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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Electronic transport in two-dimensional high dielectric constant nanosystems

There has been remarkable recent progress in engineering high-dielectric constant two dimensional (2D) materials, which are being actively pursued for applications in nanoelectronics in capacitor and memory devices, energy storage, and high-frequency modulation in communication devices. Yet many of the unique properties of these systems are poorly understood and remain unexplored. Here we report a numerical study of hopping conductivity of the lateral network of capacitors, which models two-dimensional insulators, and demonstrate that 2D long-range Coulomb interactions lead to peculiar size effects. We find that the characteristic energy governing electronic transport scales logarithmically with either system size or electrostatic screening length depending on which one is shorter. Our results are relevant well beyond their immediate context, explaining, for example, recent experimental observations of logarithmic size dependence of electric conductivity of thin superconducting films in the critical vicinity of superconductor-insulator transition where a giant dielectric constant develops. Our findings mark a radical departure from the orthodox view of conductivity in 2D systems as a local characteristic of materials and establish its macroscopic global character as a generic property of high-dielectric constant 2D nanomaterials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Insights into colour-tuning of chlorophyll optical response in green plants

Here, we performed first-principles calculations within the framework of real-space time-dependent density functional theory for the complete chlorophyll (Chl) network of the light-harvesting complex from green plants, LHC-II. A local-dipole analysis method developed for this work has made possible the studies of the optical response of individual Chl molecules subjected to the influence of the remainder of the chromophore network. The spectra calculated using our real-space TDDFT method agree with previous suggestions that weak interaction with the protein microenvironment should produce only minor changes in the absorption spectrum of Chl chromophores in LHC-II. In addition, relative shifting of Chl absorption energies leads the stromal and lumenal sides of LHC-II to absorb in slightly different parts of the visible spectrum providing greater coverage of the available light frequencies. The site-specific alterations in Chl excitation energies support the existence of intrinsic energy transfer pathways within the LHC-II complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Thermally and photoinduced polymerization of ultrathin sexithiophene films

The thermally-induced polymerization of α-sexithiophene (6T) molecules on Ag(001) and Au(001) gives rise to long unbranched polymer chains or branched polymer networks depending on the annealing parameters. There, the onset temperature for polymerization depends on the strength of interaction with the underlying substrate. Similar polymerization processes are also induced by ultraviolet radiation with photon energies between 3.0 and 4.2 eV. Radical formation by an electronic excitation in the 6T molecule is proposed as the driving mechanism that necessitates the interplay with the metallic substrate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Applications of machine learning to a compact magnetic spectrometer for high repetition rate, laser-driven particle acceleration

Accurately and rapidly diagnosing laser–plasma interactions is often difficult due to the time-intensive nature of the analysis and will only become more so with the rise of high repetition rate lasers and the desire to implement feedback on a commensurate timescale. Diagnostic analysis employing machine learning techniques can help address this problem while maintaining a high degree of accuracy. We report on the application of machine learning to the analysis of a scintillator-based electron spectrometer for experiments on high intensity, laser–plasma interactions at the Colorado State University Advanced Lasers and Extreme Photonics facility. Our approach utilizes a neural network trained on synthetic data and tested on experiments to extract the accelerated electron temperature. By leveraging transfer learning, we demonstrate an improvement in the neural network accuracy, decreasing the network error by 50%.

Swanson, K. K. (ORCID:0000000286880568)↗
DOE OSTI2021

Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis

Significance Liquid cell transmission electron microscopy (LCTEM) is an emerging technique, which enables nanoscale visualization and tracking of single nanoparticles near interfaces with unprecedented spatial resolution. Here, we studied the diffusion of nanoparticles in LCTEM experiments using techniques powered by deep neural networks and statistical tests. We observed two underlying regimes of diffusive behavior which are governed by the interaction of the electron beam, the nanoparticle, the nearby substrate, and the liquid environment. This understanding forms the foundation to use LCTEM for single-nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.

Jamali, Vida↗
DOE OSTI2021

Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis

This study aims to effectively analyze and visualize the concept to concept network derived from the COVID-19 Open Research Dataset (CORD-19) dataset, where we have more than 48,000 concepts with more than 300,000 relationships between concepts. In analyzing networks, we focus on finding relationship patterns between the coronavirus disease 2019 (COVID-19) concepts and other concepts. Given the node and edge datasets, we construct directional graphs and calculate all pair shortest paths based on multiple edge weight schemes. However, statistical metrics are not sufficient to identify specific relationships represented in the network. Therefore, we also propose a visual analytics approach to effectively understand the knowledge graph. Our highly interactive visual analytics allows users to effectively analyze the evolving graphs and (COVID-19) concept nodes and other nodes related to the COVID-19 nodes. We envision that this study will pave the path to develop strategies to provide more accurate and scalable predictive analysis on knowledge graphs related to CORD19 and other biomedical knowledge graphs.

