The major aim of the proposed research is the development of a self-healing structural system that can self-repair after extreme events, such as multiple dynamic impacts, and also self-assess the effectiveness of the repair. This self-healing capability will be accomplished through a room temperature cure epoxy resin healing system delivered through embedded glass fibers. The delivery system will be integrated with an optical fiber sensor network that self-repairs through the phenomenon of self-writing in photopolymerizable resins. A multi-physics predictive computational model of the entire process that accounts for the interrelated processes of photopolymerization, lightwave propagation in a heterogeneous structure, opto-mechanical interactions and sandwich composite damage mechanics will also be derived. The model will be applied to lifetime predictions of the self-healed structural system. The ability of a structural system to survive catastrophic events such as earthquakes, blast loads, or multiple impacts through self-repair of critical components would significantly improve the safety of critical airframes and civil infrastructure systems. Furthermore, the capability of the repaired structure to assess its structural condition would be invaluable to evaluate the necessity of further repairs, and would reduce the loss of life for rescue workers or future users of the structural system. Undergraduate students will be recruited to work with the research group through the NSF REU program and the PIs will participate in programs for academically gifted high school students from underrepresented groups across the state of North Carolina.
A team of scientists at North Carolina State University has demonstrated a vascular network self-healing system for large-scale sandwich composite structural systems. The vascular network delivers resin and hardener components to the site of critical damage zones, such as those due to blasts, earthquake or impact events. These resin and hardener components react to rebuild a significant portion of the structural performance after the critical event without the need for external stimuli. This structural performance can delay the potential failure of the structure. The ability of a structural system to survive catastrophic events such as earthquakes, blast loads, or multiple impacts through self-repair of critical components would significantly improve the safety of critical structural infrastructures. Furthermore, the capability of the repaired structure to assess its structural condition would be invaluable to evaluate the necessity of further repairs, and it would reduce the loss of life for rescue workers or future users of the structural system. The researchers have also experimentally demonstrated a polymer waveguide strain sensor that self-writes and self-repairs to measure the effectiveness of the structural self-healing in a particular structure. The self-repairing strain sensor can be embedded into the structural system, self-repair within a few seconds measure absolute strain from the unloaded condition, and self-repair multiple times. This data on the restoration of structural performance would increase the reliability of critical information available to rescue operations. In addition to being the sensors the, sensors located near the point of impact are those most likely to fail and those that potentially provide the most information on the current integrity of a structural system. Intelligent signal processing for sensor networks can reduce errors due to sensor failures; however they cannot recreate the same level of detail that would be obtained from active sensors at such critical locations. The self-healing structural system is the first time that such a self-healing structural system that can assess the condition and predict the lifetime of the repair has been achieved. Finally, multi-physics computational models were developed for improved design of the sensor network regeneration. These models could be applied to fundamentally improve the fabrication of micro- and nano-systems through 3D photolithography.
