Assessment of Regularized Ensemble Kalman Method for Inversion of Turbulence Quantity Fields

This paper introduces an ensemble-based field inversion framework to augment the turbulence models by incorporating prior physical knowledge. Different types of prior knowledge such as smoothness, prior values, and sparsity are enforced to improve the inference of the eddy viscosity and laminar–turbulent intermittency. This work first assesses the method on the problems of inferring eddy viscosity in the Reynolds-averaged Navier–Stokes equation from the velocity observation data for separated flows over periodic hills. Further, the method is used to infer the intermittency field in the transport equation of turbulent kinetic energy from measurements of the friction coefficient for transitional flows over a plate. The results demonstrate the performance of the regularized ensemble method by enforcing prior knowledge into the inference. The method serves as a useful inverse modeling tool to augment the turbulence model from observation data.