Back to Search Start Over

Applications of intelligent methods in various types of heat exchangers: a review

Authors :
Akbar Maleki
Mamdouh El Haj Assad
Mostafa Safdari Shadloo
Mohammad Ghalandari
Misagh Irandoost Shahrestani
Complexe de recherche interprofessionnel en aérothermochimie (CORIA)
Université de Rouen Normandie (UNIROUEN)
Normandie Université (NU)-Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie)
Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
Source :
Journal of Thermal Analysis and Calorimetry, Journal of Thermal Analysis and Calorimetry, Springer Verlag, 2021, ⟨10.1007/s10973-020-10425-3⟩
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

Heat exchangers are applicable in different industries and technologies, and their performance is influenced by different parameters. In addition to experimental and time-consuming computational approaches, intelligent methods can be used for the investigation of heat exchanger performance due to their abilities in accurate prediction and relatively fast performance. The accuracy and applicability of machine learning methods, mainly based on intelligent techniques, in modeling and forecasting the performance of heat exchangers are dependent on some factors including architecture of algorithm, inputs of the model, and complexity of the system. Owing to the aforementioned facts, it would be crucial to consider the influential factors in the proposed mode to produce models with the greatest accuracy. In this work, different applications of intelligent methods in performance modeling heat exchangers are reviewed, and the key outcomes of the reviewed works are represented. Moreover, the items influencing the performance of these methods are investigated. In the final stage of the current paper, some ideas are recommended for future works in the relevant fields.

Details

Language :
English
ISSN :
13886150 and 15882926
Database :
OpenAIRE
Journal :
Journal of Thermal Analysis and Calorimetry, Journal of Thermal Analysis and Calorimetry, Springer Verlag, 2021, ⟨10.1007/s10973-020-10425-3⟩
Accession number :
edsair.doi.dedup.....b5e72c81df9f597cd5d35ecbd0bca035
Full Text :
https://doi.org/10.1007/s10973-020-10425-3⟩