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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Novel candidate taxa contribute to key metabolic processes in Fennoscandian Shield deep groundwaters

The continental deep biosphere contains a vast reservoir of microorganisms, although a large proportion of its diversity remains both uncultured and undescribed. In this study, the metabolic potential (metagenomes) and activity (metatranscriptomes) of the microbial communities in Fennoscandian Shield deep subsurface groundwaters were characterized with a focus on novel taxa. DNA sequencing generated 1270 de-replicated metagenome-assembled genomes and single-amplified genomes, containing 7 novel classes, 34 orders, and 72 families. The majority of novel taxa were affiliated with Patescibacteria, whereas among novel archaea taxa, Thermoproteota and Nanoarchaeota representatives dominated. Metatranscriptomes revealed that 30 of the 112 novel taxa at the class, order, and family levels were active in at least one investigated groundwater sample, implying that novel taxa represent a partially active but hitherto uncharacterized deep biosphere component. The novel taxa genomes coded for carbon fixation predominantly via the Wood–Ljungdahl pathway, nitrogen fixation, sulfur plus hydrogen oxidation, and fermentative pathways, including acetogenesis. These metabolic processes contributed significantly to the total community’s capacity, with up to 9.9% of fermentation, 6.4% of the Wood–Ljungdahl pathway, 6.8% of sulfur plus 8.6% of hydrogen oxidation, and energy conservation via nitrate (4.4%) and sulfate (6.0%) reduction. Key novel taxa included the UBA9089 phylum, with representatives having a prominent role in carbon fixation, nitrate and sulfate reduction, and organic and inorganic electron donor oxidation. These data provided insights into deep biosphere microbial diversity and their contribution to nutrient and energy cycling in this ecosystem.

Candidatus↗
NASA NTRS2011

Initial Characterization of Optical Communications with Disruption-Tolerant Network Protocols

Disruption-tolerant networks (DTNs) are groups of network assets connected with a suite of communication protocol technologies designed to mitigate the effects of link delay and disruption. Application of DTN protocols to diverse groups of network resources in multiple sub-networks results in an overlay network-of-networks with autonomous data routing capability. In space environments where delay or disruption is expected, performance of this type of architecture (such as an interplanetary internet) can increase with the inclusion of new communications mediums and techniques. Space-based optical communication links are therefore an excellent building block of space DTN architectures. When compared to traditional radio frequency (RF) communications, optical systems can provide extremely power-efficient and high bandwidth links bridging sub-networks. Because optical links are more susceptible to link disruption and experience the same light-speed delays as RF, optical-enabled DTN architectures can lessen potential drawbacks and maintain the benefits of autonomous optical communications over deep space distances. These environment-driven expectations - link delay and interruption, along with asymmetric data rates - are the purpose of the proof-of-concept experiment outlined herein. In recognizing the potential of these two technologies, we report an initial experiment and characterization of the performance of a DTN-enabled space optical link. The experiment design employs a point-to-point free-space optical link configured to have asymmetric bandwidth. This link connects two networked systems running a DTN protocol implementation designed and written at JPL for use on spacecraft, and further configured for higher bandwidth performance. Comparing baseline data transmission metrics with and without periodic optical link interruptions, the experiment confirmed the DTN protocols' ability to handle real-world unexpected link outages while maintaining capability of reliably delivering data at relatively high rates. Finally, performance characterizations from this data suggest performance optimizations to configuration and protocols for future optical-specific DTN space link scenarios.

Space networking↗
DOE OSTI2022

DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey data sets, we designed ZipperNet, a multibranch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories—no lens, galaxy-galaxy lens, lensed Type-Ia supernova, lensed core-collapse supernova—within high-fidelity simulations of three cosmic survey data sets: the Dark Energy Survey, Rubin Observatory’s Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like data set, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1–2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1984

Radio interferometric determination of intercontinental baselines and earth orientation utilizing deep space network antennas - 1971 to 1980

Progress has been made toward the realization of the potential of radio interferometry for measuring crustal motions and global rotations of the earth with accuracies at the centimeter level. In this connection, a series of experiments, primarily with NASA's Deep Space Network (DSN) antennas, has been conducted to develop two generations of very long baseline interferometric (VLBI) systems. A description is presented of the employed techniques, an analysis of the experiments, and the results of geophysical significance. Attention is given to the interferometry technique, the geometric delay model, propagation media calibrations, and the observing strategy.

