Modeling and simulation on speed prediction of bypass pipeline inspection gauge in medium of water and crude oil

Zhang, Zengmeng; Yang, Yong; Hou, Jian; Gong, Yongjun

doi:10.1177/0020294020947123

Cited by 11 publications

(8 citation statements)

References 11 publications

(17 reference statements)

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“…In order to predict differential pressure between upstream and downstream section of the PIG which essentially drives the PIG forward, a correlation was developed by using curve fitting of data in this study that enables quick prediction of this important flow characteristics in future use. The developed correlation is a function of various parameters of the PIG geometry including the bypass opening percentage as well as gas density and inlet velocity, which is given by ∆ = ( 22 ) ( ) ℎ (12) where D is pipe diameter; d is horizontal bypass PIG diameter; h is bypass opening percentages; is disk diameter; is horizontal bypass length of PIG; is the gas density; and is the inlet velocity of the gas.…”

Section: Correlation Of Differential Pressurementioning

confidence: 99%

“…The PIG movement is greatly influenced by the pipeline flow characteristics as the PIG propels inside the pipeline due to differential pressure developed between its tail and front [9][10][11]. Therefore, the flow characteristics of fluid inside the pipeline including the fluid velocity and pressure need to be determined properly in order to control the PIG speed in pigging operations [6,12]. Additionally, new designs of PIG have evolved from time to time in order to achieve better control of PIG speed and movement towards improving the pigging performance.…”

Section: Introductionmentioning

confidence: 99%

“…A number of experimental and numerical studies on bypass PIG have been reported in literature with only few has considered disk bypass PIG as the latter was introduced more recently. A pigging operation has been simulated using a model that combines the equation of the PIG to the energy, momentum, and mass equations for the fluid flow [12,[15][16][17]. Utilizing the commercial computational fluid dynamics (CFD) software, ANSYS Fluent, Singh and Henkes [18] analysed a simple bypass PIG model for both single-phase and two-phase flow.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al, [12] carried out a numerical study on bypass PIG in the medium of water and crude oil. The study found that as the bypass hole diameter raised from 0.1 to 0.5 m under the specific research conditions, the PIG speed was reduced from 2.777 to 0.373 m/s for crude oil and from 2.779 to 0.589 m/s for water.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Flow Characteristics of Disk Bypass Pipeline Inspection Gauge (PIG) in Natural Gas Pipelines using Computational Fluid Dynamics

Rabby

Masuri

Kamal³

et al. 2021

CFDL

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Disk bypass pipeline inspection gauge (PIG) is considered as an efficient device for pigging operations including cleaning, maintaining and inspecting pipelines. The PIG performance is influenced by the fluid flow characteristics as PIG moves forward due to differential pressure of fluid around the PIG. This study focuses on flow characterization of fluid around disk bypass PIG for natural gases pipelines including methane, ethane, and butane using computational fluid dynamics approach. The control volume method with steady state Turbulent k-? model was applied for simulation purposes using ANSYS Fluent 19 software. Fluid velocities at different sections around PIG and differential pressure were investigated for various bypass opening percentages. The results showed that by increasing bypass opening percentages from 5% to 15%, fluid velocity at bypass opening section has reduced 28.28%, 40.43%, and 21.21% for ethane, butane, and methane, respectively, while differential pressure reduced 88%, 86% and 89%. This indicated that 15% bypass opening percentage provided the best flow characteristics among all cases considered. At 15% bypass opening percentage, methane resulted in the lowest fluid velocity at bypass opening section and lowest differential pressure compared to others. Additionally, a correlation of differential pressure of these gases as a function of bypass opening percentage and other parameters was also developed for first time. All results are important for design selection of PIG parameters for efficient pigging operation.

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Section: Correlation Of Differential Pressurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flow Characteristics of Disk Bypass Pipeline Inspection Gauge (PIG) in Natural Gas Pipelines using Computational Fluid Dynamics

Rabby

Masuri

Kamal³

et al. 2021

CFDL

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“…Moisture content change graphs can be got in convective drying with hot air using the curve-fitting tool. 12 By the way, the drying stages of products can be modeled to manage the drying process.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of smart and automatic oven for food drying

Tümay

Ünver

2021

Measurement and Control

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Fruits and vegetables ripen at certain times of the year and must be ripe for consumption. However, in the short-term ripening period, some of the fresh vegetables and fruits that are more than the consumable amount deteriorate before they can be consumed. Picking up fruits and vegetables when they are ripe and drying the surplus for later use is the most common storage method. In recent years, where technology has developed rapidly, instead of drying in the sun, solutions are produced in which the drying processes are managed automatically by using the drying kinematics of the products. The most recent techniques manage the drying process by measuring the weight of the wet and dried products during heating. Also, different types of ovens such as microwave ovens are tried to increase the efficiency of the drying process. These are rather complex solutions. In this study, a smart system that manages the drying process in real-time by using the humidity in the environment instead of weight together with the drying kinematics of the product is designed. So the complexity of the system is simplified. Also, the total duration of the drying process is exactly estimated by using the moisture content in the environment and the drying model of the product. In the study, firstly, data on the drying stage were collected with the experiments made for each product. These data were processed in a Matlab environment and a drying model with a curve fitting method was developed for each product. The drying models developed in the study were loaded into the processor of the smart oven and the entire drying process was managed in real-time. With the developed system solution, when the process is started, the drying time is estimated according to the amount processed and the type of product, and the drying time of the drying process is estimated by using the moisture content in the environment and the drying model of the product. In this way, pre-drying and post-drying stages can be planned.

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Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model

Xin

Chen

et al. 2022

Measurement

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Modeling and simulation on speed prediction of bypass pipeline inspection gauge in medium of water and crude oil

Cited by 11 publications

References 11 publications

Flow Characteristics of Disk Bypass Pipeline Inspection Gauge (PIG) in Natural Gas Pipelines using Computational Fluid Dynamics

Flow Characteristics of Disk Bypass Pipeline Inspection Gauge (PIG) in Natural Gas Pipelines using Computational Fluid Dynamics

Design and implementation of smart and automatic oven for food drying

Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model

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