Fluorescent oil film thickness approximate efficiency measurement based on Elman
-
摘要:
为解决荧光油膜灰度与厚度现标定方法（微小高度装置法）存在采集工况复杂、标定周期较长等缺陷，提出了一种新的近效测量方法，该方法只需极少量标定数据，便可达到与现标定方法相近的效果。近效方法利用了Elman动态神经网络对小样本数据进行量程扩展并引入一维插值算法使扩展后的数据项平滑，对解算出的三维荧光油膜厚度数据采用二维插值算法进行二次平滑以求得完整的厚度分布图。经模拟试验结果显示，通过该近效方法进行厚度测量最终可以清晰、准确、定量地显示荧光油膜厚度分布，与目前广泛使用的微小高度装置法相比效果相近，在荧光油膜汇集处（较厚区域）误差最大不超过±2.5 μm，在平滑适中及较薄区域误差不超过±2 μm，达到荧光油膜厚度工程测量标准，为飞行器全局摩阻测量提供了一种新标定思路，具有一定的实际工程应用意义。
Abstract:To address the shortcomings of the current calibration method for fluorescent oil film grey scale and thickness (tiny height device method) with complex acquisition conditions and a long calibration period, an approximate measurement method was proposed, by which only a very small amount of calibration data were required to achieve similar results to the current calibration method. The Elman dynamic neural network was used to extend the range of the small sample data and a one-dimensional interpolation algorithm was introduced to smooth the extended data items. The solved 3D fluorescent oil film thickness data were smoothed twice using a two-dimensional interpolation algorithm to obtain a complete thickness distribution. The simulation results showed that the thickness measurement by this near-efficient method can finally display the fluorescent oil film thickness distribution clearly, accurately and quantitatively, and the effect was similar to that of the currently widely used tiny height device method, with the maximum error not exceeding ±2.5 μm at the pool of fluorescent oil film (thicker area), and not exceeding ±2 μm in the smooth and moderate and thinner area; hence the engineering measurement standard of fluorescent oil film thickness could be met. It provides a calibration idea for the global friction measurement of aircraft, and has certain practical engineering application significance.
-
Key words:
- friction /
- oil film thickness /
- fluorescent oil film /
- range extension /
- interpolation
-
图 5 荧光油膜丝印灰度图像样例
Figure 5. Example of gray image of fluorescent oil film silk screen printing
图 13 微小高度装置法厚度分布结果
Figure 13. Measurement of thickness distribution by micro-height device method
表 1 近效法采集数据
Table 1. Data collection by the near-effect method
序号 灰度值 厚度值/μm 1 168 34.34 2 159 21.26 3 155 19.21 4 151 11.21 5 145 10.31 6 136 5.872 
下载: 导出CSV
表 2 微小高度装置采集数据
Table 2. Data collection by micro height devices
序号 灰度值 厚度值/μm 1 170 28.9563 2 169 28.1624 3 168 27.0142 4 167 25.8847 5 166 25.6792 $\vdots $ $\vdots $ $\vdots $ 40 131 6.1275
下载: 导出CSV
表 3 Baumer-HXC13工业相机参数
Table 3. Baumer-HXC13 industrial camera parameters
型号 Baumer-HXC13 黑白分辨率/像素 1280×1024 像素大小/μm 14×14 全帧/（f/s） 500
下载: 导出CSV
表 4 BBD-150风机参数
Table 4. BBD-150 fan parameters
型号 BBD-150 风量/（cm3/s） 165000 风压/Pa 500 转速/（r/min） 5000 出风口尺寸/cm 15
下载: 导出CSV
-
