Collaborative Research: CBET-EPSRC: Investigation of Coherent Structures in Elastic and Elasto-inertial Turbulence

Liquids composed of polymers exhibit unusual flow properties. These liquids contain long, flexible molecular chains that induce surprising behaviors in flow. Even when flowing slowly, they can generate a chaotic motion called elastic turbulence. When flowing rapidly, they can generate another chaotic state called elasto-inertial turbulence. These unusual flow behaviors are seen in common materials such as saliva, mucus and tree sap. They also are observed in advanced manufacturing processes, inkjet printing, energy production, and food manufacturing. Most mathematical models of these flows do not reproduce what experiments show, which limits their use in predicting and controlling polymer liquid flows. This joint project between NSF and UK's EPSRC will combine laboratory experiments, computer simulations, and machine-learning tools to construct models that capture the flow behaviors of polymeric fluids. Results will improve understanding of complex flows, support the development of more energy- and cost-efficient processing technologies, and improve the design of new materials. Undergraduate and graduate students will be trained in fluid mechanics and data science. Computational and data-analysis tools developed in the project will be shared with the scientific community. The proposal aligns with NSF priorities by supporting artificial intelligence/machine learning tools for advanced manufacturing. This project will close long-standing gaps between theoretical predictions and experimental observations of viscoelastic flows. The project will focus on three interconnected goals. First, the team will integrate two-dimensional experiments and simulations to achieve quantitative agreement in statistics, flow structures, and dynamical features. Machine-learning methods will be developed to infer optimal model parameters and to reconstruct polymer stress fields directly from experiments – an essential but historically inaccessible quantity that limits the accuracy of constitutive models. Second, the project will investigate the differences and universality of elastic-turbulent states in flows with streamline curvature and in parallel shear configurations, spanning both 2D and 3D geometries. Third, the project will explore how increasing inertia leads to the transition from elastic turbulence to elasto-inertial turbulence to examine whether these states share underlying mechanisms. Together, these efforts will expand the fundamental understanding of viscoelastic flows, delineate the parameter space in which chaotic flow arises, and generate high-fidelity datasets and modeling approaches that can be applied broadly. The project’s outcomes, including computational tools, improved models, and interdisciplinary training, will strengthen U.S. research capacity in fluid mechanics and support technological advancement in polymer processing and material design. 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: 2525868 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Paulo Arratia | Institution: University of Pennsylvania, PHILADELPHIA, PA | Award Amount: $396,789 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2525868 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2525868.html