MRI: Acquisition of an Xradia Versa 620 to Enable National Capabilities for High-throughput, Multiscale 3D/4D Materials Research
MRI:购买 Xradia Versa 620 增强国家高通量、多尺度 3D/4D 材料研究能力
基本信息
- 批准号:2216225
- 负责人:
- 金额:$ 149.01万
- 依托单位:
- 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:2022-09-01 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This Major Research Instrumentation (MRI) award supports the acquisition of an advanced x-ray microscope for examination of a broad range of natural and engineered materials. This instrument enables small-scale examinations of fundamental properties and mechanisms. In lightweight aerospace materials, early stages of cracking will be studied to improve failure and reliability models. In geological materials, fluid flow in porous media will be studied to assist in the development of new predictive models to improve energy efficiency and reduce environmental impacts. In biological materials, resistance to fracture in bone and effectiveness of medical implants will be studied. The NSF-funded National Science Data Fabric (NSDF) will be leveraged to ensure effective data storage, networking, computing, and educational resources. Integration of the instrument with NSDF will inherently extend the instrument as a resource to minority-serving institutions and disadvantaged communities. This is intended to help address the technology gap in our scientific community. High-energy X-ray experiments (HEXRE) enabled at synchrotron beamline facilities have led a new frontier of 3D/4D microstructure-sensitive investigations for a broad range of materials. However, in-situ testing and 3D/4D imaging using resources at a synchrotron beamline facility requires an application process to gain access and complete an abbreviated study, which inherently restricts the impact of these exciting methods. The primary objective of this project is to launch an Xradia Versa 620 to be utilized as a democratized, lab-scale surrogate for beamline resources to enable widespread adoption of microstructure-sensitive 3D/4D experimental studies and model development for the acceleration of materials development and qualification. Compared to synchrotron facilities, the Versa 620 improves speed of data acquisition though the ability to scan large objects (several inches in diameter) at lower resolutions, but with high throughput, thereby enabling large scans to find areas of interest that can then be the focus of high-resolution measurements. The improved scan speeds and increased accessibility will enable increased data quantities to advance this new frontier toward data science applications in materials research.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.
这项主要的研究工具(MRI)奖支持获得先进的X射线显微镜,以检查广泛的天然和工程材料。该仪器可以对基本属性和机制进行小规模检查。在轻型航空航天材料中,将研究开裂的早期阶段,以改善失败和可靠性模型。在地质材料中,将研究多孔培养基中的流体流动,以帮助开发新的预测模型,以提高能源效率并减少环境影响。在生物材料中,将研究骨折的抗骨折和医疗植入物的有效性。 NSF资助的国家科学数据结构(NSDF)将被利用,以确保有效的数据存储,网络,计算和教育资源。该工具与NSDF的集成将固有地将工具作为资源扩展到少数派服务机构和处境不利的社区。这旨在帮助解决我们科学界的技术差距。在同步器束线设备上启用了高能X射线实验(HEXRE),使3D/4D微观结构敏感的研究对广泛的材料进行了新的边界。但是,使用Synchrotron Beam线设备上资源的原位测试和3D/4D成像需要申请过程来获得访问并完成一项缩写研究,这本质上限制了这些令人兴奋的方法的影响。该项目的主要目的是推出Xradia Versa 620,用于用于光束线资源的民主化,实验室规模的替代物,以实现对微观结构敏感的3D/4D实验研究的广泛采用,并模拟材料开发和资格加速的模型开发。与同步加速器设施相比,Verma 620在较低分辨率下扫描大型物体(直径几英寸)的能力提高了数据获取速度,但是具有较高的吞吐量,从而使大型扫描能够找到感兴趣的领域,从而可以成为高分辨率测量的焦点。提高的扫描速度和提高的可访问性将使数据数量增加,从而将这一新的边界推向了材料研究中的数据科学应用。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jacob Hochhalter其他文献
Inherently interpretable machine learning solutions to differential equations
本质上可解释的微分方程的机器学习解决方案
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:1.6
- 作者:
Hongsup Oh;R. Amici;Geoffrey Bomarito;Shandian Zhe;R. Kirby;Jacob Hochhalter - 通讯作者:
Jacob Hochhalter
Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach
- DOI:
10.1016/j.mechmat.2024.105151 - 发表时间:
2024-12-01 - 期刊:
- 影响因子:
- 作者:
Karl Garbrecht;Andrea Rovinelli;Jacob Hochhalter;Paul Christodoulou;Ricardo A. Lebensohn;Laurent Capolungo - 通讯作者:
Laurent Capolungo
Stress intensity factor models using mechanics-guided decomposition and symbolic regression
- DOI:
10.1016/j.engfracmech.2024.110432 - 发表时间:
2024-11-08 - 期刊:
- 影响因子:
- 作者:
Jonas Merrell;John Emery;Robert M. Kirby;Jacob Hochhalter - 通讯作者:
Jacob Hochhalter
Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression
通过符号回归补充本构建模的连续热力学方法
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:5.3
- 作者:
K. Garbrecht;Donovan Birky;Brian Lester;John Emery;Jacob Hochhalter - 通讯作者:
Jacob Hochhalter
Computationally guided alloy design and microstructure-property relationships for non-equiatomic Ti–Zr–Nb–Ta–V–Cr alloys with tensile ductility made by laser powder bed fusion
- DOI:
10.1016/j.msea.2024.146922 - 发表时间:
2024-09-01 - 期刊:
- 影响因子:
- 作者:
Dillon Jobes;Daniel Rubio-Ejchel;Lucero Lopez;William Jenkins;Aditya Sundar;Christopher Tandoc;Jacob Hochhalter;Amit Misra;Liang Qi;Yong-Jie Hu;Jerard V. Gordon - 通讯作者:
Jerard V. Gordon
Jacob Hochhalter的其他文献
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{{ truncateString('Jacob Hochhalter', 18)}}的其他基金
CDS&E/Collaborative Research: Interpretable Machine Learning for Microstructure-Sensitive Fatigue Crack Initiation from Defects in Additive Manufactured Components
CDS
- 批准号:
2152868 - 财政年份:2022
- 资助金额:
$ 149.01万 - 项目类别:
Standard Grant
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