Multifidelity DDDAS Methods with Application to a Self-aware Aerospace Vehicle
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Author(s)
Allaire, D.
Kordonowy, D.
Lecerf, Marc A.
Mainini, Laura
Willcox, Karen E
Date Issued
2014
Journal
Procedia Computer Science
Publisher
Elsevier BV
Citation
Allaire, D., et al. “Multifidelity DDDAS Methods with Application to a Self-Aware Aerospace Vehicle.” Procedia Computer Science, vol. 29, 2014, pp. 1182–92. © The Authors
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.PROCS.2014.05.106