[1] LIU T,MONTEFORT J,WOODIGA S,et al. Global luminescent oil-film skin-friction meter[J]. AIAA Journal,2008,46(2): 476-485. doi: 10.2514/1.32219 [2] LIU T,WOODIGA S,TIAN M. Skin friction topology in a region enclosed by penetrable boundary[J]. Experiments in Fluids,2011,51(6): 1549-1562. doi: 10.1007/s00348-011-1171-6 [3] 钱泓江,董秀成,张征宇. 基于Hankel阵的荧光油膜灰度与厚度预测模型改进[J]. 航空动力学报,2021,36(10): 2061-2071. doi: 10.13224/j.cnki.jasp.20210147QIAN Hongjiang,DONG Xiucheng,ZHANG Zhengyu. Improved prediction model of gray and thickness of fluo-rescent oil film based on Hankel matrix[J]. Journal of Aerospace Power,2021,36(10): 2061-2071. (in Chinese) doi: 10.13224/j.cnki.jasp.20210147 [4] LIU Tianshu,CHEN Tao,SALAZAR D M,et al. Skin friction and surface optical flow in viscous flow[J]. Physics of Fluids,2022,34(6): 1-14. [5] 朱自强,吴宗成,丁举春. 层流流动控制技术及应用[J]. 航空学报,2011,32(5): 765-784.ZHU Ziqiang,WU Zongcheng,DING Juchun. Laminar flow control technology and application[J]. Acta Aeronautica et Astronautica Sinica,2011,32(5): 765-784. (in Chinese) [6] 钱泓江, 董秀成, 徐椰烃, 等. 基于神经网络的荧光油膜厚度与灰度研究[J]. 南京航空航天大学学报, 2021, 53(3): 470-476.QIAN Hongjiang, DONG Xiucheng, XU Yeting, et al. Fluorescence oil film thickness and gray level identi-fication based on neural network[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2021, 53(3): 470-476. (in Chinese) [7] 王帅,何国强,秦飞,等. 超声速内流道摩擦阻力分析及 减阻技术研究[J]. 航空动力学报,2019,34(4): 908-919.WANG Shuai,HE Guoqiang,QIN Fei,et al. Research on skin-friction drag and drag reduction technics in a supersonic inner flow path[J]. Journal of Aerospace Power,2019,34(4): 908-919. (in Chinese) [8] LIU T,MONTEFORT J,STANFIELD S,et al. Inverse heat transfer methods for global heat flux measurements in aerothermodynamics testing[J]. Progress in Aerospace Sciences,2019,107: 1-18. doi: 10.1016/j.paerosci.2019.04.003 [9] 周江宁,殷一民,郭秋亭,等. 基于激光诱导荧光的高速飞行粒子低温段测温方法[J]. 航空动力学报,2019,34(12): 2642-2647. doi: 10.13224/j.cnki.jasp.2019.12.013ZHOU Jiangning,YIN Yimin,GUO Qiuting,et al. Thermometry method for high speed flying particle at low temperature based on laser induced fluorescence technique[J]. Journal of Aerospace Power,2019,34(12): 2642-2647. (in Chinese) doi: 10.13224/j.cnki.jasp.2019.12.013 [10] 陈磊,朱涛,徐筠,等. 荧光油流显示技术在高超声速风洞中的应用[J]. 空气动力学学报,2017,35(6): 817-822.CHEN Lei,ZHU Tao,XU Yun,et al. Applications of fluorescence-oil-flow visualization technique in hypersonic wind tunnel test[J]. Acta Aerodynamica Sinica,2017,35(6): 817-822. (in Chinese) [11] QIAN Hongjiang,DONG Xiucheng,ZHANG Zhengyu. Research on stability of gray value of excited-state fluorescent oil film based on variable light vector angle[J]. Mathematical Problems in Engineering,2021,2021(51): 8632992.1-8632992.12. [12] 刘通,蔡晋生,屈崑. 壁面粗糙度湍流扩展模型及流动数值模拟[J]. 航空动力学报,2018,33(8): 1981-1989.LIU Tong,CAI Jinsheng,QU Kun. Roughness for wall turbulence extension model and flow numerical simulation[J]. Journal of Aerospace Power,2018,33(8): 1981-1989. (in Chinese) [13] LIU T,SHEN L. Fluid flow and optical flow[J]. Journal of Fluid Mechanics,2008,614: 253-291. doi: 10.1017/S0022112008003273 [14] LIU T,WOODIGA S,MONTEFORT J,et al. Global skin friction diagnostics in separated flows using luminescent oil[J]. Journal of Flow Visualization and Image Processing,2009,16(1): 19-39. doi: 10.1615/JFlowVisImageProc.v16.i1.20 [15] 李鹏,明晓. 风力机叶片的全局表面摩擦力测量的荧光油膜法[J]. 