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Experimental study and artificial neural network modeling of unsteady laminar forced convection in a rectangular duct

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Abstract

In this study, an artificial neural network (ANNs) for the prediction of unsteady heat transfer in a rectangular duct was studied. The ANNs has been applied for the unsteady heat transfer in a rectangular duct. An experimental study has been carried out to investigate the axial variation of inlet temperature and the impact of inlet frequency on decay indices in the thermal entrance region of a parallel plate channel. The investigation was conducted with laminar forced flows for Reynolds numbers ranging from 1,120 to 2,200 while the inlet heat input frequency varied from 0.02 to 0.24 Hz. The results revealed that the ANNs can be used for modeling unsteady heat transfer in the duct. The accuracy between experimental and ANNs approach results was achieved with a mean absolute relative error less than 39%.

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Abbreviations

a :

the width of the rectangular channel, m

b :

half height of the rectangular channel, m

d :

diameter of the orifice plate, m

D e :

equivalent diameter of rectangular test section or hydraulic diameter, m

D p :

diameter of PVC pipe, m

f :

logistic sigmoid activation function

h :

hidden layer

O :

output

OL:

output layer

m :

mass flow rate, kg/s

p :

design parameter (consequent parameter)

Pr:

Prandtl number

q :

design parameter (consequent parameter)

r :

design parameter (consequent parameter)

t :

time

T :

temperature,°C

V :

output voltage of thermocouple, mV

w :

wiring strength of a rule

W :

weights

x :

axial distance, m

X :

input

Y :

target activation of the output layer

Z :

dimensionless distance, Z=x/D e

α:

learning rate

β:

inlet frequency, Hz

δ:

error for output neuron

φ:

orifice plate diameter ratio, d/D p

ρ:

fluid density, kg/m3

μ:

dynamic viscosity, Pa s

ΔP :

pressure drop across the orifice plate, Pa

ΔT i :

temperature amplitude at the center of the inlet,°C

θ:

threshold between the input and hidden layers

η:

momentum factor

I:

input

max:

maximum

min:

minimum

o:

output

p:

value related to the redevelopment section

t:

value related to the test section

amp:

amplitude

References

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sakarya University, 54187, Sakarya, Turkey

    Nedim Sözbir & İsmail Ekmekçi

Authors
  1. Nedim Sözbir
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  2. İsmail Ekmekçi
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Correspondence to Nedim Sözbir.

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Sözbir, N., Ekmekçi, İ. Experimental study and artificial neural network modeling of unsteady laminar forced convection in a rectangular duct . Heat Mass Transfer 43, 749–758 (2007). https://doi.org/10.1007/s00231-006-0156-0

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  • DOI: https://doi.org/10.1007/s00231-006-0156-0

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