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...

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Bibliographic Details
Published in:Neural computing & applications 2023-09, Vol.35 (27), p.19719-19727
Main Authors: Ghalambaz, Mohammad, Edalatifar, Mohammad, Moradi Maryamnegari, Sara, Sheremet, Mikhail
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Artificial neural networks
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Encapsulation
Energy storage
Free convection
Heat transfer
Image classification
Image Processing and Computer Vision
Machine learning
Nanoparticles
Neural networks
Original Article
Parameters
Phase change materials
Probability and Statistics in Computer Science
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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%.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08708-5