Establishment of CNN and Encoder–Decoder Models for the Prediction of Characteristics of Flow and Heat Transfer around NACA Sections

Janghoon Seo, Hyun-Sik Yoon () and Min-Il Kim
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Janghoon Seo: Department of Naval Architecture and Ocean Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Gumjeong-gu, Busan 46241, Republic of Korea
Hyun-Sik Yoon: Department of Naval Architecture and Ocean Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Gumjeong-gu, Busan 46241, Republic of Korea
Min-Il Kim: Department of Naval Architecture and Ocean Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Gumjeong-gu, Busan 46241, Republic of Korea

Energies, 2022, vol. 15, issue 23, 1-18

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.

Keywords: convolutional neural network; encoder–decoder; aerodynamics; heat transfer; NACA section; computational fluid dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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