Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Prediction of Vibration Characteristics of a Composite Rotor Blade via Deep Neural Networks

심층신경망을 이용한 복합재 로터 블레이드의 진동특성 예측

  • Yoo, Seungho (Department of Aerospace Engineering, Jeonbuk National University) ;
  • Jeong, Inho (Department of Aerospace Engineering, Jeonbuk National University) ;
  • Kim, Hyejin (Department of Aerospace Engineering, Jeonbuk National University) ;
  • Cho, Haeseong (Future Air Mobility Research Center, Jeonbuk National University) ;
  • Kim, Taejoo (Korea Aerospace Research Institute) ;
  • Kee, Youngjung (Korea Aerospace Research Institute)
  • Received : 2022.02.19
  • Accepted : 2022.04.04
  • Published : 2022.05.01
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Abstract

In this paper, a deep neural network(DNN) model for predicting the vibration characteristics of the composite rotor blade with c-spar cross section was developed. Herein, the present DNN model is defined by using the natural frequencies obtained through the in-house code based on the nonlinear co-rotational(CR) shell element. For the present DNN model, the accuracy of the model was evaluated via the data with a random distribution of thickness and a tendency to decrease in thickness along the blade span.

본 논문에서는 c-스파 단면을 갖는 복합재 로터 블레이드에 대해 co-rotational(CR) 이론 기반 비선형 쉘 요소를 사용하는 In-house code를 통해 고유진동수를 구하고, 이를 이용하여 블레이드의 진동특성을 예측하는 심층신경망 모델을 개발하였다. 심층신경망 모델은 무작위 두께 분포를 갖는 데이터와 스팬 방향으로 두께 감소 경향성을 보이는 데이터를 통해 심층신경망 모델의 정확성을 평가하였다.

Keywords

Acknowledgement

본 연구는 2021년 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(No. 2020R1C1C1006006)과 한국전력공사의 2021년 착수 기초연구 개발 과제연구비의 지원(No. R21XO01-6)을 받아 수행된 연구입니다.

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