Ice cloud parameter identification method in icing wind tunnel based on multimodal fusion
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摘要: 结冰风洞云雾场校测通常存在仪器依赖度高的问题。针对该问题，提出了一种基于多模态融合的结冰风洞云雾参数辨识方法，该方法以试验模型结冰图像及迎角、来流速度、来流温度、结冰时长等参数作为输入，提取并融合两者特征参数，以液态水含量和水滴平均体积直径作为输出训练神经网络模型，进而实现对结冰风洞云雾参数的快速辨识。为验证该方法的有效性和可行性，以NACA0012标准翼型结冰为研究对象，开发了结冰风洞云雾参数辨识程序，分析了融合比例的影响，获得了适用于结冰风洞云雾参数辨识的最佳网络模型。在此基础上，开展了仿真和试验评估，结果表明：所提出的方法对液态水含量和水滴平均体积直径的辨识满度误差均小于12%，具有较高的辨识精度与良好的泛化性能，可为结冰风洞云雾参数辨识提供补充。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.
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图 1 基于多模态融合的结冰风洞云雾参数辨识方法流程图
Figure 1. Flow block diagram of cloud parameter identification method based on multimodal fusion
图 8 3种工况下的NACA0012翼型结冰冰形图像
Figure 8. The NACA0012 airfoil is frozen in 3 operating conditions
表 1 结冰工况参数设置
Table 1. Icing parameter setting
参数 取值 迎角α/（°） 0，3 来流速度v/（m·s−1） 80，100 来流温度T/（℃） −30，−20，−10 液态水含量LWC/（g·m−3） 0.1～1.0 水滴平均体积直径MVD/μm 20～100 结冰时长t/s 1350 
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表 2 测试集1的结冰工况
Table 2. Test set 1 icing conditions
算例 迎角
/（°）来流速度
/（m·s−1）来流
温度/（℃）结冰
时长/s水滴平均体积
直径/μm液态水含量
/（g·m−3）a 0 80 −20 1350 20 0.25 b 3 80 −30 1350 26 0.61 c 0 100 −20 1350 26 0.76 d 3 80 −20 1350 38 0.91 e 0 80 −10 1350 47 0.76 f 0 80 −20 1350 56 0.94 g 3 100 −10 1350 65 0.25 h 0 80 −30 1350 74 0.40 i 3 80 −30 1350 89 0.22
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表 3 测试集2的结冰工况
Table 3. Test set 2 icing conditions
算例 迎角
/（°）来流速度
/（m·s−1）来流
温度/（℃）结冰
时长/s水滴平均
体积直径/μm液态水含量
/（g·m−3）a 0 67.1 −23 1500 20 0.50 b 3 67.1 −23 1500 20 0.50 c 3 67.1 −7 1500 20 0.50
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表 4 测试集1的MVD与LWC辨识结果及满度误差
Table 4. Test set 1 MVD and LWC identification results and errors
算例 MVDT/μm MVDP/μm MVDe LWCT/（g·m−3） LWCP/（g·m−3） LWCe a 20 20.410 0.51% 0.25 0.251 0.11% b 26 27.891 2.36% 0.61 0.612 0.22% c 26 27.699 2.12% 0.76 0.756 0.44% d 38 38.634 0.79% 0.91 0.898 1.33% e 47 46.579 0.53% 0.76 0.745 1.67% f 56 56.677 0.85% 0.94 0.921 2.11% g 65 65.396 0.50% 0.25 0.251 0.11% h 74 74.582 0.73% 0.40 0.392 0.89% i 89 89.623 0.78% 0.22 0.224 0.44%
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表 5 测试集2的MVD与LWC辨识结果及满度误差
Table 5. Test set 2 MVD and LWC identification results and errors
算例 MVDT/μm MVDP/μm MVDe LWCT/（g·m−3） LWCP/（g·m−3） LWCe a 20 22.713 3.39% 0.50 0.528 3.11% b 20 23.504 4.38% 0.50 0.562 6.89% c 20 23.269 4.08% 0.50 0.541 4.56%
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表 6 测试集3的试验工况
Table 6. Test set 3 icing conditions
工况 迎角/（°） 来流速度/（m·s−1） 来流温度/（℃） 结冰时长/s 水滴平均体积直径/μm 液态水含量/（g·m−3） a 0 67.1 −23 1500 20 0.50 b 3 67.1 −23 1500 20 0.50 c 3 67.1 −7 1500 20 0.50
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表 7 测试集3的MVD与LWC辨识结果及满度误差
Table 7. Test set 3 MVD and LWC identification results and errors
算例 MVDT/μm MVDP/μm MVDe LWCT/（g·m−3） LWCP/（g·m−3） LWCe a 20 24.142 5.17% 0.50 0.552 5.78% b 20 25.129 6.41% 0.50 0.601 11.20% c 20 23.864 4.83% 0.50 0.578 8.67%
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