Turbulence and pressure fluctuations in rough wall boundary layers in pressure gradients

• Published in: Experiments in fluids Vol. 63; no. 9
• Main Authors: Fritsch, Daniel J., Vishwanathan, Vidya, Roy, Christopher J., Lowe, K. Todd, Devenport, William J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2022; Springer Nature B.V
• Subjects: Boundary conditions; Boundary layers; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Equivalence; Fluid flow; Fluid- and Aerodynamics; Heat and Mass Transfer; High Reynolds number; Modelling; Prediction models; Pressure dependence; Pressure gradients; Research Article; Reynolds number; Turbulent Flow Over Rough Walls; Wall flow; Wall pressure
• ISSN: 0723-4864; 1432-1114
• DOI: 10.1007/s00348-022-03476-9

Experimental and steady RANS data were generated for high-Reynolds-number rough wall flows beneath a systematically constructed family of bi-directional, continually varying pressure gradient distributions. These flows demonstrate outer-scale Reynolds number independence and qualitatively similar pressure gradient dependence, but reduced history dependence, compared to an equivalent smooth wall flow. The spectrum of fluctuating wall pressure beneath these flows is largely simplified compared to equivalent smooth wall behavior, collapsing on an outer-variable scaling and exhibiting an overlap region independent of pressure gradient and pressure gradient history. The universality of the high-frequency behavior suggests a corresponding universality in the near wall dissipative behavior, contrary to current modeling philosophies for rough wall flows. Corresponding RANS data reveal fundamental issues with the classical roughness boundary condition definition; RANS data fail to replicate the correct Reynolds number dependencies of rough wall flows. These results suggest that while a full understanding of rough wall flow physics is still lacking, such flows exhibit simple, universal relations that are exploitable for advancing our physical understanding and predictive modeling capability.
Graphical abstract