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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)

Rapidly expanding, new telecommunications infrastructure based on 5G technologies will disrupt and transform how we design, build, operate, and optimize scientific infrastructure and the experiments and services enabled by that infrastructure, from continental-scale sensor networks to centralized scientific user facilities, from intelligent Internet of Things devices to supercomputers. Concurrently, 5G will introduce, or exacerbate, challenges related to protecting infrastructure and associated scientific data as well as to fully leveraging opportunities related to expanded infrastructure scale and complexity. The U.S. Department of Energy (DOE) Office of Science operates scientific infrastructure, supporting some of the nation’s most advanced intellectual discoveries, spanning the country and including 30 world-class user facilities from supercomputers to accelerators. Along with field experiments and remote observatories, every aspect of DOE’s scientific enterprise will be affected by 5G, which amounts to a complete renovation of the underpinnings of the nation’s information infrastructure. In this report we explore the scientific opportunities and new research challenges associated with 5G, ranging from scalability to heterogeneity to cybersecurity. The rapid commercial deployment of 5G opens the opportunity to rethink and reinvent DOE’s scientific infrastructure and experimentation, from intelligent sensor networks at unprecedented scales to a digital continuum of cyberinfrastructure spanning low-power sensors, high-performance computing embedded within and at the edge of the network, and DOE’s large-scale user instrument and computing facilities. New programming paradigms, workflow and data frameworks, and AI-based system design, operation, and autonomous adaptation and optimization will be necessary in order to exploit these new opportunities. Field deployments and centralized scientific instruments can also be revolutionized, moving (without traditional performance penalties) from wired to wireless connectivity for data and control systems, improving flexibility, and opening new sensing modalities, including the use of the 5G electromagnetic spectrum itself as an environmental probe. For DOE science, in contrast to commercial 5G applications and settings, devices will be deployed in extreme environments such as cryogenically cooled instrument control systems and in remote settings with harsh conditions, requiring the design of new materials for RF communication and edge processing to operate in these regimes. Concurrently, 5G infrastructure comprises both hardware and sophisticated software systems - currently closed and proprietary. The cybersecurity challenges to 5G-empowered reinvention mirror the complexity and variety of new 5G features, from virtualization to private network slices to ubiquitous access. Research is also needed in order to accelerate the development of secure and open 5G software infrastructure, reducing reliance on hardware and software produced outside the United States and providing the transparency and rigorous evaluation and testing afforded through open software. Twelve broad research thrusts are laid out in four chapters, with a companion fifth chapter (and three additional research thrusts) underscoring the needs and opportunities for an aggressive testbed program co-designed by networking experts and scientists involved in the 15 research thrusts. The urgency of undertaking this research is fueled by a global, accelerating deployment of new telecommunications infrastructure that is designed for entertainment and commercial applications - barely scratching the surface of what 5G can do to extend U.S. leadership in scientific discovery.

