Modeling thermal conductivity of ethylene glycol-based nanofluids using multivariate adaptive regression splines and group method of data handling artificial neural network

References

• Agarwal, R., Verma, K., Agrawal, N. K., Duchaniya, R. K., & Singh, R. (2016). Synthesis, characterization, thermal conductivity and sensitivity of CuO nanofluids. Applied Thermal Engineering, 102, 1024–1036. doi: 10.1016/j.applthermaleng.2016.04.051
   Web of Science ®Google Scholar
• Ahmadi, M. H., Ahmadi, M. A., Mehrpooya, M., & Rosen, M. A. (2015). Using GMDH neural networks to model the power and torque of a Stirling engine. Sustainability (Switzerland), 7(2), 2243–2255. doi: 10.3390/su7022243
   Web of Science ®Google Scholar
• Ahmadi, M. H., Hajizadeh, F., Rahimzadeh, M., Shafii, M. B., Chamkha, A. J., Lorenzini, G., & Ghasempour, R. (2018). Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/ethylene glycol thermal conductivity. International Journal of Heat and Technology, 36(3), 773–782.
   Web of Science ®Google Scholar
• Ahmadi, M. H., Mirlohi, A., Nazari, M. A., & Ghasempour, R. (2018). A review of thermal conductivity of various nanofluids. Journal of Molecular Liquids, 265(September), 181–188. doi: 10.1016/J.MOLLIQ.2018.05.124
   Web of Science ®Google Scholar
• Ahmadi, M. H., Nazari, M. A., Ghasempour, R., Madah, H., Shafii, M. B., & Ahmadi, M. A. (2018). Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 541(March), 154–164. doi: 10.1016/J.COLSURFA.2018.01.030
   Web of Science ®Google Scholar
• Ahmadi, M. H., Sadeghzadeh, M., Raffiee, A. H., & Chau, K.-w. (2019). Applying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipes. Engineering Applications of Computational Fluid Mechanics, 13(1), 327–336. doi: 10.1080/19942060.2019.1582109
   Web of Science ®Google Scholar
• Ahmed, A., Baig, H., Sundaram, S., & Mallick, T. K. (2019). Use of nanofluids in solar PV/thermal systems. International Journal of Photoenergy, 2019(June), 1–17. doi: 10.1155/2019/8039129
   Web of Science ®Google Scholar
• Akilu, S., Baheta, A. T., Minea, A. A., & Sharma, K. V. (2017). Rheology and thermal conductivity of non-porous Silica (SiO2) in viscous glycerol and ethylene glycol based nanofluids. International Communications in Heat and Mass Transfer, 88(November), 245–253. doi: 10.1016/J.ICHEATMASSTRANSFER.2017.08.001
   Web of Science ®Google Scholar
• Alhuyi Nazari, M., Ahmadi, M. H., Lorenzini, G., Maddah, H., Alavi, M. F., & Ghasempour, R. (2018). Modeling thermal conductivity ratio of CuO/ethylene glycol nanofluid by using artificial neural network. Defect and Diffusion Forum, 388(October), 39–43. doi: 10.4028/www.scientific.net/DDF.388.39
   Google Scholar
• Arya, H., Sarafraz, M. M., & Arjomandi, M. (2018). Heat transfer and fluid flow of MgO/ethylene glycol in a corrugated heat exchanger. Journal of Mechanical Science and Technology, 32(8), 3975–3982. doi: 10.1007/s12206-018-0748-x
   Web of Science ®Google Scholar
• Esfe, M. H., Esfande, S., and Rostamian, S. H. (2017, November). Experimental evaluation, new correlation proposing and ANN modeling of thermal conductivity of ZnO-DWCNT/EG hybrid nanofluid for internal combustion engines applications. Applied Thermal Engineering, 133, 452–463. doi: 10.1016/j.applthermaleng.2017.11.131
