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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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Saturn I /SA-8/ launch vehicle and Launch Complex 37B liquid hydrogen fuel system
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
The question of what it means and what it takes for an autonomous system to consider another autonomous system justifiably trustworthy must be addressed by all who seek to integrate intelligent machine agents into real-world operations. A satisfactory answer to this question is an essential component in accepting autonomous machine decision-making in safety-critical and time-critical environments, such as aviation. Historically, simulation platforms for test and evaluation of complex systems have proven to be effective in assessing performance and contributing to decisions on the fitness of systems to operate in current general and commercial aviation airspace. Moreover, simulations have informed the definition of safety-critical constraints. However, as machine systems progressively take on responsibilities for decision-making traditionally supplied by humans, simulations require enhancement. Mixed reality simulation that integrates real-world platforms and data or high-fidelity simulation data in a sim-to-flight paradigm provides insight into agent interaction and the rationale behind autonomous agent decision-making as well as the capacity for seamless integrated implementation, testing, and operation of systems. Strong simulation capabilities are especially important in the presence of algorithms that hold great promise in decision-making yet increase the uncertainty in the system. Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) is a subproject of NASA’s Convergent Aeronautics Solutions (CAS) Project. ATTRACTOR’s objective is to build a basis for understanding trust and trustworthiness in multi-agent autonomous teams, and thus to inform future certification of safety-critical and time-critical autonomous systems in aviation. Because the concepts of trust and trustworthiness must be addressed in a context, ATTRACTOR has chosen Search and Rescue (SAR) in dynamic and unstructured environments, with emphasis on search, as its design reference mission (DRM). During dynamic planning and execution of trajectory-based operations, autonomous agents determine their trajectories given an assigned mission or missions and call for assistance from an appropriate teammate when needed. This experience along with the attendant human-machine and machine-machine interactions, serve as a platform for developing approaches to identifying and measuring trustworthiness and increasing trust. In this paper, we give an overview of some of ATTRACTOR’s research and development activities, findings, and ongoing work.
Over the last few years, extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high degree of precision. An appealing and challenging route toward engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a state-of-the-art ab initio computer simulation tool that enables a reliable and accurate simulation of light-induced changes in the physical and chemical properties of complex systems is of utmost importance. The first principles real-space-based Octopus project was born with that idea in mind, i.e., to provide a unique framework that allows us to describe non-equilibrium phenomena in molecular complexes, low dimensional materials, and extended systems by accounting for electronic, ionic, and photon quantum mechanical effects within a generalized time-dependent density functional theory. This article aims to present the new features that have been implemented over the last few years, including technical developments related to performance and massive parallelism. We also describe the major theoretical developments to address ultrafast light-driven processes, such as the new theoretical framework of quantum electrodynamics density-functional formalism for the description of novel light–matter hybrid states. Those advances, and others being released soon as part of the Octopus package, will allow the scientific community to simulate and characterize spatial and time-resolved spectroscopies, ultrafast phenomena in molecules and materials, and new emergent states of matter (quantum electrodynamical-materials).
Characterization of cyber-physical attacks requires a holistic understanding of cyber and physical behavior in a system. Machine Learning Anomaly Detection provides a compelling solution for continuously identifying suspicious behavior within these complex systems. In our software, we present an approach for holistic characterization of cyber-physical systems based on cyber and physical anomaly correlation. The approach consists of three main components: 1) an architecture for real-time data acquisition, management, and analysis of both cyber and physical data; 2) cyber and physical data driven anomaly detection systems (ADSs), 3) a metric that combines cyber and physical ADSs to provide a holistic characterization of the system..
Study was made and system defined for water-recovery and solid-waste processing for low-rise apartment complexes. System can be modified to conform with unique requirements of community, including hydrology, geology, and climate. Reclamation is accomplished by treatment process that features reverse-osmosis membranes.
Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.
Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.
