한국가시화정보학회지 (Journal of the Korean Society of Visualization)

  • 제22권1호
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  • Pages.49-60
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  • 2024
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  • 1598-8430(pISSN)
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  • 2093-808X(eISSN)
한국가시화정보학회 (The Korean Society of Visualization)
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DNN과 Decoder 모델 구축을 통한 생체모방 3차원 파형 익형의 유체역학적 특성 예측

Establishment of DNN and Decoder models to predict fluid dynamic characteristics of biomimetic three-dimensional wavy wings

  • Minki Kim (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Hyun Sik Yoon (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Janghoon Seo (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Min Il Kim (Department of Naval Architecture and Ocean Engineering, Pusan National University)
  • 투고 : 2024.03.08
  • 심사 : 2024.03.15
  • 발행 : 2024.03.31
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초록

The purpose of this study establishes the deep neural network (DNN) and Decoder models to predict the flow and thermal fields of three-dimensional wavy wings as a passive flow control. The wide ranges of the wavy geometric parameters of wave amplitude and wave number are considered for the various the angles of attack and the aspect ratios of a wing. The huge dataset for training and test of the deep learning models are generated using computational fluid dynamics (CFD). The DNN and Decoder models exhibit quantitatively accurate predictions for aerodynamic coefficients and Nusselt numbers, also qualitative pressure, limiting streamlines, and Nusselt number distributions on the surface. Particularly, Decoder model regenerates the important flow features of tiny vortices in the valleys, which makes a delay of the stall. Also, the spiral vortical formation is realized by the Decoder model, which enhances the lift.

키워드

과제정보

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

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