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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 325 records · Page 18

Micro-Pulse DIAL for Atmospheric Water Vapor Profiling (Phase I Final Report)

Water vapor plays a key role in Earth’s climate and weather patterns. Accurate, high resolution measurements of the water vapor vertical profiles in a region have been found to significantly improve the numerical weather prediction models’ short-term forecasts, precipitation forecasts, as well as severe weather forecasts. However, current measurement techniques provide very coarse resolution in space, time, and/or altitude. A network of continuously sampling water vapor profilers would greatly expand the scientific knowledge of the atmosphere and serve as a valuable input into numerical weather models. This would be of interest not only to the Department of Energy’s Environmental Sciences Division but to other institutions such as the National Weather Service, the National Center for Atmospheric Research, the Federal Emergency Management Agency, and the military.

54 ENVIRONMENTAL SCIENCES↗

Neural Network Modeling of Degradation of Solar Cells

Neural network modeling has been used to predict the degradation in conversion efficiency of solar cells in this work. The model takes intensity of light, temperature and exposure time as inputs and predicts the conversion efficiency of the solar cell. Backpropagation algorithm has been used to train the network. It is found that the neural network model satisfactorily predicts the degradation in efficiency of the solar cell with exposure time. The error in the computed results, after comparison with experimental results, lies in the range of 0.005-0.01, which is quite low.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Biology Inspired Approach for Communal Behavior in Sensor Networks

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

Jones, Kennie H.↗

Resolving intragranular stress fields in plastically deformed titanium using point-focused high-energy diffraction microscopy

Abstract The response of a polycrystalline material to a mechanical load depends not only on the response of each individual grain, but also on the interaction with its neighbors. These interactions lead to local, intragranular stress concentrations that often dictate the initiation of plastic deformation and consequently the macroscopic stress–strain behavior. However, very few experimental studies have quantified intragranular stresses across bulk, three-dimensional volumes. In this work, a synchrotron X-ray diffraction technique called point-focused high-energy diffraction microscopy (pf-HEDM) is used to characterize intragranular deformation across a bulk, plastically deformed, polycrystalline titanium specimen. The results reveal the heterogenous stress distributions within individual grains and across grain boundaries, a stress concentration between a low and high Schmid factor grain pair, and a stress gradient near an extension twinning boundary. This work demonstrates the potential for the future use of pf-HEDM for understanding the local deformation associated with networks of grains and informing mesoscale models. Graphical abstract

36 MATERIALS SCIENCE↗

Machine-learning Kohn–Sham potential from dynamics in time-dependent Kohn–Sham systems

Abstract The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn–Sham potential of a time-dependent Kohn–Sham (TDKS) system is proposed. The method is based on the dynamics of the Kohn–Sham system and does not require any data on the exact Kohn–Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn–Sham potential matches the exact Kohn–Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn–Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn–Sham potential in the TDKS system.

97 MATHEMATICS AND COMPUTING↗

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↗

6th Annual Earth System Grid Federation Face-to-Face Conference Report, December 5–9, 2016, Washington, D.C.

Since its inception in the late 1990s, the Earth System Grid Federation (ESGF) has evolved significantly as a system. As technology changed and its user base grew larger and more diverse, ESGF gradually developed into the state-of-the-art federated system it is today. Due to the enormity of its scope and the urgency of community needs, many features had to be tested in the real world during the expansion process. The rollout of Version 2.4 in 2016 culminated a series of releases and user feedback initiatives that began in 2013. During this 3-year period, ESGF hosted user group meetings to better understand how the system was being used; this information was then leveraged to begin Version 3.0 planning and preparations. The user meetings were collectively led and organized by the U.S. Department of Energy (DOE), the Infrastructure for the European Network for Earth System Modelling (IS-ENES), the U.S. National Aeronautics and Space Administration (NASA), the Australian National Computational Infrastructure (NCI), the U.S. National Oceanic and Atmospheric Administration (NOAA), and the U.S. National Science Foundation (NSF). The information emerging from the user group meetings influenced requirements, development, and operations. In 2015, representatives from a significant fraction of projects that use ESGF to disseminate and analyze data attended the Fifth Annual ESGF Face-to-Face (F2F) Conference (DOI: 10.2172/1253685). Attendees provided important feedback regarding current and future community data use cases. Discussions focused on maintaining essential operations while developing new and improved software to handle ever-increasing data variety, complexity, velocity, and volume—a task accomplished by the entire consortium.

