Abstract

Exploring parametric effects in pool boiling is challenging because the dependence of the resulting surface heat flux is often nonlinear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have been deduced by fitting relations obtained from physical models to experimental data and from correlated trends in nondimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the system physics. The investigation summarized here explores the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how a genetic algorithm and deep learning can be used to extract heat flux dependencies of a binary mixture on wall superheat, gravity, Marangoni effects, and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for nucleate boiling. Each technique demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.

References

1.
Stephan
,
K.
,
1992
,
Heat Transfer in Condensation and Boiling
,
Springer-Verlag
,
Berlin, Germany
.
2.
Collier
,
J. G.
, and
Thome
,
J. R.
,
1996
,
Convective Boiling and Condensation
, 3rd ed.,
Clarendon Press
(Oxford Engineering Science Series), New York.
3.
Kandlikar
,
S.
,
Shoji
,
M.
, and
Dhir
,
V. K.
, editors,
1999
, “
Nucleate Boiling
,”
Handbook of Phase Change
,
Taylor and Francis
,
New York,
Chap. 4.
4.
Carey
,
V. P.
,
2008
,
Liquid-Vapor Phase-Change Phenomena
, 2nd ed.,
Taylor and Francis
,
New York
.
5.
Dhir
,
V. K.
,
2001
, “
Numerical Simulations of Pool Boiling Heat Transfer
,”
AIChE J.
,
47
(
4
), pp.
813
834
.10.1002/aic.690470407
6.
Dhir
,
V. K.
,
2006
, “
Mechanistic Prediction of Nucleate Boiling Heat Transfer – Achievable or Hopeless Task?
,”
ASME J. Heat Transfer-Trans. ASME
,
128
(
1
), pp.
1
12
.10.1115/1.2136366
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.
Borishansky
,
V. M.
,
1969
, “
Correlation of the Effect of Pressure on the Critical Heat Flux and Heat Transfer Rates Using the Theory of Thermodynamic Similarity
,”
Problems of Heat Transfer and Hydraulics of Two-Phase Media
,
Pergamon Press
, Oxford, UK, pp.
16
37
.
9.
Mostinski
,
I. L.
,
1963
, “
Application of the Rule of Corresponding States for the Calculation of Heat Transfer and Critical Heat Flux
,”
Teploenergetika
,
4
, p.
66
(Also, English abstract in Brit. Chem. Eng., 8, p. 580, 1963).
10.
Rohsenow
,
W. M.
,
1952
, “
A Method of Correlating Heat Transfer Data for Surface Boiling of Liquids
,”
Trans. ASME
,
74
, pp.
969
975
.http://hdl.handle.net/1721.1/61431
11.
Vachon
,
R. I.
,
Nix
,
G. H.
, and
Tanger
,
G. E.
,
1968
, “
Evaluation of Constants for the Rohsenow Pool-Boiling Correlation
,”
ASME J. Heat Transfer-Trans. ASME
,
90
(
2
), pp.
239
247
.10.1115/1.3597489
12.
Kannengieser
,
O.
,
Colin
,
C.
,
Bergez
,
W.
, and
Lacapere
,
J.
,
2020
, “
Nucleate Pool Boiling on a Flat Plate Heater Under Microgravity Conditions: Results of Parabolic Flight, and Development of a Correlation Predicting Heat Flux Variation Due to Gravity
,”
Proceeding of the 7th ECI International Conference on Boiling Heat Transfer
, Florianopolis, Brazil, May 3–7.
13.
Raj
,
R.
,
Kim
,
J.
, and
McQuillen
,
J.
,
2009
, “
Subcooled Pool Boiling in Variable Gravity Environments
,”
ASME J. Heat Transfer-Trans. ASME
,
131
(
9
), p.
091502
.10.1115/1.3122782
14.
Raj
,
R.
,
Kim
,
J.
, and
McQuillen
,
J.
,
2012
, “
On the Scaling of Pool Boiling Heat Flux With Gravity and Heater Size
,”
ASME J. Heat Transfer-Trans. ASME
,
134
(
1
), p.
