The CrAVES project seeks to lay down intellectual foundations for credible autocoding of embedded systems, by which graphical control system specifications that satisfy given open-loop and closed-loop properties are automatically transformed into source code guaranteed to satisfy the same properties. The goal is that the correctness of these codes can be easily and independently verified by dedicated proof checking systems. During the autocoding process, the properties of control system specifications are transformed into proven assertions explicitly written in the resulting source code. Thus CrAVES aims at transforming the extensive safety and reliability analyses conducted by control system engineers, such as those based on Lyapunov theory, into rigorous, embedded analyses of the corresponding software implementations. CrAVES comes as a useful complement to current static software analysis methods, which it leverages to develop independent verification systems. Computers and computer programs used to manage documents and spreadsheets. They now also interact with physical artifacts (airplanes, power plants, automobile brakes and robotic surgeons), to create Cyber-Physical Systems. Software means complexity and bugs - bugs which can cause real tragedy, far beyond the frozen screens we associate with system crashes on our current PCs. Software autocoding is becoming the de facto recommended practice for many safety-critical applications. CrAVES aims to evolve this towards higher standards of quality and reduced design times and costs. Rigorous, mathematical arguments supporting safety-critical functionalities are the cornerstone of CrAVES. Collaborative programs involving high-school teachers will encourage the transmission of this message to STEM education in high-schools through university programs designed for that purpose.

The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.