Ice cloud parameter identification method in icing wind tunnel based on multimodal fusion

XIE Teng; XIONG Hao; PENG Bo; YI Xian

doi:10.11729/syltlx20220077

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XIE T，XIONG H，PENG B，et al. Ice cloud parameter identification method in icing wind tunnel based on multimodal fusion[J]. Journal of Experiments in Fluid Mechanics, 2022，36（X）：1-8.. DOI: 10.11729/syltlx20220077

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Ice cloud parameter identification method in icing wind tunnel based on multimodal fusion

XIE Teng^{1, 2,},
XIONG Hao²,
PENG Bo¹,
YI Xian^{2, 3, ,}

1.
School of Computer Science, Southwest Petroleum University, Chengdu　610500, China
2.
Key Laboratory of Icing and Anti/Deicing, China Aerodynamics Research and Development Center, Mianyang　621000, China
3.
State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Center, Mianyang　621000, China

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Received Date: August 15, 2022
Revised Date: September 19, 2022
Accepted Date: September 26, 2022
Available Online: November 02, 2022

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Abstract

Abstract

The cloud field calibration of icing wind tunnels usually has the disadvantage of high instrument dependence. To solve this problem, this paper proposes a method for identifying the parameters of cloud fields in icing wind tunnels based on multi-modal fusion. This method takes the icing image of the test model together with the parameters such as the angle of attack, air velocity, air temperature, and icing duration of the model as input, extracts and fuses the two characteristic parameters, and takes the liquid water content （LWC） and the average volume diameter of water droplets （MVD） as the output to train the neural network model. And then the rapid identification of icing cloud parameters is realized. In order to verify the effectiveness and feasibility of the proposed method, the paper takes NACA0012 airfoil icing as the research object, develops the cloud field identification program of the icing wind tunnel, analyzes the influence of the fusion proportion, and obtains the best network model suitable for ice parameter identification. On this basis, simulation and experimental evaluation are carried out. The full scale error of the proposed method for LWC and MVD is less than 12%, which has high identification accuracy and good generalization performance, and can provide an important supplement for the identification of cloud fields in the icing wind tunnel.
- icing wind tunnel,
- cloud and fog field measurement,
- icing condition,
- icing image,
- multimodal fusion,
- parameter identification

