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Flow rate measurement of gas-liquid annular flow through a combined multimodal ultrasonic and differential pressure sensor

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  • Wang, Mi
  • Liu, Jiegui
  • Bai, Yuxin
  • Zheng, Dandan
  • Fang, Lide

Abstract

Natural gas is an important strategic reserve resource for the country, also plays an important role in energy transition. During mining and transportation, natural gas has high liquid content and high-pressure characteristics, typically exhibiting gas-liquid annular flow state. Accurate flow metering of each individual phase without separation is significant to academic research and industrial production. Therefore, a combined ultrasonic and differential pressure sensor is presented to acquire flowing information. Based on the limited flowing information (liquid film fraction and gas velocity) measured by dual mode ultrasonic sensor, a two-fluid model is established to solve liquid velocity and droplet entrainment rate for calculating phase flow rates by analyzing the momentum balance between gas and liquid phases. The pressure drop by differential pressure sensor is introduced to make this two-fluid model closed and solvable. Considering the effects of system pressure and pipe diameter on pressure drop, a closed correlation for friction factor at the gas-liquid interface is established. Furthermore, the phase flow rates are calculated by the experimental measurement values obtained by dual mode ultrasonic sensor and the two-fluid model. The relative error of total flow rate is within ±10 % and the mean absolute percentage error is 5.220 %.

Suggested Citation

  • Wang, Mi & Liu, Jiegui & Bai, Yuxin & Zheng, Dandan & Fang, Lide, 2024. "Flow rate measurement of gas-liquid annular flow through a combined multimodal ultrasonic and differential pressure sensor," Energy, Elsevier, vol. 288(C).
  • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223032462
    DOI: 10.1016/j.energy.2023.129852
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    References listed on IDEAS

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    1. Parrales, Arianna & Colorado, Dario & Huicochea, Armando & Díaz, Juan & Alfredo Hernández, J., 2014. "Void fraction correlations analysis and their influence on heat transfer of helical double-pipe vertical evaporator," Applied Energy, Elsevier, vol. 127(C), pages 156-165.
    2. Li, Chaofan & Song, Yajing & Xu, Long & Zhao, Ning & Wang, Fan & Fang, Lide & Li, Xiaoting, 2022. "Prediction of the interfacial disturbance wave velocity in vertical upward gas-liquid annular flow via ensemble learning," Energy, Elsevier, vol. 242(C).
    3. Fan, Jingjing & Wang, Jianliang & Liu, Mingming & Sun, Wangmin & Lan, Zhixuan, 2022. "Scenario simulations of China's natural gas consumption under the dual-carbon target," Energy, Elsevier, vol. 252(C).
    4. Duan, Jimiao & Gong, Jing & Yao, Haiyuan & Deng, Tao & Zhou, Jun, 2014. "Numerical modeling for stratified gas–liquid flow and heat transfer in pipeline," Applied Energy, Elsevier, vol. 115(C), pages 83-94.
    5. Xie, Minghua & Yi, Xiangyu & Liu, Kui & Sun, Chuanwang & Kong, Qingbao, 2023. "How much natural gas does China need: An empirical study from the perspective of energy transition," Energy, Elsevier, vol. 266(C).
    6. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    7. Khan, Irfan & Hou, Fujun & Zakari, Abdulrasheed & Tawiah, Vincent Konadu, 2021. "The dynamic links among energy transitions, energy consumption, and sustainable economic growth: A novel framework for IEA countries," Energy, Elsevier, vol. 222(C).
    8. Norton, M.P. & Greenhalgh, R., 1990. "Estimation of moisture content in timber using ultrasonics," Applied Energy, Elsevier, vol. 35(4), pages 267-297.
    Full references (including those not matched with items on IDEAS)

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