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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity

The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (N CCN ) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy's Southern Great Plains (SGP) site to illustrate the dependence of N CCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (N accu ; median value 805 cm -3 ) and relatively high aerosol hygroscopicity (κ; median value ~0.25) values that result in the higher CCN activation and relatively high N CCN (median value 258–1578 cm -3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher N accu (~35%), and N CCN (230–1721 cm -3 at a SS of 0.1%–1.0%) with the lowest κ (~0.17). In contrast, relatively high nucleation mode particles (N nucl ; ~20%) and low N CCN (128–1553 cm -3 at a SS of 0.1%–1.0%) with higher κ (~0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better N CCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the N CCN prediction uncertainty at lower SS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reconstruction with velocities

ABSTRACT Reconstruction is becoming a crucial procedure of galaxy clustering analysis for future spectroscopic redshift surveys to obtain subper cent level measurement of the baryon acoustic oscillation scale. Most reconstruction algorithms rely on an estimation of the displacement field from the observed galaxy distribution. However, the displacement reconstruction degrades near the survey boundary due to incomplete data and the boundary effects extend to ${\sim}100\, \mathrm{Mpc}/h$ within the interior of the survey volume. We study the possibility of using radial velocities measured from the cosmic microwave background observation through the kinematic Sunyaev–Zeldovich effect to improve performance near the boundary. We find that the boundary effect can be reduced to ${\sim}30-40\, \mathrm{Mpc}/h$ with the velocity information from Simons Observatory. This is especially helpful for dense low redshift surveys where the volume is relatively small and a large fraction of total volume is affected by the boundary.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Constraining IGM enrichment and metallicity with the C iv forest correlation function

ABSTRACT The distribution and abundance of metals in the diffuse intergalactic medium (IGM) have implications for galaxy formation and evolution models, and has been argued to be sensitive to the Universe’s reionization history. However, reduced sensitivity in the near-IR implies that probing IGM metals at z > 4 is currently out of reach with the traditional method of detecting individual absorbers. We present a new technique based on clustering analysis that enables the detection of these weak IGM absorbers. We investigate the two-point correlation function (2PCF) of the ${\rm C\, {\small IV}}$ forest as a probe of IGM metallicity and enrichment topology by simulating the z = 4.5 IGM with models of inhomogeneous metal distributions. The 2PCF of the ${\rm C\, {\small IV}}$ forest demonstrates a clear peak at a characteristic separation corresponding to the doublet separation of the ${\rm C\, {\small IV}}$ line.The peak amplitude scales quadratically with metallicity, while enrichment topology affects both the shape and amplitude of the 2PCF. For models consistent with the distribution of metals at z ∼ 3, we find that we can constrain [C/H] to within 0.2 dex, log$\, M_{\rm {min}}$ to within 0.4 dex, and R to within 15 per cent. We show that CGM absorbers can be reliably identified and masked, thus recovering the underlying IGM signal. The auto-correlation of the metal-line forest presents a compelling avenue to constrain the IGM metallicity and enrichment topology with high precision at z > 4, thereby pushing such measurements into the Epoch of Reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Transcript and metabolite network perturbations in lignin biosynthetic mutants of Arabidopsis

Abstract Lignin, one of the most abundant polymers in plants, is derived from the phenylpropanoid pathway, which also gives rise to an array of metabolites that are essential for plant fitness. Genetic engineering of lignification can cause drastic changes in transcription and metabolite accumulation with or without an accompanying development phenotype. To understand the impact of lignin perturbation, we analyzed transcriptome and metabolite data from the rapidly lignifying stem tissue in 13 selected phenylpropanoid mutants and wild-type Arabidopsis (Arabidopsis thaliana). Our dataset contains 20,974 expressed genes, of which over 26% had altered transcript levels in at least one mutant, and 18 targeted metabolites, all of which displayed altered accumulation in at least one mutant. We found that lignin biosynthesis and phenylalanine supply via the shikimate pathway are tightly co-regulated at the transcriptional level. The hierarchical clustering analysis of differentially expressed genes (DEGs) grouped the 13 mutants into 5 subgroups with similar profiles of mis-regulated genes. Functional analysis of the DEGs in these mutants and correlation between gene expression and metabolite accumulation revealed system-wide effects on transcripts involved in multiple biological processes.

