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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fracture mechanisms of sodium silicate glasses

Reactive classical molecular dynamics simulations of sodium silicate glasses, xNa 2 O–(100 – x)SiO 2 (x = 10–30), under quasi-static loading, were performed for the analysis of molecular scale fracture mechanisms. Mechanical properties of the sodium silicate glasses were consistent with experimentally reported values, and the amount of crack propagation varied with reported fracture toughness values. The most crack propagation occurred in NS20 systems (20-mol% Na 2 O) compared with the other simulated compositions. Dissipation via two mechanisms, the first through sodium migration as a lower activation energy process and the second through structural rearrangement as a higher activation energy process, was calculated and accounted for the energy that was not stored elastically or associated with the formation of new fracture surfaces. Here, a correlation between crack propagation and energy dissipation was identified, with systems with higher crack propagation exhibiting less energy dissipation. Sodium silicate glass compositions with lower energy dissipation also exhibited the most sodium movement and structural rearrangement within 10 Å of the crack tip during loading. Therefore, high sodium mobility near the crack tip may enable energy dissipation without requiring formation of structural defects. Therefore, the varying mobilities of the network modifiers near crack tips influence the brittleness and the crack growth rate of modified amorphous oxide systems.

36 MATERIALS SCIENCE↗
DOE OSTI2015

MO-FG-303-02: BEST IN PHYSICS (THERAPY): Cherenkov Emission Dosimetry: Feasibility for Electron Radiotherapy

Purpose: To investigate from first principles, corroborated by Monte Carlo simulations and experimental measurements, the feasibility of developing a relative Cherenkov emission (CE) dosimetry protocol for electron beam radiotherapy. Methods: Monte Carlo (MC) simulations of mono-energetic electrons incident on water were carried out in Geant4. Percent depth Cherenkov emission (PDCE) and dose (PDD) distributions were scored for incidence energies of 4, 6, 9, 12, 15, and 18 MeV. PDCE-to-PDD analytical conversion models were developed from least-squares data fits generated for PDD as a function of PDCE at the same depth and at different depths. Experimental techniques for validation of these models are examined. Results: Same-depth PDD versus PDCE data fits indicate that although the relationship is linear to first order (correlation r > 0.9 for all energies), it is much more accurately approximated by separate linear and quadratic models for the build-up and drop-off regions, respectively (r > 0.999), which is theoretically underpinned. To understand the source of this relationship and its basis for developing robust conversion models, an approximate quadratic first-principles model was derived and found in agreement with MC/measured data (20% deviation at worst). Conversely, data fits of PDD versus different-depth PDCE unveiled a depth-invariant effective point of measurement of 1.5–2.1 mm downstream with 4–18 MeV incidence, respectively (r > 0.999 in the drop-off region). We present an analytical first-principles justification for this shift. This method led to errors of <1% in drop-off region PDD (<2% for PDD<20% with 4 MeV incidence) and <0.2 mm in practical range prediction. Conclusion: We present robust quantitative prediction models, derived from first-principles and supported by simulation and measurement, for relative dose from Cherenkov emission by high-energy electrons. This constitutes a major step towards development of protocols for routine clinical quality assurance as well as real-time in vivo Cherenkov dosimetry in radiotherapy. The authors acknowledge partial support by Fonds de recherche du Quebec - Nature et technologies (FRQNT), CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (NSERC), CREATE Integrated Sensor Systems grant of NSERC, the Canadian Institutes of Health Research (CIHR), and NSERC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Position Papers for the 2024 ASCR Workshop on Neuromorphic Computing for Science

Engineering novel neuromorphic computing systems with functionalities, capabilities, and energy efficiency similar to biological brains is one of the most exciting and challenging scientific endeavors of our time. This workshop aims to identify key research needs, challenges, and next steps necessary to develop biologically realistic neuromorphic circuits primitives that capture the functionality of neural systems found in nature. Moreover, simulating neuromorphic computing primitives integrated into networks will be key to under standing their behavior at scale, particularly for those computing architectures where full-scale commercial fabrication is not yet readily accessible. Appropriate neuroscience datasets and metrics will have to be established to vet proposed neuromorphic circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