97 MATHEMATICS AND COMPUTING↗

Silicon-embedded copper nanostructure network for high energy storage

Provided herein are nanostructure networks having high energy storage, electrochemically active electrode materials including nanostructure networks having high energy storage, as well as electrodes and batteries including the nanostructure networks having high energy storage. According to various implementations, the nanostructure networks have high energy density as well as long cycle life. In some implementations, the nanostructure networks include a conductive network embedded with electrochemically active material. In some implementations, silicon is used as the electrochemically active material. The conductive network may be a metal network such as a copper nanostructure network. Methods of manufacturing the nanostructure networks and electrodes are provided. In some implementations, metal nanostructures can be synthesized in a solution that contains silicon powder to make a composite network structure that contains both. The metal nanostructure growth can nucleate in solution and on silicon nanostructure surfaces.

25 ENERGY STORAGE↗

Silicon-embedded copper nanostructure network for high energy storage

Provided herein are nanostructure networks having high energy storage, electrochemically active electrode materials including nanostructure networks having high energy storage, as well as electrodes and batteries including the nanostructure networks having high energy storage. According to various implementations, the nanostructure networks have high energy density as well as long cycle life. In some implementations, the nanostructure networks include a conductive network embedded with electrochemically active material. In some implementations, silicon is used as the electrochemically active material. The conductive network may be a metal network such as a copper nanostructure network. Methods of manufacturing the nanostructure networks and electrodes are provided. In some implementations, metal nanostructures can be synthesized in a solution that contains silicon powder to make a composite network structure that contains both. The metal nanostructure growth can nucleate in solution and on silicon nanostructure surfaces.

25 ENERGY STORAGE↗

An effect of the networks of the subgrain boundaries on spectral responses of thick CdZnTe detectors

CdZnTe (CZT) crystals used for nuclear-radiation detectors often contain high concentrations of subgrain boundaries and networks of poligonized dislocations that can significantly degrade the performance of semiconductor devices. These defects exist in all commercial CZT materials, regardless of their growth techniques and their vendor. We describe our new results from examining such detectors using IR transmission microscopy and white X-ray beam diffraction topography. We emphasize the roles on the devices performances of networks of subgrain boundaries with low dislocation densities, such as poligonized dislocations and mosaic structures. Specifically, we evaluated their effects on the gamma-ray responses of thick, >10 mm, CZT detectors. Our findings set the lower limit on the energy resolution of CZT detectors containing dense networks of subgrain boundaries, and walls of dislocations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Roles of the {beta} subunit hinge domain in ATP synthase F{sub 1} sector: Hydrophobic network formed by introduced {beta}Phe174 inhibits subunit rotation

The ATP synthase {beta} subunit hinge domain ({beta}Phe148 {approx} {beta}Gly186, P-loop/{alpha}-helixB/loop/{beta}-sheet4, Escherichia coli residue numbering) dramatically changes in conformation upon nucleotide binding. We previously reported that F{sub 1} with the {beta}Ser174 to Phe mutation in the domain lowered the {gamma} subunit rotation speed, and thus decreased the ATPase activity [M. Nakanishi-Matsui, S. Kashiwagi, T. Ubukata, A. Iwamoto-Kihara, Y. Wada, M. Futai, Rotational catalysis of Escherichia coli ATP synthase F{sub 1} sector. Stochastic fluctuation and a key domain of the {beta} subunit, J. Biol. Chem. 282 (2007) 20698-20704.]. Homology modeling indicates that the amino acid replacement induces a hydrophobic network, in which the {beta}Met159, {beta}Ile163, and {beta}Ala167 residues of the {beta} subunit are involved together with the mutant {beta}Phe174. The network is expected to stabilize the conformation of {beta}{sub DP} (nucleotide-bound form of the {beta} subunit), resulting in increased activation energy for transition to {beta}{sub E} (empty {beta} subunit). The modeling further predicts that replacement of {beta}Met159 with Ala or Ile weakens the hydrophobic network. As expected, these two mutations experimentally suppressed the ATPase activities as well as subunit rotation of {beta}S174F. Furthermore, the rotation rate decreased with the increase of the strength in the hydrophobic network. These results indicate that the smooth conformational change of the {beta} subunit hinge domain is pertinent for the rotational catalysis.