Plant Sciences↗
DOE OSTI2024

Climate, food and humans predict communities of mammals in the United States

Abstract Aim The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location Conterminous United States. Time Period 2010–2021. Major Taxa Studied Twenty‐five species of mammals. Methods We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard‐mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine‐scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.

Kays, Roland↗
DOE OSTI2024

GeoCricket

SAND2025-12229O Geospatial Critical Infrastructure and Census Data Stockpile Tool (GeoCricket) is a set of functions that collect critical infrastructure and census data for use in the Resilient Node Cluster Analysis Tool (ReNCAT) and Quantum Geographic Information System Social Burden Calculator. It can also act to inform other place-based work. The code queries public-facing Representational State Transfer (REST) servers to collect geospatial data related to a specific area. It then exports that data as standard geographic information system file types or as a .csv file. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Haines, John↗
DOE OSTI2024

Understanding Observed Precipitation Change and the New Climate Normal from the Perspective of Daily Weather Types in the Southeast United States

Abstract Observed precipitation changes in the Southeast United States (SEUS) are spatially heterogeneous. Most of the inland SEUS and eastern Gulf Coast become drier, and the East Coast north of Charleston, South Carolina, and southern Florida become wetter from the old 30-yr period of 1961–90 to the recent period of 1991–2020. The observed climate change is examined from the perspective of daily weather types (WTs). A k -means clustering analysis has been conducted using daily 850-hPa circulation for 1948–2021. The obtained 10 WTs peak in different seasons, respectively. The frequencies and precipitation intensity of the WTs have been analyzed. A winter WT characterized by a western Appalachian trough (WAT) and a summer WT featuring North Atlantic subtropical high (NASH) have a rising trend of annual frequency from 1948 to 2021. An Appalachian high in the autumn has a decreasing frequency but becomes drier and stronger. Some precipitation intensity change and small location shift have also been observed. The drying up on the eastern Gulf Coast and the inland area of the SEUS is mainly caused by the weakened southwesterly low-level jet (LLJ) on the western flank of the NASH that reduces rain in the spring, the less frequent but stronger and drier Appalachian high in the summer and autumn, and the weaker and more western located Plains trough (PT) in the winter, spring, and autumn. The precipitation increase in the East Coast and southern Florida is majorly due to more frequent, stronger, and rainier troughs along the western Appalachian as well as the East Coast.

Qian, Jian-Hua↗
DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Combined EDS and EBSD dataset from TiB2 ceramic material

This is supplemental information to a paper to be submitted, tentatively titled Cluster analysis of combined EDS and EBSD data to solve ambiguous phase identifications. Data was acquired from a TiB2 ceramic provided by Missouri University of Science and Technology. Simultaneous EDS and EBSD were acquired on a Tescan MIRA3 GMH field-emission SEM equipped with Oxford Instruments Symmetry CMOS-based EBSD detector and Oxford Instruments UltimMax 170 mm2 silicon-drift detector EDS. Data was acquired using Oxford Instruments AZtec 4.0 software. Data was acquired at 20 keV, 70° tilt, and ≈1 nA probe current. An 80×60 pixel, 40×30 μm map (500 nm pixel pitch) was acquired. EBSD indexing was performed using TiB2, space group 191 P6/mmm, and TiC, space group 225 Fm \bar{3}m, crystal cards.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗
DOE OSTI2025

Selection Effect in Dark Energy Survey Y1

The discovery that the Universe expansion is accelerated poses one of the most profound mysteries in physics. Cosmic acceleration could be a sign of the fact that General Relativity breaks down on cosmological scales and has to be replaced, or it could arise from an unknown form of energy that currently dominates our Universe. That is what we call dark energy and in this case the problem moves to the discovery of its nature. Dark Energy Survey aims to study the nature of dark energy and to test General Relativity and cosmological models. The cluster analysis of the first run of DES leads to results which are incomparable with what other surveys have obtained. In particular it turns out that the matter density of the Universe is $Ω_m = 0.179^{+0.031}_{−0.038}$, very different from the 0.3 value expected. In this work we build a procedure that can be followed in order to understand whether the solution of DES Y1 problem could be a selection effect or not.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

