In much of the public literature on pin-fin heat transfer, the Nusselt number is presented as a function of Reynolds number using a power-law correlation. Power-law correlations typically have an accuracy of 20% while the experimental uncertainty of such measurements is typically between 5% and 10%. Additionally, the use of power-law correlations may require many sets of empirical constants to fully characterize heat transfer for different geometrical arrangements. In the present work, artificial neural networks were used to predict heat transfer as a function of streamwise spacing, spanwise spacing, pin-fin height, Reynolds number, and row position. When predicting experimental heat transfer data, the neural network was able to predict 73% of array-averaged heat transfer data to within 10% accuracy while published power-law correlations predicted 48% of the data to within 10% accuracy. Similarly, the neural network predicted 81% of row-averaged data to within 10% accuracy while 52% of the data was predicted to within 10% accuracy using power-law correlations. The present work shows that first-order heat transfer predictions may be simplified by using a single neural network model rather than combining or interpolating between power-law correlations. Furthermore, the neural network may be expanded to include additional pin-fin features of interest such as fillets, duct rotation, pin shape, pin inclination angle, and more making neural networks expandable and adaptable models for predicting pin-fin heat transfer.

References

References
1.
Metzger
,
D.
, and
Haley
,
S.
,
1982
, “
Heat Transfer Experiments and Flow Visualization for Arrays of Short Pin-Fins
,” ASME Paper No. 82-GT-138.
2.
Al Dabagh
,
A.
, and
Andrews
,
G.
,
1992
, “
Pin-Fin Heat Transfer: Contribution of the Wall and the Pin to the Overall Heat Transfer
,” ASME Paper No. 92-GT-242.
3.
Chyu
,
M.
,
Siw
,
S.
, and
Moon
,
H. K.
,
2009
, “
Effects of Height-to-Diameter Ratio of Pin-Fin Element on Heat Transfer From Staggered Pin-Fin Arrays
,”
ASME
Paper No. GT2009-59814. 10.1115/GT2009-59814
4.
Ostanek
,
J.
, and
Thole
,
K.
,
2012
, “
Effects of Varying Streamwise and Spanwise Spacing in Pin-Fin Arrays
,”
ASME
Paper No. GT2012-68127. 10.1115/GT2012-68127
5.
Meireles
,
M.
,
Almeida
,
P.
, and
Simões
,
M.
,
2003
, “
A Comprehensive Review for Industrial Applicability of Artificial Neural Networks
,”
IEEE Trans. Ind. Electron.
,
50
(
3
), pp.
585
601
.10.1109/TIE.2003.812470
6.
Peng
,
H.
, and
Ling
,
X.
,
2009
, “
Neural Networks Analysis of Thermal Characteristics on Plate-Fin Heat Exchangers With Limited Experimental Data
,”
Appl. Therm. Eng.
,
29
(
11-12
), pp.
2251
2256
.10.1016/j.applthermaleng.2008.11.011
7.
Tan
,
C. K.
,
Ward
,
J.
,
Wilcox
,
S. J.
, and
Payne
,
R.
,
2009
, “
Artificial Neural Network Modeling of the Thermal Performance of a Compact Heat Exchanger
,”
Appl. Therm. Eng.
,
29
(
17-18
), pp.
3609
3617
.10.1016/j.applthermaleng.2009.06.017
8.
Diaz
,
G.
,
Sen
,
M.
,
Yang
,
K. T.
, and
McClain
,
R.
,
1999
, “
Simulation of Heat Exchanger Performance by Artificial Neural Networks
,”
HVAC&R Res.
,
5
(
3
), pp.
195
208
.10.1080/10789669.1999.10391233
9.
Grimison
,
E. D.
,
1937
, “
Correlation and Utilization of New Data on Flow Resistance and Heat Transfer in Cross Flow of Gases over Tube Banks
,”
Trans. ASME
,
59
, pp.
583
594
.
10.
Metzger
,
D.
,
Shepard
,
W.
, and
Haley
,
S.
,
1986
, “
Row Resolved Heat Transfer Variations in Pin-Fin Arrays Including Effects of Non-Uniform Arrays and Flow Convergence
,” ASME Paper No. 86-GT-132.
11.
VanFossen
,
G. J.
,
1982
, “
Heat-Transfer Coefficients for Staggered Arrays of Short Pin Fins
,”
ASME J. Eng. Power
,
104
(
2
), pp.
268
274
.10.1115/1.3227275
12.
Brigham
,
B.
, and
VanFossen
,
G.
,
1984
, “
Length to Diameter Ratio and Row Number Effects in Short Pin Fin Heat Transfer
,”
ASME J. Eng. Gas Turbines Power
,
106
(1), pp.
241
244
.10.1115/1.3239541
13.
Armstrong
and
Winstanley
,
1988
, “
Review of Staggered Array Pin Fin Heat Transfer for Turbine Cooling Applications
,”
ASME J. Turbomach.
,
110
, pp.
94
103
.10.1115/1.3262173
14.
Faulkner
,
F. E.
,
1971
, “
Analytical Investigation of Chord Size and Cooling Methods on Turbine Blade Cooling Requirements
,” NASA CR-120883.
15.
Hornik
,
K.
,
Stinchcombe
,
M.
, and
White
,
H.
,
1989
, “
Multilayer Feedforward Networks are Universal Approximators
,”
Neural Networks
,
2
(
5
), pp.
359
366
.10.1016/0893-6080(89)90020-8
16.
Castro
,
J. L.
,
Mantas
,
C. J.
, and
Benitez
,
J. M.
,
2000
, “
Neural Networks With a Continuous Squashing Function in the Output are Universal Approximators
,”
Neural Networks
,
13
(
6
), pp.
561
563
.10.1016/S0893-6080(00)00031-9
17.
Wojciechowski
,
M.
,
2011
, “
Feed-Forward Neural Network for Python
,” Technical University of Lodz (Poland), Department of Civil Engineering, Architecture and Environmental Engineering, http://ffnet.sourceforge.net
18.
Nocedal
and
Wright
,
1999
,
Numerical Optimization
,
Springer-Verlag
,
New York
.
19.
Ghorbanian
,
K.
, and
Gholamrezaei
,
M.
,
2009
, “
An Artificial Neural Network Approach to Compressor Performance Prediction
,”
Appl. Energy
,
86
(
7-8
), pp.
1210
1221
.10.1016/j.apenergy.2008.06.006
20.
Thibault
,
J.
, and
Grandjean
,
B.
,
1991
, “
A Neural Network Methodology for Heat Transfer Data Analysis
,”
Int. J. Heat and Mass Transfer
,
34
(
8
), pp.
2063
2070
.10.1016/0017-9310(91)90217-3
21.
Ostanek
,
J.
,
2012
, “
Flowfield Interactions in Low Aspect Ratio Pin-Fin Arrays
,” Ph.D. thesis, Pennsylvania State University, University Park, PA.
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