“…The data used for the investigation of three slug characteristics which are the slug length ( Supplementary Table 1 ), slug frequency ( Supplementary Table 2 ), and the slug translational velocity ( Supplementary Table 3 ) are provided to save time and effort. The data provided was computed from the recorded videos ( Supplementary file 4 ) using a developed Matlab code which explained in (Abdalellah et al) [2] . Fig.…”
Section: Datamentioning
confidence: 99%
“… The data can be used by other researchers to develop different image processing techniques from the utilized technique in the analysis of this paper. The utilized image process technique is explained in details by (Mohmmed et al 2016) [2] . …”
The data presented in this article were the basis for the study reported in the research articles entitled “Statistical assessment of experimental observation on the slug body length and slug translational velocity in a horizontal pipe” (Al-Kayiem et al., 2017) [1] which presents an experimental investigation of the slug velocity and slug body length for air-water tow phase flow in horizontal pipe. Here, in this article, the experimental set-up and the major instruments used for obtaining the computed data were explained in details. This data will be presented in the form of tables and videos.
“…The data used for the investigation of three slug characteristics which are the slug length ( Supplementary Table 1 ), slug frequency ( Supplementary Table 2 ), and the slug translational velocity ( Supplementary Table 3 ) are provided to save time and effort. The data provided was computed from the recorded videos ( Supplementary file 4 ) using a developed Matlab code which explained in (Abdalellah et al) [2] . Fig.…”
Section: Datamentioning
confidence: 99%
“… The data can be used by other researchers to develop different image processing techniques from the utilized technique in the analysis of this paper. The utilized image process technique is explained in details by (Mohmmed et al 2016) [2] . …”
The data presented in this article were the basis for the study reported in the research articles entitled “Statistical assessment of experimental observation on the slug body length and slug translational velocity in a horizontal pipe” (Al-Kayiem et al., 2017) [1] which presents an experimental investigation of the slug velocity and slug body length for air-water tow phase flow in horizontal pipe. Here, in this article, the experimental set-up and the major instruments used for obtaining the computed data were explained in details. This data will be presented in the form of tables and videos.
“…Concerning the flow measurement of two-phase flow, there are many technologies that have been used. Mohmmed et al and Abbagoni and Yeung introduced high-speed cameras (with transparent tube segments) to take high-speed shooting records for convection type [4,5], while Dong et al used ultrasonic Doppler sensors to estimate the total surface speed of the oil-water two phase flow [6]. P. Aarabi JeshvaghanI et al proposed and implemented a flow rate measurement method that was machines, genetic algorithms, etc.…”
In modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the experiment the flow patterns of the oil-gas two-phase flow will change after passing through the venturi tube with the same flow rates. Under the different oil-gas flow rate, the change will be diverse. Being motivated by the above experiments, we use the dual ECT sensors to collect the capacitance values before and after the venturi tube, respectively. Additionally, we use the linear projection algorithm (LBP) algorithm to reconstruct the image of flow patterns. This paper discusses the relationship between the change of flow patterns and the flow rates. Furthermore, a convolutional neural network (CNN) algorithm is proposed to predict the oil flow rate, gas flow rate, and GVF (gas void fraction, especially referring to sectional gas fraction) of the two-phase flow. We use ElasticNet regression as the loss function to effectively avoid possible overfitting problems. In actual experiments, we compare the Typical-ECT-imaging-based-GVF algorithm and SVM (Support Vector Machine) algorithm with CNN algorithm based on three different ECT datasets. Three different sets of ECT data are used to predict the gas flow rate, oil flow rate, and GVF, and they are respectively using the venturi front-based ECT data only, while using the venturi behind-based ECT data and using both these data.
“…Comparisons against more established experimental techniques [6,7] and numerical simulations have been carried out [8]. Among the most widely investigated methods, one can cite edge detection, watershed segmentation [9], ensemble averaging [10] and "blob" extraction [11]. However, despite their advantages in terms of low cost, processing speed and safety, flow imaging techniques are still limited to low-pressure systems and low superficial velocities.…”
Condensation of R-134a in upward and downward annular flow was experimentally studied in a 5-mm ID transparent test section using a LED-based image processing technique. In addition to the flow orientation, the frequency of the film structures was quantified in terms of the following parameters: total mass flow rate, condensation heat transfer rates (outlet vapor quality) and test section inclination with respect to the horizontal. The image processing technique combined Fourier analysis and the Kernel density estimation technique to calculate the two-dimensional (amplitude-frequency) probability density function of the film flow structure as a function of the independent parameters. Although the tube inclination and the condensation rate showed some influence on the frequency of the interfacial waves, the most significant effect was due to the flow orientation, which resulted in abrupt frequency changes apparently linked to flow regime transitions dictated by shear and gravity forces. Keywords Condensation • Annular flow • Quantitative visualization • PDF • KDE List of symbols A Wave amplitude (px, mm) C Constant in Eq. (4) D Tube diameter (mm) E Expected value f Wave frequency (Hz) f c Characteristic frequency (Hz) g Acceleration of gravity (m s −2) G Refrigerant mass velocity (kg m −2 s −1) j Superficial velocity (m s −1) j * Dimensionless superficial velocity m Constant in Eq. (4) p Probability density function u Uncertainty U Expanded uncertainty x Vapor mass quality Greek Tube inclination (rad, •) Mass density (kg m −3) Subscripts g Vapor l Liquid max Maximum o Outlet
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