A Deep Learning Approach to Predict the Flow Field and Thermal ‎Patterns of Nonencapsulated Phase Change Materials ‎Suspensions in an Enclosure‎

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 3290 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