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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cooperation in Transmission Expansion Planning: Enhancing Grid Reliability and Efficiency Under a Changing Climate

Electricity grids are challenged to maintain reliability during more intense and frequent extreme weather events due to climate change. This challenge is exacerbated by multi-sector electrification and power sector decarbonization through increased reliance on variable renewable energy, which necessitates the expansion of transmission infrastructure. However, transmission expansion planning is often complicated by intertwined planning authorities and jurisdictions, and allocation of large capital investment needs. These factors cause authorities to manage transmission investments individually (i.e., only/mostly intraregional planning), which can lead to suboptimal transmission networks. This study investigates the potential benefits of cooperative transmission expansion planning (i.e., both intraregional and interregional planning that optimizes transmission investments across the entire physical system). Using sectoral and economic optimization, and machine learning models, it analyzes the impact of different levels of cooperation among transmission planning regions within U.S. Western Interconnection in 2019 and 2059 via an iterative investment process. Furthermore, it examines the effects of future climate change on transmission cooperation by simulating historical heat waves from 2019 under conditions of 2059. The results indicate that cooperative transmission planning leads to lower wholesale electricity prices, decreased energy outages, and reduced greenhouse gas emissions. However, the advantages of collaboration diminish during widespread heat waves, despite remaining beneficial especially for regions like California Independent System Operator with substantial solar installations. The study underscores the importance of transmission cooperation in reducing costs and enhancing reliability, emphasizing the need for strategic investments in storage to address challenges posed by future extreme weather events with varying spatial scales.

Capacity Expansion Model↗
NASA NTRS1990

Pyramid image codes

All vision systems, both human and machine, transform the spatial image into a coded representation. Particular codes may be optimized for efficiency or to extract useful image features. Researchers explored image codes based on primary visual cortex in man and other primates. Understanding these codes will advance the art in image coding, autonomous vision, and computational human factors. In cortex, imagery is coded by features that vary in size, orientation, and position. Researchers have devised a mathematical model of this transformation, called the Hexagonal oriented Orthogonal quadrature Pyramid (HOP). In a pyramid code, features are segregated by size into layers, with fewer features in the layers devoted to large features. Pyramid schemes provide scale invariance, and are useful for coarse-to-fine searching and for progressive transmission of images. The HOP Pyramid is novel in three respects: (1) it uses a hexagonal pixel lattice, (2) it uses oriented features, and (3) it accurately models most of the prominent aspects of primary visual cortex. The transform uses seven basic features (kernels), which may be regarded as three oriented edges, three oriented bars, and one non-oriented blob. Application of these kernels to non-overlapping seven-pixel neighborhoods yields six oriented, high-pass pyramid layers, and one low-pass (blob) layer.

Watson, Andrew B.↗
NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗
NASA NTRS1999

Modifying the Human-Machine Interface Based on Quantitative Measurements of the Level of Awareness

This project got underway without funding approved during the summer of 1998. The initial project steps were to identify previously published work in the fields of error classification systems, physiological measurements of awareness, and related topics. This agenda was modified at the request of NASA Ames in August, 1998 to include supporting the new Cargo Air Association (CAA) evaluation of the Human Factors related to the ADS-B technology. Additional funding was promised to fully support both efforts. Work on library research ended in the late Fall, 1998 when the SJSU project directors were informed that NASA would not be adding to the initial funding of the research project as had been initially committed. However, NASA did provide additional funding for the CAA project activity. NASA elected to leave the research grant in place to provide a pathway for the CAA project funding to SJSU (San Jose State University) to support Dr. Freund's work on the CAA tasks. Dr. Knapp essentially terminated his involvement with the project at this time.

Freund, Louis E.↗
DOE OSTI2025

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Unconditionally stable concurrent procedures for transient finite-element analysis

A family of algorithms was outlined which would appear to be particularly well-suited for implementation in a parallel environment. This is due to the fact that for any partition of the mesh each subdomain in the partition can be processed over a time step simultaneously and independently of the rest. The method eliminates the need for assembling and factorizing large global arrays while retaining the unconditional stability properties of the algorithms used at the local level. To critically appraise the proposed methodology, two limiting cases were considered: element-by-element mesh partitions, and coarse mesh partitions. It was concluded that while the proposed methodology can be useful in sequential machines, it would appear to be promising as it bears on computation. It should also be emphasized that extensions of the method to nonlinear problems are possible.

