Abstract
Machine learning (ML) offers a variety of techniques to understand many complex problems in different fields. The field of heat transfer, and thermal systems in general, are governed by complicated sets of physics that can be made tractable by reduced-order modeling and by extracting simple trends from measured data. Therefore, ML algorithms can yield computationally efficient models for more accurate predictions or to generate robust optimization frameworks. This study reviews past and present efforts that use ML techniques in heat transfer from the fundamental level to full-scale applications, including the use of ML to build reduced-order models, predict heat transfer coefficients and pressure drop, perform real-time analysis of complex experimental data, and optimize large-scale thermal systems in a variety of applications. The appropriateness of different data-driven ML models in heat transfer problems is discussed. Finally, some of the imminent opportunities and challenges that the heat transfer community faces in this exciting and rapidly growing field are identified.
Issue Section:
Review Articles
References
1.
Lawson
,
S. J.
,
Woodgate
,
M.
,
Steijl
,
R.
, and
Barakos
,
G. N.
, 2012
, “
High Performance Computing for Challenging Problems in Computational Fluid Dynamics
,” Prog. Aerosp. Sci.
,
52
, pp. 19
–29
.10.1016/j.paerosci.2012.03.0042.
Ma
,
H.
,
Hu
,
X.
,
Zhang
,
Y.
,
Thuerey
,
N.
, and
Haidn
,
O. J.
, 2020
, “
A Combined Data-Driven and Physics-Driven Method for Steady Heat Conduction Prediction Using Deep Convolutional Neural Networks
,” preprint arXiv:2005.08119.3.
Kotsiantis
,
S. B.
,
Zaharakis
,
I.
, and
Pintelas
,
P.
, 2007
, Supervised Machine Learning: A Review of Classification Techniques
,
IOS Press
,
Amsterdam, The Netherlands
.4.
Mohri
,
M.
,
Rostamizadeh
,
A.
, and
Talwalkar
,
A.
, 2018
, Foundations of Machine Learning
,
MIT Press
,
Cambridge, MA
.5.
Kvalseth
,
T. O.
, 1985
, “
Cautionary Note About R2
,” Am. Stat.
,
39
(4
), pp. 279
–285
.10.1080/00031305.1985.104794486.
Tealab
,
A.
,
Hefny
,
H.
, and
Badr
,
A.
, 2017
, “
Forecasting of Nonlinear Time Series Using ANN
,” Future Comput. Inf. J.
,
2
(1
), pp. 39
–47
.10.1016/j.fcij.2017.05.0017.
Wang
,
S.-C.
, 2003
, “
Artificial Neural Network
,” Interdisciplinary Computing in Java Programming
,
S.-C.
Wang
, ed.,
Springer US
,
Boston, MA
, pp. 81
–100
.8.
Marriott
,
J.
, and
Newbold
,
P.
, 1998
, “
Bayesian Comparison of ARIMA and Stationary ARMA Models
,” Int. Stat. Rev.
,
66
(3
), pp. 323
–336
.10.1111/j.1751-5823.1998.tb00376.x9.
Schapire
,
R. E.
, 2003
, “
The Boosting Approach to Machine Learning: An Overview
,” Nonlinear Estimation and Classification
,
D. D.
Denison
,
M. H.
Hansen
,
C. C.
Holmes
,
B.
Mallick
, and
B.
Yu
, eds.,
Springer
,
New York
, pp. 149
–171
.10.
Albawi
,
S.
,
Mohammed
,
T. A.
, and
Al-Zawi
,
S.
, 2017
, “
Understanding of a Convolutional Neural Network
,” 2017 International Conference on Engineering and Technology (ICET
),
Antalya, Turkey
, Aug. 21–23, pp. 1
–6
.10.1109/ICEngTechnol.2017.830818611.
Ali
,
J.
,
Khan
,
R.
,
Ahmad
,
N.
, and
Maqsood
,
I.
, 2012
, “
Random Forests and Decision Trees
,” Int. J. Comput. Sci. Issues (IJCSI)
,
9
(5
), p. 272
. https://ijcsi.org/papers/IJCSI-9-5-3-272-278.pdf12.
Zheng
,
B.
, and
Agresti
,
A.
, 2000
, “
Summarizing the Predictive Power of a Generalized Linear Model
,” Stat. Med.
,
19
(13
), pp. 1771
–1781
.10.1002/1097-0258(20000715)19:13<1771::AID-SIM485>3.0.CO;2-P13.
Ranstam
,
J.
, and
Cook
,
J. A.
, 2018
, “
LASSO Regression
,” Br. J. Surg.
,
105
(10
), p. 1348
.10.1002/bjs.1089514.
Balakrishnama
,
S.
, and
Ganapathiraju
,
A.
, 1998
, “
Linear Discriminant Analysis—A Brief Tutorial
,” Inst. Signal Inf. Process.
,
18
(1998
), pp. 1
–8
.http://www.music.mcgill.ca/~ich/classes/mumt611_07/classifiers/lda_theory.pdf15.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
, 1997
, “
Long Short-Term Memory
,” Neural Comput.
,
9
(8
), pp. 1735
–1780
.10.1162/neco.1997.9.8.173516.
Wold
,
S.
,
Esbensen
,
K.
, and
Geladi
,
P.
, 1987
, “
Principal Component Analysis
,” Chemom. Intell. Lab. Syst.
,
2
(1–3
), pp. 37
–52
.10.1016/0169-7439(87)80084-917.
Noble
,
W. S.
, 2006
, “
What Is a Support Vector Machine?
,” Nat. Biotechnol.
,
24
(12
), pp. 1565
–1567
.10.1038/nbt1206-156518.
Awad
,
M.
, and
Khanna
,
R.
, 2015
, “
Support Vector Regression
,” Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
,
M.
Awad
, and
R.
Khanna
, eds.,
Apress
,
Berkeley, CA
, pp. 67
–80
.10.1007/978-1-4302-5990-9_419.
Yadav
,
S.
, and
Shukla
,
S.
, 2016
, “
Analysis of k-Fold Cross-Validation Over Hold-Out Validation on Colossal Datasets for Quality Classification
,” 2016 IEEE Sixth International Conference on Advanced Computing (IACC
),
Bhimavaram, India
, Feb. 27–28, pp. 78
–83
.10.1109/IACC.2016.2520.
Raissi
,
M.
,
Perdikaris
,
P.
, and
Karniadakis
,
G. E.
, 2019
, “
Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
,” J. Comput. Phys.
,
378
, pp. 686
–707
.10.1016/j.jcp.2018.10.04521.
Cai
,
S.
,
Wang
,
Z.
,
Wang
,
S.
,
Perdikaris
,
P.
