Bicriteria optimization of subcooled flow boiling in graphene-coated microchannels using response surface methodology

Abstract

This study investigates the optimization of subcooled flow boiling in microchannels coated with graphene nanoplatelet (GNP) surfaces of varying wettability and roughness using response surface methodology (RSM). Four surface treatments: uncoated, SHi-GNP (superhydrophilic), SHo-GNP (superhydrophobic), and U-GNP (dual-wettability), are compared to assess the influence on key performance parameters, including Nusselt number (Nu) and pressure drop (Δp). Experimental results show that SHi-GNP achieves the best balance between high heat transfer performance (Nu_max = 82.9) and low pressure drop (Δp_min = 441.4 Pa), attributed to its enhanced wetting properties that promote effective rewetting and bubble nucleation. U-GNP exhibits the highest Nusselt number (Nu_max = 108.9), but at the cost of the highest pressure drop (Δp_min = 2017 Pa), due to its combined hydrophilic and hydrophobic regions that enhance nucleation but increase fluid resistance due to high surface roughness. SHo-GNP manifests moderate improvement in both heat transfer and pressure drop, while the uncoated surface shows the lowest performance. RSM proves effective in identifying optimal Reynolds numbers and power inputs for each surface treatment, facilitating bicriteria optimization of heat transfer and fluid flow performance. These findings provide valuable insights into the design of microchannel heat sinks for high-performance electronics cooling.