An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks
A deep neural network is utilized to classify the parameters of a natural convection heat transfer of a nano-encapsulated phase change material suspension using the isotherm images for the first time. A natural convection flow and heat transfer simulation dataset were created and used as a training...
| Published in: | Neural computing and applications Vol. 35, № 27. P. 19719-19727 |
|---|---|
| Other Authors: | , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Online Access: | http://vital.lib.tsu.ru/vital/access/manager/Repository/koha:001133658 Перейти в каталог НБ ТГУ |
| Summary: | A deep neural network is utilized to classify the parameters of a natural convection heat transfer of a nano-encapsulated phase change material suspension using the isotherm images for the first time. A natural convection flow and heat transfer simulation dataset were created and used as a training and validation tool. Then, a deep neural network, consisting of three parts, was used for the classification task. The first part was made of several conventional layers, and a rectified linear unit activation layer supported each layer. The second part was a preparation layer for reshaping from 2D images to 1D classification. The third layer was made of a classifier layer. The results showed that the impact of the Rayleigh number and volume concentrations of nanoparticles could be classified by 99.8 and 93.32% accuracy, respectively. However, the Stefan number was classified weakly. As a part of the current research, a transfer learning approach was used to improve accuracy. The learning transfer approach was quite effective and improved the accuracy of the Stefan number classification by 16.6%. |
|---|---|
| Bibliography: | Библиогр.: 36 назв. |
| ISSN: | 0941-0643 |