Experimental and artificial neural network based study on the heat transfer and flow performance of ZnO-EG/water nanofluid in a mini-channel with serrated fins

Tao Wen, Guangya Zhu, Lin Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

The adoptions of mini-channel and nanofluid are potential technologies to improve the heat transfer capability. The present study experimentally investigated the flow and thermal performance of EG/water based ZnO nanofluid inside a mini-channel with serrated fins with an equivalent diameter of 1.59 mm. EG/water mixture with a mass fraction of 40%/60% served as the base fluid. Nanofluids with a volumetric concentration of 0.75 vol% and 1.5 vol% were prepared. The thermophysical properties of viscosity, density and thermal conductivity were measured. It reveals that although the adding of nanoparticles is able to improve the thermal conductivity by 4.2% on average, the viscosity also enlarges remarkably by up to 18.9%. The measured density and thermal conductivity can be predicted accurately by existing formulations. As all correlations underestimate the viscosity, a new one is proposed for the fabricated nanofluid with a small Mean Absolute Relative Deviation (MARD) of 0.1%. The Nusselt number can be averagely improved by 10.6% and 13.2% for the 0.75 vol% and 1.5 vol% nanofluid with a corresponding sacrifice of friction factor augmentation of 31.2% and 47.3% respectively due to the significant augmentation of viscosity, which results in a relatively small heat transfer coefficient enhancement ratio of 0.92–1.09 under the same pumping power. In addition, the heat transfer enhancement shows a nonlinear increment with the concentration and a higher relative enhancement is found at a lower concentration. This indirectly indicates that the heat transfer does not always increase with the concentration. Even though some published correlations present acceptable predictions for the tested flow and thermal performance of EG/water based ZnO nanofluid, the newly developed Backpropagation Artificial Neural Networks (BP-ANNs) show even better prediction accuracies for Nusselt number and friction factor with the MARDs of 0.39% and 0.35% respectively. Our study is expected to give valuable data sources and inspiration for future investigation on hydrodynamic and thermal behavior of nanofluids.

Original languageEnglish
Article number107149
JournalInternational Journal of Thermal Sciences
Volume170
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Backpropagation artificial neural network
  • EG/water based ZnO nanofluid
  • Enhancement ratio
  • Mini-channel
  • Serrated fins
  • Thermophysical property

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Engineering(all)

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