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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A recently developed algorithm for nonlinear system performance analysis has been applied to an F16 aircraft to begin evaluating the suitability of the method for aerospace problems. The algorithm has a potential to be much more efficient than the current methods in performance analysis for aircraft. This paper is the initial step in evaluating this potential.
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Stochastic robustness, a simple technique used to estimate the robustness of linear, time invariant systems, is applied to a single-link robot arm control system. Concepts behind stochastic stability robustness are extended to systems with estimators and to stochastic performance robustness. Stochastic performance robustness measures based on classical design specifications are introduced, and the relationship between stochastic robustness measures and control system design parameters are discussed. The application of stochastic performance robustness, and the relationship between performance objectives and design parameters are demonstrated by means of example. The results prove stochastic robustness to be a good overall robustness analysis method that can relate robustness characteristics to control system design parameters.
Stochastic robustness, a simple technique used to estimate the robustness of linear, time-invariant systems, is applied to a single-link robot arm control system. Concepts behind stochastic stability robustness are extended to systems with estimators and to stochastic performance robustness. Stochastic performance robustness measures based on classical design specifications are introduced, and the relationship between stochastic robustness measures and control system design parameters is discussed. The application of stochastic performance robustness and the relationship between performance objectives and design parameters are demonstrated by means of the example. The results prove stochastic robustness to be a good overall robustness analysis method that can relate robustness characteristics to control system design parameters.
Stochastic robustness analysis guides the synthesis of robust linear quadratic Gaussian (LQG) regulators for a benchmark control problem. Probabilities of exceeding allowable design limits, including stability, setting time, and control usage, are estimated by Monte Carlo evaluation. Robust, low-gain compensators that fulfill objectives are designed by numerically minimizing quadratic functions of these probabilities. The method is straightforward and makes use of uncomplicated design principles.
The robustness of 10 solutions to a benchmark control design problem presented at the 1990 American Control Conference has been evaluated. The 10 controllers have second-to-eighth-order transfer functions and have been designed using several different methods, including H-infinity optimization, loop-transfer recovery, imaginary-axis shifting, constrained optimization, structured covariance, game theory, and the internal model principle. Stochastic robustness analysis quantifies the controllers' stability and performance robustness with structured uncertainties in up to six system parameters. The analysis provides insights into system response that are not readily derived from other robustness criteria and provides a common ground for judging controllers produced by alternative methods. One important conclusion is that gain and phase margins are not reliable indicators of the probability of instability. Furthermore, parameter variations actually may improve the likelihood of achieving selected performance metrics, as demonstrated by results for the probability of settling-time exceedance.
Energy storage technologies are positioned to play a substantial role in power delivery systems. They are being touted as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, due to their relative infancy, there is a lack of knowledge on how these resources truly operate over time. Data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verifying the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data the provider supplies for analysis. After review of energy storage data received from several providers, it has become clear that some of these data are inconsistent and incomplete, raising the question of their efficacy for robust analysis. This report reviews and proposes general guidelines such as sampling rates and data points that providers must supply for robust data analysis to take place. Consistent guidelines are the basis of the proper protocol and ensuing standards to (a) reduce the time it takes data to reach those who are providing analysis; (b) allow them to better understand the energy storage installations; and (c) enable them to provide high-quality analysis of the installations. This report is intended to serve as a starting point for what data points should be provided when monitoring. As battery technologies continue to advance and the industry expands, this report will be updated to remain current.
Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.
Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.
The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.
The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.
This paper describes two linked technology development projects to support Space Shuttle ground operations personnel, both during mission preparation analysis and related analyses in missions. The Space Propulsion Robust Analysis Tool (SPRAT) will provide intelligent support and automation for mission analysis setup, interpretation, reporting and documentation. SPRAT models the actions taken by flight support personnel during mission preparation and uses this model to generate an action plan. CONFIG will provide intelligent automation for procedure analyses and failure impact analyses, by simulating the interactions between operations and systems with embedded failures. CONFIG models the actions taken by crew during space vehicle malfunctions and simulates how the planned action sequences in procedures affect a device model. Jointly the SPRAT and CONFIG projects provide an opportunity to investigate how the nature of a task affects the representation of actions, and to determine a more general action representation supporting a broad range of tasks. This paper describes the problems in representing actions for mission preparation and their relation to planning and scheduling.
