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Thermal and flow characteristics of buoyancy-driven non-Newtonian flows at a high Rayleigh number of 107 and predictions from an artificial neural network

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Abstract

The thermal and flow characteristics at Ra = 107 were evaluated in a square cavity containing a circular cylinder in different places along the diagonal and horizontal centerlines. The enclosure contained non-Newtonian fluids of pseudoplastic and dilatant natures. The power-law index was varied in the range of 0.6–1.6 with an interval of 0.2 and a fixed Prandtl number of 10. The effects on the laminar natural convection are reported. The flow regimes were categorized as steady symmetric, steady asymmetric, non-periodic unsteady symmetric, non-periodic unsteady asymmetric, periodic unsteady symmetric, and periodic unsteady asymmetric. Artificial neural network was used to predict the thermal performance in the enclosure. The thermal transport in cases of n < 1 was much higher than that in cases of n > 1. The ANN model was effective in estimating the heat transfer performance with appropriate training.

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Abbreviations

D :

Cylinder diameter

H :

Effective or apparent viscosity

k :

Thermal conductivity

m :

Consistency index

n :

Power-law index

T :

Temperature

t :

Time

u,v :

Velocities

U,V :

Non-dimensional velocities

x,y :

Cartesian coordinates

X,Y :

Non-dimensional coordinates

α :

Thermal diffusivity

τ :

Non-dimensional time

β :

Thermal expansion coefficient

ω :

Distance of cylinder from the enclosure center measured horizontally

η :

Effective viscosity

ρ :

Density

θ :

Non-dimensional temperature

ψ :

Distance of cylinder from the enclosure center measured diagonally

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A5A808320112).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, Korea

    Sudhanshu Pandey & Man Yeong Ha

  2. Rolls-Royce and Pusan National University Technology Centre in Thermal Management, Jang Jeon 2-Dong, Geum Jeong Gu, Busan, 609-735, Korea

    Hyun Woo Cho & Young Min Seo

  3. School of Mechanical Engineering, Changwon National University, 20, Changwondaehak-ro, Uichang-gu, Changwon, 51140, Korea

    Hoon Ki Choi & Yong Gap Park

Authors
  1. Sudhanshu Pandey
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  2. Hyun Woo Cho
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  3. Hoon Ki Choi
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  4. Yong Gap Park
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  5. Young Min Seo
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  6. Man Yeong Ha
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Corresponding authors

Correspondence to Young Min Seo or Man Yeong Ha.

Additional information

Recommended by Editor Yang Na

Sudhanshu Pandey received his M.S. degree from Indian Institute of Technology Madras, India in 2016, and pursuing Ph.D. from Pusan National University, Korea. His research interests are focused on natural convection, non-Newtonian fluids and computational fluid dynamics.

Young Min Seo received his undergraduate degree from Pusan National University, South Korea in 2013, his Ph.D. degree from Pusan National University, Korea in 2019. Dr. Seo is currently a Post-doctoral researcher at Rolls-Royce and Pusan National University Technology Centre in Thermal Management in Busan, Korea. His research interests are focused on natural convection, finite volume method and computational fluid dynamics.

Man Yeong Ha received his B.S. degree from Pusan National University, Korea, in 1981, M.S. degree, in 1983, from Korea Advanced Institute of Science and Technology, Korea, and Ph.D. degree from Pennsylvania State University, USA in 1990. Dr. Ha is currently a Professor at the School of Mechanical Engineering at Pusan National University in Busan, Korea. He served as an Editor of the Journal of Mechanical Science and Technology. He is the member of Honorary Editorial Advisory Board of the International Journal of Heat and Mass Transfer. His research interests are focused on thermal management, computational fluid dynamics, and micro/nano fluidics.

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Pandey, S., Cho, H.W., Choi, H.K. et al. Thermal and flow characteristics of buoyancy-driven non-Newtonian flows at a high Rayleigh number of 107 and predictions from an artificial neural network. J Mech Sci Technol 35, 1791–1805 (2021). https://doi.org/10.1007/s12206-021-0341-6

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  • DOI: https://doi.org/10.1007/s12206-021-0341-6

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