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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Empirical Study on the Use of the Rancor Microworld Simulator to Support Full-scope Data Collection

A lack of data has been identified as a major challenge in human reliability analysis (HRA). Accordingly, several institutes and researchers have tried to collect HRA data from different data sources such as actual historical measurements, expert judgements, or simulator studies. While the most recent studies predominantly focus on collecting data using full-scope simulators with actual operators, Idaho National Laboratory (INL) has begun to collect HRA data using a simplified simulator, i.e., the Rancor Microworld simulator, with student participants. Full-scope studies have been known to have several intrinsic challenges to securing enough quantity of the data due to many reasons like the high cost for performing experiments or requiring actual operators’ cooperation. The ultimate goal of INL’s effort aims to infer actual operators’ data collected from a full-scope simulator on the basis of microworld data with student subjects as well as collect additional data that could be missed in the full-scope research. As a first step to achieve this goal, this paper projects an experimental plan for investigating the differences in human performance between individuals in two groups: 1) an actual operator and 2) a student when using the Rancor Microworld simulator. A randomized factorial experiment design has been developed with two independent variables, i.e., type of scenario and type of subject. Six human performance measures, i.e., 1) time, 2) error, 3) workload, 4) situation awareness, 5) patterns of attention and 6) the number of manipulations were selected. A couple of scenarios and their procedures available to the Rancor Microworld simulator have been developed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Stochastic parametric skeletal dosimetry model for humans: General approach and application to active marrow exposure from bone-seeking beta-particle emitters

The objective of this study is to develop a skeleton model for assessing active marrow dose from bone-seeking beta-emitting radionuclides. This article explains the modeling methodology which accounts for individual variability of the macro- and microstructure of bone tissue. Bone sites with active hematopoiesis are assessed by dividing them into small segments described by simple geometric shapes. Spongiosa, which fills the segments, is modeled as an isotropic three-dimensional grid (framework) of rod-like trabeculae that “run through” the bone marrow. Randomized multiple framework deformations are simulated by changing the positions of the grid nodes and the thickness of the rods. Model grid parameters are selected in accordance with the parameters of spongiosa microstructures taken from the published papers. Stochastic modeling of radiation transport in heterogeneous media simulating the distribution of bone tissue and marrow in each of the segments is performed by Monte Carlo methods. Model output for the human femur at different ages is provided as an example. The uncertainty of dosimetric characteristics associated with individual variability of bone structure was evaluated. An advantage of this methodology for the calculation of doses absorbed in the marrow from bone-seeking radionuclides is that it does not require additional studies of autopsy material. The biokinetic model results will be used in the future to calculate individual doses to members of a cohort exposed to 89,90 Sr from liquid radioactive waste discharged to the Techa River by the Mayak Production Association in 1949–1956. Further study of these unique cohorts provides an opportunity to gain more in-depth knowledge about the effects of chronic radiation on the hematopoietic system. In addition, the proposed model can be used to assess the doses to active marrow under any other scenarios of 90 Sr and 89 Sr intake to humans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Quantifying climate–growth relationships at the stand level in a mature mixed‐species conifer forest

Abstract A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year‐to‐year variability in growth. Numerous dendrochronological (tree‐ring) studies have identified climate factors that influence year‐to‐year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand‐level (as opposed to species‐level) growth. We argue that stand‐level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed‐species stand at the Howland Research Forest of central Maine, USA , we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand‐level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year‐to‐year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand‐level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species‐ and canopy‐position level. Our climate models were better fit to stand‐level biomass increment than to species‐level or canopy‐position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand‐level growth varies less from to year‐to‐year than species‐level or canopy‐position growth responses. By assessing stand‐level growth response to climate, we provide an alternative perspective on climate–growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate.

Teets, Aaron↗
DOE OSTI2025

Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach

Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Rosette core fungal resistance in Arabidopsis thaliana

