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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Brazil-USA Collaborative Research: Modifications by Anthropogenic Pollution of the Natural Atmospheric Chemistry and Particle Microphysics of the Tropical Rain Forest During the GoAmazon Intensive Operating Periods (IOPs)

Manaus, a city of nearly two million people, represents an isolated urban area having a distinct urban pollution plume within the otherwise pristine Amazon Basin. The plume has high concentrations of oxides of nitrogen and sulfur, carbon monoxide, particle concentrations, and soot, among other pollutants. Critically, the distinct plume in the setting of the surrounding tropical rain forest serves as a natural laboratory to allow direct comparisons between periods of pollution influence to those of pristine conditions. The funded activity of this report is related to the Brazil-USA collaborative project during the two Intensive Operating Periods (wet season, 1 Feb - 31 Mar 2014; dry season, 15 Aug - 15 Oct 2014) of GoAmazon2014/5. The project addresses key science questions regarding the modification of the natural atmospheric chemistry and particle microphysics of the forest by present and future anthropogenic pollution. The first objective of the project was to understand and quantify the interactions of biogenic and anthropogenic emissions with respect to the production of secondary organic material. In clean conditions in the Amazon basin, secondary organic material dominates the diameter distribution of the submicron particles. How and why is the diameter distribution shifted by pollution? The second objective followed from the first in that, although the diameter distribution is dominated by secondary organic material, the actual source of new particle production remains uncertain (i.e., the number concentration). The second objective was to test the hypothesis that new particles under natural conditions are produced as a result of evaporation of primary particles emitted by fungal spores as well as to investigate any shifts in this mechanism under pollution conditions, e.g., in consequence to the high concentrations of SO 2 in the pollution plume. Combined, the number-diameter distribution is the key connection to upscaling to the effects of aerosol particles on clouds and climate. Understanding this upscaling under clean and pollution conditions, including differences, was the third objective.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Entrainment–Mixing Characteristics through Direct Comparisons of Drop Size Distributions Using In Situ Observations from ACE-ENA

Abstract Constraining the impacts of entrainment and associated mixing (i.e., entrainment–mixing) on cloud properties continues to be difficult, partly due to observational uncertainties as well as a lacking consensus of which methodologies for diagnosing entrainment–mixing are most appropriate. This study introduces a novel method to evaluate the presence and degree of inhomogeneous and homogeneous mixing using ∼100 h of in situ observations from a research aircraft over the northeastern Atlantic. Specifically, drop size distributions are compared between regions containing negligible and significant entrainment for select flight legs, making a direct characterization of the degree of homogeneous and inhomogeneous mixing possible. A measure of drop concentration variance is used as a proxy variable to diagnose entrainment–mixing. Results correspond well with entrainment–mixing metrics, showing lower Damköhler numbers where drop size distributions shift toward smaller drop sizes (i.e., inhomogeneous mixing) and greater transition length scales where drop size distributions do not (i.e., homogeneous mixing). Inhomogeneous mixing occurs in most samples from Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA) (regardless of homogeneous mixing frequencies increasing with increasing spatial resolution from ∼100 to ∼10 m) and is associated with decreased drop size relative dispersion and both greater aerosol and drop concentrations compared with homogeneous mixing. Precipitating clouds have a greater frequency of homogeneous mixing compared with nonprecipitating clouds. The proposed methodology is similarly applied to in situ observations of southeast Pacific stratocumulus, shallow convective clouds over central Oklahoma and low-level clouds over the Southern Ocean. All four locations are primarily dominated by inhomogeneous mixing with minimal variability among each region.

Clouds↗
DOE OSTI2016

Generating multiple orbital angular momentum vortex beams using a metasurface in radio frequency domain

In this paper, an electromagnetic metasurface is designed, fabricated, and experimentally demonstrated to generate multiple orbital angular momentum (OAM) vortex beams in radio frequency domain. Theoretical formula of compensated phase-shift distribution is deduced and used to design the metasurface to produce multiple vortex radio waves in different directions with different OAM modes. The prototype of a practical configuration of square-patch metasurface is designed, fabricated, and measured to validate the theoretical analysis at 5.8 GHz. The simulated and experimental results verify that multiple OAM vortex waves can be simultaneously generated by using a single electromagnetic metasurface. The proposed method paves an effective way to generate multiple OAM vortex waves in radio and microwave wireless communication applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
DOE OSTI2018

