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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Observation and characterization of a cosmic muon neutrino flux from the northern hemisphere using six years of IceCube data

The IceCube Collaboration has previously discovered a high-energy astrophysical neutrino flux using neutrino events with interaction vertices contained within the instrumented volume of the IceCube detector. We present a complementary measurement using charged current muon neutrino events where the interaction vertex can be outside this volume. As a consequence of the large muon range the effective area is significantly larger but the field of view is restricted to the Northern Hemisphere. IceCube data from 2009 through 2015 have been analyzed using a likelihood approach based on the reconstructed muon energy and zenith angle. At the highest neutrino energies between 194 TeV and 7.8 PeV a significant astrophysical contribution is observed, excluding a purely atmospheric origin of these events at 5.6σ significance. The data are well described by an isotropic, unbroken power-law flux with a normalization at 100 TeV neutrino energy of (0.90{sub −0.27}{sup +0.30})×10{sup −18} GeV{sup −1} cm{sup −2} s{sup −1} sr{sup −1} and a hard spectral index of γ=2.13±0.13. The observed spectrum is harder in comparison to previous IceCube analyses with lower energy thresholds which may indicate a break in the astrophysical neutrino spectrum of unknown origin. The highest-energy event observed has a reconstructed muon energy of (4.5±1.2) PeV which implies a probability of less than 0.005% for this event to be of atmospheric origin. Analyzing the arrival directions of all events with reconstructed muon energies above 200 TeV no correlation with known γ-ray sources was found. Using the high statistics of atmospheric neutrinos we report the current best constraints on a prompt atmospheric muon neutrino flux originating from charmed meson decays which is below 1.06 in units of the flux normalization of the model in Enberg et al.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy

We present details on the observing strategy, data-processing techniques, and spectroscopic targeting algorithms for the first three years of operation for the Dark Energy Survey Supernova Program (DES-SN). This five-year program using the Dark Energy Camera mounted on the 4 m Blanco telescope in Chile was designed to discover and follow supernovae (SNe) Ia over a wide redshift range (0.05 < z < 1.2) to measure the equation-of-state parameter of dark energy. We describe the SN program in full: strategy, observations, data reduction, spectroscopic follow-up observations, and classification. From three seasons of data, we have discovered 12,015 likely SNe, 308 of which have been spectroscopically confirmed, including 251 SNe Ia over a redshift range of 0.017 < z < 0.85. We determine the effective spectroscopic selection function for our sample and use it to investigate the redshift-dependent bias on the distance moduli of SNe Ia we have classified. The data presented here are used for the first cosmology analysis by DES-SN (“DES-SN3YR”), the results of which are given in Dark Energy Survey Collaboration et al. The 489 spectra that are used to define the DES-SN3YR sample are publicly available at https://des.ncsa.illinois.edu/releases/sn.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Measuring Alignments between Galaxies and the Cosmic Web at z ∼ 2–3 Using IGM Tomography

Many galaxy formation models predict alignments between galaxy spin and the cosmic web (i.e., directions of filaments and sheets), leading to an intrinsic alignment between galaxies that creates a systematic error in weak-lensing measurements. These effects are often predicted to be stronger at high redshifts (z ≳ 1) that are inaccessible to massive galaxy surveys on foreseeable instrumentation, but IGM tomography of the Lyα forest from closely spaced quasars and galaxies is starting to measure the z ∼ 2–3 cosmic web with requisite fidelity. Using mock surveys from hydrodynamical simulations, we examine the utility of this technique, in conjunction with coeval galaxy samples, to measure alignment between galaxies and the cosmic web at z ∼ 2.5. We show that IGM tomography surveys with ≲5 h {sup −1} Mpc sightline spacing can accurately recover the eigenvectors of the tidal tensor, which we use to define the directions of the cosmic web. For galaxy spins and shapes, we use a model parameterized by the alignment strength, Δ〈cos θ〉, with respect to the tidal tensor eigenvectors from the underlying density field, and also consider observational effects such as errors in the galaxy position angle, inclination, and redshift. Measurements using the upcoming ∼1 deg{sup 2} CLAMATO tomographic survey and 600 coeval zCOSMOS-Deep galaxies should place 3σ limits on extreme alignment models with Δ〈cos θ〉∼0.1, but much larger surveys encompassing >10,000 galaxies, such as Subaru PFS, will be required to constrain models with Δ〈cos θ〉∼0.03. These measurements will constrain models of galaxy–cosmic web alignment and test tidal torque theory at z ∼ 2, improving our understanding of the physics of intrinsic alignments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios

Abstract. To support the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on Scenarios and Models is carrying out an intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM). The goals of BES-SIM are (1) to project the global impacts of land-use and climate change on biodiversity and ecosystem services (i.e., nature's contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models. BES-SIM uses three scenarios combining specific Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) – SSP1xRCP2.6, SSP3xRCP6.0, SSP5xRCP8.6 – to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenario selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem services models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modeling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modeling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios.