Sovers, O. J.↗
DOE OSTI2023

Deep anoxic aquifers could act as sinks for uranium through microbial-assisted mineral trapping

Uptake of uranium (U) by secondary minerals, such as carbonates and iron (Fe)-sulfides, that occur ubiquitously on Earth, may be substantial in deep anoxic environments compared to surficial settings due to different environment-specific conditions. Yet, knowledge of U reductive removal pathways and related fractionation between 238 U and 235 U isotopes in deep anoxic groundwater systems remain elusive. Here we show bacteria-driven degradation of organic constituents that influences formation of sulfidic species facilitating reduction of geochemically mobile U(VI) with subsequent trapping of U(IV) by calcite and Fe-sulfides. The isotopic signatures recorded for U and Ca in fracture water and calcite samples provide additional insights on U(VI) reduction behaviour and calcite growth rate. The removal efficiency of U from groundwater reaching 75% in borehole sections in fractured granite, and selective U accumulation in secondary minerals in exceedingly U-deficient groundwater shows the potential of these widespread mineralogical sinks for U in deep anoxic environments.

36 MATERIALS SCIENCE↗
NASA NTRS2021

Acoustic Communication in Deep Ice at Ocean Worlds

One of NASA priorities is the in-situ exploration of ocean worlds in the solar system where potentially there might be life under the ice shell. This requires reaching the ocean below extremely cold through significant deep ice. Jupiter’s moon, Europa, is such a challenging body, where it is estimated to have a 40 km thick ice shell. An approach for reaching the ocean has been conceived using a melting probe Cryobot concept that has been studied for a potential future mission. A lander is assumed to be the platform from which the Cryobot would be deployed. The ice penetrating vehicle concept consists of a cylindrical, narrow-body probe that encases a radioisotope heat/power source that would be used to do the penetration by melting through the icy crust. The baseline design of the probe includes a suite of science instruments to analyze the ice during descent and the liquid ocean underneath. For communication, a set of fiber optic wire as well as wireless RF in the very cold porous top layer is assumed, and then acoustic modules would be used for the communication in warmer denser ice over distance of 25 km between the modules. In addition to the acoustic communication modules, a sonar is part of the concept, for obstacle avoidance. The focus of this paper is on the use of elastic waves in the 1kHz range.

Lih, Shyh-Shiuh↗
DOE OSTI2022

Virioplankton assemblages from challenger deep, the deepest place in the oceans

Hadal ocean biosphere, that is, the deepest part of the world’s oceans, harbors a unique microbial community, suggesting a potential uncovered co-occurring virioplankton assemblage. Herein, we reveal the unique virioplankton assemblages of the Challenger Deep, comprising 95,813 non-redundant viral contigs from the surface to the hadal zone. Almost all of the dominant viral contigs in the hadal zone were unclassified, potentially related to Alteromonadales and Oceanospirillales. 2,586 viral auxiliary metabolic genes from 132 different KEGG orthologous groups were mainly related to the carbon, nitrogen, sulfur, and arsenic metabolism. Lysogenic viral production and integrase genes were augmented in the hadal zone, suggesting the prevalence of viral lysogenic life strategy. Abundant rve genes in the hadal zone, which function as transposase in the caudoviruses, further suggest the prevalence of viral-mediated horizontal gene transfer. This study provides fundamental insights into the virioplankton assemblages of the hadal zone, reinforcing the necessity of incorporating virioplankton into the hadal biogeochemical cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hydropower: Supply Chain Deep Dive Assessment

This report examines the hydropower supply chain to identify potential bottlenecks, challenges, and opportunities, particularly if the U.S. demand for hydropower components grows significantly to meet decarbonization targets. It is part of a series of DOE reports directed by Executive Order 14017 "America's Supply Chains"