42 ENGINEERING↗
NASA NTRS1989

PRIME: A bottom-up approach to probabilistic rule development

PRIME is a system to be used by an intelligent machine to allow it to operate in an abstract but uncertain (or stochastic) environment. It maintains a model of the effects of the machine's actions in the form of a rule base, which is induced from experience. This bottom-up approach to rule development allows the model to adapt to changes in the environment. Each rule consists of a condition under which the rule is active, an action, the effect of the action on the environment, and an estimate of the probability of this effect occurring. The effect probabilities are used to model the uncertainty in the environment, permitting multiple possible effects for a single action under a particular set of conditions. The objective of the intelligent machine is to satisfy user-specified goals with maximum probability of success. PRIME fulfills this requirement in two ways: it continuously updates the rule base with the most recent information, to ensure the validity of the model; and it generates plans which have the maximum probability of achieving the goals, based on the probability estimates in the rule base. PRIME is composed of three main processes: exploration, generalization, and planning. In exploration, the machine executes various randomly chosen actions, observes the effects on the environment, and updates the rule base accordingly. This process is used to develop the rule base in simulation, as well as to supplement the current knowledge during normal operation. Generalization is the procedure used to induce general rules from experience, which is encoded in the form of specific rules. These general rules extend the machine's knowledge to situations which have not been encountered yet, thereby increasing the capability of the machine to plan effectively. Planning is the process of constructing an optimal sequence of actions to satisfy a goal, using the rule base to predict the effects of these actions and to determine the probability of success of the plan. The rule representation and many other data structures were specifically chosen to maximize the efficiency of these processes. A simulated environment was designed to test the performance of PRIME. The results of experimentation were largely negative. The main problem was that the domain coverage of the rules was inadequate for the number of rules stored in the rule base, due to redundancies in general rules and numerous rules covering ineffective actions. It was determined that a more efficient generalization, and some form of goal-directed exploration, are necessary in order to solve most of the current deficiencies in PRIME.

Miller, Scott A.↗
DOE OSTI2016

Fault detection and analysis in nuclear research facility using artificial intelligence methods

In this article, an online detection of transducer and actuator condition is discussed. A case study is on the reading of area radiation monitor (ARM) installed at the chimney of PUSPATI TRIGA nuclear reactor building, located at Bangi, Malaysia. There are at least five categories of abnormal ARM reading that could happen during the transducer failure, namely either the reading becomes very high, or very low/ zero, or with high fluctuation and noise. Moreover, the reading may be significantly higher or significantly lower as compared to the normal reading. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are good methods for modeling this plant dynamics. The failure of equipment is based on ARM reading so it is then to compare with the estimated ARM data from ANN/ ANFIS function. The failure categories in either ‘yes’ or ‘no’ state are obtained from a comparison between the actual online data and the estimated output from ANN/ ANFIS function. It is found that this system design can correctly report the condition of ARM equipment in a simulated environment and later be implemented for online monitoring. This approach can also be extended to other transducers, such as the temperature profile of reactor core and also to include other critical actuator conditions such as the valves and pumps in the reactor facility provided that the failure symptoms are clearly defined.

42 ENGINEERING↗
NASA NTRS2009

Adaptive Behavior for Mobile Robots

The term "System for Mobility and Access to Rough Terrain" (SMART) denotes a theoretical framework, a control architecture, and an algorithm that implements the framework and architecture, for enabling a land-mobile robot to adapt to changing conditions. SMART is intended to enable the robot to recognize adverse terrain conditions beyond its optimal operational envelope, and, in response, to intelligently reconfigure itself (e.g., adjust suspension heights or baseline distances between suspension points) or adapt its driving techniques (e.g., engage in a crabbing motion as a switchback technique for ascending steep terrain). Conceived for original application aboard Mars rovers and similar autonomous or semi-autonomous mobile robots used in exploration of remote planets, SMART could also be applied to autonomous terrestrial vehicles to be used for search, rescue, and/or exploration on rough terrain.

Huntsberger, Terrance↗
DOE OSTI2025

Toward particle accelerator machine state embeddings as a modality for large language models

Understanding and diagnosing the state of a particle accelerator requires navigating high-dimensional control system data, often involving hundreds of interdependent parameters. We propose a novel multimodal embedding framework that jointly learns representations of machine states from both numerical control system readouts and natural language descriptions. This enables the translation of complex machine conditions into human-readable summaries while maintaining fidelity to the underlying physical system. The obtained embeddings are subsequently adapted to an open-weights large language model via cross-attention conditioning. We demonstrate a first implementation trained on European XFEL machine state data. This work covers the embedding model architecture, training methodology, and presents initial examples demonstrating the model's capabilities in action. Due to the general concept of machine state, the model can be easily adapted to other facilities and control system environments.

