Surface qualitative analysis and ANN modelling for pool boiling heat transfer using Al2O3-water based nanofluids

Abstract

The present investigation is focussed on the surface associated qualitative analysis for nucleate pool boiling heat transfer characteristics of alumina-water based nanofluids. An optimal ANN design has been developed with various training algorithms and hidden layer based on experimental results. The nanofluids were prepared using distilled water and alumina nanoparticles of 40 nm size. The particle concentrations varied form 0.01 wt.% to 1 wt.%. The characterisation of nanoparticles and nanofluids was done using FESEM, EDAX, zeta potential stability test and thermophysical analysis. The pool boiling setup validation was done by comparing experimental results with Rohsenow’s correlation outputs. The boiling heat transfer was performed by varying input heat flux and calculating heat transfer coefficients with surface temperatures. The post boiling analysis was also performed on the nanoparticle deposited surfaces. The effect of nanoparticle concentrations and microlayer particle deposition is studied using surface roughness analysis, contact angle measurement and microscopic images. The pool boiling heat transfer with nanofluids showed an enhancement of up to 70 % as compared to distilled water. The improved thermophysical properties of nanofluids and the enhanced surface wettability provided an overall increase in heat transfer coefficients. The particle deposited surfaces showed heat transfer deterioration of up to 40 % with water. The ANN model analysis showed that the LM (Levenberg Marquardt) algorithm provided the most optimised structure. The combination of number of epochs and MSEs (Mean square errors) were taken for selecting the best model.