TY - JOUR
T1 - Recent advances in the applications of machine learning methods for heat exchanger modeling—a review
AU - Zou, Junjia
AU - Hirokawa, Tomoki
AU - An, Jiabao
AU - Huang, Long
AU - Camm, Joseph
N1 - Publisher Copyright:
Copyright © 2023 Zou, Hirokawa, An, Huang and Camm.
PY - 2023/11/14
Y1 - 2023/11/14
N2 - Heat exchanger modeling has been widely employed in recent years for performance calculation, design optimizations, real-time simulations for control analysis, as well as transient performance predictions. Among these applications, the model’s computational speed and robustness are of great interest, particularly for the purpose of optimization studies. Machine learning models built upon experimental or numerical data can contribute to improving the state-of-the-art simulation approaches, provided careful consideration is given to algorithm selection and implementation, to the quality of the database, and to the input parameters and variables. This comprehensive review covers machine learning methods applied to heat exchanger applications in the last 8 years. The reviews are generally categorized based on the types of heat exchangers and also consider common factors of concern, such as fouling, thermodynamic properties, and flow regimes. In addition, the limitations of machine learning methods for heat exchanger modeling and potential solutions are discussed, along with an analysis of emerging trends. As a regression classification tool, machine learning is an attractive data-driven method to estimate heat exchanger parameters, showing a promising prediction capability. Based on this review article, researchers can choose appropriate models for analyzing and improving heat exchanger modeling.
AB - Heat exchanger modeling has been widely employed in recent years for performance calculation, design optimizations, real-time simulations for control analysis, as well as transient performance predictions. Among these applications, the model’s computational speed and robustness are of great interest, particularly for the purpose of optimization studies. Machine learning models built upon experimental or numerical data can contribute to improving the state-of-the-art simulation approaches, provided careful consideration is given to algorithm selection and implementation, to the quality of the database, and to the input parameters and variables. This comprehensive review covers machine learning methods applied to heat exchanger applications in the last 8 years. The reviews are generally categorized based on the types of heat exchangers and also consider common factors of concern, such as fouling, thermodynamic properties, and flow regimes. In addition, the limitations of machine learning methods for heat exchanger modeling and potential solutions are discussed, along with an analysis of emerging trends. As a regression classification tool, machine learning is an attractive data-driven method to estimate heat exchanger parameters, showing a promising prediction capability. Based on this review article, researchers can choose appropriate models for analyzing and improving heat exchanger modeling.
KW - design optimization
KW - heat exchanger
KW - machine learning
KW - modeling
KW - performance prediction
UR - http://www.scopus.com/inward/record.url?scp=85177865050&partnerID=8YFLogxK
U2 - 10.3389/fenrg.2023.1294531
DO - 10.3389/fenrg.2023.1294531
M3 - Review article
AN - SCOPUS:85177865050
SN - 2296-598X
VL - 11
JO - Frontiers in Energy Research
JF - Frontiers in Energy Research
M1 - 1294531
ER -