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Develop artificial neural network numerical modeling to study fluid flow and heat transfer of dispersed nanoparticles through base liquid
International Journal of Numerical Methods for Heat & Fluid Flow ( IF 4.2 ) Pub Date : 2021-02-15 , DOI: 10.1108/hff-11-2020-0697
Tawfeeq Abdullah Alkanhal

Purpose

This paper aims to disperse the silicon dioxide in water (as the mono nanofluid [MN]) and then, carbon nanotube (CNT)-silica composite in water (as the hybrid nanofluid [HN]).

Design/methodology/approach

Nanofluids have gained lots of attention through the recent years. Due to their usage in the industries and also medical applications, they have high protentional to be studied in different aspects. The most common study for the nanofluids is to understand the heat transfer capacity for each material in each fluid. These material(s) or fluid(s) can be one (mono nanofluid) or more than one (hybrid nanofluid).

Findings

The mixture of two solids is to assess the unique properties of each material and also to decrease the cost of experiments. The heat transfers for both MN and HN were measured at volume fractions up to 1.0%, and temperatures up to 50°C. Also, the heat transfers were compared. By more CNT, thermal conductivity was enhanced about 17.39% (from 12.42% of MN to 29.81% of HN).

Originality/value

X-Ray diffraction and field emission scanning electron microscope (FESEM) were examined for mono solids and the composite. After the experimental study, for MN and HN, four novel correlations calculated.



中文翻译:

开发人工神经网络数值模型来研究分散纳米粒子通过基液的流体流动和传热

目的

本文旨在将二氧化硅分散在水中(作为单纳米流体 [MN]),然后将碳纳米管(CNT)-二氧化硅复合物分散在水中(作为混合纳米流体 [HN])。

设计/方法/方法

近年来,纳米流体受到了很多关注。由于它们在工业和医疗应用中的使用,它们在不同方面具有很高的研究价值。纳米流体最常见的研究是了解每种流体中每种材料的传热能力。这些材料或流体可以是一种(单纳米流体)或多于一种(混合纳米流体)。

发现

两种固体的混合物是为了评估每种材料的独特性能,也是为了降低实验成本。MN 和 HN 的热传递是在体积分数高达 1.0% 和温度高达 50°C 的情况下测量的。此外,还比较了热传递。通过更多的 CNT,热导率提高了约 17.39%(从 MN 的 12.42% 到 HN 的 29.81%)。

原创性/价值

检查单固体和复合材料的 X 射线衍射和场发射扫描电子显微镜 (FESEM)。经过实验研究,对于 MN 和 HN,计算了四个新的相关性。

更新日期:2021-02-15
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