Prediction Model of Aircraft Icing Based on Deep Neural Network

基于深度神经网络的飞机结冰冰形预测模型

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.

结冰是威胁航空飞行安全的重要因素,适航条例要求需要根据不同结冰工况开展结冰安全评估。由于飞机结冰过程十分复杂,快速预测结冰冰形仍是当前面临的重要挑战。基于深度置信神经网络(Deep belief network,DBN)及栈式自动编码器(Stacked autoencoder,SAE)深度神经网络建立了一种高效的飞机结冰预测模型。在完成网络结构详细设计的基础上,利用结冰数值计算方法构建的冰形样本空间,实现神经网络训练。以NACA0012翼型为研究对象,开展了冰形预测研究。结果表明:构建的预测模型能够准确地捕捉飞机结冰过程中的非线性行为,进而实现冰形的高准确度预测。预测模型为飞机结冰分析提供了一种有效的方法。