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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Generalized parton distributions and exclusive processes

In last fifteen years, GPDs have emerged as a powerful tool to reveal such aspects of the QCD structure of the nucleon as: - 3D parton correlations and distributions; - spin content of the nucleon. Further advances in the field of GPDs and hard exclusive processes rely on: - developments in theory and new methods in phenomenology such as new flexible parameterizations, neural networks, global QCD fits - new high-precision data covering unexplored kinematics: JLab at 6 and 12 GeV, Hermes with recoil detector, Compass, EIC. This slide-show presents: Nucleon structure in QCD, particularly hard processes, factorization and parton distributions; and a brief overview of GPD phenomenology, including basic properties of GPDs, GPDs and QCD structure of the nucleon, and constraining GPDs from experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation based robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.

42 ENGINEERING↗
DOE OSTI2019

Distribution of Ni, Co, Precious, and Platinum Group Metals in Copper Making Process

The distribution coefficients of Ni, Co, Ag, Au, Pt, and Pd between molten copper and silica-saturated iron silicate slags (L{sub Me}{sup Cu/s}) were measured experimentally. The distribution behaviors were studied under typical conditions of copper converting and fire refining, i.e., from 1250 °C to 1350 °C, and from 10{sup −8} to 10{sup −4} atm oxygen partial pressure. The coefficients were determined as the ratios of the trace element weight concentrations measured in situ, directly from the equilibrated metal and slag phases. For the quantitative elemental analysis of the phases, state-of-the-art analytic techniques, including electron probe microanalysis and laser ablation-inductively coupled plasma-mass spectrometry, were employed. The distribution coefficients L{sub Me}{sup Cu/s} determined can be arranged in the following order: Pt > Au > Pd >> Ag > (Cu) > Ni > Co > (Fe).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tracking snowmelt during hydrological surface processes using a distributed hydrological model in a mesoscale basin on the Tibetan Plateau

We report that mountain snowpack is an important water resource for the high altitude and latitude regions where the terrain is complex. However, the snowmelt pathway and its actual contribution to streamflow and soil moisture are rarely reported and remain unclear in such regions. To fill in this knowledge gap, we incorporate a snowmelt pathway tracking algorithm to a high-resolution physics-based distributed-hydrology-soil-vegetation model (DHSVM), to track snowmelt movement and quantify snowmelt contributions in the surface hydrologic processes. A simple reservoir operation scheme is also incorporated in the model. The modified model is applied to a dammed meso-scale watershed in the northeast Tibetan Plateau, China to study the snow and reservoir effects. The results show that annual snow contribution to soil moisture (SC-SM) and snow contribution to streamflow (SC-S) significantly decrease over 1965-2019. At a monthly scale, SC-SM has the largest amplitude at the top soil layer and its peak in the deeper layer lags behind the upper layer, and mean monthly SC-S at all stations show bimodal distributions corresponding to snowfall season. Reservoir regulation has subtle impacts (≤2.0%) on SC-S. If the current climate change rate continues, monthly and annual streamflow at the outlet will decrease primarily due to snowpack reduction. To mitigate climate change impacts, better water resource management is needed in this watershed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Solar Automated Permit Processing Software for Distributed PV (Cooperative Research and Development Final Report, CRADA Number CRD-19-00825)

NREL and its project partners built and commercialized the novel Solar Automated Permitting Processing Plus (SolarAPP+) software platform that delivers instant residential rooftop solar permits. The tool provides installers with a standard portal for entering permit information for residential solar systems across all Authorities Having Jurisdiction (AHJs) using SolarAPP+, thereby providing a streamlined plan review form, transparency into permitting timelines, and required specifications. The project has proven to reduce permitting timelines from as many as 20 business days to zero, delivering solar projects faster in the four participating AHJs using the platform. Streamlining permitting timelines, all else equal, results in consumers installing their systems and reducing their electricity bills faster. The continued expansion of SolarAPP+ features and adoption nationwide will expand the benefits of the SolarAPP+ beyond the current user base.

14 SOLAR ENERGY↗
DOE OSTI2025

S-QGPU: Shared quantum gate processing unit for distributed quantum computing

We propose a distributed quantum computing (DQC) architecture in which individual small-sized quantum computers are connected to a shared quantum gate processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit gate modules for remote gate operations. In contrast to conventional DQC systems, where each quantum computer is equipped with dedicated communication qubits, S-QGPU effectively pools the resources (e.g., the communication qubits) together for remote gate operations, and, thus, significantly reduces the cost of not only the local quantum computers but also the overall distributed system. Our preliminary analysis and simulation show that S-QGPU's shared resources for remote gate operations enable efficient resource utilization. When not all computing qubits (also called data qubits) in the system require simultaneous remote gate operations, S-QGPU-based DQC architecture demands fewer communication qubits, further decreasing the overall cost. Alternatively, with the same number of communication qubits, it can support a larger number of simultaneous remote gate operations more efficiently, especially when these operations occur in a burst mode.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Reconstructing probability distributions with Gaussian processes

Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). Here, the reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new chain with 40 times as many points in only 30 min. Our likelihood reconstruction tool is made publicly available online.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Microstructure Evolution and Microhardness Distribution of Copper Processed Using Multiple Passes of Elliptical Cross-Sectional Spiral Equal-Channel Extrusion