Declining population trends of European mountain birds

Abstract Mountain areas often hold special species communities, and they are high on the list of conservation concern. Global warming and changes in human land use, such as grazing pressure and afforestation, have been suggested to be major threats for biodiversity in the mountain areas, affecting species abundance and causing distribution shifts towards mountaintops. Population shifts towards poles and mountaintops have been documented in several areas, indicating that climate change is one of the key drivers of species’ distribution changes. Despite the high conservation concern, relatively little is known about the population trends of species in mountain areas due to low accessibility and difficult working conditions. Thanks to the recent improvement of bird monitoring schemes around Europe, we can here report a first account of population trends of 44 bird species from four major European mountain regions: Fennoscandia, UK upland, south‐western (Iberia) and south‐central mountains (Alps), covering 12 countries. Overall, the mountain bird species declined significantly (−7%) during 2002–2014, which is similar to the declining rate in common birds in Europe during the same period. Mountain specialists showed a significant −10% decline in population numbers. The slope for mountain generalists was also negative, but not significantly so. The slopes of specialists and generalists did not differ from each other. Fennoscandian and Iberian populations were on average declining, while in United Kingdom and Alps, trends were nonsignificant. Temperature change or migratory behaviour was not significantly associated with regional population trends of species. Alpine habitats are highly vulnerable to climate change, and this is certainly one of the main drivers of mountain bird population trends. However, observed declines can also be partly linked with local land use practices. More efforts should be undertaken to identify the causes of decline and to increase conservation efforts for these populations.

Lehikoinen, Aleksi↗
DOE OSTI2021

Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2018

Climate Shifts Within Major Agricultural Seasons for +1.5 and +2.0 °C Worlds: HAPPI Projections and AgMIP Modeling Scenarios

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 °C World but overestimates the CO2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 °C and +2.0 °C worlds for adaptation planning in these climate stabilization targets.

Alexander C. Ruane↗
DOE OSTI2024

Elucidating the Impact of Cis – Trans Organic Structure Directing Agent Isomer Ratios on the Aluminum Distribution Within SSZ-39

Despite their widespread use, the mechanisms governing the synthesis of zeolite catalysts are still poorly understood. A notable example of this problem is the uncertainty surrounding the influence of synthesis conditions on the placement of Al atoms in the zeolite framework which determines the active sites available for catalytic species. In this work, the role of the cis to trans isomer ratio of the OSDA N,N-dimethyl-3-5-dimethylpiperidinium on the energetics of 26 distinct Al pair distributions in SSZ-39 is examined both in the presence and absence of Na using density functional theory calculations. The initial orientation of the OSDA was found to have a significant impact on the final energies present, necessitating the screening of a large number of initial orientations with force field calculations and single point DFT calculations. Ground state energies were found to vary significantly with the ratio of cis to trans OSDAs with a Boltzmann distribution revealing the most likely Al pair distributions shift from sharing the same 8 membered rings to sharing the same double six membered rings to having no shared subunits as one increases the amount of cis OSDA present within the framework. The presence of Na was found to favor Al pair distributions where both Als occupied the same 6-membered ring. When an implicit solvent model was used to evaluate ground state energies the ideal Na sites shifted from 6-membered rings to empty SSZ-39 cages while OSDA positions and orientations remained largely the same. To provide insight on how kinetic factors may influence Al distributions, formation energies we calculated for connected double six membered rings. Further, these formation energies revealed a preference for Al pairs to occupy the same 4-membered ring which indicates kinetic and thermodynamic control may lead to different Al distributions in SSZ-39.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Improving the sensitivity of J coupling measurements in solids with application to disordered materials

It has been shown previously that for magic angle spinning (MAS) solid state NMR the refocused INADEQUATE spin-echo (REINE) experiment can usefully quantify scalar (J) couplings in disordered solids. This paper focuses on the two z filter components in the original REINE pulse sequence, and investigates by means of a product operator analysis and fits to density matrix simulations the effects that their removal has on the sensitivity of the experiment and on the accuracy of the extracted J couplings. The first z filter proves unnecessary in all the cases investigated here and removing it increases the sensitivity of the experiment by a factor ∼1.1–2.0. Furthermore, for systems with broad isotropic chemical shift distributions (namely whose full widths at half maximum are greater than 30 times the mean J coupling strength), the second z filter can also be removed, thus allowing whole-echo acquisition and providing an additional √2 gain in sensitivity. Considering both random and systematic errors in the values obtained, J couplings determined by fitting the intensity modulations of REINE experiments carry an uncertainty of 0.2–1.0 Hz (∼1−10 %).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Polydispersity-Driven Block Copolymer Amphiphile Self-Assembly into Prolate-Spheroid Micelles

