Maximizing heat transfer potential with graphene nanofluids along with structured copper surfaces for pool boiling

Abstract

Increasing the demand and depletion of energy sources confront every technological development with energy conversation and its utilization. Boiling heat transfer is the most applicable heat transfer method in many industries, from electronic chip manufacturing to heavy nuclear power plants. Hence, the augmentation of it is of prime importance for future requirements. The present investigation focused on the augmentation of boiling heat transfer by considering a novel V-pattern staggered copper surface for pool boiling in graphene nanofluid media. The study encompasses a detailed exploration of nanofluid concentrations ranging from 0.1% to 0.40 vol% prepared in equal volumes of dimethylformamide and distilled water. The nanofluid's stability is observed to be quite promising, and the results are presented. The wall superheat was drastically reduced to 48.73%, and the maximum heat transfer coefficient enhancement of 83.47% was achieved for 0.04% concentration of graphene nanofluid with a V-pattern surface compared to a smooth copper surface with water. This is due to the micro-level structured convection patterns promoted by the microstructures. The thermophysical properties of nanofluid and peculiar microstructures enhanced the relative heat transfer coefficient by 63.37%. On the other hand, two popular machine learning models, Random Forest Regression (RFR) and Multi-Layer Perceptron (MLP), are used to predict the boiling heat transfer coefficient. Moreover, it is observed that the RFR model fitted the data well and yielded the lowest mean absolute percentage error within the bounds of computational time.