摘要
互相关光流混合算法是目前粒子图像测速的主流算法,但是重建速度场精度不高。利用高斯径向基函数插值替换互相关光流混合算法中的双三次插值,减小了重建角度误差。进行朗肯涡流仿真实验,使用高斯径向基函数插值混合算法进行粒子速度场重建,其均方根误差和平均角度误差比传统混合算法分别降低了27.36%和38.32%,并分析了位移和粒径大小对重建误差的影响。搭建了二维粒子图像测速技术(PIV)实验系统,采用粒径为100μm的聚酰胺粒子作为示踪粒子,进行旋转实验和注水实验,分别模拟涡流场和射流场。采用高斯径向基函数插值混合算法进行速度场重建,可以获得与传统混合算法相近的粒子速度场。
Objective Fluid motion is a common phenomenon in observed nature and utilized in industries.Mastering the fluid flow is an important prerequisite for an in-depth study of fluid mechanics.The particle image velocimetry(PIV)is a non-contact global flow-field measurement and display technology that provides accurate data for flow-field measurements without affecting the flow field.The particle image velocimetry is mainly divided into two categories:cross-correlation and optical flow algorithms.The optical flow algorithm is primarily used in small-displacement scenarios.When the particle displacement is significantly larger than the particle size,the optical flow method cannot yield accurate results.The cross-correlation algorithm is mainly used in large displacement scenarios,and the combination of the two algorithms can satisfy more application scenarios.Although the hybrid algorithm has higher accuracy than the traditional algorithm in large-and small-displacement scenarios,the angle information is not well retained in the case of complex fluid.Because the image of the particle conforms to the Airy spot model and the light intensity satisfies the two-dimensional Gaussian distribution,if the Gaussian radial basis function interpolation is used,the velocity field refinement will be transformed into a surface reconstruction problem,and the reconstructed velocity field will have a higher accuracy.Therefore,we propose a cross-correlation optical flow mixing algorithm based on the Gaussian radial basis function interpolation to reduce the angular error.Methods Based on the traditional hybrid algorithm,in this study,the Gaussian radial basis function interpolation is used to replace bicubic interpolation and design a cross-correlation optical flow hybrid algorithm.First,a pair of particle images is inputted,and a cross-correlation method is used to extract the relatively large particle motion in each query window.A Gaussian radial basis function is used for data interpolation to fill the speed vector in each pixel.For each pixel,the image displacement is processed to remove the speed vector detected in the image.Subsequently,the initial velocity vector is determined using the HS optical flow method,and the residual velocity field is refined using the variable spectral flow method based on the dynamic illumination equation.The Gaussian radial basis function interpolation method is used to interpolate the velocity field at each layer,and the more refined velocity field vectors are obtained.Finally,the velocity field vectors obtained by the cross-correlation and optical flow algorithms are superimposed to obtain an accurate velocity field.The algorithm is quantitatively evaluated through a Rankine vortex simulation experiment.The influence of displacement and particle size on the accuracy of the algorithm is studied.Subsequently,a two-dimensional PIV experimental system is built,and rotation and water injection experiments are performed to simulate the vortex current field and jet field,respectively.The practicability of the proposed algorithm is verified.Results and Discussions In the Rankine vortex simulation experiment,the manifold reconstructed by the proposed method is more in line with the characteristics of the Rankine vortex and closer to the ground truth(Fig.3).The root mean square error(RMSE)and average angular error of the cross-correlation optical flow hybrid algorithm based on Gaussian radial basis function interpolation are 27.36%and 38.32%lower than those of the Hybrid method 2020,respectively(Table 1).With an increase in the maximum displacement,the root mean square error gradually increases.In most cases,the hybrid algorithm based on Gaussian radial basis function interpolation is superior to the Hybrid method 2020.In the case of a small displacement,the RMSE can be decreased by approximately 45%,whereas in the case of a large displacement,the RMSE can be decreased by approximately 15%(Fig.5).With an increase in particle size,the angle error first decreases and then the best reconstruction result is obtained when the particle size is 3 pixel.The proposed method can obtain good reconstruction results in the cases of both small and large particle sizes.In the case of particle size of 2-4 pixel,the average angle error of the proposed method is approximately 15%lower than that of the Hybrid method 2020(Fig.6).The results of water injection and rotation experiments verify the performance of the proposed algorithm in practical applications.Conclusions In this study,based on the traditional hybrid algorithm,the Gaussian radial basis function interpolation is used to replace bicubic interpolation,and a cross-correlation optical flow hybrid algorithm based on Gaussian radial basis function interpolation is proposed.This approach preserves the angle information in the complex flow field,which is not possible using the traditional hybrid algorithm.It changes considerably with velocity,and the method can accurately reconstruct flow fields.The proposed algorithm and the Hybrid method 2020 algorithm are used to reconstruct the velocity field in an experiment.The results show that the two algorithms can maintain high consistency in the entire manifold,and the proposed algorithm can retain more angle information.This verifies that the proposed algorithm can accurately reconstruct the actual complex flow field and has potential for practical applications.
作者
熊俊哲
孔明
洪波
施飞杨
简娟
詹虹晖
单良
Xiong Junzhe;Kong Ming;Hong Bo;Shi Feiyang;Jian Juan;Zhan Honghui;Shan Liang(Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,College of Information Engineering,China Jiliang University,Hangzhou 310018,Zhejiang,China;College of Metrology&Measurement Engineering,China Jiliang University,Hangzhou 310018,Zhejiang,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2023年第6期47-56,共10页
Chinese Journal of Lasers
基金
国家自然科学基金(51874264,52076200)。
关键词
测量
粒子图像测速
高斯径向基函数
朗肯涡流
平均角度误差
measurement
particle image velocimetry
Gaussian radial basis function
Rankine vortex
average angle error
作者简介
通信作者:单良,lshan@cjlu.edu.cn。