International Safeguards and the Pacific Northwest National Laboratory

Established in 1965, Pacific Northwest National Laboratory’s (PNNL) strong technical ties and shared heritage with the nearby U.S. Department of Energy Hanford Site were central to the early development of expertise in nuclear fuel cycle signatures, separations chemistry, plutonium chemistry, environmental monitoring, modeling and analysis of reactor systems, and nuclear material safeguards and security. From these Hanford origins, PNNL has grown into a multi-program science and engineering enterprise that utilizes this diversity to strengthen the international safeguards regime. Today, PNNL supports the International Atomic Energy Agency (IAEA) in its mission to provide assurances to the international community that nations do not use nuclear materials and equipment outside of peaceful uses. PNNL also serves in the IAEA’s Network of Analytical Laboratories (NWAL) by providing analysis of environmental samples gathered around the world. PNNL is involved in safeguards research and development activities in support of many U.S. Government programs such as the National Nuclear Security Administration’s (NNSA) Office of Research and Development, NNSA Office of Nonproliferation and Arms Control, and the U.S. Support Program to IAEA Safeguards. In addition to these programs, PNNL invests internal resources including safeguards-specific training opportunities for staff, and laboratory-directed research and development funding to further ideas that may grow into new capabilities. This paper and accompanying presentation highlight some of PNNL’s contributions in technology development, implementation concepts and approaches, policy, capacity building, and human capital development, in the field of international safeguards.

Olsen, Khris B.↗
DOE OSTI2013

Representativeness-based Sampling Network Design for the State of Alaska

This data set collection consists of data products described in Hoffman et. al., 2013. Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks is described here. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km2 resolution to define multiple sets of ecoregions across two decadal time periods. Maps of ecoregions for the present (2000-2009) and future (2090-2099) were produced, showing how combinations of 37 characteristics are distributed and how they may shift in the future. Representative sampling locations are identified on present and future ecoregion maps. A representativeness metric was developed, and representativeness maps for eight candidate sampling locations were produced. This metric was used to characterize the environmental similarity of each site. This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measurements, and provides a down-scaling approach for integration of models and measurements. These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns. This dataset contains one zipped file, one .txt file, and one .sh file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

High-resolution virtual Frisch grid gamma-ray detectors based on as-grown CdZnTeSe with reduced defects

X- and gamma-ray detectors arequickly becoming essential tools for science and technology in various fields. These detectors offer broad applications such as homeland security, nonproliferation, nuclear security, medical imaging, astrophysics, and high energy physics. All these applications necessitate high-resolution detectors operable at room temperature and available at a reasonable cost. CdZnTe (CZT) is the material of choice for this purpose; however, the material still suffers from intrinsic defects such as highly decorated subgrain boundary networks and a high concentration of secondary phases. These defects not only hinder the charge transport but also create a spatial inhomogeneity in the charge transport properties, subsequently causing substantial degradation in detector response particularly for relatively thick (>1 cm) detectors. Some of the material deficiencies suffered by CZT have been addressed by adding selenium into the CZT matrix. Selenium was found to be very effective in producing material that is principally free from a subgrain boundary network with the occasional appearance of subgrain boundaries with reduced secondary phases decorating these boundaries. The resulting quaternary compound CdZnTeSe (CZTS) reflected excellent material quality mitigating some major deficiencies suffered by CZT. Virtual Frisch grid detectors were fabricated from the as-grown CZTS ingots, and they demonstrated high resolution spectroscopic grade. The excellent CZTS material contained very low defects and was found to potentially increase the yield of high-quality detectors as compared to CZT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Iron loading effects in Fe/SSZ-13 NH3-SCR catalysts: nature of the Fe-ions and structure-function relationships

