An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks M. Ghalambaz, M. Edalatifar, S. M. Maryamnegar, M. A. Sheremet
Material type: ArticleContent type: Текст Media type: электронный Subject(s): теплопередача | естественная конвекция | инкапсулированные суспензии | материалы с фазовым переходом | глубокие нейронные сетиGenre/Form: статьи в журналах Online resources: Click here to access online In: Neural computing and applications Vol. 35, № 27. P. 19719-19727Abstract: 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%.Библиогр.: 36 назв.
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%.
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