, and
Karniadakis
,
G. E.
, 2021
, “
Physics-Informed Neural Networks for Heat Transfer Problems
,” ASME J. Heat Transfer-Trans. ASME
,
143
(6
), p. 060801
.10.1115/1.405054222.
Hansson
,
J.
,
Nilsson
,
T. M. J.
,
Ye
,
L.
, and
Liu
,
J.
, 2018
, “
Novel Nanostructured Thermal Interface Materials: A Review
,” Int. Mater. Rev.
,
63
(1
), pp. 22
–45
.10.1080/09506608.2017.130101423.
Peng
,
J.-Z.
,
Liu
,
X.
,
Aubry
,
N.
,
Chen
,
Z.
, and
Wu
,
W.-T.
, 2020
, “
Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer Based on Convolutional Neural Networks: Heat Conduction
,” preprint arXiv:2010.03854.24.
Gadala
,
M.
, and
Vakili
,
S.
, 2011
, “
Assessment of Various Methods in Solving Inverse Heat Conduction Problems
,” Heat Conduction—Basic Research
,
V.
Vikhrenko
, ed.,
InTech
,
Rijeka, Croatia
, pp. 37
–62
.25.
Szénási
,
S.
,
Fried
,
Z.
, and
Felde
,
I.
, 2020
, “
Training of Artificial Neural Network to Solve the Inverse Heat Conduction Problem
,” 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI
),
Herlany, Slovakia
, Jan. 23–25, pp. 293
–298
.10.1109/SAMI48414.2020.910873326.
Tsourkas
,
P.
, and
Rubinsky
,
B.
, 2005
, “
A Parallel Genetic Algorithm for Heat Conduction Problems
,” Numer. Heat Transfer, Part B
,
47
(2
), pp. 97
–110
.10.1080/1040779049051577427.
Vakili
,
S.
, and
Gadala
,
M. S.
, 2009
, “
Effectiveness and Efficiency of Particle Swarm Optimization Technique in Inverse Heat Conduction Analysis
,” Numer. Heat Transfer, Part B
,
56
(2
), pp. 119
–141
.10.1080/1040779090311646928.
Ostrowski
,
Z.
,
Białecki
,
R. A.
, and
Kassab
,
A. J.
, 2008
, “
Solving Inverse Heat Conduction Problems Using Trained POD-RBF Network Inverse Method
,” Inverse Probl. Sci. Eng.
,
16
(1
), pp. 39
–54
.10.1080/1741597070119829029.
Wu
,
Y.-J.
,
Fang
,
L.
, and
Xu
,
Y.
, 2019
, “
Predicting Interfacial Thermal Resistance by Machine Learning
,” npj Comput. Mater.
,
5
(1
), p. 56
.10.1038/s41524-019-0193-030.
Zhan
,
T.
,
Fang
,
L.
, and
Xu
,
Y.
, 2017
, “
Prediction of Thermal Boundary Resistance by the Machine Learning Method
,” Sci. Rep.
,
7
(1
), p. 7109
.10.1038/s41598-017-07150-731.
Wei
,
H.
,
Zhao
,
S.
,
Rong
,
Q.
, and
Bao
,
H.
, 2018
, “
Predicting the Effective Thermal Conductivities of Composite Materials and Porous Media by Machine Learning Methods
,” Int. J. Heat Mass Transfer
,
127
, pp. 908
–916
.10.1016/j.ijheatmasstransfer.2018.08.08232.
Wei
,
H.
,
Bao
,
H.
, and
Ruan
,
X.
, 2020
, “
Machine Learning Prediction of Thermal Transport in Porous Media With Physics-Based Descriptors
,” Int. J. Heat Mass Transfer
,
160
, p. 120176
.10.1016/j.ijheatmasstransfer.2020.12017633.
Kwon
,
B.
,
Ejaz
,
F.
, and
Hwang
,
L. K.
, 2020
, “
Machine Learning for Heat Transfer Correlations
,” Int. Commun. Heat Mass Transfer
,
116
, p. 104694
.10.1016/j.icheatmasstransfer.2020.10469434.
Souayeh
,
B.
,
Bhattacharyya
,
S.
,
Hdhiri
,
N.
, and
Waqas Alam
,
M.
, 2021
, “
Heat and Fluid Flow Analysis and ANN-Based Prediction of a Novel Spring Corrugated Tape
,” Sustainability
,
13
(6
), p. 3023
.10.3390/su1306302335.
Baghban
,
A.
,
Kahani
,
M.
,
Nazari
,
M. A.
,
Ahmadi
,
M. H.
, and
Yan
,
W.-M.
, 2019
, “
Sensitivity Analysis and Application of Machine Learning Methods to Predict the Heat Transfer Performance of CNT/Water Nanofluid Flows Through Coils
,” Int. J. Heat Mass Transfer
,
128
, pp. 825
–835
.10.1016/j.ijheatmasstransfer.2018.09.04136.
Alizadeh
,
R.
,
Abad
,
J. M. N.
,
Fattahi
,
A.
,
Mohebbi
,
M. R.
,
Doranehgard
,
M. H.
,
Li
,
L. K. B.
,
Alhajri
,
E.
, and
Karimi
,
N.
, 2021
, “
A Machine Learning Approach to Predicting the Heat Convection and Thermodynamics of an External Flow of Hybrid Nanofluid
,” ASME J. Energy Res. Technol.
,
143
(7
), p. 070908
.10.1115/1.404945437.
Ma
,
T.
,
Guo
,
Z.
,
Lin
,
M.
, and
Wang
,
Q.
, 2021
, “
Recent Trends on Nanofluid Heat Transfer Machine Learning Research Applied to Renewable Energy
,” Renewable Sustainable Energy Rev.
,
138
, p. 110494
.10.1016/j.rser.2020.11049438.
Ghiaasiaan
,
S. M.
, 2007
, Two-Phase Flow, Boiling, and Condensation: In Conventional and Miniature Systems
,
Cambridge University Press
,
Cambridge, UK
.39.
Buhendwa
,
A. B.
,
Adami
,
S.
, and
Adams
,
N. A.
, 2021
, “
Inferring Incompressible Two-Phase Flow Fields From the Interface Motion Using Physics-Informed Neural Networks
,” Mach. Learn. Appl.
,
4
, p. 100029
.10.1016/j.mlwa.2021.10002940.
Zhou
,
Y.
,
Chen
,
F.
, and
Sun
,
B.
, 2008
, “
Identification Method of Gas-Liquid Two-Phase Flow Regime Based on Image Multi-Feature Fusion and Support Vector Machine
,” Chin. J. Chem. Eng.
,
16
(6
), pp. 832
–840
.10.1016/S1004-9541(09)60002-141.