Main conclusion: Unlike rosette leaves, the mature Arabidopsis rosette core can display full resistance to Botrytis cinerea revealing the importance for spatial and developmental aspects of plant fungal resistance. Abstract: Arabidopsis thaliana is a model host to investigate plant defense against fungi. However, many of the reports investigating Arabidopsis fungal defense against the necrotrophic fungus, Botrytis cinerea, utilize rosette leaves as host tissue. Here we report organ-dependent differences in B. cinerea resistance of Arabidopsis. Although wild-type Arabidopsis rosette leaves mount a jasmonate-dependent defense that slows fungal growth, this defense is incapable of resisting fungal devastation. In contrast, as the fungus spreads through infected leaf petioles towards the plant center, or rosette core, there is a jasmonate- and age-dependent fungal penetration blockage into the rosette core. In this paper, we report evidence for induced and preformed resistance in the rosette core, as direct rosette core inoculation can also result in resistance, but at a lower penetrance relative to infections that approach the core from infected leaf petioles. The Arabidopsis rosette core displays a distinct transcriptome relative to other plant organs, and BLADE ON PETIOLE (BOP) transcripts are abundant in the rosette core. The BOP genes, with known roles in abscission zone formation, are required for full Arabidopsis rosette core B. cinerea resistance, suggesting a possible role for BOP-dependent modifications that may help to restrict fungal susceptibility of the rosette core. Finally, we demonstrate that cabbage and cauliflower, common Brassicaceae crops, also display leaf susceptibility and rosette core resistance to B. cinerea that can involve leaf abscission. Thus, spatial and developmental aspects of plant host resistance play critical roles in resistance to necrotrophic fungal pathogens and are important to our understanding of plant defense mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Assessing the impacts of water abstractions on river ecosystem services: an eco-hydraulic modelling approach

The provision of important river ecosystem services (ES) is dependent on the flow regime. This requires methods to assess the impacts on ES caused by interventions on rivers that affect flow regime, such as water abstractions. This study proposes a method to i) quantify the provision of a set of river ES, ii) simulate the effects of water abstraction alternatives that differ in location and abstracted flow, and iii) assess the impact of water abstraction alternatives on the selected ES. The method is based on river modelling science, and integrates spatially distributed hydrological, hydraulic and habitat models at different spatial and temporal scales. The method is applied to the hydropeaked upper Noce River (Northern Italy), which is regulated by hydropower operations. We selected locally relevant river ES: habitat suitability for the adult marble trout, white-water rafting suitability, hydroelectricity production from run-of-river (RoR) plants. Our results quantify the seasonality of river ES response variables and their intrinsic non-linearity, which explains why the same abstracted flow can produce different effects on trout habitat and rafting suitability depending on the morphology of the abstracted reach. An economic valuation of the examined river ES suggests that incomes from RoR hydropower plants are of comparable magnitude to touristic revenue losses related to the decrease in rafting suitability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Contributions of major tau kinase activation and phospho-tau accumulation to cortical and hippocampal tangle formation and cognition in older adults

Aberrant activation of tau kinases (tauK) has been proposed as a major step in tau hyperphosphorylation and misfolding, and subsequent formation of neurofibrillary tangles (NFT) in Alzheimer's disease (AD). However, evidence of tauK hyperactivation in actual AD brains is scarce and inconsistent, and their role in age-related cognitive decline remains undocumented. We evaluated activated/inhibited species of CDK5/p35/p25, GSK3a/ß, and ERK1/2 as well as ten tau/phospho-tau (ptau) peptides (mapping Ser 202 , Thr 217 , Ser 262 , Ser 305 , and Ser 404 phospho-residues) by Western blot or selected reaction monitoring proteomics, respectively, in postmortem dorsolateral prefrontal cortex (DLPFC) and hippocampal samples of 150 participants from the Rush Memory and Aging Project (MAP). Regression models and mediation analyses assessed the contributions of these variables to tau phosphorylation, NFT deposition and antemortem cognitive status of MAP participants. Surprisingly, greater p25 and p35 (indices for CDK5 activation) and lower pSer 21/9 -GSK3a/ß (inhibited species) immunodensities were associated with lower ptau peptide amounts. Individuals with higher p25 cortical densities displayed better cognitive outcomes, particularly working memory. Statistical mediation analyses indicated that the beneficial effect of CDK5/p25 on cognition was mediated by lower densities of phospho-Thr 217 -tau and NFT deposition in DLPFC, and also identified Thr 217 and Ser 262 as the ptau sites with greatest influence in both NFT accumulation and cognitive impairment. The present data suggest that tau hyperphosphorylation, tangle deposition, and the subsequent cognitive impairment do not rely on aberrant activation of major tauKs. Additionally, novel evidence was provided for the beneficial contribution of cortical CDK5/p25 to the maintenance of working memory.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Balancing Water Sustainability and Productivity Objectives in Microalgae Cultivation: Siting Open Ponds by Considering Seasonal Water-Stress Impact Using AWARE-US