Testing and validation of real-time systems is always difficult to perform since neither the error generation process nor the fault propagation problem is easy to comprehend. There is no better substitute to results based on actual measurements and experimentation. Such results are essential for developing a rational basis for evaluation and validation of real-time systems. However, with physical experimentation, controllability and observability are limited to external instrumentation that can be hooked-up to the system under test. And this process is quite a difficult, if not impossible, task for a complex system. Also, to set up such experiments for measurements, physical hardware must exist. On the other hand, a simulation approach allows flexibility that is unequaled by any other existing method for system evaluation. A simulation methodology for system evaluation was successfully developed and implemented and the environment was demonstrated using existing real-time avionic systems. The research was oriented toward evaluating the impact of permanent and transient faults in aircraft control computers. Results were obtained for the Bendix BDX 930 system and Hamilton Standard EEC131 jet engine controller. The studies showed that simulated fault injection is valuable, in the design stage, to evaluate the susceptibility of computing sytems to different types of failures.
A Galerkin method for systems of PDE's in circular geometries is presented with motivating problems being drawn from structural, acoustic, and structural acoustic applications. Depending upon the application under consideration, piecewise splines or Legendre polynomials are used when approximating the system dynamics with modifications included to incorporate the analytic solution decay near the coordinate singularity. This provides an efficient method which retains its accuracy throughout the circular domain without degradation at singularity. Because the problems under consideration are linear or weakly nonlinear with constant or piecewise constant coefficients, transform methods for the problems are not investigated. While the specific method is developed for the two dimensional wave equations on a circular domain and the equation of transverse motion for a thin circular plate, examples demonstrating the extension of the techniques to a fully coupled structural acoustic system are used to illustrate the flexibility of the method when approximating the dynamics of more complex systems.
The initial part of the study was begun with a combination of literature searches, World Wide Web searches, and contacts with individuals and companies who were known to members of our team to have an interest in topics that seemed to be related to our study. There is a long list of such topics, such as concurrent engineering, design for manufacture, life-cycle engineering, systems engineering, systems integration, systems design, design systems, integrated product and process approaches, enterprise integration, integrated product realization, and similar terms. These all capture, at least in part, the flavor of what we describe here as integrated design systems. An inhibiting factor in this inquiry was the absence of agreed terminology for the study of integrated design systems. It is common for the term to be applied to what are essentially augmented Computer-Aided Design (CAD) systems, which are integrated only to the extent that agreements have been reached to attach proprietary extensions to proprietary CAD programs. It is also common for some to use the term integrated design systems to mean a system that applies only, or mainly, to the design phase of a product life cycle. It is likewise common for many of the terms listed earlier to be used as synonyms for integrated design systems. We tried to avoid this ambiguity by adopting the definition of integrated design systems that is implied in the introductory notes that we provided to our contacts, cited earlier. We thus arrived at this definition: Integrated Design Systems refers to the integration of the different tools and processes that comprise the engineering, of complex systems. It takes a broad view of the engineering of systems, to include consideration of the entire product realization process and the product life cycle. An important aspect of integrated design systems is the extent to which they integrate existing, "islands of automation" into a comprehensive design and product realization environment. As the study progressed, we relied increasingly upon a networking approach to lead us to new information. The departure point for such searches often was a government-sponsored project or a company initiative. The advantage of this approach was that short conversations with knowledgeable persons would usually cut through confusion over differences of terminology, thereby somewhat reducing the search space of the study. Even so, it was not until late in our eight-month inquiry that we began to see signs of convergence of the search, in the sense that a number of the latest inquiries began to turn up references to earlier contacts. As suggested above, this convergence often occurred with respect to particular government or company projects.