The aim of this work is to study the effect of six-pass elliptical cross-sectional spiral equal-channel extrusion (ECSEE) on the microstructure and performance of ultrafine-grained (UFG) copper. Equiaxed grains of average grain size of less than 1 μm are formed into shear bands in the low strain region of ECSEE deformed specimen. More homogeneous and equiaxed microstructure with high misorientation angles is obtained in the high strain. Moreover, the microstructure evolution of ECSEE-induced copper is a dynamic equilibrium process of shear deformation accompanying the interactions of high dislocation density, cellular structure and high-angle grain boundaries. The grain ECSEE refinement mechanism is described as the formation process of dislocations, cells, local grain sub-boundaries rotation and large angle grain restructure. The significantly non-uniform hardness distribution is consistent with the deformation behavior and microstructure refinement in the ECSEE-induced specimen. The homogeneity of microstructure and hardness improves as the ECSEE pass increases.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Distributed computing strategies for processing of FT-ICR MS imaging datasets for continuous mode data visualization

High-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging enables the spatial mapping and identification of biomolecules from complex surfaces. The need for long time-domain transients, and thus large raw file sizes, results in a large amount of raw data (“big data”) that must be processed efficiently and rapidly. This can be compounded by largearea imaging and/or high spatial resolution imaging. For FT-ICR, data processing and data reduction must not compromise the high mass resolution afforded by the mass spectrometer. The continuous mode “Mosaic Datacube” approach allows high mass resolution visualization (0.001 Da) of mass spectrometry imaging data, but requires additional processing as compared to featurebased processing. We describe the use of distributed computing for processing of FT-ICR MS imaging datasets with generation of continuous mode Mosaic Datacubes for high mass resolution visualization. An eight-fold improvement in processing time is demonstrated using a Dutch nationally available cloud service.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Understanding and Enhancing Silicon Nanoparticle Distribution during Electrode Processing

Silicon-dominant anodes are of great interest because of their potential to boost the cell-level energy of state-of-the-art Li-ion batteries. While silicon materials have been extensively studied, understanding interactions at the electrode level has recieved little attention, especially the coating process of Si particles, which plays an equally important role in unlocking the full potential of silicon anodes. Herein, the electrode processing of a Si-dominated anode (52.8 wt%, 3.5–4.5 mAh cm −2 ) is being investigated to understand the relationship of processing on the morphology and properties of Si anodes at the electrode level. It has been found that almost-undetectable Si agglomerates easily form during electrode processing, which grow into largeprotrusions after lithiation and trigger potential internal shorting and self-discharge problems. A facile slurry filtration step is proposed to homogenize the particle distribution within Si-dominant electrodes which improves the electrochemical performance and storage stability of Si-based Li ion batteries.

Wu, Bingbin↗
DOE OSTI2011

Testing the process dependence of the Sivers function via hadron distributions inside a jet

We study the process dependence of the Sivers function by considering the impact of color-gauge invariant initial and final state interactions on transverse spin asymmetries in proton-proton scattering reactions within the framework of the transverse momentum dependent (TMD), generalized parton model. To this aim, we consider the azimuthal distribution of leading pions inside a fragmenting jet as well as single inclusive jet asymmetry in polarized proton-proton collisions. In contrast to single inclusive pion production, in both cases we can isolate the Sivers contribution and thereby study its process dependence. The predictions for the Sivers asymmetry obtained with and without inclusion of color gauge factors are comparable in size but with opposite signs. We conclude that both processes represent unique opportunities to discriminate among the two approaches and test the universality properties of the Sivers function in hadronic scattering reactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Morphostatistical characterization of the spatial galaxy distribution through Gibbs point processes

ABSTRACT This paper proposes a morphostatistical characterization of the galaxy distribution through spatial statistical modelling based on inhomogeneous Gibbs point processes. The galaxy distribution is supposed to exhibit two components. The first one is related to the major geometrical features exhibited by the observed galaxy field, here, its corresponding filamentary pattern. The second one is related to the interactions exhibited by the galaxies. Gibbs point processes are statistical models able to integrate these two aspects in a probability density, controlled by some parameters. Several such models are fitted to real observational data via the ABC shadow algorithm. This algorithm provides simultaneous parameter estimation and posterior-based inference, hence allowing the derivation of the statistical significance of the obtained results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Distributed caching for processing raw arrays

As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority -- by as much as two orders of magnitude -- of the proposed framework over existing techniques in terms of cache overhead and workload execution time.

Zhao, Weijie↗
DOE OSTI2018

Multifaceted processes controlling the distribution of hazardous compounds in the spontaneous combustion of coal and the effect of these compounds on human health

Highlights: • Electron bean reveals the interactions among nanoparticles and hazardous compounds. • Samples were obtained from the most big Brazilian coal power plant. • Overall, exposure to hazardous compounds in coal fire pose health risks. Pollution generated by hazardous elements and persistent organic compounds that affect coal fire is a major environmental concern because of its toxic nature, persistence, and potential risk to human health. The coal mining activities are growing in the state of Santa Catarina in Brazil, thus the collateral impacts on the health and economy are yet to be analyzed. In addition, the environment is also enduring the collateral damage as the waste materials directly influence the coal by-products applied in civil constructions. This study was aimed to establish the relationships between the composition, morphology, and structural characteristics of ultrafine particles emitted by coal mine fires. In Brazil, the self-combustions produced by Al–Ca–Fe–Mg–Si coal spheres are rich in chalcophile elements (As, Cd, Cu, Hg, Pb, Sb, Se, Sn, and Zn), lithophile elements (Ce, Hf, In, La, Th, and U), and siderophile elements (Co, Cr, Mo, Fe, Ni, and V). The relationship between nanomineralogy and the production of hazardous elements as analyzed by advanced methods for the geochemical analysis of different materials were also delineated. The information obtained by the mineral substance analysis may provide a better idea for the understanding of coal-fire development and assessing the response of particular coal in different combustion processes.

54 ENVIRONMENTAL SCIENCES↗