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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Incremental nonnegative matrix factorization based on correlation and graph regularization for matrix completion

Matrix factorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegative matrix factorization and its incremental variants have attracted much attention. The existing incremental algorithms are established based on the assumption of samples are independent and only update the new latent variable of weighting coefficient matrix when the new sample comes, which may lead to inferior solutions. To address this issue, we investigate a novel incremental nonnegative matrix factorization algorithm based on correlation and graph regularizer (ICGNMF). The correlation is mainly used for finding out those correlated rows to be updated, that is, we assume that samples are dependent on each other. We derive the updating rules for ICGNMF by considering the correlation. We also present tests on widely used image datasets, and show ICGNMF reduces the error by comparing other methods.

Zhang, Xiaoxia↗
DOE OSTI2025

Back trajectories for TBS flights (c1-level)

The ARMTRAJ VAP provides four trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The four trajectory datasets support aerosol, cloud, and planetary boundary layer research. Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). For each sample in each of the four datasets, HYSPLIT will also be run at multiple starting locations surrounding ARM deployments, enabling an ensemble of runs from which the mean and variability (estimated uncertainty) of each sample's trajectory coordinates, thermodynamic properties, or other fields will be reported.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Conformational space exploration of cryo-EM structures by variability refinement

Cryo-EM observation of biological samples enables visualization of sample heterogeneity, in the form of discrete states that are separable, or continuous heterogeneity as a result of local protein motion before flash freezing. Variability analysis of this continuous heterogeneity describes the variance between a particle stack and a volume, and results in a map series describing the various steps undertaken by the sample in the particle stack. While this observation is absolutely stunning, it is very hard to pinpoint structural details to elements of the maps. Here, in order to bridge the gap between observation and explanation, we designed a tool that refines an ensemble of structures into all the maps from variability analysis. Using this bundle of structures, it is easy to spot variable parts of the structure, as well as the parts that are not moving. Comparison with molecular dynamics simulations highlights the fact that the movements follow the same directions, albeit with different amplitudes. Ligand can also be investigated using this method. Variability refinement is available in the Phenix software suite, accessible under the program name phenix.varref.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2021

Variability in NGC 3201 Giant Stars and Its Impact on Their Spectroscopic [Fe/H] Determination

We present the analysis of 34 light curves in V and I of 17 giant stars in the globular cluster NGC 3201 to check if such stars are variable and if their variability has some kind of impact on the iron abundance as obtained from spectroscopic measurements. First, we computed the generalized Lomb–Scargle and phase dispersion minimization periodograms of the sample to check if the stars were variables. In this way, seven stars of the sample were found to be nonvariable, two stars are considered as possible variables, and eight stars were found to be variable, with periods ranging from 0.0881 ± 0.0001 to 0.5418 ± 0.0027 days. According to the literature, the variables have distinct values of [Fe I/H]: the three most metal-rich stars are in the red giant branch (RGB) stage, one has [Fe I/H] = −1.37 dex, while the other two have [Fe I/H] = −1.31 dex. The two most metal-poor variables have [Fe I/H] = −1.61 dex and [Fe I/H] = −1.62 dex, and are AGB stars; the remaining variables have [Fe I/H] = −1.44, −1.48, and −1.50 dex, the first two being RGB stars while the last is an AGB star. On the other hand, stars that appear to be nonvariable have −1.56 ≤ [Fe I/H] ≤ −1.40. We conclude that variability somehow affects the spectroscopic determination of the iron content of giant stars in NGC 3201, increasing the iron spread of the cluster. If variability is not taken into account, this spread could be incorrectly interpreted as due to an intrinsic iron spread affecting the stars of the cluster.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterizing soil water content variability across spatial scales from optimized high-resolution distributed temperature sensing technique

