Application of Statistical Analysis Techniques to Pipeline On-Bottom Stability Analysis

Author(s):  
Bassem S. Youssef ◽  
Mark J. Cassidy ◽  
Yinghui Tian

Offshore pipelines are increasingly being employed to transport offshore hydrocarbons to onshore processing facilities. Pipelines laid directly on the seabed are subject to a considerable hydrodynamic loading from waves and currents and must be accurately designed for on-bottom stability. Confidence in the stability of pipelines requires appropriate models for their assessment and, in this paper, particular emphasis is placed on achieving an integrated and balanced approach in considering the nonlinearities and uncertainties in the pipe structure, the reaction of the restraining soil, and the hydrodynamic loading applied. A statistical approach is followed by developing a response surface model for the pipeline maximum horizontal displacement within a storm, while including variability in parameters. The Monte Carlo simulation method is used in combination with the developed response surface model to calculate the extreme response statistics. The benefit of this approach is demonstrated and also used to investigate the sensitivity of the on-bottom pipeline simulation for a variety of model input parameters. These results provide guidance to engineers as to what uncertainties are worth reducing, if possible, before a pipe is designed.

2009 ◽  
Vol 419-420 ◽  
pp. 89-92
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Zai Bai Qin

Cylinder shell stiffened by rings is used commonly in submersibles, and structure strength should be verified in the initial design stage considering the thickness of the shell, the number of rings, the shape of ring section and so on. Based on the statistical techniques, a strategy for optimization design of pressure hull is proposed in this paper. Its central idea is that: firstly the design variables are chosen by referring criterion for structure strength, then the samples for analysis are created in the design space; secondly finite element models corresponding to the samples are built and analyzed; thirdly the approximations of these analysis are constructed using these samples and responses obtained by finite element model; finally optimization design result is obtained using response surface model. The result shows that this method that can improve the efficiency and achieve optimal intention has valuable reference information for engineering application.


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