冲击实验中复杂流场的准静止区检测  

Quasi-static region detection of complex flow field in the impact experiments

作　　者：徐悦 曾强 李然[1] 陈泉 李一鸣 牟彤彤 杨晖[1] XU Yue;ZENG Qiang;LI Ran;CHEN Quan;LI Yiming;MU Tongtong;YANG Hui(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《上海理工大学学报》2024年第2期171-178,共8页Journal of University of Shanghai For Science and Technology

基　　金：国家自然科学基金资助项目(12072200,12002213);青岛海洋科学与技术国家实验室鳌山科技创新计划项目(2021QNLM020002-7);上海市自然科学基金资助项目(20ZR1438800)。

摘　　要：颗粒流场特征检测通常采用粒子图像测速(PIV)技术测得速度场,再对多帧连续图像进行人工识别,检测结果存在一定的主观误差。因此,提出了一种在单帧流场图像中识别准静止区的DC-UNet++网络。首先,通过电荷耦合器件(CCD)采集小球冲击颗粒床形成的流场图像,再用PIV技术分析并制作数据集。然后,在多组数据集上训练CNN模型、UNet++模型和提出的DCUNet++模型,验证分析其在单帧图像上检测准静止区的可行性与准确性。最后,讨论了该模型在非透明和透明两类颗粒材料中的低速冲击流场上的泛化能力。实验结果表明:DC-UNet++网络在非透明和透明颗粒材料上的准确率分别达到87.76%和72.91%。DC-UNet++网络实现了在单帧图像上检测目标特征的任务,且对透明颗粒材料复杂流场下的特征仍具有较为准确的检测结果。Current feature detection in particle flow fields was commonly measured by the particle image velocimetry(PIV)followed by manual identification of multiple consecutive frames,which is subject to some subjective errors.Therefore,a DC-UNet++network for identifying the quasi-static regions in single-frame flow field images was proposed.Firstly,the particle flow field images formed by the impact experiments between the small ball and particle bed were captured by a high-speed charge coupled device(CCD)camera.Next,the raw images were used for PIV analysis and a dataset was produced.Then,the CNN model,UNet++model and the proposed DC-UNet++model were trained on multiple datasets to validate and analyze their feasibility and accuracy in detecting the quasi-static regions on a single frame image.Finally,the ability of the model to generalize over low velocity impact flow fields in non-transparent and transparent granular materials was discussed.The experimental results showed that the DC-UNet++network achieved an accuracy of 87.76%and 72.91%on non-transparent and transparent granular materials,respectively.The DC-UNet++network achieved the task of detecting the target features on a single image frame and still had a relatively accurate detection of features under complex flow fields in transparent granular materials.

关 键 词：光学测量 PIV速度场 图像处理 扩张卷积 特征检测 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] O313.4[自动化与计算机技术—计算机科学与技术]