Data-driven methods for estimating the effective thermal conductivity of nanofluids: A comprehensive review
作者： Alireza Zendehboudi , R. Saidur , I.M. Mahbubul , S.H. Hosseini
DOI: 10.1016/J.IJHEATMASSTRANSFER.2018.11.053
关键词:
摘要: Abstract There is a growing body of work in the field nanofluids and several investigations have been conducted on their thermal conductivities. While experimental works require considerable investments to provide highly equipped laboratory proper instruments, predictive models are useful promote understanding under different operational conditions. However, restrictions traditional scholars develop reliable that capable simulating thermophysical properties nanofluids. Data-driven attracted huge attention widely applied various fields. no specific review application data-driven conductivity till now thus this first paper which gives state art research progress study. It was identified effective reflected well by new models. The authors believe fast, reliable, simple-to-use, practical. This may open door for scientists, engineers, researchers nanofluid calculating with excellent precision. investigation leads recognition main problems opportunities area future works.
cheric.org参考文章(101)
V. Ya. Shevchenko, A. E. Madison, Structure of Nanoparticles: I. Generalized Crystallography of Nanoparticles and Magic Numbers Glass Physics and Chemistry. ,vol. 28, pp. 40- 43 ,(2002) , 10.1023/A:1014201530029
Mohammd Hemmat Esfe, Somchai Wongwises, Ali Naderi, Amin Asadi, Mohammad Reza Safaei, Hadi Rostamian, Mahidzal Dahari, Arash Karimipour, Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation☆ International Communications in Heat and Mass Transfer. ,vol. 66, pp. 100- 104 ,(2015) , 10.1016/J.ICHEATMASSTRANSFER.2015.05.014
Mohammad Hemmat Esfe, Masoud Afrand, Wei-Mon Yan, Mohammad Akbari, None, Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data International Communications in Heat and Mass Transfer. ,vol. 66, pp. 246- 249 ,(2015) , 10.1016/J.ICHEATMASSTRANSFER.2015.06.002
Mohammad Hemmat Esfe, Masoud Afrand, Somchai Wongwises, Ali Naderi, Amin Asadi, Sara Rostami, Mohammad Akbari, None, Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH) 2 –EG using experimental data International Communications in Heat and Mass Transfer. ,vol. 67, pp. 46- 50 ,(2015) , 10.1016/J.ICHEATMASSTRANSFER.2015.06.015
Mohammad Hemmat Esfe, Hadi Rostamian, Masoud Afrand, Arash Karimipour, Mohsen Hassani, None, Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation International Communications in Heat and Mass Transfer. ,vol. 68, pp. 98- 103 ,(2015) , 10.1016/J.ICHEATMASSTRANSFER.2015.08.015
Fei Duan, Thermal Property Measurement of Al2O3-Water Nanofluids InTech. ,(2012) , 10.5772/33830
Shuai Deng, Wen Su, Li Zhao, A neural network for predicting normal boiling point of pure refrigerants using molecular groups and a topological index International Journal of Refrigeration-revue Internationale Du Froid. ,vol. 63, pp. 63- 71 ,(2016) , 10.1016/J.IJREFRIG.2015.10.025
Rohit S. Khedkar, Shriram S. Sonawane, Kailas L. Wasewar, Influence of CuO nanoparticles in enhancing the thermal conductivity of water and monoethylene glycol based nanofluids International Communications in Heat and Mass Transfer. ,vol. 39, pp. 665- 669 ,(2012) , 10.1016/J.ICHEATMASSTRANSFER.2012.03.012
Huaqing Xie, Motoo Fujii, Xing Zhang, Effect of interfacial nanolayer on the effective thermal conductivity of nanoparticle-fluid mixture International Journal of Heat and Mass Transfer. ,vol. 48, pp. 2926- 2932 ,(2005) , 10.1016/J.IJHEATMASSTRANSFER.2004.10.040
Hrishikesh E. Patel, T. Sundararajan, Sarit K. Das, An experimental investigation into the thermal conductivity enhancement in oxide and metallic nanofluids Journal of Nanoparticle Research. ,vol. 12, pp. 1015- 1031 ,(2010) , 10.1007/S11051-009-9658-2