Designing an artificial neural network using radial basis function (RBF-ANN) to model thermal conductivity of ethylene glycol–water-based TiO2 nanofluids
详细信息 查看全文
- 作者：Mohammad Hemmat Esfe
- 关键词：Thermal conductivity ; Artificial neural network ; Temperature ; Modeling
- 刊名：Journal of Thermal Analysis and Calorimetry
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：127
- 期：3
- 页码：2125-2131
- 全文大小：
- 刊物类别：Chemistry and Materials Science
- 刊物主题：Physical Chemistry; Analytical Chemistry; Polymer Sciences; Inorganic Chemistry; Measurement Science and Instrumentation;
- 出版者：Springer Netherlands
- ISSN：1588-2926
- 卷排序：127
文摘
In this article, thermal conductivity of ethylene glycol–water-based TiO2 nanofluids has been modeled by artificial neural network. For this purpose, thermal conductivity of nanofluids with volume fractions of 0.2–1 % has been collected in temperatures of 30–70 °C. These data were modeled by artificial neural networks. So two common types of neural networks were used, and the results were compared with each other. One of these networks was multilayer perceptron, and the other one was radial basis approximation. Finally, an experimental relationship was suggested for calculating thermal conductivity of this nanofluid and the results were compared with the results of radial basis neural network. This comparison shows that neural networks are very powerful in modeling the nanofluids experimental data and are able to follow the patterns of these data with a high precision.