EOS at the Fermilab LHC Physics Center(LPC)

The Fermilab CMS LPC center has operated an EOS instance since initial testing began in June 2012, transitioning to production storage in 2013 with an initial capacity of 600\,TB. Today, the system provides approximately 16\,PB of storage to support the 4{,}500-core LPC user analysis cluster, which serves several hundred active CMS users at any given time. In this talk, we will provide an update on our system setup and outline near-term plans for the CMS LPC production EOS system. We will share operational experiences from running our EOS system over the past year. Additionally, we will briefly discuss our recent efforts within the Fermilab Disk Evolution Project to consider the future architecture of HEP data storage at Fermilab for the HL-LHC era.

Wu, Yujun↗
DOE OSTI2026

EOS at the Fermilab LHC Physics Center(LPC)

The Fermilab CMS LPC center has operated an EOS instance since initial testing began in June 2012, transitioning to production storage in 2013 with an initial capacity of 600\,TB. Today, the system provides approximately 16\,PB of storage to support the 4{,}500-core LPC user analysis cluster, which serves several hundred active CMS users at any given time. In this talk, we will provide an update on our system setup and outline near-term plans for the CMS LPC production EOS system. We will share operational experiences from running our EOS system over the past year. Additionally, we will briefly discuss our recent efforts within the Fermilab Disk Evolution Project to consider the future architecture of HEP data storage at Fermilab for the HL-LHC era.

Wu, Yujun↗
DOE OSTI2024

Effect of realistic routing on the social burden metric

The distance people travel to reach critical services is a key input to the Social Burden metric used by Sandia’s Resilient Node Cluster Analysis Tool (ReNCAT) in the optimization’s objective function. By default, ReNCAT utilizes Euclidian distances between population blocks and critical facilities when calculating Social Burden. However, these straight-line distances do not reflect how most residents or goods would travel throughout the area. As distance is a vital input to the burden calculation, a more realistic distance calculation will yield more realistic burden values. This work uses real road networks and calculates the shortest distance path between population centers and critical facilities using a standard graph theory approach. These realistic route distances are then used to compute Social Burden for four areas of study. It was found that distances using real road routes are generally, but not always, longer than the Euclidean distance. The increased length increases the final Social Burden metric, however, the overall burden percent change ranged between 17% and 52%, which means the impact of realistic routes relies heavily upon the area’s road topology. It was found that rural locations within an area may have larger burden increases than urban areas as more dense road networks allow routes to more closely follow a straight-line path. Additionally, using the most straight forward routing algorithms requires high computational effort for areas with large road networks. While it is believed this process can be made more performant, that task is beyond this scope of work.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Insights of Molecular Mechanism of Xylem Development in Five Black Poplar Cultivars

Black poplar (Populus deltoides, P. nigra, and their hybrids) is the main poplar cultivars in China. It offers interesting options of large-scale biomass production for bioenergy due to its rapid growth and high yield. Poplar wood properties were associated with chemical components and physical structures during wood formation. In this study, five poplar cultivars, P. euramericana ‘Zhonglin46’ (Pe1), P. euramericana ‘Guariento’ (Pe2), P. nigra ‘N179’ (Pn1), P. deltoides ‘Danhong’ (Pd1), and P. deltoides ‘Nanyang’ (Pd2), were used to explore the molecular mechanism of xylem development. We analyzed the structural differences of developing xylem in the five cultivars and profiled the transcriptome-wide gene expression patterns through RNA sequencing. The cross sections of the developing xylem showed that the cell wall thickness of developed fiber in Pd1 was thickest and the number of xylem vessels of Pn1 was the least. A total of 10,331 differentially expressed genes were identified among 10 pairwise comparisons of the five cultivars, most of them were related to programmed cell death and secondary cell wall thickening. K-means cluster analysis and Gene Ontology enrichment analysis showed that the genes highly expressed in Pd1 were related to nucleotide decomposition, metabolic process, transferase, and microtubule cytoskeleton; whereas the genes highly expressed in Pn1 were involved in cell wall macromolecule decomposition and polysaccharide binding processes. Based on a weighted gene co-expression network analysis, a large number of candidate regulators for xylem development were identified. And their potential regulatory roles to cell wall biosynthesis genes were validated by a transient overexpression system. This study provides a set of promising candidate regulators for genetic engineering to improve feedstock and enhance biofuel conversion in the bioenergy crop Populus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Environmental impacts of dam reservoir filling in the East Amazon

