TY - JOUR
T1 - Rough surfaces in underexplored surface morphology space and their implications on roughness modelling
AU - Nair, Shyam S.
AU - Wadhai, Vishal A.
AU - Kunz, Robert F.
AU - Yang, Xiang I.A.
N1 - Publisher Copyright:
© The Author(s), 2024. Published by Cambridge University Press.
PY - 2024/11/19
Y1 - 2024/11/19
N2 - We report direct numerical simulations results of the rough-wall channel, focusing on roughness with high statistics but small to negative statistics, and we study the implications of this new dataset on rough-wall modelling. Here, is the root mean square, is the first-order moment of roughness height, and is the skewness. The effects of packing density, skewness and arrangement of roughness elements on mean streamwise velocity, equivalent roughness height and Reynolds and dispersive stresses have been studied. We demonstrate that two-point correlation lengths of roughness height statistics play an important role in characterizing rough surfaces with identical moments of roughness height but different arrangements of roughness elements. Analysis of the present as well as historical data suggests that the task of rough-wall modelling is to identify geometric parameters that distinguish the rough surfaces within the calibration dataset. We demonstrate a novel feature selection procedure to determine these parameters. Further, since there is no finite set of roughness statistics that distinguish between all rough surfaces, we argue that obtaining a universal rough-wall model for making equivalent sand-grain roughness predictions would be challenging, and that each rough-wall model would have its applicable range. This motivates the development of group-based rough-wall models. The applicability of multi-variate polynomial regression and feedforward neural networks for building such group-based rough-wall models using the selected features has been shown.
AB - We report direct numerical simulations results of the rough-wall channel, focusing on roughness with high statistics but small to negative statistics, and we study the implications of this new dataset on rough-wall modelling. Here, is the root mean square, is the first-order moment of roughness height, and is the skewness. The effects of packing density, skewness and arrangement of roughness elements on mean streamwise velocity, equivalent roughness height and Reynolds and dispersive stresses have been studied. We demonstrate that two-point correlation lengths of roughness height statistics play an important role in characterizing rough surfaces with identical moments of roughness height but different arrangements of roughness elements. Analysis of the present as well as historical data suggests that the task of rough-wall modelling is to identify geometric parameters that distinguish the rough surfaces within the calibration dataset. We demonstrate a novel feature selection procedure to determine these parameters. Further, since there is no finite set of roughness statistics that distinguish between all rough surfaces, we argue that obtaining a universal rough-wall model for making equivalent sand-grain roughness predictions would be challenging, and that each rough-wall model would have its applicable range. This motivates the development of group-based rough-wall models. The applicability of multi-variate polynomial regression and feedforward neural networks for building such group-based rough-wall models using the selected features has been shown.
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U2 - 10.1017/jfm.2024.956
DO - 10.1017/jfm.2024.956
M3 - Article
AN - SCOPUS:85210286547
SN - 0022-1120
VL - 999
JO - Journal of Fluid Mechanics
JF - Journal of Fluid Mechanics
M1 - 2400956
ER -