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

• DOI: 10.1016/j.procs.2014.05.106
• Publication Date: 2014-06-06T02:11:36Z
• Authors: D. Allaire; D. Kordonowy; M. Lecerf; L. Mainini; K. Willcox
• Keywords: 0203 mechanical engineering; Computer Science (all); 02 engineering and technology

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