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

民用基础设施、人体和地球地下的可靠性和可持续性都取决于我们监测现有和不断演变的损害的能力。 损坏是民用基础设施、人体组成部分和地下储存失败的一个主要模式，但它对于成功地提高地热和传统地下储层的能源生产至关重要。 随着人工智能方法在检测传感器数据中的异常信号方面的进步，需要将这些读数与故障的基本物理/力学联系起来的方法，以确定故障是否即将发生。这需要强大的计算方法，捕捉故障的物理和识别故障的可测量的签名。虽然有许多用于模拟损伤的计算方法，但很少用已知的实验数据或盲数据集进行地面实况测试。这项研究将产生一个基准的实验室数据集，启动一个损伤力学的挑战，比较计算方法在脆韧性材料的损伤演化。该数据集的生成将极大地有利于材料模型的进步，不同数值方法之间的预测比较，最重要的是创建一个高质量的实验数据数据库，可供工程界在未来使用。 更广泛的影响是对用于预测损坏和故障的一系列计算方法的关键测试。理解材料的失效与当今国家的当前利益特别相关？在日益老化的基础设施和需要裂缝网络来优化生产的增强型地热系统中，我们的推广目标是获得一个基准数据集的计算挑战和培训研究生和本科生的方法，用于验证计算模型;提供一个论坛，公开讨论失败的数字方法，并提供一个由科学家和工程师组成的经过审查的计算社区，以解决损害/将为损伤力学挑战生成基准实验室数据集，以比较脆韧性材料损伤演化的计算方法。实验设计是作为2019年2月在普渡大学举行的损伤力学研讨会上的社区努力而开发的，其中包括损伤力学领域的首席计算科学家和工程师。基准实验室数据集将包括来自传统数字载荷-位移传感器的空间和时间测量值、用于映射表面变形的3D数字图像相关性、用于裂纹失效几何形状的3D X射线显微镜以及用于捕获表面粗糙度的激光轮廓测量。这些样品将通过增材制造方法（例如3D打印）制造，以生产可重复的样品，这些样品旨在以受控的方式失效。选择这些方法是为了确保参与者定义的可重复和无偏见的指标可用于定量评估和衡量理论和数据驱动模型的质量，考虑到固有的不确定性和可变性对故障的发生和模式的重大影响，该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.