Hobold
,
G. M.
, and
da Silva
,
A. K.
, 2018
, “
Machine Learning Classification of Boiling Regimes With Low Speed, Direct and Indirect Visualization
,” Int. J. Heat Mass Transfer
,
125
, pp. 1296
–1309
.10.1016/j.ijheatmasstransfer.2018.04.15642.
Mask
,
G.
,
Wu
,
X.
, and
Ling
,
K.
, 2019
, “
An Improved Model for Gas-Liquid Flow Pattern Prediction Based on Machine Learning
,” J. Pet. Sci. Eng.
,
183
, p. 106370
.10.1016/j.petrol.2019.10637043.
Quintino
,
A. M.
,
da Rocha
,
D. L. L. N.
,
Fonseca Júnior
,
R.
, and
Rodriguez
,
O. M. H.
, 2020
, “
Flow Pattern Transition in Pipes Using Data-Driven and Physics-Informed Machine Learning
,” ASME J. Fluids Eng.
,
143
(3
), p. 031401
.10.1115/1.404887644.
Taitel
,
Y.
, and
Dukler
,
A. E.
, 1976
, “
A Model for Predicting Flow Regime Transitions in Horizontal and Near Horizontal Gas‐Liquid Flow
,” AIChE J.
,
22
(1
), pp. 47
–55
.10.1002/aic.69022010545.
Guo-Hua
,
Q.
,
Feng
,
D.
,
Van-Bin
,
X.
,
Meng-Meng
,
W.
, and
Jun
,
H.
, 2005
, “
Gas/Liquid Two-Phase Flow Regime Identification in Horizontal Pipe Using Support Vector Machines
,” 2005 International Conference on Machine Learning and Cybernetics
, Vol.
1743
,
Guangzhou, China
, Aug. 18–21, pp. 1746
–1751
.10.1109/ICMLC.2005.152722746.
Wang
,
H.
, and
Zhang
,
L.
, 2009
, “
Identification of Two-Phase Flow Regimes Based on Support Vector Machine and Electrical Capacitance Tomography
,” Meas. Sci. Technol.
,
20
(11
), p. 114007
.10.1088/0957-0233/20/11/11400747.
Zhang
,
Y.
,
Azman
,
A. N.
,
Xu
,
K.-W.
,
Kang
,
C.
, and
Kim
,
H.-B.
, 2020
, “
Two-Phase Flow Regime Identification Based on the Liquid-Phase Velocity Information and Machine Learning
,” Exp. Fluids
,
61
(10
), p. 212
.10.1007/s00348-020-03046-x48.
Nnabuife
,
S. G.
,
Pilario
,
K. E. S.
,
Lao
,
L.
,
Cao
,
Y.
, and
Shafiee
,
M.
, 2019
, “
Identification of Gas-Liquid Flow Regimes Using a Non-Intrusive Doppler Ultrasonic Sensor and Virtual Flow Regime Maps
,” Flow Meas. Instrum.
,
68
, p. 101568
.10.1016/j.flowmeasinst.2019.05.00249.
Saito
,
Y.
,
Torisaki
,
S.
, and
Miwa
,
S.
, 2018
, “
Two-Phase Flow Regime Identification Using Fluctuating Force Signals Under Machine Learning Techniques
,” ASME
Paper No. ICONE26-81288.10.1115/ICONE26-8128850.
Xu
,
Q.
,
Liu
,
C.
,
Wang
,
X.
,
Cao
,
Y.
,
Yu
,
H.
,
Li
,
W.
, and
Guo
,
L.
, 2021
, “
Machine Learning Classification of Flow Regimes in a Long Pipeline-Riser System With Differential Pressure Signal
,” Chem. Eng. Sci.
,
233
, p. 116402
.10.1016/j.ces.2020.11640251.
Sami
,
N. A.
, and
Ibrahim
,
D. S.
, 2021
, “
Forecasting Multiphase Flowing Bottom-Hole Pressure of Vertical Oil Wells Using Three Machine Learning Techniques
,” Pet. Res.
, epub.10.1016/j.ptlrs.2021.05.00452.
Ahmadi
,
M. A.
, and
Chen
,
Z.
, 2019
, “
Machine Learning Models to Predict Bottom Hole Pressure in Multi-Phase Flow in Vertical Oil Production Wells
,” Can. J. Chem. Eng.
,
97
(11
), pp. 2928
–2940
.10.1002/cjce.2352653.
Khamehchi
,
E.
, and
Bemani
,
A.
, 2021
, “
Prediction of Pressure in Different Two-Phase Flow Conditions: Machine Learning Applications
,” Measurement
,
173
, p. 108665
.10.1016/j.measurement.2020.10866554.
Santamaria
,
A. D.
,
Mortazavi
,
M.
,
Chauhan
,
V.
,
Benner
,
J.
,
Philbrick
,
O.
,
Clemente
,
R.
,
Jia
,
H.
, and
Ling
,
C.
, 2021
, “
Machine Learning Applications of Two-Phase Flow Data in Polymer Electrolyte Fuel Cell Reactant Channels
,” J. Electrochem. Soc.
,
168
(5
), p. 054505
.10.1149/1945-7111/abfa5c55.
Chauhan
,
V.
,
Mortazavi
,
M.
,
Benner
,
J. Z.
, and
Santamaria
,
A. D.
, 2020
, “
Two-Phase Flow Characterization in PEM Fuel Cells Using Machine Learning
,” Energy Rep.
,
6
, pp. 2713
–2719
.10.1016/j.egyr.2020.09.03756.
Kanin
,
E. A.
,
Osiptsov
,
A. A.
,
Vainshtein
,
A. L.
, and
Burnaev
,
E. V.
, 2019
, “
A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data
,” J. Pet. Sci. Eng.
,
180
, pp. 727
–746
.10.1016/j.petrol.2019.05.05557.
Shadloo
,
M. S.
,
Rahmat
,
A.
,
Karimipour
,
A.
, and
Wongwises
,
S.
, 2020
, “
Estimation of Pressure Drop of Two-Phase Flow in Horizontal Long Pipes Using Artificial Neural Networks
,” ASME J. Energy Res. Technol.
,
142
(11
), p. 112110
.10.1115/1.404759358.
Hughes
,
M. T.
,
Fronk
,
B. M.
, and
Garimella
,
S.
, 2021
, “
Universal Condensation Heat Transfer and Pressure Drop Model and the Role of Machine Learning Techniques to Improve Predictive Capabilities
,” Int. J. Heat Mass Transfer
,
179
, p. 121712
.10.1016/j.ijheatmasstransfer.2021.12171259.
Qiu
,
Y.
