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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Knowledge representation system for assembly using robots

Assembly robots combine the benefits of speed and accuracy with the capability of adaptation to changes in the work environment. However, an impediment to the use of robots is the complexity of the man-machine interface. This interface can be improved by providing a means of using a priori-knowledge and reasoning capabilities for controlling and monitoring the tasks performed by robots. Robots ought to be able to perform complex assembly tasks with the help of only supervisory guidance from human operators. For such supervisory quidance, it is important to express the commands in terms of the effects desired, rather than in terms of the motion the robot must undertake in order to achieve these effects. A suitable knowledge representation can facilitate the conversion of task level descriptions into explicit instructions to the robot. Such a system would use symbolic relationships describing the a priori information about the robot, its environment, and the tasks specified by the operator to generate the commands for the robot.

Jain, A.↗
NASA NTRS1991

Passive acquisition of CLIPS rules

The automated acquisition of knowledge by machine has not lived up to expectations, and knowledge engineering remains a human intensive task. Part of the reason for the lack of success is the difference in the cognitive focus of the expert. The expert must shift his or her focus from the subject domain to that of the representation environment. In doing so this cognitive shift introduces opportunity for errors and omissions. Presented here is work that observes the expert interact with a simulation of the domain. The system logs changes in the simulation objects and the expert's actions in response to those changes. This is followed by the application of inductive reasoning to move the domain specific rules observed to general domain rules.

Kovarik, Vincent J., Jr.↗
NASA NTRS1994

Toward Automatic Scalability Analysis of Message Passing Programs: A Case Study

Scalability analysis forms an important component of any performance debugging cycle, for massively parallel machines. However, tools that help in performing such analysis for parallel programs are non-existent. The primary reason for lack of such tools is the complexity involved in capturing program dynamics such as communication-computation overlap, communication latencies and memory hierarchy reference patterns. In this paper, we highlight some simple techniques that can be used to study scalability of explicit message-passing parallel programs that consider the above issues. We start from the high level source code and use a methodology for deducing communication characteristics and its impact on the total execution time of the program. The approach is validated with the help of a pipelined method for solving scalar tri-diagonal systems, using both simulations and symbolic cost models on the Intel hypercube.

Sarukkai, Sekhar R.↗
DOE OSTI2021

Unleashing the Power of Industrial Big Data through Scalable Manual Labeling

Big Data plays a central role in the remarkable results achieved by Machine Learning (ML) and especially Deep Learning (DL) in the recent years. However, the difficulty in obtaining a reasonable amount of labeled samples limits ML/DL application in various domains, including industrial equipment and system monitoring. In this paper the need for methods that turn manual labeling into a scalable process is highlighted. A real world problem is analyzed for which weak supervision methods, successfully employed in other domains, did not produce acceptable results. An alternative approach based on clustering ensembles is described and tested, achieving good performance.

Paes Leao, Bruno↗
DOE OSTI2025

Data as a Key Resource in Catalysis: A Community Account

The deployment of artificial intelligence (AI) is transforming the scientific fields central to interdisciplinary catalysis research. By enabling more effective use of data, AI (including simpler machine learning and data science tools) holds great promise for accelerating discoveries. However, progress has so far been modest, largely due to the lack of standardized, machine-readable, and openly shared catalysis data. This perspective, accounting for community insights emerging at conferences, analyses the underlying reasons for these challenges and proposes solutions to a future whereFAIR data management becomes an integral part of research in catalysis. In the short-term, we deem that mandatory FAIR data depositing prior to scientific publications along with consensualized top-down guidelines on data sharing powered by ease-to-use tools can make the necessary step change happen to catalyse data as key resource in our community.

36 - MATERIALS SCIENCE↗
NASA NTRS1990

The sixth generation robot in space

The knowledge based simulator developed in the artificial intelligence laboratory has become a working test bed for experimenting with intelligent reasoning architectures. With this simulator, recently, small experiments have been done with an aim to simulate robot behavior to avoid colliding paths. An automatic extension of such experiments to intelligently planning robots in space demands advanced reasoning architectures. One such architecture for general purpose problem solving is explored. The robot, seen as a knowledge base machine, goes via predesigned abstraction mechanism for problem understanding and response generation. The three phases in one such abstraction scheme are: abstraction for representation, abstraction for evaluation, and abstraction for resolution. Such abstractions require multimodality. This multimodality requires the use of intensional variables to deal with beliefs in the system. Abstraction mechanisms help in synthesizing possible propagating lattices for such beliefs. The machine controller enters into a sixth generation paradigm.

