Development of the ANN-KIM composed model to predict the nanofluid energetic thermal conductivity via various types of nano-powders dispersed in oil

被引：6

作者：

Mei, Xueqing ^{[1
]}

Li, Zhixiong ^{[1
]}

Bagherzadeh, Seyed Amin ^{[2
]}

Karimipour, Aliakbar ^{[3
]}

Bahrami, Mehrdad ^{[2
]}

Karimipour, Arash ^{[2
]}

机构：

[1] Hubei Univ Arts & Sci, Hubei Key Lab Power Syst Design & Test Elect Vehi, Xiangyang 441053, Peoples R China

[2] Islamic Azad Univ, Najafabad Branch, Dept Mech Engn, Najafabad, Iran

[3] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam

来源：

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY | 2021年 / 145卷 / 04期

关键词：

Nano-powders; Thermal conductivity; Optimization; ANN; Thermodynamics; WATER/GRAPHENE OXIDE NANOFLUID; WALL CARBON NANOTUBES; HEAT-TRANSFER; ENTROPY GENERATION; FLOW; VISCOSITY; NANOPARTICLES; MICROCHANNEL; TEMPERATURE; STABILITY;

D O I：

10.1007/s10973-020-10212-0

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Artificial neural network/kriging interpolation method optimization method which is evaluated concerned the hybrid nanofluid composed of iron oxide (Fe2O3) and aluminum oxide (Al2O3) nano-powders to improve the thermal properties of 10w40 engine oil at different amounts of volume fraction and temperature. An input-target dataset contains 30 input-target pairs. The proposed model is examined at non-trained inputs throughout the investigated intervals beside the first derivatives of the thermal conductivity with respect to temperature and nanoparticle mass fraction. It can be seen that the obtained results have suitable smoothness and continuity. Accordingly, the kriging method shows the acceptable outcomes through the experimental prediction of Al2O3/Fe(2)O(3)nanoparticles dispersed in 10w40 engine oil.

引用

页码：2123 / 2128

页数：6

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