Transactions of Nanjing University of Aeronautics & Astronautics
Prediction Model of Aircraft Icing Based on Deep Neural Network
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Abstract:
Icing is an important factor threatening aircraft flight safety. According to the requirements of airworthiness regulations， aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions. Due to the complexity of the icing process， the rapid assessment of ice shape remains an important challenge. In this paper， an efficient prediction model of aircraft icing is established based on the deep belief network （DBN） and the stacked auto-encoder （SAE）， which are all deep neural networks. The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation. After that the model is applied on the ice shape evaluation of NACA0012 airfoil. The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction. The model provides an important tool for aircraft icing analysis.
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Project Supported:
This work was supported in part by the National Natural Science Foundation of China （No.51606213） and the National Major Science and Technology Projects （No.J2019-III-0010-0054）.
Clc Number:
O359
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