基于深度神经网络的任意翼型结冰预测方法

屈经国; 彭博; 易贤; 马乙楗

doi:10.7638/kqdlxxb-2022.0116

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基于深度神经网络的任意翼型结冰预测方法

研究论文 | 更新时间：2026-05-28

- 基于深度神经网络的任意翼型结冰预测方法
- Icing prediction method for arbitrary airfoil using deep neural networks
- 空气动力学学报 2023年41卷第7期页码：48-55
- 作者机构：
  
  1. 西南石油大学计算机科学学院,成都,610500
  2. 中国空气动力研究与发展中心结冰与防除冰重点实验室,绵阳,621000
  3. 空气动力学国家重点实验室,绵阳,621000
- 作者简介：
  
  屈经国（1996-），男，硕士研究生，研究方向：深度学习与飞机结冰. E-mail：xvyn@qq.com
  易贤*（1977-），男，研究员，研究方向：航空宇航科学与技术. E-mail：yixian_2000@163.com
- 基金信息：
  
  国家自然科学重点基金（12132019）;国家自然科学面上基金（12172372）;国家重大科技专项（J2019-III-0010-0054）
- DOI：10.7638/kqdlxxb-2022.0116
  中图分类号： V211.41
- 纸质出版：2023
- 稿件说明：
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屈经国, 彭博, 易贤, 等. 基于深度神经网络的任意翼型结冰预测方法[J]. 空气动力学学报, 2023, 41(7): 48−55.

QU Jingguo, PENG Bo, YI Xian, et al. Icing prediction method for arbitrary airfoil using deep neural networks[J]. 2023, 41(7): 48-55.
屈经国, 彭博, 易贤, 等. 基于深度神经网络的任意翼型结冰预测方法[J]. 空气动力学学报, 2023, 41(7): 48−55. DOI： 10.7638/kqdlxxb-2022.0116

QU Jingguo, PENG Bo, YI Xian, et al. Icing prediction method for arbitrary airfoil using deep neural networks[J]. 2023, 41(7): 48-55. DOI： 10.7638/kqdlxxb-2022.0116.

摘要

目前针对翼型结冰的机器学习预测模型只能预测特定翼型或某一类翼型的结冰情况，尚不具备面向任意翼型进行结冰预测的普适性。为解决该问题，提出了一种适用于低速不可压流动、基于深度神经网络的任意翼型结冰预测方法。该方法采用翼型压力系数对任意翼型特征进行抽象，结合流场与云雾场等参数共同作为输入，使用二维冰形曲线傅里叶级数拟合系数作为输出。基于该思路，建立了一种基于深度神经网络的预测模型，初步实现了任意翼型的结冰预测。多种算例实验结果表明，提出的方法针对单一翼型或任意翼型均表现出良好的冰形预测效果，预测冰形的主要特征参数相对误差均不超过15%。

Abstract

The icing of the aircraft will affect the aerodynamic performance and is the main factor affecting flight safety. Accurate prediction of ice shape can provide strong support for anti-icing work and is of great significance for ensuring flight safety. The traditional numerical icing research methods can hardly meet the requirements of ice accumulation evaluation under multiple icing conditions. Neural network methods provide a robust way for the ice prediction task. The current machine-learning prediction model for airfoil icing can only predict the icing shape of a specific airfoil or a class of airfoils and does not have the universality of icing prediction for a general airfoil. To solve this problem

a deep neural network-based icing prediction method for a general airfoil is proposed

which is suitable for low-speed incompressible flow. The method uses the airfoil pressure coefficient to abstract the characteristics of airfoils

combines the parameters of the flow field and the cloud field as the input

and uses the Fourier series fitting coefficient of the two-dimensional ice curve as the output. By this means

a prediction model using a deep neural network is established

and the icing prediction task of a general airfoil is preliminarily realized. The experimental results of various examples show that the proposed method has a good ice shape prediction effect for a single airfoil or a general airfoil

and the relative error of the main characteristic parameters of the ice shape prediction is not more than 15%.

关键词

Keywords

references

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