The Sandia Fracture Challenge: blind round robin predictions of ductile tearing

Existing and emerging methods in computational mechanics are rarely validated against problems with an unknown outcome. For this reason, Sandia National Laboratories, in partnership with US National Science Foundation and Naval Surface Warfare Center Carderock Division, launched a computational challenge in mid-summer, 2012. Researchers and engineers were invited to predict crack initiation and propagation in a simple but novel geometry fabricated from a common off-the-shelf commercial engineering alloy. The goal of this international Sandia Fracture Challenge was to benchmark the capabilities for the prediction of deformation and damage evolution associated with ductile tearing in structural metals, including physics models, computational methods, and numerical implementations currently available in the computational fracture community. Thirteen teams participated, reporting blind predictions for the outcome of the Challenge. The simulations and experiments were performed independently and kept confidential. The methods for fracture prediction taken by the thirteen teams ranged from very simple engineering calculations to complicated multiscale simulations. The wide variation in modeling results showed a striking lack of consistency across research groups in addressing problems of ductile fracture. While some methods were more successful than others, it is clear that the problem of ductile fracture prediction continues to be challenging. Specific areas of deficiency have been identified through this effort. Also, the effort has underscored the need for additional blind prediction-based assessments

Derivation of the Orthotropic Nonlinear Elastic Material Law Driven by Low-Cost Data (DDONE)

Orthotropic nonlinear elastic materials are common in nature and widely used by various industries. However, there are only limited constitutive models available in today\u27s commercial software (e.g., ABAQUS, ANSYS, etc.) that adequately describe their mechanical behavior. Moreover, the material parameters in these constitutive models are also difficult to calibrate through low-cost, widely available experimental setups. Therefore, it is paramount to develop new ways to model orthotropic nonlinear elastic materials. In this work, a data-driven orthotropic nonlinear elastic (DDONE) approach is proposed, which builds the constitutive response from stress–strain data sets obtained from three designed uniaxial tensile experiments. The DDONE approach is then embedded into a finite element (FE) analysis framework to solve boundary-value problems (BVPs). Illustrative examples (e.g., structures with an orthotropic nonlinear elastic material) are presented, which agree well with the simulation results based on the reference material model. The DDONE approach generally makes accurate predictions, but it may lose accuracy when certain stress–strain states that appear in the engineering structure depart significantly from those covered in the data sets. Our DDONE approach is thus further strengthened by a mapping function, which is verified by additional numerical examples that demonstrate the effectiveness of our modified approach. Moreover, artificial neural networks (ANNs) are employed to further improve the computational efficiency and stability of the proposed DDONE approach