Accelerator Physics↗
NASA NTRS1988

An expert system based intelligent control scheme for space bioreactors

An expert system based intelligent control scheme is being developed for the effective control and full automation of bioreactor systems in space. The scheme developed will have the capability to capture information from various resources including heuristic information from process researchers and operators. The knowledge base of the expert system should contain enough expertise to perform on-line system identification and thus be able to adapt the controllers accordingly with minimal human supervision.

San, Ka-Yiu↗
DOE OSTI2017

foresee™ (SWR-17-15)

foresee™ is a home energy management system (HEMS) that provides a user centric energy automation solution for residential building occupants. Built upon advanced control and machine learning algorithms, foresee™ intelligently manages the home appliances and distributed energy resources (DERs) such as photovoltaics and battery storage in a home. Unlike existing HEMS in the market, foresee™ provides a tailored home automation solution for individual occupants by learning and adapting to their preferences on cost, comfort, convenience and carbon. foresee™ improves not only the energy efficiency of the home but also its capability to provide grid services such as demand response. Highly reliable demand response services are likely to be incentivized by utility companies, making foresee™ economically viable for most homes.

Source record↗
NASA NTRS1988

Blindness in designing intelligent systems

New investigations of the foundations of artificial intelligence are challenging the hypothesis that problem solving is the cornerstone of intelligence. New distinctions among three domains of concern for humans--description, action, and commitment--have revealed that the design process for programmable machines, such as expert systems, is based on descriptions of actions and induces blindness to nonanalytic action and commitment. Design processes focusing in the domain of description are likely to yield programs like burearcracies: rigid, obtuse, impersonal, and unable to adapt to changing circumstances. Systems that learn from their past actions, and systems that organize information for interpretation by human experts, are more likely to be successful in areas where expert systems have failed.

Denning, Peter J.↗
DOE OSTI2025

Responsible Artificial Intelligence for Insider Threat Mitigation

This report examines the application of artificial intelligence (AI) technologies for insider threat mitigation (ITM) programs in nuclear security facilities. Insider threat detection presents unique challenges due to the subtle and adaptive nature of these threats, the complex signatures involved, and the scarcity of available data for analysis. Traditional human-centered approaches, while essential, face limitations in processing large amounts of data continuously and detecting subtle patterns across multiple systems. AI technologies can potentially address these limitations by providing 24/7 monitoring capabilities, identifying complex patterns that might escape human observation, and offering consistent application of security criteria. However, the deployment of AI in nuclear security contexts introduces significant new risks, including workflow disruption, expanded attack surfaces, potential for misuse, and ethical concerns regarding privacy, fairness, transparency, safety, and security. The high-consequence nature of nuclear security decisions demands careful consideration of these risks and systematic approaches to their mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1989

Application of Artificial Intelligence (AI) Programming Techniques to Tactical Guidance for Fighter Aircraft

A research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within-Visual-Range (WVR) air combat engagements is discussed. The application of AI methods for development and implementation of the TDG is presented. The history of the Adaptive Maneuvering Logic (AML) program is traced and current versions of the AML program are compared and contrasted with the TDG system. The Knowledge-Based Systems (KBS) used by the TDG to aid in the decision-making process are outlined in detail and example rules are presented. The results of tests to evaluate the performance of the TDG versus a version of AML and versus human pilots in the Langley Differential Maneuvering Simulator (DMS) are presented. To date, these results have shown significant performance gains in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify than the updated FORTRAN AML programs.

