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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Data-Link and Surface Map Traffic Intent Displays for NextGen 4DT and Equivalent Visual Surface Operations

By 2025, U.S. air traffic is predicted to increase 3-fold and may strain the current air traffic management system, which may not be able to accommodate this growth. In response to this challenge, a consortium of industry, academia and government agencies have proposed a revolutionary new concept for U.S. aviation operations, termed the Next Generation Air Transportation System or "NextGen". Many key capabilities are being identified to enable NextGen, including the concept of "net-centric" operations whereby each aircraft and air services provider shares information to allow real-time adaptability to ever-changing factors such as weather, traffic, flight trajectories, and security. Data-link is likely to be the primary source of communication in NextGen. Because NextGen represents a radically different approach to air traffic management and requires a dramatic shift in the tasks, roles, and responsibilities for the flight deck, there are numerous research issues and challenges that must be overcome to ensure a safe, sustainable air transportation system. Flight deck display and crew-vehicle interaction concepts are being developed that proactively investigate and overcome potential technology and safety barriers that might otherwise constrain the full realization of NextGen.

Shelton, Kevin J.↗
NASA NTRS1990

Multiple Access Trade Study

The Personal Access Satellite System (PASS) strawman design uses a hybrid Time Division Multiple Access (TDMA)/Frequency Division Multiple Access (FDMA) implementation. TDMA is used for the forward direction (from Suppliers to Users), and FDMA for the return direction (from Users to Suppliers). An alternative architecture is proposed that will require minimal real time coordination and yet provide a fast access method by using random access Code Division Multiple Access (CDMA). The CDMA system issues are addressed such as connecting suppliers and users, both of whom may be located anywhere in the CONUS, when the user terminals are constrained in size and weight; and providing efficient traffic routing under highly variable traffic requirements. It is assumed that bandwidth efficiency is not of paramount importance. CDMA or Spread Spectrum Multiple Access (SSMA) communication is a method in which a group of carriers operate at the same nominal center frequency but are separable from each other by the low cross correlation of the spreading codes used. Interference and multipath rejection capability, ease of selective addressing and message screening, low density power spectra for signal hiding and security, and high resolution ranging are among the benefits of spread spectrum communications.

Motamedi, Masoud↗
DOE OSTI2020

The future of the NPT: REVCON and beyond

The Treaty on the Nonproliferation of Nuclear Weapons (NPT), with its quinquennial review conferences (RevCons), has become the foundation of the international nonproliferation regime over the course of its 50-year history. With more states parties than any other arms control agreement (currently 191), the Treaty provides a legally binding framework for all states parties, constraining proliferation of nuclear weapons and committing the five recognized states who possess nuclear weapons to work toward disarmament. The NPT has enjoyed wide success in constraining the spread of nuclear weapons. Only one state party has left the agreement and gone on to acquire these weapons, and the overall track record of compliance with IAEA safeguards has been robust. However, for all its effectiveness in the area of nonproliferation, its achievements on the disarmament front have been relatively unimpressive. While the NWS – most notably the United States and the Soviet Union/Russia – have decreased the overall size of their nuclear arsenals significantly since 1970, these arsenals have remained an important feature of NWS security policies. The limited progress towards disarmament has created friction between the NNWS and NWS and has been the source of mounting tension within the NPT regime. The 2020 RevCon, the 50th anniversary of the NPT and 25th anniversary of its indefinite extension, faces a range of contentious challenges, old and new, which call into question the Treaty’s future viability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

In-Cylinder Diagnostics to Overcome Efficiency Barriers in Natural Gas Engines (LDRD 200166)

The high-level objective of this project is to solve national-security problems associated with petroleum use, cost, and environmental impacts by enabling more efficient use of natural-gas-fueled internal combustion engines. An improved science-base on end-gas autoignition, or “knock,” is required to support engineering of more efficient engine designs through predictive modeling. An existing optical diesel engine facility is retrofitted for natural gas fueling with laser-spark-ignition combustion to provide in-cylinder imaging and pressure data under knocking combustion. Zero-dimensional chemical-kinetic modeling of autoignition, adiabatically constrained by the measured cylinder pressure, isolates the role of autoignition chemistry. OH* chemiluminescence imaging reveals six different categories of knock onset that depend on proximity to engine surfaces and the in-cylinder deflagration. Modeling results show excellent prediction regardless of the knock category, thereby validating state-of-the-art kinetic mechanisms. The results also provide guidance for future work to build a science base on the factors that affect the deflagration rate.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2022

