Online Learning of a Spectral Eddy Viscosity Closure for Large Eddy Simulation Using Ensemble Kalman Inversion

Abstract

Large Eddy Simulation (LES) resolves only the large, energy-containing structures of turbulent flows while modeling the effects of unresolved subgrid scales. As a result, the ac- curacy of LES strongly depends on the subgrid-scale closure. This work investigates the online calibration of a spectral viscosity closure for forced two-dimensional turbulence using ensemble Kalman Inversion (EKI). The closure consists of a wavenumber-dependent viscosity function, which is parameterized by a 16-dimensional vector. An ensemble of potential parameter vec- tors is passed through coarse resolution LES simulations, and the resulting kinetic energy spec- tra are compared with a DNS reference spectrum up to the LES-resolved wavenumber range. Then, ensemble statistics iteratively update the spectral viscosity parameters in order to reduce the model-data discrepancy. The convergence of the spectral mismatch is used to assess the accuracy of the calibrated closure by comparing LES with the filtered DNS data in spectral space. The results demonstrate the potential of EKI-based online calibration as a derivative- free framework for constructing data-driven turbulence closures with multiple parameters.