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.
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机译:计算力学中的现有方法和新兴方法很少针对结果未知的问题进行验证。因此,桑迪亚国家实验室(Sandia National Laboratories)与美国国家科学基金会和海军地面作战中心卡德洛克分校合作,于2012年仲夏发起了计算挑战。邀请研究人员和工程师以简单但新颖的方式预测裂纹的产生和扩展。由常见的现成商业工程合金制成的几何形状。此次国际桑迪亚骨折挑战赛的目标是对与结构金属的韧性撕裂相关的变形和损伤演化的预测能力进行基准测试,包括物理模型,计算方法以及计算裂缝社区中当前可用的数值实现。 13个团队参加了比赛,他们对挑战的结果进行了盲目预测。模拟和实验是独立进行的,并且要保密。 13个小组采用的裂缝预测方法从非常简单的工程计算到复杂的多尺度模拟。建模结果的巨大差异表明,各研究组在解决延性骨折问题上缺乏一致性。尽管某些方法比其他方法更成功,但很明显,韧性断裂预测问题仍然具有挑战性。通过这项工作,已经确定了特定的缺陷领域。此外,这项工作还强调了需要额外的基于盲目预测的评估。
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