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Optimal Position of Rectangular Vortex Generator for Heat Transfer Enhancement in a Flat-Plate Channel

[+] Author Affiliations
Tariq Amin Khan, Wei Li, Zhengjiang Zhang, Jincai Du, Fu Liu

Zhejiang University, Hangzhou, China

Sadiq Amin Khan

Beihang University, Beijing, China

Paper No. HT2017-4904, pp. V001T02A005; 8 pages
doi:10.1115/HT2017-4904
From:
  • ASME 2017 Heat Transfer Summer Conference
  • Volume 1: Aerospace Heat Transfer; Computational Heat Transfer; Education; Environmental Heat Transfer; Fire and Combustion Systems; Gas Turbine Heat Transfer; Heat Transfer in Electronic Equipment; Heat Transfer in Energy Systems
  • Bellevue, Washington, USA, July 9–12, 2017
  • Conference Sponsors: Heat Transfer Division
  • ISBN: 978-0-7918-5788-5
  • Copyright © 2017 by ASME

abstract

Heat transfer is a naturally occurring phenomenon which can be greatly enhanced by introducing longitudinal vortex generators (VGs). As the longitudinal vortices can potentially enhance heat transfer with small pressure loss penalty, VGs are widely used to enhance the heat transfer of flat-plate type heat exchangers. However, there are few researches which deal with its thermal optimization. Three dimensional numerical simulations are performed to study the effect of angle of attack and attach angle (angle between VG and wall) of vortex generator on the fluid flow and heat transfer characteristics of a flat-plate channel. The flow is assumed as steady state, incompressible and laminar within the range of studied Reynolds numbers (Re = 380, 760, 1140). In the present work, the average and local Nusselt number and pressure drop are investigated for Rectangular vortex generator (RVG) with varying angle of attack and attach angle. The numerical results indicate that the heat transfer and pressure drop increases with increasing the angle of attack to a certain range and then decreases with increasing angle of attack. Moreover, the attach angle also plays an importance role; a 90° attach angle is not necessary for enhancing the heat transfer. Usually, heat transfer enhancement is achieved at the expense of pressure drop penalty. To find the optimal position of vortex generator to obtain maximum heat transfer and minimum pressure drop, the data obtained from numerical simulations are used to train a BRANN (Bayesian-regularized artificial neural network). This in turn is used to drive multi-objective genetic algorithm (MOGA) to find the optimal parameters of VGs in the form of Pareto front. The optimal values of these parameters are finally presented.

Copyright © 2017 by ASME

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