Benchmark Data Set for Damage Mechanics Challenge on Brittle-Ductile Materials

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The reliability and sustainability of civil infrastructure, the human body and the Earth's subsurface all depend on our ability to monitor existing and evolving damage. Damage is a key mode of failure of civil infrastructure, components of the human body and subsurface storage, but it is of the highest importance for the success of enhanced energy production from geothermal and traditional subsurface reservoirs. As artificial intelligence methods advance in the detection of anomalous signals in data from sensors, methods are needed to link these readings to the underlying physics/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 failure. While there are many computational approaches for simulating damage, few have been ground-truth tested with either known experimental data or with blind data sets. This research will generate a benchmark laboratory data set to initiate a damage mechanics challenge to compare computational approaches on damage evolution in brittle-ductile material. The generation of this dataset will be of great benefit to the advancement of material models, to the comparison of predictions among different numerical approaches, and most importantly create a high-quality database of experimental data that can be used in the future by the engineering community. The broader impact is critical testing of an array of computational methods used to predict damage and failure. Understanding the failure of materials is particularly relevant today with the current interest in the nation?s aging infrastructure and in enhanced geothermal systems which require a network of fractures to optimize production. Our outreach objective is to obtain a benchmark dataset for a computational challenge and for training graduate and undergraduate students in methods for verification of computational models; to provide a forum for open discussions of numerical approaches for failure, and to provide a vetted computational community of scientists and engineers to address damage/failure issues and to work with industry.A benchmark laboratory data set will be generated for a damage mechanics challenge to compare computational approaches on damage evolution in brittle-ductile materials. The experimental design was developed as a community effort at a Damage Mechanics Workshop held at Purdue University in February 2019, which included lead computational scientists and engineers in the field of damage mechanics. The benchmark laboratory datasets will include spatial and temporal measurements from traditional digital load-displacement sensors, 3D digital image correlation to map surface deformations, 3D X-ray microscopy to ground-truth the crack-failure geometry, and laser profilometry to capture surface roughness. The samples will be fabricated through additive manufacturing methods (e.g. 3D printing) to produce repeatable samples designed to fail in controlled ways. These methods were selected to ensure that participant-defined repeatable and unbiased metrics were available to quantitatively assess and measure the quality of the theoretical and data-driven models, given the significant influence of inherent uncertainty and variability on the onset and mode of failure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.