Development of a Real-Time In-Flight Ice Detection System via Computational Aeroacoustics and Bayesian Neural Networks

Reliable real-time detection of ice formation is a critical enabling technology in improving rotorcraft safety. The development of a real-time in-flight ice detection system using computational aeroacoustics and Bayesian neural networks is presented and evaluated within this paper. A finite NACA0012 airfoil section undergoing sinusoidal pitching is used to represent the cyclic motion characteristic of a rotor in forward flight. Experimental ice shape measurements from the NASA Glenn Icing Research Wind Tunnel are used for validation of the numerical ice shapes. The flow-fields of the computed ice shapes are then determined using a hybrid RANS/LES approach and the acoustic noise signals of the ice shapes are predicted using the solid-surface Ffowcs Williams and Hawkings (FWH) analogy. A Bayesian neural network is then trained using the ice shapes and the performance indicators are mapped to the acoustic noise signals. This framework thus allows for the detection of ice and significantly the type of ice accreted over the pitching wing through differentiating between iced noise signals.