Microalgae have great potential as an energy crop. Scaling-up algal biofuel production in the United States (US) should be done with careful attention to water stress. This study evaluates the regional and seasonal water-stress impact of potential algae-pond deployments in the US. Three site-selection strategies focusing on biomass yield, water-use efficiency (WUE), and water-stress impact, respectively, are applied and compared to meet a US algae biomass production target of 30 million metric tons/yr ash-free dry weight, which converts to 20.8 billion L renewable diesel, via hydrothermal liquefaction. Ranking algae ponds based on biomass yield leads to freshwater consumption of 2.66 km3/yr, resulting in the highest water-stress impact (39.1 US equivalent km3). Under the WUE scenario, water consumption is reduced by 81%, but biomass yield is reduced by 12%. In contrast, adding a water-stress constraint to the biomass-yield ranking reduces water consumption by 50% and water-stress impact by 97%, with a small yield reduction (1.7%). Results show that pond location has a significant effect on water stress and that water stress is not proportional to water consumption or yield. Furthermore, capturing seasonal water patterns is critical for planning because sites in water-abundant regions can have short-term but significant water-stress impacts.

algae, Biofuel, water scarcity footprint, hydrothe↗
DOE OSTI2024

First-Principles Insights into the Thermodynamics of Variable-Temperature Ammonia Synthesis on Transition-Metal-Doped Cu (100) and (111)

Ammonia (NH 3 ) is one of the most produced chemicals worldwide. NH 3 synthesis predominantly utilizes the Haber–Bosch (HB) process, requiring high temperatures and pressures. Despite significant process advances, ample opportunity remains for improving the rate, selectivity, catalyst stability, and energy efficiency. Inspired by a recently developed programmable heating and quenching (PHQ) technique, we present in this paper a first-principles screening of candidate single-atom alloy catalysts generated from doping (111) and (100) surfaces of copper (Cu), an ineffective HB catalyst in its pure form. We predict the thermodynamics of two rate-limiting reactions, N 2 dissociative adsorption and the final hydrogenation step leading up to NH 3 release, at 400 and 900 K. Thermodynamically, the former reaction is favored at low temperatures, while the latter is favored at high temperatures. Vanadium-, chromium-, and molybdenum-doped Cu surfaces, due to intermediate M–N covalent bonding character, emerge as appealing candidate catalysts for PHQ NH 3 synthesis, as they balance the thermodynamics of the above-mentioned reaction steps at their respective optimal temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years. Furthermore, both large parameter spaces and high sampling variability necessitate a large number of experiments. As such, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users. We combine two key elements to reduce the optimization cost: an early-prediction model, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

25 ENERGY STORAGE↗
DOE OSTI2022

Genotype–environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence

Abstract Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype–environment associations in the hermit thrush ( Catharus guttatus ), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype–environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype–environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns

The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. In order to address these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spatiotemporal Variability in Wind Turbine Blade Leading Edge Erosion

Wind turbine blade leading edge erosion (LEE) reduces energy production and increases wind energy operation and maintenance costs. Degradation of the blade coating and ultimately damage to the underlying blade structure are caused by collisions of falling hydrometeors with rotating blades. The selection of optimal methods to mitigate/reduce LEE are critically dependent on the rates of coating fatigue accumulation at a given location and the time variance in the accumulation of material stresses. However, no such assessment currently exists for the United States of America (USA). To address this research gap, blade coating lifetimes at 883 sites across the USA are generated based on high-frequency (5-min) estimates of material fatigue derived using a mechanistic model and robust meteorological measurements. Results indicate blade coating failure at some sites in as few as 4 years, and that the frequency and intensity of material stresses are both highly episodic and spatially varying. Time series analyses indicate that up to one-third of blade coating lifetime is exhausted in just 360 5-min periods in the Southern Great Plains (SGP). Conversely, sites in the Pacific Northwest (PNW) exhibit the same level of coating lifetime depletion in over three times as many time periods. Thus, it may be more cost-effective to use wind turbine deregulation (erosion-safe mode) for damage reduction and blade lifetime extension in the SGP, while the application of blade leading edge protective measures may be more appropriate in the PNW. Annual total precipitation and mean wind speed are shown to be poor predictors of blade coating lifetime, re-emphasizing the need for detailed modeling studies such as that presented herein.

