CRII:OAC: Machine Learning- Enhanced Multiscale Simulation of Fiber Composites

CRII：OAC：机器学习 - 纤维复合材料的增强多尺度模拟

• 批准号: 2103708
• 负责人: Ramin Bostanabad
• 金额: $ 17.5万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103708&HistoricalAwards=false
• 关键词: CRII; OAC; Machine; Learning; Enhanced

Many engineered materials such as fiber composites have a hierarchical structure that spans multiple length scales. The analysis and design of these materials rely on multiscale simulations whose computational costs significantly increase if the structure is large and if the material deformation depends on its loading history. These high costs prohibit computationally intensive studies such as uncertainty propagation and design optimization. To tackle this challenge, the project employs recent advances in high performance computing and machine learning to accelerate multiscale simulations by orders of magnitude without compromising accuracy. The developed methods and tools are applicable to many materials systems and the testbed on fiber composites benefits a wide range of academic and industrial efforts since these materials are heavily used in, for example, the automobile and aerospace industries.This work develops cyberinfrastructure foundations that will enable acceleration of multiscale simulations while (1) minimizing the information loss incurred in inter-scale communication, and (2) considering various uncertainty sources such as spatial variation of microstructural properties and morphologies. The project builds mechanistic machine learning (ML) models that emulate complex and history-dependent microstructural deformations that embody a broad range of nanoscale and mesoscale effects to ensure transferability. The ML models are integrated with a message passing interface (MPI) design that leverages the hierarchical nature of the multiscale simulation to achieve two-level parallelism, both within and across the computational nodes of a compute cluster. The message passing is employed to manage inter-scale and intra-scale data transfer during multiscale simulation of a light-weight fiber composite whose microstructures spatially vary due to manufacturing uncertainties. The simulations on composite materials aim to increase understanding of how their properties are affected by inter- and intra- microstructural uncertainties, constituent properties, deformation history, and microstructure morphology.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.

许多工程材料（如纤维复合材料）具有跨越多个长度尺度的分层结构。这些材料的分析和设计依赖于多尺度模拟，如果结构很大，并且材料变形取决于其加载历史，则其计算成本显着增加。这些高成本阻碍了计算密集型研究，如不确定性传播和设计优化。为了应对这一挑战，该项目采用了高性能计算和机器学习的最新进展，以在不影响准确性的情况下将多尺度模拟加速几个数量级。所开发的方法和工具适用于许多材料系统，纤维复合材料的试验台有利于广泛的学术和工业努力，因为这些材料大量用于汽车和航空航天工业。这项工作开发了网络基础设施基础，将能够加速多尺度模拟，同时（1）最大限度地减少尺度间通信中的信息损失，以及（2）考虑各种不确定性来源，例如微观结构性质和形态的空间变化。该项目建立了机械机器学习（ML）模型，模拟复杂和历史相关的微观结构变形，体现了广泛的纳米级和中尺度效应，以确保可转移性。ML模型与消息传递接口（MPI）设计相集成，该设计利用多尺度仿真的分层性质，在计算集群的计算节点内和计算节点之间实现两级并行。消息传递是用来管理的多尺度模拟的轻质纤维复合材料的微观结构空间变化，由于制造的不确定性的尺度间和尺度内的数据传输。复合材料的模拟旨在提高对微观结构间和内部不确定性、成分特性、变形历史和微观结构形态如何影响其性能的理解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis

并发多尺度损伤分析中的自适应时空降维

• DOI: 10.1007/s00466-023-02299-7
• 发表时间: 2023
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: Deng, Shiguang;Apelian, Diran;Bostanabad, Ramin
• 通讯作者: Bostanabad, Ramin

Data-Driven Calibration of Multifidelity Multiscale Fracture Models Via Latent Map Gaussian Process

通过潜图高斯过程对多保真多尺度断裂模型进行数据驱动校准

• DOI: 10.1115/1.4055951
• 发表时间: 2023
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Deng, Shiguang;Mora, Carlos;Apelian, Diran;Bostanabad, Ramin
• 通讯作者: Bostanabad, Ramin

Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains

• DOI: 10.1016/j.cma.2021.114424
• 发表时间: 2021-04
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Hengjie Wang;R. Planas;Aparna Chandramowlishwaran;R. Bostanabad

Reduced-order multiscale modeling of plastic deformations in 3D alloys with spatially varying porosity by deflated clustering analysis

• DOI: 10.1007/s00466-022-02177-8
• 发表时间: 2021-08
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: Shiguang Deng;Carl Soderhjelm;D. Apelian;R. Bostanabad

Multi-Fidelity Reduced-Order Models for Multiscale Damage Analyses With Automatic Calibration

用于具有自动校准功能的多尺度损伤分析的多保真降阶模型

• DOI: 10.1115/detc2022-90163
• 发表时间: 2022
• 期刊: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Deng, Shiguang;Mora, Carlos;Apelian, Diran;Bostanabad, Ramin
• 通讯作者: Bostanabad, Ramin