The aqueous self-assembly behavior of polydisperse poly(ethylene oxide-b-1,4-butadiene-b-ethylene oxide) (OBO) macromolecular triblock amphiphiles is examined to discern the implications of continuous polydispersity in the hydrophobic block on the resulting aqueous micellar morphologies of otherwise monodisperse polymer surfactants. The chain length polydispersity and implicit composition polydispersity of these samples furnishes a distribution of preferred interfacial curvatures, resulting in dilute aqueous block copolymer dispersions exhibiting coexisting spherical and rod-like micelles with vesicles in a single sample with a O weight fraction, w{sub O}, of 0.18. At higher w{sub O} = 0.51-0.68, the peak in the interfacial curvature distribution shifts and we observe the formation of only American football-shaped micelles. We rationalize the formation of these anisotropically shaped aggregates based on the intrinsic distribution of preferred curvatures adopted by the polydisperse copolymer amphiphiles and on the relief of core block chain stretching by chain-length-dependent intramicellar segregation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Poleward Expansion of the White-Footed Mouse ( Peromyscus leucopus ) under Climate Change: Implications for the Spread of Lyme Disease

The white-footed mouse (Peromyscus leucopus) is an important reservoir host for Borrelia burgdorferi, the pathogen responsible for Lyme disease, and its distribution is expanding northward. We used an Ecological Niche Factor Analysis to identify the climatic factors associated with the distribution shift of the white-footed mouse over the last 30 years at the northern edge of its range, and modeled its current and potential future (2050) distributions using the platform BIOMOD. A mild and shorter winter is favouring the northern expansion of the white-footed mouse in Québec. With more favorable winter conditions projected by 2050, the distribution range of the white-footed mouse is expected to expand further northward by 3° latitude. We also show that today in southern Québec, the occurrence of B. burgdorferi is associated with high probability of presence of the white-footed mouse. Changes in the distribution of the white-footed mouse will likely alter the geographical range of B. burgdorferi and impact the public health in northern regions that have yet to be exposed to Lyme disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Lens Modeling of STRIDES Strongly Lensed Quasars Using Neural Posterior Estimation

Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lenses, which will soon become available from surveys like the Rubin Observatory’s Legacy Survey of Space and Time and the Euclid Wide Survey, we require fast and standardizable modeling techniques. To address this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale strongly lensed quasars from the Strong Lensing Insights into the Dark Energy Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to implicitly marginalize over nuisance parameters. We extend this method by employing sequential NPE to increase precision of mass model posteriors. We then fold individual lens models into a hierarchical Bayesian inference to recover the population distribution of lens mass parameters, accounting for out-of-distribution shift. After verifying our method using simulated analogs of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope single-filter observations. We find the population mean of the power-law elliptical mass distribution slope, γ lens , to be $\mathcal{M}_γ$ lens = 2.13 ± 0.06. Our result represents the first population-level constraint for these systems. This population-level inference from fully automated modeling is an important stepping stone toward cosmological inference with large samples of strongly lensed quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A review of the application of 2D isotropic-anisotropic correlation NMR spectroscopy in structural studies of chalcogenide glasses

The application of solid-state high-resolution NMR spectroscopy in the structural investigation of chalcogenide glasses in Ge/As/P/Si-X (X = S, Se,Te) systems has remained challenging even for the spin-1/2 nuclides ( 29 Si, 31 P, 77 Se, 125 Te), owing to their low natural abundance (except for 31 P), slow spin-lattice relaxation rate and large CSA and chemical shift distribution induced line broadening effects. However, most of these deleterious effects can be successfully overcome in two-dimensional (2D) isotropic-anisotropic correlation NMR experiments, especially when performed at high magnetic field and in conjunction with the Car-Purcell-Meiboom-Gill (CPMG) echo train acquisition. Here, we present a short introduction to the basic principles of such experiments and review their applications over the last decade in deciphering various short- and intermediate- range structural characteristics of chalcogenide glasses in S-Se, Se-Te, Ge-Se, As-Se and Si-Se systems as well as in investigating the molecular dynamics in a P-Se supercooled liquid. We anticipate possible future applications of these 2D isotropic-anisotropic correlation NMR experiments, particularly in conjunction with density functional theory-based calculations of NMR chemical shift tensor parameters and additional signal enhancement schemes, in addressing complex structural correlations and distributions in chalcogenide glasses.