The results of the entropy generation and availability energy loss analysis under conditions of oscillating pressure and oscillating helium gas flow in two Massachusetts Institute of Technology (MIT) test rigs piston-cylinder and piston-cylinder-heat exchanger are presented. Two solution domains, the gas spring (single-space) in the piston-cylinder test rig and the gas spring + heat exchanger (two-space) in the piston-cylinder-heat exchanger test rig are of interest. Sage and CFD-ACE+ commercial numerical codes are used to obtain 1-D and 2-D computer models, respectively, of each of the two solution domains and to simulate the oscillating gas flow and heat transfer effects in these domains. Second law analysis is used to characterize the entropy generation and availability energy losses inside the two solution domains. Internal and external entropy generation and availability energy loss results predicted by Sage and CFD-ACE+ are compared. Thermodynamic loss analysis of simple systems such as the MIT test rigs are often useful to understand some important features of complex pattern forming processes in more complex systems like the Stirling engine. This study is aimed at improving numerical codes for the prediction of thermodynamic losses via the development of a loss post-processor. The incorporation of loss post-processors in Stirling engine numerical codes will facilitate Stirling engine performance optimization. Loss analysis using entropy-generation rates due to heat and fluid flow is a relatively new technique for assessing component performance. It offers a deep insight into the flow phenomena, allows a more exact calculation of losses than is possible with traditional means involving the application of loss correlations and provides an effective tool for improving component and overall system performance.
A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a "war game" is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test. The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.
Functional fault models (FFMs) are a directed graph representation of the failure effect propagation paths within a system's physical architecture and are used to support development and real-time diagnostics of complex systems. Verification of these models is required to confirm that the FFMs are correctly built and accurately represent the underlying physical system. However, a manual, comprehensive verification process applied to the FFMs was found to be error prone due to the intensive and customized process necessary to verify each individual component model and to require a burdensome level of resources. To address this problem, automated verification tools have been developed and utilized to mitigate these key pitfalls. This paper discusses the verification of the FFMs and presents the tools that were developed to make the verification process more efficient and effective.
This presentation presents an overview of work performed at NASA Ames Research Center in 2017. The work concerns the analysis of current aircraft system management displays, and the initial development of an interface for providing information about aircraft system status. The new interface proposes a shift away from current aircraft system alerting interfaces that report the status of physical components, and towards displaying the implications of degradations on mission capability. The proposed interface describes these component failures in terms of operational consequences of aircraft system degradations. The research activity was an effort to examine the utility of different representations of complex systems and operating environments to support real-time decision making of off-nominal situations. A specific focus was to develop representations that provide better integrated information to allow pilots to more easily reason about the operational consequences of the off-nominal situations. The work is also seen as a pathway to autonomy, as information is integrated and understood in a form that automated responses could be developed for the off-nominal situations in the future.
Model Based Systems Engineering (MBSE) is being introduced and used to support many aspects of the engineering of complex systems. Our focus is on space systems, with a particular interest in Mission Assurance of those systems. We seek to identify where Mission Assurance can potentially benefit when a space system is developed using an MBSE approach. To do so we consider how MBSE can help provide the evidence that Mission Assurance needs, using the recently developed NASA Reliability and Maintainability Objective Hierarchy as the exposition of those needs. We illustrate this MBSE’s potential contributions using examples drawn from our prior survey of the MBSE literature addressing space mission assurance concerns. The overall conclusion we draw from this is that there are numerous ways in which MBSE may, and in some cases already does, support Mission Assurance.
As power systems evolve, becoming increasingly renewable, distributed, dynamic, and digital, managing them becomes ever more challenging and complex. System operators serve as the "air traffic controllers" for electricity grids, working to balance supply and demand, manage evolving electricity markets, and ensure the safety and reliability of electricity systems. To adapt to the rapidly changing energy landscape and support the transition to modern, zero carbon power systems, system operators must develop and adopt new technologies, approaches, and frameworks. The Global Power System Transformation (G-PST) Consortium is a network of system operators, research institutions, and industry partners working to accelerate and scale the technical solutions required to transform our world's power systems. By developing and sharing robust models and tools, research and demonstration results, knowledge, and innovation, the G-PST Consortium is empowering system operators to transition to 100% renewable energy while ensuring the reliability, safety, and cost-effectiveness of grid operations.
Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.