58 GEOSCIENCES↗
DOE OSTI2015

Parameterizing deep convection using the assumed probability density function method

Due to their coarse horizontal resolution, present-day climate models must parameterize deep convection. This paper presents single-column simulations of deep convection using a probability density function (PDF) parameterization. The PDF parameterization predicts the PDF of subgrid variability of turbulence, clouds, and hydrometeors. That variability is interfaced to a prognostic microphysics scheme using a Monte Carlo sampling method.The PDF parameterization is used to simulate tropical deep convection, the transition from shallow to deep convection over land, and mid-latitude deep convection.These parameterized single-column simulations are compared with 3-D reference simulations. The agreement is satisfactory except when the convective forcing is weak. The same PDF parameterization is also used to simulate shallow cumulus and stratocumulus layers. The PDF method is sufficiently general to adequately simulate these five deep, shallow, and stratiform cloud cases with a single equation set. This raises hopes that it may be possible in the future, with further refinements at coarse time step and grid spacing, to parameterize all cloud types in a large-scale model in a unified way.

Storer, R. L.↗
DOE OSTI2014

Trade-offs between Biofuels Energy Production, Land Use and Water Use in Florida

Objective of the presentation is to document land use and water use implications of biomass production to demonstrate the overall resources implications associated with bioethanol production for Florida’s transportation sector needs. Rationale for using biofuels (BF) is explained, so are advantages & challenges of BF production and use. Land use changes (LUC) in Florida are presented and consequences outlined. It is documented that Florida’s agricultural land is a very limited resource, with only 0.43 ac/person comparing to the global average of 1.71 ac/person. The direct relation of increased biofuels production causing increased water use is explained. Favorable climate, water resources, advanced research, traditional leading agricultural role, minor oil reserves, no refineries and increasing energy demands are the main reasons why Florida considers pursuing BF production in large scale. Eight various bioethanol crops produced in Florida were considered in this study (Miscanthus, Switchgrass, Sweet Sorghum, Corn, Elephantgrass, Sugarcane, Energycane, Eucalyptus). Biomass yield and bioethanol yield of these crops are documented. Bioethanol needs of Florida are estimated and related land requirements for the needed bioethanol production calculated. Projections for various bioethanol blends (E15 to E85) are then presented. Finally, water demand for biofuels production is quantified. It is concluded that land use requirement for production of all ethanol in E85 fuel blend in Florida is roughly the same as the total available ag land in Florida for the best yielding biofuels crops (energycane, eucalyptus). Water demand for production of all ethanol needed for E100 would increase current overall water consumption in Florida between 65% and 100% for the most common biofuels crops. Vehicular energy is only 33% of Floridians energy consumption, so even all Florida’s agricultural land was given up for biofuels, it would still produce only 33% of Florida’s total energy needs. Still, bioethanol (primarily cellulosic) produced in Florida has a potential to meet a significant portion of the State’s transportation needs. Assuming no change in food production and consumption habits in Florida, the likely result of biofuels sector expansion would be the conversion of natural lands or low-intensity agricultural lands into high-intensity biomass production and the associated increased water consumption and water quality implications.

09 BIOMASS FUELS↗
DOE OSTI2018

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Highlights: • We assessed how well a year-2006 satellite-based LUR model captures historical NO{sub 2}. • We used three methods to estimate annual mean NO{sub 2} during 1990–2005. • We measured their performance using standard LUR validation techniques. • Back-extrapolated 2006 levels captured up to 76% of spatial variability (90–05). Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO{sub 2}) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO{sub 2} predictions: (1) ‘do nothing’ (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006–2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R{sup 2} and mean-square error R{sup 2} (MSE-R{sup 2}), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R{sup 2} = 31%) and 80% (2003; MSE-R{sup 2} = 78%) of spatial variability in NO{sub 2} in a given year, and 76% (MSE-R{sup 2} = 72%) averaged over 1990–2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO{sub 2} estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning

Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Nitrogen use efficiency evaluation of aerobic rice under field capacity water potential using {sup 15}N isotopic tracer technique

This study was carried out to evaluate the efficiency use of the nitrogen fertilizer on aerobic rice varieties MR219-4 and MR219-9 which were grown aerobically under field capacity water potential at the controlled environment area or shield house. Direct {sup 15}N isotope tracer method was used in this study, whereby the {sup 15}N isotope was utilized as a tracer for nitrogen nutrient uptake. {sup 15}N isotope presence in the samples is determined by using emission spectrometer analysis and percentage of total nitrogen is determined by using Kjeldahl method. {sup 15}N atom access value contained in the sample will be used in determining the effectiveness of the use of nitrogen in fertilizers through the specific calculation formulas. In this work, the data several data of nitrogen derived from fertilizer (Ndff), total nitrogen, nitrogen uptake and nitrogen use efficiency was obtained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2020

Activity Time-Use Patterns and Experienced Satisfaction: Travel Behavioral Dynamics from the 2010–2013 American Time-Use Survey Data