,
Garg
,
D.
,
Kim
,
S.-M.
,
Mudawar
,
I.
, and
Kharangate
,
C. R.
, 2021
, “
Machine Learning Algorithms to Predict Flow Boiling Pressure Drop in Mini/Micro-Channels Based on Universal Consolidated Data
,” Int. J. Heat Mass Transfer
,
178
, p. 121607
.10.1016/j.ijheatmasstransfer.2021.12160760.
Khosravi
,
A.
,
Pabon
,
J. J. G.
,
Koury
,
R. N. N.
, and
Machado
,
L.
, 2018
, “
Using Machine Learning Algorithms to Predict the Pressure Drop During Evaporation of R407C
,” Appl. Therm. Eng.
,
133
, pp. 361
–370
.10.1016/j.applthermaleng.2018.01.08461.
Longo
,
G. A.
,
Mancin
,
S.
,
Righetti
,
G.
,
Zilio
,
C.
,
Ceccato
,
R.
, and
Salmaso
,
L.
, 2020
, “
Machine Learning Approach for Predicting Refrigerant Two-Phase Pressure Drop Inside Brazed Plate Heat Exchangers (BPHE)
,” Int. J. Heat Mass Transfer
,
163
, p. 120450
.10.1016/j.ijheatmasstransfer.2020.12045062.
Najafi
,
B.
,
Ardam
,
K.
,
Hanušovský
,
A.
,
Rinaldi
,
F.
, and
Colombo
,
L. P. M.
, 2021
, “
Machine Learning Based Models for Pressure Drop Estimation of Two-Phase Adiabatic Air-Water Flow in Micro-Finned Tubes: Determination of the Most Promising Dimensionless Feature Set
,” Chem. Eng. Res. Des.
,
167
, pp. 252
–267
.10.1016/j.cherd.2021.01.00263.
Suh
,
Y.
,
Bostanabad
,
R.
, and
Won
,
Y.
, 2021
, “
Deep Learning Predicts Boiling Heat Transfer
,” Sci. Rep.
,
11
(1
), p. 5622
.10.1038/s41598-021-85150-464.
Hobold
,
G. M.
, and
da Silva
,
A. K.
, 2019
, “
Visualization-Based Nucleate Boiling Heat Flux Quantification Using Machine Learning
,” Int. J. Heat Mass Transfer
,
134
, pp. 511
–520
.10.1016/j.ijheatmasstransfer.2018.12.17065.
Rassoulinejad-Mousavi
,
S. M.
,
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.2021.11684966.
Ravichandran
,
M.
, and
Bucci
,
M.
, 2019
, “
Online, Quasi-Real-Time Analysis of High-Resolution, Infrared, Boiling Heat Transfer Investigations Using Artificial Neural Networks
,” Appl. Therm. Eng.
,
163
, p. 114357
.10.1016/j.applthermaleng.2019.11435767.
Sinha
,
K. N. R.
,
Kumar
,
V.
,
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
(3
), p. 100382
.10.1016/j.xcrp.2021.10038268.
Alic
,
E.
,
Das
,
M.
, and
Kaska
,
O.
, 2019
, “
Heat Flux Estimation at Pool Boiling Processes With Computational Intelligence Methods
,” Processes
,
7
(5
), p. 293
.10.3390/pr705029369.
Bahman
,
A. M.
, and
Ebrahim
,
S. A.
, 2020
, “
Prediction of the Minimum Film Boiling Temperature Using Artificial Neural Network
,” Int. J. Heat Mass Transfer
,
155
, p. 119834
.10.1016/j.ijheatmasstransfer.2020.11983470.
Huang
,
J.
,
Zheng
,
G.
,
Li
,
M.
,
Xiao
,
Q.
, and
Wang
,
H.
, 2021
, “
Assessing the Effects of Fluids Flow on Heat Transfer Performance in Direct Contact Heat Transfer Process Through EMD-LSSVM Model: An Experimental Study
,” Appl. Therm. Eng.
,
189
, p. 116732
.10.1016/j.applthermaleng.2021.11673271.
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.2019.11921172.
Balcilar
,
M.
,
Dalkilic
,
A. S.
,
Sonmez
,
A. C.
, and
Wongwises
,
S.
, 2014
, “
Classification of in-Tube Boiling R134a Data Belonging to the Smooth and Corrugated Tubes
,” Int. Commun. Heat Mass Transfer
,
53
, pp. 185
–194
.10.1016/j.icheatmasstransfer.2014.02.02073.
Mehralizadeh
,
A.
,
Shabanian
,
S. R.
, and
Bakeri
,
G.
, 2020
, “
Investigation of Boiling Heat Transfer Coefficients of Different Refrigerants for Low Fin, Turbo-B and Thermoexcel-E Enhanced Tubes Using Computational Smart Schemes
,” J. Therm. Anal. Calorim.
,
141
(3
), pp. 1221
–1242
.10.1007/s10973-019-09075-x74.
Zhu
,
G.
,
Wen
,
T.
, and
Zhang
,
D.
, 2021
, “
Machine Learning Based Approach for the Prediction of Flow Boiling/Condensation Heat Transfer Performance in Mini Channels With Serrated Fins
,” Int. J. Heat Mass Transfer
,
166
, p. 120783
.10.1016/j.ijheatmasstransfer.2020.12078375.
Park
,
H. M.
,
Lee
,
J. H.
, and
Kim
,
K. D.
, 2020
, “
Wall Temperature Prediction at Critical Heat Flux Using a Machine Learning Model
,” Ann. Nucl. Energy
,
141
, p. 107334
.10.1016/j.anucene.2020.10733476.
Wang
,
H.
,
Longman
,
A.
,
Gruenwald
,
J. T.
,
Tusar
,
J.
, and
Vilim
,
R.
, 2019
, “
Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors
,” Nucl. Technol.
,
205
(8
), pp. 1003
–1020
.10.1080/00295450.2019.158395777.
Zhao
,
X.
,
Shirvan
,
K.
,
Salko
,
R. K.
, and
Guo
,
F.
, 2020
, “
On the Prediction of Critical Heat Flux Using a Physics-Informed Machine Learning-Aided Framework
,” Appl. Therm. Eng.
,
164
, p. 114540
.10.1016/j.applthermaleng.2019.11454078.
Groeneveld
,
D. C.
,
Shan
,
J. Q.
,
Vasić
,
A. Z.
,
Leung
,
L. K. H.
,
Durmayaz
,
A.
,
Yang
,
J.
,
Cheng
,
S. C.
, and
Tanase
,
A.
, 2007
, “
The 2006 CHF Look-Up Table
,” Nucl. Eng. Des.
