RETRACTED: Performance of joined artificial neural network and genetic algorithm to study the effect of temperature and mass fraction of nanoparticles dispersed in ethanol
Quyen Nguyen, Parisa Ghorbani, Seyed Amin Bagherzadeh, Omid Malekahmadi, Arash Karimipour
Abstract
There are many experimental results in the field of finding nanofluids in optimal conditions, which are useful and effective in using artificial neural network methods for better analysis of these results. In this study, by using an artificial neural network, this paper checked the effect of temperature and concentration of different types of nanoparticles, such as copper oxide or Multi Wall Carbon Nano Tubes (MWCNT), on thermal conductivity and the interaction of nanofluid particles. The temperature and mass fraction of the nanoparticles are considered as the inputs of the neural network, and the thermal conductivity and molecular interaction as the outputs of this network are considered. The experimental data extracted for this study change in four temperatures of 25°C, 40°C, 55°C, and 70°C, as well as the mass fraction of nanoparticles in the range of 0.007%–7%. This paper considered the best network with a hidden layer, 15 neurons, and the Levenberg–Marquardt training method. The results showed that at a concentration between 2% and 4% of nanoparticles and at higher temperatures than 60°C, maximum thermal conductivity and molecular interaction occurred. Nevertheless, considering that the maximum heat conductivity combined with minimum fluid interaction with nanofluid (for more stability) is our desire, so this paper used a genetic algorithm to optimize the results obtained.