Ortiz, Michael↗
NASA NTRS1991

Transparent process migration: Design alternatives and the Sprite implementation

The Sprite operating system allows executing processes to be moved between hosts at any time. We use this process migration mechanism to offload work onto idle machines, and also to evict migrated processes when idle workstations are reclaimed by their owners. Sprite's migration mechanism provides a high degree of transparency both for migrated processes and for users. Idle machines are identified, and eviction is invoked, automatically by daemon processes. On Sprite it takes up to a few hundred milliseconds on SPARCstation 1 workstations to perform a remote exec, while evictions typically occur in a few seconds. The pmake program uses remote invocation to invoke tasks concurrently. Compilations commonly obtain speedup factors in the range of three to six; they are limited primarily by contention for centralized resources such as file servers. CPU-bound tasks such as simulations can make more effective use of idle hosts, obtaining as much as eight-fold speedup over a period of hours. Process migration has been in regular service for over two years.

Douglis, Fred↗
NASA NTRS1993

Thermal conductance of pressed metallic contacts augmented with Indium foil or Apiezon-N (tm) grease at liquid helium temperatures

The thermal conductance of pressed contacts which have been augmented with Indium foil or Apiezon-N (tm) grease was measured over the temperature range of 1.6 to 6.0 K, with applied forces from 22 N to 670 N. The sample pairs were fabricated from OFHC copper, 6061-T6 aluminum, free-machining brass, and 304 stainless steel. Although the thermal conductance was found to increase with increasing applied contact force, the force dependence was less than in earlier work. The addition of Indium foil or Apiezon-NT grease between the contact surfaces resulted in an improvement over uncoated surfaces ranging from a factor of approximately 3 for stainless steel to an order of magnitude for copper contacts.

Salerno, Louis J.↗
NASA NTRS2002

Raytracing for Multi-Spacecraft Missions

The numerical simulation of in-situ instruments allows the instrument design to be optimized for particle throughput and allows for the iteration of the design parameters before fabrication. This effort leads to greater efficiency since the hardware itself does not have to be machined and fabricated to test each design change. More importantly for multi-spacecraft missions, the numerical raytracing allows the assessment of manufacturing tolerances so that a low relative accuracy can be obtained between the instruments. We will discuss our experience in optimizing and raytracing our charged particle instruments, obtaining the geometry factor from these simulations, and the comparison with the results of laboratory testing. This discussion with others will support the ISSI Scientific Report on Calibration Techniques for In-Situ Plasma Instrumentation.

Coffey, Victoria↗
NASA NTRS1999

Voice loops as coordination aids in space shuttle mission control

Voice loops, an auditory groupware technology, are essential coordination support tools for experienced practitioners in domains such as air traffic management, aircraft carrier operations and space shuttle mission control. They support synchronous communication on multiple channels among groups of people who are spatially distributed. In this paper, we suggest reasons for why the voice loop system is a successful medium for supporting coordination in space shuttle mission control based on over 130 hours of direct observation. Voice loops allow practitioners to listen in on relevant communications without disrupting their own activities or the activities of others. In addition, the voice loop system is structured around the mission control organization, and therefore directly supports the demands of the domain. By understanding how voice loops meet the particular demands of the mission control environment, insight can be gained for the design of groupware tools to support cooperative activity in other event-driven domains.

STS Shuttle Project↗
NASA NTRS2016

Tailoring a Human Reliability Analysis to Your Industry Needs

Companies at risk of accidents caused by human error that result in catastrophic consequences include: airline industry mishaps, medical malpractice, medication mistakes, aerospace failures, major oil spills, transportation mishaps, power production failures and manufacturing facility incidents. Human Reliability Assessment (HRA) is used to analyze the inherent risk of human behavior or actions introducing errors into the operation of a system or process. These assessments can be used to identify where errors are most likely to arise and the potential risks involved if they do occur. Using the basic concepts of HRA, an evolving group of methodologies are used to meet various industry needs. Determining which methodology or combination of techniques will provide a quality human reliability assessment is a key element to developing effective strategies for understanding and dealing with risks caused by human errors. There are a number of concerns and difficulties in "tailoring" a Human Reliability Assessment (HRA) for different industries. Although a variety of HRA methodologies are available to analyze human error events, determining the most appropriate tools to provide the most useful results can depend on industry specific cultures and requirements. Methodology selection may be based on a variety of factors that include: 1) how people act and react in different industries, 2) expectations based on industry standards, 3) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 4) type and availability of data, 5) how the industry views risk & reliability, and 6) types of emergencies, contingencies and routine tasks. Other considerations for methodology selection should be based on what information is needed from the assessment. If the principal concern is determination of the primary risk factors contributing to the potential human error, a more detailed analysis method may be employed versus a requirement to provide a numerical value as part of a probabilistic risk assessment. Industries involved with humans operating large equipment or transport systems (ex. railroads or airlines) would have more need to address the man machine interface than medical workers administering medications. Human error occurs in every industry; in most cases the consequences are relatively benign and occasionally beneficial. In cases where the results can have disastrous consequences, the use of Human Reliability techniques to identify and classify the risk of human errors allows a company more opportunities to mitigate or eliminate these types of risks and prevent costly tragedies.