011502
.10.1115/1.4004370
15.
Kim
,
J.
, and
Raj
,
R.
,
2014
, “
Gravity and Heater Size Effects on Pool Boiling Heat Transfer
,” NASA, Washington, DC, Report No. NASA/CR—2014-216672.
16.
Holland
,
J. H.
,
1975
,
Adaptation in Natural and Artificial Systems
,
University of Michigan Press
,
Ann Arbor, MI
.
17.
Goldberg
,
D. E.
,
1989
, “
Genetic Algorithms in Search
,”
Optimization, and Machine Learning
,
Addison-Wesley
,
Reading, MA
.
18.
Han
,
H.
,
Yu
,
R.
,
Li
,
B.
,
Zhang
,
Y.
,
Wang
,
W.
, and
Chen
,
X.
,
2019
, “
Multi-Objective Optimization of Corrugated Tube With Loose-Fit Twisted Tape Using RSM and NSGA-II
,”
Int. J. Heat Mass Transfer
,
131
, pp.
781
794
.10.1016/j.ijheatmasstransfer.2018.10.128
19.
Tang
,
S. Z.
,
Wang
,
F. L.
,
He
,
Y. L.
,
Yu
,
Y.
, and
Tong
,
Z. X.
,
2019
, “
Parametric Optimization of H-Type Finned Tube With Longitudinal Vortex Generators by Response Surface Model and Genetic Algorithm
,”
Appl. Energy
,
239
, pp.
908
918
.10.1016/j.apenergy.2019.01.122
20.
Sodagar-Abardeh
,
J.
,
Ebrahimi-Moghadam
,
A.
,
Farzaneh-Gord
,
M.
, and
Norouzi
,
A.
,
2019
, “
Optimizing Chevron Plate Heat Exchangers Based on the Second Law of Thermodynamics and Genetic Algorithm
,”
J. Therm. Anal. Calorim.
,
139
, pp.
3562
3576
.10.1007/s10973-019-08742-3
21.
Wu
,
R.
,
Zhang
,
X.
,
Fan
,
Y.
,
Hu
,
R.
, and
Luo
,
X.
,
2019
, “
A Bi-Layer Compact Thermal Model for Uniform Chip Temperature Control With Non-Uniform Heat Sources by Genetic-Algorithm Optimized Microchannel Cooling
,”
Int. J. Therm. Sci.
,
136
, pp.
337
346
.10.1016/j.ijthermalsci.2018.10.047
22.
Parveen
,
N.
,
Zaidi
,
S.
, and
Danish
,
M.
,
2020
, “
Comparative Analysis for the Prediction of Boiling Heat Transfer Coefficient of R134a in Micro/Mini Channels Using Artificial Intelligence (AI)-Based Techniques
,”
Int. J. Modell. Simul.
,
40
(
2
), pp.
114
129
.10.1080/02286203.2018.1564809
23.
Ahmadi
,
M. H.
,
Ghazvini
,
M.
,
Maddah
,
H.
,
Kahani
,
M.
,
Pourfarhang
,
S.
,
Pourfarhang
,
A.
, and
Heris
,
S. Z.
,
2020
, “
Prediction of the Pressure Drop for Cuo/(Ethylene Glycol-Water) Nanofluid Flows in Car Radiator by Means of Artificial Neural Networks Analysis Integrated With Genetic Algorithm
,”
Phys. A
,
546
, p.
124008
.10.1016/j.physa.2019.124008
24.
Alade
,
I. O.
,
Rahman
,
M. A. A.
, and
Saleh
,
T. A.
,
2019
, “
Modeling and Prediction of the Specific Heat Capacity of Al2O3/Water Nanofluids Using Hybrid Genetic Algorithm/Support Vector Regression Model
,”
Nano-Struct. Nano-Objects
,
17
, pp.
103
111
.10.1016/j.nanoso.2018.12.001
25.
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
26.
Rosenblatt
,
F.
,
1958
, “
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
,”
Psychol. Rev.
,
68
, pp.
386
408
.10.1037/h0042519
27.
