Burst pressure prediction of collinear crack in steel pipeline
The predictions of pipeline burst pressure in the early stage are very importance in order to provide assessment for future inspection and maintenances activities. The failures of pipelines contribute to economic implications, fatal injuries and also constitute serious hazards to the environment due...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2013
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| Online Access: | http://umpir.ump.edu.my/id/eprint/8354/ http://umpir.ump.edu.my/id/eprint/8354/ http://umpir.ump.edu.my/id/eprint/8354/1/CD8052_%40_69.pdf |
| Summary: | The predictions of pipeline burst pressure in the early stage are very importance in order to provide assessment for future inspection and maintenances activities. The failures of pipelines contribute to economic implications, fatal injuries and also constitute serious hazards to the environment due to leakage. This project is a study on interaction effect of the distance between cracks for material grade B steel pipe using finite element analysis (FEA). The objectives for this project are to study the interaction of two linear cracks and analyze the maximum pressure defect for various distances between crack and crack length. This project include the analysis of the material grade B steel by using MSC Patran 2008 r1 software as pre-processor and MSC Marc 2008 r1 software as a solver. This analysis investigates one failure criterions that is von Mises stress as to predict the failure of defective pipe. Half of pipe was simulated by applying the symmetrical condition. The pipe is modeled in 3D with outer diameter of 60.5 mm, wall thickness of 4 mm and different defect parameters. Result shows that the maximum pressure increases when the distance between cracks increase and the crack length decrease. The results have been compared to available design codes for corroded pipelines such as ASME B31G, Modified ASME B31G and DNV RP F101. The comparison with design codes have shown that FEA burst pressure gives higher values |
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