An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material

Deep neural networks

verfasst von: Mohammad Ghalambaz, Mohammad Edalatifar, Sara Moradi Maryamnegari, Mikhail Sheremet
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%.
Organisationseinheit(en): Institut für Elektroprozesstechnik
Externe Organisation(en): Tomsk State University
K.N. Toosi University of Technology
Typ: Artikel
Journal: Neural Computing and Applications
Band: 35
Seiten: 19719-19727
Anzahl der Seiten: 9
ISSN: 0941-0643
Publikationsdatum: 09.2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Software, Artificial intelligence
Elektronische Version(en): https://doi.org/10.1007/s00521-023-08708-5 (Zugang: Geschlossen)

BibTeX

@article{d6941854ede94a2ab52392cf809e3b52,
title = "An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks",
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",
author = "Mohammad Ghalambaz and Mohammad Edalatifar and {Moradi Maryamnegari}, Sara and Mikhail Sheremet",
note = "Funding Information: This research of Mohammad Ghalambaz and Mikhail Sheremet was supported by the Tomsk State University Development Program (Priority-2030). ",
year = "2023",
month = sep,
doi = "10.1007/s00521-023-08708-5",
language = "English",
volume = "35",
pages = "19719--19727",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "27",
}