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Classification of Flow Modes for Natural Convection in a Square Enclosure with an Eccentric Circular Cylinder

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  • Hyun-Sik Yoon

    (Department of Naval Architecture and Ocean Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Gumjeong-gu, Busan 46241, Korea)

  • Yoo-Jeong Shim

    (Department of Naval Architecture and Ocean Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Gumjeong-gu, Busan 46241, Korea)

Abstract

The present study investigated the natural convection for a hot circular cylinder embedded in a cold square enclosure. The numerical simulations are performed to solve a two-dimensional steady natural convection for three Rayleigh numbers of 10 3 , 10 4 and 10 5 at a fixed Prandtl number of 0.7. This study considered the wide range of the inner cylinder positions to identify the eccentric effect of the cylinder on flow and thermal structures. The present study classifies the flow structures according to the cylinder position. Finally, the present study provides the map for the flow structures at each Rayleigh number ( Ra ). The Ra = 10 3 and 10 4 form the four modes of the flow structures. These modes are classified by mainly the large circulation and inner vortices. When Ra = 10 5 , one mode that existed at Ra = 10 3 and 10 4 , disappears in the map of the flow structures. The new three modes appear, resulting in total six modes of flow structures at Ra = 10 5 . New modes at Ra = 10 5 are characterized by the top side secondary vortices. The corresponding isotherms are presented to explain the bifurcation of the flow structure.

Suggested Citation

  • Hyun-Sik Yoon & Yoo-Jeong Shim, 2021. "Classification of Flow Modes for Natural Convection in a Square Enclosure with an Eccentric Circular Cylinder," Energies, MDPI, vol. 14(10), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2788-:d:553392
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    1. Goodarzi, Marjan & D’Orazio, Annunziata & Keshavarzi, Ahmad & Mousavi, Sayedali & Karimipour, Arash, 2018. "Develop the nano scale method of lattice Boltzmann to predict the fluid flow and heat transfer of air in the inclined lid driven cavity with a large heat source inside, Two case studies: Pure natural ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 210-233.
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