Butcher, A.↗
NASA NTRS1991

Materials and processes laboratory composite materials characterization task, part 1. Damage tolerance

A test run was performed on IM6/3501-6 carbon-epoxy in which the material was processed, machined into specimens, and tested for damage tolerance capabilities. Nondestructive test data played a major role in this element of composite characterization. A time chart was produced showing the time the composite material spent within each Branch or Division in order to identify those areas which produce a long turnaround time. Instrumented drop weight testing was performed on the specimens with nondestructive evaluation being performed before and after the impacts. Destructive testing in the form of cross-sectional photomicrography and compression-after-impact testing were used. Results show that the processing and machining steps needed to be performed more rapidly if data on composite material is to be collected within a reasonable timeframe. The results of the damage tolerance testing showed that IM6/3501-6 is a brittle material that is very susceptible to impact damage.

Nettles, A. T.↗
NASA NTRSDate not supplied

Artificial Intelligence Enhancements to Imagery for Space Operations

Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.

Rodney Grubbs↗
DOE OSTI2025

Laser Powder Bed Fusion Manufactured Alloys 625 and 282 and Properties Relevant to Advanced Nuclear Components

Under the Advanced Materials and Manufacturing Technologies program, two Ni-based alloys fabricated by laser powder bed fusion (LPBF) have been evaluated: γ′-strengthened Haynes 282 and solution-strengthened Inconel 625. Large printing defects were observed in the LPBF 282 alloy fabricated using a Renishaw AM250 machine, likely due to particle spattering during printing. Annealing at 1,180°C for 1 h followed by 4 h at 800°C in a high density of 50 nm γ′-strengthening precipitates as well as partial recrystallization resulted in a bimodal grain distribution. Creep testing performed at 750°C revealed lower creep life and ductility for the LPBF 282 compared with wrought 282. X-ray computed tomography combined with optical and scanning electron microscopy microstructural characterization revealed crack formation during creep testing, initiated either from printing defects or from creep cavitation at grain boundaries. Printing defects were the likely reason for the lower creep performance of LPBF 282 and could be suppressed by optimizing the build configuration. Printing 625 on an EOS M290 machine using the recommended EOS parameters resulted in a very low defect density. Superior creep strength at 725°C was observed for the as-printed LPBF 625 along the build direction when compared with wrought 625. No defect larger than ~50 µm could be detected by X-ray computed tomography in the as-printed conditions and after creep testing for 1,000 h at 725°C and 150 MPa. Small, needle-like δ-phase precipitates were observed after creep testing for 100 h at 725°C and 200 MPa. The rapid formation of the γ phase is directly related to the initial Nb and Mo segregation in the LPBF 625 cell walls, and its effect on the alloy’s high-temperature performance will be evaluated. Solution annealing was carried out at 1,150°C for 1 h and led to full recrystallization of the alloy. A decrease in strength and increase in ductility were observed after solution annealing, and the alloy recrystallization resulted in isotropic tensile properties at room temperature, contrary to what was observed for the as-printed LPBF 625.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator ( LAMMPS ) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Cuillier, Paul↗
NASA NTRS1989

Telerobotic Architecture for an on-Orbit Servicer

An on-orbit servicer system has unique functional and human factors requirements. The servicing, whether it be teleoperation task, a supervised control task or an autonomous robotic task, the man-machine interface function is likely to be a bottleneck to the operation of the whole system. The man-machine interface system for a space servicer, namely the operator control station, includes several subsystems with a hierarchical architecture. Those subsystems include a reasoning and planning subsystem (also known as the artificial intelligence planner), a run-time control subsystem, a manipulator control and mechanization subsystem, and a sensing and perception subsystem. Indicative of these potentials, certain generic tasks, suggestive of space assembly, maintenance and repair, were performed in a testbed environment. Through performance in several modes: direct teleoperation, shared control, traded control, and robotic operation, the benefits of the individual technology contributions to the operation were quantized and recommendations for use in telerobotic systems were established.

Operator Control Station↗
NASA NTRS1986

The NASA automation and robotics technology program

The development and objectives of the NASA automation and robotics technology program are reviewed. The objectives of the program are to utilize AI and robotics to increase the probability of mission success; decrease the cost of ground control; and increase the capability and flexibility of space operations. There is a need for real-time computational capability; an effective man-machine interface; and techniques to validate automated systems. Current programs in the areas of sensing and perception, task planning and reasoning, control execution, operator interface, and system architecture and integration are described. Programs aimed at demonstrating the capabilities of telerobotics and system autonomy are discussed.

