Session: DA-09-01 Piping and Equipment Dynamics and Dynamic Response Analysis-1

Paper Number: 122867

122867 - Lift-Off Simulation and Revision of the Fluid-Driven Mfl Pipeline Inspection Robot Under Obstacle Excitation 

     The use of fluid-driven Magnetic Flux Leakage (MFL) pipeline inspection robot to maintain oil and gas pipelines is an essential method of ensuring safe operation. The internal excitations of the pipe, such as girth weld and depressions, cause the vibration response of the robot and the variation of the Lift-off value of the sensors. In this study, the Lift-off value of the detection probe and the vibration response of a multi-sealing disc pipeline inspection robot passing through a girth weld under different design parameters are investigated by co-simulation. Considering the coupling between the fluid and robot, the fluid-structure interactions simulation model of Adams and MATLAB/SIMULINK is established. Simplify the modeling of probe detection arm and study the change trend of Lift-off value under excitation. The results show that the change of sealing disc interval and running speed has a significant influence on the dynamic response. The extreme value of vertical acceleration increases with the increase of the interval, but the increase of sealing disc interval has little influence on the change of tendency and the extreme value of axial acceleration. As the running speed increases, the extreme value of the vertical acceleration decreases significantly, and the degree of reciprocating oscillation in the time history curve decreases significantly. The extreme value and the range of variation of the Lift-off value at different locations are determined through result analysis and show a weakening trend at different speeds. The present analysis is beneficial for structural fatigue analysis and Lift-off correction.

Presenting Author: Minghao Chen China University of Petroleum (Beijing)

Presenting Author Biography: Minghao Chen, male, born in 2000, PhD candidate, graduated from Beijing Institute of Petrochemical Engineering with a master degree in mechanical Engineering in 2022. He is currently pursuing the Ph.D. degree at China
University of Petroleum, Beijing. His main research interests include oil and gas intelligent equipment design theory and service reliability.

Authors:

Minghao Chen China University of Petroleum (Beijing)
Xinna Shi China University of Petroleum (Beijing)
Tong Pang China University of Petroleum (Beijing)
Xiaoying Zhang China University of Petroleum (Beijing)
Hang Zhang China University of Petroleum (Beijing)