Experimental evaluation and artificial neural network modeling of heat transfer performance of aerosolized magnesium oxide nanoparticles flow through pipes

Abstract

This study presents a novel approach to modeling the convective heat transfer coefficient (CHTC) of aerosolized magnesium oxide (MgO) nanoparticles in a circular pipe using artificial neural network (ANN) technique, by leveraging experimental data. The work addresses a gap in existing research on the heat transfer characteristics of nanoaerosols under varying thermal and flow conditions. MgO nanoparticles (30-50 nm) were dispersed in compressed air at volume fractions of 0.005, 0.01, and 0.05 to generate the nanoaerosol. This aerosol was then driven through the pipe at volumetric flow rates between 10 and 50 liters per minute (lpm). The pipe was subjected to controlled heat fluxes of 4546.83 W/m², 9093.66 W/m², and 13640.49 W/m² to evaluate the aerosol heat transfer coefficient (AHTC). Experimental results demonstrated that incorporating MgO nanoparticles significantly enhanced the heat transfer coefficient by up to 1.4 %, 111 %, and 89.7 % at the specified heat flux values, corresponding to increases in the volumetric flow rate from 10 lpm to 50 lpm, respectively. An ANN-based correlation was developed to model the heat transfer coefficient in relation to heat flux, particle volume fraction, and volumetric flow rate. This model accurately predicted the experimental data, achieving a mean absolute percentage error (MAPE) of 9.9 × 10^-5, a mean square error (MSE) of 0.038433, and a coefficient of determination (R²) of 0.99. These findings confirm the ANN model's efficacy in predicting the enhancement of the nanoaerosol heat transfer coefficient and provide a robust tool for future thermal management applications involving nanofluids.