   Web of Science ®Google Scholar
• Gandomkar, A., Saidi, M. H., Shafii, M. B., Vandadi, M., & Kalan, K. (2017). Visualization and comparative investigations of pulsating Ferro-fluid heat pipe. Applied Thermal Engineering, 116(April), 56–65. doi: 10.1016/J.APPLTHERMALENG.2017.01.068
   Web of Science ®Google Scholar
• Gao, Y., Wang, H., Sasmito, A. P., & Mujumdar, A. S. (2018). Measurement and modeling of thermal conductivity of graphene Nanoplatelet water and ethylene glycol base nanofluids. International Journal of Heat and Mass Transfer, 123(August), 97–109. doi: 10.1016/J.IJHEATMASSTRANSFER.2018.02.089
   Web of Science ®Google Scholar
• Goudarzi, K., & Jamali, H. (2017). Heat transfer enhancement of Al2O3-EG nanofluid in a car radiator with wire coil inserts. Applied Thermal Engineering, 118(May), 510–517. doi: 10.1016/J.APPLTHERMALENG.2017.03.016
   Web of Science ®Google Scholar
• Hemmat Esfe, M., Saedodin, S., Bahiraei, M., Toghraie, D., Mahian, O., & Wongwises, S. (2014). Thermal conductivity modeling of MgO/EG nanofluids using experimental data and Artificial Neural network. Journal of Thermal Analysis and Calorimetry, 118(1), 287–294. doi: 10.1007/s10973-014-4002-1
   Web of Science ®Google Scholar
• Islam, R., & Shabani, B. (2019). Prediction of electrical conductivity of TiO2 water and ethylene glycol-based nanofluids for cooling application in low temperature PEM fuel cells. In Energy Procedia, 160:550–57. Elsevier Ltd. doi: 10.1016/j.egypro.2019.02.205
   Google Scholar
• Izadkhah, M.-S., Erfan-Niya, H., & Heris, S. Z. (2019). Influence of graphene oxide nanosheets on the stability and thermal conductivity of nanofluids. Journal of Thermal Analysis and Calorimetry, 135(1), 581–595. doi: 10.1007/s10973-018-7100-7
   Web of Science ®Google Scholar
• Jiang, H., Zhang, Q., & Shi, L. (2015). Effective thermal conductivity of carbon nanotube-based nanofluid. Journal of the Taiwan Institute of Chemical Engineers, 55(October), 76–81. doi: 10.1016/j.jtice.2015.03.037
   Web of Science ®Google Scholar
• Komeilibirjandi, A., Raffiee, A. H., Maleki, A., Nazari, M. A., and Shadloo, M. S. (2019, September). Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network. Journal of Thermal Analysis and Calorimetry, 1–11. doi: 10.1007/s10973-019-08838-w
   Web of Science ®Google Scholar
• Lee, S., Choi, S. U.-S., Li, S., & Eastman, J. A. (1999). Measuring thermal conductivity of fluids containing oxide nanoparticles. Journal of Heat Transfer, 121(2), 280–289. doi: 10.1115/1.2825978
   Web of Science ®Google Scholar
• Li, C. H., & Peterson, G. P. (2007). The effect of particle size on the effective thermal conductivity of Al2O3-water nanofluids. Journal of Applied Physics, 101(4), 044312. doi: 10.1063/1.2436472
   Web of Science ®Google Scholar
• Li, X., Zou, C., Lei, X., & Li, W. (2015). Stability and enhanced thermal conductivity of ethylene glycol-based SiC nanofluids. International Journal of Heat and Mass Transfer, 89(October), 613–619. doi: 10.1016/J.IJHEATMASSTRANSFER.2015.05.096
   Web of Science ®Google Scholar
• Liu, M.-S., Lin, M. C.-C., Huang, I.-T., & Wang, C.-C. (2006). Enhancement of thermal conductivity with CuO for nanofluids. Chemical Engineering & Technology, 29(1), 72–77. doi: 10.1002/ceat.200500184
