Cost-based recurrence analysis of conductance time series for gas–liquid two-phase flow system

2021 ◽  
pp. 2150161
Author(s):  
Lusheng Zhai ◽  
Yuqing Wang ◽  
Jie Yang ◽  
Yinglin Wu
Keyword(s):  
Flow Pattern ◽  
Lorenz System ◽  
Flow System ◽  
Recurrence Plot ◽  
Flow Structures ◽  
Two Phase ◽  
Structure Information ◽  
Flow Pattern Transition ◽  
Gas Liquid Flow ◽  
Liquid Flows

Gas–liquid two-phase flows are frequently encountered in chemical and nuclear industries. The study of gas–liquid flow structures is of great significance for understanding the mechanisms of the flow pattern transition. In this paper, a direct-image multi-electrode conductance sensor (DMCS) was used to detect the structure information of vertical gas–liquid flows. Recurrence plot (RP) and cost-based recurrence plot (CBRP) are validated using typical nonlinear systems, i.e. Lorenz system and Hénon map, and used to analyze the signals collected by the DMCS. The results indicate that the determinism (DET) derived from the CBRP is sensitive to flow pattern evolution, and can also demonstrate the internal differences in the same flow patterns.

Flow Pattern Transition in Pipes Using Data-Driven and Physics-Informed Machine Learning

2020 ◽  
Author(s):  
André M. Quintino ◽  
Davi L. L. N. da Rocha ◽  
Roberto Fonseca Jr. ◽  
Oscar M. H. Rodriguez
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Flow Pattern ◽  
Liquid Flow ◽  
Two Phase Flow ◽  
Data Driven ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Pattern Transition ◽  
Gas Liquid Flow

Abstract Flow pattern is an important engineering design factor in two-phase flow in the chemical, nuclear and energy industries, given its effects on pressure drop, holdup, and heat and mass transfer. The prediction of two-phase flow patterns through phenomenological models is widely used in both industry and academy. In contrast, as more experimental data become available for gas-liquid flow in pipes, the use of data-driven models to predict flow-pattern transition, such as machine learning, has become more reliable. This type of heuristic modeling has a high demand for experimental data, which may not be available in some industrial applications. As a consequence, it may fail to deliver a sufficiently generalized transition prediction. Incorporation of physics in machine learning is being proposed as an alternative to improve prediction and also to reduce the demand for experimental data. This paper evaluates the use of hybrid-physics-data machine learning to predict gas-liquid flow-pattern transition in pipes. Random forest and artificial neural network are the chosen tools. A database of experiments available in the open literature was collected and is shared in this work. The performance of the proposed hybrid model is compared with phenomenological and data-driven machine learning models through confusion matrices and graphics. The results show improvement in prediction performance even with a low amount of data for training. The study also suggests that graphical comparison of flow-pttern transition boundaries provides better understanding of the performance of the models than the traditional metric

Crisp and Fuzzy Classifiers in the Two-Phase Gas-Liquid Flow Diagnostics

2012 ◽  
Vol 17 (4) ◽  
pp. 385-394
Author(s):  
Paweł Fiderek ◽  
Tomasz Jaworski ◽  
Robert Banasiak ◽  
Jacek Kucharski
Keyword(s):  
Pattern Recognition ◽  
Flow Pattern ◽  
Liquid Flow ◽  
Confusion Matrix ◽  
Clustering Algorithms ◽  
Prediction Performance ◽  
Two Phase ◽  
Fuzzy Classifiers ◽  
Gas Liquid Flow ◽  
Liquid Flows

Abstract The following paper presents results of common clustering algorithms use, both crisp and fuzzy, for flow pattern recognition of two-phase gas-liquid flows observed in horizontal pipeline. Obtained results of HCM, FCM, and kNN clustering algorithms were presented in a form of confusion matrix and compared via its prediction performance.

