• 文献标题:   Comprehensive study concerned graphene nano-sheets dispersed in ethylene glycol: Experimental study and theoretical prediction of thermal conductivity
  • 文献类型:   Article
  • 作  者:   IBRAHIM M, SAEED T, CHU YM, ALI HM, CHERAGHIAN G, KALBASI R
  • 作者关键词:   graphene nanosheet, thermal conductivity, sensitivity, artificial neural network
  • 出版物名称:   POWDER TECHNOLOGY
  • ISSN:   0032-5910 EI 1873-328X
  • 通讯作者地址:  
  • 被引频次:   35
  • DOI:   10.1016/j.powtec.2021.03.028 EA MAR 2021
  • 出版年:   2021

▎ 摘  要

In this study, thermal conductivity of graphene nano-sheets (GNs)/ethylene glycol (EG) nanofluidwas compared with EG thermal conductivity at 25-70 degrees C and 0.005-0.5 wt% to examine the effects of GNs nanoparticles. For all samples, presence of nanoparticles intensifies EG thermal conductivity up to 54.6%. Moreover, loading GNs into EG inverts the dependency of the thermal conductivity to temperature. As the temperature rises, the thermal conductivity of the base fluid decreases, while for nanofluid, thermal conductivity increases. Based on the results, by incorporating more nanoparticles, the positive effects of nanoparticles on thermal conductivity s reduced. It was concluded that with increasing temperature, the effect of adding GNs on the thermal conductivity is strengthened. Neural network implementation showed that this method can forecast k(GNs)/EC/k(EG) with maximum error of less than 3%. (C) 2021 Elsevier B.V. All rights reserved.