A Deep Learning Approach to Predict the Flow Field and Thermal Patterns of Nonencapsulated Phase Change Materials Suspensions in an Enclosure
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JACM-8-4_011
تاریخ نمایه سازی: 15 تیر 1401
Abstract:
The flow and heat transfer of a novel type of functional phase change nanofluids, nano-encapsulated phase change suspensions, is investigated in the present study using a deep neural networks framework. A deep neural network was used to learn the natural convection flow and heat transfer of the phase change nanofluid in an enclosure. A dataset of flow and heat transfer samples containing ۳۲۹۰ samples of the flow field and temperature distributions was used to train the deep neural network. The design variables were fusion temperature of nanoparticles, Stefan number, and Rayleigh number. The results showed that the proposed combination of a feed-forward neural network and a convolutional neural network as a deep neural network could robustly learn the complex physics of flow and heat transfer of phase change nanofluids. The trained neural network could estimate the flow and heat transfer without iterative and costly numerical computations. The present neural network framework is a promising tool for the design and prediction of complex physical systems.
Keywords:
Nanoencapsulated phase-change suspension , deep convolutional neural networks , Natural convection heat transfer , deep learning
Authors
Mohammad Edalatifar
Department of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, Iran
Mohammad Bagher Tavakoli
Department of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, Iran
Farbod Setoudeh
Faculty of Electrical Engineering, Arak University of Technology, Arak, Iran
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