GAS-LIQUID FLOW-PATTERN TRANSITION IN HORIZONTAL PIPES – ANALYSIS OF REPORTED MODELS

2018 ◽  
Author(s):  
André Mendes Quintino ◽  
Davi Lotfi Lavor Navarro da Rocha ◽  
Oscar Mauricio Hernandez Rodriguez
Keyword(s):  
Flow Pattern ◽  
Liquid Flow ◽  
Horizontal Pipes ◽  
Flow Pattern Transition ◽  
Gas Liquid Flow

Detecting the Flow Pattern Transition in the Gas-Liquid Two-Phase Flow Using Multivariate Multi-Scale Entropy Analysis

2019 ◽  
Vol 74 (10) ◽  
pp. 837-848 ◽  
Author(s):  
Yudong Liu ◽  
Dayang Wang ◽  
Yingyu Ren ◽  
Ningde Jin
Keyword(s):  
Flow Pattern ◽  
Two Phase Flow ◽  
Flow Patterns ◽  
Mean Value ◽  
Entropy Rate ◽  
Permutation Entropy ◽  
Phase Flow ◽  
Two Phase ◽  
Multi Scale ◽  
Flow Pattern Transition

AbstractDue to the complex flow structure and non-uniform phase distribution in the vertical upward gas-liquid two-phase flow, an eight-electrode rotating electric field conductance sensor is used to obtain multi-channel conductance signals. The flow patterns of the vertical upward gas-liquid two-phase flow are classified according to the images obtained from a high-speed camera. Then, we employ the multivariate weighted multi-scale permutation entropy (MWMPE) to detect the instability of flow pattern transition in the gas-liquid two-phase flow. Afterwards, we compare the results of the MWMPE with those of the single-channel weighted multi-scale permutation entropy (SCWMPE) and multivariate multi-scale sample entropy (MMSE). The comparison results indicate that, compared with the SCWMPE and MMSE, the MWMPE has superior performance in terms of the high-resolution presentation of flow instability in the gas-liquid two-phase flow. Finally, we extract the mean value of the MWMPE in whole scales and the entropy rate of the MWMPE in the small scales. The results indicate that the normalized mean value and normalized entropy rate of MWMPE are very sensitive to the transitions of flow patterns, thus allowing the detection of the instability of flow pattern transition.

An Experimental Study on Air-Water Two-Phase Flow Patterns in Pebble Beds

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Bai Bofeng ◽  
Liu Maolong ◽  
Su Wang ◽  
Zhang Xiaojie
Keyword(s):  
Experimental Study ◽  
Flow Pattern ◽  
Test Section ◽  
Slug Flow ◽  
Annular Flow ◽  
Two Phase Flow ◽  
Flow Patterns ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Pattern Transition

An experimental study was conducted on the air-water two-phase flow patterns in the bed of rectangular cross sections containing spheres of regular distribution. Three kinds of glass spheres with different diameters (3 mm, 6 mm, and 8 mm) were used for the establishment of the test section. By means of visual observations of the two-phase flow through the test section, it was discovered that five different flow patterns occurred within the experimental parameter ranges, namely, bubbly flow, bubbly-slug flow, slug flow, slug-annular flow, and annular flow. A correlation for the bubble and slug diameter in the packed beds was proposed, which was an extended expression of the Tung/Dhir model, Jamialahmadi’s model, and Schmidt’s model. Three correlations were proposed to calculate the void friction of the flow pattern transition in bubble flow, slug flow, and annular flow based on the bubble model in the pore region. The experimental result showed that the modified Tung and Dhir model of the flow pattern transition was in better agreement with the experimental data compared with Tung and Dhir’s model.

