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GMDH-type neural network modeling and genetic algorithm-based multi-objective optimization of thermal and friction characteristics in heat exchanger tubes with wire-rod bundles

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Abstract

The group method of data handling (GMDH) technique was used to predict heat transfer and friction characteristics in heat exchanger tubes equipped with wire-rod bundles. Nusselt number and friction factor were determined as functions of wire-rod bundle geometric parameters and Reynolds number. The performance of the developed GMDH-type neural networks was found to be superior in comparison with the proposed empirical correlations. For optimization, the genetic algorithm-based multi-objective optimization was applied.

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Abbreviations

A:

Heat transfer area (m2)

Cp:

Specific heat capacity (kJ/kg K)

D:

Diameter (m)

h:

Heat transfer coefficient (W/m2 K)

f :

Friction factor

k:

Thermal conductivity (W/m K)

l:

Length of wire-rod (m)

L:

Length of test section (m)

m:

Mass flow rate (kg/s)

N:

Number of data points/wire-rod number per bundle

Nu:

Nusselt number

P:

Pitch lengths (m)

Pr:

Prandtl number

Q:

Heat transfer rate (W)

t:

Target data

T:

Temperature (K)

Re:

Reynolds number

V:

Velocity (m/s)

y:

Predicted value

ν:

Kinematic viscosity (m2/s)

ρ:

Density (kg/m3)

b:

Bulk

i:

Inlet

o:

Outlet

w:

Wall

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Authors and Affiliations

  1. Chemical Engineering Department, CFD Research Center, Razi University, Taghe Bostan, Kermanshah, Iran

    Masoud Rahimi, Reza Beigzadeh & Mehdi Parvizi

  2. Department of Mechanical Engineering, Faculty of Engineering, Mahanakorn University of Technology, Bangkok, 10530, Thailand

    Smith Eiamsa-ard

Authors
  1. Masoud Rahimi
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  2. Reza Beigzadeh
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  3. Mehdi Parvizi
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  4. Smith Eiamsa-ard
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Correspondence to Masoud Rahimi.

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Rahimi, M., Beigzadeh, R., Parvizi, M. et al. GMDH-type neural network modeling and genetic algorithm-based multi-objective optimization of thermal and friction characteristics in heat exchanger tubes with wire-rod bundles. Heat Mass Transfer 52, 1585–1593 (2016). https://doi.org/10.1007/s00231-015-1681-5

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  • DOI: https://doi.org/10.1007/s00231-015-1681-5

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