Parametric study of vortex generators on a fin-and-tube heat exchanger

ABSTRACT

Heat exchangers are intended to be as efficient, inexpensive, lightweight, and small as possible. Consequently, the inclusion of flow control devices aimed at enhancing heat transfer in heat exchangers has emerged as a prominent research topic. This study focuses on investigating the impact of incorporating vane-type Vortex Generators (VGs) in a fin-and-tube heat exchanger. In contrast with previous studies, the VGs proposed on this study are lower than the fin pitch, leading to the generation of vortexes and enhancing heat transfer. To achieve this objective, several simulations are performed by means of Computational Fluid Dynamics (CFD), using various Reynolds numbers (Re) and angles of attack (α) of the VGs. These simulations aim to assess the hydraulic and thermal efficiency of the heat exchanger across different operating modes. In addition, the generated vortexes and temperature, pressure and velocity fields are analyzed; with the aim of obtaining a more comprehensive understanding of the flow characteristics within the heat exchanger. The findings show that the implementation of VGs, in comparison with the benchmark case which does not contain VGs, improves thermal performance in all the studied cases, achieving a maximum enhancement between 79% and 97%, depending on the Re; but it also reduces hydraulic performance. Nevertheless, by the implementation of VGs, a maximum overall performance increase between 13% and 59% is achieved, depending on Re. Considering both hydraulic and thermal performance, the results show that the greatest performance improvements are obtained with the lowest angles of attack. Additionally, an Artificial Neural Network (ANN) is developed and trained for modeling the tested case, showing that the best overall performance is obtained with Re = 500 and α = 11.33°.

KEYWORDS:

• Computational Fluid Dynamics (CFD)
• heat exchanger
• vortex generator (VG)
• fin-and-tube heat exchanger (FTHE)
• artificial neural network (ANN)

Nomenclature

ANN	=	Artificial Neural Network
BP-MLP	=	Multilayer Perceptron with Backpropagation
CFD	=	Computational Fluid Dynamics
FTHE	=	Fin-and-Tube Heat Exchanger
LE	=	Leading Edge
LMTD	=	Logarithmic-Mean Temperature Difference
RANS	=	Reynolds-Average Navier-Stokes
SBVG	=	Sub-Boundary layer Vortex Generator
SST	=	Shear Stress Transport
TE	=	Trailing Edge
VG	=	Vortex Generator
α	=	Angle of attack
∆P	=	Pressure drop
$A_c$	=	Cross-sectional area
$A_o$	=	Heat transfer surface
$A_t$	=	Total heat transfer area
$C_p$	=	Specific heat
D	=	Tube diameter
$f$	=	Fanning friction factor
$F_L$	=	Fin length
$F_p$	=	Fin pitch
$F_w$	=	Fin width
$G_c$	=	Mass flux of the air based on the minimum flow area
h	=	Heat transfer coefficient
$j$	=	j-Colburn factor
k	=	Thermal conductivity
$K_c$	=	Abrupt contraction pressure-loss coefficient
$K_e$	=	Abrupt expansion pressure-loss coefficient
$\dot{m}$	=	Mass flow rate
$\eta$	=	Thermal performance factor
N	=	Number of tubes
Nu	=	Nusselt number
Ω	=	Spin tensor
$\rho$	=	Density
p	=	Order of accuracy (Richardson Extrapolation)
$P_{inlet}$	=	Inlet mean pressure
$P_l$	=	Longitudinal tube spacing
$P_{outlet}$	=	Outlet mean pressure
$P_t$	=	Transverse tube spacing
Pr	=	Prandtl number
Q	=	Transferred heat
$Q$	=	Q-criterion value
R	=	Convergence condition (Richardson Extrapolation)
R-value	=	Correlation coefficient
Re	=	Reynolds number
RE	=	Exact value (Richardson Extrapolation)
${\delta }_f$	=	Fin thickness
$\sigma$	=	Ration of the minimum flow area to frontal area
$\sigma$	=	Hidden layer threshold parameters (ANN)
${\sigma }^{\prime }$	=	Output layer threshold parameters (ANN)
S	=	Strain-rate tensor
$T_{inlet}$	=	Inlet mean temperature
$T_{outlet}$	=	Outlet mean temperature
$T_{wall}$	=	Tube mean temperature
$\mu$	=	Dynamic viscosity
u	=	Inlet velocity
${\omega }_i$	=	Weights of the output layers (ANN)
${\omega }_{ij}$	=	Weights of the input hidden layers (ANN)