Lim, Seung-Hwan↗
DOE OSTI2015

SU-E-T-521: Investigation of the Uncertainties Involved in Secondary Neutron/gamma Production in Geant4/MCNP6 Monte Carlo Codes for Proton Therapy Application

Purpose: A major concern in proton therapy is the production of secondary neutrons causing secondary cancers, especially in young adults and children. Most utilized Monte Carlo codes in proton therapy are Geant4 and MCNP. However, the default versions of Geant4 and MCNP6 do not have suitable cross sections or physical models to properly handle secondary particle production in proton energy ranges used for therapy. In this study, default versions of Geant4 and MCNP6 were modified to better handle production of secondaries by adding the TENDL-2012 cross-section library. Methods: In-water proton depth-dose was measured at the “The Svedberg Laboratory” in Uppsala (Sweden). The proton beam was mono-energetic with mean energy of 178.25±0.2 MeV. The measurement set-up was simulated by Geant4 version 10.00 (default and modified version) and MCNP6. Proton depth-dose, primary and secondary particle fluence and neutron equivalent dose were calculated. In case of Geant4, the secondary particle fluence was filtered by all the physics processes to identify the main process responsible for the difference between the default and modified version. Results: The proton depth-dose curves and primary proton fluence show a good agreement between both Geant4 versions and MCNP6. With respect to the modified version, default Geant4 underestimates the production of secondary neutrons while overestimates that of gammas. The “ProtonInElastic” process was identified as the main responsible process for the difference between the two versions. MCNP6 shows higher neutron production and lower gamma production than both Geant4 versions. Conclusion: Despite the good agreement on the proton depth dose curve and primary proton fluence, there is a significant discrepancy on secondary neutron production between MCNP6 and both versions of Geant4. Further studies are thus in order to find the possible cause of this discrepancy or more accurate cross-sections/models to handle the nuclear interactions of protons with energy ranges used for therapy. NSERC-CRSNG’ CREATE Medical Physics Research Training Network compute calcul CANADA; McGill University health center research institute the fast foundation; LM acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering; Research Council Grant number 432290.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Chemotaxonomic patterns in intracellular metabolites of marine microbial plankton

Most biological diversity on Earth is contained within microbial communities. In the ocean, these communities dominate processes related to carbon fixation and nutrient recycling. Yet, specific factors that determine community composition and metabolic activity are difficult to resolve in complex microbial populations, complicating predictions of microbial processes in a changing ocean. Microbial metabolism generates small organic molecules that reflect both the biochemical and physiological diversity as well as the taxonomic specificity of these biological processes. These small molecules serve as the conduit for taxon-specific signaling and exchange. Here, we use liquid chromatography-mass spectrometry (LC-MS)-based metabolomics to taxonomically categorize 111 metabolites that include small molecules in central and secondary metabolism across 42 taxa representing numerically dominant and metabolically important lineages of microbial autotrophs and heterotrophs. Patterns in metabolite presence-absence broadly reflected taxonomic lineages. A subset of metabolites that includes osmolytes, sulfur-containing metabolites, sugars, and amino acid derivatives provided chemotaxonomic information among phytoplankton taxa. A variety of phytohormones and signaling molecules were predominantly found in the heterotrophic bacteria and archaea, expanding knowledge of metabolites implicated in modulating interactions between microbes. This chemotaxonomic inventory of marine microbial metabolites is a key step in deciphering metabolic networks that influence ocean biogeochemical cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Lower functional redundancy in “narrow” than “broad” functions in global soil metagenomics

Understanding the relationship between soil microbial taxonomic compositions and functional profiles is essential for predicting ecosystem functions under various environmental disturbances. However, even though microbial communities are sensitive to disturbance, ecosystem functions remain relatively stable, as soil microbes are likely to be functionally redundant. Microbial functional redundancy may be more associated with “broad” functions carried out by a wide range of microbes than with “narrow” functions in which specific microorganisms specialize. Thus, a comprehensive study to evaluate how microbial taxonomic compositions correlate with broad and narrow functional profiles is necessary. Here, we evaluated soil metagenomes worldwide to assess whether functional and taxonomic diversities differ significantly between the five broad and the five narrow functions that we chose. Our results revealed that, compared with the five broad functions, soil microbes capable of performing the five narrow functions were more taxonomically diverse, and thus their functional diversity was more dependent on taxonomic diversity, implying lower levels of functional redundancy in narrow functions. Co-occurrence networks indicated that microorganisms conducting broad functions were positively related, but microbes specializing in narrow functions were interacting mostly negatively. Our study provides strong evidence to support our hypothesis that functional redundancy is significantly different between broad and narrow functions in soil microbes, as the association of functional diversity with taxonomy was greater in the five narrow than in the five broad functions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

