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Thermo-hydraulic performance prediction for offset-strip fin heat exchangers using artificial neural networks

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Abstract

In this study, an artificial neural network (ANN) model was applied to predict the thermo-hydraulic performance of the offset-strip fin heat exchanger. The ANN model was used for predicting the thermo-hydraulic performance to improve the prediction accuracy and extend the applicable range compared to the results of using the empirical equations reported in previous studies. The main parameters of these empirical equations were fin height (0.134 < α < 0.997), fin length (0.012 < δ < 0.048), fin width (0.041 < γ < 0.121), and Reynolds number (120 < Re <104). In addition, the Fanning friction factor f and the Colburn factor j were considered as the outputs of the ANN model. The impact of the parameters on the thermo-hydraulic performance of the heat exchanger was quantitatively evaluated, and the prediction accuracy was improved over a wide range for the thermo-hydraulic performance generated by the ANN model. Thus, the results obtained using the ANN model agreed well with the experimental data over a wider range than possible for the previous empirical correlations, showing extremely high accuracy and validity of the ANN model in comparison to the empirical equations.

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Abbreviations

B :

Bias vector

c p :

Specific heat

D h :

Hydraulic diameter of fin

f :

Fanning friction factor

j :

Colburn factor

G :

Mass flux

h :

Fin height

I :

Relative impact

l :

Fin length

Pr :

Prandtl number

q :

Heat flux

t :

Fin thickness

R 2 :

Coefficient of regression

Re :

Reynolds number

RMRE :

Root mean relative error

RMSE :

Root-mean-square error

St :

Stanton number

T :

Temperature

w :

Fin width

W :

Weight

α :

Dimensionless fin height

β :

Thermal expansion coefficient

δ :

Dimensionless fin length

γ :

Dimensionless fin width

σ err :

Standard deviation for relative error

τ :

Wall shear stress

λ :

Thermal conductivity

μ :

Dynamic viscosity

ρ :

Density

w :

Wall

m :

Average

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Acknowledgments

This research was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [NRF-2019R1A5A8083201]. This work was partly supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20193310100050, Technology development of gas turbine blade reengineering specialized for domestic operating environment).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, Busan, Korea

    Sangmin Kim & Man Yeong Ha

  2. Korea Electrotechnology Research Institute, Changwon, Korea

    Young Min Seo

  3. Rolls-Royce and Pusan National University Technology Centre, Busan, Korea

    Sang Youl Yoon & Sudhanshu Pandey

  4. Department of Mechanical Engineering, Changwon National University, Changwon, 641-773, Korea

    Seokho Kim

Authors
  1. Sangmin Kim
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  2. Young Min Seo
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  3. Sang Youl Yoon
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  4. Seokho Kim
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  5. Sudhanshu Pandey
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  6. Man Yeong Ha
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Corresponding author

Correspondence to Man Yeong Ha.

Additional information

Sangmin Kim received his undergraduate degree from Pusan National University, South Korea in 2018. Currently, he is pursuing integrated Ph.D. at Pusan National University, Korea. His research interests are focused on Heat Exchanger, finite volume method, gas turbine blade, ANN and computational fluid dynamics.

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.

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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Kim, S., Seo, Y.M., Yoon, S.Y. et al. Thermo-hydraulic performance prediction for offset-strip fin heat exchangers using artificial neural networks. J Mech Sci Technol 37, 2623–2638 (2023). https://doi.org/10.1007/s12206-023-0436-3

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  • DOI: https://doi.org/10.1007/s12206-023-0436-3

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