DeMott, D. L.↗
NASA NTRS2014

Human Factor Challenges of Remotely Piloted Aircraft

The control stations of many unmanned systems have been characterized by inadequate human-system interfaces. Some of the interface problems may have been prevented had an existing regulation or cockpit design principle been applied. In other cases, the design problems may indicate a lack of suitable guidance material. The human factors of unmanned operations will be reviewed, and a NASA program to develop human-factor guidelines for control stations will be described. To be effective, guidelines must be relevant to a wide range of systems, must not be overly prescriptive, and must not impose premature standardization on evolving technologies. Several types of guidelines are described. These relate to required capabilities, information requirements, properties of the human machine interface, and general cognitive engineering principles.

Hobbs, Alan↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
DOE OSTI2025

Deep Learning Advances Arctic River Water Temperature Predictions

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
NASA NTRS2014

Addressing Challenges to the Design & Test of Operational Lighting Environments for the International Space Station

In our day to day lives, the availability of light, with which to see our environment, is often taken for granted. The designers of land based lighting systems use sunlight and artificial light as their toolset. The availability of power, quantity of light sources, and variety of design options are often unlimited. The accessibility of most land based lighting systems makes it easy for the architect and engineer to verify and validate their design ideas. Failures with an implementation, while sometimes costly, can easily be addressed by renovation. Consider now, an architectural facility orbiting in space, 260 miles above the surface of the earth. This human rated architectural facility, the International Space Station (ISS) must maintain operations every day, including life support and appropriate human comforts without fail. The facility must also handle logistics of regular shipments of cargo, including new passengers. The ISS requires accommodations necessary for human control of machine systems. Additionally, the ISS is a research facility and supports investigations performed inside and outside its livable volume. Finally, the facility must support remote operations and observations by ground controllers. All of these architectural needs require a functional, safe, and even an aesthetic lighting environment. At Johnson Space Center, our Habitability and Human Factors team assists our diverse customers with their lighting environment challenges, via physical test and computer based analysis. Because of the complexity of ISS operational environment, our team has learned and developed processes that help ISS operate safely. Because of the dynamic exterior lighting environment, uses computational modeling to predict the lighting environment. The ISS' orbit exposes it to a sunrise every 90 minutes, causing work surfaces to quickly change from direct sunlight to earthshine to total darkness. Proper planning of vehicle approaches, robotics operations, and crewed Extra Vehicular Activities are mandatory to ensure safety to the crew and all others involved. Innovation in testing techniques is important as well. The advent of Solid State Lighting technology and the lack of stable national and international standards for its implementation pose new challenges on how to design, test and verify individual light fixtures and the environment that uses them. The ISS will soon be replacing its internal fluorescent lighting system to a solid state LED system. The Solid State Lighting Assembly will be used not only for general lighting, but also as a medical countermeasure to control the circadian rhythm of the crew. The new light source has performance criteria very specific to its spectral fingerprint, creating new challenges that were originally not as significant during the original design of the ISS. This presentation will showcase findings and toolsets our team is using to assist in the planning of tasks, and design of operational lighting environments on the International Space Station.

Clark, Toni A.↗
DOE OSTI2026

Transforming jet flavour tagging at ATLAS

Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton–proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c-jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b-jet tagging efficiency, compared to the previous algorithm. GN2 provides substantial benefits for physics analyses involving heavy-flavour jets, such as measurements of Higgs boson pair production and the couplings of bottom and charm quarks to the Higgs boson, and demonstrates the impact of advanced machine learning methods in experimental particle physics.

Characterization and analytical techniques↗
NASA NTRS1988

A simulation system for Space Station extravehicular activity

A simulation program has been developed which addresses the human factors required to provide a crewmember with productive information during Space Station EVA. The operation of the voice recognition and control system and helmet-mounted projection display in the Space Station Extravehicular Mobility Unit (EMU) is reviewed. The features of the simulation program are discussed, including logic flow, information types, and the man-machine interface techniques used in the simulation program, voice recognizer, and helmet-mounted display.

Marmolejo, Jose A.↗