{"title":"基于深度学习的气液两相气泡流气泡快速跟踪和监测算法","authors":"Lide Fang, Yiming Lei, Jianan Ning, Jingchi Zhang, Yue Feng","doi":"10.1063/5.0222856","DOIUrl":null,"url":null,"abstract":"Gas–liquid two-phase bubbly flow has significant applications across multiple fields, including reactor design and separation processes in chemical engineering, oil well extraction and pipeline transportation in the oil and gas industry, cooling systems in the nuclear industry, and wastewater treatment in environmental engineering. Bubble monitoring is crucial in these applications as it can enhance mass and heat transfer efficiency, improve flow stability, and ensure the safe operation of systems. This study developed an advanced algorithm aimed at precisely detecting and segmenting small bubbles at the gas–liquid interface using semantic segmentation techniques. This technology leverages deep learning models to analyze images, automatically identifying bubbles at the gas–liquid interface and accurately delineating their boundaries. The technique provides precise contours for each bubble, offering essential foundational data for further bubble dynamics analysis. Building on this, the deep learning detection algorithm was combined with the Deep Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) algorithm, tracking algorithm, enabling the system to rapidly and accurately identify and track the movement of the same bubble across consecutive frames.","PeriodicalId":20066,"journal":{"name":"Physics of Fluids","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep learning-based algorithm for rapid tracking and monitoring of gas–liquid two-phase bubbly flow bubbles\",\"authors\":\"Lide Fang, Yiming Lei, Jianan Ning, Jingchi Zhang, Yue Feng\",\"doi\":\"10.1063/5.0222856\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gas–liquid two-phase bubbly flow has significant applications across multiple fields, including reactor design and separation processes in chemical engineering, oil well extraction and pipeline transportation in the oil and gas industry, cooling systems in the nuclear industry, and wastewater treatment in environmental engineering. Bubble monitoring is crucial in these applications as it can enhance mass and heat transfer efficiency, improve flow stability, and ensure the safe operation of systems. This study developed an advanced algorithm aimed at precisely detecting and segmenting small bubbles at the gas–liquid interface using semantic segmentation techniques. This technology leverages deep learning models to analyze images, automatically identifying bubbles at the gas–liquid interface and accurately delineating their boundaries. The technique provides precise contours for each bubble, offering essential foundational data for further bubble dynamics analysis. Building on this, the deep learning detection algorithm was combined with the Deep Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) algorithm, tracking algorithm, enabling the system to rapidly and accurately identify and track the movement of the same bubble across consecutive frames.\",\"PeriodicalId\":20066,\"journal\":{\"name\":\"Physics of Fluids\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physics of Fluids\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0222856\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics of Fluids","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1063/5.0222856","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MECHANICS","Score":null,"Total":0}
A deep learning-based algorithm for rapid tracking and monitoring of gas–liquid two-phase bubbly flow bubbles
Gas–liquid two-phase bubbly flow has significant applications across multiple fields, including reactor design and separation processes in chemical engineering, oil well extraction and pipeline transportation in the oil and gas industry, cooling systems in the nuclear industry, and wastewater treatment in environmental engineering. Bubble monitoring is crucial in these applications as it can enhance mass and heat transfer efficiency, improve flow stability, and ensure the safe operation of systems. This study developed an advanced algorithm aimed at precisely detecting and segmenting small bubbles at the gas–liquid interface using semantic segmentation techniques. This technology leverages deep learning models to analyze images, automatically identifying bubbles at the gas–liquid interface and accurately delineating their boundaries. The technique provides precise contours for each bubble, offering essential foundational data for further bubble dynamics analysis. Building on this, the deep learning detection algorithm was combined with the Deep Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) algorithm, tracking algorithm, enabling the system to rapidly and accurately identify and track the movement of the same bubble across consecutive frames.
期刊介绍:
Physics of Fluids (PoF) is a preeminent journal devoted to publishing original theoretical, computational, and experimental contributions to the understanding of the dynamics of gases, liquids, and complex or multiphase fluids. Topics published in PoF are diverse and reflect the most important subjects in fluid dynamics, including, but not limited to:
-Acoustics
-Aerospace and aeronautical flow
-Astrophysical flow
-Biofluid mechanics
-Cavitation and cavitating flows
-Combustion flows
-Complex fluids
-Compressible flow
-Computational fluid dynamics
-Contact lines
-Continuum mechanics
-Convection
-Cryogenic flow
-Droplets
-Electrical and magnetic effects in fluid flow
-Foam, bubble, and film mechanics
-Flow control
-Flow instability and transition
-Flow orientation and anisotropy
-Flows with other transport phenomena
-Flows with complex boundary conditions
-Flow visualization
-Fluid mechanics
-Fluid physical properties
-Fluid–structure interactions
-Free surface flows
-Geophysical flow
-Interfacial flow
-Knudsen flow
-Laminar flow
-Liquid crystals
-Mathematics of fluids
-Micro- and nanofluid mechanics
-Mixing
-Molecular theory
-Nanofluidics
-Particulate, multiphase, and granular flow
-Processing flows
-Relativistic fluid mechanics
-Rotating flows
-Shock wave phenomena
-Soft matter
-Stratified flows
-Supercritical fluids
-Superfluidity
-Thermodynamics of flow systems
-Transonic flow
-Turbulent flow
-Viscous and non-Newtonian flow
-Viscoelasticity
-Vortex dynamics
-Waves