Eigenspace perturbations for subgrid modeling in large-eddy simulations.

The method of eigenspace perturbations has been developed to characterize structural uncertainty in turbulence closure models. In the framework of large eddy simulations, the eigenvalues and eigenvectors of the modeled subgrid stress tensor can be used to drive realizable perturbations towards the limiting states of turbulence anisotropy thus creating a family of models that represent solution envelopes around a baseline prediction. In the present work, we apply the principles of eigenspace perturbations and information from the resolved scales with the goal of improving the prediction of a baseline subgrid model. A target stress that is computable on the grid is identified; then, perturbations are applied to the modeled subgrid tensor towards the eigenspace of the target tensor, with a view to correcting the shape and orientation of the baseline subgrid tensor. The approach involves minimal computing overhead, and is self-contained, only using information already generated as part of the simulation.