Prediction of ice shape characteristic parameters based on BP nerual network

CHAI Congcong; YI Xian; GUO Lei; WANG Jun

doi:10.11729/syltlx20200016

Volume 35 Issue 3

Jun. 2021

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CHAI Congcong, YI Xian, GUO Lei, et al. Prediction of ice shape characteristic parameters based on BP nerual network[J]. Journal of Experiments in Fluid Mechanics, 2021, 35(3): 16-21. doi: 10.11729/syltlx20200016

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Prediction of ice shape characteristic parameters based on BP nerual network

doi: 10.11729/syltlx20200016

CHAI Congcong^{1, 2
,},
YI Xian^{2
,
,},
GUO Lei^{1
,
,},
WANG Jun¹

1.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
Key Laboratory of Icing and Anti/De-icing, China Aerodynamics Research and Development Center, Mianyang Sichuan 621000, China

Received Date: 2020-02-12
Rev Recd Date: 2020-04-18
Publish Date: 2021-06-25

Abstract

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.
- airfoil icing,
- ice shape characteristic parameters,
- neural network,
- k-fold cross validation

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References(19)

References

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