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Spatio–temporal analysis of hydrodynamic forces on the particle bed in an oscillatory flow environment
Published online by Cambridge University Press: 20 February 2018
Abstract
A numerical investigation of unsteady hydrodynamic forces on the particle bed in an oscillatory flow environment is performed by means of direct numerical simulations. Statistical descriptions of drag and lift forces for two particle sizes of diameter 372 and 125 in wall units in a very rough turbulent flow regime are reported. Characterization of unsteady forces in terms of spatial distribution, temporal autocorrelation, force spectrum as well as cross-correlations with measurable flow variables is carried out. Based on the concept of impulse, intermittency in the drag and lift forces is also investigated. Temporal correlations show drag and lift to be positively correlated with a time delay that is approximately equal to the Taylor micro-scale related to the drag/lift fluctuations. The force spectra for drag and lift reveal roughly two scaling regions, $-11/3$ and $-7/3$; the former typically represents turbulence–mean-shear interactions, whereas the latter indicates dominance of turbulence–turbulence interactions. Particle forces are strongly correlated with streamwise velocity and pressure fluctuations in the near-bed region for both flow cases. In comparison to the large-diameter particle case, the spatial extent of these correlations is 2–3 times larger in homogeneous directions for the small sized particle, a feature that is reminiscent of longer near-bed structures. For both large- and small-particle cases, it is shown that the distributions of drag (lift) fluctuations, in particular, peakedness and long tails, match remarkably well with fourth-order Gram–Charlier distributions of velocity (pressure) fluctuations. Furthermore, it is demonstrated that the intermittency is larger in the case of the lift force compared to that for the drag in both flow cases. Distributions of impulse events are heavily and positively skewed and are well described by a generalized extreme value distribution.
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- © 2018 Cambridge University Press
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