Abstract

In this work, artificial neural networks (ANNs) is used to predict nucleate boiling heat flux by learning from a dataset of twelve experimental parameters across 231 independent samples. An approach to reduce the number of parameters involved and to increase model accuracy is proposed and implemented. The proposed approach consists of two steps. In the first step, a feature importance study is performed to determine the most significant parameters. Only important features are used in the second step. In the second step, dimensional analysis is performed on these important parameters. Neural network analysis is then conducted based on dimensionless parameters. The results indicate that the proposed feature importance study and dimensional analysis can significantly improve the ANNs performance. It also show that model errors based on the reduced dataset are considerably lower than those based on the initial dataset. The study based on other machine learning models also shows that the reduced dataset generate better results. The results conclude that ANNs outperform other machine learning algorithms and outperform a well-known boiling correlation equation. Additionally, the feature importance study concludes that wall superheat, gravity and liquid subcooling are the three most significant parameters in the prediction of heat flux for nucleate boiling. Novel results quantifying parameter significance in surface tension dominated (SDB) and buoyancy dominated (BDB) boiling regimes have been reported. The results show that surface tension and liquid subcooling are the most significant parameters in SDB regime with a combined contribution percentage of 60%, while wall superheat and gravity are the most significant parameters in BDB regime with a combined contribution percentage of 70%.

References

1.
Faghri
,
A.
, and
Zhang
,
Y.
,
2020
, “
Boiling
,”
Fundamentals of Multiphase Heat Transfer and Flow
,
Springer
, Berlin, pp.
469
534
.
2.
Dhir
,
V. K.
,
2006
, “
Mechanistic Prediction of Nucleate Boiling Heat Transfer-Achievable or a Hopeless Task?
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
128
(
1
), pp.
1
12
.10.1115/1.2136366
3.
Dhir
,
V. K.
,
2001
, “
Numerical Simulations of Pool-Boiling Heat Transfer
,”
AIChE J.
,
47
(
4
), pp.
813
834
.10.1002/aic.690470407
4.
Banerjee
,
S.
,
Lian
,
Y.
,
Liu
,
Y.
, and
Sussman
,
M.
,
2022
, “
A New Method for Estimating Bubble Diameter at Different Gravity Levels for Nucleate Pool Boiling
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
144
(
2
), p. 021601.10.1115/1.4053102
5.
Banerjee
,
S.
,
Liu
,
Y.
,
Sussman
,
M.
, and
Lian
,
Y.
,
2022
, “
Depletable Micro-Layer for Nucleate Boiling Simulations in Micro-Gravity Conditions: A New Approach
,”
Int. J. Heat Mass Transfer
,
190
, p.
122642
.10.1016/j.ijheatmasstransfer.2022.122642
6.
Rohsenow
,
W.
,
1952
, “
A Method of Correlating Heat Transfer Data for Surface Boiling Liquids
,”
Trans. ASME
,
74
, p.
966
.
7.
Stephan
,
K.
, and
Abdelsalam
,
M.
,
1980
, “
Heat-Transfer Correlations for Natural Convection Boiling
,”
Int. J. Heat Mass Transfer
,
23
(
1
), pp.
73
87
.10.1016/0017-9310(80)90140-4
8.
Liaw
,
S.-P.
, and
Dhir
,
V.
,
1989
, “
Void Fraction Measurements During Saturated Pool Boiling of Water on Partially Wetted Vertical Surfaces
,”
ASME J. Heat Mass Transfer-Trans. ASME
, 111(3), pp.
731
738
.10.1115/1.3250744
9.
Fritz
,
W.
,
1935
, “
Maximum Volume of Vapor Bubbles
,”
Phys. Z
,
36
, pp.
379
384
.
10.
Gorenflo
,
D.
,
Knabe
,
V.
, and
Bieling
,
V.
,
1986
, “
Bubble Density on Surfaces With Nucleate Boiling-Its Influence on Heat Transfer and Burnout Heat Flux at Elevated Saturation Pressures
,”
International Heat Transfer Conference Digital Library
,
Begel House Inc
., Danbury, CT.
11.
Wang
,
C.
, and
Dhir
,
V.
,
1993
, “
Effect of Surface Wettability on Active Nucleation Site Density During Pool Boiling of Water on a Vertical Surface
,”
ASME J. Heat Transfer
, 115(3), pp.
659
669
.10.1115/1.2910737
12.