This study seeks to identify existing frameworks or develop requirements and recommendations for a new framework that can consistently characterize physical attacks, analogous to MITRE ATT&CK®. MITRE ATT&CK is widely used across government, research organizations, and the cyber security community to characterize cyber attack tactics, techniques, and procedures (TTPs) in a consistent and commonly understood manner. While physical attack taxonomies, methodologies, and other tools for evaluating physical security do exist, many are sector and/or facility-type specific—and therefore not able to provide comparable scenarios across sectors—or are more focused on security assessment instead of the characterization of attacks themselves. A MITRE ATT&CK analog for physical attacks on critical infrastructure would provide a common language and structure for analysis of physical attacks. Existing attack characterization methodologies do not robustly address cyber-physical security risks. To fully understand a facility’s security needs, it is important to understand the entire vulnerability landscape from both a physical and a cyber perspective. To underscore this need, organizations such as the Cybersecurity and Infrastructure Security Agency (CISA) are calling for a coordinated approach to cyber and physical security, which they refer to as cyber and physical security convergence. A physical attack characterization framework that could be used jointly with MITRE ATT&CK would help support a more robust analysis in support of convergence, enabling the consistent characterization of attacks that utilize both cyber and physical tactics and techniques. This could provide analysts and stakeholders with a clearer understanding of how security mitigations deployed in the physical realm impact security risks in the cyber realm, and vice versa. In this study, the project team evaluates existing physical security taxonomies and methodologies to assess whether an existing method can be used to create a “physical half” of MITRE ATT&CK. This study then provides requirements and recommendations for a framework that can leverage aspects of existing methodologies. The goal of the final framework is for it to be widely adopted and referenced, regardless of critical infrastructure sector, facility type, or facility components. This study also identifies applicable use cases for when and how a framework could be applied across the various critical infrastructure sectors for a variety of attack types or motivations. Through a literature review of existing security-focused methodologies and taxonomies, engagement with relative stakeholders, evaluation of potential physical attack framework use cases, and subsequent identification of requirements, this study identified the following key findings and recommendations: There is a need for a new physical attack characterization framework; A physical attack framework should be interoperable with the MITRE ATT&CK framework; A physical attack framework should be broadly applicable, but with detailed tactics, techniques, and procedures that encompass the entire attack path; A physical attack framework should be based on observed or feasible events; A physical attack framework should adapt features from existing methodologies, frameworks, and taxonomies; A physical attack framework should be owned, overseen, and maintained by one organization.
A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed- gradient projection algorithm. Further, the intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.
Computational icing simulation methods are making the transition from the realm of research to commonplace use in design and certification. As such, standards of software management, design, validation, and documentation must be adjusted to accommodate the increased expectations of the user community with respect to accuracy, reliability, capability, and usability. With this in mind, in collaboration with Glenn's Engineering Design and Analysis Division, the Icing Branch of the NASA Glenn Research Center at Lewis Field began a software improvement project focused on the two-dimensional ice accretion simulation tool LEWICE. This project is serving as an introduction to the concepts of software management and is intended to serve as a pilot project for future icing simulation code development. The LEWICE 2.0 Software Development Project consisted of two major elements: software management and software validation. The software management element consisted of identifying features of well-designed and well-managed software that are appropriate for an analytical prediction tool such as LEWICE and applying them to a revised version of the code. This element included tasks such as identification of software requirements, development and implementation of coding standards, and implementation of software revision control practices. With the application of these techniques, the LEWICE ice accretion code became a more stable and reliable software product. In addition, the lessons learned about software development and maintenance can be factored into future software projects at the outset. The software validation activity was an integral part of our effort to make LEWICE a more accurate and reliable analysis tool. Because of the efforts taken to extensively validate this software, LEWICE 2.0 is more robust than previous releases and can reproduce results accurately across several computing platforms. It also differs from previous versions in the extensive quantitative comparisons of its results with a data base of ice shapes that were generated in Glenn's Icing Research Tunnel. The results of the shape comparisons were analyzed to determine the range of meteorological conditions under which LEWICE 2.0 is within the repeatability found for ice shapes obtained from experiments. These comparisons show that the average variation of LEWICE 2.0 from experimental data is 7.2 percent, whereas the overall variability of the experimental data is 2.5 percent. The LEWICE 2.0 Software Development Project resulted in the release, at the recent Icing Branch LEWICE Workshop, of a more useful and robust analysis tool than was previously available to the icing community. The lessons learned from this project will be applied to the Icing Branch s future software development efforts.
In conjunction with NASA and the Department of Defense, the Johns Hopkins University Applied Physics Laboratory (JHU/APL) has been investigating analytical techniques to address many of the fundamental issues associated with solar exploration spacecraft and high-speed atmospheric vehicle systems. These issues include: thermo-structural response including the effects of thermal management via the use of surface optical properties for high-temperature composite structures; aerodynamics with the effects of non-equilibrium chemistry and gas radiation; and aero-thermodynamics with the effects of material ablation for a wide range of thermal protection system (TPS) materials. The need exists to integrate these discrete tools into a common framework that enables the investigation of interdisciplinary interactions (including analysis tool, applied load, and environment uncertainties) to provide high fidelity solutions. In addition to developing robust tools for the coupling of aerodynamically induced thermal and mechanical loads, JHU/APL has been studying the optimal design of high-speed vehicles as a function of their trajectory. Under traditional design methodology the optimization of system level mission parameters such as range and time of flight is performed independently of the optimization for thermal and mechanical constraints such as stress and temperature. A truly optimal trajectory should optimize over the entire range of mission and thermo-mechanical constraints. Under this research, a framework for the robust analysis of high-speed spacecraft and atmospheric vehicle systems has been developed. It has been built around a generic, loosely coupled framework such that a variety of readily available analysis tools can be used. The methodology immediately addresses many of the current analysis inadequacies and allows for future extension in order to handle more complex problems.