Mathematical Simulation for Forecasting an Uneven Distribution of the Stressed-Strain State of Metal When Expanding Large-Diameter Pipes

2021 ◽  
pp. 966-975
Author(s):  
D. C. Nguyen ◽  
D. B. Yefremov
Keyword(s):  
Mathematical Simulation ◽  
Strain State ◽  
Large Diameter ◽  
Uneven Distribution ◽  
Large Diameter Pipes

Load-carrying ability of large-diameter pipes

1978 ◽  
Vol 10 (1) ◽  
pp. 29-34 ◽  
Author(s):  
V. V. Chelyshev ◽  
V. G. Burdukovskii ◽  
B. N. Gubashov ◽  
V. V. Kirichenko
Keyword(s):  
Large Diameter ◽  
Load Carrying ◽  
Large Diameter Pipes

Factory-applied anticorrosive insulation for large-diameter pipes

Metallurgist ◽  
1987 ◽  
Vol 31 (10) ◽  
pp. 320-321
Author(s):  
V. M. Ryabov ◽  
L. A. Usova
Keyword(s):  
Large Diameter ◽  
Large Diameter Pipes

Numerical Simulation of UOE Pipe Process and its Effect on Pipe Mechanical Behavior in Deep-Water Applications

2015 ◽  
Author(s):  
Giannoula Chatzopoulou ◽  
Spyros A. Karamanos ◽  
George E. Varelis
Keyword(s):  
Deep Water ◽  
Manufacturing Process ◽  
Large Diameter ◽  
Structural Response ◽  
Structural Instability ◽  
External Pressure ◽  
Line Pipe ◽  
Simulation Tools ◽  
Large Diameter Pipes ◽  
Pipe Manufacturing

Large-diameter thick-walled steel pipes during their installation in deep-water are subjected to a combination of loading in terms of external pressure, bending and axial tension, which may trigger structural instability due to excessive pipe ovalization with catastrophic effects. In the present study, the UOE pipe manufacturing process, commonly adopted for producing large-diameter pipes of significant thickness, is considered. The study examines the effect of UOE line pipe manufacturing process on the structural response and resistance of offshore pipes during the installation process using nonlinear finite element simulation tools.

High end inspection by filmless radiography on LSAW large diameter pipes

2010 ◽  
Vol 43 (3) ◽  
pp. 206-209 ◽  
Author(s):  
Thomas Kersting ◽  
Norbert Schönartz ◽  
Ludwig Oesterlein ◽  
Andreas Liessem
Keyword(s):  
Large Diameter ◽  
Large Diameter Pipes

Effects of Pipe Diameter and Stokes Number on Erosion in Elbows

2020 ◽  
Author(s):  
Soroor Karimi ◽  
Alireza Asgharpour ◽  
Elham Fallah ◽  
Siamack A. Shirazi
Keyword(s):  
Experimental Data ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Large Diameter ◽  
Stokes Number ◽  
Gas Production ◽  
Flow Conditions ◽  
Cfd Simulations ◽  
Oil And Gas Production ◽  
Large Diameter Pipes

Abstract Large diameter pipes and elbows are vastly used in industry especially in mining and oil and gas production. Solid particle erosion is a common issue in these pipelines, and it is important to predict it to avoid failures. Currently, laboratory experiments reported in the literature are limited to diameters less than 4 inches. Therefore, there is not much experimental data available for large diameter elbows. However, the erosion can be predicted by CFD simulations and applying erosion equations in such elbows. The goal of this project is to examine the effects of elbow diameter and Stokes number on erosion patterns and magnitude for various flow conditions for elbow diameters of 2, 4, 8, and 12 inches. The approach of this work is to first perform CFD simulations of liquid-solid and gas-solid flows in 2-inch and 4-inch elbows, respectively, and evaluate the results by available experimental data. Then CFD simulations are carried for 2, 4, 8, and 12-inch standard elbows for various Stokes numbers corresponding to gas dominant flows with the velocity of 30 m/s, and liquid dominant flows with the velocities of 6 m/s. For gas dominant flows erosion in air and for liquid dominant flows erosion in water is investigated. All these simulations are carried for four particle sizes of 25, 75, 150, and 300 microns. The results indicate that Stokes number and diameter of elbows have significant effects on erosion patterns as well as magnitudes in this geometry. This work will have various applications, including validating mechanistic models of erosion predictions in elbows and developing an Artificial Intelligence (machine learning) algorithm to predict erosion for various flow conditions. Such algorithms are limited to the range of conditions they are trained for. Therefore, it is important to expand the database these codes are accessing. Overall, the CFD database of large diameter elbows will reduce the computational costs in the future.

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