Establishment of CNN and Encoder–Decoder Models for the Prediction of Characteristics of Flow and Heat Transfer around NACA Sections
The present study established two different models based on the convolutional neural network (CNN) and the encoder–decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The...
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| Vydané v: | Energies (Basel) Ročník 15; číslo 23; s. 9204 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Basel
MDPI AG
01.12.2022
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | The present study established two different models based on the convolutional neural network (CNN) and the encoder–decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well with the true data. The contours of the velocity components and the pressure coefficients reasonably explained the variation of the aerodynamic coefficients according to the geometric parameter of the NACA section. In order to physically interpret the heat transfer performance, more quantitative and qualitative information are needed owing to the lack of the correlation and the resolution of the thermal fields. Consequently, the present approaches will be useful to design the NACA section-based shape giving higher aerodynamic and heat transfer performances by quickly predicting the force and heat transfer coefficients. In addition, the predicted flow and thermal fields will provide the physical interpretation of the aerodynamic and heat transfer performances. |
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| AbstractList | The present study established two different models based on the convolutional neural network (CNN) and the encoder–decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well with the true data. The contours of the velocity components and the pressure coefficients reasonably explained the variation of the aerodynamic coefficients according to the geometric parameter of the NACA section. In order to physically interpret the heat transfer performance, more quantitative and qualitative information are needed owing to the lack of the correlation and the resolution of the thermal fields. Consequently, the present approaches will be useful to design the NACA section-based shape giving higher aerodynamic and heat transfer performances by quickly predicting the force and heat transfer coefficients. In addition, the predicted flow and thermal fields will provide the physical interpretation of the aerodynamic and heat transfer performances. |
| Audience | Academic |
| Author | Kim, Min-Il Seo, Janghoon Yoon, Hyun-Sik |
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| Cites_doi | 10.1063/5.0075784 10.1002/(SICI)1097-0363(19981215)28:9<1281::AID-FLD759>3.0.CO;2-# 10.2514/6.2017-3660 10.1007/s00521-020-05461-x 10.1007/s10973-020-09875-6 10.1007/s00466-019-01740-0 10.1038/323533a0 10.2514/1.C034415 10.1016/j.ijthermalsci.2016.12.016 10.1115/1.483224 10.2514/1.C033621 10.1016/j.ijheatmasstransfer.2021.121333 10.1016/j.applthermaleng.2016.11.187 10.1063/1.5086884 10.1016/j.ijmecsci.2021.106701 10.1016/j.tsep.2021.101011 10.1016/j.applthermaleng.2021.117908 10.1260/1756-8293.7.3.301 10.1016/0017-9310(72)90054-3 10.1038/nature14539 10.1038/s41598-022-12157-w 10.2514/6.2018-1903 10.3390/sym12040544 10.2514/1.J057894 |
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| SubjectTerms | Aerodynamics Analysis Boundary conditions computational fluid dynamics convolutional neural network Datasets Deep learning Design optimization encoder–decoder Heat transfer NACA section Neural networks Reynolds number Simulation methods Velocity |
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| Title | Establishment of CNN and Encoder–Decoder Models for the Prediction of Characteristics of Flow and Heat Transfer around NACA Sections |
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