Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network

Yue Hua, Jiang-Zhou Peng, Zhi-Fu Zhou, Wei-Tao Wu, Yong He () and Mehrdad Massoudi ()
Additional contact information
Yue Hua: Sino-French Engineer School, Nanjing University of Science and Technology, Nanjing 210094, China
Jiang-Zhou Peng: Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Zhi-Fu Zhou: State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Wei-Tao Wu: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Yong He: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Mehrdad Massoudi: U.S. Department of Energy, National Energy Technology Laboratory (NETL), 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA

Energies, 2022, vol. 15, issue 21, 1-16

Abstract: 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 data, the convolutional neural network (CNN)-based predictor enables an accurate end-to-end mapping from the geometry input and the desired nanofluid physical field. Taking the computational fluid dynamics (CFD) calculation as the basis of our approach, the obtained results show that the average accuracy of the predicted temperature field and the coefficient of determination R 2 are more than 99.9% and 0.998 accurate for single-inner cylinder nanofluid-filled annulus; while for the more complex case of double-inner cylinder, the results are still very close, higher than 99.8% and 0.99, respectively. Furthermore, the predictor takes only 0.038 s for each nanofluid field prediction, four orders of magnitude faster than the numerical simulation. The high accuracy and the fast speed estimation of the proposed predictor show the great potential of this approach to perform efficient inner cylinder configuration design and optimization for nanofluid-filled annulus.

Keywords: nanofluids; geometry adaptive; deep convolutional neural network; inner cylinder configuration design (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/21/8195/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/21/8195/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:21:p:8195-:d:961966

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().