Turbulence is widespread in the atmosphere, and safe aircraft operations require predictions of turbulence to reduce encounters with severe and extreme turbulence, and appropriate responses when hazardous turbulence is encountered. Current airspace operations rely largely on humans to mitigate turbulence hazards at the flight planning stage and in flight. The future air transportation system with greatly expanded use of UAS and with new types of Urban Air Mobility operations will have increasing levels of automation and autonomy, and many operations will be conducted without a skilled human operator onboard. Automated systems will be needed to perform turbulence hazard mitigation tasks currently allocated to humans. Future operations will also involve new types of vehicles, especially distributed electric propulsion (DEP) VTOL vehicles, and a greatly increased density of operations in environments with unique turbulence characteristics, including the urban canyon. Enabling increasing levels of autonomy envisioned for future airspace operations without compromising flight safety will require novel technologies for predicting turbulence, automatically planning missions that minimize turbulence hazards, recognizing and quantifying turbulence in flight, and appropriately and automatically responding to inflight turbulence. In the context of a passenger carrying DEP VTOL vehicle, Phase I demonstrated the feasibility of quantifying turbulence in flight, of learning a turbulence environment over multiple flights, and of mission planning based on the learned environment. Phase II examine the sensitivity of DEP VTOL vehicles to a broader range of turbulence characteristics, select a set of turbulence models that captures the most relevant turbulence characteristics, extend the online turbulence quantification and learning algorithms to this expanded set of models, and demonstrate expanded mission planning capabilities supporting a broader range of missions.
The proposed technology will help to enable increasingly autonomous operations, directly supporting the goals of the UAS Integration in the NAS project started by ARMD in 2011, and the UAS Traffic Management (UTM) project begun in 2015. The proposed work will also support NASA's emerging interest in Urban Air Mobility, enhancing safety for both manned and unmanned vehicles, and benefitting passenger comfort.
The proposed technology will enable increasingly autonomous SUAS operations, such as BVLOS inspection operations and package delivery by commercial operators such as Amazon and Uber Eats. The proposed technology will benefit other future vehicles that operate with a high level of automation or autonomy, especially vehicles such as air taxis that will operate at low altitudes in urban areas.