Purpose

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

Design/methodology/approach

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

Findings

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

Originality/value

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

中文翻译：

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基于几何约束增强神经网络的飞机积冰预测

 目的

本研究的目的是利用具有几何约束的深度学习建立一种新颖的机翼结冰预测模型，称为几何约束增强神经网络，与非几何约束模型相比，提高预测精度。

设计/方法论/途径

该模型以飞行速度、环境温度、液态水含量、中位体积直径和结冰时间作为输入，以结冰厚度作为输出。为了提高结冰预测的准确性，该模型在损失函数中加入了几何约束。然后根据数值模拟获得的二维NACA0012翼型结冰样本对模型进行训练。

 发现

结果表明，几何约束的介入通过减弱波动特征的出现，有效地提高了冰形的预测精度。经过训练后，机翼结冰预测模型可用于快速预测机翼结冰。

 原创性/价值

这项工作涉及机翼结冰预测模型中的几何约束。所提出的模型在飞机结冰的快速评估方面具有合理的能力。