Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, d...Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.展开更多
基金financial support from the Brazilian Federal Agency for Support and Evaluation of Graduate Education(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior—CAPES,scholarship process no BEX 0506/15-0)the Brazilian National Agency of Petroleum,Natural Gas and Biofuels(Agencia Nacional do Petroleo,Gas Natural e Biocombustiveis—ANP),in cooperation with the Brazilian Financier of Studies and Projects(Financiadora de Estudos e Projetos—FINEP)the Brazilian Ministry of Science,Technology and Innovation(Ministério da Ciencia,Tecnologia e Inovacao—MCTI)through the ANP’s Human Resources Program of the State University of Sao Paulo(Universidade Estadual Paulista—UNESP)for the Oil and Gas Sector PRH-ANP/MCTI no 48(PRH48).
文摘Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.
文摘针对利用MATLAB官方提供的Web组件将在线虚拟仿真实验平台打包部署到Web端后,客户端出现浏览器端加载应用程序缓慢的问题,文章不使用MATLAB官方的Web组件,在使用超文本标记语言(HyperText Markup Language,HTML)和Vue搭建前端界面的同时,使用异步Web框架FastAPI搭建后端应用程序接口(Application Program Interface,API)并进行服务部署。开展数字信号处理中的图像处理实验,结果表明文章提出的方法可有效地解决Web应用加载速度缓慢等问题。