,
237
(15–17
), pp. 1909
–1922
.10.1016/j.nucengdes.2007.02.01479.
Chang
,
C.-W.
,
Dinh
,
N.
, and
Cetiner
,
S. M.
, 2017
, “
Physics-Constrained Machine Learning for Two-Phase Flow Simulation Using Deep Learning-Based Closure Relation
,” Proceedings of the American Nuclear Society Winter Meeting
,
Washington, DC
, Oct. 29–Nov. 2, pp. 1749
–1752
.https://www.researchgate.net/publication/322695361_Physics-Constrained_Machine_Learning_for_Two-Phase_Flow_Simulation_Using_Deep_Learning-Based_Closure_Relation80.
Zhao
,
X.
,
Salko
,
R. K.
, and
Shirvan
,
K.
, 2021
, “
Improved Departure From Nucleate Boiling Prediction in Rod Bundles Using a Physics-Informed Machine Learning-Aided Framework
,” Nucl. Eng. Des.
,
374
, p. 111084
.10.1016/j.nucengdes.2021.11108481.
Yoo
,
J. M.
,
Lee
,
D. H.
,
Hong
,
D. J.
, and
Jeong
,
J. J.
, 2021
, “
Application of Machine Learning Technique in Predicting Condensation Heat Transfer Coefficient and Droplet Entrainment Rate
,” Transactions of the Korean Nuclear Society Virtual Spring Meeting
,
Jeju, Korea
, May 13–14.https://www.kns.org/files/pre_paper/45/21S-175-%EC%9C%A0%EC%A7%80%EB%AF%BC.pdf82.
Balcilar
,
M.
,
Dalkilic
,
A. S.
, and
Wongwises
,
S.
, 2011
, “
Artificial Neural Network Techniques for the Determination of Condensation Heat Transfer Characteristics During Downward Annular Flow of R134a Inside a Vertical Smooth Tube
,” Int. Commun. Heat Mass Transfer
,
38
(1
), pp. 75
–84
.10.1016/j.icheatmasstransfer.2010.10.00983.
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.2020.12035184.
Du
,
M.
,
Yin
,
H.
,
Chen
,
X.
, and
Wang
,
X.
, 2019
, “
Oil-in-Water Two-Phase Flow Pattern Identification From Experimental Snapshots Using Convolutional Neural Network
,” IEEE Access
,
7
, pp. 6219
–6225
.10.1109/ACCESS.2018.288873385.
Rosa
,
E. S.
,
Salgado
,
R. M.
,
Ohishi
,
T.
, and
Mastelari
,
N.
, 2010
, “
Performance Comparison of Artificial Neural Networks and Expert Systems Applied to Flow Pattern Identification in Vertical Ascendant Gas–Liquid Flows
,” Int. J. Multiphase Flow
,
36
(9
), pp. 738
–754
.10.1016/j.ijmultiphaseflow.2010.05.00186.
Manjrekar
,
O. N.
, and
Dudukovic
,
M. P.
, 2019
, “
Identification of Flow Regime in a Bubble Column Reactor With a Combination of Optical Probe Data and Machine Learning Technique
,” Chem. Eng. Sci.: X
,
2
, p. 100023
.10.1016/j.cesx.2019.10002387.
Qin
,
L.
,
Hu
,
L.
,
Mao
,
K.
,
Chen
,
W.
, and
Fu
,
X.
, 2016
, “
Application of Extreme Learning Machine to Gas Flow Measurement With Multipath Acoustic Transducers
,” Flow Meas. Instrum.
,
49
, pp. 31
–39
.10.1016/j.flowmeasinst.2016.03.00388.
Shen
,
W.
,
Vaca
,
D.
, and
Kumar
,
S.
, 2020
, “
Reconsidering Uncertainty From Frequency Domain Thermoreflectance Measurement and Novel Data Analysis by Deep Learning
,” Nanoscale Microscale Thermophys. Eng.
,
24
(3–4
), pp. 138
–149
.10.1080/15567265.2020.180766289.
Kim
,
T. H.
,
Kommer
,
E.
,
Dessiatoun
,
S.
, and
Kim
,
J.
, 2012
, “
Measurement of Two-Phase Flow and Heat Transfer Parameters Using Infrared Thermometry
,” Int. J. Multiphase Flow
,
40
, pp. 56
–67
.10.1016/j.ijmultiphaseflow.2011.11.01290.
Smith
,
M. K.
,
Castello
,
C. C.
, and
New
,
J. R.
, 2013
, “
Machine Learning Techniques Applied to Sensor Data Correction in Building Technologies
,” Proceedings of the 2013 12th International Conference on Machine Learning and Applications
, Miami, FL, Dec. 4–7, pp. 305
–308
.10.1109/ICMLA.2013.6291.
Jaques
,
N.
,
Taylor
,
S.
,
Sano
,
A.
, and
Picard
,
R.
, 2017
, “
Multimodal Autoencoder: A Deep Learning Approach to Filling in Missing Sensor Data and Enabling Better Mood Prediction
,” 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII
),
San Antonio, TX
, Oct. 23–26, pp. 202
–208
.https://affect.media.mit.edu/pdfs/17.Jaques_autoencoder_ACII.pdf92.
Cascini
,
A.
,
Gamberi
,
M.
,
Mora
,
C.
,
Rosano
,
M.
, and
Bortolini
,
M.
, 2016
, “
Comparative Carbon Footprint Assessment of Commercial Walk-In Refrigeration Systems Under Different Use Configurations
,” J. Cleaner Prod.
,
112
, pp. 3998
–4011
.10.1016/j.jclepro.2015.08.07593.
Zhang
,
G.
,
Eddy Patuwo
,
B.
, and
Hu
,
M. Y.
, 1998
, “
Forecasting With Artificial Neural Networks:: The State of the Art
,” Int. J. Forecast.
,
14
(1
), pp. 35
–62
.10.1016/S0169-2070(97)00044-794.
Pai
,
P.-F.
, and
Hong
,
W.-C.
, 2005
, “
Forecasting Regional Electricity Load Based on Recurrent Support Vector Machines With Genetic Algorithms
,” Electr. Power Syst. Res.
,
74
(3
), pp. 417
–425
.10.1016/j.epsr.2005.01.00695.
Powell
,
K. M.
,
Sriprasad
,
A.
,
Cole
,
W. J.
, and
Edgar
,
T. F.
, 2014
, “
Heating, Cooling, and Electrical Load Forecasting for a Large-Scale District Energy System
,” Energy
,
74
, pp. 877
–885
.10.1016/j.energy.2014.07.06496.
Xuemei
,
L.
,
Lixing
,
D.
,
Ming
,
S.
,
Gang
,
X.
