Stochastic deconvolution of wall statistics in Reynolds‐averaged Navier–Stokes simulations based on one‐dimensional turbulence

Abstract

AbstractReynolds‐averaged Navier–Stokes simulation (RaNS) is state‐of‐the‐art for numerical analysis of complex flows at high Reynolds number. Standalone RaNS may yield a reasonable estimate of the wall‐shear stress and turbulent drag if a proper wall‐function is prescribed, but detailed turbulence statistics cannot be obtained, especially at the wall. This lack in modeling is addressed here by a stochastic deconvolution strategy based on a stochastic one‐dimensional turbulence (ODT) model. Here, a one‐way coupling strategy is proposed in which a forcing term is computed from the balanced RaNS solution that is in turn utilized in the ODT model. The temporally developing ODT solution exhibits turbulent perturbations but relaxes toward the local RaNS solution due to resolved molecular‐diffusive processes. It is demonstrated that the approach is able to recover the distribution of positive wall‐shear stress fluctuations in turbulent channel flow. When formulated as post‐processing tool, it is suggested that RaNS can be enhanced by ODT providing economical means for local high‐fidelity numerical modeling based on a low‐fidelity flow solution.