Prediction of icing wind tunnel temperature field with machine learning

ZHANG Xinghuan; ZHANG Pingtao; PENG Bo; YI Xian

doi:10.11729/syltlx20210196

Volume 36 Issue 5

Sep. 2022

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Journal of Experiments in Fluid Mechanics > 2022 > 36(5): 8-15. > DOI: 10.11729/syltlx20210196

ZHANG X H，ZHANG P T，PENG B，et al. Prediction of icing wind tunnel temperature field with machine learning[J]. Journal of Experiments in Fluid Mechanics, 2022，36（5）：8-15.. DOI: 10.11729/syltlx20210196

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Prediction of icing wind tunnel temperature field with machine learning

ZHANG Xinghuan^{1, 2,},
ZHANG Pingtao²,
PENG Bo^1, ,,
YI Xian^2, ,

1.
School of Computer Science, Southwest Petroleum University, Chengdu　610500, China
2.
Key Laboratory of Icing and Anti/De-icing, China Aerodynamics Research and Development Center, Mianyang　621000, China

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Received Date: December 30, 2021
Revised Date: April 20, 2022
Accepted Date: April 24, 2022

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Abstract

Abstract

Icing wind tunnel is an important infrastructure for research on aircraft icing and anti-deicing in which refrigeration system realizes precise control of the airflow temperature in the wind tunnel by adjusting the suction pressure of the compressor unit. The suction pressure control and cooling methods affect the wind tunnel test efficiency. In this paper, aiming at accurate prediction of compressor suction pressure, the support vector regression （APSO–SVR） optimized by adaptive particle swarm algorithm is used to establish pressure prediction model to conduct pressure prediction and experimental research. In order to further improve the efficiency of icing wind tunnel testing, multi-layer perceptron （MLP） neural network is used to establish an analysis model to analyze the influence of test parameters on the cooling rate of wind tunnel. The results show that the average absolute percentage error （E_MAP） between the predicted and test value of the compressor suction pressure is less than 4%, and the mean square error （E_MS） is less than 0.003; the parameters affecting the wind tunnel cooling rate are mainly airflow density, test wind speed, compressor suction pressure and the initial temperature of the heat exchanger outlet. Among them, the compressor suction pressure has the most significant effect on it.
- icing wind tunnel,
- refrigeration system,
- compressor suction pressure,
- cooling rate,
- APSO–SVR,
- MLP