, and
Jibin
,
L.
, 2009
, “
A Novel Air-Conditioning Load Prediction Based on ARIMA and BPNN Model
,” 2009 Asia-Pacific Conference on Information Processing
,
Shenzhen, China
, July 18–19, pp. 51
–54
.10.1109/AP CIP.2009.2197.
Díaz
,
G.
,
Sen
,
M.
,
Yang
,
K. T.
, and
McClain
,
R. L.
, 1999
, “
Simulation of Heat Exchanger Performance by Artificial Neural Networks
,” HVACR Res.
,
5
(3
), pp. 195
–208
.10.1080/10789669.1999.1039123398.
Tan
,
C. K.
,
Ward
,
J.
,
Wilcox
,
S. J.
, and
Payne
,
R.
, 2009
, “
Artificial Neural Network Modelling of the Thermal Performance of a Compact Heat Exchanger
,” Appl. Therm. Eng.
,
29
(17–18
), pp. 3609
–3617
.10.1016/j.applthermaleng.2009.06.01799.
Pacheco-Vega
,
A.
,
Díaz
,
G.
,
Sen
,
M.
,
Yang
,
K. T.
, and
McClain
,
R. L.
, 2001
, “
Heat Rate Predictions in Humid Air-Water Heat Exchangers Using Correlations and Neural Networks
,” ASME J. Heat Transfer-Trans. ASME
,
123
(2
), pp. 348
–354
.10.1115/1.1351167100.
Ertunc
,
H. M.
, and
Hosoz
,
M.
, 2006
, “
Artificial Neural Network Analysis of a Refrigeration System With an Evaporative Condenser
,” Appl. Therm. Eng.
,
26
(5–6
), pp. 627
–635
.10.1016/j.applthermaleng.2005.06.002101.
Ghalandari
,
M.
,
Irandoost Shahrestani
,
M.
,
Maleki
,
A.
,
Safdari Shadloo
,
M.
, and
El Haj Assad
,
M.
, 2021
, “
Applications of Intelligent Methods in Various Types of Heat Exchangers: A Review
,” J. Therm. Anal. Calorim.
,
145
(4
), pp. 1837
–1848
.10.1007/s10973-020-10425-3102.
Behnam
,
P.
,
Faegh
,
M.
,
Shafii
,
M. B.
, and
Khiadani
,
M.
, 2021
, “
A Comparative Study of Various Machine Learning Methods for Performance Prediction of an Evaporative Condenser
,” Int. J. Refrig.
,
126
, pp. 280
–290
.10.1016/j.ijrefrig.2021.02.009103.
El-Said
,
E. M. S.
,
Abd Elaziz
,
M.
, and
Elsheikh
,
A. H.
, 2021
, “
Machine Learning Algorithms for Improving the Prediction of Air Injection Effect on the Thermohydraulic Performance of Shell and Tube Heat Exchanger
,” Appl. Therm. Eng.
,
185
, p. 116471
.10.1016/j.applthermaleng.2020.116471104.
Krzywanski
,
J.
, 2019
, “
A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods
,” Energies
,
12
(23
), p. 4441
.10.3390/en12234441105.
Ledesma
,
S.
,
Belman-Flores
,
J. M.
, and
Barroso-Maldonado
,
J. M.
, 2015
, “
Analysis and Modeling of a Variable Speed Reciprocating Compressor Using ANN
,” Int. J. Refrig.
,
59
, pp. 190
–197
.10.1016/j.ijrefrig.2015.08.009106.
Wan
,
H.
,
Cao
,
T.
,
Hwang
,
Y.
,
Chang
,
S.-D.
, and
Yoon
,
Y.-J.
, 2021
, “
Machine-Learning-Based Compressor Models: A Case Study for Variable Refrigerant Flow Systems
,” Int. J. Refrig.
,
123
, pp. 23
–33
.10.1016/j.ijrefrig.2020.12.003107.
Warey
,
A.
,
Kaushik
,
S.
,
Khalighi
,
B.
,
Cruse
,
M.
, and
Venkatesan
,
G.
, 2020
, “
Data-Driven Prediction of Vehicle Cabin Thermal Comfort: Using Machine Learning and High-Fidelity Simulation Results
,” Int. J. Heat Mass Transfer
,
148
, p. 119083
.10.1016/j.ijheatmasstransfer.2019.119083108.
Swider
,
D. J.
,
Browne
,
M. W.
,
Bansal
,
P. K.
, and
Kecman
,
V.
, 2001
, “
Modelling of Vapour-Compression Liquid Chillers With Neural Networks
,” Appl. Therm. Eng.
,
21
(3
), pp. 311
–329
.10.1016/S1359-4311(00)00036-3109.
Navarro-Esbrí
,
J.
,
Berbegall
,
V.
,
Verdu
,
G.
,
Cabello
,
R.
, and
Llopis
,
R.
, 2007
, “
A Low Data Requirement Model of a Variable-Speed Vapour Compression Refrigeration System Based on Neural Networks
,” Int. J. Refrig.
,
30
(8
), pp. 1452
–1459
.10.1016/j.ijrefrig.2007.03.007110.
Mohanraj
,
M.
,
Jayaraj
,
S.
, and
Muraleedharan
,
C.
, 2012
, “
Applications of Artificial Neural Networks for Refrigeration, Air-Conditioning and Heat Pump Systems—A Review
,” Renewable Sustainable Energy Rev.
,
16
(2
), pp. 1340
–1358
.10.1016/j.rser.2011.10.015111.
Wang
,
Y.
,
Li
,
W.
,
Zhang
,
Z.
,
Shi
,
J.
, and
Chen
,
J.
, 2019
, “
Performance Evaluation and Prediction for Electric Vehicle Heat Pump Using Machine Learning Method
,” Appl. Therm. Eng.
,
159
, p. 113901
.10.1016/j.applthermaleng.2019.113901112.
Ho
,
W. T.
, and
Yu
,
F. W.
, 2021
, “
Predicting Chiller System Performance Using ARIMA-Regression Models
,” J. Build. Eng.
,
33
, p. 101871
.10.1016/j.jobe.2020.101871113.
Yu
,
C. W.
,
Chen
,
J. W.
, and
Chen
,
Y. L.
, 2021
, “
Enhanced LSTM Framework for Water-Cooled Chiller COP Forecasting
,” 2021 IEEE International Conference on Consumer Electronics (ICCE
),
Berlin
, Jan. 10–12, pp. 1
–3
.10.1109/ICCE50685.2021.9427706114.
Staedter
,
M. A.
, and
Garimella
,
S.
, 2019
, “
A Data Driven Control Strategy for Optimal and Flexible Operation of Absorption Systems
,” Int. J. Refrig.