13 HYDRO ENERGY↗
NASA NTRS2021

Extending the Licklider Transmission Protocol to Multi-Band Links

Most deep space missions return data to Earth using links operating at a single frequency band. Indeed, their data requirements are low enough that bandwidth regulations do not constrain the system. In contrast, spacecraft such as Kepler or Europa Clipper are transitioning to a new operational paradigm where engineering and science data are transmitted through simultaneous links operating at different frequency bands (henceforth termed multi-band links). This ensures, for instance, that critical data is correctly received using a well characterized X-band link, while science data at a much larger data rate can be returned efficiently (both in terms of bandwidth and energy) through a Ka-band link.Having a spacecraft establish two simultaneous links with a ground station opens a large span of potential improvements for space communications and mission operations. In this paper, we consider the problem of running a Licklider Transmission Protocol (LTP) session over a multi-band link. LTP is an implementation of a selective Automatic Repeat reQuest (ARQ) protocol, i.e. it ensures correct delivery of data over an error prone link with potentially long propagation delays. To maximize its efficiency in deep space environments, LTP operates in deferred-ACK mode and is typically included as one of the core protocols in the Delay Tolerant Networking (DTN) suite.The contributions of this paper are as follows: First, we propose an extension to LTP for multi-band links (denoted MBLTP) and sketch how it can be implemented without modifying the current definition of LTP data units. Next, we develop bounds on the performance of MBLTP when transmitting a single data file over a multi-band link. Three metrics are considered, file expected delivery time, total energy spent and bundle jitter. The results of the analytic model are first benchmarked against simulations to ensure validity, and then compared against the performance of both traditional LTP and Parallel LTP (PLTP).We demonstrate that MBLTP can significantly reduce the latency and jitter with which data products are delivered to destination over a deep space link compared to LTP at moderate energy cost. Similarly, we also demonstrate that MBLTP outperforms PLTP in all considered metrics.

Sanchez Net, Marc↗
NASA NTRS1993

The tropopause cold trap in the Australian Monsoon during STEP/AMEX 1987

The relationship between deep convection and tropopause cold trap conditions is examined for the tropical northern Australia region during the 1986-87 summer monsoon season, emphasizing the Australia Monsoon Experiment (AMEX) period when the NASA Stratosphere-Troposphere Exchange Project (STEP) was being conducted. The factors related to the spatial and temporal variability of the cold point potential temperature (CPPT) are investigated. A framework is developed for describing the relationships among surface average equivalent potential temperature in the surface layer (AEPTSL) the height of deep convection, and stratosphere-troposphere exchange. The time-mean pattern of convection, large-scale circulation, and surface AEPTSL in the Australian monsoon and the evolution of the convective environment during the monsoon period and the extended transition season which preceded it are described. The time-mean fields of cold point level variables are examined and the statistical relationships between mean CPPT, surface AEPTSL, and deep convection are described. Day-to-day variations of CPPT are examined in terms of these time mean relationships.

Selkirk, Henry B.↗
DOE OSTI2020

Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology

Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.

Weber, Maridee A.↗
DOE OSTI2021

Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities

The deep sea, the largest ocean’s compartment, drives planetary-scale biogeochemical cycling. Yet, the functional exploration of its microbial communities lags far behind other environments. Here we analyze 58 metagenomes from tropical and subtropical deep oceans to generate the Malaspina Gene Database. Free-living or particle-attached lifestyles drive functional differences in bathypelagic prokaryotic communities, regardless of their biogeography. Ammonia and CO oxidation pathways are enriched in the free-living microbial communities and dissimilatory nitrate reduction to ammonium and H 2 oxidation pathways in the particle-attached, while the Calvin Benson-Bassham cycle is the most prevalent inorganic carbon fixation pathway in both size fractions. Reconstruction of the Malaspina Deep Metagenome-Assembled Genomes reveals unique non-cyanobacterial diazotrophic bacteria and chemolithoautotrophic prokaryotes. The widespread potential to grow both autotrophically and heterotrophically suggests that mixotrophy is an ecologically relevant trait in the deep ocean. These results expand our understanding of the functional microbial structure and metabolic capabilities of the largest Earth aquatic ecosystem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
DOE OSTI2020

WRF Modelling of Deep Convection and Hail for Wind Power Applications

Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the United States Southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modelling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates and precipitation totals show good agreement with observations. Here, the occurrence of precipitation during power producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modelled with a mean proportion correct of 0.77 and an odds ratio 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry and there is evidence for positive model bias in cloud reflectivity.

