Thermal performance in convection flow of nanofluids using a deep convolutional neural network
This study develops a geometry adaptive, physical field predictor for the combined forced
and natural convection flow of a nanofluid in horizontal single or double-inner cylinder
annular pipes with various inner cylinder sizes and placements based on deep learning.
The predictor is built with a convolutional-deconvolutional structure, where the input is the
annulus cross-section geometry and the output is the temperature and the Nusselt number
for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like …
and natural convection flow of a nanofluid in horizontal single or double-inner cylinder
annular pipes with various inner cylinder sizes and placements based on deep learning.
The predictor is built with a convolutional-deconvolutional structure, where the input is the
annulus cross-section geometry and the output is the temperature and the Nusselt number
for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like …
[PDF][PDF] Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network. Energies 2022, 15, 8195
This study develops a geometry adaptive, physical field predictor for the combined forced
and natural convection flow of a nanofluid in horizontal single or double-inner cylinder
annular pipes with various inner cylinder sizes and placements based on deep learning.
The predictor is built with a convolutional-deconvolutional structure, where the input is the
annulus cross-section geometry and the output is the temperature and the Nusselt number
for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like …
and natural convection flow of a nanofluid in horizontal single or double-inner cylinder
annular pipes with various inner cylinder sizes and placements based on deep learning.
The predictor is built with a convolutional-deconvolutional structure, where the input is the
annulus cross-section geometry and the output is the temperature and the Nusselt number
for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like …
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