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

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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.