南京航空航天大学学报,2011,43(5): 581-585. doi: 10.3969/j.issn.1005-2615.2011.05.002LI Peng,MING Xiao. Fluorescent oil film method for global surface friction measurement of wind turbine blades[J]. Journal of Nanjing University of Aeronautics and Astronautics,2011,43(5): 581-585. (in Chinese) doi: 10.3969/j.issn.1005-2615.2011.05.002 [16] 黄湛,王宏伟,魏连风,等. 基于荧光油膜的全局表面摩阻测量技术研究[J]. 空气动力学学报,2016,34(3): 373-378,403.HUANG Zhan,WANG Hongwei,WEI Lianfeng,et al. Research of global skin friction measurement based on fluorescent oil film[J]. Acta Aerodynamica Sinica,2016,34(3): 373-378,403. (in Chinese) [17] 邹易峰,张征宇,王学渊,等. 风洞试验模型表面的荧光油膜路径运动速度测量[J]. 航空学报,2019,40(6): 92-105. doi: 10.7527/S1000-6893.2018.22595ZOU Yifeng,ZHNAG Zhengyu,WANG Xueyuan,et al. Velocity measurement of fluorescent oil film path movement on wind tunnel testing model surface[J]. Acta Aeronautica et Astronautica Sinica,2019,40(6): 92-105. (in Chinese) doi: 10.7527/S1000-6893.2018.22595 [18] 李鹏. 全局表面摩擦应力直接测量技术研究[D]. 南京: 南京航空航天大学, 2012.LI Peng. Research on direct measurement technology of global surface friction stress[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012. (in Chinese) [19] MA Xinyu,ZHANG Xiaohong. A short-term prediction model to forecast power of photovoltaic based on MFA-Elman[J]. Energy Reports,2022,8(4): 495-507. [20] 皮骏,黄江博. 基于IPSO-Elman神经网络的航空发动机故障诊断[J]. 航空动力学报,2017,32(12): 3031-3038. doi: 10.13224/j.cnki.jasp.2017.12.028PI Jun,HUANG Jiangbo. Aero-engine fault diagnosis based on IPSO-Elman neural network[J]. Journal of Aerospace Power,2017,32(12): 3031-3038. (in Chinese) doi: 10.13224/j.cnki.jasp.2017.12.028 [21] ZHANG Yufei,ZHAO Jianping,WANG Limin,et al. An improved OIF Elman neural network based on CSO algorithm and its applications[J]. Computer Communications,2021,171: 148-156. doi: 10.1016/j.comcom.2021.01.035 [22] MU B Q,CHEN H F. Hankel matcices for system identification[J]. Journal of Mathematical Analysis & Applications,2014,409(1): 494-508. [23] 李海涛,邵泽东. 空间插值分析算法综述[J]. 计算机系统应用,2019,28(7): 1-8. doi: 10.15888/j.cnki.csa.006988LI Haitao,SHAO Zedong. Review of spatial interpolation analysis algorithm[J]. Computer Systems & Applications,2019,28(7): 1-8. (in Chinese) doi: 10.15888/j.cnki.csa.006988 [24] 孙思梦. 关于连分式插值方法的若干研究[D]. 安徽 淮南: 安徽理工大学, 2020.SUN Simeng. Some research on continued fraction interpolation method[D]. Huainan Anhui: Anhui University of Science and Technology, 2020. (in Chinese) [25] 张营,左洪福,佟佩声,等. 基于谱插值和奇异值差分谱的滚动轴承静电监测信号去噪方法[J]. 航空动力学报,2014,29(8): 1996-2002. doi: 10.13224/j.cnki.jasp.2014.08.030ZHANG Ying,ZUO Hongfu,TONG Peisheng,et al. Denoising method for electrostatic monitoring signal of roller bearing on spectrum interpolation and difference spectrum of singular value[J]. Journal of Aerospace Power,2014,29(8): 1996-2002. (in Chinese) doi: 10.13224/j.cnki.jasp.2014.08.030 [26] ZHANG H D,ALEXANDRINA R,ABDELAZIZ B. An enhanced N-point interpolation method to eliminate average precision distortion[J]. Pattern Recognition Letters,2022,158: 111-116. doi: 10.1016/j.patrec.2022.04.028 -
点击查看大图
计量
- 文章访问数: 230
- HTML浏览量: 45
- PDF量: 71
- 被引次数: 0