Jones
,
B. J.
,
McHale
,
J. P.
, and
Garimella
,
S. V.
,
2009
, “
The Influence of Surface Roughness on Nucleate Pool Boiling Heat Transfer
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
131
(
12
), p. 121009.10.1115/1.3220144
13.
Voulodimos
,
A.
,
Doulamis
,
N.
,
Doulamis
,
A.
, and
Protopapadakis
,
E.
,
2018
, “
Deep Learning for Computer Vision: A Brief Review
,”
Comput. Intell. Neurosci.
,
2018
, pp.
1
13
.10.1155/2018/7068349
14.
Young
,
T.
,
Hazarika
,
D.
,
Poria
,
S.
, and
Cambria
,
E.
,
2018
, “
Recent Trends in Deep Learning Based Natural Language Processing
,”
IEEE Comput. Intell. Mag.
,
13
(
3
), pp.
55
75
.10.1109/MCI.2018.2840738
15.
Chatterjee
,
P.
,
Damevski
,
K.
, and
Pollock
,
L.
,
2021
, “
Automatic Extraction of Opinion-Based Q&A From Online Developer Chats
,”
Proceedings of the 43rd International Conference on Software Engineering (ICSE)
, Madrid, ES, May 22–30, pp.
1260
1272
.
16.
Banerjee
,
S.
, and
Lian
,
Y.
,
2022
, “
Data Driven Covid-19 Spread Prediction Based on Mobility and Mask Mandate Information
,”
Appl. Intell.
,
52
(
2
), pp.
1969
1978
.10.1007/s10489-021-02381-8
17.
Jakaria
,
A. H.
,
Hossain
,
M. M.
, and
Rahman
,
M. A.
,
2020
, “
Smart Weather Forecasting Using Machine Learning: A Case Study in Tennessee
,” preprint arXiv:2008.10789.
18.
Alizadehdakhel
,
A.
,
Rahimi
,
M.
,
Sanjari
,
J.
, and
Alsairafi
,
A. A.
,
2009
, “
CFD and Artificial Neural Network Modeling of Two-Phase Flow Pressure Drop
,”
Int. Commun. Heat Mass Transfer
,
36
(
8
), pp.
850
856
.10.1016/j.icheatmasstransfer.2009.05.005
19.
Jambunathan
,
K.
,
Hartle
,
S.
,
Ashforth-Frost
,
S.
, and
Fontama
,
V.
,
1996
, “
Evaluating Convective Heat Transfer Coefficients Using Neural Networks
,”
Int. J. Heat Mass Transfer
,
39
(
11
), pp.
2329
2332
.10.1016/0017-9310(95)00332-0
20.
Ling
,
J.
, and
Templeton
,
J.
,
2015
, “
Evaluation of Machine Learning Algorithms for Prediction of Regions of High Reynolds Averaged Navier Stokes Uncertainty
,”
Phys. Fluids
,
27
(
8
), p.
085103
.10.1063/1.4927765
21.
Rajendran
,
V.
,
Kelly
,
K. Y.
,
Leonardi
,
E.
, and
Menzies
,
K.
,
2018
, “
Vortex Detection on Unsteady CFD Simulations Using Recurrent Neural Networks
,”
AIAA
Paper No. 2018-372410.2514/6.2018-3724.
22.
Singh
,
S.
, and
Abbassi
,
H.
,
2018
, “
1D/3D Transient HVAC Thermal Modeling of an Off-Highway Machinery Cabin Using CFD-ANN Hybrid Method
,”
Appl. Therm. Eng.
,
135
, pp.
406
417
.10.1016/j.applthermaleng.2018.02.054
23.
Mohan
,
A. T.
, and
Gaitonde
,
D. V.
,
2018
, “
A Deep Learning Based Approach to Reduced Order Modeling for Turbulent Flow Control Using LSTM Neural Networks
,” preprint arXiv:1804.09269.
24.
Banerjee
,
S.
,
Ayala
,
O.
, and
Wang
,
L.-P.
,
2020
, “
Direct Numerical Simulations of Small Particles in Turbulent Flows of Low Dissipation Rates Using Asymptotic Expansion
,” 5th Thermal and Fluids Engineering Conference (
TFEC
), New Orleans, LA, Apr. 5–8, pp.
659
668
.10.1615/T FEC2020.tfl.032308
25.
Naphon
,
P.
