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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

MODELING POTENTIAL IMPACTS OF SO2 CO-INJECTED WITH CO2 ON THE KNOX GROUP, WESTERN KENTUCKY

Understanding potential long-term impacts of CO2 impurities, such as sulfur and nitrogen compounds, on deep carbon storage reservoirs is of considerable interest because co-injection of the impurities with CO2 can bring significant economic and environmental benefits. The Cambrian–Ordovician Knox Group, a thick sequence of dolostone (Beekmantown Dolomite) with minor dolomitic sandstone (Gunter Sandstone), in western Kentucky, USA, has been evaluated as a prospective CO2 sequestration target. In this study, TOUGHREACT was used to build 1-D radial models to simulate the potential impacts of co-injected CO2 and SO2 on minerals, pore fluids, and porosity and permeability in the Beekmantown Dolomite and the Gunter Sandstone. Co-injection of a mass ratio of 2.5 percent SO2 and 97.5 percent CO2, representative of flue gas from coal-fired plants, was simulated and the co-injection simulations were compared to models with CO2 only injections. The model results suggest that the major impacts of added SO2 for both the Beekmantown and the Gunter rocks were significant enhancement of dissolution of dolomite and precipitation of anhydrite, leading to noticeable increases in porosity and permeability. The Gunter Sandstone appeared to be more active with SO2 than the Beekmantown Dolomite. More dolomite was dissolved in the Gunter than in the Beekmantown with the same SO2 impurity. Consequently, porosity was raised more in the Gunter than in the Beekmantown. On the other hand, the impacts on aluminosilicate minerals appeared to be insignificant in both reservoirs, slightly changing the rates of precipitation/dissolution but the overall reaction paths remained the same.

Zhu, Junfeng↗
NASA NTRSDate not supplied

The BioSensor Instrument Beyond LEIA: a Versatile Platform for Lunar Biology

Introduction: The BioSensor is a deep-space-compatible automated microfluidic culturing instrument. While originally designed to measure the effects of deep space radiation on yeast growth for the BioSentinel mission, it has the potential to host a diverse range of life science experiments with single- and/or multi-celled organisms and can be adapted to interface with a diverse range of platforms in both crewed and uncrewed settings. It is therefore a leading candidate for hosting life sciences experimentation associated with a lunar surface habitat. BioSensor design: The function of the BioSensor is to monitor the growth and metabolic activity of samples in batch fluidic culture, without the need for crew involvement. The current configuration houses organisms in 16 wells within 16 microfluidic cards, accommodating a total of 256 samples, replicates, and controls. Each well has an optical system including three LEDs and a photodiode detector to measure absorbance at three wavelengths, enabling measurement of optical density, color change in dyes such as alamarBlue, and bioproduction of pigmented compounds. Organisms are loaded into fluidic wells and air-dried for storage during integration, launch, and transit, then activated by the introduction of culture medium from storage bags via manifolds that fill one card at a time. Temperature is controlled by individual card heaters, and timing of all activities (fluidics fills, optical measurements, temperatures) is directed by an experiment script. The self-contained BioSensor payload is roughly 4U in volume; with electrical/mechanical/thermal interface, e.g. for operation on ISS or a lunar lander, as well as a linear energy-transfer (LET) charged-particle radiation spectrometer, the entire system is closer to 6U. BioSentinel and LEIA: Flown on the ISS and in a deep-space free flyer for BioSentinel, the BioSensor has been modified for use in the LEIA mission, including improvements to reduce the sensitivity to lengthy launch delays. LEIA will monitor yeast growth in the radiation and reduced-gravity environment of the lunar surface no earlier than 2026, on a CLPS lander [4]. Changes include accommodating additional culture media and an additional LED color for a new biological assay (bioproduction of carotenoids-- dietary antioxidants), as well as modifications to the housing to allow late-load biology changeout and improved isolation between fluidics and electronics. Future prospects: Future work with the BioSensor, beyond LEIA, will include expanding the range and diversity of organisms and assays accommodated. Preliminary work has demonstrated the growth of Arabidopsis seedlings in BioSensor fluidic cards, including optical measurements of growth rate over time. Minor modifications could allow measurement of phenotypes related to photosynthetic capacity in both plants and cyanobacteria. The experimental capabilities of the BioSensor could be dramatically increased by introducing the capability for fluorescence measurements, and/or the design of novel biological assays using luminescence. The BioSensor can also be adapted for new platforms and experiment settings; in addition to free-flyer, ISS, and CLPS lander, a preliminary design concept has been developed for crewed deployment directly to the lunar surface. The instrument could be accommodated inside a lunar habitat, where its automated operation would make it an excellent candidate for experiments from fundamental investigations into the response of organisms to lunar surface conditions to applied-science purposes such as screening engineered strains of various organisms for bioproduction capability.

