Wall Velocity Field Research Articles

Neural Network Modeling for Near Wall Turbulent Flow

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control.

Flow over convergent and divergent wall riblets

Fast swimming sharks have small riblets on their skin, which are assumed to improve the swimming performance of the fish. Fluid dynamic experiments in water as well as in air confirm this assumption. With riblet surfaces as compared to smooth surfaces, drag reductions up to about 10% were measured. The overall riblet pattern on sharks shows parallel riblets directed from head to tail, but besides this overall pattern fast swimming sharks have also small areas with converging riblets and others with diverging riblets. In the present study the velocity field over convergent and divergent riblet patterns is investigated by hot-wire measurements in turbulent pipe flow. Significant changes in the near wall velocity field were found.