Estimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learning Article Swipe
Andaç Batur Çolak
,
Dogan Akgul
,
Hatice Mercan
,
Ahmet Selim Dalkılıç
,
Somchai Wongwises
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.csite.2023.102713
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.csite.2023.102713
Shell and helically coiled tube heat exchangers (SHCTHEXs) are heat exchangers that only take up a small space and enable greater heat transfer area compared to traditional models. Information on 21 different SHCTHEXs obtained from catalog was considered for the modeling. Two other artificial neural network structures have been created to forecast the heat transfer coefficient, pressure drop, Nusselt number, and performance evaluation criteria values as outputs. In contrast, tubing and coil diameters, Reynolds and Dean numbers, curvature ratio, and mass flow rate are designed as inputs. In the network structures with 105 data points, 70% of the data was used for training, 15% for validation, and 15% for the testing stages. The Levenberg-Marquardt procedure was evaluated as the training algorithm in multi-layer perceptron network models. The coefficient of determination was as higher than 0.99. The mean deviation was less than 0.01%. The results show that the created artificial neural network structures can acqurately estimate the outputs.
Related Topics
Concepts
Artificial neural network
Nusselt number
Reynolds number
Heat transfer coefficient
Heat exchanger
Pressure drop
Perceptron
Heat transfer
Curvature
Computer science
Concentric tube heat exchanger
NTU method
Mass flow rate
Shell and tube heat exchanger
Multilayer perceptron
Mechanics
Materials science
Thermodynamics
Micro heat exchanger
Artificial intelligence
Mathematics
Physics
Geometry
Turbulence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.csite.2023.102713
- OA Status
- gold
- Cited By
- 34
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313556289
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313556289Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.csite.2023.102713Digital Object Identifier
- Title
-
Estimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-01-05Full publication date if available
- Authors
-
Andaç Batur Çolak, Dogan Akgul, Hatice Mercan, Ahmet Selim Dalkılıç, Somchai WongwisesList of authors in order
- Landing page
-
https://doi.org/10.1016/j.csite.2023.102713Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.csite.2023.102713Direct OA link when available
- Concepts
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Artificial neural network, Nusselt number, Reynolds number, Heat transfer coefficient, Heat exchanger, Pressure drop, Perceptron, Heat transfer, Curvature, Computer science, Concentric tube heat exchanger, NTU method, Mass flow rate, Shell and tube heat exchanger, Multilayer perceptron, Mechanics, Materials science, Thermodynamics, Micro heat exchanger, Artificial intelligence, Mathematics, Physics, Geometry, TurbulenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
34Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 12, 2023: 8Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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