The rapid development of electronic devices, such as wearable and wireless sensors networks integrated into the Internet of Things, has created the need for efficient, environmentally-friendly energy generators capable of recharging batteries to prolong their lifespan. Vibrational energy harvesting, a process for scavenging energy from natural or forced vibrations such as structural movement or human activity, provides a promising means for this energy generation. This grant leverages a synergy between mathematical innovation and data-driven modeling, yielding transformative tools for advancing highly promising yet surprisingly complex systems for energy harvesting and targeted energy transfer. The research contributes to future economic and environmental impacts by supporting the widespread use of self-recharging wireless sensors, such as on bridges, buildings, and off-shore renewable energy generators. Powering wireless devices in health and structural safety monitoring systems will significantly reduce operation and maintenance costs associated with the wired technologies. The environmental contributions are therefore two-fold: direct, as a green energy device, and indirect through enabling effective diagnostics and prognostics in the renewable energy sector.
The goals of this grant address gaps and opportunities in mathematical developments and in practical applications for impact-based engineering devices and systems with non-smooth dynamics, critical for understanding the harvesting of vibrational energy. The main research objective of this grant is to develop a universal suite of mathematical methodologies for the analysis and optimized performance of deterministic and stochastic non-smooth engineering systems. These approaches are pursued with a focus on practical engineering models of vibro-impacting energy harvesting systems and nonlinear dynamic dampers, within the broad area of targeted energy transfer. Integrating novel nonlinear, stochastic, and computational approaches with experimental and real-world data both for energy harvesting and for devices that mitigate vibration will pave new pathways for analysis-based design and model validation. Synergistic connections between analysis, computations and data-driven mathematical innovation are pursued both to drive new mathematical approaches and to obtain practical predictions for device design. This grant will also serve as an excellent cross-disciplinary training ground for the involved postdocs and graduate students.
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.
