CAREER: Material design rules and predictive modeling of multiphase particulate systems

Suspensions of particles in liquids appear in systems such as food, pharmaceutical products, and construction mixtures. The flow of these suspensions during processing strongly affects product quality, production speed, and energy consumption. However, predicting the flow behavior of suspensions is difficult, particularly when the suspended particles vary in size and surface properties and are dispersed in a polymeric carrier fluid. As a result, industrial processes involving suspensions often depend on trial and error. This leads to material and energy wastes. This award will develop physics-based models to better predict the behavior of complex suspensions. The insights will aid in selecting particle and liquid properties to achieve desired material properties. Educational efforts will include development of virtual, mixed, and augmented reality modules, allowing students and teachers to interact with realistic models of particle-based materials. The award will promote STEM workforce development through exposure to computational and data-driven research methods. This award will develop a multiscale modeling framework to provide a first principles understanding of the flow behavior of real-world multiphase particulate suspensions. Unlike current models, which often assume uniform particle sizes in Newtonian liquids, this award will address three key scientific questions: (1) the effect of viscoelastic carrier fluids on particle migration and rearrangement under flow, (2) how particle size variations and particle–fluid interfacial chemistry influence flow dynamics, and (3) how data-driven models to a priori identify particle and fluid properties that produce targeted material responses. The acquired data will be integrated with data-driven techniques to develop predictive models that facilitate reverse engineering of particle and fluid characteristics and establish design rules linking formulation and processing conditions to flow behavior in dense, multicomponent suspensions. The project will also develop interactive visualization tools that use computational models and 3D printing to engage high school, university students, and K–12 teachers. These activities will employ virtual, mixed, and augmented reality headsets to create an immersive visualization environment, enabling students to manipulate simulation parameters and visualize their effects in real time. 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. NSF Award ID: 2543134 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Abhinendra Singh | Institution: Case Western Reserve University, CLEVELAND, OH | Award Amount: $550,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543134 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543134.html