic extension of the approximate deconvolution model

The approximate deconvolution model (ADM) for large-eddy simulation exploits a range of represented but non-resolved scales as buffer region for emulating the subgrid-scale energy transfer. ADM can be related to Langevin models for turbulence when filter operators are interpreted as stochastic kernel estimators. The objective of this paper is to introduce the concept of the Eulerian formulation of the Langevin model in a consistent form, allowing for stable numerical integration, and to show how this model can be used for a modified way of subfilter-scale estimation. An initial verification of the concept has been performed for the tree-dimensional Taylor-Green vortex.