Dynamic Data Driven Methods for Self-aware Aerospace Vechicles
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. To make self-aware aerospace vehicles a reality, we need new algorithms that drive decision-making through dynamic response to uncertain data, while incorporating information from multiple modeling sources and sensors. We propose transformative advances in two DDDAS areas, Applications Modeling and Mathematical and Statistical Algorithms. Our advances will show the large potential benefit of DDDAS to future Air Force systems, enabling a revolutionary new generation of self-aware aerospace vehicles that can perform missions that are impossible using current design, planning, and operating paradigms. Our research program is highly multidisciplinary, drawing on our team s collective expertise in the fields of model reduction, inference, uncertainty quantification, optimization, multi-fidelity modeling, sensing, composite damage modeling, aerostructural modeling, and vehicle design. Our specific research objectives are: (1) To systematically relate component-model fidelity to vehicle-level performance estimation, and to accordingly enrich our collection of multi-fidelity models through the development of physics-based models of composite damage. (2) To develop DDDAS methods that guide construction of an offline damage library given mission information, storage limits, data retrieval capability, and sensing capability. (3) To develop DDDAS methods to exploit online sensor information for decision-making and for model adaptation, explicitly considering the opportunities associated with multiple modalities of sensor data. (4) To develop design methods for DDDAS-enabled self-aware aircraft. Our research will result in new DDDAS methods to manage and represent information generated by physics-based models across a range of scales and experimental data, new DDDAS strategies to dynamically manage online data, including sensor management and online adaptation of reduced- order models, new approaches to exploit the opportunities of multiple data modalities, and new design methods for DDDAS-enabled aircraft.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 24, 2016
- Source ID
- FA95501610108
Entities
People
- Karen Willcox
Organizations
- Air Force Office of Scientific Research
- Harvard University
- United States Air Force