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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MBARI WEC 2021 deployment

This dataset includes data from the Monterey Bay Aquarium Research Institute (MBARI) wave energy converter (WEC) and a nearby located Sofar Spotter buoy. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to autonomous underwater vehicle, sensor system, and even aquaculture farm needs. For more information on the MBARI WEC see the research journal attached in the submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Performance of DSRC V2V Communication Networks in an Autonomous Semi-Truck Platoon Application

Autonomy for multiple trucks to drive in a fixedheadway platoon formation is achieved by adding precision GPS and V2V communications to a conventional adaptive cruise control (ACC) system. The performance of the Cooperative ACC (CACC) system depends heavily on the reliability of the underlying V2V communications network. Using data recorded on precision-instrumented trucks at both ACM and NCAT test tracks, we provide an understanding of various effects on V2V network performance: Occlusions - non-line-of-sight (NLOS) between the Tx and Rx antenna may cause network signal loss. Rain - water droplets in the air may cause network signal degradation. Antenna position - antennas at higher elevation may have less ground clutter to deal with. RF interference - interference may cause network packet loss. GPS outage - outages caused by tree cover, tunnels, etc. may result in degraded performance. Road curvature - curves may affect antenna diversity. Road grade - antenna may have limited vertical coverage. Our results, which include multiple plots and graphs and their analysis, build on those reported by others and could be of interest to researchers, practitioners, and end-users in the broader autonomous-connected vehicle community.

V2V communications, GPS, truck platoon, semi-auton↗
NASA NTRS2003

F/A-18 Performance Benefits Measured During the Autonomous Formation Flight Project

The Autonomous Formation Flight (AFF) project at the NASA Dryden Flight Research Center (Edwards, California) investigated performance benefits resulting from formation flight, such as reduced aerodynamic drag and fuel consumption. To obtain data on performance benefits, a trailing F/A-18 airplane flew within the wing tip-shed vortex of a leading F/A-18 airplane. The pilot of the trail airplane used advanced station-keeping technology to aid in positioning the trail airplane at precise locations behind the lead airplane. The specially instrumented trail airplane was able to obtain accurate fuel flow measurements and to calculate engine thrust and vehicle drag. A maneuver technique developed for this test provided a direct comparison of performance values while flying in and out of the vortex. Based on performance within the vortex as a function of changes in vertical, lateral, and longitudinal positioning, these tests explored design-drivers for autonomous stationkeeping control systems. Observations showed significant performance improvements over a large range of trail positions tested. Calculations revealed maximum drag reductions of over 20 percent, and demonstrated maximum reductions in fuel flow of just over 18 percent.

Vachon, M. Jake↗
NASA NTRS2007

Dryden Flight Research Highlights; August 2006 - July 2007

A DVD highlights the research activities of the Dryden Flight Research Center. The video includes coverage of space-based communication, intelligent flight controls, autonomous refueling demonstration, QuietSpike, sonic boom tests, G-III radar pod, X-48B blended wing body, Altair fire mission, Ikhana UAV, STS-117 Atlantis, and SOFIA Telescope research efforts.

Wright, David A.↗
DOE OSTI2021

Cyber Threat Assessment Methodology for Autonomous and Remote Operations for Advanced Reactors (Conference Presentation)

The next generation of Advanced Reactors include planned capabilities for both Autonomous (operating without human interaction for a set period-of-time) and Remote (operating with human interaction from a separate physical location) Operations. Existing Nuclear Reactor architectures include a set of safety and security constraints tightly coupled with personnel policies and procedures. As Advanced Reactors are fielded with these new Autonomous and Remote operational capabilities, the architectures and associated infrastructure services and components will perceivably expand the overall attack surface and risk calculations with regards to safe and secure operations. This paper is part of an FY21 work program focused on ensuring Advanced Reactor designs are informed with threat-based guidance on design and operation of Secure Architectures with a specific focus on the deployment of Autonomous Systems in support of Advanced Reactor Operations. The next phase of this research program is to complement produce a methodology for assessment of the cyber threat against these architectures as well as a catalogue of Use Cases to support the Advanced Reactor community in their implementation of Autonomous and Remote Operations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Autonomous Formation Flight

NASA's Strategic Plan for the Aerospace Technology Enterprise includes ambitious objectives focused on affordable air travel, reduced emissions, and expanded aviation-system capacity. NASA Dryden Flight Research Center, in cooperation with NASA Ames Research Center, the Boeing Company, and the University of California, Los Angeles, has embarked on an autonomous-formation-flight project that promises to make significant strides towards these goals. For millions of years, birds have taken advantage of the aerodynamic benefit of flying in formation. The traditional "V" formation flown by many species of birds (including gulls, pelicans, and geese) enables each of the trailing birds to fly in the upwash flow field that exists just outboard of the bird immediately ahead in the formation. The result for each trailing bird is a decrease in induced drag and thus a reduction in the energy needed to maintain a given speed. Hence, for migratory birds, formation flight extends the range of the system of birds over the range of birds flying solo. The Autonomous Formation Flight (AFF) Project is seeking to extend this symbiotic relationship to aircraft.