Fiber-optic Distributed Temperature Sensing, when combined with the Single-probe Heat-pulse technique can measure soil moisture (θ) across spatial scales. The key limitation of this system is in obtaining the relationship between soil thermal conductivity (λ) and θ for a specific field. Using the Department of Energy Atmospheric Radiation Measurement (ARM) site, this study tested a new methodology to account for the spatial variability in the λ-θ relationship using a Gaussian processes model. The resulting accurate θ measurements (RMSE = 0.03 m 3 m –3 ) were used to characterize the spatial variability of θ across scales and to develop an empirical equation that can correct for the changes in the θ spatial variability observed at different spatial resolutions. In addition, the number of required samples to accurately characterize θ and its variability over scales ranging from 5 m and 350 m were estimated. Finally, these findings provide key information to scale soil moisture from centimeters to hundreds of meters for process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Estimation of Tropical Cyclone Intensity in the North Atlantic and Northeastern Pacific Basins Using TRMM Satellite Passive Microwave Observations

Here, a statistical passive microwave intensity estimation (PMW-IE) algorithm for estimating the intensity of tropical cyclones (TCs) in the North Atlantic and northeastern and central Pacific basins is developed and tested. The algorithm is derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 85-GHz brightness temperatures and near-surface rain-rate retrievals to provide objective estimates of current maximum sustained surface winds (Vmax) and 6-h future Vmax of TCs. The full record of TRMM data (1998–2013) including 2326 TMI overpasses of 503 TCs is separated into dependent samples (1998–2010) for model development and independent samples (2011–13) for model verification. The best track intensities are used as dependent variables in a stepwise multiple-regression approach. Separately for each basin, three regression models are derived using selected 1) 85-GHz-only variables, 2) rain-rate-only variables, and 3) combined 85-GHz and rain variables. The algorithms are assessed using independent samples and those with contemporaneous aircraft-reconnaissance measurements. Rain-only and combined models perform better than the 85-GHz-only model. Lower errors are found for estimating the 6-h future Vmax than estimating the current Vmax using all three models. This implies that it is optimal to use passive-microwave-retrieved rain variables observed a few hours earlier to estimate TC intensity. The MAE (RMSE) of 6-h future Vmax is 9 (12) kt (1 kt ≈ 0.51 m s -1 ) when testing the combined models with ATL and EPA independent samples. Aircraft-reconnaissance-based independent samples yields a MAE of 9.6 kt and RMSE of 12.6 kt for estimating 6-h future Vmax.

58 GEOSCIENCES↗
DOE OSTI2019

Ionization-driven intrinsic absorption line variability of BAL quasars in the Stripe 82 region

Abstract We investigate the connection between the intrinsic C iv absorption line variability and the continuum flux changes of broad absorption line (BAL) quasars using a sample of 78 sources in the Stripe 82 region. The absorption trough variability parameters are measured using the archival multi-epoch spectroscopic data from the Sloan Digital Sky Survey (SDSS), and the continuum flux variability parameters are estimated from the photometric light curves obtained by the SDSS and the Catalina Real-Time Survey surveys. We find evidence for weak correlations (ρs ∼ 0.3) between the intrinsic C iv absorption line variability and the quasar continuum variability for the final sample of 78 BAL quasars. The correlation strengths improve (ρs ∼ 0.5) for the ‘high-signal-to-noise ratio (SNR)’ sample sources that have higher spectral SNR. Using two subsets of the high-SNR sample differing on the absorption trough depth, we find that the shallow-trough subset shows an even stronger correlation (ρs ∼ 0.6), whereas the deep-trough subset does not show any correlation between the absorption line variability and the continuum variability. These results point to the important role of saturation effects in the correlation between the absorption line variability and the continuum variability of BAL quasars. Considering other effects that can also smear the correlation, we conclude that the actual correlation between the absorption line and continuum variability is even stronger.

Vivek, M.↗
DOE OSTI2020

An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer?