Experimental Study on Flow Patterns of Decaying Swirling Gas-Liquid Flow in a Horizontal Pipe

2020 ◽  
Author(s):  
Shuai Liu ◽  
Li Liu ◽  
Jiarong Zhang ◽  
Hanyang Gu
Keyword(s):  
Flow Pattern ◽  
Liquid Flow ◽  
Separation Efficiency ◽  
Swirling Flow ◽  
Flow Patterns ◽  
Two Phase ◽  
Horizontal Pipe ◽  
Gas Removal ◽  
Swirl Vane ◽  
Gas Liquid Flow

Abstract Swirling flow is one of the well-recognized techniques to control the working process. This special flow is widely adopted in swirl vane separators in nuclear steam generator (SG) for water droplet separation and the fission gas removal system in Thorium Molten Salt Reactor (TMSR) for gas bubble separation. Since the parameters such as separation efficiency, pressure drop and mass and heat transfer rate are strongly dependent on the flow pattern, the accurate prediction of flow patterns and their transitions is extremely important for the proper design, operation and optimization of swirling two-phase flow systems. In this paper, using air and water as working fluids, a visualization experiment is carried out to study the gas-liquid flow in a horizontal pipe containing a swirler with four helical vanes. The test pipe is 5 m in length and 30 mm in diameter. Firstly, five typical flow patterns of swirling gas-liquid flow at the outlet of the swirler are classified and defined, these being spiral chain, swirling gas column, swirling intermittent, swirling annular and swirling ribbon flow. Being affected by the different gas and liquid flow rate of non-swirling flow, it is found that the same non-swirling flow can change into different swirling flow patterns. After that, the evolution of various swirling flow patterns along the streamwise direction is analyzed considering the influence of swirl attenuation. The results indicate that the same swirling flow pattern can transform into a variety of swirling flow patterns and subsequent non-swirling flow patterns. Finally, the flow pattern maps at different positions downstream of the swirler are presented.

High-Viscosity Liquid/Gas Flow Pattern Transitions in Upward Vertical Pipe Flow

SPE Journal ◽  
2020 ◽  
Vol 25 (03) ◽  
pp. 1155-1173
Author(s):  
Eissa Al-Safran ◽  
Mohammad Ghasemi ◽  
Feras Al-Ruhaimani
Keyword(s):  
Flow Pattern ◽  
Two Phase Flow ◽  
Bubble Diameter ◽  
High Viscosity ◽  
Transition Model ◽  
Phase Flow ◽  
Liquid Viscosity ◽  
Two Phase ◽  
Flow Pattern Transition ◽  
Transition Models

Summary High-viscosity liquid two-phase upward vertical flow in wells and risers presents a new challenge for predicting pressure gradient and liquid holdup due to the poor understanding and prediction of flow pattern. The objective of this study is to investigate the effect of liquid viscosity on two-phase flow pattern in vertical pipe flow. Further objective is to develop new/improve existing mechanistic flow-pattern transition models for high-viscosity liquid two-phase-flow vertical pipes. High-viscosity liquid flow pattern two-phase flow data were collected from open literature, against which existing flow-pattern transition models were evaluated to identify discrepancies and potential improvements. The evaluation revealed that existing flow transition models do not capture the effect of liquid viscosity, resulting in poor prediction. Therefore, two bubble flow (BL)/dispersed bubble flow (DB) pattern transitions are proposed in this study for two different ranges of liquid viscosity. The first proposed transition model modifies Brodkey's critical bubble diameter (Brodkey 1967) by including liquid viscosity, which is applicable for liquid viscosity up to 100 mPa·s. The second model, which is applicable for liquid viscosities above 100 mPa·s, proposes a new critical bubble diameter on the basis of Galileo's dimensionless number. Furthermore, the existing bubbly/intermittent flow (INT) transition model on the basis of a critical gas void fraction of 0.25 (Taitel et al. 1980) is modified to account for liquid viscosity. For the INT/annular flow (AN) transition, the Wallis transition model (Wallis 1969) was evaluated and found to be able to predict the high-viscosity liquid flow pattern data more accurately than the existing models. A validation study of the proposed transition models against the entire high-viscosity liquid experimental data set revealed a significant improvement with an average error of 22.6%. Specifically, the model over-performed existing models in BL/INT and INT/AN pattern transitions.

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