Schkolnik, Gerard S.↗
NASA NTRS2007

Propulsion Controls and Diagnostics Research at NASA Glenn Research Center

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. Also the propulsion systems required to enable the National Aeronautics and Space Administration (NASA) Vision for Space Exploration in an affordable manner will need to have high reliability, safety and autonomous operation capability. The Controls and Dynamics Branch (CDB) at NASA Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of Intelligent Propulsion Systems. This paper describes the current activities of the CDB under the NASA Aeronautics Research and Exploration Systems Missions. The programmatic structure of the CDB activities is described along with a brief overview of each of the CDB tasks including research objectives, technical challenges, and recent accomplishments. These tasks include active control of propulsion system components, intelligent propulsion diagnostics and control for reliable fault identification and accommodation, distributed engine control, and investigations into unsteady propulsion systems.

Garg, Sanjay↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2000

From Livingstone to SMV: Formal Verification for Autonomous Spacecrafts

To fulfill the needs of its deep space exploration program, NASA is actively supporting research and development in autonomy software. However, the reliable and cost-effective development and validation of autonomy systems poses a tough challenge. Traditional scenario-based testing methods fall short because of the combinatorial explosion of possible situations to be analyzed, and formal verification techniques typically require a tedious, manual modelling by formal method experts. This paper presents the application of formal verification techniques in the development of autonomous controllers based on Livingstone, a model-based health-monitoring system that can detect and diagnose anomalies and suggest possible recovery actions. We present a translator that converts the models used by Livingstone into specifications that can be verified with the SMV model checker. The translation frees the Livingstone developer from the tedious conversion of his design to SMV, and isolates him from the technical details of the SMV program. We describe different aspects of the translation and briefly discuss its application to several NASA domains.

Pecheur, Charles↗
DOE OSTI2023

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frame-works), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.

Castellana, Vito G.↗
NASA NTRS2005

Bio-Nanobattery Development and Characterization

A bio-nanobattery is an electrical energy storage device that utilizes organic materials and processes on an atomic, or nanometer-scale. The bio-nanobattery under development at NASA s Langley Research Center provides new capabilities for electrical power generation, storage, and distribution as compared to conventional power storage systems. Most currently available electronic systems and devices rely on a single, centralized power source to supply electrical power to a specified location in the circuit. As electronic devices and associated components continue to shrink in size towards the nanometer-scale, a single centralized power source becomes impractical. Small systems, such as these, will require distributed power elements to reduce Joule heating, to minimize wiring quantities, and to allow autonomous operation of the various functions performed by the circuit. Our research involves the development and characterization of a bio-nanobattery using ferritins reconstituted with both an iron core (Fe-ferritin) and a cobalt core (Co-ferritin). Synthesis and characterization of the Co-ferritin and Fe-ferritin electrodes were performed, including reducing capability and the half-cell electrical potentials. Electrical output of nearly 0.5 V for the battery cell was measured. Ferritin utilizing other metallic cores were also considered to increase the overall electrical output. Two dimensional ferritin arrays were produced on various substrates to demonstrate the feasibility of a thin-film nano-scaled power storage system for distributed power storage applications. The bio-nanobattery will be ideal for nanometerscaled electronic applications, due to the small size, high energy density, and flexible thin-film structure. A five-cell demonstration article was produced for concept verification and bio-nanobattery characterization. Challenges to be addressed include the development of a multi-layered thin-film, increasing the energy density, dry-cell bionanobattery development, and selection of ferritin core materials to allow the broadest range of applications. The potential applications for the distributed power system include autonomously-operating intelligent chips, flexible thin-film electronic circuits, nanoelectromechanical systems (NEMS), ultra-high density data storage devices, nanoelectromagnetics, quantum electronic devices, biochips, nanorobots for medical applications and mechanical nano-fabrication, etc.