Mitigating the environmental impacts caused by hydroelectric dams is a worldwide challenge. The aquatic ecosystem is the most impacted during the reservoir filling phase, yet biogeochemical dynamics at this stage are not well-studied. Here we evaluate water quality and hydraulic parameters in the Araguari River (Amapá/Brazil) during the filling of the Ferreira Gomes Hydroelectric Power Plant reservoir (UHEFG). Five field campaigns were performed from July 2014 to August 2015 across nine sample sites within the reservoir (P1, P2, …P9) and only one downstream (P10). The following key variables were monitored: Trophic State Index (TSI), Total Coliforms (TC), E. coli (EC) and Chlorophyll-a (Chl-a), along with physical and chemical variables (Temperature, Suspended Solids, Total Dissolved Solids, Electrical Conductivity, Turbidity, Color, pH, DO, Al3+, NH4+, Cl-, Mg1+, Ca2+, total Phosphorous, NH3, NO3-, and SO42). Besides that, hydraulic-operational variables were also investigated: inflows (Qa), outflows (Qd), and variation in reservoir volume (Vol%). Multiple Regression Analyses showed that the key parameters were both significantly influenced by physio-chemical and hydraulic variables (0.46=R2adj=0.99, p<0.05). The DO showed significant spatial variation, being influenced by the turbulence from the Coaracy Nunes dam (UHECN) upstream and the UHEFG dam downstream. The Vol% influenced the TSI, which ranged from oligotrophic to hypertrophic and eventually stabilized at mesotrophic. The levels of TSI, TC, and Chl-a decreased and the level of E. coli increased (p<0.05) as a function of Vol%. A Cluster Analysis showed the formation of three spatial groups - two inside the reservoir and one downstream (P10). This suggests that in the rainy season or transition season, the hydraulic residence time in the reservoir is very low (16 = thr = 36 h) when hydrodynamic processes are dominant. In the dry season (thr ˜ 1 month), biogeochemical and hydrodynamic processes occur at similar time scales to the nutrient and microbial abundance. These results confirm the hypothesis that the filling phase has significant impacts on the key parameters of the water quality (p<0.05). We conclude that the filling phase of the UHEFG reservoir generated significant environmental impacts, which have repercussions even retrospectively.

Amazon, biogeochemistry, ecosystem, extreme event,↗
DOE OSTI2022

ENSO Impact on Winter Precipitation in the Southeast United States through a Synoptic Climate Approach

The ENSO impact on winter precipitation in the Southeast United States was analyzed from the perspective of daily weather types (WTs). We calculated the dynamic contribution associated with the change in frequency of the WTs and the thermodynamic contribution due to changes in the spatial patterns of the environmental fields of the WTs. Six WTs were obtained using a k-means clustering analysis of 850 hPa winds in reanalysis data from November to February of 1948–2022. All the WTs can only persist for a few days. The most frequent winter weather type is WT1 (shallow trough in Eastern U.S.), which can persist or likely transfer to WT4 (Mississippi River Valley ridge). WT1 becomes less frequent in El Niño years, while the frequency of WT4 does not change much. WTs 2–6 correspond to a loop of eastward propagating waves with troughs and ridges in the mid-latitude westerlies. Three WTs with a deep trough in the Southeast U.S., which are WT2 (east coast trough), WT3 (off east coast trough) and WT6 (plains trough), become more frequent in El Niño years. The more frequent deep troughs (WTs 2, 3 and 6) and less frequent shallow trough (WT1) result in above-normal precipitation in the coastal Southeast U.S. in the winter of El Niño years. WT5 (off coast Carolina High), with maximum precipitation extending from Mississippi Valley to the Great Lakes, becomes less frequent in El Niño years, which corresponds to the below-normal precipitation from the Great Lakes to Upper Mississippi and Ohio River Valley in El Niño years, and vice versa in La Niña years. The relative contribution of the thermodynamic and dynamic contribution is location dependent. On the east coast, the two contributions are similar in magnitude.

Qian, Jian-Hua↗