Investigation and inference of soft material deformation mechanisms unlocked at large speeds, finite deformations, and many cycles: W911NF-23-S-0001
Abstract
Focused energy arising from munitions, shock waves, explosions, and directed energy weapons is a damage-causing agent for soft, engineered materials and warfighter biotissues. While the mechanical behavior of these materials is often known or characterized at slow rates, the response of any materials subject to such rapid expansions is difficult to predict. Predictive capability requires overcoming experimental and theory-based limitations. For example, repeatable experiments of simultaneous and high-rate finite deformations is challenging. At the same time, the micromechanisms of high-rate deformation physics are unknown. The PIs recently developed a material characterization platform called inertial cavitation rheometry (IMR). This fast, finite rheology method combines laser-induced cavitation with physical models. The technique was recently extended to leverage modeling advances like ensemble-based data assimilation. However, a critical feedback loop of using model results to inform subsequent generations of experiments has yet to be closed. As more soft materials with different chemical compositions and network paradigms are considered in the context of Army applications, a method of solely measuring single material properties is insufficient. This is of particular relevance to the practical use cases that also introduce a need to include more sophisticated model physics. Thus, characterization strategies should be cognizant of experimental uncertainty and the relative observability of parameters. They should also be maximally predictive for a given experimental budget (sample size) and reveal the mechanistic origins of the observations. The proposed work will integrate experimental and computational approaches to construct mutuallyinformed experiments and simulations and infer the first-principles associated with the mechanical mechanisms. The experiments will link load cycle number, amplitude, and rate with the ultra-high rate material behavior and properties of soft gels. Beginning as multiple single-mode tests, the group of PI J. Estrada (Michigan) will develop and implement a multimodal experimental platform capable of characterizing material networks before, during, and after several cycles of uniaxial and cavitation loading. At the same time, the group of PI S. Bryngelson (Georgia Tech) will leverage advances in modeling and assimilating experimental data to identify material parameters and the physical models and theory on which they are based. Together, the PIs plan to integrate the experimental and computational methods into an iterative strategy to optimally identify soft material behavior. They will use a library of plausible constitutive behavior terms and regress them to identify the basis functions for the material model. This strategy is increasingly predictive with additional computational?experimental iterations, and will continue until the model is sufficiently accurate. Two material case studies relevant to the Army Research Office are chosen to establish the method?s efficacy. The proposed work will first consider methacrylated soft hydrogels, polymerized via exposure to ultraviolet light, and model them in terms of their crosslinking and network geometry. The second case study will focus on time-dependent crosslinking, modeling the behavior of fast self-healing resins. Such resins exhibit both viscoelastic behavior and recovery of elasticity. In both cases, the iterations between computations and experiments will lead to a physically faithful model. The model, buttressed with validated simulations and revealing experiments, will be scrutinized to infer the origins of the material behaviors and mechanisms of their deterioration.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 03, 2023
- Source ID
- W911NF2310324
Entities
People
- Jonathan Estrada
Organizations
- Army Contracting Command
- United States Army
- University of Michigan