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Jignesh Thaker

Bio: Jignesh Thaker is an academic researcher from Sardar Vallabhbhai National Institute of Technology, Surat. The author has contributed to research in topics: Slug flow & Flow visualization. The author has an hindex of 4, co-authored 11 publications receiving 130 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the internal structure of different sub-regimes of slug flow is analyzed and five distinct slug flow subregimes are identified based on the visual observations, which correspond to slug at formation stage (onset of slug); less aerated slug; highly aerated slugs; slug and plug; and slug and wavy.

52 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed a regime map showing possible erosion and corrosion phenomena due to intermittent flows in pipe, which is represented in terms of non-dimensional superficial Reynolds numbers of both the phases to account for pipe diameter, flow rate and fluid viscosity.

51 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the mechanism responsible for plug to slug transition and associated dynamics of bubble detachment from the elongated bubble using flow visualization and local velocity measurements using FASTCAM Photron camera and a 3D automated traverse system.

32 citations

Journal ArticleDOI
TL;DR: In this paper, a flow visualisation experiment was conducted for the intermittent regime of gas-liquid two-phase flow and the results showed that the correlations proposed in literature for slug flow do not accurately predict the flow characteristics in the plug flow regime.

29 citations

Journal ArticleDOI
TL;DR: In this paper, a unified correlation for prediction of two-phase frictional pressure drop for intermittent flow regime is proposed, which will help in avoiding the erosion-corrosion in piping system.

5 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a gas-liquid slug two-phase flow in a horizontal pipe was carried out to investigate the initiation and flow development mechanisms, and the slug initiation mechanisms were explained by visual observation and pressure fluctuations.

68 citations

Journal ArticleDOI
15 Apr 2017-Wear
TL;DR: In this article, the authors used three-dimensional confocal microscopy and computational fluid dynamics to characterize the flow patterns and distribution of sand particles in a horizontal steel elbow through which a sand slurry was passed at different velocities.

53 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed a regime map showing possible erosion and corrosion phenomena due to intermittent flows in pipe, which is represented in terms of non-dimensional superficial Reynolds numbers of both the phases to account for pipe diameter, flow rate and fluid viscosity.

51 citations

Journal ArticleDOI
TL;DR: In this article, a two-phase flow regime identification in a horizontal pipe was realized based on the liquid phase velocity information and the machine learning method using statistical features extracted from the velocity time series data, such as mean, root mean square and power spectral density.
Abstract: Two-phase flow regime identification in a horizontal pipe was realized based on the liquid phase velocity information and the machine learning method. Ultrasound Doppler velocimetry was employed to measure the liquid velocity. Statistical features extracted from the velocity time series data, such as mean, root mean square, and power spectral density, were used to realize real-time flow regime identification. In addition, two novel parameters—maximum velocity ratio and maximum velocity difference ratio—were proposed to identify plug and decaying slug flow. Different classification algorithms were employed to achieve a high identification accuracy. Moreover, transient flow regime identification with a fast response was realized based on two classification algorithms—long–short term memory and convolutional neural network. The results show that the accuracy of real-time flow regime identification based on a flow regime map can reach up to 93.1% using support vector machine, the maximum velocity ratio and maximum velocity difference ratio are effective in identifying plug and decaying slug flow, and transient flow regime identification under slug flow condition can be realized with an accuracy of 94% based on a convolutional neural network (CNN). Decaying slugs with long lengths confuse the CNN and are responsible for the error in identification. The results presented herein are expected to expand the available knowledge on two-phase flow regime identification.

49 citations

Posted Content
01 Jan 2017
TL;DR: In this paper, the authors used a seeding technique to ensure that the surface tension of the water layer remains unaffected upon contact with the tracer particles in the gas-phase and thus allowed small scale interfacial structures to occur and evolve naturally.
Abstract: Abstract Simultaneous Particle Image Velocimetry (PIV) measurements of stratified turbulent air/water flow in a horizontal pipe have been performed using small water droplets, d p ‾ = 6 μ m , as tracer particles in the gas-phase. This seeding technique ensures that the surface tension of the water layer remains unaffected upon contact with the tracer particles in the gas-phase and thus allows small scale interfacial structures, such as capillary waves to occur and evolve naturally. Experiments have been conducted in a 31 m long, 100 mm in diameter PVC pipe using air and water at atmospheric pressure as test fluids. For the purpose of validation of the experimental set-up and the suggested seeding technique, gas single-phase measurements were performed at Re D = 45 , 000 and compared to existing DNS results from the literature with similar Re-number, showing very good agreement. Two stratified flow cases, i.e. smooth and wavy, are extensively discussed with emphasis on the effect of the interface pattern on the gas streamwise turbulence profile u g ′ . A simple analysis using the u g ′ -profiles of 17 stratified flows suggests the presence of a correlation between the turbulence structure of the gas-phase and global flow conditions such as the pressure drop and the bulk velocity.

42 citations