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

脆性材料损伤力学挑战的基准数据集

• 批准号: 1932312
• 负责人: Laura Pyrak-Nolte
• 金额: $ 8.99万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932312&HistoricalAwards=false
• 关键词: Benchmark; Data; Set; Damage; Mechanics

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.

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发展的损害的能力。 损坏是民用基础设施，人体和地下存储组成部分的关键模式，但对于从地热和传统地下储层增强能源生产的成功至关重要。 随着人工智能方法在传感器数据中发现异常信号时提出了进步，因此需要方法将这些读数与未能确定失败是否迫在眉睫的基础物理学/机制联系起来。这需要强大的计算方法来捕获失败的物理并确定可测量的失败签名。尽管有许多用于模拟损害的计算方法，但很少有通过已知的实验数据或盲目数据集对基地进行测试。这项研究将生成基准实验室数据集，以引发损害力学挑战，以比较脆性延性材料中损伤演变的计算方法。该数据集的产生将对材料模型的进步，对不同数值方法之间的预测进行比较，这将是很大的好处，最重要的是创建了一个高质量的实验数据数据库，该数据库将来可以在工程界使用。 更广泛的影响是对用于预测损伤和失败的一系列计算方法的关键测试。今天了解材料的失败与当前对国家老化基础设施的兴趣以及需要裂缝网络以优化生产的增强的地热系统有关。我们的推广目标是获得一个基准数据集，以进行计算挑战，并为培训研究生和本科生提供计算模型的方法；提供一个论坛，以公开讨论失败的数值方法，并为科学家和工程师提供审查的计算社区，以解决损害/失败问题并与行业合作。将生成基准实验室数据集，以进行损害机制挑战，以比较巨大的差异材料的损害进化的计算方法。实验设计是在2019年2月在普渡大学举行的损害力学研讨会上开发的，其中包括损害力学领域的主要计算科学家和工程师。基准实验室数据集将包括传统数字载荷传感器，3D数字图像相关到地图表面变形，3D X射线显微镜的空间和时间测量，以接地裂纹型 - 实现裂纹几何形状以及激光器的轮廓仪，以捕获表面粗糙度。样品将通过添加剂制造方法（例如3D打印）制造，以生成旨在以受控方式失败的可重复样品。选择这些方法是为了确保参与者定义的可重复和公正指标可用于定量评估和衡量理论和数据驱动模型的质量，鉴于固有的不确定性和变异性对失败的发作和模式的重大影响，该奖项反映了NSF的法定任务，并通过评估了基金会的范围，并通过评估了基金会的MERITIAL和BRODITIAL的支持。