GRO 95577 (CR1) as a mineralogical analogue for asteroid (101955) Bennu

Orbital spectra of asteroid (101955) Bennu collected by NASA's Origins, Spectral Interpretation, Resource Identification, Security–Regolith Explorer (OSIRIS–REx) spacecraft have identified ungrouped C, CI, and CM meteorites having petrologic types 1, 1/2, and 2 as the best mineralogical analogues to Bennu to date. Here we present spectral evidence that Grosvenor Mountains (GRO) 95577, a CR1, is a better analogue for Bennu's bulk surface mineralogy. CR-like parent bodies are targets of interest because they contain some of the most pristine materials from the solar nebula and can contain substantial amounts of H2O and OH− in addition to exotic organics. Unfortunately, terrestrial weathering makes constraining their indigenous mineralogy and organics challenging. Analysis of samples retrieved directly from an asteroid would help us disentangle the effects of terrestrial weathering and asteroidal aqueous alteration and hence whether some of the exotic organics and large populations of presolar grains were affected by terrestrial processes in meteorites. If Bennu is comprised of CR1(−like) material, in whole or in part, the OSIRIS–REx returned sample represents a tremendous opportunity to explore in depth what is currently a unique material among carbonaceous chondrites.

Asteroids↗
DOE OSTI2023

A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments

To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.

Sundararajan, Aditya↗
DOE OSTI2024

System and method for geometrically constrained axisymmetric adjustable kinematic mount

The present disclosure relates to an adjustable mount apparatus. The apparatus may have a material support having a plurality of support legs, with each one of the support legs having a distal portion. A base assembly is used which has a plurality of laterally extending leg portions. A plurality of adjustably positionable capture subassemblies are slidably disposed within the laterally extending leg portions for releasably coupling to the distal portions of the support legs to hold the material support in a stable manner on the base assembly. A plurality of moveable elements, each being independently associated with one of the capture subassemblies, enable highly controlled linear movement of the capture subassemblies. Each capture subassembly engages with the distal portion of one of the support legs to exert a clamping force thereon, to affix the material support securely to the base assembly.

Guzorek, Steven J.↗
DOE OSTI2024

Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach

Recent advancements in privacy-preserving artificial intelligence (AI) have paved the way for enhanced privacy in computational processes. A standing challenge, however, is the robust privacy preservation in AI algorithms, especially when integrated into edge devices and Internet-of-Thing (IoT) infrastructures. Most prevailing solutions have adopted traditional encryption methods which, though secure, often introduce significant overhead and potential dips in accuracy. In this study, we put forth an innovative approach, utilizing the CKKS encryption scheme, aiming to harmoniously balance computational efficiency with stringent data privacy. By harnessing the capabilities of Full Homomorphic Encryption (FHE) under the CKKS scheme, we ensure the preservation of privacy, successfully curbing the inherent noise traditionally linked with accuracy reductions in similar encryption-oriented solutions. Through comprehensive experiments, our approach showcased its potential as a strong contender for privacy preservation, demonstrating commendable performance across all tests, affirming that FHE is indeed viable for devices with constrained computational power and energy resources.

Khan, Muhammad Jahanzeb↗
DOE OSTI2024

A semi-supervised learning method to produce explainable radioisotope proportion estimates for NaI-based synthetic and measured gamma spectra

Quantifying the radioactive sources present in gamma spectra is an ever-present and growing national security mission and a time-consuming process for human analysts. While machine learning models exist that are trained to estimate radioisotope proportions in gamma spectra, few address the eventual need to provide explanatory outputs beyond the estimation task. In this work, we develop two machine learning models for a NaI detector measurements: one to perform the estimation task, and the other to characterize the first model’s ability to provide reasonable estimates. To ensure the first model exhibits a behavior that can be characterized by the second model, the first model is trained using a custom, semi-supervised loss function which constrains proportion estimates to be explainable in terms of a spectral reconstruction. The second auxiliary model is an out-of-distribution detection function (a type of meta-model) leveraging the proportion estimates of the first model to identify when a spectrum is sufficiently unique from the training domain and thus is out-of-scope for the model. In demonstrating the efficacy of this approach, we encourage the use of meta-models to better explain ML outputs used in radiation detection and increase trust.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

Utilization of Ancillary Data Sets for Conceptual SMAP Mission Algorithm Development and Product Generation

