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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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Queuing strategy for communications along shared data bus minimizes number of data lines while always assuring user of highest priority given access to bus. New system handles up to 32 user demands on 17 data lines that previously serviced only 17 demands.
Vertical-Bloch-line memory is developmental very-large-scale integrated-circuit block-access magnetic memory. Stores data in form of localized pairs of twists (VBL pairs) in magnetic field at edge of ferromagnetic domain in each stripe. Presence or absence of VBL pair at bit position denotes one or zero, respectively. Offers advantages of resistance to ionizing radiation, potential areal storage density approximately less than 1 Gb/cm squared, data rates approximately less than 1 Gb/s, and average access times of order of milliseconds. Furthermore, mass, volume, and demand for power less than other magnetic and electronic memories.
Space Services and Logistics, Inc. represents the complete engineering design of a technically and financially viable commercial space company. The final proposal offers an economically sound program of space vehicles and systems designed to substantially affect a variety of space markets and produce a vertically integrated structure within the next 20 years. Throughout this design process, particular stress has been placed on attaining the highest possible levels of safety and reliability. The final program financial design requires a considerable initial outlay, but promises a relatively quick return on invested capital, culminating in large annual profits by the end of the 20-year scope of the cost outlook. The overall design has been extensively researched and was primarily driven by the present and near-term projected market demands for services uniquely or competitively offered only by space-oriented operations. Heretofore, available capabilities, rather than these market demands, have determined the degree and type of commercial market access. Removing this limitation through extensive use of modularity and reconfigurability allows the company to gear itself to the market, while still remaining extremely competitive with existing systems. The markets identified as lucrative, and that have governed much of the design requirements, are: low-cost launch services to LEO over a wide range of payload masses and inclinations; upper stage payload delivery from LEO to GEO; manned space operations and human transport to and from orbit; EVA assembly and maintenance of large space structures; satellite servicing and repair by both humans and telerobotic operations; a line of customized satellites designed for extended life and capable of reconfiguration or technology upgrade on orbit; small-scale microgravity experimentation and manufacturing supported by spacecraft retrieval capabilities for experimental specimens and manufactured goods; and a full-range of payload integration, testing, design, and support services before launch and once in orbit.
Thermal management is critical to space exploration efforts. In particular, efficient transfer and control of heat flow is essential when operating high energy sources such as nuclear reactors. Thermal energy must be transferred to various energy conversion devices, and to radiators for safe and efficient rejection of excess thermal energy. Applications for space power demand exceptionally long periods of time with equipment that is accessible for limited maintenance only. Equally critical is the hostile and alien environment which includes high radiation from the reactor and from space (galactic) radiation. In space or lunar applications high vacuum is an issue, while in Martian operations the systems will encounter a CO2 atmosphere. The effect of contact at high temperature with local soil (regolith) in surface operations on the moon or other terrestrial bodies (Mars, asteroids) must be considered.
The air transportation system is a significant driver of the U.S. economy, providing safe, affordable, and rapid transportation. During the past three decades airspace and airport capacity has not grown in step with demand for air transportation; the failure to increase capacity at the same rate as the growth in demand results in unreliable service and systemic delay. This report describes the results of an analysis of airline strategic decision-making that affects geographic access, economic access, and airline finances, extending the analysis of these factors using historic data (from Part 1 of the report). The Airline Schedule Optimization Model (ASOM) was used to evaluate how exogenous factors (passenger demand, airline operating costs, and airport capacity limits) affect geographic access (markets-served, scheduled flights, aircraft size), economic access (airfares), airline finances (profit), and air transportation efficiency (aircraft size). This analysis captures the impact of the implementation of airport capacity limits, as well as the effect of increased hedged fuel prices, which serve as a proxy for increased costs per flight that might occur if auctions or congestion pricing are imposed; also incorporated are demand elasticity curves based on historical data that provide information about how passenger demand is affected by airfare changes.
