STRAIN BASED DAMAGE MODEL PREDICTIONS OF DUCTILE CRACK GROWTH IN MULTIPLE FRACTURE SPECIMEN GEOMETRIES

Modeling damage and ductile crack growth in metallic materials has been of interest over the last four decades. Damage models of increasing complexity have been employed to characterize and predict the ductile crack growth in materials. A short review of the existing ductile crack growth models has been provided. Recently, a strain-based damage model has been advanced by researchers, which is capable of capturing the stress and strain states for the incipient damage within the material while being able to capture the triaxiality parameter. In this work a strain based ductile fracture damage model has been employed to model specimens with three different crack geometries, namely a single-edge notch tension SEN(T), compact-tension C(T), and outer diameter (OD) axial surface-cracked pipe. The predictions from the ductile crack growth model have been compared to experimental findings of the crack growth (obtained using a d-c Electric Potential measurement technique) and the corresponding load levels and crack opening displacements (CODs). The points of similarity between the experimental measurements and the fracture surface observations of crack growth and the predictions from the finite element modeling (FEM) approach have been discussed. The same X80 material properties and damage model parameters were employed to benchmark the ductile crack growth in two different SEN(T) specimens with differing normalized crack depths (crack/width ratios, a/W), a C(T) specimen (a/W=0.5), and an OD SC pipe (a/t=0.6) to shed light on the predictability of the crack initiation event and the subsequent ductile crack growth until failure. The findings provide credence to the applicability of the new approach for piping materials while providing a framework for flaw evaluation methodologies. The investigation also opens the doors for regions where mesh regularization methods and modeling approaches along with mathematical relations can be developed to form a more efficient framework for modeling specimens with diverse constraints efficiently.