Holcomb, Lee B.↗
NASA NTRS1989

Work, exercise, and space flight. 3: Exercise devices and protocols

Preservation of locomotor capacity by earth equivalent, exercise in space is the crucial component of inflight exercise. At this time the treadmill appears to be the only way possible to do this. Work is underway on appropriate hardware but this and a proposed protocol to reduce exercise time must be tested. Such exercise will preserve muscle, bone Ca(++) and cardiovascular-respiratory capacity. In addition, reasonable upper body exercise can be supplied by a new force generator/measurement system-optional exercise might include a rowing machine and bicycle ergometer. A subject centered monitoring-evaluation program will allow real time adjustments as required. Absolute protection for any astronaut will not be possible and those with hypertrophied capacities such as marathoners or weight lifters will suffer significant loss. However, the program described should return the crew to earth with adequate capacity of typical activity on earth including immediate ambulation and minimal recovery time and without permanent change. An understanding of the practical mechanics and biomechanics involved is essential to a solution of the problem.

Thornton, William↗
NASA NTRS1930

Simplified aerodynamic analysis of the cyclogiro rotating wing system

A simplified aerodynamic theory of the cyclogiro rotating wing is presented herein. In addition, examples have been calculated showing the effect on the rotor characteristics of varying the design parameters of the rotor. A performance prediction, on the basis of the theory here developed, is appended, showing the performance to be expected of a machine employing this system of sustentation. The aerodynamic principles of the cyclogiro are sound; hovering flight, vertical climb, and a reasonable forward speed may be obtained with a normal expenditure of power. Auto rotation in a gliding descent is available in the event of a power-plant failure.

Wheatley, John B↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
NASA NTRS1989

Human machine interaction via the transfer of power and information signals

Robot manipulators are designed to perform tasks which would otherwise be executed by a human operator. No manipulator can even approach the speed and accuracy with which humans execute these tasks. But manipulators have the capability to exceed human ability in one particular area: strength. Through any reasonable observation and experience, the human's ability to perform a variety of physical tasks is limited not by his intelligence, but by his physical strength. If, in the appropriate environment, we can more closely integrate the mechanical power of a machine with intellectually driven human hand under the supervisory control of the human's intellect, we will then have a system which is superior to a loosely-integrated combination of a human and his fully automated robot as in the present day robotic systems. We must therefore develop a fundamental approach to the problem of this extending human mechanical power in certain environments. Extenders will be a class of robots worn by humans to increase human mechanical ability, while the wearer's intellect remains the central intelligent control system for manipulating the extender. The human body, in physical contact with the extender, exchanges information signals and power with the extender. Commands are transferred to the extender via the contact forces between the wearer and the extender as opposed to use of joystick (master arm), push-button or key-board to execute such commands that were used in previous man amplifiers. Instead, the operator becomes an integral part of the extender while executing the task. In this unique configuration the mechanical power transfer between the human and extender occurs in addition to information signal transfer. When the wearer uses the extender to touch and manipulate an object, the extender transfers to the wearer's hand, in feedback fashion, a scaled-down value of the actual external load which the extender is manipulating. This natural feedback force on the wearer's hand allows him to feel the scaled-down value of the external forces in the manipulations. Extenders can be utilized to maneuver very heavy loads in factories, shipyards, airports, and construction sites. In some instances, for example, extenders can replace forklifts. The experimental results for a prototype extender are discussed.

Kazerooni, H.↗
DOE OSTI2023

Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site

The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.

machine learning↗
DOE OSTI2023

Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

The development and deployment of advanced reactors, such as the sodium-cooled fast reactor (SFR), relies on sophisticated modeling tools to ensure the safety of the design under various transients. The predictive capability of these advanced modeling tools requires validation to garner trust in supporting the licensing of the advanced reactors. For this reason, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13.In this study, we present and discuss the benchmarking efforts of the modern system code SAM on the FFTF LOFWOS Test #13. Further, the SAM baseline model was developed according to the benchmark specification, which included a detailed core model with reactivity feedback. Generally, good agreement was observed between the baseline results and benchmark measurements; however, discrepancies persisted, particularly in predicted fuel assembly coolant outlet temperatures. Utilizing the baseline model, uncertainty quantification (UQ) and sensitivity analysis (SA) were conducted with the assistance of various statistical learning and machine learning methods, including kernel density estimation, Gaussian processes, and Sobol indices. Following the baseline model prediction and UQ and SA results, we discuss the reasons for the simulation discrepancies and propose further improvements to the model. This benchmarking effort adheres to the best-estimate plus uncertainty approach and can serve as a valuable example for supporting risk-informed licensing of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