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Manassas, VA, United States

Patent
Aurora Flight Sciences Corporation | Date: 2015-09-25

A composite structure cured by the process of: electrically coupling a first lead to a first portion of said composite structure; electrically coupling a second lead to a second portion of said composite structure; and using an electric power source to pass electric current through said composite structure from said first portion to said second portion, wherein passing said electric current through said composite structure causes the temperature of at least a portion of said composite structure increase to a predetermined temperature.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 499.51K | Year: 2015

ABSTRACT:Aurora Flight Sciences, in collaboration with Worcester Polytechnic Institute, proposes the development of the Real-Time Safety-Assured Autonomous Aircraft (RTS3A) system. The overall goal of the proposed work is to develop a modular and flexible flight management system in order to enable condition-aware flight. The RTS3A system incorporates multi-disciplinary, physics-based models and sensor suites to fully functionalize the flight environment of an aircraft with respect to its structural and propulsion capabilities, which allows for optimization of mission execution as well as condition based maintenance. Specifically, the aim is to develop a standalone package representing the RTS3A system incorporating an open architecture that that will ensure modularity and a high degree of flexibility in terms of subsystem interchangeability. This open architecture will establish standardized subsystem classification, interface protocols, data formatting and processing standards, and software design standards for a general condition-aware system. Initial implementation of the RTS3A system on current unmanned systems will be explored and a candidate platform for further development will be identified.BENEFIT:There are three main avenues that Aurora can pursue for commercialization of the RTS3A aircraft: Unmanned Military Aircraft, Manned Military Aircraft, and Commercial Aircraft. The unmanned military aircraft platform would most likely be the first avenue of approach for commercializing this technology. The RTS3A system is comprised of two major sections: the path planning algorithm and the subsystem prognostic health monitoring systems. The path planning algorithm is most applicable to unmanned systems, where the path planner can be used to inform the vehicle flight path. Optimizing the flight path based on real-time updated vehicle heath state constraints will result in risk reduction: maximum mission performance would be ensured given the current capabilities of the vehicle, as well as cost reduction: fuel savings by operating the vehicle at the most efficient operating conditions. For manned flight, the prognostic health monitoring algorithms are of more interest, although the benefits herein are also applicable to unmanned aircraft. The data of the actual state of the various subsystems can be used to inform maintenance schedules, allowing for longer time intervals between vehicle overhauls, and additionally can potentially pinpoint problem areas with higher than normal levels of degradation to further inform the focus of maintenance activities. This will result in additional cost savings over the lifetime of the vehicle, as well as a running history of the subsystem conditions which will give better estimates of remaining useful life.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT: Aurora Flight Sciences, in collaboration with Worcester Polytechnic Institute, proposes the development of the Real-Time Safety-Assured Autonomous Aircraft (RTS3A) system. The system dynamically performs decision-making based on both sensed and predictive information to carry out adaptive missions and maintenance. This system leverages sensing capabilities distributed throughout subsystems of the aircraft that measure characteristics to allow prediction of the conditional response of the aircraft. The RTS3A system incorporates multi-disciplinary, physics-based models and sensor suites to fully functionalize the flight environment of an aircraft with respect to its structural and propulsion capabilities, which allows for optimization of mission execution as well as condition based maintenance. Distributed sensors and reasoning agents will generate real-time information on subsystems of the vehicle, and each subsystem will communicate with a higher-level system-reasoning agent. A central reasoning agent, informed by the lower level agents as well as real-time updates to the flight environment, will manage mission control systems in order to adapt the maneuver envelop which influences the vehicle"s control authority. The central reasoning agent will ensure minimum margins of flight safety under uncertain and constantly changing conditions. Initial implementation of the self-aware vehicle concept is suggested on unmanned systems, such as Aurora"s Orion Unmanned Aerial System. BENEFIT: This Phase I program will demonstrate the feasibility of a prototype RTS3A system applied to two subsystems, airframe and the propulsion, of a typical aircraft. Prognosis will be based on physics-based models with data to reduce and manage uncertainty. Critical and typical failure modes for the subsystems will be incorporated into the software, allowing aircraft safety to be monitored during uncertain flight conditions. Prognosis of faults and failures in composite airframes allow for the extension of vehicle health management onto the structure, which typically represents the largest portion of the aircraft weight and cost for both maintenance and replacement. Incorporating the propulsion subsystem allows initial realization of the self-aware vehicle concept. Prognostics would allow for condition-based maintenance of airframe and propulsion components, reducing maintenance costs associated with an air vehicle while allowing real-time safety of the vehicle to be monitored.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.98K | Year: 2015