Central to the theory of activity-based approaches is the role of time-use and its impacts on various decision processes related to individual activity-travel patterns, thereby determining ones’ daily well-being. This research aims at quantifying and investigating the underlying correlation of time-use and happiness using the repeated cross-section data from the 2010-2013 American Time Use Survey Well-Being module. The survey specifically records reported levels of activity satisfaction as measured through – happiness, meaningfulness, tiredness, sadness and stress experienced from activity participation. Using these scores, we first construct a composite degree of activity satisfaction that a person experiences using latent class cluster analysis. A series of regression models are then estimated to jointly analyze episodic duration and satisfaction separately for each of years from 2010-2013, by controlling for socioeconomics, household lifecycle stages (29-groups based on age, gender, marital status and presence of children), activity characteristics and global life and health assessment measures. Overall, model estimation results reveal a strong negative correlation between episodic duration and happiness from individual activity engagement patterns. This correlation pattern is temporally stable for the years 2010-2013, with minor variations. Results also suggest that household lifecycle stage has a major impact on activity time-use and satisfaction behavior. Further, single parent and younger couples (25 years to 45 years) with children are more likely to engage in longer and less happy episodes due to family commitments. The model findings also show a strong correlation between health conditions and activity happiness.

Ravulaparthy, Srinath↗
DOE OSTI2025

Land-use analysis using infrastructure representations and high-resolution flood inundation mapping techniques

In the face of climate change and population growth in coastal regions, land-use analysis efforts are more challenging than ever. Land-use decision-makers in coastal communities are burdened with the difficult choices of where to place new homes versus other assets. While there has been an increased focus on hazard mitigation and disaster resilience in the field of planning, evidence points towards continued development in risk-prone areas including flood zones. Residential development within flood zones specifically continues to be a major issue. To help counter this trend, this study introduces a novel land-use analysis method, coupling topographic flood inundation mapping techniques with digital elevation model (DEM) adaptations. This Topographic Model Scenario Generation workflow can be used by planners early in the land-use decision making process and provides an alternative to high-computational hydraulic models. The analysis also includes the identification of strengths and weaknesses of topographic models' recognition of built infrastructure assets, adding to a limited body of knowledge addressing recommended uses of such models. Levees and canals prove particularly functional in this context while detention ponds less so, likely due to a lack of total water mass accountability. Lastly, we provide a functional demonstration in Southeast Texas to illustrate the workflow's ability to create multiple infrastructure scenarios and visualize their effects across different flood events.

42 ENGINEERING↗
DOE OSTI2018

Climate extremes, land–climate feedbacks and land-use forcing at 1.5°C

This article investigates projected changes in temperature and water cycle extremes at 1.5°C global warming, and highlights the role of land processes and land-use changes (LUC) for these projections. We provide new comparisons of changes in climate at 1.5°C vs 2°C based on empirical sampling analyses of transient simulations vs simulations from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) multi-model experiment. The two approaches yield overall similar results regarding changes in climate extremes on land, and reveal a substantial difference in regional extremes occurrence at 1.5°C vs 2°C. Land processes mediated through soil moisture feedbacks and land-use forcing play a major role for projected changes in extremes at 1.5°C in most mid-latitude regions, including densely populated areas in North America, Europe and Asia. This has important implications for low-emissions scenarios derived from Integrated Assessment Models (IAMs), which include major LUC in ambitious mitigation pathways (e.g. associated with increased bioenergy use), but are also shown to differ in the simulated LUC patterns. Biogeophysical effects from LUC are not considered in the development of IAM scenarios, but play an important role for projected regional changes in climate extremes, and are thus of high relevance for sustainable development pathways.

Seneviratne, Sonia I. (ORCID:0000000195282917)↗
DOE OSTI2023

Cybersecurity Assessment for a Behind-the-Meter Solar PV System: A Use Case for the DER-CF

The world's energy production is shifting toward lower-cost, cleaner, more efficient, and sustainable sources. The increasing numbers of distributed energy resources (DERs) are allowing for the rapid transformation of electric grids toward achieving the goal of energy decarbonization. Along with cleaner and more efficient energy, however, we must also aim for a secure energy future. Solar photovoltaic (PV) systems are an important part of this transition. This paper discusses a cybersecurity risk assessment for behind-the-meter DERs using a solar PV system as a use case of the Distributed Energy Resource Cybersecurity Framework (DER-CF) developed by the National Renewable Energy Laboratory. This poster presents a conference paper on the risk assessment processes and summarizes the DER-CF's use case recommendations to strengthen the cybersecurity posture of the electric grid.

cybersecurity↗
DOE OSTI2014

Increasing the Aromatic Selectivity of Quinoline Hydrogenolysis Using Pd/MOx–Al2O3

Catalysts consisting of Pd nanoparticles supported on highly dispersed TiOx–Al2O3, TaOx–Al2O3, and MoOx–Al2O3 are studied for catalytic quinoline hydrogenation and selective C–N bond cleavage at 275 °C and 20 bar H2. The Pd/MOx–Al2O3 materials exhibit significantly greater aromatic product selectivity and thus 10–15 % less required H2 for a given level of denitrogenation relative to an unmodified Pd/Al2O3 catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