Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 344, Issue -, Pages 1-12Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2018.09.025
Keywords
Nanofluid; Heat transfer; Neural Network; Porous duct; Brownian motion; Expansion ratio
Funding
- National Sciences Foundation of China (NSFC) [U1610109]
- Yingcai Project of CUMT, China [YC2017001]
- UOW, Australia
- PAPD Vice-Chancellor's Postdoctoral Research Fellowship, Australia
- Babol Noshirvani University of Technology, Iran [BNUT/390051/97]
Ask authors/readers for more resources
Heat transfer studying in channels is crucial for transport of the fluids in the oil and gas industry. In this study, numerical approach is applied to analyze the thermal behavior of alumina nanofluid in a duct. Brownian motion impact has been included for predicting nanofluid properties. Neural Network was employed to estimate the heat transfer rate. Numerical data has been obtained via Runge-Kutta method. Our outputs display that GMDH achieved an operative method for an efficient recognition of trends in data. Impact of expansion ratio, nanoparticle concentration, power law index and Reynolds number on Nu was also studied. Our findings reveal that heat transfer intensifies by rise of nanoparticle concentration while it has a reducing trend with rise of expansion ratio. (C) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available