NASA National Aeronautics and Space Administration NASA Urban Air Mobility Fatigue Prediction Aaron Crawford – NASA ULI Innovative Manufacturing, Operation, and Certification of Advanced By Dr. Pankaj Dhussa

1. Urban Air Mobility Fatigue Prediction
Aaron Crawford – NASA ULI Innovative Manufacturing, Operation, and Certification of Advanced
Structures for Civil Vertical Lift Vehicles (IMOCAS)
Challenge
- Urban air mobility vehicles are very new and not fully
understood
- Safety is a critical factor for taxi vehicles
- Terminal operations, especially in cities, may pose
more of a safety concern than other more traditional
aircraft or rotorcraft
- New materials and configurations have yet to be
studied in detail
- Cost reduction through extended lifespan allows for
cleaner and more accessible travel
High Fidelity CFD at Reduced Computational
Cost
Reduced Order Modeling with Machine Learning
Expected Impacts
- Accurate fatigue estimates allow for the safest
operation of any maned or unmanned vehicle
- Accurate monitoring of safety and fatigue will allow for
the lifecycle of vehicle parts to be extended, thus driving
down costs, improving accessibility, and reducing the
consumption of parts and materials.
- UAM companies will be able to both plan operations as
well as monitor ongoing operations
Partners and/or Participants
• Georgia Institute of Technology
• University of South Carolina
• North Carolina A&T
• Middle Georgia State University - Aviation
• Qarbon
• NASA
Solution
- Development of Operational Analysis Tool that is
generalized for any vehicle that provides both
prediction and real time monitoring of environmental
conditions, fatigue, safety and cost.
- Provide methodology and tool to UAM companies as a
free open-source framework for continued use and
upgrades
Results
Computational cost poses significant barrier to fatigue modeling.
Terminal operations must be modeled though a representative environment.
High Fidelity Structural Analysis
Fatigue Predictions and Real Time Monitoring
Next Steps
This work is still ongoing. Next steps include …
- Understanding the impact of realistic environment on a UAM vehicle.
- Understanding how much various environmental factors impact
fatigue
- Test machine learning methods to determine best practices for data
set selection and ROM creation
- Work with UAM/UAV companies to help deliver a fully developed tool