| 基于BP神经网络的冰形特征参数预测 |
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| 引用本文： | 柴聪聪,易贤,郭磊,王俊.基于BP神经网络的冰形特征参数预测[J].实验流体力学,2021,35(3):16-21. |
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| 作者姓名： | 柴聪聪 易贤 郭磊 王俊 |
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| 作者单位： | 1.电子科技大学 计算机科学与工程学院, 成都 611731 |
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| 基金项目： | 国家自然科学基金11472296 |
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| 摘 要： | 机翼结冰影响了飞机飞行的气动特性，严重时将会引起事故，对冰形特征参数进行预测对翼型气动特性研究以及后续防除冰措施具有重要的意义。本文利用BP神经网络，建立翼型冰形特征参数预测模型，并采用k折交叉验证进行网络结构选择，以气象与飞行条件作为输入，结冰极限、冰角高度和角度等冰形特征参数作为输出。结果表明:预测的冰形特征参数（除下冰角高度外）与数值结果相对误差低于5%，证明该方法具有较好的预测效果。
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| 关 键 词： | 机翼结冰 冰形特征参数 神经网络 k折交叉验证 |
| 收稿时间： | 2020-02-12 |
Prediction of ice shape characteristic parameters based on BP nerual network |
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| Affiliation: | 1.School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China2.Key Laboratory of Icing and Anti/De-icing, China Aerodynamics Research and Development Center, Mianyang Sichuan 621000, China |
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| Abstract: | Airfoil icing affects the aerodynamic characteristics of aircraft flight, which can lead to accidents when it is serious. The prediction of ice shape parameters can effectively prevent accidents. In this paper, BP neural network is used to establish the prediction model of airfoil ice shape characteristic parameters, and k-fold cross validation is used to select the network structure, in which the meteorological and flight conditions are the inputs, and ice shape characteristic parameters such as the ice limit, the ice angle height and angle are the outputs. The experimental results show that the relative error between the predicted ice shape parameters (except for the height of the lower ice angle) and the numerical results is less than 5%, which proves that the method has a good prediction ability. |
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