60 APPLIED LIFE SCIENCES↗

Understanding the Effect of Single Atom Cationic Defect Sites in an Al 2 O 3 (012) Surface on Altering Selenate and Sulfate Adsorption: An Ab Initio Study

Adsorption is a promising under-the-sink selenate remediation technique for distributed water systems. Recently it was shown that adsorption induced water network rearrangement control adsorption energetics on the α-Al 2 O 3 (012) surface. Here, we aim to elucidate the relative importance of the water network effects and surface cation identity on controlling selenate and sulfate adsorption energy using density functional theory calculations. Density functional theory (DFT) calculations predicted the adsorption energies of selenate and sulfate on nine transition metal cations (Sc–Cu) and two alkali metal cations (Ga and In) in the α-Al 2 O 3 (012) surface under simulated acidic and neutral pH conditions. We find that the water network effects had a larger impact on the adsorption energy than the cationic identity. However, cation identity secondarily controlled adsorption. Most cations decreased the adsorption energy, weakening the overall performance, the larger Sc and In cations enabled inner-sphere adsorption in acidic conditions because they relaxed outward from the surface, providing more space for adsorption. Additionally, only Ti induced Se selectivity over S by reducing the adsorbing selenate to selenite but not reducing the sulfate. Altogether, this study indicates that tuning water network structure will likely have a larger impact than tuning cation–selenate interactions for increasing adsorbate effectiveness.

Adsorption↗

Construction of Blaze at the University of Illinois at Chicago: A Shared, High-Performance, Visual Computer for Next-Generation Cyberinfrastructure-Accelerated Scientific, Engineering, Medical and Public Policy Research

The Blaze high-performance visual computing system serves the high-performance computing research and education needs of University of Illinois at Chicago (UIC). Blaze consists of a state-of-the-art, networked, computer cluster and ultra-high-resolution visualization system called CAVE2(TM) that is currently not available anywhere in Illinois. This system is connected via a high-speed 100-Gigabit network to the State of Illinois' I-WIRE optical network, as well as to national and international high speed networks, such as the Internet2, and the Global Lambda Integrated Facility. This enables Blaze to serve as an on-ramp to national cyberinfrastructure, such as the National Science Foundation’s Blue Waters petascale computer at the National Center for Supercomputing Applications at the University of Illinois at Chicago and the Department of Energy’s Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. DOE award # DE-SC005067, leveraged with NSF award #CNS-0959053 for “Development of the Next-Generation CAVE Virtual Environment (NG-CAVE),” enabled us to create a first-of-its-kind high-performance visual computing system. The UIC Electronic Visualization Laboratory (EVL) worked with two U.S. companies to advance their commercial products and maintain U.S. leadership in the global information technology economy. New applications are being enabled with the CAVE2/Blaze visual computing system that is advancing scientific research and education in the U.S. and globally, and help train the next-generation workforce.

47 OTHER INSTRUMENTATION↗

The structure of liquid water up to 360 MPa from x-ray diffraction measurements using a high Q-range and from molecular simulation

X-ray diffraction measurements of liquid water are reported at pressures up to 360 MPa corresponding to a density of 0.0373 molecules per Å{sup 3}. The measurements were conducted at a spatial resolution corresponding to Q{sub max} = 16 Å{sup −1}. The method of data analysis and measurement in this study follows the earlier benchmark results reported for water under ambient conditions having a density of 0.0333 molecules per Å{sup 3} and Q{sub max} = 20 Å{sup −1} [J. Chem. Phys. 138, 074506 (2013)] and at 70 °C having a density of 0.0327 molecules per Å{sup 3} and Q{sub max} = 20 Å{sup −1} [J. Chem. Phys. 141, 214507 (2014)]. The structure of water is very different at these three different T and P state points and thus they provide the basis for evaluating the fidelity of molecular simulation. Measurements show that at 360 MPa, the 4 waters residing in the region between 2.3 and 3 Å are nearly unchanged: the peak position, shape, and coordination number are nearly identical to their values under ambient conditions. However, in the region above 3 Å, large structural changes occur with the collapse of the well-defined 2nd shell and shifting of higher shells to shorter distances. The measured structure is compared to simulated structure using intermolecular potentials described by both first-principles methods (revPBE-D3) and classical potentials (TIP4P/2005, MB-pol, and mW). The DFT-based, revPBE-D3, method and the many-body empirical potential model, MB-pol, provide the best overall representation of the ambient, high-temperature, and high-pressure data. The revPBE-D3, MB-pol, and the TIP4P/2005 models capture the densification mechanism, whereby the non-bonded 5th nearest neighbor molecule, which partially encroaches the 1st shell at ambient pressure, is pushed further into the local tetrahedral arrangement at higher pressures by the more distant molecules filling the void space in the network between the 1st and 2nd shells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗

Sample Complexity of Power System State Estimation Using Matrix Completion

In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system - a problem known as sample complexity analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically underdetermined due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on matrix completion. In particular, a constrained matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.

24 POWER TRANSMISSION AND DISTRIBUTION↗