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    Analysis of bulging, bursting, and reliability-based design and optimization for pipelines

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    dissertation (3.154Mb)
    Date
    2018-05
    Author
    Memon, Shabbir
    Advisor
    Lankarani, Hamid M.
    Metadata
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    Abstract
    Pipelines are subjected to bursting failure under many operating conditions. Predicting the burst capacities of corroded pipelines is significantly relevant to the pipeline industry. The tube bulge test is an advanced testing process used to predict burst capacity, whereby a tube is placed in a die cavity, sealed from both ends, and pressurized to bulge at the center. In the present study, the bulge test was modeled using the finite element method (FEM). The objective of this study was to examine and optimize the process parameters and corrosion rates for obtaining maximum bulge height and uniform effective strain and thickness distributions, and the effect of material parameters, process parameters, geometric parameters, pre-straining, and pipe curvature on bulging. The Taguchi methodology was utilized for optimizing the operating, geometric, and material parameters. The analysis of variance (ANOVA) approach was also used to study the relative contribution of material properties, process parameters, and tube thickness. This study also analyzed and extended the pipeline bursting failure model to include failure probability, and a new bursting failure prediction model for curved pipes was developed. The Monte Carlo simulation (MCS) scheme was used throughout this work in a probabilistic approach to predict the probability of failure. Also, a sensitivity analysis was carried out to investigate the influence of corrosion parameters on the probability of failure, which reflects the effect of axial-radial corrosion rates and depth length of the corrosion defect on failure probability. Finally, optimized corrosion rates were predicted using the reliability-based design and optimization (RBDO) technique, in which the probabilistic constraint is utilized based on the Shell-92 pipeline failure prediction model.
    Description
    Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Mechanical Engineering
    URI
    http://hdl.handle.net/10057/15420
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