Computational Methods for Large Deformation and Multiphysics in Soft Materials Using Immersed Isogeometric Analysis

Soft polymer materials such as hydrogels, elastomers, and charged polymer networks can absorb large amounts of fluid and undergo substantial shape changes in response to mechanical, chemical, and electrical stimuli. These adaptive behaviors enable emerging technologies in medicine, water purification, energy systems, and soft robotics, including drug delivery platforms, adaptive membranes, bio-inspired medical devices, and responsive actuators. Despite their broad potential, the behavior of these materials remains difficult to predict because it arises from strongly coupled interactions among deformation, solvent transport, ion migration, and electrostatic effects that evolve over time and across multiple length scales. This research aims to establish a unified theoretical and computational framework capable of accurately capturing these coupled phenomena under large deformations and non-equilibrium conditions. The resulting predictive capabilities will advance the scientific understanding of soft active materials and support the design of next-generation technologies for healthcare, clean water, and energy-efficient systems. The project will also contribute to education and workforce development by training graduate and undergraduate students in interdisciplinary research spanning mechanics, materials science, and computational engineering, while providing open-source simulation tools for use by the broader scientific community. The project will develop a thermodynamically consistent multiphysics framework for modeling the coupled mechanical, chemical, and electrical behavior of soft polymer materials, including hydrogels, elastomers, and polyelectrolyte gels. The formulation will integrate large-deformation mechanics, solvent transport, ion diffusion, and electrostatic interactions within a unified variational framework capable of describing transient and history-dependent processes. To address major computational challenges associated with strongly coupled large-deformation systems, the research will introduce a hybrid numerical methodology based on Isogeometric Analysis, employing smooth high-order basis functions to achieve improved accuracy, stability, and geometric fidelity compared to conventional finite-element approaches. The framework will combine a moving material description with a fixed high-order computational grid, enabling accurate tracking of evolving internal variables while avoiding numerical difficulties associated with mesh distortion. Additional developments will include robust techniques for enforcing boundary and interface conditions, maintaining numerical stability, and performing accurate integration over evolving and geometrically complex domains. The theory and computational tools will be validated through simulations of benchmark problems and experimentally observed phenomena such as swelling-induced instabilities, electrically driven deformation, and coupled transport processes in membrane systems. The expected outcomes include predictive design capabilities for soft active materials and new computational methods for strongly coupled multiphysics systems undergoing large deformations. 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: 2549543 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Rui Huang | Institution: University of Texas at Austin, AUSTIN, TX | Award Amount: $747,979 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2549543 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2549543.html