, and
Arisariyawong
,
T.
,
2016
, “
Heat Transfer Analysis Using Artificial Neural Networks of the Spirally Fluted Tubes
,”
J. Res. Appl. Mech. Eng.
,
4
(
2
), pp.
135
147
.
26.
Guo
,
X.
,
Li
,
W.
, and
Iorio
,
F.
,
2016
, “
Convolutional Neural Networks for Steady Flow Approximation
,”
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, San Francisco, CA, Aug. 13–17, pp.
481
490
.https://dl.acm.org/doi/10.1145/2939672.2939738
27.
Wang
,
J.-X.
,
Wu
,
J.-L.
, and
Xiao
,
H.
,
2017
, “
Physics-Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data
,”
Phys. Rev. Fluids
,
2
(
3
), p.
034603
.10.1103/PhysRevFluids.2.034603
28.
Liu
,
Y.
,
Dinh
,
N.
,
Sato
,
Y.
, and
Niceno
,
B.
,
2018
, “
Data-Driven Modeling for Boiling Heat Transfer: Using Deep Neural Networks and High-Fidelity Simulation Results
,”
Appl. Therm. Eng.
,
144
, pp.
305
320
.10.1016/j.applthermaleng.2018.08.041
29.
Hassanpour
,
M.
,
Vaferi
,
B.
, and
Masoumi
,
M. E.
,
2018
, “
Estimation of Pool Boiling Heat Transfer Coefficient of Alumina Water-Based Nanofluids by Various Artificial Intelligence (AI) Approaches
,”
Appl. Therm. Eng.
,
128
, pp.
1208
1222
.10.1016/j.applthermaleng.2017.09.066
30.
Mazzola
,
A.
,
1997
, “
Integrating Artificial Neural Networks and Empirical Correlations for the Prediction of Water-Subcooled Critical Heat Flux
,”
Revue Générale de Thermique
,
36
(
11
), pp.
799
806
.10.1016/S0035-3159(97)87750-1
31.
Alimoradi
,
H.
, and
Shams
,
M.
,
2017
, “
Optimization of Subcooled Flow Boiling in a Vertical Pipe by Using Artificial Neural Network and Multi Objective Genetic Algorithm
,”
Appl. Therm. Eng.
,
111
, pp.
1039
1051
.10.1016/j.applthermaleng.2016.09.114
32.
Scalabrin
,
G.
,
Condosta
,
M.
, and
Marchi
,
P.
,
2006
, “
Modeling Flow Boiling Heat Transfer of Pure Fluids Through Artificial Neural Networks
,”
Int. J. Therm. Sci.
,
45
(
7
), pp.
643
663
.10.1016/j.ijthermalsci.2005.09.009
33.
Qiu
,
Y.
,
Garg
,
D.
,
Zhou
,
L.
,
Kharangate
,
C. R.
,
Kim
,
S.-M.
, and
Mudawar
,
I.
,
2020
, “
An Artificial Neural Network Model to Predict Mini/Micro-Channels Saturated Flow Boiling Heat Transfer Coefficient Based on Universal Consolidated Data
,”
Int. J. Heat Mass Transfer
,
149
, p.
119211
.10.1016/j.ijheatmasstransfer.2019.119211
34.
Zhou
,
L.
,
Garg
,
D.
,
Qiu
,
Y.
,
Kim
,
S.-M.
,
Mudawar
,
I.
, and
Kharangate
,
C. R.
,
2020
, “
Machine Learning Algorithms to Predict Flow Condensation Heat Transfer Coefficient in Mini/Micro-Channel Utilizing Universal Data
,”
Int. J. Heat Mass Transfer
,
162
, p.
120351
.10.1016/j.ijheatmasstransfer.2020.120351
35.
Suh
,
Y.
,
Bostanabad
,
R.
, and
Won
,
Y.
,
2021
, “
Deep Learning Predicts Boiling Heat Transfer
,”
Sci. Rep.
,
11
(
1
), pp.
1
10
.10.1038/s41598-021-85150-4
36.
McClure
,
E. R.
, and
Carey
,
V. P.
,
2021
, “
Genetic Algorithm and Deep Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
143
(
12
), p.
121602
.10.1115/1.4052435
37.
James
,
G.
,
Witten
,
D.
,
Hastie
,
T.
, and
Tibshirani
,
R.
,
2013
,
An Introduction to Statistical Learning
, Vol.