McManus, John W.↗
NASA NTRS2021

Role of Cyber-Physical Testing in Developing Resilient Extraterrestrial Habitats

Extraterrestrial long-term habitat systems (henceforth referred to as habitat systems) require groundbreaking technological advances to overcome the extreme demands introduced by isolation and challenging environments. A habitat system must operate as intended under continuous disruptive conditions. Designing for the demands that challenging environments will place on habitat systems (e.g., wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts, vibrations, and solar particle events) represents one of the greatest challenges in this endeavor. This engineering problem necessitates that we design and manage habitat systems to be resilient. System resilience requires a comprehensive approach that accounts for disruptions through the design process and adapts to them in operation. As the habitat system evolves—growing in physical size, complexity, population, and connectivity—and diversifies in operations, it must continue to be safe and resilient. In this endeavor, we should take advantage of lessons learned in developing civil infrastructure responsive to catastrophic natural hazards, autonomous robotics platforms, smart buildings, cyber-physical testing, complex systems, and diagnostics and prognostics for intelligent health management. This study highlights the importance of system resilience and cyber-physical testing to address the grand challenge of developing habitat systems.

Public health and safety↗
DOE OSTI2025

Designing Future Energy Systems with Generative AI

Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1998

Case-Based Capture and Reuse of Aerospace Design Rationale

The goal of this project is to apply artificial intelligence techniques to facilitate capture and reuse of aerospace design rationale. The project applies case-based reasoning (CBR) and concept mapping (CMAP) tools to the task of capturing, organizing, and interactively accessing experiences or "cases" encapsulating the methods and rationale underlying expert aerospace design. As stipulated in the award, Indiana University and Ames personnel are collaborating on performance of research and determining the direction of research, to assure that the project focuses on high-value tasks. In the first five months of the project, we have made two visits to Ames Research Center to consult with our NASA collaborators, to learn about the advanced aerospace design tools being developed there, and to identify specific needs for intelligent design support. These meetings identified a number of task areas for applying CBR and concept mapping technology. We jointly selected a first task area to focus on: Acquiring the convergence criteria that experts use to guide the selection of useful data from a set of numerical simulations of high-lift systems. During the first funding period, we developed two software systems. First, we have adapted a CBR system developed at Indiana University into a prototype case-based reasoning shell to capture and retrieve information about design experiences, with the sample task of capturing and reusing experts' intuitive criteria for determining convergence (work conducted at Indiana University). Second, we have also adapted and refined existing concept mapping tools that will be used to clarify and capture the rationale underlying those experiences, to facilitate understanding of the expert's reasoning and guide future reuse of captured information (work conducted at the University of West Florida). The tools we have developed are designed to be the basis for a general framework for facilitating tasks within systems developed by the Advanced Design Technologies Testbed (ADTT) project at ARC. The tenets of our framework are (1) that the systems developed should leverage a designer's knowledge, rather than attempting to replace it; (2) that learning and user feedback must play a central role, so that the system can adapt to how it is used, and (3) that the learning and feedback processes must be as natural and as unobtrusive as possible. In the second funding period we will extend our current work, applying the tools to capturing higher-level design rationale.

Leake, David B.↗
NASA NTRS2019

The Space Communications and Navigation Testbed aboard International Space Station: Seven Years of Space-based Reconfigurable Software Defined Communications, Navigation, and Networking

