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Applications of intelligent methods in various types of heat exchangers: a review
- 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.
- Subjects :
- Work (thermodynamics)
Computer science
020209 energy
Mode (statistics)
02 engineering and technology
Condensed Matter Physics
01 natural sciences
Industrial engineering
010406 physical chemistry
0104 chemical sciences
[SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph]
Heat exchanger
0202 electrical engineering, electronic engineering, information engineering
Key (cryptography)
Physical and Theoretical Chemistry
ComputingMilieux_MISCELLANEOUS
Subjects
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⟩