J A Lee↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the magnitude of 105, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but training only on simulated data results in poor performance on real data. Domain adaptation can bridge the gap between simulated and real datasets. We adopt domain adaptation on the estimation of Einstein radius in simulated galaxy-scale gravitational lensing images. We evaluate two domain adaptation techniques - domain adversarial neural networks (DANN) and maximum mean discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain with emulation of DES survey conditions. We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain datasets. Our results show the potential of using domain adaptation to perform analysis on future survey data with a deep neural network trained on simulated data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2004

High-Power Krypton Hall Thruster Technology Being Developed for Nuclear-Powered Applications

The NASA Glenn Research Center has been performing research and development of moderate specific impulse, xenon-fueled, high-power Hall thrusters for potential solar electric propulsion applications. These applications include Mars missions, reusable tugs for low-Earth-orbit to geosynchronous-Earth-orbit transportation, and missions that require transportation to libration points. This research and development effort resulted in the design and fabrication of the NASA-457M Hall thruster that has been tested at input powers up to 95 kW. During project year 2003, NASA established Project Prometheus to develop technology in the areas of nuclear power and propulsion, which are enabling for deep-space science missions. One of the Project-Prometheus-sponsored Nuclear Propulsion Research tasks is to investigate alternate propellants for high-power Hall thruster electric propulsion. The motivation for alternate propellants includes the disadvantageous cost and availability of xenon propellant for extremely large scale, xenon-fueled propulsion systems and the potential system performance benefits of using alternate propellants. The alternate propellant krypton was investigated because of its low cost relative to xenon. Krypton propellant also has potential performance benefits for deep-space missions because the theoretical specific impulse for a given voltage is 20 percent higher than for xenon because of krypton's lower molecular weight. During project year 2003, the performance of the high-power NASA-457M Hall thruster was measured using krypton as the propellant at power levels ranging from 6.4 to 72.5 kW. The thrust produced ranged from 0.3 to 2.5 N at a discharge specific impulse up to 4500 sec.

Jacobson, David T.↗
DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗
DOE OSTI2017

Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams

Analysis of an organization’s computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach’s potential to greatly reduce analyst workloads.

Tuor, Aaron R.↗
DOE OSTI2022

High Temperature, High Pressure Stability of Aqueous Foams for Potential Application in Enhanced Geothermal System (EGS)

Enhanced Geothermal Systems (EGS) are typically constructed by injecting high-pressure water into deep hot dry rocks (HDR) under carefully controlled conditions to create new or re-open existing fractures, which usually uses an immense quantity of water. In lieu, a more sustainable technique is to utilize aqueous foams as fracturing fluid to reduce water consumption and waste-water treatments of conventional hydraulic fracturing. Although foam-based fracturing has shown promising results in oil and gas industries, its feasibility is not demonstrated in EGS conditions that usually involve high temperature and high pressures. One potential barrier of utilizing foams in EGS applications is that foams are thermodynamically unstable and will become more unstable with increasing temperature due to liquid drainage, bubble coarsening, and coalescence. This work focuses on evaluation of the stability of selected aqueous foams under high-temperature and high-pressure conditions. Specifically, foams generated with surfactant alfa olefin sulfonate (AOS) were studied at temperature up to 200oC, while the pressure ranged between 100 psi and 1000 psi. The effect of additional stabilizing agents was also examined, including guar gum, bentonite clay, borate salt crosslinker, silicon dioxide nanoparticles (SiO2), and graphene oxide (GO) dispersion. Results showed that the stabilizing agents can enhance the foam thermal stability. Foams made with AOS and the borate salt crosslinker exhibited the longest half-life of 20 min at 200°C when the pressure was at 1000 psi. Data fitting showed that foam stability decreased exponentially as temperature increased. On the other hand, pressure increased the foam half-life which followed a power model. This study indicates that it may be possible to obtain highly stable foams at high-temperature and high-pressure conditions with appropriate stabilizing agents.

