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

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Temporal Prediction of Ice Accretion Using Reduced-order Modeling

차원축소모델을 활용한 시간에 따른 착빙 형상 예측 연구

  • Kang, Yu-Eop (Department of Aerospace Engineering, Seoul National University) ;
  • Yee, Kwanjung (Institute of Advanced Aerospace Technology, Seoul National University)
  • Received : 2021.11.09
  • Accepted : 2022.02.08
  • Published : 2022.03.01
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Abstract

The accumulated ice and snow during the operation of aircraft and railway vehicles can degrade aerodynamic performance or damage the major components of vehicles. Therefore, it is crucial to predict the temporal growth of ice for operational safety. Numerical simulation of ice is widely used owing to the fact that it is economically cheaper and free from similarity problems compared to experimental methods. However, numerical simulation of ice generally divides the analysis into multi-step and assumes the quasi-steady assumption that considers every time step as steady state. Although this method enables efficient analysis, it has a disadvantage in that it cannot track continuous ice evolution. The purpose of this study is to construct a surrogate model that can predict the temporal evolution of ice shape using reduced-order modeling. Reduced-order modeling technique was validated for various ice shape generated under 100 different icing conditions, and the effect of the number of training data and the icing conditions on the prediction error of model was analyzed.

항공기 및 철도차량 운용 중 발생하는 착빙 및 착설 현상은 공력 성능 감소와 주요 부품의 파손을 야기하기 때문에 시간에 따른 얼음 증식을 예측하는 것이 운용 안전 측면에서 매우 중요하다. 결빙수치해석은 실험적 방법에 비해 경제적으로 저렴하고 상사성 문제로부터 자유롭다는 점에서 결빙 형상을 예측하기 위한 수단으로 널리 사용되고 있다. 그러나 결빙수치해석은 착빙노출시간을 multi-step으로 나누어 매 단계별로 정상상태를 가정하는 준정상상태(quasi-steady) 가정을 이용한다. 이러한 방법은 효율적인 해석이 가능하지만 연속적인 결빙 형상을 얻지 못한다는 단점을 가지고 있다. 본 연구에서는 차원축소기법을 활용하여 결빙 형상 데이터를 보간함으로써 시간에 따른 결빙 형상을 연속적으로 예측할 수 있는 모델을 만드는 것을 목적으로 한다. 서로 다른 100개의 결빙 조건에서 형성된 결빙 데이터에 대하여 차원축소모델을 적용하였으며, 학습 데이터의 수와 결빙 조건이 차원축소모델의 예측 오차에 미치는 영향을 분석하였다.

Keywords

Acknowledgement

본 연구는 국토교통부의 재원으로 국토교통과학기술진흥원 철도기술연구사업(22RTRP-B146019-05)의 지원으로 작성되었습니다.

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