Interplay of roughness and wettability in microchannel fluid flows—Elucidating hydrodynamic details assisted by deep learning

Abstract

Under microconfinement, the complex interaction between surface roughness and fluid slippage yields unexpected variations in friction factor and drag reduction. These variations arise from the combined effects of roughness and hydrophobic interactions of the surface with the hydrodynamic field. Our study investigates alterations in frictional characteristics within long microchannels, considering fluid slippage, hydraulic diameter, and roughness. This exploration holds promise for precise drag reduction control applications for lab-on-a-chip and small-scale devices. To address computational limitations in analyzing diverse hydrodynamic conditions, we employ an artificial neural network prediction model, validated with experimental and numerical results. Contrary to the macroscopic conclusions obtained from the Moody chart, our findings indicate that fluid slippage, apart from surface roughness, significantly influences the friction factor. The interdependencies of friction factor on the flow and fluid parameters are thoroughly studied toward the proposition of a new slip-modified constricted flow friction factor formula, predicting friction in microchannels with combined roughness and hydrophobicity effects. This combined numerical and machine-learning approach presents a noteworthy counterpart to the moody chart at microscales offering the potential for a unified continuum-based description to include interfacial effects.