17 WIND ENERGY↗
DOE OSTI2021

Trapping interlayer excitons in van der Waals heterostructures by potential arrays

Transition metal dichalcogenide heterostructures can host interlayer excitons (IXs), which consist of electrons and holes spatially separated in different layers. IXs possess permanent dipoles and have proven to offer a wealth of novel physics. Here we develop a discrete, random-walk model which includes annihilation and repulsion interactions among IXs. Using this model, we simulate the trapping of IXs in traps of different depths, densities, and shapes. Our results show that dipole-dipole interactions play an important role in regulating IX trapping. The effects of dipole interactions can be mitigated with small, deep traps which are realizable with atomic defects and moire potentials.

2-dimensional systems↗
NASA NTRS2016

Beaconless Pointing for Deep-Space Optical Communication

Free space optical communication is of interest to NASA as a complement to existing radio frequency communication methods. The potential for an increase in science data return capability over current radio-frequency communications is the primary objective. Deep space optical communication requires laser beam pointing accuracy on the order of a few microradians. The laser beam pointing approach discussed here operates without the aid of a terrestrial uplink beacon. Precision pointing is obtained from an on-board star tracker in combination with inertial rate sensors and an outgoing beam reference vector. The beaconless optical pointing system presented in this work is the current approach for the Integrated Radio and Optical Communication (iROC) project.

free-space optical communication↗
NASA NTRS2015

Exploring Our Solar System with CubeSats and NanoSats

The Jet Propulsion Laboratory (JPL) is NASA's lead center for robotic exploration of our solar system. We are known for our large, flagship missions, such as Voyager, which gave humanity its first close look at Jupiter and Saturn; and the Mars Rovers, which have excited millions worldwide with their daring landing exploits. Less familiar to those outside NASA may be our role in developing the Kepler mission, which has discovered more than 2000 planets around other stars; or the recently launched Soil Moisture Active Passive (SMAP) mission, one of many JPL Earth Science missions. A recent JPL initiative has emphasized low cost missions that use rapidly evolving technology developed for CubeSats and NanoSat s to explore our solar system. Costs are significantly lower (by one or two orders of magnitude) than for conventional JPL missions, and development time is also significantly shorter. At present 21 such CubeSat flight projects are under way at the laboratory with various partners : some in flight, some in development, some in advanced formulation. Four are planned as deep space missions. To succeed in exploring deep space CubeSat/NanoSat missions have to address several challenges: the more severe radiation environment, communications and navigation at a distance, propulsion, and packaging of instruments that can return valuable science into a compact volume/mass envelope. Instrument technologies, including cameras, magnetometers, spectrometers, radiometers, and even radars are undergoing miniaturization to fit on these smaller platforms. Other key technologies are being matured for smallsats and NanoSats in deep space, including micro -electric propulsion, compact radio (and optical) communications, and onboard data reduction. This paper will describe missions that utilize these developments including the first two deep space CubeSats (INSPIRE), planned for launch in 2017; the first pair of CubeSats to be sent to another planet (MARCO), manifested with the InSight Mars lander launch in March of 2016; a helicopter "drone" on Mars to extend the reach of future rovers; plans for a Lunar Flashlight mission to shine a light on the permanently shadowed craters of the Moon's poles; a Near Earth Asteroid CubeSat missio n; and a CubeSat constellation to demonstrate time series measurements of storm systems on Earth. From these beginnings, the potential for CubeSats and NanoSats to add to our knowledge of the solar system could easily grow exponentially. Imagine if every deep space mission carried one or more CubeSats that could operate independently (even for a brief period) on arrival at their target body. At only incremental additional cost, such spacecraft could go closer, probe deeper, and provide science measurements that we would not risk with the host spacecraft. This paper will describe examples including a NanoSat to probe the composition of Venus' atmosphere, impactors and close flybys of Europa, lunar probes, and soft landers for the moons of Mars. Low cost access to deep space also offers the potential for independent CubeSat/NanoSat missions - allowing us to characterize the population of near Earth asteroids for example, deploy a constellation around Venus, or take closer looks at the asteroid belt.

Deep Space↗