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[1]	林贵平, 卜雪琴, 申晓斌. 飞机结冰与防冰技术[M]. 北京: 北京航空航天大学出版社, 2016. LIN G P, BU X Q, SHEN X B. Aircraft icing and anti-icing technology[M]. Beijing: Beijing University of Aeronautics & Astronautics Press, 2016.
[2]	VAN-ZANTE J, IDE R, STEEN L C. NASA Glenn icing research tunnel: 2012 cloud calibration procedure and results[C]//Proc of the 4th AIAA Atmospheric and Space Environments Conference. 2012: 2933. doi: 10.2514/6.2012-2933 http://dx. doi.org/10.2514/6.2012-2933
[3]	郭向东,张平涛,张珂,等. 3 m × 2 m结冰风洞热流场品质提高及评估[J]. 实验流体力学,2021,35(4):41-51. DOI: 10.11729/syltlx20200118 GUO X D,ZHANG P T,ZHANG K,et al. Improvement and evaluation of thermal flow-field quality in CARDC icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2021,35(4):41-51. doi: 10.11729/syltlx20200118
[4]	柴聪聪,王强,易贤,等. 基于卷积神经网络的结冰翼型气动参数预测[J]. 飞行力学,2021,39(5):13-18. DOI: 10.13645/j.cnki.f.d.20210811.001 CHAI C C,WANG Q,YI X,et al. Aerodynamic parameters prediction of airfoil ice accretion based on convolutional neural network[J]. Flight Dynamics,2021,39(5):13-18. doi: 10.13645/j.cnki.f.d.20210811.001
[5]	陈海,钱炜祺,何磊. 基于深度学习的翼型气动系数预测[J]. 空气动力学学报,2018,36(2):294-299. CHEN H,QIAN W Q,HE L. Aerodynamic coefficient prediction of airfoils based on deep learning[J]. Acta Aerodynamica Sinica,2018,36(2):294-299.
[6]	何磊,钱炜祺,易贤,等. 基于转置卷积神经网络的翼型结冰冰形图像化预测方法[J]. 国防科技大学学报,2021,43(3):98-106. DOI: 10.11887/j.cn.202103013 HE L,QIAN W Q,YI X,et al. Graphical prediction method of airfoil ice shape based on transposed convolution neural networks[J]. Journal of National University of Defense Technology,2021,43(3):98-106. doi: 10.11887/j.cn.202103013
[7]	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
[8]	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
[9]	SURESH S,OMKAR S N,MANI V,et al. Lift coefficient prediction at high angle of attack using recurrent neural network[J]. Aerospace Science and Technology,2003,7(8):595-602. doi: 10.1016/S1270-9638(03)00053-1
[10]	兰其龙. 基于神经网络预测控制的低温风洞多变量控制策略研究[D]. 绵阳: 中国空气动力研究与发展中心, 2016. LAN Q L. Research on multivariable control strategy based on neural network predictive control in cryogenic wind tunnel[D]. Mianyang: China Aerodynamics Research and Development Center, 2016.
[11]	郭向东,柳庆林,赖庆仁,等. 大型结冰风洞气流场适航符合性验证[J]. 空气动力学学报,2021,39(2):184-195. DOI: 10.7638/kqdlxxb-2019.0086 GUO X D,LIU Q L,LAI Q R,et al. Airworthiness compliance verification of aerodynamic flowfield of a large-scale icing wind tunnel[J]. Acta Aerodynamica Sinica,2021,39(2):184-195. doi: 10.7638/kqdlxxb-2019.0086
[12]	郭向东,张平涛,赵献礼,等. 大型结冰风洞热流场符合性验证[J]. 实验流体力学,2020,34(5):79-88. DOI: 10.11729/syltlx20190113 GUO X D,ZHANG P T,ZHAO X L,et al. The compliance verification of thermodynamic flowfield in the large icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2020,34(5):79-88. doi: 10.11729/syltlx20190113
[13]	KENNEDY J, EBERHART R C. Particle swarm optimization[C]//Proc of the Proceedings of ICNN'95 - International Conference on Neural Networks. 1995. doi: 10.1109/ICNN. 1995.488968 http://dx. doi.org/10.1109/ICNN.1995.488968
[14]	EBERHART R C, SHI Y H. Particle swarm optimization: developments, applications and resources[C]//Proceedings of the 2001 Congress on Evolutionary Computation （IEEE Cat. No. 01TH8546）. 2001. doi: 10.1109/CEC. 2001.934374 http://dx. doi.org/10.1109/CEC.2001.934374
[15]	SHI Y H, EBERHART R C. A modified particle swarm optimizer[C]//Proc of IEEE Icec Conference. 2009
[16]	CORTES C,VAPNIK V. Support-vector networks[J]. Machine Learning,1995,20(3):273-297. doi: 10.1007/BF00994018
[17]	HAYKIN S. 神经网络与机器学习[M]. 北京: 机械工业出版社, 2011. HAYKIN S. Neural networks and machine learning[M]. Beingjing: Neural Networks and Machine Learning, 2011.
[18]	BURGES C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery,1998,2(2):121-167. doi: 10.1023/A:1009715923555
[19]	ABOUHAWWASH M,SEADA H,DEB K. Towards faster convergence of evolutionary multi-criterion optimization algorithms using Karush Kuhn Tucker optimality based local search[J]. Computers & Operations Research,2017,79:331-346. doi: 10.1016/j.cor.2016.04.026
[20]	韩玲. 基于人工神经网络: 多层感知器(MLP)的遥感影像分类模型[J]. 测绘通报,2004(9):29-30,42. DOI: 10.3969/j.issn.0494-0911.2004.09.010 HAN L. The classification model of RS images based on artificial neural network—MLP[J]. Bulletin of Surveying and Mapping,2004(9):29-30,42. doi: 10.3969/j.issn.0494-0911.2004.09.010

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