ND (neutron diffraction) and SANS (small-angle neutron scattering) techniques can assess residual stress in and the microstructure of hydrocarbon pipelines. Forecasting tools based on predictive failure models incorporating these techniques can produce such tangible benfits as improved material selection and pipeline design, greater standardization of material, and more thoroughly examined welding requirements. They can also allow a more comprehensive understanding of ideal wall thickness, taking into account that thinner pipe walls can reduce RS (residual stress) gradients that cause failures while also reducing installation and material costs. Such tools will also help operators prioritize maintenance, repair, and rehabilitation needs. Neutron techniques are also complementary to other testing methods, extending their benefit to pipeline operators by revealing hidden cause-effect relationships leading to pipeline failures and identifying potential weaknesses in protection systems. This article explains how ND and SANS techniques can address problems in current pipeline-integrity assessment methodologies.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Neutron diffraction-based tools aid failure forecasting models


    Contributors:

    Published in:

    Oil and Gas Journal ; 106 , 11 ; 56-61


    Publication date :

    2008


    Size :

    5 Seiten, 6 Bilder, 5 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    In-situ neutron diffraction study of micromechanical shear failure in an aerospace composite

    Wang, Bing / Seffen, Keith A. / Guest, Simon D. et al. | AIAA | 2020



    Profitability Forecasting Models

    Abdelghany, Ahmed / Abdelghany, Khaled | Wiley | 2018


    Elasticity-Based Rural Traffic Forecasting Models

    Stokes, R. W. | British Library Online Contents | 1995


    Software Forecasting Models

    Schneidewind, Norman | AIAA | 2009