Scale Bridging in Ductile Fracture via Kernel-based Machine Learning

通过基于内核的机器学习实现延性断裂中的尺度桥接

基本信息

批准号：
2034074
负责人：
Ryan Sills
金额：
$ 57.69万
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：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034074&HistoricalAwards=false
关键词：
Scale Bridging Ductile Fracture via

项目摘要

Ductile metals such as aluminum and steel are used ubiquitously across a variety of industries such as energy, defense, and transportation, and thus impact everyone’s daily life. Unfortunately, the mechanisms by which ductile metals fail or crack are poorly understood, making it difficult to accurately predict when fracture will occur. These knowledge gaps drive up the costs of products, decrease their energy efficiency, and limit our ability to develop stronger, more fracture-resistant materials needed for future energy and defense applications, e.g., high-temperature materials. The goal of this project is to formulate a physics-informed, machine-learning-enabled model for ductile fracture that will lead to reduced product costs and higher energy efficiency. For example, the ability to produce lighter vehicle structures would reduce vehicle carbon emissions while simultaneously reducing fuel costs for consumers. The project will promote science and engineering to undergraduates through research projects and enriched classroom instruction, and to high school students through the development of new modeling- and machine learning-focused modules for high school classrooms. In collaboration with a high school educator, these learning modules will be piloted in a high school physics classroom. Models of ductile fracture via nucleation, growth and coalescence of voids are largely phenomenological with many uncertain parameters that are difficult to determine. Under this project, a predictive, micromechanically informed model for void nucleation will be developed by coupling molecular dynamics simulations with kernel-based machine learning. Molecular dynamics simulations will reveal the fundamental mechanics underlying void nucleation at hard particles while quantifying the void nucleation rate. This rate is affected by a large set of features, including stress, temperature, and defects such as vacancies, solutes, and dislocations. To render model development tractable, a set of kernel-based machine learning models and algorithms will be formulated with two objectives: (i) to extract and engineer a minimally sized set of physical features for quantifying nucleation rates in microstructures, and (ii) to probabilistically establish an accurate closed-form statistical mapping between this microstructural feature set and the nucleation rates. Finally, the resulting machine learning model will be implemented into the finite element method as part of the commonly used Gurson-Tvergaard-Needleman model for ductile fracture. This effort will serve as a prototype for the use of machine learning towards upscaling of nano- and microscale information to engineering scale models.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.

在能源，防御和运输等各种行业中，铝制金属（例如铝和钢铁）使用普遍存在，从而影响每个人的日常生活。不幸的是，延性金属失效或裂纹的机制知之甚少，因此很难准确预测何时发生断裂。这些知识差距提高了产品的成本，降低了其能源效率，并限制了我们开发更强大，更抗骨折的材料，即将来的能源和国防应用所需的材料，例如高温材料。该项目的目的是形成一种具有物理信息的，具有机器学习的延展性骨折模型，这将导致产品成本降低和较高的能源效率。例如，生产较轻的车辆结构的能力将减少车辆碳排放，同时降低消费者的燃油成本。该项目将通过研究项目和丰富的课堂指导来促进科学和工程学，并通过开发针对高中教室的新建模和机器学习的模块来开发高中生。这些学习模块将与高中教育者合作，将在高中物理教室中进行试验。通过成核，空隙的生长和聚结的延性裂缝模型在很大程度上是现象学，许多不确定的参数难以确定。在该项目下，通过将分子动力学模拟与基于内核的机器学习耦合，将开发一个预测性的微机械知情模型。分子动力学模拟将揭示硬颗粒处的基本力学基础力学，同时量化空隙成核速率。该速率受一系列特征的影响，包括压力，温度以及空置，溶质和脱位等缺陷。为了使模型开发可处理，将采用两个目标来制定一组基于内核的机器学习模型和算法：（i）提取和工程师一组最小尺寸的物理特征集，以量化微观结构中的成核速率，（ii）以正确地建立了这种微观结构特征集合率和成量化速率的准确封闭形式映射。最后，作为常用的Gurson-Tvergaard-needleman模型用于延性裂缝的一部分，最终的机器学习模型将被实施到有限元方法中。这项工作将是将机器学习用于将纳米和微观尺度信息提升到工程规模模型的原型。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来审查标准，被认为是通过评估来获得的支持。