Collaborative Research: CDS&E: data-enabled dynamic microstructural modeling of flowing complex fluids

合作研究：CDS

• 批准号: 2347345
• 负责人: Matthew Helgeson
• 金额: $ 7.98万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347345&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS&E; data; enabled; Collaborative; Research; CDS&E; data; enabled

Many important technologies including printed electronics, photonics, wearable sensors, and solar cells are manufactured with processes that involve complex flows of polymeric materials. In many cases, the details of the alignment and relative positions (microstructure) of the polymer molecules play a key role in their performance, and these features depend sensitively on the flows used to manufacture them. This award will exploit the recent development of experimental methods that engender rich data sets incorporating both flow and microstructure for complex polymeric materials. The availability of these data provides the opportunity to develop and apply machine learning, data science, and polymer physics toward development of predictive mathematical models that will ultimately enable design of advanced manufacturing processes involving complex materials. Transformational progress in exploiting modern data-driven methods toward modeling flowing complex fluids requires (1) large experimental data sets involving time-evolution of flow and microstructure in a diverse range of flows, and (2) new modeling frameworks to exploit these data sets. This award advances these two themes toward development of a tool to rapidly develop predictive models of fluid structure and stress in material classes for which no first-principles-based models currently exist. Rich and realistic experimental data sets will be used, which come from simultaneous flow and spatially-resolved scattering measurements for complex fluids in complex flows. The new modeling frameworks will integrate machine learning, data assimilation, dimension reduction, and data-driven dynamic modeling, informed by the physics of flowing complex fluids. These methods will be applied to a grand challenge problem in materials processing: discovery of new physical descriptions of non-dilute orientable particle dispersions, informing the construction of new first principles models to describe the coupling of flow with particle interactions.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.

许多重要的技术，包括印刷电子产品，光子学，可穿戴传感器和太阳能电池，都是由涉及复杂聚合物材料流的工艺制造的。在许多情况下，聚合物分子的比对和相对位置（微观结构）的细节在其性能中起关键作用，并且这些特征敏感地取决于用于制造它们的流量。该奖项将利用实验方法的最新开发，这些方法可导致丰富的数据集，这些数据集纳入了复杂聚合物材料的流量和微观结构。这些数据的可用性为开发和应用机器学习，数据科学和聚合物物理学提供了一个机会，以开发预测性数学模型，最终将设计涉及复杂材料的先进制造过程。在利用现代数据驱动的方法对流动复杂流体进行建模的转型进步需要（1）大型实验数据集涉及各种流量中流量和微观结构的时间进化，以及（2）新的建模框架来利用这些数据集。该奖项推进了这两个主题开发工具，以快速开发材料类别中流体结构和压力的预测模型，目前不存在基于第一原则的模型。将使用丰富而现实的实验数据集，这些数据集来自复杂流中复杂流体的同时流动和空间分辨的散射测量。新的建模框架将整合机器学习，数据同化，缩小尺寸和数据驱动的动态建模，并由流动复杂流体的物理学告知。 这些方法将应用于材料处理中的一个巨大挑战问题：发现非剥离定向粒子分散的新物理描述，为构建新的第一原理模型的构建，以描述流动与粒子相互作用的耦合。该奖项反映了NSF的统计任务，并被认为是通过基金会的智力综述和宽广的评论来评估的，并且值得一提。