Using a traditional aqueous solution ion-exchange method under a protecting atmosphere of N2, a series of Fe/SSZ-13 catalysts with various Fe loadings were synthesized. UV-Vis, EPR and Mössbauer spectroscopies, coupled with temperature programmed reduction and desorption techniques, were used to probe the nature of the Fe sites. The major monomeric and dimeric Fe species are extra-framework [Fe(OH)2]+ and [HO-Fe-O-Fe-OH]2+. Larger oligomers with unknown nuclearity, poorly crystallized Fe2O3 particles, together with isolated Fe2+ ions, are minor Fe-containing moieties. Reaction rate and Fe loading correlations suggest that isolated Fe3+ ions are the active sites for standard SCR while the dimeric sites are the active centers for NO oxidation. NH3 oxidation, on the other hand, is catalyzed by sites with higher nuclearity. A low-temperature standard SCR reaction network is proposed that includes redox cycling of both monomeric and dimeric Fe species, for SCR and NO2 generation, respectively. The authors gratefully acknowledge the US Department of Energy (DOE), Energy Efficiency and Renewable Energy, Vehicle Technologies Program for the support of this work. The research described in this paper was performed at the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the DOE’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is operated for the US DOE by Battelle.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗
DOE OSTI2018

The Selection of Heat Treatment Parameters to Obtain Austempered Ductile Iron with the Required Impact Strength

The effect of austempering parameters on impact energy of non-alloyed cast iron with nodular graphite was examined. The analysis involved the austenitizing temperature in the range of 830-950 °C for the time of 60 min and the austempering temperature in the range of 300-400 °C for the time of 8, 16, 32 and 64 min. The value of the impact energy KV was determined. It has been found that the austenitizing temperature and austempering conditions affect the impact energy of ADI. The conducted analytical studies helped to develop a method for the selection of heat treatment parameters to obtain the ausferritic ductile iron with appropriate impact energy KV. The applied machine learning methods included support vector machine, decision trees, artificial neural networks and the method of variational component analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Self-supported Zn/Si core-shell arrays as advanced electrodes for lithium ion batteries

Graphical abstract: Zn/Si core-shell arrays prepared by combined hydrothermal plus sputtering method show good performance with high specific capacity and excellent cycling stability. - Highlights: • Construct Zn/Si core-shell arrays via sputtering. • The Zn/Si core-shell arrays show high capacity and good cycles. • Core-shell arrays is favorable for fast ion/electron transfer. - Abstract: Silicon is considered as the most promising substitute for the commercial graphite anode of lithium ion batteries (LIBs) due to its high capacity and low working voltage. In this work, we realize the rational combination between Zn nanorods and Si forming self-supported Zn/Si core-shell arrays with the aid of hydrothermal and sputtering methods. Amorphous Si layer is well coated on the backbone of Zn nanorods with diameters of 70–80 nm. As a preliminary test, the Zn/Si core-shell arrays are used as the anode of LIBs and demonstrated with a high initial reversible capacity of 2795 mAh g{sup −1} at 0.1C, good high-rate capability with 1985 mAh g{sup −1} at 1C and stable cycling life. The good performance is due to the highly conductive Zn nanorod network and porous core-shell structure providing good mechanical support and fast transport of ions/electrons.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Atomistic Simulation of Water Percolation and Proton Hopping in Nafion Fuel Cell Membrane

We have performed a detailed analysis of water clustering and percolation in hydrated Nafion configurations generated by classical molecular dynamics simulations. Our results show that at low hydration levels H2O molecules are isolated and a continuous hydrogen-bonded network forms as the hydration level is increased. Our quantitative analysis has established a hydration level (λ) between 5 and 6 H2O/SO3- as the percolation threshold of Nafion. We have also examined the effect of such a network on proton transport by studying the structural diffusion of protons using the quantum hopping molecular dynamics method. The mean residence time of the proton on a water molecule decreases by two orders of magnitude when the λ value is increased from 5 to 15. The proton diffusion coefficient in Nafion at a λ value of 15 is about 1.1x10-5 cm2/s in agreement with experiment. The results provide quantitative atomic-level evidence of water network percolation in Nafion and its effect on proton conductivity.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Matrix Diffusion in Fractured Media: New Insights Into Power Law Scaling of Breakthrough Curves

Here, we develop a theoretical model for power law tailing behavior of transport in fractured rock based on the relative dominance of the decay rate of the advective travel time distribution, modeled using a Pareto distribution (with tail decaying as ~ time -(1+α) ), versus matrix diffusion, modeled using a Lévy distribution. The theory predicts that when the advective travel time distribution decays sufficiently slowly (α<1), the late-time decay rate of the breakthrough curve is -(1+α/2) rather than the classical -3/2. Yet, if α>1, the -3/2 decay rate is recovered. For weak matrix diffusion or short advective first breakthrough times, we identify an early-time regime where the breakthrough curve follows the Pareto distribution, before transitioning to the late-time decay rate. The theoretical predictions are validated against particle tracking simulations in the three-dimensional discrete fracture network simulator dfnWorks, where matrix diffusion is incorporated using a time domain random walk.

