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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Journal of Experiments in Fluid Mechanics > 2021 > 35(3): 16-21. > DOI: 10.11729/syltlx20200016

CHAI Congcong, YI Xian, GUO Lei, WANG Jun. 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

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

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Received Date: February 11, 2020
Revised Date: April 17, 2020

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

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[1]	CEBECI T, KAFYEKE F. Aircrafticing[J]. Annual Review of Fluid Mechanics, 2003, 35(1): 11-21. doi: 10.1146/annurev.fluid.35.101101.161217
[2]	王建涛, 易贤, 肖中云, 等. ARJ21-700飞机冰脱落数值模拟[J]. 空气动力学学报, 2013, 31(4): 430-436. https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX201304004.htm WANG J T, YI X, XIAO Z Y, et al. Numerical simulation of ice shedding from ARJ21-700[J]. Acta Aerodynamica Sinica, 2013, 31(4): 430-436. https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX201304004.htm
[3]	RATVASKY T P, BARNHART B P, LEE S. Current methods modeling and simulating icing effects on aircraft performance, stability, control[J]. Journal of Aircraft, 2010, 47(1): 201-211. doi: 10.2514/1.44650
[4]	闫鹏庆, 牛亚宏. 人工模拟结冰飞行试验技术研究[J]. 民用飞机设计与研究, 2018(1): 71-74. DOI: 10.19416/j.cnki.1674-9804.2018.01.013 YAN P Q, NIU Y H. Research on artificial icing flight test technology[J]. Civil Aircraft Design & Research, 2018(1): 71-74. doi: 10.19416/j.cnki.1674-9804.2018.01.013
[5]	高郭池, 丁丽, 李保良, 等. 气动除冰类飞机结冰风洞试验适航审定技术[J]. 实验流体力学, 2019, 33(2): 85-94. DOI: 10.11729/syltlx20180067 GAO G C, DING L, LI B L, et al. Airworthiness certification technology about icing wind tunnel test for pneumatic de-icing aircraft[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(2): 85-94. doi: 10.11729/syltlx20180067
[6]	李鑫, 白俊强, 王昆. 机翼积冰数值模拟[J]. 航空动力学报, 2013, 28(12): 2663-2670. DOI: 10.13224/j.cnki.jasp.2013.12.005 LI X, BAI J Q, WANG K. Numerical simulation of ice accretions on aircraft wing[J]. Journal of Aerospace Power, 2013, 28(12): 2663-2670. doi: 10.13224/j.cnki.jasp.2013.12.005
[7]	张强, 陈迎春, 周涛, 等. 民用飞机机翼结冰试验与数值预测[J]. 航空动力学报, 2017, 32(1): 22-26. DOI: 10.13224/j.cnki.jasp.2017.01.004 ZHANG Q, CHEN Y C, ZHOU T, et al. Test and numerical prediction of ice accretions on civil aircraft wing[J]. Journal of Aerospace Power, 2017, 32(1): 22-26. doi: 10.13224/j.cnki.jasp.2017.01.004
[8]	SAE. ARP5904, Airborne Icing Tankers[S]. Washington: Society of Automotive Engineers, 2007.
[9]	FOSSATI M, HABASHI W G. Multiparameter analysis of aero-icing problems using proper orthogonal decomposition and multidimensional interpolation[J]. AIAA Journal, 2013, 51(4): 946-960. doi: 10.2514/1.J051877
[10]	OGRETIM E, HUEBSCH W, SHINN A. Aircraft ice accretion prediction based on neural networks[J]. Journal of Aircraft, 2006, 43(1): 233-240. doi: 10.2514/1.16241
[11]	潘环, 艾剑良. 飞机结冰冰形预测的建模与仿真[J]. 系统仿真学报, 2014, 26(1): 221-224, 229. DOI: 10.16182/j.cnki.joss.2014.01.009 PAN H, AI J L. Modeling and simulation of aircraft ice shape prediction[J]. Journal of System Simulation, 2014, 26(1): 221-224, 229. doi: 10.16182/j.cnki.joss.2014.01.009
[12]	CHANG S N, LENG M Y, WU H W, et al. Aircraft ice accretion prediction using neural network and wavelet packet transform[J]. Aircraft Engineering and Aerospace Technology, 2016, 88(1): 128-136. doi: 10.1108/aeat-05-2014-0057
[13]	张强, 高正红. 基于神经网络的翼型积冰预测[J]. 飞行力学, 2011, 29(2): 6-9. DOI: 10.13645/j.cnki.f.d.2011.02.002 ZHANG Q, GAO Z H. Prediction of ice accretions based on the neural net[J]. Flight Dynamics, 2011, 29(2): 6-9. doi: 10.13645/j.cnki.f.d.2011.02.002
[14]	KIM H, BRAGG M. Effects of leading-edge ice accretion geometry on airfoil performance[C]//Proc of the 17th Applied Aerodynamics Conference. 1999. doi: 10.2514/6.1999-3150
[15]	BRAGG M, BROEREN A, ADDY H, et al. Airfoil ice-accretion aerodynamic simulation[R]. AIAA-2007-0085, 2007. doi: 10.2514/6.2007-85
[16]	RUFF G, ANDERSON D. Quantification of ice accretions for icing scaling evaluations[R]. AIAA-98-0915, 1998. doi: 10.2514/6.1998-195
[17]	SAE. AIR5666, Icing wind tunnel interfacility comparison tests[S]. Washington: SAE Aero-space Information Report, 2012.
[18]	易贤. 飞机积冰的数值计算与积冰试验相似准则研究[D]. 绵阳: 中国空气动力研究与发展中心, 2007. YI X. Numerical computation of aircraft icing and study on icing test scaling law[D]. Mianyang: China Aerodynamics Research and Development Center, 2007.
[19]	王小川, 史峰, 郁磊. MATLAB神经网络43个案例分析[M]. 北京: 北京航空航天大学出版社, 2013. WANG X C, SHI F, YU L. Analysis of 43 cases of MATLAB neural network[M]. Beijing: Beijing University of Aeronautics & Astronautics Press, 2013.

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