AIRSHIELD
Detecting aircraft damage with onboard sensors and machine learning.
The idea
AIRSHIELD combines fiber optic sensors, piezoelectric transducers, and machine learning to monitor aircraft structures. Our research, preliminary experiments, and cost analysis supported its commercial potential.

My contribution
My work focused on onboard sensing, damage mechanisms, structural simulation, and an initial GNN study.
Onboard sensing
I researched fiber Bragg grating sensors and piezoelectric transducers, and how they can work together to detect aircraft damage.

Damage mechanisms
I compared impact, corrosion, and fatigue damage and their physical effects. I used Ansys LS DYNA for bird strike simulations and Ansys Mechanical for structural analysis.

Graph neural networks
I used PyTorch to explore GNNs for spatial sensor data and ran a small study to test whether they could recognize damage patterns.


Presenting at NASA
As one of eight national finalists, we presented at NASA Langley Research Center. Our team received $9,000 in research funding and won the Best Infographic Award.
