CAREER: Using Physics-Based Machine Learning to Reconcile the Crack Tip with the Plastic Zone during Fracture of Metals

职业：使用基于物理的机器学习来协调金属断裂过程中的裂纹尖端与塑性区

• 批准号: 2237039
• 负责人: Ryan Sills
• 金额: $ 62.22万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237039&HistoricalAwards=false
• 关键词: CAREER; Using; Physics; Based; Machine

Metal fracture occurs when cracks or other flaws grow, leading to failure of metallic structures such as buildings and aircraft that has been estimated to cost 4 percent of US gross domestic product. Current theories and models used to predict fracture are based on an incomplete picture of the fracture process, which lead to inaccurate predictions of crack growth, making it difficult to design engineering structures and next-generation structural metals. This Faculty Early Career Development (CAREER) award supports research to develop better material models used in the design of fracture-resistant engineering structures relevant to energy, defense, aerospace, and transportation applications, thereby supporting the U.S. economy and defense. The machine learning methods developed under this project will be broadly applicable to many fields of science and engineering. Through a collaboration with local and national science teaching organizations, the research findings from this project will be used to develop a multi-day lesson-plan for the high school science classroom wherein students learn about fracture of metals using computer simulations. This lesson-plan will be made available to science teachers nationwide. Prevailing theories of metal fracture focus on either the crack tip where dislocation nucleation governs the propensity for brittle fracture, or the plastic zone surrounding the crack tip where plastic dissipation governs the fracture toughness. Recent work has shown, however, that there are important interplays between the crack tip and plastic zone which affect various fracture behaviors such as dislocation multiplication and growth of fatigue cracks. This project is to develop a three-dimensional discrete dislocation dynamics model which simultaneously accounts for the crack tip and the plastic zone by capturing all relevant dislocation and bond-breaking processes in one unified model. To achieve this goal, a physics-informed machine learning-based image solver will be developed which utilizes a new convolutional architecture that couples to the finite element method. Atomistic simulations will be used to quantify the dislocation nucleation rate near the crack tip, grain boundary weakening due to dislocation adsorption and/or emission, and grain boundary decohesion. Predictions from the fracture model will be compared with state-of-the-art fracture experiments. Insights gained from this research will directly inform fracture models used in engineering design, prediction of embrittlement, and alloy design.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.

当裂缝或其他缺陷增长时，会发生金属断裂，从而导致金属结构（例如建筑物和飞机）的失败，估计占美国国内生产总值的4％。用于预测断裂的当前理论和模型是基于断裂过程的不完整的，这导致对裂纹生长的预测不准确，因此很难设计工程结构和下一代结构金属。这项教师早期职业发展（职业）奖支持研究，以开发与能源，国防，航空航天和运输应用相关的抗裂缝工程结构设计中使用的更好的材料模型，从而支持美国的经济和国防。该项目下开发的机器学习方法将广泛适用于许多科学和工程领域。通过与地方和国家科学教学组织的合作，该项目的研究结果将用于为高中科学课堂开发多天的课程计划，其中学生使用计算机模拟了解金属的裂缝。本课程计划将提供给全国科学教师。 金属断裂的盛行理论集中在裂纹尖端上，其中脱位成核决定了脆性断裂的倾向，或者塑料耗散控制裂缝韧性的裂纹尖端周围的塑料区域。然而，最近的工作表明，裂纹尖端和塑料区域之间存在重要的相互作用，这些相互作用会影响各种断裂行为，例如位错乘法和疲劳裂纹的生长。该项目将开发一个三维离散的脱位动力学模型，该模型通过在一个统一模型中捕获所有相关的脱位和破坏键的过程，同时考虑裂纹尖端和塑料区域。为了实现这一目标，将开发一个基于物理学的机器学习图像求解器，该图像求解器利用新的卷积体系结构将其与有限的元素方法耦合。原子模拟将用于量化裂纹尖端附近的脱位成核速率，晶界由于吸附和/或发射而导致的晶界弱化以及晶界的破坏。骨折模型的预测将与最新断裂实验进行比较。从这项研究中获得的见解将直接告知用于工程设计，互惠预测和合金设计的断裂模型。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来支持的。