In complex oxides, charge carriers often couple strongly with lattice vibrations to form polarons–entangled electron–phonon quasiparticles whose transport properties remain difficult to characterize. Experimental access to intrinsic polaronic transport requires ultraclean samples, while theoretical description demands methods beyond low-order perturbation theory. Here, we show a predictive theory–experiment workflow to study polaron transport in complex oxides. Focusing on a prototypical polaronic oxide, anatase TiO 2 , we combine growth of high-quality oxygen-vacancy-doped films using hybrid molecular beam epitaxy with a first-principles electron–phonon diagrammatic Monte-Carlo (FEP-DMC) framework recently developed for accurate polaron predictions. Our films exhibit record-high electron mobility for anatase TiO 2 , in excellent agreement with FEP-DMC calculations conducted prior to experiment, which predict a room-temperature mobility of 45 ± 15 cm −2 V −1 s −1 and a mobility-temperature scaling of μ ∝ T −1.9 ± 0.077 . Microscopic analysis using scanning transmission electron microscopy and x-ray photoelectron spectroscopy reveals the role of oxygen vacancies in modulating transport at lower temperatures. FEP-DMC further provides quantitative insight into polaron formation energy, phonon cloud distribution, lattice distortion around the polaron, and the polaronic contribution to mobility. Together, these results provide a deeper microscopic understanding of large-polaron transport in a complex oxide and provide the blueprint to characterize other polaronic materials.
Abstract The complexity and sheer volume of information — encompassing documents, papers, data, and other resources — from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.To tackle this issue, a Retrieval Augmented Generation (RAG)-based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain [1], which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail. The application can be accessed athttps://rags4eic-ai4eic.streamlit.app.[A tagged version of the source code can be found inhttps://github.com/ai4eic/EIC-RAG-Project/releases/tag/AI4EIC2023_PROCEEDING.]
Large platforms have been proposed for supporting multipurpose communication payloads to exploit economy of scale, reduce congestion in the geostationary orbit, provide interconnectivity between diverse earth stations, and obtain significant frequency reuse with large multibeam antennas. This paper addresses a specific system design, starting with traffic projections in the next two decades and discussing tradeoffs and design approaches for major components including: antennas, transponders, and switches. Other issues explored are selection of frequency bands, modulation, multiple access, switching methods, and techniques for servicing areas with nonuniform traffic demands. Three-major services are considered: a high-volume trunking system, a direct-to-user system, and a broadcast system for video distribution and similar functions. Estimates of payload weight and d.c. power requirements are presented. Other subjects treated are: considerations of equipment layout for servicing by an orbit transfer vehicle, mechanical stability requirements for the large antennas, and reliability aspects of the large number of transponders employed.
Health care services delivered from a distance, known collectively as telemedicine, are being increasingly demonstrated on various transmission media. Telemedicine activities have included diagnosis by a doctor at a remote location, emergency and disaster medical assistance, medical education, and medical informatics. The ability of communications satellites to offer communication channels and bandwidth on demand, connectivity to mobile, remote and under served regions, and global access will afford them a critical role for telemedicine applications within the National and Global Information Infrastructure (NII/GII). The importance that communications satellites will have in telemedicine applications within the NII/GII the differences in requirements for NII vs. GII, the major issues such as interoperability, confidentiality, quality, availability, and costs, and preliminary conclusions for future usability based on the review of several recent trails at national and global levels are presented.
Next-generation launch vehicles demand several technological improvements to achieve lower cost and more reliable access to space. One technology area whose performance gains may far exceed others is densified propellants. The ideal rocket engine propellant is characterized by high specific impulse, high density, and low vapor pressure. A propellant combination of liquid hydrogen and liquid oxygen (LH2/LOX) is one of the highest performance propellants, but LH2 stored at standard conditions has a relatively low density and high vapor pressure. Propellant densification can significantly improve this propellant's properties relative to vehicle design and engine performance. Vehicle performance calculations based on an average of existing launch vehicles indicate that densified propellants may allow an increase in payload mass of up to 5 percent. Since the NASA Lewis Research Center became involved with the National Aerospace Plane program in the 1980's, it has been leading the way in making densified propellants a viable fuel for next-generation launch vehicles. Lewis researchers have been working to provide a method and critical data for continuous production of densified hydrogen and oxygen.
