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

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.

许多重要技术，包括印刷电子、光子学、可穿戴传感器和太阳能电池，都是通过涉及复杂的聚合物材料流程的工艺制造的。在许多情况下，聚合物分子的排列和相对位置（微观结构）的细节对其性能起着关键作用，并且这些特征敏感地取决于用于制造它们的流程。该奖项将利用最新发展的实验方法，产生包含复杂聚合物材料的流动和微观结构的丰富数据集。这些数据的可用性提供了开发和应用机器学习、数据科学和聚合物物理学的机会，以开发预测数学模型，最终实现涉及复杂材料的先进制造工艺的设计。利用现代数据驱动方法对流动的复杂流体进行建模的变革性进展需要（1）涉及各种流动中流动和微观结构的时间演化的大型实验数据集，以及（2）利用这些数据集的新建模框架。该奖项将这两个主题推进到开发一种工具，以快速开发当前不存在基于第一原理的模型的材料类别中的流体结构和应力的预测模型。将使用丰富且真实的实验数据集，这些数据集来自复杂流动中复杂流体的同步流动和空间分辨散射测量。新的建模框架将集成机器学习、数据同化、降维和数据驱动的动态建模，并以流动复杂流体的物理原理为基础。 这些方法将应用于材料加工中的一个重大挑战问题：发现非稀释可定向颗粒分散体的新物理描述，为构建新的第一原理模型来描述流动与颗粒相互作用的耦合提供信息。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。