3.8 Proceedings Paper

Multiobjective optimization of thermophysical properties of indonesian fly-ash nanofluid

期刊

MATERIALS TODAY-PROCEEDINGS
卷 49, 期 -, 页码 1255-1262

出版社

ELSEVIER
DOI: 10.1016/j.matpr.2021.06.304

关键词

Fly-ash; Water dispersion; Nanofluid; Optimization; RSM

资金

  1. Department of Chemical Engineering
  2. University of Jeddah, Jeddah, Saudi Arabia

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This study utilizes nanofluids containing fly-ash nanoparticles dispersed in water to model and optimize the thermal and viscous temperature-dependent characteristics. The objective is to find the optimal fly-ash nanoparticle concentration and thermophysical properties.
Nanofluids with thermal and viscous tunable properties are seen to be smart fluids of the future generation. In determining the viscous and heat transfer characteristics, the concentration of nanomaterials is important. Generally, an optimal concentration of nanomaterial is needed to design a sustainable and cost-effective heat transfer process. This study includes modeling and optimization of various thermal and viscous temperature-dependent characteristics for differing nanofluid concentrations. The nanofluids containing fly-ash nanoparticles dispersed in water are used in this work. The Response Surface Methodology (RSM) is used to model and optimize the thermophysical properties of nanofluids. The higher volume of nanoparticles in the solution not only increases the heat transfer but also increases the fluid's viscosity leading to higher energy requirements for transportation. The study's key objective is to find the optimal fly-ash nanoparticle concentration and thermophysical characteristics. Temperature and volume concentration were considered as input parameters, while viscosity, thermal conductivity, specific heat, and density were selected as a response. The optimum value for viscosity, thermal conductivity, specific heat, and density are estimated as 0.0466536 Pa.s, 0.686725 W/m degrees C, 4040 j/kg degrees C and 984.368 kg/m3 for the input value of 60 degrees C and 0.1041741 percent nanoparticle loading. (c) 2021 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the scientific committee of the Global Conference on Recent Advances in Sustainable Materials 2021.

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