Dynamic data driven methods for structural assessment and self-aware air-vehicles
Laura Mainini - Massachusetts Institute of Technology and Politecnico Torino - will discuss aspects of "situational awareness" of next generation of aerospace vehicles.
Next generation of aerospace vehicles will be able to autonomously operate accounting for the evolution of their health (self-awareness) and the dynamic change of the surrounding environment (situational awareness).
This form of autonomous reasoning can be formalized as instance of the general Sense-Infer-Plan-Act flow that processes data into information, information into knowledge, and knowledge into intelligent decisions.
It will be discussed the specific problem of supporting self-awareness and associate the Sense-Infer-Plan-Act flow with measurements (physical quantities that can be monitored with sensors), and capabilities (quantities that evolve with the state of the system and limit the operational space). In this framework, we obtain real-time estimates of capabilities from sensor measurements. To achieve this goal, it will be developed an offline-online methodology that combines data and physics-based models through a Multi-Step Reduced Order Modeling (MultiStep-ROM) procedure. In addition, it will be proposed a novel approach for the identification of the most informative sensor locations. We apply our methodologies to the practical case of autonomous aerospace vehicles that dynamically adapt their mission to the evolution of their structural state.