首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Experimental and Theoretical Investigation of Thermophysical Properties of Synthesized Hybrid Nanofluid Developed by Modeling Approaches
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Experimental and Theoretical Investigation of Thermophysical Properties of Synthesized Hybrid Nanofluid Developed by Modeling Approaches

机译:用建模方法开发合成杂交纳米流体热物理性质的实验与理论研究

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Although, titanium oxide ( TiO_2) has appropriate mechanical and chemical stability used in different applications, its thermal conductivity slightly increases with an increasing temperature and concentration compared with other metal oxides such as aluminum oxide ( Al_2O_3). Thus, synthesized aluminum oxide nanoparticles were incorporated on the surfaces of titanium oxide in ultrasonication condition with purpose of thermophysical properties modification. The scanning electron microscopy and X-ray diffraction were used to investigate the structure and morphology of synthesized nanocomposite. The impact of variables (temperature, volume fraction and nanoparticle size) on the thermal conductivity and viscosity of prepared hybrid nanofluid was investigated using KD2Pro instrument and Brookfield DVII viscometer, respectively. Results showed a significant improvement of thermophysical properties of prepared hybrid nanofluid, compared to water or untreated titanium oxide–water. The results showed that three mentioned variables considerably affect the thermophysical properties of hybrid nanofluid; as an increasing volume fraction, reducing nanoparticle size and temperature led to an increasing viscosity while enhanced thermal conductivity was resulted from an increasing nanofluid volume fraction and temperature, and a decreasing nanoparticle size. This was confirmed using two computer-modeling approaches, which allow optimization of the thermophysical properties of hybrid nanofluid. Modifying Response Surface Methodology-Central Composite Design (RSM-CCD) estimated accurately the optimal conditions for thermal conductivity and viscosity. The best artificial neural network model was chosen based on its predictive accuracy for estimation of thermophysical properties; having seven neurons in hidden layer and minimum error, demonstrated the most accurate approach for modeling the considered task.
机译:尽管氧化钛(TiO_2)具有适当的机械和化学稳定性在不同的应用中,其导热率随着诸如氧化铝(Al_2O_3)的其他金属氧化物(Al_2O_3)而相比,其温度和浓度越来越大。因此,具有热物理性质改性的目的,合成的氧化铝纳米粒子掺入超声氧化钛的表面上。扫描电子显微镜和X射线衍射用于研究合成纳米复合材料的结构和形态。使用KD2Pro仪器和Brookfield DVII粘度计研究了变量(温度,体积分数和纳米粒径)对制备的杂交纳米流体的导热率和粘度的影响。结果表明,与水或未处理的氧化钛 - 水相比,制备杂种纳米流体的热物理性质的显着改善。结果表明,三种上述变量显着影响杂交纳米流体的热物理性质;随着体积分数的增加,减少纳米颗粒尺寸和温度导致粘度的增加,同时由增加的纳米流体体积分数和温度产生增强的导热率,以及纳米颗粒尺寸的降低导致导热率。这是使用两种计算机建模方法确认的,这允许优化杂交纳米流体的热性性质。改变响应面方法 - 中央复合设计(RSM-CCD)精确估计热导率和粘度的最佳条件。基于其预测精度来选择最佳的人工神经网络模型,用于估计热神经性质;在隐藏层和最小误差中具有七个神经元,展示了用于建模考虑的任务的最准确的方法。

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