King, Glen C.↗
NASA NTRS2018

Autonomous Guidance Algorithms for NASA Learn-to-Fly Technology Development

Learn-to-Fly (L2F) is an advanced technology development effort under the NASA Transformative Aeronautics Concepts Program (TACP) that is aimed at assessing the feasibility of self-learning flight vehicles. Specifically, research has been conducted to demonstrate the potential to merge two enabling technologies; real-time aerodynamic modeling and adaptive controls, to substantially reduce the typical ground and flight testing requirements for air vehicle design. The approach to this effort involved development of unique airframes and on-board algorithms to demonstrate key L2F technologies on a fully autonomous flight test vehicle. This research, that included an aggressive flight test program, was intended to rapidly advance these technologies and demonstrate capabilities of the L2F approach. Key components of the L2F architecture include real-time aerodynamic modeling, adaptive controls and control allocation, and guidance. This paper provides an overview of the guidance algorithm which primarily served as an executive function to coordinate control commands for range navigation and the desired test conditions, provide autonomous envelope limiting/expansion and enable automatic landing to touchdown with no intervention from a human operator. A discussion of the L2F concept-of-operations and unique flight testing considerations, which influenced the guidance functional requirements, is included and results of recent flight testing are presented.

Foster, John V.↗
NASA NTRS1990

Autonomous integrated GPS/INS navigation experiment for OMV. Phase 1: Feasibility study

The phase 1 research focused on the experiment definition. A tightly integrated Global Positioning System/Inertial Navigation System (GPS/INS) navigation filter design was analyzed and was shown, via detailed computer simulation, to provide precise position, velocity, and attitude (alignment) data to support navigation and attitude control requirements of future NASA missions. The application of the integrated filter was also shown to provide the opportunity to calibrate inertial instrument errors which is particularly useful in reducing INS error growth during times of GPS outages. While the Orbital Maneuvering Vehicle (OMV) provides a good target platform for demonstration and for possible flight implementation to provide improved capability, a successful proof-of-concept ground demonstration can be obtained using any simulated mission scenario data, such as Space Transfer Vehicle, Shuttle-C, Space Station.

Upadhyay, Triveni N.↗
NASA NTRS2006

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Jacklin, Stephen A.↗
NASA NTRS2016

Development of the NASA MCAT Auxiliary Telescope for Orbital Debris Research

The National Aeronautical and Space Administration (NASA) has recently deployed the Meter Class Autonomous Telescope (MCAT) to Ascension Island. MCAT will provide NASA with a dedicated optical sensor for observations of orbital debris with the goal of statistically sampling the orbital and photometric characteristics of the population from low Earth to Geosynchronous orbits. Additionally, a small auxiliary telescope, co-located with MCAT, is being deployed to augment its observations by providing near-simultaneous photometry and astrometry, as well as offloading low priority targets from MCAT's observing queue. It will also be available to provide observational measurements to the Space Surveillance Network for the United States Air Force.

Frith, James↗
NASA NTRS2021

Anomaly Detection, Active Learning, Precursor Identification,and Human Knowledge for Autonomous System Safety

The project Autonomy Teaming and TRajectories for ComplexTrusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.

machine learning↗
NASA NTRS1994

Very fast motion planning for highly dexterous-articulated robots

Due to the inherent danger of space exploration, the need for greater use of teleoperated and autonomous robotic systems in space-based applications has long been apparent. Autonomous and semi-autonomous robotic devices have been proposed for carrying out routine functions associated with scientific experiments aboard the shuttle and space station. Finally, research into the use of such devices for planetary exploration continues. To accomplish their assigned tasks, all such autonomous and semi-autonomous devices will require the ability to move themselves through space without hitting themselves or the objects which surround them. In space it is important to execute the necessary motions correctly when they are first attempted because repositioning is expensive in terms of both time and resources (e.g., fuel). Finally, such devices will have to function in a variety of different environments. Given these constraints, a means for fast motion planning to insure the correct movement of robotic devices would be ideal. Unfortunately, motion planning algorithms are rarely used in practice because of their computational complexity. Fast methods have been developed for detecting imminent collisions, but the more general problem of motion planning remains computationally intractable. However, in this paper we show how the use of multicomputers and appropriate parallel algorithms can substantially reduce the time required to synthesize paths for dexterous articulated robots with a large number of joints. We have developed a parallel formulation of the Randomized Path Planner proposed by Barraquand and Latombe. We have shown that our parallel formulation is capable of formulating plans in a few seconds or less on various parallel architectures including: the nCUBE2 multicomputer with up to 1024 processors (nCUBE2 is a registered trademark of the nCUBE corporation), and a network of workstations.

Challou, Daniel J.↗
DOE OSTI2024

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