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DMREF: Data-Driven Integration of Experiments and Multi-Scale Modeling for Accelerated Development of Aluminum Alloys

DMREF：数据驱动的实验和多尺度建模集成，加速铝合金的开发

基本信息

批准号：
1921959
负责人：
Todd Hufnagel
金额：
$ 205.64万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921959&HistoricalAwards=false
关键词：
DMREF Data Driven Integration Experiments

项目摘要

Traditionally, materials design is an iterative process in which the properties of the material are gradually improved by adjusting process by which the material is made. This approach can be accelerated by introducing computational models into the loop and comparing the output with experimental measurements of the structure and properties of the material at each step. The cycle is slowed, however, by the need for humans to be involved at almost every step for data collection, analysis, and reduction. This Designing Materials to Revolutionize and Engineer our Future (DMREF) award supports research to facilitate rapid adoption of advanced materials into engineering applications by leveraging modern data science tools to develop a data framework and infrastructure for seamless integration of experiments and models in complex materials development problems. This framework will be broadly applicable, allowing researchers to adopt it to particular problems and allowing the ideas to be integrated across a wide range of materials and application areas. The concepts and tools developed will be disseminated through freely-available software, open source modules, online tutorials, open access data sets, and training for users to apply the tools in new contexts.This work seeks to establish a new paradigm for the materials design loop in which the flow of data, rather than individual modeling or experimental tasks, is viewed as central. Modern data science tools will be leveraged to create the semantic framework and physical infrastructure necessary for seamless integration of experiments and models in complex materials development problems. This framework will define and describe classes of material data, and connections among these classes, that are required to create an automated data flow in which each experimental and computational task in the design loop automatically pushes information to, or pulls information from, a data layer common to all tasks. This will accelerate the materials design process by minimizing the required human intervention, while still allowing human input to the loop where essential. A specific instantiation of this approach will be the implementation of a multi-scale modeling framework for the resistance of commercial aluminum alloys to spall failure, with automated connections to advanced microstructural characterization and high-throughput laser shock testing through a centralized data layer. The design loop will be closed by further coupling to models of microstructure development during processing, with machine-learning algorithms trained to optimize the microstructure to resist the nucleation and growth of spall voids.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.

传统上，材料设计是一个迭代过程，其中材料的性能通过调整材料的制造工艺而逐渐改善。这种方法可以通过在循环中引入计算模型并在每一步将输出与材料结构和性能的实验测量进行比较来加速。然而，由于几乎每一步都需要人类参与数据收集、分析和还原，这一周期被放慢了。该奖项旨在通过利用现代数据科学工具开发数据框架和基础设施，以促进先进材料在工程应用中的快速采用，从而实现复杂材料开发问题中实验和模型的无缝集成。该框架将具有广泛的适用性，允许研究人员将其应用于特定问题，并允许将这些想法整合到广泛的材料和应用领域。开发的概念和工具将通过免费提供的软件，开源模块，在线教程，开放访问数据集和培训用户在新的context.This工作旨在建立一个新的范例材料设计循环中的数据流，而不是个别建模或实验任务，被视为中央。现代数据科学工具将被用来创建语义框架和物理基础设施，以实现复杂材料开发问题中实验和模型的无缝集成。该框架将定义和描述材料数据的类别以及这些类别之间的连接，这些类别是创建自动化数据流所需的，其中设计循环中的每个实验和计算任务自动将信息推送到所有任务共同的数据层或从其中提取信息。这将通过最大限度地减少所需的人为干预来加速材料设计过程，同时仍然允许在必要时对回路进行人为输入。这种方法的一个具体实例将是实施一个多尺度建模框架，用于商业铝合金抗剥落失效，通过集中数据层自动连接到先进的微观结构表征和高通量激光冲击测试。设计循环将通过进一步耦合到加工过程中的微观结构发展模型来闭合，机器学习算法经过培训，以优化微观结构，以抵抗剥落空隙的成核和生长。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。