4.7 Article

Model-based design of riblets for turbulent drag reduction

Journal

JOURNAL OF FLUID MECHANICS
Volume 906, Issue -, Pages -

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/jfm.2020.722

Keywords

drag reduction; turbulence control; turbulence modelling

Funding

  1. Office of Naval Research [N00014-17-1-2308]
  2. Air Force Office of Scientific Research [FA9550-16-1-0009, FA9550-18-1-0422]
  3. Center for High-Performance Computing at the University of Southern California

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This paper develops a model-based framework to quantify the effect of streamwise-aligned spanwise-periodic riblets on kinetic energy and skin-friction drag in turbulent channel flow. The study shows that triangular riblets can reliably predict drag-reducing trends, but may lead to performance deterioration for large riblets.
Both experiments and direct numerical simulations have been used to demonstrate that riblets can reduce turbulent drag by as much as , but their systematic design remains an open challenge. In this paper we develop a model-based framework to quantify the effect of streamwise-aligned spanwise-periodic riblets on kinetic energy and skin-friction drag in turbulent channel flow. We model the effect of riblets as a volume penalization in the Navier-Stokes equations and use the statistical response of the eddy-viscosity-enhanced linearized equations to quantify the effect of background turbulence on the mean velocity and skin-friction drag. For triangular riblets, our simulation-free approach reliably predicts drag-reducing trends as well as mechanisms that lead to performance deterioration for large riblets. We investigate the effect of height and spacing on drag reduction and demonstrate a correlation between energy suppression and drag reduction for appropriately sized riblets. We also analyse the effect of riblets on drag-reduction mechanisms and turbulent flow structures including very large-scale motions. Our results demonstrate the utility of our approach in capturing the effect of riblets on turbulent flows using models that are tractable for analysis and optimization.

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