Turbulence Modeling and Risk-Based Planning to Enable Safe Autonomous Operations

Aircraft routinely encounter turbulence, and appropriate responses to this turbulence are critical to maintain safe aircraft operation within prescribed operating limits and to effectively accomplish missions. Current airspace operations rely primarily on onboard human pilots to assess the severity of turbulence and respond appropriately. The future air transportation system will have increasing levels of autonomy, and many operations will be conducted without a skilled human operator onboard. Automated systems are thus needed to assess how turbulence is impacting an air vehicle and to respond appropriately. Future airspace operations will also occur in different environments. In particular, dramatically expanded low-altitude operations, including operations in urban environments, will occur as concepts such as those for Urban Air Mobility (UAM) become a reality. Much of the effort that has been devoted to turbulence monitoring and forecasting in the past has targeted commercial transport operations, especially high-altitude cruise conditions. Turbulence in the urban canyon is driven by different physical processes, and it has different characteristics than high-altitude turbulence. New models of turbulence need to be developed to support prediction of turbulence in low-altitude environments, including the urban canyon, and to perform onboard turbulence identification. The proposed work will develop the Turbulence Modeling and Decision Support System for UAS, with a focus on very low altitude operations by future air vehicles. It will include development of new model structures that capture the characteristics of turbulence in the urban boundary layer, the roughness sub layer, and the urban canopy layer. It will include development of onboard approaches to identify turbulence levels, methods to predict turbulence, and decision making tools for both short term responses to turbulence and mission planning based on turbulence predictions.