Acknowledgements

The authors are grateful for the support provided by the SGIker of UPV/EHU.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Conceptualization, K.P.-P. and U.F.-G.; methodology, O.I.; software, E.Z.; validation, K.P.-P. and U.F.G.; formal analysis, R.G-F.; investigation, K.P.-P.; resources, U.F.-G.; data curation, K.P.-P.; writing – original draft preparation, K.P.-P.; writing – review and editing, R.G-F.; visualization, O.I.; supervision, U.F.-G.; project administration, E.Z.; funding acquisition, U.F.-G. All authors have read and agreed to the published version of the manuscript.

Data availability statement

The data that support the findings of this study are available from the corresponding author, U.F.-G., upon reasonable request.

Additional information

Funding

The authors are thankful to the government of the Basque Country for the financial support of ELKARTEK20/78 KK-2020/00114 and ELKARTEK21/10 KK-2021/00014 research programs.

Notes on contributors

Koldo Portal-Porras

Koldo Portal-Porras received the B.Sc. degree in Mechanical Engineering and B.Sc. degree in Industrial Electronics and Automation Engineering from the University of the Basque Country, Vitoria-Gasteiz, Spain, in 2020 and 2021, respectively, and the M.Sc. degree in Sustainable Energy Engineering from the same university in 2022. Since 2020 he has been a researcher in the Department of Energy Engineering, University of the Basque Country, Vitoria-Gasteiz, Spain. His research interests include computational fluids dynamics and deep learning.

Unai Fernandez-Gamiz

Unai Fernandez-Gamiz received the B.Sc. and M.Sc. degrees in mechanical engineering from the University of the Basque Country, Vitoria-Gasteiz, Spain, in 1999 and 2004, respectively, and the Ph.D. degree in the Mechanical Engineering, Fluids and Aeronautics from the UPC-Barcelona, Barcelona, Spain, in 2013. Since 2008, he has been a Full Lecturer with the Department of Energy Engineering, University of the Basque Country, Vitoria-Gasteiz, Spain. His research interests include renewable energy sources, flow control devices and applied computational fluids dynamics methods.

Ekaitz Zulueta

Ekaitz Zulueta received the B.Sc. degree in electronic engineering from Mondragon University, Arrasate, Spain, in 1997, the M.Sc. degree in electrical engineering from Swiss Institute of Technology Lausanne, Lausanne, Switzerland, in 2000, and the Ph.D. degree in control engineering from the University of the Basque Country, Vizcaya, Spain, in 2005. From 2000 to 2002, he was a Research Engineer with Ideko, Elgoibar, Spain, and Fagor Automation, Arrasate-Mondragon, Spain. Since 2002, he has been a Lecturer with the University of the Basque Country. His research interests include computational intelligence, including image processing, and wind turbines.

Roberto Garcia-Fernandez

Roberto Garcia-Fernandez received a Master's degree in mechanical engineering from the University of the Basque Country, Spain, in 2008. Since 2005 he has been working in different companies as a mechanical engineer, acquiring more and more responsibilities in this field. He is currently in charge of the innovation department at SUNSUNDEGUI (bus and coach manufacturer) where he is deeply involved in the research of new structural composite materials, the fluid dynamic effect on body shape and the applicability of Artificial Intelligence in different areas of the company. Oscar Irigaray Pérez de San Román

Oscar Irigaray

Oscar Irigaray obtained his B.S.c. in Automotive Engineering from the University of the Basque Country, Vitoria-Gasteiz in 2021 and his M.S.c in Motorsport Engineering in 2022 from the Oxford Brookes University, specializing in Aerodynamics and Computational Fluid Dynamics. During his studies he participated in Formula Student projects for a total of 4 years as an Aerodynamicist winnig the FSUK design event in 2022. From September 2022 on, he has been working at the Department of Energy Engineering of the University of the Basque Country as an Assistant Researcher.