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Fracture propagation control (FPC) is important in design and operation of pipelines for gas transport. Predicting and understanding the velocity of a running ductile fracture remains as one of the toughest challenges in the fracture research community and is of paramount importance in currently used FPC methods. Current methods use semi-empirical models, based on the Battelle Two Curve Method (BTCM), developed about 40 years ago. However, this approach has shown being inaccurate and unreliable, especially when applied to today's high toughness steels or when non-ideal gases (e.g. CO 2 and rich natural gas) are considered. The main reason for this is a poor understanding of the governing factors for the velocity of a running ductile fracture, and represents a huge challenge and source of non-conservatism when applying these mainly empirical tools for ensuring crack arrest. This paper aims to further understand ductile fracture velocity and arrest in pipelines, and to find the main material parameters that control the velocity of a running ductile fracture. A fully coupled fluid-structure interaction framework for modelling a running ductile fracture in a pressurized pipeline have been used to do a systematic parameter analysis of the most important pipeline parameters controlling the fracture velocity. [ABSTRACT FROM AUTHOR]

Magnetic flux leakage techniques are used extensively to detect and characterise defects in natural gas and oil transmission pipelines. Based on the least squares support vector machines (LS-SVMs) technique, this paper presents a novel approach for the three-dimensional (3-D) defect profile reconstructed from magnetic flux leakage signals. The basic theory of LS-SVM for function estimates is given. The hyper-parameters of the LS-SVMs problem formulations are tuned using a 10-fold cross validation procedure and a grid search mechanism, and applying the pruning algorithm to impose sparseness on the LS-SVMs. The training data are composed of the measured and simulated data. A mapping from MFL signals to 3-D profiles of defects is established, the reconstruction of 3-D profiles of defects from magnetic flux leakage inspection signals is achieved and 3-D error of reconstruction results is analysed. The experimental results show that the LS-SVM has high precision, good generalisation ability and capability of tolerating noise. [ABSTRACT FROM AUTHOR]