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:
- 金额:$ 48.64万
- 依托单位:
- 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:
- 关键词:
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.
空间定制材料是由两种或多种不同材料组成的金属陶瓷复合材料。每种材料的体积分数在空间中不断变化。与传统复合材料相比,这些复合材料具有优于传统复合材料的优势,因为它们为设计师提供了更多机会来根据操作条件和环境设计其性能。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以不同分辨率水平(例如纳米尺度和微米尺度)描述物理现象的预测模型。该奖项支持开发一种创新的多尺度方法,通过机器学习技术增强并得到实验数据的支持,以研究机械和热载荷下空间定制材料的力学。所提出的方法将加速用于汽车、航空航天和生物医学行业的下一代金属陶瓷复合材料的设计。此外,该奖项还将利用大学项目来支持:(1)数据科学与工程本科生教学; (2) 招收女性、代表性不足的少数族裔和 LGBTQ 学生; 3) 向 K-12 学生进行外展活动。 当前空间定制材料数值建模的最先进实践使用微观力学原理来弥合从较低尺度到宏观尺度的差距。然而,大多数方法无法解释分子界面相互作用和微观结构不确定性,必须考虑这两个问题才能准确预测更高长度尺度的材料响应。即使解决了这些问题,也会导致难以明确导出有效的材料属性以及本构关系或失效关系。该项目利用数据科学技术和机器学习来克服空间定制钛合金-二硼化钛金属-陶瓷材料系统的多尺度材料建模中的这些挑战。该项目将产生以下总体成果:(1)在分层多尺度建模中开发数据驱动的方法,以跨多个长度和时间尺度链接和接口信息(包括不确定性); (2) 开发一种有效的方法来生成解释微观结构不确定性的异质复合材料的均质微尺度模型; (3)开发随着数据积累而更新的自适应机器学习框架; (4) 在各种热机械载荷条件下设计多尺度实验。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优点和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Investigating the strength of Ti/TiB interfaces at multiple scales using density functional theory, molecular dynamics, and cohesive zone modeling
- DOI:10.1016/j.ceramint.2022.07.259
- 发表时间:2022-08
- 期刊:
- 影响因子:5.2
- 作者:S. Attarian;S. Xiao
- 通讯作者:S. Attarian;S. Xiao
Multiscale Modeling of Metal-Ceramic Spatially Tailored Materials via Gaussian Process Regression and Peridynamics
通过高斯过程回归和近场动力学对金属陶瓷空间定制材料进行多尺度建模
- DOI:10.1142/s0219876222500256
- 发表时间:2022
- 期刊:
- 影响因子:1.7
- 作者:El Tuhami, Ahmed;Xiao, Shaoping
- 通讯作者:Xiao, Shaoping
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Shaoping Xiao其他文献
Model-free reinforcement learning for motion planning of autonomous agents with complex tasks in partially observable environments
用于在部分可观察环境中执行复杂任务的自主代理的运动规划的无模型强化学习
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Junchao Li;Mingyu Cai;Zhen Kan;Shaoping Xiao - 通讯作者:
Shaoping Xiao
Molecular dynamics modeling and simulation of lubricant between sliding solids
滑动固体间润滑剂的分子动力学建模与模拟
- DOI:
10.1142/s2424913017500096 - 发表时间:
2017-06 - 期刊:
- 影响因子:0
- 作者:
Mir Ali Ghaffari;Yan Zhang;Shaoping Xiao - 通讯作者:
Shaoping Xiao
Reinforcement learning-based motion planning in partially observable environments under ethical constraints
道德约束下部分可观察环境中基于强化学习的运动规划
- DOI:
10.1007/s43681-024-00441-6 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Junchao Li;Mingyu Cai;Shaoping Xiao - 通讯作者:
Shaoping Xiao
Peridynamics with Corrected Boundary Conditions and Its Implementation in Multiscale Modeling of Rolling Contact Fatigue
修正边界条件的近场动力学及其在滚动接触疲劳多尺度建模中的实现
- DOI:
10.1142/s1756973718410032 - 发表时间:
2019-05 - 期刊:
- 影响因子:1.5
- 作者:
Mir Ali Ghaffari;Yanjue Gong;Siamak Attarian;Shaoping Xiao - 通讯作者:
Shaoping Xiao
Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties
考虑N2O排放和不确定性气候变化的智能农业管理
- DOI:
10.48550/arxiv.2402.08832 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Zhaoan Wang;Shaoping Xiao;Jun Wang;Ashwin Parab;Shivam Patel - 通讯作者:
Shivam Patel
Shaoping Xiao的其他文献
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{{ truncateString('Shaoping Xiao', 18)}}的其他基金
BRITE Pivot: Learning-based Optimal Control of Streamflow with Potentially Infeasible Time-bound Constraints for Flood Mitigation
BRITE Pivot:基于学习的水流优化控制,具有可能不可行的防洪时限约束
- 批准号:
2226936 - 财政年份:2023
- 资助金额:
$ 48.64万 - 项目类别:
Standard Grant
SGER: A Nanoelectromechanical Design for Carbon Nanotube-Based Memory Cells at Finite Temperatures
SGER:有限温度下基于碳纳米管的存储单元的纳米机电设计
- 批准号:
0630153 - 财政年份:2006
- 资助金额:
$ 48.64万 - 项目类别:
Standard Grant
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