The 1990 annual statistics and highlights report

The National Space Science Data Center (NSSDC) has archived over 6 terabytes of space and Earth science data accumulated over nearly 25 years. It now expects these holdings to nearly double every two years. The science user community needs rapid access to this archival data and information about data. The NSSDC has been set on course to provide just that. Five years ago the NSSDC came on line, becoming easily reachable for thousands of scientists around the world through electronic networks it managed and other international electronic networks to which it connected. Since that time, the data center has developed and implemented over 15 interactive systems, operational nearly 24 hours per day, and is reachable through DECnet, TCP/IP, X25, and BITnet communication protocols. The NSSDC is a clearinghouse for the science user to find data needed through the Master Directory system whether it is at the NSSDC or deposited in over 50 other archives and data management facilities around the world. Over 13,000 users accessed the NSSDC electronic systems, during the past year. Thousands of requests for data have been satisfied, resulting in the NSSDC's sending out a volume of data last year that nearly exceeded a quarter of its holdings. This document reports on some of the highlights and distribution statistics for most of the basic NSSDC operational services for fiscal year 1990. It is intended to be the first of a series of annual reports on how well NSSDC is doing in supporting the space and Earth science user communities.

Green, James L.↗
NASA NTRS1999

Digital Earth Initiative: A Joint Interagency Program

The Digital Earth is a virtual representation of our planet that enables a person to explore and interact with the vast amounts of natural and cultural information gathered about the Earth. The Digital Earth comprises data interfaces and standards enabling access to geo-referenced data from remote sensing, cartographic, demographic, medical, and other sources to respond to questions posed by the user. In a recent address at the California Science Center in Los Angeles, Vice President Al Gore articulated a Digital Earth Vision. That vision spoke to developing a multi-resolution, three-dimensional representation of the planet, into which we can roam and zoom into vast quantities of embedded geo-referenced data. The vision was not limited to moving through space but also allowing travel over a time-line, which can be set for days, years, centuries, or even geological epochs. As prototypes become available, it would also be possible to interact with the Digital Earth in multiple places around the country with access to high-speed networks and at a more limited level of access over the Internet. NASA was asked by the Vice President to lead an interagency initiative that would take steps to bring this vision to the public. This talk describes the start-up and plans of the Digital Earth Interagency Working Group in the formulation of its charter, an architecture reference model for Digital Earth, public/private partnerships, cooperative agreement notices, Digital Earth prototypes, and testbeds. Animations employing technologies for virtual roaming and zooming through multi-resolution satellite data set as prototype systems will be presented along with examples of potential user scenarios. Plans for engaging academia and industry in implementing the Digital Earth initiative will be discussed.

Halem, Milton↗
NASA NTRS2016

Satellite-Surface Perspectives of Air Quality and Aerosol-Cloud Effects on the Environment: An Overview of 7-SEAS BASELInE

The objectives of 7-SEASBASELInE (Seven SouthEast Asian Studies Biomass-burning Aerosols and Stratocumulus Environment: Lifecycles and Interactions Experiment) campaigns in spring 2013-2015 were to synergize measurements from uniquely distributed ground-based networks (e.g., AERONET (AErosol RObotic NETwork)), MPLNET ( NASA Micro-Pulse Lidar Network)) and sophisticated platforms (e.g.,SMARTLabs (Surface-based Mobile Atmospheric Research and Testbed Laboratories), regional contributing instruments), along with satellite observations retrievals and regional atmospheric transport chemical models to establish a critically needed database, and to advance our understanding of biomass-burning aerosols and trace gases in Southeast Asia (SEA). We present a satellite-surface perspective of 7-SEASBASELInE and highlight scientific findings concerning: (1) regional meteorology of moisture fields conducive to the production and maintenance of low-level stratiform clouds over land; (2) atmospheric composition in a biomass-burning environment, particularly tracers-markers to serve as important indicators for assessing the state and evolution of atmospheric constituents; (3) applications of remote sensing to air quality and impact on radiative energetics, examining the effect of diurnal variability of boundary-layer height on aerosol loading; (4) aerosol hygroscopicity and ground-based cloud radar measurements in aerosol-cloud processes by advanced cloud ensemble models; and (5) implications of air quality, in terms of toxicity of nanoparticles and trace gases, to human health. This volume is the third 7-SEAS special issue (after Atmospheric Research, vol. 122, 2013; and Atmospheric Environment, vol. 78, 2013) and includes 27 papers published, with emphasis on air quality and aerosol-cloud effects on the environment. BASELInE observations of stratiform clouds over SEA are unique, such clouds are embedded in a heavy aerosol-laden environment and feature characteristically greater stability over land than over ocean, with minimal radar surface clutter at a high vertical spatial resolution. To facilitate an improved understanding of regional aerosol-cloud effects, we envision that future BASELInE-like measurement modeling needs fall into two categories: (1) efficient yet critical in-situ profiling of the boundary layer for validating remote-sensing retrievals and for initializing regional transport chemical and cloud ensemble models; and (2) fully utilizing the high observing frequencies of geostationary satellites for resolving the diurnal cycle of the boundary layerheight as it affects the loading of biomass-burning aerosols, air quality and radiative energetics.

