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.

铝和钢等延展性金属广泛应用于能源、国防和运输等各个行业，从而影响着每个人的日常生活。不幸的是，人们对延展性金属失效或破裂的机制知之甚少，因此很难理解。这些知识差距推高了产品成本，降低了能源效率，并限制了我们开发未来能源和国防应用所需的更坚固、更耐断裂的材料（例如高温材料）的能力。该项目的目标是制定一种基于物理的、支持机器学习的延性断裂模型，从而降低产品成本并提高能源效率，例如，生产更轻的车辆结构的能力将减少车辆的碳排放。同时降低消费者的燃料成本。该项目将通过研究项目和丰富的课堂教学向本科生推广科学和工程，并通过与高中课堂合作开发新的以建模和机器学习为重点的模块来向高中生推广科学和工程。高中教育者，这些学习模块将在高中物理课堂上进行试点。通过空洞的成核、生长和合并形成的延性断裂模型主要是现象学的，具有许多难以确定的不确定参数。在该项目中，将建立一个基于微观力学的空洞成核模型。通过将分子动力学模拟与基于内核的机器学习结合起来开发的分子动力学模拟将揭示硬颗粒处空穴成核的基本机制，同时量化空穴成核率。为了使模型开发易于处理，将制定一组基于内核的机器学习模型和算法，其目标有两个：（i）提取和设计一组最小尺寸的物理特征，用于量化微观结构中的成核率，并且（ii）概率性地在该微观结构特征集和成核率之间建立精确的封闭形式统计映射。由此产生的机器学习模型将被应用到有限元方法中，作为常用的延性断裂 Gurson-Tvergaard-Needleman 模型的一部分。这项工作将作为使用机器学习来升级纳米和微米级信息的原型。这反映了 NSF 的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。