The Damage Mechanics Challenge Results: Participant Predictions Compared with Experiment

ABSTRACT We present results from a recent exercise where participating organizations were asked to provide model-based blind predictions of damage evolution in 3D-printed geomaterial analogue test articles. Participants were provided with a range of data characterizing both the undamaged state (e.g.: ultrasonic measurements) and damage evolution (e.g.: 3-point bending, unconfined compression, and Brazilian testing) of the material. In this paper, we focus on comparisons between the participants’ predictions and the previously secret challenge problem experimental observations. We present valuable lessons learned for the application of numerical methods to deformation and failure in brittle-ductile materials. The exercise also allows us to identify which specific types of calibration data were of most utility to the participants in developing their predictions. Further, we identify additional data that would have been useful for participants to improve the confidence of their predictions. Consequently, this work improves our understanding of how to better characterize a material to enable accurate prediction of damage and failure propagation in natural and engineered brittle-ductile materials. INTRODUCTION Understanding the failure of materials is particularly relevant today with the current interest in aging infrastructure and in enhanced geothermal systems which require a network of fractures to optimize production. As more and more sensors are used to monitor infrastructure and the subsurface, methods are required to detect anomalous signals in data and link these signals to the underlying physics and mechanics of failure to determine if failure is imminent. This requires robust computational methods that capture the physics of failure and identify the measurable signatures of failures. While there are many computational approaches for simulating damage evolution in materials, few have been ground-truth tested with either known experimental data or with blind data sets. The Fracture Challenge concept (e.g.: Boyce et al., 2014) has demonstrated that valuable lessons can be learned by comparing computational methods and workflow approaches taken by different teams in the blind prediction of controlled fracturing experiments. Previous challenges have focused upon failure in metals. Here, we present results from a recent challenge that considered damage evolution in additively manufactured samples where the mechanical response is similar to the brittle-ductile response of rock, yet highly reproducible from one test article to another by virtue of the 3D printing process.