Details
| Original language | English |
|---|---|
| Pages (from-to) | 19719-19727 |
| Number of pages | 9 |
| Journal | Neural Computing and Applications |
| Volume | 35 |
| Issue number | 27 |
| Early online date | 3 Jul 2023 |
| Publication status | Published - Sept 2023 |
Abstract
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%.
Keywords
- Deep learning, Nano-encapsulated phase change suspension, Natural convection heat transfer, Physical characteristics classification
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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In: Neural Computing and Applications, Vol. 35, No. 27, 09.2023, p. 19719-19727.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material
T2 - Deep neural networks
AU - Ghalambaz, Mohammad
AU - Edalatifar, Mohammad
AU - Moradi Maryamnegari, Sara
AU - Sheremet, Mikhail
N1 - Funding Information: This research of Mohammad Ghalambaz and Mikhail Sheremet was supported by the Tomsk State University Development Program (Priority-2030).
PY - 2023/9
Y1 - 2023/9
N2 - 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%.
AB - 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%.
KW - Deep learning
KW - Nano-encapsulated phase change suspension
KW - Natural convection heat transfer
KW - Physical characteristics classification
UR - http://www.scopus.com/inward/record.url?scp=85163883896&partnerID=8YFLogxK
U2 - 10.1007/s00521-023-08708-5
DO - 10.1007/s00521-023-08708-5
M3 - Article
AN - SCOPUS:85163883896
VL - 35
SP - 19719
EP - 19727
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 27
ER -