ABSTRACT: During the Phase I Program, Aurora will perform a conceptual-level design of an air-launched sensor platform featuring integrated antennas, with the goal of maximizing range and endurance thanks to the improved aerodynamic qualities. The technology is expected to replace many traditional antenna installations on military UAVs, and then find applications in the commercial world, including uses unrelated to air vehicles. The problem of antenna integration is tightly coupled with that of structural layout and overall vehicle configuration; the Aurora/Resonant Sciences team will work in parallel to identify preferred solutions and demonstrate the feasibility of the concept. Based on the findings of the study, one antenna concept will be selected and a sample part built to start assessing the manufacturing aspects of the technology. The proposed Phase I program will pave the way for a detailed structural design, antenna breadboard testing, and the demonstration of an adaptive wing actuation under load in a wind tunnel in Phase II. BENEFIT: The need for structurally embedded antennas is primarily being driven by the U.S. defense industry for future aircraft development programs. The technology developed as a result of this program would find applications in both military and commercial aircraft applications. Aurora will work to develop this technology and transition any processes or techniques developed during the course of this program to the military and commercial sectors. Application of this technology to Department of Defense programs will allow a cleaner aerodynamic design, longer range and endurance, as well as better sensor coverage, and provide the airframe designer with increased design possibilities. Aurora will market the design and manufacturing techniques developed during this program as a tool in the acquisition and development of new aircraft programs in both the military and commercial sectors. It is expected that the early market will include Department of Defense contractors and specifically programs focused on the development of advanced composite aircraft structures.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.92K | Year: 2015

ABSTRACT:Airdrop accuracy is of paramount importance since airdrop systems landing in unintended locations could create internationally damaging outcomes. There is an opportunity to compensate for local effects by learning their impact on the airdrop trajectory from previous missions. Aurora Flight Sciences is teaming with Boston University to apply deep learning techniques to historical airdrop data at particular drop zones to determine site- and mission-specific biases and increase accuracy at that drop zone. Deep learning can identify complex multi-layer relationships within data and discover patterns without any prior knowledge, enabling detection of trends that are only present when multiple conditions are present. Although deep learning calculations are computationally expensive, they can be run offline to generate a database that can be accessed online based on real-time mission information. The algorithms can ingest additional data as it is collected to update and refine outputs for even greater accuracy. The conceptual tool developed in this Phase I SBIR will plan flight paths and computed air release points based on knowledge learned from past airdrops with the ultimate goal of showing the feasibility of using historical data to improve accuracy at a particular drop zone.BENEFIT:Recent operations in Afghanistan and Syria have increased the interest in precision airdrop as a low cost and safe method of tactical resupply, and mission commanders are continuing to seek ways to improve airdrop accuracy and reliability. Calculation of site- and mission-specific airdrop adjustments will break through the current barrier in airdrop accuracy created by limitations in weather forecasting. Increases in airdrop accuracy reduce risk to our ground and air forces, increase bundle recovery rates, and enable missions at more highly constrained drop zones. Further, this deep learning based technique can be extended to other airdrop system types including humanitarian, dispersion, and guided systems. The tool developed in this effort can be integrated into multiple airdrop mission planning programs including the Consolidated Airdrop Tool, XDrop, and PARANAVSYS. Additionally, it can be extended to high altitude sonobuoy deployment for the Navy.

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