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In this paper we develop initial offline and online capabilities for a self-aware aerospace vehicle. Such a vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings via sensors and responding intelligently. The key challenge to enabling such a self-aware aerospace vehicle is to achieve tasks of dynamically and autonomously sensing, planning, and acting in real time. Our first steps towards achieving this goal are presented here, where we consider the execution of online mapping strategies from sensed data to expected vehicle capability while accounting for uncertainty. Libraries of strain, capability, and maneuever loading are generated offline using vehicle and mission modeling capabilities we have developed in this work. These libraries are used dynamically online as part of a Bayesian classification process for estimating the capability state of the vehicle. Failure probabilities are then computed online for specific maneuvers. We demonstrate our models and methodology on decisions surrounding a standard rate turn maneuver.

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Adhesively co-bonded and co-cured structural joints are attractive for aircraft applications due to the potential reduction in weight and part count as compared to traditional mechanical joints. In this research, a damage tolerant structure using co-bonded hybrid T-joints was designed and manufactured for structural application on a remotely piloted aircraft. The T-joint structure was constructed from GLAss fiber REinforced aluminum laminates (GLARE) 4-2/1 skin, carbon fiber composite web, and three-dimensional woven glass fiber fabric Pi-preform in a co-bonded/co-cured process. The goal of this research is to demonstrate the damage tolerance, structural capability, and ancillary benefits of the co-bonded T-joint. In order to evaluate the structural design and investigate the strength of manufactured T-joint, experiments such as pull-out tests were performed. Additionally, the T-joint was manufactured and tested with defects inserted intentionally within the co-bonded interface between the GLARE and Pi-preform at two locations: a) underneath the vertical woven carbon composite web, and b) along the edge of the glass fiber Pi-preform and the GLARE flange. Pull-out tests showed the effect of intentionally inserted defects on the strength of the co-bonded T-joint structure. The benefit of co-bonded joints featuring damage tolerant skins and high strength composite webs has been demonstrated.

A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. Achieving this DDDAS paradigm enables a revolutionary new generation of self-aware aerospace vehicles that can perform missions that are impossible using current design, flight, and mission planning paradigms. To make self-aware aerospace vehicles a reality, fundamentally new algorithms are needed that drive decision-making through dynamic response to uncertain data, while incorporating information from multiple modeling sources and multiple sensor fidelities.In this work, the specific challenge of a vehicle that can dynamically and autonomously sense, plan, and act is considered. 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. We employ a multifidelity approach to inference, prediction and planning-an approach that incorporates information from multiple modeling sources, multiple sensor data sources, and multiple fidelities.