Modeling and Multi-Objective Optimization of Thermophysical Properties for Thermal Conductivity and Reynolds number of CuO-Water Nanofluid using Artificial Neural Network.

Journal of Research in Engineering and Applied Sciences

Amin Moslemi Petrudi¹ 

[1] Department of Mechanical Engineering, Tehran University, Tehran, Iran.;

关键词: multi-objective optimization;    parto-front;    nsga-ii algorithm;    nanofluid.;   

DOI  :  10.46565/jreas.2020.v05i04.002

来源: DOAJ

【 摘 要 】

In nanofluids, due to the small size of the particles, they greatly reduce the problems caused by corrosion, impurities, andpressure drop, and the stability of fluids against sediment is significantly improved. Due to the high conductivity of nanoparticles, with the distribution in the base fluid, they increase the thermal conductivity of the fluid, which is one of the basic parameters of heat transfer.In this paper, properties using experimental data and artificial neural networks, to maximize thermal conductivity, temperaturechanges, and nanofluid volumefraction of NSGA-II optimization algorithm and also to obtain thermalconductivity valuesfrom154experimentaldata,artificialneuralnetworkmodelingisused.VariousindicesincludingR-squaredandMean SquareError(MSE)havebeenusedtoevaluatethemodelingaccuracyinprediction,Reynoldsnumber,andnanofluidthermal conductivity.Thecoefficientofdeterminationoftherelation(R-squared)isequalto0.9988,whichindicatestheacceptable agreement of the proposed relationship with the experimental data. To optimize, the results are presented as a target function, the Parto-front,anditsoptimalpoints.Optimalresultsshowedthatthemaximumthermalconductivitycoefficientandtheoptimal Reynolds number occur in a volume fraction of 2%.

【 授权许可】

Unknown