   Web of Science ®Google Scholar
• Michael, M., Zagabathuni, A., Ghosh, S., & Pabi, S. K. (2019). Thermo-physical properties of pure ethylene glycol and water–ethylene glycol mixture-based boron nitride nanofluids. Journal of Thermal Analysis and Calorimetry, 137(2), 369–380. doi: 10.1007/s10973-018-7965-5
   Web of Science ®Google Scholar
• Nazari, M. A., Ghasempour, R., Ahmadi, M. H., Heydarian, G., & Shafii, M. B. (2018). Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. International Communications in Heat and Mass Transfer, 91, 90–94. doi: 10.1016/j.icheatmasstransfer.2017.12.006
   Web of Science ®Google Scholar
• Niranjan, G., Chilambarasan, L., Raja Sekhar, Y., & Vikranthreddy, D. (2017). Performance studies on solar collector with grooved absorber tube configuration using aqueous ZnO–ethylene glycol nanofluids. Applied Solar Energy (English Translation of Geliotekhnika, 53(3), 215–221. doi: 10.3103/S0003701X17030082
   Google Scholar
• Oduro, S. D., Metia, S., Duc, H., and Ha, Q. P. (2015). Predicting carbon monoxide emissions with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs). In Proceedings of the 32nd International Symposium on Automation and Robotics in Construction and Mining (ISARC 2015). doi: 10.22260/ISARC2015/0122
   Google Scholar
• Omrani, A. N., Esmaeilzadeh, E., Jafari, M., & Behzadmehr, A. (2019). Effects of multi walled carbon nanotubes shape and size on thermal conductivity and viscosity of nanofluids. Diamond and Related Materials, 93(March), 96–104. doi: 10.1016/J.DIAMOND.2019.02.002
   Web of Science ®Google Scholar
• Put, R., Xu, Q. S., Massart, D. L., & Vander Heyden, Y. (2004). Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure-retention relationship studies. Journal of Chromatography A, 1055(1–2), 11–19. doi: 10.1016/j.chroma.2004.07.112
   PubMed Web of Science ®Google Scholar
• Ramezanizadeh, M., Ahmadi, M. H., Nazari, M. A., Sadeghzadeh, M., & Chen, L. (2019). A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids. Renewable and Sustainable Energy Reviews, 114(October), 109345. doi: 10.1016/J.RSER.2019.109345
   Web of Science ®Google Scholar
• Ramezanizadeh, M., and Nazari, M. A. (2019, August). Modeling thermal conductivity of Ag/water nanofluid by applying a mathematical correlation and artificial neural network. International Journal of Low-Carbon Technologies, 14, 468–474. doi: 10.1093/ijlct/ctz030
   Web of Science ®Google Scholar
• Ramezanizadeh, M., Nazari, M. A., Ahmadi, M. H., & Açıkkalp, E. (2018). Application of nanofluids in thermosyphons: A review. Journal of Molecular Liquids, 272(December), 395–402. doi: 10.1016/J.MOLLIQ.2018.09.101
   Web of Science ®Google Scholar
• Ramezanizadeh, M., Nazari, M. A., Ahmadi, M. H., & Chen, L. (2019). A review on the approaches applied for cooling fuel cells. International Journal of Heat and Mass Transfer, 139(August), 517–525. doi: 10.1016/J.IJHEATMASSTRANSFER.2019.05.032
   Web of Science ®Google Scholar
• Ramezanizadeh, M., Nazari, M. A., Ahmadi, M. H., Lorenzini, G., & Pop, I. (2019, March). A review on the applications of intelligence methods in predicting thermal conductivity of nanofluids. Journal of Thermal Analysis and Calorimetry, 1–17. doi: 10.1007/s10973-019-08154-3
