Divergence-free turbulent inflow data from realistic covariance tensor

Abstract

Scale-resolving computational fluid dynamics (CFD) methods require carefully constructed boundary conditions to produce accurate results. The inflow data should be unsteady and the successive realizations must follow specific statistics while ideally having a particular correlation in both space and time. A method for generating synthetic correlated stochastic data from uncorrelated sequences is detailed and applied to the problem of inflow turbulence generation for CFD simulations. The technique constructs divergence-free anisotropic random fields with the sensible spectrum and complete complex correlation in space and time. A realistic two-point correlation tensor is inferred from first and second moments and a set of heuristic recommendations based on turbulent flow observations. These statistics are readily available in most practical cases making the technique highly versatile. The approach is computationally efficient with the use of eigendecomposition to reduce the resources required depending on the accuracy needed. Demonstration of the method is provided with the simulation of a turbulent channel flow and a square duct flow, and validation is done against existing numerical data.