Pryor, Sara C. (ORCID:0000000348473440)↗
DOE OSTI2023

Strong Dark Matter Self-interactions Diversify Halo Populations within and surrounding the Milky Way

Abstract We perform a high-resolution cosmological zoom-in simulation of a Milky Way (MW)–like system, which includes a realistic Large Magellanic Cloud analog, using a large differential elastic dark matter self-interaction cross section that reaches ≈100 cm 2 g −1 at relative velocities of ≈10 km s −1 , motivated by the diverse and orbitally dependent central densities of dwarf galaxies within and surrounding the MW. We explore the effects of dark matter self-interactions on satellite, splashback, and isolated halos through their abundance, central densities, maximum circular velocities, orbital parameters, and correlations between these variables. We use an effective constant cross section model to analytically predict the stages of our simulated halos’ gravothermal evolution, demonstrating that deviations from the collisionless R max – V max relation can be used to select deeply core-collapsed halos, where V max is a halo’s maximum circular velocity, and R max is the radius at which it occurs. We predict that a sizable fraction (≈20%) of subhalos with masses down to ≈10 8 M ⊙ is deeply core collapsed in our SIDM model. Core-collapsed systems form ≈10% of the isolated halo population down to the same mass; these isolated, core-collapsed halos would host faint dwarf field galaxies with extremely steep central density profiles. Finally, most halos with masses above ≈10 9 M ⊙ are core-forming in our simulation. Our study thus demonstrates how self-interactions diversify halo populations in an environmentally dependent fashion within and surrounding MW-mass hosts, providing a compelling avenue to address the diverse dark matter distributions of observed dwarf galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Maximizing ozone signals among chemical, meteorological, and climatological variability

The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NO x State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Biomass production of herbaceous energy crops in the United States: field trial results and yield potential maps from the multiyear regional feedstock partnership

Current knowledge of yield potential and best agronomic management practices for perennial bioenergy grasses is primarily derived from small-scale and short-term studies, yet these studies inform policy at the national scale. In an effort to learn more about how bioenergy grasses perform across multiple locations and years, the U.S. Department of Energy (US DOE)/Sun Grant Initiative Regional Feedstock Partnership was initiated in 2008. The objectives of the Feedstock Partnership were to (1) provide a wide range of information for feedstock selection (species choice) and management practice options for a variety of regions and (2) develop national maps of potential feedstock yield for each of the herbaceous species evaluated. The Feedstock Partnership expands our previous understanding of the bioenergy potential of switchgrass, Miscanthus, sorghum, energycane, and prairie mixtures on Conservation Reserve Program land by conducting long-term, replicated trials of each species at diverse environments in the U.S. Trials were initiated between 2008 and 2010 and completed between 2012 and 2015 depending on species. Field-scale plots were utilized for switchgrass and Conservation Reserve Program trials to use traditional agricultural machinery. This is important as we know that the smaller scale studies often overestimated yield potential of some of these species. Insufficient vegetative propagules of energycane and Miscanthus prohibited farm-scale trials of these species. The Feedstock Partnership studies also confirmed that environmental differences across years and across sites had a large impact on biomass production. Nitrogen application had variable effects across feedstocks, but some nitrogen fertilizer generally had a positive effect. National yield potential maps were developed using PRISM-ELM for each species in the Feedstock Partnership. This manuscript, with the accompanying supplemental data, will be useful in making decisions about feedstock selection as well as agronomic practices across a wide region of the country.

09 BIOMASS FUELS↗
DOE OSTI2018

Extreme Variability Quasars from the Sloan Digital Sky Survey and the Dark Energy Survey

Here, we perform a systematic search for long-term extreme variability quasars (EVQs) in the overlapping Sloan Digital Sky Survey (SDSS) and 3-Year Dark Energy Survey (DES) imaging, which provide light curves spanning more than 15 years. We also identified 1000 EVQs with a maximum g band magnitude change of more than 1 mag over this period, about 10% of all quasars searched. The EVQs have L bol 10 45 - 10 47 erg s -1 and L=L Edd 0:01-1. Accounting for selection effects, we estimate an intrinsic EVQ fraction of 30-50% among all g.22 quasars over a baseline of 15 years. These EVQs are good candidates for so-called “changing-look quasars”, where a spectral transition between the two types of quasars (broad-line and narrow-line) is observed between the dim and bright states. We performed detailed multi-wavelength, spectral and variability analyses for the EVQs and compared to their parent quasar sample. We found that EVQs are distinct from a control sample of quasars matched in redshift and optical luminosity: (1) their UV broad emission lines have larger equivalent widths; (2) their Eddington ratios are systematically lower; and (3) they are more variable on all timescales. The intrinsic difference in quasar properties for EVQs suggest that internal processes associated with accretion are the main driver for the observed extreme long-term variability. In spit of their different properties, EVQs seem to be in the tail of a continuous distribution of quasar properties, rather than standing out as a distinct population. We speculate that EVQs are normal quasars accreting at relatively low accretion rates, where the accretion flow is more likely to experience instabilities that drive the factor of few changes in flux on multi-year timescales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

Siegel, Jared E.↗