   Web of Science ®Google Scholar
• Shu, L., Zhang, J., Fu, B., Xu, J., Tao, P., Song, C., … Deng, T. (2019). Ethylene glycol-based solar-thermal fluids dispersed with reduced graphene oxide. RSC Advances, 9(18), 10282–10288. doi: 10.1039/C8RA09533G
   PubMed Web of Science ®Google Scholar
• Simpson, S., Schelfhout, A., Golden, C., & Vafaei, S. (2019). Nanofluid thermal conductivity and effective parameters. Applied Sciences, 9(1), 87. doi: 10.3390/app9010087
   Google Scholar
• Taherialekouhi, R., Rasouli, S., & Khosravi, A. (2019). An experimental study on stability and thermal conductivity of water-graphene oxide/aluminum oxide nanoparticles as a cooling hybrid nanofluid. International Journal of Heat and Mass Transfer, 145(December), 118751. doi: 10.1016/J.IJHEATMASSTRANSFER.2019.118751
   Web of Science ®Google Scholar
• Talluri, L., Manfrida, G., & Fiaschi, D. (2019). Thermoelectric energy storage with geothermal heat integration – exergy and exergo-economic analysis. Energy Conversion and Management, 199(November), 111883. doi: 10.1016/J.ENCONMAN.2019.111883
   Web of Science ®Google Scholar
• Vasudev, V., & Dondapati, R. S. (2017). Experimental and exergy analysis of ammonia/water absorption system using ethylene glycol [C2H4(OH)2] in the evaporator. Energy Procedia, 109(March), 401–408. doi: 10.1016/J.EGYPRO.2017.03.039
   Google Scholar
• Wang, X., Xu, X., & Choi, S. U. S. (1999). Thermal conductivity of nanoparticle – fluid mixture. Journal of Thermophysics and Heat Transfer, 13(4), 474–480. doi: 10.2514/2.6486
   Web of Science ®Google Scholar
• Warrier, P., & Teja, A. (2011). Effect of particle size on the thermal conductivity of nanofluids containing metallic nanoparticles. Nanoscale Research Letters, 6(1), 247. doi: 10.1186/1556-276X-6-247
   PubMed Web of Science ®Google Scholar
• Xie, H., Yu, W., & Chen, W. (2010). Mgo nanofluids: Higher thermal conductivity and lower viscosity among ethylene glycol-based nanofluids containing oxide nanoparticles. Journal of Experimental Nanoscience, 5(5), 463–472. doi: 10.1080/17458081003628949
   Web of Science ®Google Scholar
• Xu, Q.-S., Daszykowski, M., Walczak, B., Daeyaert, F., de Jonge, M. R., Heeres, J., … Massarta, D. L. (2004). Multivariate adaptive regression splines – studies of HIV reverse transcriptase inhibitors. Chemometrics and Intelligent Laboratory Systems, 72(1), 27–34. doi: 10.1016/J.CHEMOLAB.2004.02.007
   Web of Science ®Google Scholar
• Zamzamian, A., Oskouie, S. N., Doosthoseini, A., Joneidi, A., & Pazouki, M. (2011). Experimental investigation of forced convective heat transfer coefficient in nanofluids of Al2O3/EG and CuO/EG in a double pipe and plate heat exchangers under turbulent flow. Experimental Thermal and Fluid Science, 35(3), 495–502. doi: 10.1016/j.expthermflusci.2010.11.013
   Web of Science ®Google Scholar
• Zoqi, M. J., Ghamgosar, M., Ganji, M., & Fallahi, S. (2016). Application of GMDH and genetic algorithm in fraction in biogas from landfill modeling. Journal of Environmental Science and Technology, 18(3), 1–12.
   Google Scholar
• Żyła, G. (2017). Viscosity and thermal conductivity of MgO–EG nanofluids. Journal of Thermal Analysis and Calorimetry, 129(1), 171–180. doi: 10.1007/s10973-017-6130-x
   Web of Science ®Google Scholar