A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces

264 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Superhydrophobic turbulent drag reduction as a function of surface grating parameters (2014, methodology): “2013), scaling issue (Fukagata et al. 2006; Jeffs et al. 2010; Busse & Sandham 2012; Park et al. 2013), and effects of directional slip (Min & Kim 2004; Fukagata et al.”
On the mechanism of turbulent drag reduction with super-hydrophobic surfaces (2015, background): “…24506Reb 6 4000, g=w=30 µm, MG on one wall; ——, DR=Us/Ub in (b), and DR=L+0s /[1+ (Reb/Reτ0)] with Reb = 3600, Reτ0 ≈ 223 in (d); – · –, – ·· –, analytical expressions of Fukagata et al. (2006) for uniform streamwise slip and uniform combined slip, respectively, at Reτ0 ≈ 223. or slightly…”
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows (2020, background): “To investigate the applicability of machine learning based spatio-temporal super resolution reconstruction to three-dimensional turbulence, let us consider a turbulent channel ﬂow at Re τ = 180 (Fukagata et al. 2006).”