한국가시화정보학회지 (Journal of the Korean Society of Visualization)

  • 제20권3호
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  • Pages.109-116
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  • 2022
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  • 1598-8430(pISSN)
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  • 2093-808X(eISSN)
한국가시화정보학회 (The Korean Society of Visualization)
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VGG16 과 U-Net 구조를 이용한 공력특성 예측

Prediction of aerodynamics using VGG16 and U-Net

  • Bo Ra, Kim (Division of Mechanical Engineering, Korea Maritime and Ocean University) ;
  • Seung Hun, Lee (Division of Mechanical Engineering, Korea Maritime and Ocean University) ;
  • Seung Hyun, Jang (Division of Mechanical Engineering, Korea Maritime and Ocean University) ;
  • Gwang Il, Hwang (Division of Mechanical Engineering, Korea Maritime and Ocean University) ;
  • Min, Yoon (Division of Mechanical Engineering, Korea Maritime and Ocean University)
  • 투고 : 2022.10.31
  • 심사 : 2022.11.18
  • 발행 : 2022.11.30
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초록

The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.

키워드

과제정보

본 연구는 한국연구재단의 지원 (No. 2021-R1F1A1053438)을 받아 수행되었습니다.

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