文献类型：期刊文献

英文题名：Quantitative Evaluation of Submerged Cavitation Jet Performance Based on Image Processing Method

作者：Zhong, Xiao[1];Dong, Jingming[1];Meng, Rongxuan[1];Liu, Mushan[1];Pan, Xinxiang[1,2]

机构：[1]Dalian Maritime Univ, Marine Engn Coll, Dalian 116026, Peoples R China;[2]Guangdong Ocean Univ, Coll Ocean Engn, Zhanjiang 524088, Peoples R China

年份：2022

卷号：10

期号：10

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000873109200001)、、Scopus(收录号：2-s2.0-85140778109)、WOS

基金：This research was funded by National Natural Science Foundation of China, grant number 51979022, and Fundamental Research Funds for the Central Universities grant number 3132019330.

语种：英文

外文关键词：submerged cavitation jet; image processing; dimensionless cavitation intensity; maximum mass loss; quantitative evaluation

外文摘要：The submerged cavitation jet is suitable for ocean engineering activities such as ship fouling cleaning, organic wastewater treatment, offshore oil drilling, and natural gas hydrate extraction due to its superior hydraulic performance and erosion capacity. As an intuitive analysis method, image processing is widely used to investigate the characteristics of submerged cavitation jets. However, due to the lack of quantitative evaluation of the cavitation cloud in image processing, it is difficult to establish the relationship between cavitation cloud image and cavitation performance. Therefore, a novel image processing method based on dimensionless grayscale intensity is proposed in this paper. This method was used under different sample spaces to obtain the maximum mass loss of the sample. The results showed that the method could accurately calculate the maximum mass loss of the sample based on the image processing results. When the sample space is 200 images and the working pressure is 20 MPa, the calculation error of the image processing method for the maximum mass loss of the sample is 1.26%. For the sample spaces of 10-5000 images, the maximum calculation error of the image processing method for the maximum mass loss of the samples is 3.29%. The image processing method proposed in this paper establishes the relationship between the cavitation cloud image and the maximum mass loss of the samples, which provides help for further understanding and application of submerged cavitation jets.