The planned Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond [1]. Scheduled to launch late in 2014, the proposed SMAP mission would provide high resolution and frequent revisit global mapping of soil moisture and freeze/thaw state, utilizing enhanced Radio Frequency Interference (RFI) mitigation approaches to collect new measurements of the hydrological condition of the Earth's surface. The SMAP instrument design incorporates an L-band radar (3 km) and an L band radiometer (40 km) sharing a single 6-meter rotating mesh antenna to provide measurements of soil moisture and landscape freeze/thaw state [2]. These observations would (1) improve our understanding of linkages between the Earth's water, energy, and carbon cycles, (2) benefit many application areas including numerical weather and climate prediction, flood and drought monitoring, agricultural productivity, human health, and national security, (3) help to address priority questions on climate change, and (4) potentially provide continuity with brightness temperature and soil moisture measurements from ESA's SMOS (Soil Moisture Ocean Salinity) and NASA's Aquarius missions. In the planned SMAP mission prelaunch time frame, baseline algorithms are being developed for generating (1) soil moisture products both from radiometer measurements on a 36 km grid and from combined radar/radiometer measurements on a 9 km grid, and (2) freeze/thaw products from radar measurements on a 3 km grid. These retrieval algorithms need a variety of global ancillary data, both static and dynamic, to run the retrieval models, constrain the retrievals, and provide flags for indicating retrieval quality. The choice of which ancillary dataset to use for a particular SMAP product would be based on a number of factors, including its availability and ease of use, its inherent error and resulting impact on the overall soil moisture or freeze/thaw retrieval accuracy, and its compatibility with similar choices made by the SMOS mission. All decisions regarding SMAP ancillary data sources would be fully documented by the SMAP Project and made available to the user community.

freeze/thaw↗
DOE OSTI2016

Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristics-based scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidth-constrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Opportunities for Joint Water–Energy Management: Sensitivity of the 2010 Western U.S. Electricity Grid Operations to Climate Oscillations

The 2016 SECURE Water Act report’s natural water availability benchmark, combined with the 2010 level of water demand from an integrated assessment model, is used as input to drive a large-scale water management model. The regulated flow at hydropower plants and thermoelectric plants in the Western U.S. electricity grid (WECC) is translated into potential hydropower generation and generation capacity constraints. The impact on reliability (unserved energy, reserve margin) and cost (production cost, carbon emissions) of water constraints on 2010-level WECC power system operations is assessed using an electricity production cost model (PCM). Use of the PCM reveals the changes in generation dispatch that reflect the inter-regional interdependencies in water-constrained generation and the ability to use other generation resources to meet all electricity loads in the WECC. August grid operational benchmarks show a range of sensitivity in production cost (-8 to +11%) and carbon emissions (-7 to 11%). The reference reserve margin threshold of 15% above peak load is maintained in the scenarios analyzed, but in 5 out of 55 years unserved energy is observed when normal operations are maintained. There is 1 chance in 10 that a year will demonstrate unserved energy in August, which defines the system’s historical performance threshold to support impact, vulnerability, and adaptation analysis. For seasonal and longer term planning, i.e., multi-year drought, we demonstrate how the Water Scarcity Grid Impact Factor and climate oscillations (ENSO, PDO) can be used to plan for joint water-electricity management to maintain grid reliability.

adaptation↗
NASA NTRS2013

OSIRIS-REx Will Return a Sample of Asteroid 1999 RQ36 for Astrochemistry

Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) is the third mission in NASA's New Frontiers program. OSIRIS-REx launches in 2016 and will return the first pristine samples of carbonaceous material from the surface of a primitive asteroid in 2023. The target, (101955) 1999 RQ36, is 1/2 km diameter roughly spherical Apollo near-Earth asteroid. It is expected to be carbonaceous, similar to CI or CM carbonaceous chondrites. This organic-rich remnant from the early Solar System is also among the most potentially hazardous asteroids known with a 1 in 2500 chance of impacting the Earth in the late 22nd century. OSIRIS-REx will measure the Yarkovsky effect to better constrain future orbit and impact potential of this and other asteroids. OSIRIS-REx will image, map, and spectrally characterize RQ36 from 0.4-50 icron. The returned sample of regolith will be >60g (and up to 2kg) and thus the largest extraterrestrial sample returned from space since Apollo 17. Unlike meteorites, the sample will come from a known, well-characterized source and will be collected and transported to Earth pristine from terrestrial contamination. It will be available for study by the global astrochemistry community to address fundamental questions about the origin and evolution of the solar system and the life it harbors.