Hopfield
,
J. J.
,
1982
, “
Neural Networks and Physical Systems With Emergent Collective Computational Abilities
,”
Proc. Natl. Acad. Sci.
,
79
(
8
), pp.
2554
2558
.10.1073/pnas.79.8.2554
28.
Hakeem
,
M. A.
,
Kamil
,
M.
, and
Asif
,
M.
,
2014
, “
Prediction of Boiling Heat Transfer Coefficients in Pool Boiling of Liquids Using Artificial Neural Network
,”
J. Sci. Ind. Res.
,
43
, pp.
536
540
.http://nopr.niscair.res.in/handle/123456789/29198
29.
Alic
,
E.
,
Das
,
M.
, and
Kaska
,
O.
,
2019
, “
Heat Flux Estimation at Pool Boiling Processes With Computational Intelligence Methods
,”
Processes
,
293
, pp.
1
16
.https://doi.org/10.3390/pr7050293
30.
Wang
,
W. J.
,
Zhao
,
L. X.
, and
Zhang
,
C. L.
,
2006
, “
Generalized Neural Network Correlation for Flow Boiling Heat Transfer of R22 and Its Alternative Refrigerants Inside Horizontal Smooth Tubes
,”
Int. J. Heat Mass Transfer
,
49
(
15–16
), pp.
2458
2465
.10.1016/j.ijheatmasstransfer.2005.12.021
31.
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
–1
123
.10.1016/j.applthermaleng.2017.09.066
32.
Balcilar
,
M.
,
Dalkilic
,
A. D.
,
Suriyawong
,
A.
,
Yiamsawas
,
T.
, and
Wongwises
,
S.
,
2012
, “
Investigation of Pool Boiling of Nanofluids Using Artificial Neural Network and Correlation Development Techniques
,”
Int. Commun. Heat Mass Transfer
,
39
(
3
), pp.
424
431
.10.1016/j.icheatmasstransfer.2012.01.008
33.
Sayahi
,
T.
,
Tatar
,
A.
, and
Bahrami
,
M.
,
2016
, “
A RBF Model for Predicting the Pool Boiling Behavior of Nanofluids Over a Horizontal Rod Heater
,”
Int. J. Therm. Sci.
,
99
, pp.
180
194
.10.1016/j.ijthermalsci.2015.08.010
34.
Sajjad
,
U.
, and
Hussain
,
I.
,
2021
, “
A High-Fidelity Approach to Correlate the Nucleate Pool Boiling Data of Roughened Surfaces
,”
Int. J. Therm. Sci.
,
142
, p.
103719
.
35.
Cong
,
T.
,
Chen
,
R.
,
Su
,
G.
,
Qiu
,
S.
, and
Tian
,
W.
,
2011
, “
Analysis of CHF in Saturated Forced Convective Boiling on a Heated Surface With Impinging Jets Using Artificial Neural Network and Genetic Algorithm
,”
Nuclear Eng. Des.
,
241
(
9
), pp.
3945
3951
.10.1016/j.nucengdes.2011.07.029
36.
K, Kumar, T
,
S.
,
Kumar
,
N.
,
Thakur
,
A.
, and
Raj
,
R.
,
2021
, “
Deep Learning the Sound of Boiling for Advance Prediction of Boiling Crisis
,”
Cell Rep. Phys. Sci.
,
2
, p.
100382
.10.1016/j.xcrp.2021.100382
37.
Suh
,
Y.
,
Bostanabad
,
R.
, and
Won
,
Y.
,
2021
, “
Deep Learning Predicts Boiling Heat Transfer
,”
Sci. Rep.
,
11
(
1
), p.
5622
.10.1038/s41598-021-85150-4
38.
Rassoulinejad-Mousavi
,
S.
,
Al-Hindawi
,
F.
,
Soori
,
T.
,
Rokoni
,
A.
,
Yoon
,
H.
,
Hu
,
H.
,
Wu
,
T.
, and
Sun
,
Y.
,
2021
, “
Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling
,”
Appl. Therm. Eng.
,
190
, p.
116849
.10.1016/j.applthermaleng.2021.116849
39.