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[1]	郭向东,张平涛,赵照,等. 大型结冰风洞云雾场适航应用符合性验证[J]. 航空学报,2020,41(10):123879. DOI: 10.7527/S1000-6893.2020.23879 GUO X D,ZHANG P T,ZHAO Z,et al. Airworthiness application compliance verification of cloud flowfield in large icing wind tunnel[J]. Acta Aeronautica et Astronautica Sinica,2020,41(10):123879. doi: 10.7527/S1000-6893.2020.23879
[2]	战培国. 结冰风洞校准标准研究[J]. 标准科学,2020(5):87-90. DOI: 10.3969/j.issn.1674-5698.2020.05.014 ZHAN P G. Research on standards of icing wind tunnel calibration[J]. Standard Science,2020(5):87-90. doi: 10.3969/j.issn.1674-5698.2020.05.014
[3]	周靓,战培国,王梓旭. 结冰风洞云雾场校准标准初探[J]. 标准科学,2020(9):94-98. DOI: 10.3969/j.issn.1674-5698.2020.09.019 ZHOU L,ZHAN P G,WANG Z X. Preliminary study on calibration standard of cloud field in icing wind tunnel[J]. Standard Science,2020(9):94-98. doi: 10.3969/j.issn.1674-5698.2020.09.019
[4]	易贤,桂业伟,肖春华,等. 结冰风洞液态水含量测量方法研究[J]. 科技导报,2009,27(21):86-90. DOI: 10.3321/j.issn:1000-7857.2009.21.018 YI X,GUI Y W,XIAO C H,et al. A method of liquid water content measurement in icing wind tunnel[J]. Science & Technology Review,2009,27(21):86-90. doi: 10.3321/j.issn:1000-7857.2009.21.018
[5]	赖庆仁,郭龙,李明,等. 结冰风洞液态水含量测量装置设计与实现[J]. 空气动力学学报,2016,34(6):750-755. DOI: 10.7638/kqdlxxb-2015.0137 LAI Q R,GUO L,LI M,et al. Design and implementation of the device for liquid water content measurement in icing wind tunnel[J]. Acta Aerodynamica Sinica,2016,34(6):750-755. doi: 10.7638/kqdlxxb-2015.0137
[6]	战培国. 结冰云小水滴粒径测量设备综述[J]. 测控技术,2020,39(6):1-7. DOI: 10.19708/j.ckjs.2020.04.217 ZHAN P G. Review of measuring instruments for water droplet sizing in icing clouds[J]. Measurement & Control Technology,2020,39(6):1-7. doi: 10.19708/j.ckjs.2020.04.217
[7]	HOVENAC E, IDE R. Performance of the forward scattering spectrometer probe in NASA's Icing Research Tunnel[C]// Proc of the 27th Aerospace Sciences Meeting. 1989: 769. doi: 10.2514/6.1989-769
[8]	VECCHIONE L, DE MATTEIS P. An overview of the CIRA icing wind tunnel[C]//Proc of the 41st Aerospace Sciences Meeting and Exhibit. 2003: 900. doi: 10.2514/6.2003-900
[9]	王梓旭,沈浩,郭龙,等. 3m×2m结冰风洞云雾参数校测方法[J]. 实验流体力学,2018,32(2):61-67. DOI: 10.11729/syltlx20170163 WANG Z X,SHEN H,GUO L,et al. Cloud calibration method of 3m×2m icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2018,32(2):61-67. doi: 10.11729/syltlx20170163
[10]	AC-9C Aircraft Icing Technology Committee. Calibration and acceptance of icing wind tunnels[R/OL]. SAE International[2022-06-24]. https://www.sae.org/content/arp5905. doi: 10.4271/ARP5905
[11]	何俊,张彩庆,李小珍,等. 面向深度学习的多模态融合技术研究综述[J]. 计算机工程,2020,46(5):1-11. DOI: 10.19678/j.issn.1000-3428.0057370 HE J,ZHANG C Q,LI X Z,et al. Survey of research on multimodal fusion technology for deep learning[J]. Computer Engineering,2020,46(5):1-11. doi: 10.19678/j.issn.1000-3428.0057370
[12]	易贤,王强,柴聪聪,等. 基于深度神经网络的飞机结冰冰形预测模型[J]. 南京航空航天大学学报(英文版),2021,38(4):535-544. DOI: 10.16356/j.1005-1120.2021.04.001 YI X,WANG Q,CHAI C C,et al. Prediction model of aircraft icing based on deep neural network[J]. Transactions of Nanjing University of Aeronautics and Astronautics,2021,38(4):535-544. doi: 10.16356/j.1005-1120.2021.04.001
[13]	易贤,朱国林,王开春,等. 翼型积冰的数值模拟[J]. 空气动力学学报,2002,20(4):428-433. DOI: 10.3969/j.issn.0258-1825.2002.04.009 YI X,ZHU G L,WANG K C,et al. Numerically simulating of ice accretion on airfoil[J]. Acta Aerodynamica Sinica,2002,20(4):428-433. doi: 10.3969/j.issn.0258-1825.2002.04.009
[14]	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
[15]	柴聪聪,易贤,郭磊,等. 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. DOI: 10.11729/syltlx20200016 CHAI C C,YI X,GUO L,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
[16]	HUBER P J. Robust estimation of a location parameter[J]. The Annals of Mathematical Statistics,1964,35(1):73-101. doi: 10.1214/aoms/1177703732
[17]	JAMES G, WITTEN D, HASTIE T, et al. An introduction to statistical learning: with Applications in R[M]. New York: Springer, 2013.
[18]	易贤,郭龙,符澄,等. 结冰风洞试验段水滴分布特性分析[J]. 实验流体力学,2016,30(3):2-7,1. DOI: 10.11729/syltlx20160034 YI X,GUO L,FU C,et al. Analysis of water droplets distribution in the test section of an icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2016,30(3):2-7,1. doi: 10.11729/syltlx20160034

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