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http://dx.doi.org/10.5139/JKSAS.2022.50.3.147

Temporal Prediction of Ice Accretion Using Reduced-order Modeling  

Kang, Yu-Eop (Department of Aerospace Engineering, Seoul National University)
Yee, Kwanjung (Institute of Advanced Aerospace Technology, Seoul National University)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.50, no.3, 2022 , pp. 147-155 More about this Journal
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.
Keywords
Aircraft Icing; Reduced-order Modeling; Proper Orthogonal Decomposition;
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