Low-Dimensional Models for Aerofoil Icing Predictions
نویسندگان
چکیده
Determining the aero-icing characteristics is key for safety assurance in aviation, but it may be a computationally expensive task. This work presents framework development of low-dimensional models application to aerofoil icing. The builds on: an adaptive sampling strategy identify local, nonlinear features across icing envelope continuous intermittent icing; classic technique based on Proper Orthogonal Decomposition, and modern Neural Network architecture. extreme diversity simulated ice shapes, from smooth streamlined rugged irregular motivated use unsupervised classification shapes. allowed deploying Decomposition locally within each sub-region, sensibly improving prediction accuracy over global model. On other hand, architecture convolutional auto-encoder were found insensitive complexity A strong correlation was exist between shape, resulting mass aerodynamic performance iced aerofoil, both terms average variance. average, rime causes loss maximum lift coefficient 21.5% compared clean thickness 0.9% chord. For glaze ice, 46.5% 2.1%. Glaze also have three times more surface coverage than ice.
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ژورنال
عنوان ژورنال: Aerospace
سال: 2023
ISSN: ['2226-4310']
DOI: https://doi.org/10.3390/aerospace10050444