58 GEOSCIENCES↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Sono-Assisted Surface Energy Driven Assembly of 2D Materials on Flexible Polymer Substrates: A Green Assembly Method Using Water

The challenges in achieving a green and scalable integration of two-dimensional (2D) materials with flexible polymer substrates present a major barrier for the application of 2D materials, such as graphene, MoS2, and h-BN for flexible devices. Here, we create a sono-assisted surface energy driven assembly (SASEDA) method that can achieve foot-scale to micrometer-scale assembly of 2D materials, form a conductive network in as short as 10 s, and build hierarchical and hybrid flexible devices such as sensors, resistors, and capacitors by using water as the dispersion solvent. SASEDA highlights two counterintuitive innovations. First, we use an 'unfavorable' solvent (i.e., water) for both 2D materials (e.g., graphene, MoS2, and h-BN) and polymer substrates (e.g., polydimethylsiloxane) to drive the assembly process. Second, we use a weak sono-field (0.3 W/cm2) generated by a regular sonication bath cleaner to enhance the assembly efficiency and reorganize and unify the assembly network. This method and its principle pave the way toward affordable large-scale 2D material-based flexible devices.

2D material↗
DOE OSTI2022

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Layout-Aware I/O Scheduling for Terabits Data Movement

Many science facilities, such as the Department of Energy s Leadership Computing Facilities and experimental facilities including the Spallation Neutron Source, Stanford Linear Accelerator Center, and Advanced Photon Source, produce massive amounts of experimental and simulation data. These data are often shared among the facilities and with collaborating institutions. Moving large datasets over the wide- area network (WAN) is a major problem inhibiting collaboration. Next- generation, terabit-networks will help alleviate the problem, however, the parallel storage systems on the end-system hosts at these institutions can become a bottleneck for terabit data movement. The parallel storage system (PFS) is shared by simulation systems, experimental systems, analysis and visualization clusters, in addition to wide-area data movers. These competing uses often induce temporary, but significant, I/O load imbalances on the storage system, which impact the performance of all the users. The problem is a serious concern because some resources are more expensive (e.g. super computers) or have time-critical deadlines (e.g. experimental data from a light source), but parallel file systems handle all requests fairly even if some storage servers are under heavy load. This paper investigates the problem of competing workloads accessing the parallel file system and how the performance of wide-area data movement can be improved in these environments. First, we study the I/O load imbalance problems using actual I/O performance data collected from the Spider storage system at the Oak Ridge Leadership Computing Facility. Second, we present I/O optimization solutions with layout-awareness on end-system hosts for bulk data movement. With our evaluation, we show that our I/O optimization techniques can avoid the I/O congested disk groups, improving storage I/O times on parallel storage systems for terabit data movement.

I/O and Storage↗
DOE OSTI2020

Replication Data for: Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Overall Water Splitting with Room-Temperature Synthesized NiFe Oxyfluoride Nanoporous Films

Freestanding and lightweight thin-films were rationally designed to serve as robust electrodes for renewable energy applications. A facile and scalable nanomanufacturing process was developed to fabricate these transformative thin-film electrodes (iron group mixed oxides) that exhibit a nanoporous structure and controllable composition. More specifically, electrodeposition and anodic treatments were employed to produce freestanding and lightweight metal oxides nanoporous layers (NPL). These NPL can be directly used as flexible and additive-free electrodes for renewable energy generation (water splitting) and storage (supercapacitor) applications without requiring binders and current collector and other additives. Significantly enhanced electrochemical performance was achieved due to the unique merits of the NPL: i) highly porous structure considerably increases the electrode/electrolyte interface, which facilitate electrochemical reactions; ii) NPL substantially increase the number of active sites that are favorable in electrochemical reactions; iii) residual metal network within the NPL forms a conductive framework, drastically improving electrode strength, flexibility and conductivity.

36 MATERIALS SCIENCE↗