The adoption of software defined radios offers space missions a new way to develop and operate space transceivers for communications and navigation.The SCaN Testbed on-board the ISS led groundbreaking efforts to champion use of software defined radios for space communications. The SCaN Testbed has allowed NASA, industry, academia, and international partners to pursue their respective interests in joint collaboration with NASA, and move this technology and it's applications to the space domain. Launched in 2012, The SCaN Testbed has logged over 4000 hours of operation exploring the development, reconfiguration, and operation of software defined radios and their software applications. Over the past seven years, experimenters and organizations from across the United States and around the world, have advanced the applications of software defined radios and networks using the SCaN Tested. Some of SCaN Testbed's successful experiments include the demonstration of the first Ka-band full duplex space transceiver, which became an R&D 100 award winning technology, and was inducted into the Space Technology Hall of Fame, following the launch and space deployment of a successful commercial product line based on the Testbed radios.Experiments have focused on new software development and operations concepts for understanding how to manage and apply this relatively new technology to space to improve communications and navigation for space science and exploration missions. The advanced capabilities of the software radios allow for multiple applications or functions (e.g., communication and navigation) to operate from the same radio platform. Multiple software waveform applications enable software component reuse and improve efficiency for multiple applications operating over different mission phases. The new capabilities of software defined radios such as on-orbit reconfiguration, also present new challenges such as increased operational complexity. Experiments of the SCaN testbed include more intelligent or cognitive applications to improve communications efficiency and manage the complexity of the radios, the communication channels, and the network itself. The software defined radios on the SCaN Testbed are each compliant to NASA's Space Telecommunications Radio System (STRS) Architecture. The STRS Architecture provides commonality among radio developments from different providers and different mission applications, and is designed to reduce the cost, risk, and complexity of unique and custom radio developments. This radio architecture standard defines common waveform software interfaces, methods of instantiation, operation, and documentation. As the SCaN Testbed concludes its operations on ISS, this presentation explores the advancements and accomplishments made to advance software defined radio technology and its applications for exploration. The accomplishments cover a number of experiment areas in Ka-band and S-band communications with TDRS, high rate communications, adaptive waveform operation, navigation using both GPA and Galileo constellations, complex networking and disruptive tolerant link protocols, user initiative service, and initial experiments with intelligent and cognitive applications which demonstrate the significant potential of software defined and cognitive radios.

Mortensen, Dale↗
DOE OSTI2020

Measuring Cities with Software-Defined Sensors

The Chicago Array of Things (AoT) project, funded by the US National Science Foundation, created an experimental, urban-scale measurement capability to support diverse scientific studies. Initially conceived as a traditional sensor network, collaborations with many science communities guided the project to design a system that is remotely programmable to implement Artificial Intelligence (AI) within the devices-at the “edge” of the network-as a means for measuring urban factors that heretofore had only been possible with human observers, such as human behavior including social interaction. The concept of “software-defined sensors” emerged from these design discussions, opening new possibilities, such as stronger privacy protections and autonomous, adaptive measurements triggered by events or conditions. We provide examples of current and planned social and behavioral science investigations uniquely enabled by software-defined sensors as part of the SAGE project, an expanded follow-on effort that includes AoT.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2022

Adaptive IV&V for Increasingly Complex Software Systems

As NASA software systems continue to innovate, becoming more complex and nondeterministic, the need for the NASA IV&V Program to become systemically adaptive to ensure mission success is paramount. To ensure adaptability within resource constraints, IV&V has developed an agile, risk-based approach to identify, characterize, scope, focus, and prioritize mission assurance activities. This risk based adaptive framework has been applied to trends such as increased reliance on data driven algorithms for safety and mission critical software behavior, use of MBSE in system design, and application of agile principles to embedded software development. The framework is enabled by continuous innovation of new approaches such as software only test beds, assurance design tools, and initiatives that augment IV&V assurance methods with artificial intelligence and machine learning techniques. This presentation will highlight the trends the NASA IV&V Program is seeing, the innovative steps it is taking to address those challenges, and how it is postured to address evolving risk and constantly changing and new technologies.

Wesley W Deadrick↗
NASA NTRS1994

Active vision in satellite scene analysis

In earth observation or planetary exploration it is necessary to have more and, more autonomous systems, able to adapt to unpredictable situations. This imposes the use, in artificial systems, of new concepts in cognition, based on the fact that perception should not be separated from recognition and decision making levels. This means that low level signal processing (perception level) should interact with symbolic and high level processing (decision level). This paper is going to describe the new concept of active vision, implemented in Distributed Artificial Intelligence by Dassault Aviation following a 'structuralist' principle. An application to spatial image interpretation is given, oriented toward flexible robotics.

