Performance model evaluation of turbine flow meter in vertical gas-liquid two-phase flows

Tang, Ziyan; Jin, Ningde; Zhou, Yiyu; Ren, Weikai

doi:10.1016/j.flowmeasinst.2023.102356

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…The experimental and numerical data used in this study are available from Park et al (2018), Rzadkiewicz et al (1997) and Spinewine and Zech (2007). Software code for numerical simulation developed and used in this study is available at in Tang et al (2023).…”

Section: Appendix A: Numerical Detailsmentioning

confidence: 99%

A Mixture Model With Slip Velocity for Saturated Granular‐Liquid Free‐Surface Flows

Tang,

Lin,

Cui

2024

Water Resources Research

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In this paper, a model is presented for modeling saturated granular‐liquid free‐surface flows, in which the volume‐averaged mixture bulk velocity is employed to derive the balance equations for the mass and momentum of mixture flow. Additionally, an evolution equation of the slip velocity between granular‐and liquid constituents is derived to describe the separation between these constituents. The frictional‐collisional constitutive relation for granular‐constituent is employed to determine the stress due to particles interaction. The governing equations for mixture flows are numerically solved by a finite difference two‐step projection method. The volume of fluid (VOF) method is employed to track the free surface of the mixture flow in the present numerical model. Good agreements between numerical results and experimental data are observed by modeling the dam‐break process of granular‐liquid mixture flow, dam‐break waves over the saturated erodible beds and surge waves induced by submarine landslides along an inclined plane. Furthermore, the difference between the volume‐averaged mixture bulk velocity and mass‐averaged mixture bulk velocity is found to vary as the instinct density ratio of granular‐constituent and liquid‐constituent and the volumetric concentration ns of the granular‐constituent, and the evolution in the slip velocity during the process of the settlement of sediments is numerically analyzed.

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Section: Appendix A: Numerical Detailsmentioning

confidence: 99%

A Mixture Model With Slip Velocity for Saturated Granular‐Liquid Free‐Surface Flows

Tang,

Lin,

Cui

2024

Water Resources Research

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show abstract

“…To avoid direct measurement of gas-liquid velocity, many indirect flow measurement methods have been developed [11][12][13], among which the research on virtual flow meters on oil and gas pipelines has introduced a new idea for flow measurement. These measurement methods usually collect existing status detection sensor data on the pipeline, such as pressure, temperature, and flow rate data, and train artificial neural networks to achieve flow measurement.…”

Section: Introductionmentioning

confidence: 99%

Measurement of air-water counter-current flow rates in vertical annulus using multiple differential pressure signals and machine learning

Cao,

Dang,

Dang

et al. 2024

Meas. Sci. Technol.

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Gas-liquid counter-current flow in vertical annulus is involved in multiple industrial fields such as petroleum engineering. For instance, in coalbed methane wells where liquid pumping is utilized, obtaining real-time gas-liquid flow in the annulus is crucial for the development and management of coalbed methane wells. However, due to complex flow conditions, this requirement is difficult to achieve through traditional flow measurement means. Therefore, this paper proposes a flow prediction method based on multiple sets of differential pressure signals and machine learning techniques. Experiments on air-water two-phase flow were conducted on a vertical annulus pipe with an inner/outer diameter of 75mm/125mm and adjustable eccentricity. The probability density function and power spectral density function of three sets of differential pressure signals collected at different heights in the annulus pipe were used as model inputs, and gas-liquid flow rate as output. A gas-liquid two-phase flow prediction model was constructed based on the artificial neural network model, and the hyper-parameters of the model were optimized using Bayesian optimization. The results show that on a test dataset of 440 combinations of conditions with air superficial velocity of 0.06~5.04m/s, water superficial velocity of 0.03~0.25m/s, and pipe eccentricity of 0, 0.25, 0.5, 0.75, 1, the model can achieve average prediction errors of 9.12% and 29.34% for gas and water flow, respectively. This indicates that the method can be applied to non-throttling, non-intrusive measurement of phase flow under annulus gas-liquid counter-current flow conditions.

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Not-A-Knot cubic spline model to predict accurately the viscosity calibration curve of turbine flow meter with experiment validation

Prakosa,

Alias,

Purwowibowo

et al. 2025

Flow Measurement and Instrumentation

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Performance model evaluation of turbine flow meter in vertical gas-liquid two-phase flows

Cited by 8 publications

References 27 publications

A Mixture Model With Slip Velocity for Saturated Granular‐Liquid Free‐Surface Flows

A Mixture Model With Slip Velocity for Saturated Granular‐Liquid Free‐Surface Flows

Measurement of air-water counter-current flow rates in vertical annulus using multiple differential pressure signals and machine learning

Not-A-Knot cubic spline model to predict accurately the viscosity calibration curve of turbine flow meter with experiment validation

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