Press
,
W. H.
,
Teukolsky
,
S. A.
,
Vetterling
,
W. T.
, and
Flannery
,
B. P.
,
2007
, “
Section 10.5. Downhill Simplex Method in Multidimensions
,”
Numerical Recipes: The Art of Scientific Computing
, 3rd ed.,
Cambridge University Press
,
New York
.
40.
Nelder
,
J. A.
, and
Mead
,
R.
,
1965
, “
A Simplex Method for Function Minimization
,”
Comput. J.
,
7
(
4
), pp.
308
313
.10.1093/comjnl/7.4.308
41.
Haupt
,
R. L.
, and
Haupt
,
S. E.
,
2004
,
Practical Genetic Algorithms
, 2nd ed.,
Wiley
,
New York
.
42.
Kröse
,
B.
, and
Smagt
,
P.
,
1996
,
An Introduction to Neural Networks
, 8th ed.,
The University of Amsterdam
, Amsterdam, The Netherlands.
43.
Chollet
,
F.
,
2015
, “
Keras
,” accessed Aug. 1, 2019, https://keras.io
44.
Boger
,
Z.
, and
Guterman
,
H.
,
1997
, “
Knowledge Extraction From Artificial Neural Network Models
,”
IEEE Systems, Man and Cybernetics Conference
, Orlando, FL.
45.
Berry
,
M. J. A.
, and
Linoff
,
G.
,
1997
,
Data Mining Techniques
,
Wiley
,
New York
.
46.
Blum
,
A.
,
1992
,
Neural Networks in C++
,
Wiley
,
New York
.
47.
Hinton
,
G.
, Srivastava, N., and Swersky, K.,
2012
, “
Lecture 6a - Overview of Mini-Batch Gradient Descent
,” Lecture Notes Distributed in CSC321 of University of Toronto, accessed Aug. 2019, https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
48.
Clevert
,
D. A.
,
Unterthiner
,
T.
, and
Hochreiter
,
S.
,
2016
, “
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
,”
International Conference on Learning Representations (ICLR)
, arXiv:1511.07289v5.
49.
McGillis
,
W. R.
, and
Carey
,
V. P.
,
1996
, “
On the Role of Marangoni Effects on the Critical Heat Flux for Pool Boiling of Binary Mixtures
,”
ASME J. Heat Transfer-Trans. ASME
,
118
(
1
), pp.
103
109
.10.1115/1.2824021
50.
Ahmed
,
S.
, and
Carey
,
V. P.
,
1998
, “
Effects of Gravity on Boiling of Binary Fluid Mixtures
,”
Int. J. Heat Mass Transfer
,
41
(
16
), pp.
2469
2483
.10.1016/S0017-9310(97)00334-7
51.
Ahmed
,
S.
, and
Carey
,
V. P.
,
1999
, “
Effects of Surface Orientation on Pool Boiling Heat Transfer in Binary Mixtures
,”
ASME J. Heat Transfer-Trans. ASME
,
121
(
1
), pp.
80
88
.10.1115/1.2825972
52.
Sun
,
C.-L.
, and
Carey
,
V. P.
,
2004
, “
Marangoni Effects on the Boiling of 2-Propanol/Water Mixtures in a Confined Space
,”
Int. J. Heat Mass Transfer
,
47
(
25
), pp.
5417
5426
.10.1016/j.ijheatmasstransfer.2004.07.014
53.
Sun
,
C- L.
, and
Carey
,
V. P.
,
2007
, “
Effects of Gap Geometry and Gravity on Boiling Around a Constrained Bubble in 2-Propanol/Water Mixtures
,”
ASME J. Heat Transfer-Trans. ASME
,
129
(
2
), pp.
114
123
.10.1115/1.2402178
54.
Glorot
,
X.
, and
Bengio
,
Y.
,
2010
, “
Understanding the Difficulty of Training Deep Feedforward Neural Networks
,” Proceedings of the International Conference on Artificial Intelligence and Statistics (
AISTATS10
),
Society for Artificial Intelligence and Statistics
, Sardinia, Italy, May 13–15, pp.
249
256
. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
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