Machine Learning–Enhanced Multiscale Modeling of Spatially Tailored Materials

机器学习 - 空间定制材料的增强多尺度建模

基本信息

批准号：
2104383
负责人：
Shaoping Xiao
金额：
$ 48.64万
依托单位：
University of Iowa
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104383&HistoricalAwards=false
关键词：
Machine Learning Enhanced Multiscale Modeling

项目摘要

Spatially tailored materials are metal-ceramic composites consisting of two or more different materials. The volume fraction of each material continuously changes in space. Compared to traditional composites, these composites offer advantages over traditional ones for they provide more opportunities to designers to fashion their performance for the operating conditions and environment. Since these composites have features spanning multiple length scales, it is usually necessary to develop predictive models capable of describing physical phenomena at different levels of resolution, e.g., at the nanoscale as well as the microscale. This award supports the development of an innovative multiscale method, enhanced by machine learning techniques and supported by experimental data, to investigate the mechanics of spatially tailored materials under mechanical and thermal loading. The proposed method will accelerate the design of the next generation of metal-ceramic composites for use in the automotive, aerospace, and biomedical industries. In addition, the award will leverage university programs to support: (1) undergraduate teaching and learning in data science and engineering; (2) recruitment of female, underrepresented minority, and LGBTQ students; and 3) outreach to K-12 students. Current state-of-the-art practices for numerical modeling of spatially tailored materials use the principles of micromechanics to bridge the gap from the lower scales under consideration to the macroscale. However, most methods cannot account for the molecular interfacial interactions and the microstructure uncertainties, both of which must be considered to accurately predict material responses at the higher length scales. And even when these considerations are addressed, it results in the difficulty of explicitly deriving effective material properties and constitutive or failure relationships. This project utilizes data science techniques and machine learning to overcome these challenges in the multiscale material modeling of a spatially tailored titanium alloy-titanium diboride metal-ceramic material system. The project will lead to the following general outcomes: (1) development of a data-enabled approach in hierarchical multiscale modeling to link and interface information (including uncertainties) across multiple length and time scales; (2) development of an efficient way to generate homogenized microscale models of heterogeneous composites that account for microstructure uncertainties; (3) development of an adaptive machine learning framework that updates with data accumulation; and (4) design of multiscale experiments under a variety of thermomechanical loading conditions.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.

量身定制的材料是由两种或更多不同材料组成的金属陶瓷复合材料。每种材料的体积分数不断变化。与传统的复合材料相比，这些复合材料比传统的复合材料提供了优势，因为它们为设计师提供了更多的机会，可以在运营条件和环境中塑造其性能。由于这些复合材料具有跨越多个长度尺度的特征，因此通常有必要开发能够在不同级别分辨率（例如，在纳米级和微观上）描述物理现象的预测模型。该奖项支持了一种创新的多尺寸方法的开发，该方法通过机器学习技术增强并得到了实验数据的支持，以研究机械和热载荷下的空间量身定制材料的机制。所提出的方法将加速下一代金属陶瓷复合材料的设计，用于汽车，航空航天和生物医学行业。此外，该奖项将利用大学计划支持：（1）数据科学和工程学的本科教学；（2）招募女性，代表性不足的少数民族和LGBTQ学生； 3）向K-12学生推广。空间量身定制材料的数值建模的当前最新实践使用微力学的原理来弥合从所考虑的下部尺度到宏观的差距。但是，大多数方法无法解释分子界面相互作用和微结构不确定性，必须考虑两者以准确预测更高长度的材料响应。即使解决了这些考虑，它也会导致明确得出有效的材料特性以及本构或失败关系的困难。该项目利用数据科学技术和机器学习来克服这些挑战，这些挑战是在空间量身定制的钛合金二烷二苯胺金属陶瓷材料系统的多尺度材料建模中。该项目将导致以下一般成果：（1）在层次多长度和时间范围内开发层次多尺度建模中启用数据的方法，以链接和接口信息（包括不确定性）；（2）开发一种有效的方法来产生均质的复合材料的均质微观模型，以解释微观结构不确定性；（3）开发随着数据积累更新的自适应机学习框架；（4）在各种热机械加载条件下设计多尺度实验的设计。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。