Two-phase flow regime identification based on the liquid-phase velocity information and machine learning

Zhang, Yongchao; Azman, Amirah Nabilah; Xu, Kewei; Kang, Can; Kim, Hyoung-Bum

doi:10.1007/s00348-020-03046-x

Cited by 74 publications

(14 citation statements)

References 45 publications

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“…Hernandez et al [38] developed a decision-tree-based classifier to identify flow regimes and select appropriate predictive models for several two-phase flow systems. Zhang et al [39] proposed two different machine learning classification algorithms for two-phase nuclear systems. The first one was designed for real-time flow regime identification based on SVMs, and the second classifier was designed for transient flow regime classification using CNNs.…”

Section: Machine Learning Algorithms For Two-phase Flow Heat Transfermentioning

confidence: 99%

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Loyola-Fuentes¹,

Pietrasanta

Marengo

et al. 2022

Energies

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Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes.

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Section: Machine Learning Algorithms For Two-phase Flow Heat Transfermentioning

confidence: 99%

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Loyola-Fuentes¹,

Pietrasanta

Marengo

et al. 2022

Energies

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“…There have been several applications of ML in the context of studies on weirs [11][12][13][14] and scour in various fields [15][16][17]. However, there is a lack of applications of ML algorithms on issues concerning two-phase air-water flows [18,19].…”

Section: Introductionmentioning

confidence: 99%

Air Entrainment in Drop Shafts: A Novel Approach Based on Machine Learning Algorithms and Hybrid Models

Granata

Nunno

2022

Fluids

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Air entrainment phenomena have a strong influence on the hydraulic operation of a plunging drop shaft. An insufficient air intake from the outside can lead to poor operating conditions, with the onset of negative pressures inside the drop shaft, and the choking or backwater effects of the downstream and upstream flows, respectively. Air entrainment phenomena are very complex; moreover, it is impossible to define simple functional relationships between the airflow and the hydrodynamic and geometric variables on which it depends. However, this problem can be correctly addressed using prediction models based on machine learning (ML) algorithms, which can provide reliable tools to tackle highly nonlinear problems concerning experimental hydrodynamics. Furthermore, hybrid models can be developed by combining different machine learning algorithms. Hybridization may lead to an improvement in prediction accuracy. Two different models were built to predict the overall entrained airflow using data obtained during an extensive experimental campaign. The models were based on different combinations of predictors. For each model, four different hybrid variants were developed, starting from the three individual algorithms: KStar, random forest, and support vector regression. The best predictions were obtained with the model based on the largest number of predictors. Moreover, across all variants, the one based on all three algorithms proved to be the most accurate.

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“…is high dependence on many parameters leads to large errors in measurements. Ultrasound Doppler velocimetry was employed to measure the liquid velocity, where the velocity is used to realize real-time flow regime identification with the help of machine learning [7]. Recently, the use of microwave attenuation and phase shift appeared as an emerging technique used to overcome some deficiencies related to the previous techniques.…”

Section: Introductionmentioning

confidence: 99%

Design of Microwave Antenna Array for Imaging of Multiphase Flows in Polypropylene Pipes

Alkhalifeh

Rmili

Hakim

et al. 2021

International Journal of Antennas and Propagation

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The oil and gas industry requires accurate sensors to control fluid flow in pipelines during the production process from horizontal and near horizontal wells. The extracted crude oil is usually a multiphase mixture of oil, water, and gas, and the accurate measurement of the ratio of each multiphase within the pipeline is an important parameter to manage wells efficiently by maximizing the hydrocarbons that can be extracted. Various methods have been developed for determining the phase ratio including mechanical, optical, X-ray or gamma ray, ultrasound, nuclear magnetic resonance (NMR), and rarely microwave techniques. However, these methods do not permit the knowledge of the real-time evolution of the phase ratio and are less precise. Here, we propose and develop by simulation two microwave systems, in horizontal and vertical polarizations, to choose the optimal configuration for crude pipeline imaging applications. First, the pipeline containing crude oil was modeled and its thermal and dielectric properties are proposed. Then, the antennas array performances were optimized and assembled to the pipeline. Different numbers of antenna elements were successfully investigated using CST simulation in both vertical and horizontal polarizations to find the optimal number of antenna elements for the pipeline applications.

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Two-phase flow regime identification based on the liquid-phase velocity information and machine learning

Cited by 74 publications

References 45 publications

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Air Entrainment in Drop Shafts: A Novel Approach Based on Machine Learning Algorithms and Hybrid Models

Design of Microwave Antenna Array for Imaging of Multiphase Flows in Polypropylene Pipes

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