首页    期刊浏览 2024年12月01日 星期日
登录注册

文章基本信息

  • 标题:Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs
  • 本地全文:下载
  • 作者:Farid Soltani ; Mehdi Hajian ; Davood Toghraie
  • 期刊名称:Case Studies in Thermal Engineering
  • 印刷版ISSN:2214-157X
  • 电子版ISSN:2214-157X
  • 出版年度:2021
  • 卷号:26
  • 页码:101122
  • DOI:10.1016/j.csite.2021.101122
  • 出版社:Elsevier B.V.
  • 摘要:This paper aims to determine the thermal conductivity (k nf ) of oxide of tungsten (WO 3 )-MWCNTs/hybrid engine oil, through an Artificial Neural Network (ANN). Nanofluid were prepared by the suspension of nanoparticles in engine oil. The experiments were conducted at a volume fraction of nanoparticles ϕ = 0.05 to ϕ = 0.6%, as well as a temperature range of T = 20°C–60 °C. The ANN was then used to estimate the k nf , and the optimum neuron number was 7. Results showed that the absolute error values of the ANN method in many points are zero. Also, the ANN had smaller error values compared to the correlation method. ANN showed acceptable performance and correlation coefficient. Also, a correlation method was used to predict k nf .
  • 关键词:Engine oil–based nanofluids ; Hybrid nanofluid ; Artificial eural network ; Thermal characteristics
国家哲学社会科学文献中心版权所有