,
97
, pp. 157
–168
.10.1016/j.ijrefrig.2018.09.002115.
Beghi
,
A.
,
Rampazzo
,
M.
, and
Zorzi
,
S.
, 2017
, “
Reinforcement Learning Control of Transcritical Carbon Dioxide Supermarket Refrigeration Systems
,” IFAC-PapersOnLine
,
50
(1
), pp. 13754
–13759
.10.1016/j.ifacol.2017.08.2565116.
Goyal
,
A.
, and
Garimella
,
S.
, 2019
, “
Computing Thermodynamic Properties of Ammonia–Water Mixtures Using Artificial Neural Networks
,” Int. J. Refrig.
,
100
, pp. 315
–325
.10.1016/j.ijrefrig.2019.02.011117.
Ekren
,
O.
,
Sahin
,
S.
, and
Isler
,
Y.
, 2010
, “
Comparison of Different Controllers for Variable Speed Compressor and Electronic Expansion Valve
,” Int. J. Refrig.
,
33
(6
), pp. 1161
–1168
.10.1016/j.ijrefrig.2010.05.005118.
Goyal
,
A.
,
Staedter
,
M. A.
, and
Garimella
,
S.
, 2019
, “
A Review of Control Methodologies for Vapor Compression and Absorption Heat Pumps
,” Int. J. Refrig.
,
97
, pp. 1
–20
.10.1016/j.ijrefrig.2018.08.026119.
Kulkarni
,
K.
,
Devi
,
U.
,
Sirighee
,
A.
,
Hazra
,
J.
, and
Rao
,
P.
, 2018
, “
Predictive Maintenance for Supermarket Refrigeration Systems Using Only Case Temperature Data
,” 2018 Annual American Control Conference (ACC
),
Milwaukee, WI
, June 27–29, pp. 4640
–4645
.10.23919/ACC.2018.8431901120.
Kocyigit
,
N.
, 2015
, “
Fault and Sensor Error Diagnostic Strategies for a Vapor Compression Refrigeration System by Using Fuzzy Inference Systems and Artificial Neural Network
,” Int. J. Refrig.
,
50
, pp. 69
–79
.10.1016/j.ijrefrig.2014.10.017121.
Liang
,
J.
, and
Du
,
R.
, 2007
, “
Model-Based Fault Detection and Diagnosis of HVAC Systems Using Support Vector Machine Method
,” Int. J. Refrig.
,
30
(6
), pp. 1104
–1114
.10.1016/j.ijrefrig.2006.12.012122.
Han
,
H.
,
Cui
,
X.
,
Fan
,
Y.
, and
Qing
,
H.
, 2019
, “
Least Squares Support Vector Machine (LS-SVM)-Based Chiller Fault Diagnosis Using Fault Indicative Features
,” Appl. Therm. Eng.
,
154
, pp. 540
–547
.10.1016/j.applthermaleng.2019.03.111123.
Li
,
G.
,
Hu
,
Y.
,
Chen
,
H.
,
Wang
,
J.
,
Guo
,
Y.
,
Liu
,
J.
, and
Li
,
J.
, 2017
, “
Identification and Isolation of Outdoor Fouling Faults Using Only Built-In Sensors in Variable Refrigerant Flow System: A Data Mining Approach
,” Energy Build.
,
146
, pp. 257
–270
.10.1016/j.enbuild.2017.04.041124.
Yan
,
R.
,
Ma
,
Z.
,
Zhao
,
Y.
, and
Kokogiannakis
,
G.
, 2016
, “
A Decision Tree Based Data-Driven Diagnostic Strategy for Air Handling Units
,” Energy Build.
,
133
, pp. 37
–45
.10.1016/j.enbuild.2016.09.039125.
Guo
,
Y.
,
Tan
,
Z.
,
Chen
,
H.
,
Li
,
G.
,
Wang
,
J.
,
Huang
,
R.
,
Liu
,
J.
, and
Ahmad
,
T.
, 2018
, “
Deep Learning-Based Fault Diagnosis of Variable Refrigerant Flow Air-Conditioning System for Building Energy Saving
,” Appl. Energy
,
225
, pp. 732
–745
.10.1016/j.apenergy.2018.05.075126.
Han
,
H.
,
Gu
,
B.
,
Wang
,
T.
, and
Li
,
Z. R.
, 2011
, “
Important Sensors for Chiller Fault Detection and Diagnosis (FDD) From the Perspective of Feature Selection and Machine Learning
,” Int. J. Refrig.
,
34
(2
), pp. 586
–599
.10.1016/j.ijrefrig.2010.08.011127.
Comstock
,
M. C.
, and
Braun
,
J.
, 1999
, “
Experimental Data From Fault Detection and Diagnostic Studies on a Centrifugal Chiller
,” ASHRAE, Atlanta, GA, Report No. 4036-1 Research Project 1043.128.
Pagani
,
S.
,
Manoj
,
P. D. S.
,
Jantsch
,
A.
, and
Henkel
,
J.
, 2020
, “
Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey
,” IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.
,
39
(1
), pp. 101
–116
.10.1109/TCAD.2018.2878168129.
Athavale
,
J.
,
Yoda
,
M.
, and
Joshi
,
Y.
, 2018
, “
Chapter Three—Thermal Modeling of Data Centers for Control and Energy Usage Optimization
,” Advances in Heat Transfer
,
E. M.
Sparrow
,
J. P.
Abraham
, and
J. M.
Gorman
, eds.,
Elsevier
,
Amsterdam, The Netherlands
, pp. 123
–186
.130.
Moore
,
A. L.
, and
Shi
,
L.
, 2014
, “
Emerging Challenges and Materials for Thermal Management of Electronics
,” Mater. Today
,
17
(4
), pp. 163
–174
.10.1016/j.mattod.2014.04.003131.
Jung
,
H.
, and
Pedram
,
M.
, 2010
, “
Supervised Learning Based Power Management for Multicore Processors
,” IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.
,
29
(9
), pp. 1395
–1408
.10.1109/TCAD.2010.2059270132.
Lu
,
H.
,
Lu
,
Y.
,
Tang
,
Z.
, and
Wang
,
S.
, 2006
, “
SOC Dynamic Power Management Using Artificial Neural Network
,” Proceedings Advances in Natural Computation
,
L.
Jiao
,
L.
Wang
,
X.-B.
Gao
,
J.
Liu
, and
F.
Wu
, eds.,
Springer
,
Berlin, Heidelberg
, pp. 555
–564
.133.
Hantao
,
H.
,
Manoj
,
P. D. S.
,
Xu
,
D.
,
Yu
,
H.
