This project focuses on investigations into methodologies to enable real-time decision-making for mission-critical structural systems experiencing high-rate dynamics. Examples of mission-critical structures that experience high-rate dynamics include hypersonic vehicles, space crafts, ballistics packages, and active blast mitigation. Enabling real-time decision-making for these structures increases mission success rates by enhancing the structure's survivability, providing on-time guidance corrections, and adopting the mission goals/outcome to changing conditions. Additionally, the methodologies developed by this project increase the robustness, safety, and commercial viability of structures operating in extreme dynamic environments. The development of algorithms and methodologies for structures experiencing high-rate dynamics serves the national interest and fulfills the NSF's mission: to promote the progress of science; to advance the national health, prosperity and welfare; or to secure the national defense. This project provides research experience and mentors a diverse and inclusive group of students, and introduces a graduate level class on surrogate modeling of complex systems.
A structural system operating in a high-rate dynamic environment can experience sudden and unmodeled plastic deformation of the structure that may further lead to damaged electronics, sensors, and/or delicate payloads. This research focuses on enabling a real-time decision-making module to take corrective actions. To achieve this goal, a parallelization framework is developed that enables a structural system to be decomposed into its constituent components where each component can be monitored, modeled, and extrapolated into the future. Once the degradation trajectories for each component have been estimated, they are recombined into a single system-level model to be used for real-time decision making. The project is organized into two research thrusts and one experimental design challenge. Thrust 1 investigates surrogate modeling techniques with the goal of developing component-level models that can converge within the required time constraints. These surrogate models use data obtained from dense sensor networks to generate data-driven damage-sensitive features that can be used as the degradation parameters for component-level prognostics. Thrust 2 explores and formulates methodologies for real-time component-level prognostics. These component-level predictions are then recombined into a single structural model that is used to develop potential corrective actions. Lastly, the experimental design challenge validates the developed algorithms and methodologies using a high-rate dynamic test bench.
This project is jointly funded by Office of Advanced Cyberinfrastructure (OAC) and the Established Program to Stimulate Competitive Research (EPSCoR).
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.
