Modeling Thermal Conductivity Ratio of CuO/Ethylene Glycol Nanofluid by Using Artificial Neural Network

Abstract:

The thermal conductivity of nanofluids depends on several factors such as temperature, concentration, and temperature. These parameters have the most significant effect on thermal conductivity compared with other factors. In the present study, the accuracy of trained Perceptron neural network with 10 neurons and three input variables including size of nanoparticles, temperature, and concentration is evaluated. The sum of squared errors and the correlation coefficient of the trained neural network are equal to 0.99293 and 0.00031, respectively.

• Periodical: Defect and Diffusion Forum (Volume 388)
• Pages: 39-43
• DOI: https://doi.org/10.4028/www.scientific.net/DDF.388.39
• Online since: October 2018
• Authors: Mohammad Alhuyi Nazari, Mohammad Hossein Ahmadi*, Giulio Lorenzini, Heydar Maddah, Morteza Fahim Alavi, Roghayeh Ghasempour
• Keywords: Artificial Neural Network, CuO/EG, Nano Particles, Thermal Conductivity

References

[1] Narei H, Ghasempour R, Noorollahi Y. The effect of employing nanofluid on reducing the bore length of a vertical ground-source heat pump. Energy Convers Manag 2016;123:581–91.

DOI: 10.1016/j.enconman.2016.06.079

[2] Wang B, Wang X, Lou W, Hao J. Thermal conductivity and rheological properties of graphite/oil nanofluids. Colloids Surfaces A Physicochem Eng Asp 2012;414:125–31.

DOI: 10.1016/j.colsurfa.2012.08.008

[3] Toghraie D, Chaharsoghi VA, Afrand M. Measurement of thermal conductivity of ZnO–TiO2/EG hybrid nanofluid. J Therm Anal Calorim 2016;125:527–35.

DOI: 10.1007/s10973-016-5436-4

[4] Ahmadloo E, Azizi S. Prediction of thermal conductivity of various nanofluids using artificial neural network. Int Commun Heat Mass Transf 2016;74:69–75.

DOI: 10.1016/j.icheatmasstransfer.2016.03.008

[5] Ahmadi MH, Alhuyi Nazari M, Ghasempour R, Madah H, Shafii MB, Ahmadi MA. Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods. Colloids Surfaces A Physicochem Eng Asp 2018;541:154–64.

DOI: 10.1016/j.colsurfa.2018.01.030

[6] Nazari MA, Ghasempour R, Ahmadi MH, Heydarian G, Shafii MB. Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. Int Commun Heat Mass Transf 2018;91:90–4.

DOI: 10.1016/j.icheatmasstransfer.2017.12.006

[7] Ahmadi MH, Mirlohi A, Nazari MA, Ghasempour R. A review of thermal conductivity of various nanofluids. J Mol Liq 2018.

[8] Aramesh M, Pourfayaz F, Kasaeian A. Numerical investigation of the nanofluid effects on the heat extraction process of solar ponds in the transient step. Sol Energy 2017;157:869–79.

DOI: 10.1016/j.solener.2017.09.011

[9] Manimaran R, Palaniradja K, Alagumurthi N, Hussain J. Experimental comparative study of heat pipe performance using CuO and TiO 2 nanofluids. Int J Energy Res 2014;38:573–80.

DOI: 10.1002/er.3058

[10] Wongcharee K, Chuwattanakul V, Eiamsa-ard S. Influence of CuO/water nanofluid concentration and swirling flow on jet impingement cooling. Int Commun Heat Mass Transf 2017.

DOI: 10.1016/j.icheatmasstransfer.2017.08.020

[11] Maheshwary PB, Handa CC, Nemade KR. A comprehensive study of effect of concentration, particle size and particle shape on thermal conductivity of titania/water based nanofluid. Appl Therm Eng 2017;119:79–88.

DOI: 10.1016/j.applthermaleng.2017.03.054

[12] Hossein Karimi Darvanjooghi M, Nasr Esfahany M. Experimental investigation of the effect of nanoparticle size on thermal conductivity of in-situ prepared silica–ethanol nanofluid. Int Commun Heat Mass Transf 2016;77:148–54.

DOI: 10.1016/j.icheatmasstransfer.2016.08.001

[13] Kasaeian A, Ghalamchi M, Ahmadi MH, Ghalamchi M. GMDH algorithm for modeling the outlet temperatures of a solar chimney based on the ambient temperature. Mech Ind 2017;18:216.

DOI: 10.1051/meca/2016034

[14] Ahmadi MH, Ahmadi MA, Mehrpooya M, Rosen MA. Using GMDH neural networks to model the power and torque of a stirling engine. Sustain 2015;7:2243–55.

DOI: 10.3390/su7022243

[15] Pourkiaei SM, Ahmadi MH, Hasheminejad SM. Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network. Mech Ind 2016;17:105.

DOI: 10.1051/meca/2015050

[16] Agarwal R, Verma K, Agrawal NK, Duchaniya RK, Singh R. Synthesis, characterization, thermal conductivity and sensitivity of CuO nanofluids. Appl Therm Eng 2016;102:1024–36.

DOI: 10.1016/j.applthermaleng.2016.04.051

[17] Liu M, Lin M, Wang C. Enhancements of thermal conductivities with Cu, CuO, and carbon nanotube nanofluids and application of MWNT/water nanofluid on a water chiller system. Nanoscale Res Lett 2011;6:297.

DOI: 10.1186/1556-276x-6-297

[18] Wang X, Xu X, S. Choi SU. Thermal Conductivity of Nanoparticle - Fluid Mixture. J Thermophys Heat Transf 1999;13:474–80.

[19] Lee S, Choi SU-S, Li S, Eastman JA. Measuring Thermal Conductivity of Fluids Containing Oxide Nanoparticles. J Heat Transfer 1999;121:280.

DOI: 10.1115/1.2825978

[20] Liu MS, Lin MCC, Huang I Te, Wang CC. Enhancement of thermal conductivity with CuO for nanofluids. Chem Eng Technol 2006;29:72–7.