No abstract

No abstract

The Gurson-Tvergaard-Needleman (GTN) damage model is widely used to predict ductile failure initiation and propagation. However, the material-dependent parameters can show a significant spread when determined for the same steel grade material. Different calibration procedures and optimisation processes cause a significant variation in the obtained parameter values. Furthermore, there is no clear consensus on which parameters require calibration. In this study, the influence of the material-dependent parameters used to model the dynamic ductile fracture behaviour of X70 grade pipeline steel is investigated. A sensitivity analysis is performed on a finite element model of a Charpy V-Notch (CVN) specimen. Seven GTN model parameters are considered in a total of 70 simulations. A feedforward back-propagating artificial neural network (ANN) is constructed and trained using data obtained through the numerical simulations. A Connected Weights (CW) algorithm allows to determine the relative influence of each parameter on the fracture energy. It was observed that the void growth acceleration factor plays an important role with respect to the parameter influences.Remarkably, the mean nucleation strain, N  has the highest relative importance whilst the critical void volume fraction, c fwhich is considered as a crucial damage parametershowed the smallest influence when the acceleration factor is low. On the contrary, when considering a high acceleration factor, c f becomes the most influential parameter. Based on the obtained importance for each parameter, it is suggested that parameters 0 f , c f , F f , and N f should be selected for calibration in each individual application. Finally, the applied machine learning approach is used to predict the fracture energy for a given set of damage parameters for X70 grade steel. It is observed that the trained neural network is able to provide a satisfactory approximation of the CVN fracture energy.

Initiation and propagation of ductile fractures are major consideration during the design of high-pressure pipelines. Consequences of a pipeline failure can be catastrophic thus structural integrity must be ensured over several decades. Traditional lab-scale experiments such as the Charpy V-Notch (CVN) and Drop Weight Tear Test (DWTT), impact experiments on a notched three-point bending sample, are widely used to measure the fracture toughness of a material. However, with increasing wall thickness and the transition to high-grade steels in the pipeline industry, the size-effect of the specimen and inverse fracture became prominent issues. A new testing methodology called the Dynamic Tensile Tear Test (DT3) is currently investigated as to address the issues presented by the current state of the art. In this study, a numerical investigation is conducted on the CVN, DWTT and DT3 experiments to compare the modelling of dynamic ductile fracture propagation in three different testing scales using the Gurson-Tvergaard-Needleman (GTN) damage model. X70 and X100 pipeline steel grades are used to model material behaviour. For each considered lab-scale experiment, the dynamic ductile fracture behaviour was successfully reproduced using the GTN damage model.