Artificial Neural-Network Aided Model-Parameter Identification for Zero-Dimensional Radiator Heat Balance Models
A numerical procedure using an artificial neural network (ANN) has been developed to identify the model parameters of 0-dimensional radiator heat balance models. This model parameter identification method includes two steps as follows. The first step is an ANN learning step. The second step is a computational analysis step to identify the model parameters using learned ANN. The model parameters to be identified are radiator-water thermal conductance as a function of water flow rate, radiator-air thermal conductance as a function of air velocity, and radiator heat capacity as a constant. The validation results indicate that the present method can be applied to identify model parameters.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
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Authors:
- Shimamura, Takeshi
- Miyamoto, Takeshi
- Kuboyama, Tatsuya
- Moriyoshi, Yasuo
- Publication Date: 2023-1
Language
- English
- Japanese
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 181-186
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Serial:
- Transactions of Society of Automotive Engineers of Japan
- Volume: 54
- Issue Number: 1
- Publisher: Society of Automotive Engineers of Japan
- ISSN: 0287-8321
- EISSN: 1883-0811
- Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Heat balance; Machine learning; Mathematical models; Neural networks; Radiators; Validation
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01876872
- Record Type: Publication
- Source Agency: Japan Science and Technology Agency (JST)
- Files: TRIS, JSTAGE
- Created Date: Mar 23 2023 10:20AM