Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH) 2 –EG using experimental data
作者： Mohammad Hemmat Esfe , Masoud Afrand , Somchai Wongwises , Ali Naderi , Amin Asadi
DOI: 10.1016/J.ICHEATMASSTRANSFER.2015.06.015
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摘要: Abstract This paper presents an investigation on the thermal conductivity of nanofluids using experimental data, neural networks, and correlation for modeling conductivity. The Mg(OH)2 nanoparticles with mean diameter 10 nm dispersed in ethylene glycol was determined by a KD2-pro analyzer. Based data at different solid volume fractions temperatures, is proposed terms fraction temperature. Then, model relative as function temperature developed via network based measured data. A two hidden layers 5 neurons each layer has lowest error highest fitting coefficient. By comparing performance derived from empirical it revealed that can more accurately predict Mg(OH)2–EG nanofluids'
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