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 |
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| 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 |
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| 024 | 7 | |a 10.1007/s00521-023-08708-5 |2 doi | |
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| 245 | 1 | 3 | |a An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks |c M. Ghalambaz, M. Edalatifar, S. M. Maryamnegar, M. A. Sheremet |
| 336 | |a Текст | ||
| 337 | |a электронный | ||
| 504 | |a Библиогр.: 36 назв. | ||
| 520 | 3 | |a 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%. | |
| 653 | |a теплопередача | ||
| 653 | |a естественная конвекция | ||
| 653 | |a инкапсулированные суспензии | ||
| 653 | |a материалы с фазовым переходом | ||
| 653 | |a глубокие нейронные сети | ||
| 655 | 4 | |a статьи в журналах |9 958260 | |
| 700 | 1 | |a Ghalambaz, Mohammad |9 458771 | |
| 700 | 1 | |a Edalatifar, Mohammad |9 958261 | |
| 700 | 1 | |a Maryamnegar, Sara Moradi |9 958262 | |
| 700 | 1 | |a Sheremet, Mikhail A. |9 89131 | |
| 773 | 0 | |t Neural computing and applications |d 2023 |g Vol. 35, № 27. P. 19719-19727 |x 0941-0643 | |
| 852 | 4 | |a RU-ToGU | |
| 856 | 4 | |u http://vital.lib.tsu.ru/vital/access/manager/Repository/koha:001133658 | |
| 856 | |y Перейти в каталог НБ ТГУ |u https://koha.lib.tsu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=1133658 | ||
| 908 | |a статья | ||
| 999 | |c 1133658 |d 1133658 | ||
| 039 | |b 100 | ||