112
,
Springer
, New York.
38.
Van Der Maaten
,
L.
,
Postma
,
E.
, and
Van den Herik
,
J.
,
2009
, “
Dimensionality Reduction: A Comparative
,”
J. Mach. Learn Res.
,
10
(
66–71
), p.
13
.
39.
McCulloch
,
W. S.
, and
Pitts
,
W.
,
1943
, “
A Logical Calculus of the Ideas Immanent in Nervous Activity
,”
Bull. Math. Biophys.
,
5
(
4
), pp.
115
133
.10.1007/BF02478259
40.
Abadi
,
M.
,
Barham
,
P.
,
Chen
,
J.
,
Chen
,
Z.
,
Davis
,
A.
,
Dean
,
J.
,
Devin
,
M.
, et al.,
2016
, “
TensorFlow: A System for Large-Scale Machine Learning
,”
Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation
(
OSDI'16
), USENIX Association, Savannah, GA, Nov. 2–4, pp.
265
283
.https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf
41.
Chollet
,
F.
, et al.,
2015
, “
Keras
,” accessed Jan. 3, 2023, https://github.com/fchollet/keras
42.
Pedregosa
,
F.
,
Varoquaux
,
G.
,
Gramfort
,
A.
,
Michel
,
V.
,
Thirion
,
B.
,
Grisel
,
O.
, et al.,
2011
, “
Scikit-Learn: Machine Learning in Python
,”
J. Mach. Learn. Res.
,
12
, pp.
2825
2830
.
43.
Oka
,
T.
,
Abe
,
Y.
,
Mori
,
Y. H.
, and
Nagashima
,
A.
,
1995
, “
Pool Boiling of n-Pentane, CFC-113 and Water Under Reduced Gravity: Parabolic Flight Experiments With a Transparent Heater
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
117
(
2
), pp.
408
417
.10.1115/1.2822537
44.
Merte
,
H.
, Jr.
,
Lee
,
H.
, and
Keller
,
R.
,
1996
, “
Report on Pool Boiling Experiment Flown on STS-47 (PBE-IA)
,” STS-57 (PBE-IB), and STS-60 (PBE-IC). Final report,
Michigan University
, Ann Arbor, MI, Report No. N-96-27393; NASA-CR-198465; E-10154; NAS-1.26: 198465; NIPS-96-35673.
45.
Straub
,
J.
,
2001
, “
Boiling Heat Transfer and Bubble Dynamics in Microgravity
,”
Adv. Heat Transfer
,
35
, pp.
57
172
.10.1016/S0065-2717(01)80020-4
46.
Raj
,
R.
,
Kim
,
J.
, and
McQuillen
,
J.
,
2012
, “
Pool Boiling Heat Transfer on the International Space Station: Experimental Results and Model Verification
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
134
(
10
), p.
101504
.10.1115/1.4006846
47.
Warrier
,
G. R.
,
Dhir
,
V. K.
, and
Chao
,
D. F.
,
2015
, “
Nucleate Pool Boiling eXperiment (NPBX) in Microgravity: International Space Station
,”
Int. J. Heat Mass Transfer
,
83
, pp.
781
798
.10.1016/j.ijheatmasstransfer.2014.12.054
48.
Pearson
,
K.
,
1920
, “
Notes on the History of Correlation
,”
Biometrika
,
13
(
1
), pp.
25
45
.10.1093/biomet/13.1.25
49.
Morgan
,
A.
,
Bromley
,
L.
, and
Wilke
,
C.
,
1949
, “
Effect of Surface Tension on Heat Transfer in Boiling
,”
Ind. Eng. Chem.
,
41
(
12
), pp.
2767
2769
.10.1021/ie50480a025
50.
Baron de Fourier
,
J. B. J.
,
1822
,
Théorie Analytique de la Chaleur
,
Firmin Didot
.
51.
Zuber
,
N.
,
1961
, “
The Hydrodynamic Crisis in Pool Boiling of Saturated and Subcooled Liquids
,” Int. Developments in Heat Transfer, 27, pp.
230
236
.
52.
Raj
,
R.
, and
Kim
,
J.
,
2010
, “
Heater Size and Gravity Based Pool Boiling Regime Map: Transition Criteria Between Buoyancy and Surface Tension Dominated Boiling
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
132
(
9
), p. 091503.10.1115/1.4001635
You do not currently have access to this content.