Analysis of interfacial dynamics in stratified and wavy-stratified flow using Laser Doppler Velocimetry

Saini, Sunny; Thaker, Jignesh; Banerjee, Jyotirmay

doi:10.1007/s42757-020-0083-1

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…Under certain conditions, the liquid and gas phases may ow alternately and irregularly as elongated bubble (also called gas pocket or gas slug) and bulk of liquid regions lling the entire pipe as liquid slug (Mohmmed et al, 2021, resulting in the intermittence of ow parameters such as pressure, void fraction, ow rates of both phases, heat and mass coe cients, etc. This ow regime, called intermittent ow, represents a destructive ow pattern (Saini et al, 2022;Adouni et al, 2022), which makes it challenging for the engineers. Indeed, the alternating ow of the two structures causes several industrial problems such as instability of heat and mass exchange in chemical reactors and liquid over ow in upstream installations.…”

Section: Introductionmentioning

confidence: 99%

Identifying the intermittent flow sub-regimes using pressure drop time series fluctuations

Arabi

Salhi

Zenati

et al. 2023

Preprint

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From a literature survey, it has been noticed a lack of objective methods to identify and distinct between the intermittent flow sub-regimes. Thus, the aim of this study is to analyze pressure drop fluctuations using statistical methods in order to propose simple tools to characterize the sub-regimes of the intermittent flow. The pressure drop time series were collected from experiments carried out using air-water mixture and 30 mm ID pipe. The utilization of transparent pipes allowed to identify three sub-regimes which are Plug flow, Less Aerated Slug flow (LAS flow), and Highly Aerated Slug flow (HAS flow). It was found that the PDF displayed a unimodal distribution in all cases studied. Meanwhile, the representation of the base width and maximum values of PDF as function of the gas-to-liquid superficial velocities ratio and the mixture Froude number, respectively, showed that the points for each sub-regime are clustered in the same zone. So, these two feature spaces can be used to identify the sub-regimes. A third feature space, based on the statistical parameters standard deviation and kurtosis, was also proposed as a tool to distinguish between the three sub-regimes.

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Section: Introductionmentioning

confidence: 99%