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Research Papers

Optimal Configuration of Vortex Generator for Heat Transfer Enhancement in a Plate-Fin Channel

[+] Author and Article Information
Tariq Amin Khan

Department of Energy Engineering,
Zhejiang University,
Hangzhou 310027, China;
Co-innovation Center for
Advanced Aero-Engine,
College of Energy Engineering,
Zhejiang University,
Hangzhou 310027, China
e-mail: tariqamin4u@yahoo.com

Wei Li

Department of Energy Engineering,
Zhejiang University,
Hangzhou 310027, China
e-mail: weil96@zju.edu.cn

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received April 8, 2017; final manuscript received September 7, 2017; published online December 20, 2017. Assoc. Editor: Amir Jokar.

J. Thermal Sci. Eng. Appl 10(2), 021013 (Dec 20, 2017) (11 pages) Paper No: TSEA-17-1112; doi: 10.1115/1.4038418 History: Received April 08, 2017; Revised September 07, 2017

Heat transfer is a naturally occurring phenomenon and its augmentation is a vital research topic for many years. Although, vortex generators (VGs) are widely used to enhance the heat transfer of plate-fin type heat exchangers, few researches deal with its thermal optimization. This work is dedicated to the numerical investigation and optimization of VGs configuration in a plate-fin channel. Three-dimensional (3D) numerical simulations are performed to study the effect of angle of attack and attach angle (angle between VG and wall) and shape of VG on the fluid flow and heat transfer characteristics. The flow is assumed as steady-state, incompressible, and laminar within the range of studied Reynolds numbers (Re = 380–1140). Results are presented in the form average and local Nusselt number and friction factor. The effect of attach angle is highlighted and the results show that the attach angle of 90 deg may not be necessary for enhancing the heat transfer. The flow structure and heat transfer characteristics of certain cases are examined in detail. The parameters of VG are then optimized for maximum heat transfer and minimum pressure drop. The three independent design parameters are considered for the two objective functions. For this purpose, computation fluid dynamics (CFD) data, response surface methodology (RSM) and a multi-objective optimization algorithm (MOA) are combined. The data obtained from numerical simulations are used to train a Bayesian-regularized artificial neural network (BRANN). This in turn is used to drive a MOA to find the optimal parameters of VGs in the form of Pareto front. The optimal values of these parameters are finally presented.

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Figures

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Fig. 2

Grid independence analysis

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Fig. 1

(a) Plane view and (b) isometric view of the computational domain showing the boundary conditions

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Fig. 8

Temperature distribution on different cross sections for RVG at α = 45 deg: (a) β = 90 deg and (b) β = 60 deg

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Fig. 11

(a) Distribution of Nu along the flow direction at Re = 760 and (b) average Nu for different Reynolds number

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Fig. 9

Temperature distribution on different cross sections for DVG at α = 45 deg: (a) β = 90 deg and (b) β = 60 deg

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Fig. 10

Spanwise local heat flux for RVG at x = 0.1m, Re = 760: (a) lower wall and (b) upper wall

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Fig. 3

The combined flow chart of CFD, ANN, and MOA

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Fig. 4

Comparison with the experimental work of Wu and Tao

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Fig. 5

Longitudinal vortices on different cross sections for RVG at α = 45 deg: (a) β = 90 deg and (b) β = 60 deg

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Fig. 6

Longitudinal vortices on different cross sections for DVG at α = 45 deg: (a) β = 90 deg and (b) β = 60 deg

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Fig. 7

Nu for varying α and β at Re = 760: (a) RVG and (b) DVG

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Fig. 12

Friction factor f for varying α and β at Re = 760: (a) RVG and (b) DVG

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Fig. 13

(a) Local pressure drop CP for RVG at Re = 760 and (b) average f for different Reynolds number

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Fig. 14

Prediction with training data for (a) Nu and (b) f

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Fig. 15

Pareto front of two objectives, Nu and f, with CFD data

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