Thakore, Viren↗
DOE OSTI2019

Deep Isolation - Innovation for the Storage and Disposal of High-level Waste and Spent Nuclear Fuel - 19367

Deep Isolation, Inc. is a private, Berkeley based company that has brought innovation to nuclear waste disposal. Deep Isolation has developed a disposal technique that adapts mature drilling technology and customizes it for the purpose of providing a safe and secure solution. Though the stalled nuclear waste program in the US has created unwanted burdens and costs, it has also provided object lessons which Deep Isolation has learned from and designed a novel approach. The solution proposed by Deep Isolation combines the best of the geosciences and social sciences to present the US and other countries with an optimized and tailored approach to disposing of a variety of nuclear waste. This system can complement existing potential storage and disposal sites, or in the case of countries with smaller amounts of waste--the national solution. Deep Isolation innovates where others have stalled and offers a means of deep geological disposal that costs less, takes less time to implement, and cuts down and in some cases eliminates the need for transportation of the waste. The technical solution optimized by Deep Isolation uses established directional drilling technology from the oil and gas industry to drill a vertical drill hole thousands of meters deep and then transition to a horizontal drill hole that is thousands of meters in length. The target media is in or below shale formations, which are present at various depths throughout much of the United States and throughout the world. The availability of suitable geology allows for permanent disposal at multiple locations - including existing nuclear sites, without a need to transport the waste to a central repository. This innovative solution not only offers options for commercial used nuclear fuel and defense spent nuclear fuel and high-level waste, but also other nuclear wastes such as those considered by DoE's past deep bore-hole program, i.e. cesium and strontium capsules. Greater-Than-Class-C (GTCC) waste is also an opportunity for Deep Isolation. DoE's preferred alternative for GTCC includes 'land disposal at generic commercial facilities.' Deep Isolation could provide safe, secure, and permanent disposal at a commercial facility. Deep Isolation complements the technical innovation with best practices from the social sciences. In acknowledgement that the past challenges to implement nuclear waste disposal facilities have been primarily due to socio-political failings, we have designed a means to deeply engage with public and stakeholders. Our approach is committed to partnering with communities, local jurisdictions, states, and Tribes to determine a path forward that benefits all involved, that is fully transparent, collaborative and accountable. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Sequim Bay Underwater UXO (SBU2) Prototype Demonstration Site: Field Operations Summary, 2019

The U.S. Department of Defense (DoD) has identified more than 400 underwater sites that are potentially contaminated with munitions. Many of these sites are in shallow water (0–35 m deep) where the munitions pose a threat to human health and the environmental. To address this challenge, the Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) held a workshop in 2018 to establish the requirements and framework for developing a series of underwater unexploded ordnance (UXO) Standardized Demonstration sites (“test beds”) to support the evaluation of sensors, equipment, and technologies to detect UXO. Sequim Bay in Washington State was evaluated as a potential test bed and was determined to meet initial qualifying criteria with respect to environmental setting, and operational, logistical, and facilities support provided by the Pacific Northwest National Laboratory (PNNL).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Time-lapse seismic data inversion for estimating reservoir parameters using deep learning

Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

Geochemistry & Geophysics↗
DOE OSTI2017

Novel Geothermal Development of Deep Sedimentary Systems in the United States

Economic and reservoir engineering models show that stratigraphic reservoirs have the potential to contribute significant geothermal power in the U.S. If the reservoir temperature exceeds about 150 – 200 °C at 2 – 4 km depth, respectively, and there is good permeability, then these resources can generate power with a levelized cost of electricity (LCOE) of close to 10 ¢/kWh (without subsidies) on a 100 MW power plant scale. There is considerable evidence from both groundwater geology and petroleum reservoir geology that relatively clean carbonates and sandstones have, and can sustain, the required high permeability to depths of at least 5 km. This paper identifies four attractive stratigraphic reservoir prospects which are all located in the eastern Great Basin, and have temperatures of 160 – 230 °C at 3 – 3.5 km depth. They are the Elko basins (Nevada), North Steptoe Valley (Nevada), Pavant Butte (Utah), and the Idaho Thrust Belt. The reservoir lithologies are Paleozoic carbonates in the first three, and Jurassic sandstone and carbonate in the Idaho Thrust Belt. All reservoir lithologies are known to have high permeability characteristics. At North Steptoe Valley and Pavant Butte, nearby transmission line options allow interconnection to the California power market. Modern techniques for drilling and developing tight oil and gas reservoirs are expected to have application to geothermal development of these reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Photoluminescence Dynamics Defined by Exciton Trapping Potential of Coupled Defect States in DNA-Functionalized Carbon Nanotubes