AERONET↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
NASA NTRS2010

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

Kuhn, D. Richard↗
DOE OSTI2015

Modeling of Propagation of Interacting Cracks Under Hydraulic Pressure Gradient

A robust and reliable numerical model for fracture initiation and propagation, which includes the interactions among propagating fractures and the coupling between deformation, fracturing and fluid flow in fracture apertures and in the permeable rock matrix, would be an important tool for developing a better understanding of fracturing behaviors of crystalline brittle rocks driven by thermal and (or) hydraulic pressure gradients. In this paper, we present a physics-based hydraulic fracturing simulator based on coupling a quasi-static discrete element model (DEM) for deformation and fracturing with conjugate lattice network flow model for fluid flow in both fractures and porous matrix. Fracturing is represented explicitly by removing broken bonds from the network to represent microcracks. Initiation of new microfractures and growth and coalescence of the microcracks leads to the formation of macroscopic fractures when external and/or internal loads are applied. The coupled DEM-network flow model reproduces realistic growth pattern of hydraulic fractures. In particular, simulation results of perforated horizontal wellbore clearly demonstrate that elastic interactions among multiple propagating fractures, fluid viscosity, strong coupling between fluid pressure fluctuations within fractures and fracturing, and lower length scale heterogeneities, collectively lead to complicated fracturing patterns.

hydraulic fracturing↗
DOE OSTI2021

Modeling and Evaluation of Cyber-Attacks on Grid-Interactive Efficient Buildings

Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.

Fu, Yanyang↗
DOE OSTI2023

A multifidelity deep operator network approach to closure for multiscale systems

Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables. Despite their success, PROMs can be susceptible to inaccuracies, even instabilities, due to the improper accounting of the interaction between the resolved and unresolved scales of the multiscale system (known as the closure problem). In the current work, we interpret closure as a multifidelity problem and use a multifidelity deep operator network (DeepONet) framework to address it. In addition, to enhance the stability and/or accuracy of the multifidelity-based closure, we employ the recently developed ``in-the-loop" training approach from the literature on coupling physics and machine learning models. The resulting approach is tested on shock advection for the one-dimensional viscous Burgers equation and vortex merging for the two-dimensional Navier-Stokes equations. Furthermore, the numerical experiments show significant improvement of the predictive ability of the closure-corrected PROM over the un-corrected one both in the interpolative and the extrapolative regimes.

42 ENGINEERING↗
DOE OSTI2023

CommAwareNet: Towards Communication‐Aware Smart Facilities: Designing an Energy‐Efficient High‐Data‐Rate and Reliable Hybrid THz/VLC Comm. Arch. Reinforced with Intelligent Surfaces for Future Network

This project will develop foundations for energy-efficient, high-data-rate, and reliable communication links for next-generation advanced wireless technologies. The Internet of Everything is emerging as a major technology that can establish a massively connected network among both human-type users and machine-type devices. Particularly, massive machine-type communication is becoming the dominant communication paradigm for numerous emerging areas, including data center networking and wireless backhaul, healthcare (e-health), manufacturing (industry 4.0), utilities, transportation (connected cars and public safety), and virtual or augmented reality for human-machine interaction applications. The rising demand for high data rates, especially in indoor scenarios and densely populated outdoor environments, will eventually overload conventional RF-based technologies. This project aims to conduct fundamental research to design, optimize, and validate a communication-aware, energy-efficient, and reliable network (CommAwareNet) architecture for future smart facilities and environments that require unprecedented high throughput needs for massive numbers of the machine- and human-type users. The scientific and technical merit of this work lies in the research required to bring the proposed transformative CommAwareNet solution into fully functional and practical architecture.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