Low-Dimensional Models for Aerofoil Icing Predictions

Abstract

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

Keywords

• icing
• aerodynamics
• POD
• neural networks
• convolutional auto-encoder
• stall characteristics