• 文献标题:   Increase thermal conductivity of aqueous mixture by additives graphene nanoparticles in water via an experimental/numerical study: Synthesise, characterization, conductivity measurement, and neural network modeling
  • 文献类型:   Article
  • 作  者:   ALSARRAF J, MALEKAHMADI O, KARIMIPOUR A, TLILI I, KARIMIPOUR A, GHASHANG M
  • 作者关键词:   thermal conductivity, graphene, 2d material, correlation, artificial neural network
  • 出版物名称:   INTERNATIONAL COMMUNICATIONS IN HEAT MASS TRANSFER
  • ISSN:   0735-1933 EI 1879-0178
  • 通讯作者地址:   Duy Tan Univ
  • 被引频次:   1
  • DOI:   10.1016/j.icheatmasstransfer.2020.104864
  • 出版年:   2020

▎ 摘  要

Graphene is a flexible and transparent conductor which can be used in varied material-apparatus applications, counting solar cells, phones, touch panels, and light-emitting diodes (LED). In the current experiment, Graphene preparation by Top-down method and stability of Graphene-Water nanofluid studied. Then, as the main aim, thermal conductivity (TC) of few-layered Graphene measured and numerically modeled. To analysis Microstructural observation and Phase study of nanoparticles, XRD, DLS, FTIR, FESEM-EDX, and TEM applied. Also, to read the stability of nanofluid, UV-Vis, Zeta-potential and DSC-TG applied. The range of Thermal conductivity test for mass fraction was 1.0-4.5 mg/ml, and for temperature was 25-50 degrees C. More than three months for nanofluid stability confirmed by stability tests. More ever, the thermal stability test for 1.0 mg/ml nanofluid confirmed its operational temperature range up to 1000 degrees C. Thermal conductivity enhancement (TCE) of 31.08%, measured at 4.5 mg/ml mass fraction at 50 degrees C temperature. To compute nanofluid's TC, the numerical study by new correlation (including 2.19% utmost deviation) and an Artificial neural network with R-2 = 0.999 modeled. As a result, Graphene-Water nanofluid is stable, and in thermal systems, it has agreeable heat transfer potential.