Study of the optimal location of wing sensors using model-order reduction

The overall goal of this work is to numerically determine the optimal location of sensors  for predicting the vibration behavior of a wing.  The methodology to achieve this goal will be to construct, using as starting point finite element simulations, a reduced-order model able to capture the essential vibrational characteristic of the wing

Multifidelity DDDAS Methods with Application to a Self-aware Aerospace Vehicle

A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. We consider the specific challenge of an unmanned aerial vehicle that can dynamically and autonomously sense its structural state and re-plan its mission according to its estimated current structural health. The challenge is to achieve each of these tasks in real time-executing online models and exploiting dynamic data streams-while also accounting for uncertainty. Our approach combines information from physics-based models, simulated offline to build a scenario library, together with dynamic sensor data in order to estimate current flight capability. Our physics-based models analyze the system at both the local panel level and the global vehicle level.United States. Air Force. Office of Scientific Research (Grant FA9550-11-1-0339

Multifidelity DDDAS methods with application to a self-aware aerospace vehicle

AbstractA self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. We consider the specific challenge of an unmanned aerial vehicle that can dynamically and autonomously sense its structural state and re-plan its mission according to its estimated current structural health. The challenge is to achieve each of these tasks in real time–executing online models and exploiting dynamic data streams–while also accounting for uncertainty. Our approach combines information from physics-based models, simulated offline to build a scenario library, together with dynamic sensor data in order to estimate current flight capability. Our physics-based models analyze the system at both the local panel level and the global vehicle level

United States Air Force Applications of Unmanned Aerial Systems (UAS): A Delphi Study to Examine Current and Future UAS Autonomous Mission Capabilities

As UAS technology continues to grow and enable increased autonomous capabilities, acquisition and operational decision makers must determine paths to pursue for existing and emerging mission areas. The DoD has published a number of 25-year unmanned systems integration roadmaps (USIR) to describe future capabilities and challenges. However, these roadmaps have lacked distinguishable stakeholder perspectives. Following the USIRs concept, this research focused on UAS autonomy through the lens of UAS subject matter experts (SMEs). We used the Delphi method with SMEs from USAF communities performing day-to-day operations, acquisitions, and research in UAS domains to forecast mission capabilities over the next 20 years; specifically, within the context of increased UAS autonomous capabilities. Through two rounds of questions, the study provided insight to the capabilities SMEs viewed as most important and likely to be incorporated as well as how different stakeholders view the many challenges and opportunities autonomy present for future missions