Abstract: The goal of this work is to investigate the aircraft icing severity by using numerical modeling and machine learning methods. Since the ice buildup on the wing’s leading edge may alter the original aerodynamic configuration and degrade the aerodynamic performances, the ice shape is predicted by the numerical simulation approach. This approach is developed based on the Eulerian two-phase flow theory and implemented in OpenFOAM to contribute the open-source community. The second stage of this work introduces a data-driven statistical model for aircraft icing severity evaluation. The complex process of ice accretion makes machine learning-based methods an attractive alternative to experiments and traditional numerical simulation-based approaches. Multiple conventional and ensemble machine learning models are adapted to six atmospheric and flight conditions. Multiple performance measures are employed, and the results show that proposed data-driven model has a satisfactory capability to evaluate aircraft icing severity.

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