To dramatically increase the adaptability, performance, and safety of processes in support of the Defense Waste Processing Facility (DWPF), Savannah River National Lab (SRNL) plans to perform chemical and radiological compatibility testing on a wide variety of 3D printed materials of interest. The 3D printing process provides numerous strategic operational benefits such as rapid prototyping of complex designs and geometry specific to the needs of the nuclear waste disposition process, as well as on-demand rapid prototyping and iteration with materials that aren’t as accessible through traditional manufacturing methods. Reaction chemistry in simulated waste batches can be matched closely to its radioactive counterpart, but glass reactor vessels have limitations. Vessel geometry can play a big factor in mixing transport limitations, process chemistry, and degradation reaction kinetics. In addition, additive manufacturing allows for much more detailed vessel design than traditional alternatives. Waste processing techniques in DWPF also encounter extreme chemical environments including high pH, strong acids, abrasive slurries, and significant irradiation. To meet these challenges, a matrix of various polymer, ceramic, and metal additive manufacturing materials have been exposed to a suite of chemical environments of interest as well as radioactive dose (such as gamma radiation from 60 Co) to properly test their durability under these conditions. Mass change has been monitored over a period of up to a week in these conditions, as well as added characterization for surface modification through Scanning Electron Microscopy/Electron Dispersive X-ray analysis (SEM/EDX). Further chemical characterization has been monitored through Fourier-Transform InfraRed Spectroscopy (FTIR), with planned investigation via thermal and tensile strength degradation. While the direct product of this research is identification of material(s) that can withstand specific hazardous environments encountered by the mercury water wash tank in DWPF process simulation experiments, the reference base of materials will be used for many other nuclear processes in the pursuit of rapidly developed, cost-efficient, and highly specific devices for environmental remediation and much more.
The Mobility Energy Productivity (MEP) metric quantifies the quality of mobility at a given location and evaluates how changes in the transportation system impact mobility over time, such as through infrastructure investments. This study demonstrates the integration of the MEP metric into the Delaware Department of Transportation's (DelDOT's) transportation planning process by utilizing data from its statewide travel demand model. Specifically, the study assesses MEP for the 2020 baseline conditions and three alternative scenarios - 2030, Churchman, and Old Orchard - across multiple travel modes, including driving, walking, biking, and transit. The findings highlight that mobility and accessibility in Delaware are primarily supported by the driving mode, while transit services remain relatively limited, often ranking below biking and walking in many areas. In the 2030 scenario, where network operations and opportunities expand as projected, overall statewide accessibility declines, although Kent and Sussex counties experience improvements. The results from the Churchman and Old Orchard scenarios indicate that local network enhancements can positively influence accessibility, though primarily at a localized level, demonstrating MEP's capability to capture regional accessibility changes. Further, the National Renewable Energy Laboratory team successfully transferred MEP operational knowledge to the DelDOT team through dockerization, enabling DelDOT to independently run MEP for various scenarios of interest. Integrating MEP into DelDOT's planning framework supports future project evaluations and decision-making by incorporating access to opportunities as a key dimension of transportation system assessment.
The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud‐computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high‐performance computing environments, supporting multi‐node systems required for near real‐time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on‐demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on‐demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre‐compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF‐EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher‐resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short‐term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity‐building applications that extend numerical weather prediction to developing countries are intended to provide near real‐time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather‐related hazards, or impacts that local weather events may have during the recovery phase.