2D NMR↗
DOE OSTI2015

SU-D-BRD-07: Evaluation of the Effectiveness of Statistical Process Control Methods to Detect Systematic Errors For Routine Electron Energy Verification

Purpose: To evaluate the ability of statistical process control methods to detect systematic errors when using a two dimensional (2D) detector array for routine electron beam energy verification. Methods: Electron beam energy constancy was measured using an aluminum wedge and a 2D diode array on four linear accelerators. Process control limits were established. Measurements were recorded in control charts and compared with both calculated process control limits and TG-142 recommended specification limits. The data was tested for normality, process capability and process acceptability. Additional measurements were recorded while systematic errors were intentionally introduced. Systematic errors included shifts in the alignment of the wedge, incorrect orientation of the wedge, and incorrect array calibration. Results: Control limits calculated for each beam were smaller than the recommended specification limits. Process capability and process acceptability ratios were greater than one in all cases. All data was normally distributed. Shifts in the alignment of the wedge were most apparent for low energies. The smallest shift (0.5 mm) was detectable using process control limits in some cases, while the largest shift (2 mm) was detectable using specification limits in only one case. The wedge orientation tested did not affect the measurements as this did not affect the thickness of aluminum over the detectors of interest. Array calibration dependence varied with energy and selected array calibration. 6 MeV was the least sensitive to array calibration selection while 16 MeV was the most sensitive. Conclusion: Statistical process control methods demonstrated that the data distribution was normally distributed, the process was capable of meeting specifications, and that the process was centered within the specification limits. Though not all systematic errors were distinguishable from random errors, process control limits increased the ability to detect systematic errors using routine measurement of electron beam energy constancy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Monitoring the refinement of crystal structures with {sup 15}N solid-state NMR shift tensor data

The {sup 15}N chemical shift tensor is shown to be extremely sensitive to lattice structure and a powerful metric for monitoring density functional theory refinements of crystal structures. These refinements include lattice effects and are applied here to five crystal structures. All structures improve based on a better agreement between experimental and calculated {sup 15}N tensors, with an average improvement of 47.0 ppm. Structural improvement is further indicated by a decrease in forces on the atoms by 2–3 orders of magnitude and a greater similarity in atom positions to neutron diffraction structures. These refinements change bond lengths by more than the diffraction errors including adjustments to X–Y and X–H bonds (X, Y = C, N, and O) of 0.028 ± 0.002 Å and 0.144 ± 0.036 Å, respectively. The acquisition of {sup 15}N tensors at natural abundance is challenging and this limitation is overcome by improved {sup 1}H decoupling in the FIREMAT method. This decoupling dramatically narrows linewidths, improves signal-to-noise by up to 317%, and significantly improves the accuracy of measured tensors. A total of 39 tensors are measured with shifts distributed over a range of more than 400 ppm. Overall, experimental {sup 15}N tensors are at least 5 times more sensitive to crystal structure than {sup 13}C tensors due to nitrogen’s greater polarizability and larger range of chemical shifts.

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

Declines in low-elevation subalpine tree populations outpace growth in high-elevation populations with warming

Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long life spans, the importance of climate-sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. Using spatially explicit, stochastic population models combined with data from long-term forest surveys, we explored whether the climate-sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high-elevation North American conifers. Empirically observed, warming-driven declines in recruitment led to rapid modelled population declines at the low-elevation, ‘warm edge’ of subalpine forest and slow emergence of populations beyond the high-elevation, ‘cool edge’. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modelled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations – especially in the presence of increased moisture – and rapid establishment above tree line, and, ultimately, expansion into the alpine. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high-elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

54 ENVIRONMENTAL SCIENCES↗