The fusion of deep learning and acoustic emission response methods for identifying solid particles in annular multiphase flows-Reference-Cited by-同舟云学术

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The fusion of deep learning and acoustic emission response methods for identifying solid particles in annular multiphase flows

Published:2023-06 Issue: Volume:225 Page:211685
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Wang Kai,Chang Ziang,Li Yichen,Tian Peng,Qin Min,Fu Guangming,Yin Bangtang,Wang Gang

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Elsevier BV

Reference47 articles.

1. Non-intrusive monitoring of bubbles in a gas–solid fluidized bed using vibration signature analysis;Abbasi;Powder Technol.,2009

2. Prediction of entrained droplet fraction in co-current annular gas–liquid flow in vertical pipes;Aliyu;Exp. Therm. Fluid Sci.,2017

3. Experimental Investigation of Solid Particle Erosion in Successive Elbows in Gas Dominated Flows;Asgharpour,2018

4. Sand production: a smart control framework for risk mitigation;Ben Mahmud;Petroleum,2020

5. Development and interaction of disturbance waves in downward annular gas-liquid flow;Cherdantsev;Int. J. Multiphas. Flow,2021

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4. Sand particle characterization and identification in annular multiphase flow using an intelligent method;Physics of Fluids;2024-01-01

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