Electric propulsion and autonomy are technology frontiers that offer tremendous potential to achieve low operating costs for small-aircraft. Such technologies enable simple and safe to operate vehicles that could dramatically improve regional transportation accessibility and speed through point-to-point operations. This analysis develops an understanding of the potential traffic volume and National Airspace System (NAS) capacity for small on-demand aircraft operations. Future demand projections use the Transportation Systems Analysis Model (TSAM), a tool suite developed by NASA and the Transportation Laboratory of Virginia Polytechnic Institute. Demand projections from TSAM contain the mode of travel, number of trips and geographic distribution of trips. For this study, the mode of travel can be commercial aircraft, automobile and on-demand aircraft. NASA's Airspace Concept Evaluation System (ACES) is used to assess NAS impact. This simulation takes a schedule that includes all flights: commercial passenger and cargo; conventional General Aviation and on-demand small aircraft, and operates them in the simulated NAS. The results of this analysis projects very large trip numbers for an on-demand air transportation system competitive with automobiles in cost per passenger mile. The significance is this type of air transportation can enhance mobility for communities that currently lack access to commercial air transportation. Another significant finding is that the large numbers of operations can have an impact on the current NAS infrastructure used by commercial airlines and cargo operators, even if on-demand traffic does not use the 28 airports in the Continental U.S. designated as large hubs by the FAA. Some smaller airports will experience greater demand than their current capacity allows and will require upgrading. In addition, in future years as demand grows and vehicle performance improves other non-conventional facilities such as short runways incorporated into shopping mall or transportation hub parking areas could provide additional capacity and convenience.
A declining spring snowpack is expected to have widespread effects on montane and subalpine forests in western North America and across the globe. The way that tree water demands respond to this change will have important impacts on forest health and downstream water subsidies. Here, we present data from a network of sap velocity sensors and xylem water isotope measurements from three common tree species (Picea engelmannii, Abies lasiocarpa and Populus tremuloides) across a hillslope transect in a subalpine watershed in the Upper Colorado River basin. We use these data to compare tree- and stand-level responses to the historically high spring snowpack but low summer rainfall of 2019 against the low spring snowpack but high summer rainfall amounts of 2021 and 2022. From the sap velocity data, we found that only 40 % of the trees showed an increase in cumulative transpiration in response to the large snowpack year (2019), illustrating the absence of a common response to interannual spring snowpack variability. The trees that increased water use during the year with the large spring snowpack were all found in dense canopy stands – irrespective of species – while trees in open-canopy stands were more reliant on summer rains and, thus, more active during the years with modest snow and higher summer rain amounts. Using the sap velocity data along with supporting measurements of soil moisture and snow depth, we propose three mechanisms that lead to stand density modulating the tree-level response to changing seasonality of precipitation: Topographically mediated convergence zones have consistent access to recharge from snowmelt which supports denser stands with high water demands that are more reliant and sensitive to changing snow. Interception of summer rain in dense stands reduces the throughfall of summer rain to surface soils, limiting the sensitivity of the dense stands to changes in summer rain. Shading in dense stands allows the snowpack to persist deeper into the growing season, providing high local reliance on snow during the fore-summer (early-summer) drought period. Combining data generated from natural gradients in stand density, like this experiment, with results from controlled forest-thinning experiments can be used to develop a better understanding of the responses of forested ecosystems to futures with reduced spring snowpack.
With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to establish workflow for and demonstrate a unique set of web application tools for linking NASA GRC's Integrated Computational Materials Engineering (ICME) Granta MI database schema and NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset are presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.
With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to develop a set of Python functions that exchange information between NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset and its Integrated Computational Materials Engineering (ICME), Granta MI® database schema is presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.
The state of data on industrial energy use has co-evolved over several decades with the demands of industrial energy analysis. The most recent development - analysis in support of decarbonizing the industrial sector - has changed the characteristics of industrial data that are useful for analysts and model developers. Although data and its collection processes may be cast from a conventional viewpoint as objective and free from the influence of social dynamics, this provides an incomplete picture of not only the processes by which information is generated, but also the limitations and opportunities of data to be useful for analysis. The foundational industry energy data set (FIED) is a result of the confluence of trends in open data and the demand for higher resolution industrial energy analysis. The general approach to compiling the FIED involves accessing, filtering, and formatting data published by federal organizations on the Internet for public use. Unlike most industrial energy datasets, which are published by the U.S. Energy Information Administration (EIA), the FIED relies on core datasets from the U.S. Environmental Protection Agency (EPA). The FIED addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization - including estimates of energy use, greenhouse gas emissions, and design capacities - for facilities that are identified by latitude and longitude. This enables local-level analysis of existing combustion equipment, as well as regional comparisons with traditional industrial energy data estimates. The report summarizes the general logic behind compiling the FIED. The FIED itself and its Python code are available from OpenEI and GitHub, respectively.