Void fraction measurement using modal decomposition and ensemble learning in vertical annular flow

Li, Chaofan; Liu, Miaomiao; Zhao, Ning; Wang, Fan; Zhao, Zhengyan; Guo, Suna; Fang, Luyuan; Li, Xiaoting

doi:10.1016/j.ces.2021.116929

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…The trained model was able to directly predict the key parameters from the online normalized capacitance measurement. More recently, Li et al proposed a new high-precision real-time model for predictions of the void fraction in gas–liquid two-phase flow based on ensemble learning. The model integrated the XGBoost with an empirical modal decomposition approach and a kernel ridge regression.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

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“…Challenges in the measurement were noticed when the gas-liquid interface experienced large waves. Similarly, near-infrared light sensors have also been used to measure the film thickness based on the different absorption characteristics of gas and liquid [77][78][79][80][81]. However, due to the significant impact of the entrained liquid droplets on the absorption coefficient, light guide pipes were designed and inserted into the annular flow, which ensured the light was only absorbed by the liquid film.…”

Section: Ultrasonic Flow Meter and Near-infrared Sensormentioning

confidence: 99%

Two-Phase Annular Flow in Vertical Pipes: A Critical Review of Current Research Techniques and Progress

Xue¹,

Stewart

Kelly

et al. 2022

Water

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Two-phase annular flow in vertical pipes is one of the most common and important flow regimes in fluid mechanics, particularly in the field of building drainage systems where discharges to the vertical pipe are random and the flow is unsteady. With the development of experimental techniques and analytical methods, the understanding of the fundamental mechanism of the annular two-phase flow has been significantly advanced, such as liquid film development, evolution of the disturbance wave, and droplet entrainment mechanism. Despite the hundreds of papers published so far, the mechanism of annular flow remains incompletely understood. Therefore, this paper summarizes the research on two-phase annular flow in vertical pipes mainly in the last two decades. The review is mainly divided into two parts, i.e., the investigation methodologies and the advancement of knowledge. Different experimental techniques and numerical simulations are compared to highlight their advantages and challenges. Advanced underpinning physics of the mechanism is summarized in several groups including the wavy liquid film, droplet behaviour, entrainment and void fraction. Challenges and recommendations are summarized based on the literature cited in this review.

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“…As one of the most important flow patterns of gas-liquid twophase flow, annular flow is widely found in traditional and emerging industrial applications, such as the pipeline transportation of shale gas and condensate natural gas, power plant boilers, nuclear reactor steam generators, heat and mass transfer and chemical reaction engineering equipment [1]. In an annular two-phase gas-liquid flow, part of the liquid phase flows as a liquid film on the channel walls and the remaining liquid flows as droplets entrained in the gas core of the flow * Authors to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Liquid film parameter measurement based on thermal distribution sensor in horizontal annular flow

Zhao,

Sun,

Zhang

et al. 2023

Meas. Sci. Technol.

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Liquid film is an important carrier of mass and heat transfer in annular flow, and the parameter detection of which plays an important role in the energy metering of natural gas production processes and the safe and reliable operation of cooling and heating systems. In this paper, a liquid film velocity equation is derived based on the analysis of the heat balance equation between the liquid film and tube wall. According to the constant power principle, the thermal distributed flow measurement sensor is designed by using the multi-physical field coupling simulation technique According to the momentum balance equation of the Laurinat model, considering the pump mechanism of the disturbance wave and using magnitude comparison and stress balance, a liquid film thickness circumferential distribution model in the horizontal annular flow is proposed. Utilizing the model and designed sensor, the liquid film thickness of four locations (0°, 90°, 180°, 270°) measurements are obtained for 64 horizontal annular flow conditions. Laboratory results indicate that the mean absolute percent error of the model is 32.66% and the 82.81% relative error is within the ±30% error band.

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Void fraction measurement using modal decomposition and ensemble learning in vertical annular flow

Cited by 9 publications

References 38 publications

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Two-Phase Annular Flow in Vertical Pipes: A Critical Review of Current Research Techniques and Progress

Liquid film parameter measurement based on thermal distribution sensor in horizontal annular flow

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