Chemical reactions between semiconducting single-wall carbon nanotubes (SWCNTs) and single-stranded DNA (ssDNA) achieve spatially patterned covalent functionalization sites and create coupled fluorescent quantum defects on the nanotube surface, tailoring SWCNT photophysics for applications such as single-photon emitters in quantum information technologies. The evaluation of relaxation dynamics of photoluminescence (PL) from those coupled quantum defects is essential for understanding the nanotube electronic structure and beneficial to the design of quantum light emitters. Here, we measured the PL decay for ssDNA-functionalized SWCNTs as a function of the guanine content of the ssDNA oligo that dictates the red-shifting of their PL emission peaks relative to the band-edge exciton. We then correlate the observed dependence of PL decay dynamics on energy red-shifts to the exciton potential energy landscape, which is modeled using first-principles approaches based upon the morphology of ssDNA-altered SWCNTs obtained by atomic force microscopy (AFM) imaging. In this work, our simulations illustrate that the multiple guanine defects introduced within a single ssDNA strand strongly interact to create a deep exciton trapping well, acting as a single hybrid trap. The emission decay from the distinctive trapping potential landscape is found to be biexponential for ssDNA-modified SWCNTs. We attributed the fast time component of the biexponential PL decay to the redistribution of exciton population among the lowest energy bright states and a manifold of dark states emerging from the coupling of multiple guanine defects. The long lifetime component in the biexponential decay, on the other hand, is attributed to the redistribution of exciton population among different exciton trapping sites that arise from the binding of multiple ssDNA strands along the nanotube axis. AFM measurements indicate that those trapping sites are separated on average by ~8 nm along the nanotube axis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2016

Deep Charging Evaluation of Satellite Power and Communication System Components

A set of deep charging tests has been carried out by NASA's Marshall Space Flight Center on subscale flight-like samples developed by Space Systems/Loral, LLC. The samples, which included solar array wire coupons, a photovoltaic cell coupon, and a coaxial microwave transmission cable, were placed in passive and active (powered) circuit configurations and exposed to electron radiation. The energy of the electron radiation was chosen to deeply penetrate insulating (dielectric) materials on each sample. Each circuit configuration was monitored to determine if potentially damaging electrostatic discharge events (arcs) were developed on the coupon as a result of deep charging. The motivation for the test, along with charging levels, experimental setup, sample details, and results will be discussed.

Schneider, T. A.↗
NASA NTRS1976

A triangular element based on generalized potential energy concepts

Stiffness equations are formulated for a doubly-curved triangular thin shell finite element. The strain energy component of the potential energy is first expressed in terms of displacements and displacement gradients with the aid of consistent deep shell strain-displacement equations. The element in-plane and normal displacement fields are approximated by complete cubic polynomials. These functions do not satisfy the interelement displacement admissibility conditions. Satisfaction is forced by the imposition of constraint conditions on the interelement boundaries; the constraints represent the modification of the potential energy. Some numerical results for a pinched cylinder, a cylindrical sphere, and a pinched sphere are examined.

Thomas, G. R.↗
NASA NTRS2012

Time and Energy, Exploring Trajectory Options Between Nodes in Earth-Moon Space

The Global Exploration Roadmap (GER) was released by the International Space Exploration Coordination Group (ISECG) in September of 2011. It describes mission scenarios that begin with the International Space Station and utilize it to demonstrate necessary technologies and capabilities prior to deployment of systems into Earth-Moon space. Deployment of these systems is an intermediate step in preparation for more complex deep space missions to near-Earth asteroids and eventually Mars. In one of the scenarios described in the GER, "Asteroid Next", there are activities that occur in Earth-Moon space at one of the Earth-Moon Lagrange (libration) points. In this regard, the authors examine the possible role of an intermediate staging point in an effort to illuminate potential trajectory options for conducting missions in Earth-Moon space of increasing duration, ultimately leading to deep space missions. This paper will describe several options for transits between Low Earth Orbit (LEO) and the libration points, transits between libration points, and transits between the libration points and interplanetary trajectories. The solution space provided will be constrained by selected orbital mechanics design techniques and physical characteristics of hardware to be used in both crewed missions and uncrewed missions. The relationships between time and energy required to transfer hardware between these locations will provide a better understanding of the potential trade-offs mission planners could consider in the development of capabilities, individual missions, and mission series in the context of the ISECG GER.

Martinez, Roland↗
DOE OSTI2018

Deep Vadose Zone Treatability Test of Uranium Reactive Gas Sequestration for the Hanford Central Plateau: Final Report

The uranium reactive gas sequestration (URGS) test described herein was conducted as an element of the Deep Vadose Zone Treatability Test Plan for the Hanford Central Plateau. The URGS technology was tested as a potential remedy to decrease the mobility of uranium in the vadose zone as a mechanism to protect groundwater. Information about URGS obtained from this test is intended for use in subsequent feasibility studies for Hanford Central Plateau waste sites with uranium contamination in the deep vadose zone.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