, and
Hao
,
Z.
, 2014
, “
Reinforcement Learning Based Self-Adaptive Voltage-Swing Adjustment of 2.5D I/Os for Many-Core Microprocessor and Memory Communication
,” Proceedings of the 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD
), San Jose, CA, Nov. 2–6, pp. 224
–229
.10.1109/ICCAD.2014.7001356134.
Kanesan
,
J.
,
Arunasalam
,
P.
,
Seetharamu
,
K. N.
, and
Azid
,
I. A.
, 2005
, “
Artificial Neural Network Trained, Genetic Algorithms Optimized Thermal Energy Storage Heatsinks for Electronics Cooling
,” ASME
Paper No. IPACK2005-73053.10.1115/IPACK2005-73053135.
Yang
,
H.
,
Zhang
,
Z.
,
Zhang
,
J.
, and
Zeng
,
X. C.
, 2018
, “
Machine Learning and Artificial Neural Network Prediction of Interfacial Thermal Resistance Between Graphene and Hexagonal Boron Nitride
,” Nanoscale
,
10
(40
), pp. 19092
–19099
.10.1039/C8NR05703F136.
Athavale
,
J.
,
Joshi
,
Y.
, and
Yoda
,
M.
, 2018
, “
Artificial Neural Network Based Prediction of Temperature and Flow Profile in Data Centers
,” 2018 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm
),
San Diego, CA
, May 29–June 1, pp. 871
–880
.10.1109/ITHERM.2018.8419607137.
Walekar
,
A.
,
Siddarth
,
A.
,
Guhe
,
A.
,
Sukthankar
,
N.
, and
Agonafer
,
D.
, 2018
, “
Neural Network Based Bin Analysis for Indirect/Direct Evaporative Cooling of Modular Data Centers
,” ASME
Paper No. IMECE2018-88502.10.1115/IMECE2018-88502138.
Shoukourian
,
H.
, and
Kranzlmüller
,
D.
, 2020
, “
Forecasting Power-Efficiency Related Key Performance Indicators for Modern Data Centers Using LSTMs
,” Future Gener. Comput. Syst.
,
112
, pp. 362
–382
.10.1016/j.future.2020.05.014139.
Ounifi
,
H.
,
Gherbi
,
A.
,
Kara
,
N.
, and
Li
,
W.
, 2019
, “
A Deep Neural Network Based Approach to Energy Efficiency Analysis for Cloud Data Center
,” 2019 IEEE 17th International Conference on Industrial Informatics (INDIN
),
Helsinki, Finland
, July 22–25, pp. 1397
–1404
.10.1109/INDIN41052.2019.8972019140.
Asgari
,
S.
,
MirhoseiniNejad
,
S.
,
Moazamigoodarzi
,
H.
,
Gupta
,
R.
,
Zheng
,
R.
, and
Puri
,
I. K.
, 2021
, “
A Gray-Box Model for Real-Time Transient Temperature Predictions in Data Centers
,” Appl. Therm. Eng.
,
185
, p. 116319
.10.1016/j.applthermaleng.2020.116319141.
Liu
,
H.
,
Wei
,
Z.
,
He
,
W.
, and
Zhao
,
J.
, 2017
, “
Thermal Issues About Li-Ion Batteries and Recent Progress in Battery Thermal Management Systems: A Review
,” Energy Convers. Manage.
,
150
, pp. 304
–330
.10.1016/j.enconman.2017.08.016142.
Bandhauer
,
T. M.
,
Garimella
,
S.
, and
Fuller
,
T. F.
, 2011
, “
A Critical Review of Thermal Issues in Lithium-Ion Batteries
,” J. Electrochem. Soc.
,
158
(3
), p. R1
.10.1149/1.3515880143.
Li
,
W.
,
Garg
,
A.
,
Xiao
,
M.
, and
Gao
,
L.
, 2020
, “
Optimization for Liquid Cooling Cylindrical Battery Thermal Management System Based on Gaussian Process Model
,” ASME J. Therm. Sci. Eng. Appl.
,
13
(2
), p. 021015
.10.1115/1.4047526144.
Tang
,
X.
,
Guo
,
Q.
,
Li
,
M.
,
Wei
,
C.
,
Pan
,
Z.
, and
Wang
,
Y.
, 2021
, “
Performance Analysis on Liquid-Cooled Battery Thermal Management for Electric Vehicles Based on Machine Learning
,” J. Power Sources
,
494
, p. 229727
.10.1016/j.jpowsour.2021.229727145.
Ramprasad
,
R.
,
Batra
,
R.
,
Pilania
,
G.
,
Mannodi-Kanakkithodi
,
A.
, and
Kim
,
C.
, 2017
, “
Machine Learning in Materials Informatics: Recent Applications and Prospects
,” npj Comput. Mater.
,
3
(1
), p. 54
.10.1038/s41524-017-0056-5146.
Pilania
,
G.
, 2021
, “
Machine Learning in Materials Science: From Explainable Predictions to Autonomous Design
,” Comput. Mater. Sci.
,
193
, p. 110360
.10.1016/j.commatsci.2021.110360147.
Chang
,
C.-W.
, and
Dinh
,
N. T.
, 2019
, “
Classification of Machine Learning Frameworks for Data-Driven Thermal Fluid Models
,” Int. J. Therm. Sci.
,
135
, pp. 559
–579
.10.1016/j.ijthermalsci.2018.09.002148.
Koeppe
,
A.
,
Bamer
,
F.
,
Selzer
,
M.
,
Nestler
,
B.
, and
Markert
,
B.
, 2021
, “
Explainable Artificial Intelligence for Mechanics: Physics-Informing Neural Networks for Constitutive Models
,” preprint arXiv:2104.10683.149.
Mills
,
K.
,
Spanner
,
M.
, and
Tamblyn
,
I.
, 2017
, “
Deep Learning and the Schrödinger Equation
,” Phys. Rev. A
,
96
(4
), p. 042113
.10.1103/PhysRevA.96.042113150.
Hanna
,
B. N.
,
Dinh
,
N. T.
,
Youngblood
,
R. W.
, and
Bolotnov
,
I. A.
, 2020
, “
Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD)
,” Prog. Nucl. Energy
,
118
, p. 103140
.10.1016/j.pnucene.2019.103140151.
Lundberg
,
S. M.
, and
Lee
,
S.-I.
, 2017
, “
A Unified Approach to Interpreting Model Predictions
,” Proceedings of the 31st International Conference on Neural Information Processing Systems
,
Long Beach, CA
, Dec. 4–9, pp. 4768
–4777
.https://arxiv.org/pdf/1705.07874.pdfCopyright © 2021 by ASME
You do not currently have access to this content.
