Characterization of Gas‐Liquid Two‐Phase Flows Using Laser Patterns

Jagannathan, Navisa; Chidambaram, Balachandar; Seshadri, Arunkumar; Venkatesan, M.

doi:10.1002/cjce.22242

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…[ 6,8 ] (d) Neural networks and support vector machine techniques, which are useful for detecting and describing subtle relationships in massive quantities of seemingly unrelated data. [ 12–14 ] The main disadvantages of these techniques include the number of time‐series sets of more than a single variable, the training step, and the choice of the model architecture. (e) Time‐series analysis based on non‐conventional methods, such as chaos theory, wavelets, and fractal analysis.…”

Section: Introductionmentioning

confidence: 99%

Two‐phase flow‐patterns identification in oil/gas pipelines based on fractal analysis

et al. 2021

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Flow‐pattern identification in two‐phase pipelines is essential for energy reduction as well as for the selection of suitable operating conditions for effective flow transport. The flow pattern in conventional oil/gas pipelines exhibit different flow patterns due to the underlying complex interactions of transport processes at different time scales. Fractal analysis of complex time series has received significant attention in the past few years due to its ability to extract hidden useful information from underlying phenomena in the time‐series complexity. In this work, the potential of fractal analysis for flow‐pattern identification in oil/gas pipelines is investigated using voltage measurements. Experimental data from a downward‐inclined oil/gas pipeline system at different superficial velocities of gas were analyzed. Our results indicate that fractal parameters can be useful for flow‐pattern identification and to gain insights into the complex phenomena of multiphase systems.

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Section: Introductionmentioning

confidence: 99%

Two‐phase flow‐patterns identification in oil/gas pipelines based on fractal analysis

et al. 2021

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“…So far numerous methods are proposed to perform accurate and effective identification of gasliquid flow patterns [8,9]. Among them flow pattern map is a well-accepted approach to prediction of the conditions leading to the transition of flow patterns in a variety of engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Sun¹,

Chen²,

Yao³

2019

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To find effective and practical methods to distinguish gas-liquid two-phase flow patterns, new flow pattern maps are established using the differential pressure through a classical Venturi tube. The differential pressure signal was first decomposed adaptively into a series of intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition. Hilbert marginal spectra of the IMFs showed that the flow patterns are related to the amplitude of the pressure fluctuation. The cross-correlation method was employed to sift the characteristic IMF, and then the energy ratio of the characteristic IMF to the raw signal was proposed to construct flow pattern maps with the volumetric void fraction and with the two-phase Reynolds number, respectively. The identification rates of these two maps are verified to be 91.18% and 92.65%. This approach provides a cost-effective solution to the difficult problem of identifying gas-liquid flow patterns in the industrial field.

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“…Two phase flows are of great industrial importance and throughout the years numerous techniques have been employed in order to identify the flow regime and also to determine the thin film thickness of flows. Jaganathan et al [1] characterized two phase flow patterns based on variation in the laser patterns produced. The variation was due to interaction of light with different mediums of different refractive indices.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Flow Regime Identification Using IR Sensor and 3.5mm Headphone Jack

Vasan

Venkatesan²

2018

AUSMT

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Multiphase flow occurs in variety of applications in Industries and in nature. One of the most important subcategory of multiphase flow is gas-liquid flows predominantly found in evaporators, condensers, micro-reactors, cryogenic engines and in compact heat exchangers. In the present study, air-water two phase flows in a 4.7mm inner diameter glass tube with 0.3mm thickness is characterized using an IR transceiver and a headphone jack. The output of the IR receiver is sent to the laptop using a 3.5mm headphone jack. The novelty in the present work is that this eliminates the need for a dedicated micro-controller/ DAQ system to process the data. A simple program written in python is used to process the input of the mic and provide amplitude and frequency information which identifies the flow regime. High-Speed Images captured simultaneously are used to validate the output obtained using the headphone jack.

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Characterization of Gas‐Liquid Two‐Phase Flows Using Laser Patterns

Cited by 7 publications

References 16 publications

Two‐phase flow‐patterns identification in oil/gas pipelines based on fractal analysis

Two‐phase flow‐patterns identification in oil/gas pipelines based on fractal analysis

Metrology and Measurement Systems

Intelligent Flow Regime Identification Using IR Sensor and 3.5mm Headphone Jack

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