Naillon, Martine↗
DOE OSTI2017

Innovative Patient Room Lighting System with Integrated Spectrally Adaptive Control

In December of 2013, the U.S. Department of Energy’s SSL R&D Program released a Funding Opportunity Announcement (FOA), that for the first time, contained opportunities for comprehensive application-specific system development. The FOA included opportunities for two applications, one of which was a Patient Room. Philips Lighting Research North America, submitted a proposal for the Patient Room application, and was selected for the complete project award. The award amount was for $497,127, with a Philips Research co-funding commitment 165,709 dollars. The total project value was 662,836 dollars. This project sought to redefine lighting for the patient room application. The goal was to deliver an innovative LED patient suite (patient room and bathroom) lighting system solution that was 40% more energy-efficient than traditional fluorescent incumbent technologies, and would meet all the visual and non-visual needs of patients, caregivers and visitors, and improve the patient experience. State-of-the-art multichannel LED platforms and control technologies that would provide spectral tuning and become part of an intelligent, connected lighting system drove the solution. The project was scoped into four main task areas that included a) System Concept Creation, b) Identification of the Luminaire Portfolio, c) Development of the Connected Lighting Infrastructure, and d) System Performance Validation. Each of the four main tasks were completed and validated extensively over the course the 2 ½ year project. The system concept was created by first developing a lighting design that demonstrated best practices for patient room lighting – illuminance and uniformity for task performance, reduced glare, and convenient controls, in addition to giving patients control over the lighting in their environment. A framework was defined to deliver circadian support via software behaviors. Through that process luminaires were identified from the Philips portfolio that were adaptable – by their form, dimensions, and optical materials – to mix multicolor LED platforms uniformly and deliver target design lumen levels. The Blue Sky luminaire was selected for the patient bed area to give the illusion of skylight while providing white light on the patient bed. Luminaires used existing 2-channel tunable white LED boards, and newly developed 4-channel LED boards. Red-Orange, Blue, Green, and Blue-shifted Yellow LED chips were selected based on spectral characteristics and their ability to produce high quality white light. 4-channel Power over Ethernet (PoE) drivers were developed and firmware written so they would communicate with both 2- and 4-channel boards. These components formed the backbone of the connected lighting infrastructure. Software, flexible and nuanced in its complexity, was written to set behaviors for myriad lighting scenes in the room throughout the 24 hour day – and all could be overridden by manual controls. This included a dynamic tunable white program, three color changing automatic programs that simulated degrees of sunrise to sunset palettes, and an amber night lighting system that offered visual cues for postural stability to minimize the risk of falls. All programs were carefully designed to provide visual comfort for all occupants, support critical task performance for staff, and to support the patient’s 24hr rhythms. A full scale mockup room was constructed in the Philips Cambridge Lab. The lighting system was installed, tested and functionality demonstrated to ensure smooth operation of system components – luminaires, drivers, PoE switches, wall controls, patient remote, and daylight and occupancy sensors. How did the system perform? It met visual criteria, confirmed by calculations, simulations and measurements in the field. It met non-visual criteria, confirmed by setting circadian stimulus (CS) targets and performing calculations using the calculator developed by the Lighting Research Center. Finally, human factors validation studies were conducted to gain insight from real end users in the healthcare profession; surveys were administered, data analyzed, and audio comments captured. The general consensus was positive, with requests to pilot the system in their hospitals. The importance of the research completed under this grant is that it allowed the exploration and development of a unique lighting system, one that would deliver a blend of visual and non-visual criteria in patient room design for today’s healthcare environment. The research investigated the area of multichannel LED technology, multichannel Power over Ethernet (PoE) drivers and their integration with automatic and manual controls as a system – uncovering and meeting challenges along the way. It married visual needs of patients and staff with support for 24 hour rhythms, placing value on the wellbeing of the patient – while successfully saving energy over incumbent technologies. Indications are that the market is ready and willing to invest – multiple healthcare facilities are in line to pilot this system, recognizing its value beyond energy to patient and staff well-being. Its value to the public can best be expressed by a patient support coordinator who, after spending several hours in the room being immersed in the lighting, analyzing all its features, commented: “This re-writes lighting for healthcare”.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