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Prediction of the heat transfer performance of twisted tape inserts by using artificial neural networks

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Abstract

A numerical study is undertaken to investigate the effect of twisted tape inserts on heat transfer. Twisted tapes with various aspect ratios and single, double, and triple inserts are placed inside a tube for Reynolds numbers ranging from 8000 to 12000. Numerical results show that the tube with a twisted tape and different numbers of tape is more effective than the smooth tube in terms of thermo-hydraulic performance. The highest heat transfer is achieved with the triple insert, with the highest turning number and an increment of 15 %. Then, an artificial neural network (ANN) model with a three-layer feedforward neural network is adopted to obtain the Nusselt number on the basis of four inputs for a heated tube with a twisted insert. Several configurations of the neural network are examined to optimize the number of neurons and to identify the most appropriate training algorithm. Finally, the best model is determined with one hidden layer and thirteen neurons in the layer. Bayesian regulation is chosen as the training algorithm. With the optimized algorithm, excellent precision for measuring the output is provided, with R2 = 0.97043. In addition, the optimized ANN architecture is applied to similar studies in the literature to predict the heat transfer performance of twisted tapes. The developed ANN architecture can predict the heat transfer enhancement performance of similar problems with R2 values higher than 0.93.

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Abbreviations

Cp :

Specific heat at constant pressure (j/kgK)

f :

Friction factor

h :

Heat transfer coefficient (W/m2 K)

k :

Thermal conductivity (W/m K)

L :

Pipe length (m)

N t :

Number of turnings (-)

N :

Number of neurons in a layer (-)

Q :

Heat flux (m3/s)

R 2 :

Coefficient of determination (-)

S :

Weight of the biases between layers (-)

T :

Temperature (K)

t :

Thickness (mm)

w :

Weight coefficient (-)

Δp :

Pressure drop (Pa)

ρ :

Density (kg/m3)

μ :

Dynamic viscosity (Pa·s)

ANFIS :

Adaptive network-based fuzzy inference system

ANN :

Artificial neural network

BP :

Back-propagation algorithm

BR :

Bayesian regularization algorithm

CFD :

Computational fluid dynamics

LM :

Levenberg-marquardt algorithm

MRE :

Mean relative error

Nu :

Nusselt number

Pr :

Prandtl number

R 2 :

Coefficient of determination

Re :

Reynolds number

SCG :

Scaled conjugate gradient algorithm

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Authors and Affiliations

  1. Mechanical Engineering Department, Cankaya University, Ankara, Turkey

    Ece Ayli & Eyup Kocak

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  1. Ece Ayli
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  2. Eyup Kocak
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Correspondence to Ece Ayli.

Additional information

Ece Ayli was graduated from TED Ankara College in 2006. She received her B.S., M.S., and Ph.D. from TOBB University of Economics and Technology, Department of Mechanical Engineering in 2010, 2012, and 2016. She joined the faculty of Çankaya University, Department of Mechanical Engineering in 2017. Her areas of interest are heat transfer, renewable energy and soft computing.

Eyup Kocak received his B.S. and M.S. from Gazi University, Department of Mechanical Engineering in 2013 and 2016. He joined the faculty of Çankaya University, Department of Mechanical Engineering in 2018 as research assistant and ongoing his Ph.D. studies in the same department. His areas of interest are heat transfer, renewable energy and fluid dynamics.

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Ayli, E., Kocak, E. Prediction of the heat transfer performance of twisted tape inserts by using artificial neural networks. J Mech Sci Technol 36, 4849–4858 (2022). https://doi.org/10.1007/s12206-022-0843-x

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  • DOI: https://doi.org/10.1007/s12206-022-0843-x

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