organic-rich↗
DOE OSTI2026

Asi Nuclear Energy Sensors Data Portal Chatbot And Data Structuring Tool

The Idaho National Laboratory (INL) is advancing the development of an AI-powered chatbot and data structuring tool specifically designed to accelerate data mining processes for sensor-related information and seamlessly integrate the results into the ASI Sensors Data Portal (https://nes.energy.gov/). By doing so, the software aims to enhance the accessibility, usability, and organization of sensor data for nuclear energy applications. The software initial phase focuses on retrieving comprehensive datasets, prioritizing the past five years of publicly available information from the Office of Scientific and Technical Information (OSTI). These datasets will be meticulously processed to ensure compatibility, employing cleaning and preprocessing steps to eliminate irrelevant, incomplete, or corrupted information, thus establishing a robust foundation for subsequent AI use. The data will serve as the backbone for training an AI model and chatbot, which will act as an interactive tool enabling users to ask complex, context-specific questions and receive accurate, validated answers derived from constrained literature. In parallel, the project incorporates a data structuring process supported by AI to organize sensor information from multiple sources into a standardized format. This structured data will include detailed sensor specifications, such as measurement range, applications, accuracy, and operating conditions, generated and documented with AI. These specifications will be systematically integrated into the sensor portal. To maintain the highest levels of accuracy and relevance, all AI-generated outputs will be reviewed and validated by subject matter experts (SMEs), with additional fields or parameters added as needed. Future stages of the project aim to expand the dataset beyond OSTI to include other sources and potentially incorporate unclassified controlled information (UCI) with restricted access protocols to address security and confidentiality requirements.

Mapes, NormanJ. [Idaho National Laboratory (INL), ↗
NASA NTRS2022

Towards Effective Drought Monitoring in the Middle East and North Africa (Mena) Region: Implications From Assimilating Leaf Area Index and Soil Moisture Into the Noah-Mp Land Surface Model for Morocco

The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

Wanshu Nie↗
DOE OSTI2014

Containerless Methane Storage

Technological advances over the last decade in shale gas production have led to a dramatic increase in domestic natural gas (NG) reserves and have resulted in a dramatic corresponding decrease in domestic NG prices. Widespread adoption of NG as a transportation fuel would bring a number of benefits. Imported petroleum would be replaced by domestically produced NG, increasing energy security and reducing the U.S. trade deficit. Greenhouse gas emissions would be reduced because of the low carbon content of NG compared to gasoline. The cost of transportation would be reduced, reducing costs and spurring growth throughout the economy. SRI International (SRI) is attempting to enable adoption of NG for light-duty vehicles through the Containerless Methane Storage technology. It is an Adsorbed Natural Gas (ANG) technology in which the sorbent is also the pressure vessel. This technology promises to provide a low-pressure, lightweight, conformable storage system, enabling cost-effective home refilling of NG vehicles. The technology provides minimum dead volume in and around the tank, eliminates the dead weight from the pressure container, and provides improved safety from constrained release of the fuel. ARPA-e funding was used to provide a proof-of-concept demonstration of this technology.

03 NATURAL GAS↗
DOE OSTI2016

ASSERT FY16 Analysis of Feedstock Companion Markets

Meeting Co-Optima biofuel production targets will require large quantities of mobilized biomass feedstock. Mobilization is of key importance as there is an abundance of biomass resources, yet little is available for purchase, let alone at desired quantity and quality levels needed for a continuous operation, e.g., a biorefinery. Therefore Co-Optima research includes outlining a path towards feedstock production at scale by understanding routes to mobilizing large quantities of biomass feedstock. Continuing along the vertically-integrated path that pioneer cellulosic biorefineries have taken will constrain the bioenergy industry to high biomass yield areas, limiting its ability to reach biofuel production at scale. To advance the cellulosic biofuels industry, a separation between feedstock supply and conversion is necessary. Thus, in contrast to the vertically integrated supply chain, two industries are required: a feedstock industry and a conversion industry. The split is beneficial for growers and feedstock processers as they are able to sell into multiple markets. That is, depots that produce value-add feedstock intermediates that are fully fungible in both the biofuels refining and other, so-called companion markets. As the biofuel industry is currently too small to leverage significant investment in up-stream infrastructure build-up, it requires an established (companion) market to secure demand, which de-risks potential investments and makes a build-up of processing and other logistics infrastructure more likely. A common concern to this theory however is that more demand by other markets could present a disadvantage for biofuels production as resource competition may increase prices leading to reduced availability of low-cost feedstock for biorefineries. To analyze the dynamics across multiple markets vying for the same resources, particularly the potential effects on resource price and distribution, the Companion Market Model (CMM) has been developed in this task by experts in feedstock supply chain analysis, market economics, and System Dynamics from the Idaho National Laboratory and MindsEye Computing.

09 BIOMASS FUELS↗