Since the first detailed compositional analysis of dissolved organic matter (DOM) in 1995, high resolution mass spectrometry (HRMS) has become a vital tool for DOM characterization. While the upward trend in HRMS for molecular level analysis of DOM continues, so do the challenges of data comparison and interpretation between laboratories operating instruments of differing performance and user operating conditions. It is therefore essential that the community establishes whether data and trends can be compared robustly between research groups. To this end, 4 identically prepared DOM samples were each studied by 16 laboratories, representing 17 commercially-purchased instruments located across 8 countries, using positive- and negative-mode electrospray ionization (ESI) HRMS analysis. Despite the widely reported sensitivity of ESI experiments to sample matrix, preparation, ion source operation and instrument tuning parameters, the instruments used in this study showed relatively low variability between results for the same samples, particularly H/C and AI metrics. The variation in O/C and m/z metrics was relatively higher, although z-score graphs and %BCD demonstrated that the relative difference in general chemical composition between samples was the same for each instrument included in the study. As such, these metrics can be used for fingerprinting of DOM samples and as a benchmark for quality control in participating laboratories, with future strides in environmental science building upon these efforts.

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

O Corona, where art thou? eROSITA’s view of UV-optical-IR variability-selected massive black holes in low-mass galaxies

Finding massive black holes (MBHs,M BH ≈ 10 4 –10 7 M ⊙ ) in the nuclei of low-mass galaxies $\left( {{M_*}\mathop {\mathop < \limits_ }\limits_ {{10}^{10}}{M_ \odot }} \right)$ is crucial to constrain seeding and growth of black holes over cosmic time, but it is particularly challenging due to their low accretion luminosities. Variability selection via long-term photometric ultraviolet, optical, or infrared (UVOIR) light curves has proved effective and identifies lower-Eddington ratios compared to broad and narrow optical spectral lines searches. In the inefficient accretion regime, X-ray and radio searches are effective, but they have been limited to small samples. Therefore, differences between selection techniques have remained uncertain. Here, we present the first large systematic investigation of the X-ray properties of a sample of known MBH candidates in dwarf galaxies. We extracted X-ray photometry and spectra of a sample of ~200 UVOIR variability-selected MBHs and significantly detected 17 of them in the deepest available SRG/eROSITA image, of which four are newly discovered X-ray sources and two are new secure MBHs. This implies that tens to hundreds of LSST MBHs will have SRG/eROSITA counterparts, depending on the seeding model adopted. Surprisingly, the stacked X-ray images of the many non-detected MBHs are incompatible with standard disk-corona relations, typical of active galactic nuclei, inferred from both the optical and radio fluxes. They are instead compatible with the X-ray emission predicted for normal galaxies. After careful consideration of potential biases, we identified that this X-ray weakness needs a physical origin. A possibility is that a canonical X-ray corona might be lacking in the majority of this population of UVOIR-variability selected low-mass galaxies or that unusual accretion modes and spectral energy distributions are in place for MBHs in dwarf galaxies. This result reveals the potential for severe biases in occupation fractions derived from data from only one waveband combined with SEDs and scaling relations of more massive black holes and galaxies.

Astronomy & Astrophysics↗
DOE OSTI2023

Detecting Long-period Variability in the SDSS Stripe 82 Standards Catalog

We report the results of a search for long-period (100 < P < 600 days) periodic variability in the SDSS Stripe 82 standards catalog. The SDSS coverage of Stripe 82 enables such a search because there are on average 20 observations per band in ugriz bands for about one million sources, collected over about 6 yr, with a faint limit of r ~ 22 mag and precisely calibrated 1%–2% photometry. We calculated the periods of variable source candidates in this sample using the Lomb–Scargle periodogram and considered the three highest periodogram peaks in each of the gri filters as relevant. Only those sources with gri periods consistent within 0.1% were later studied. We use the Kuiper statistic to ensure uniform distribution of data points in phased light curves. We present five sources with the spectra consistent with quasar spectra and plausible periodic variability. This SDSS-based search bodes well for future sensitive large-area surveys, such as the Rubin Observatory Legacy Survey of Space and Time, which, due to its larger sky coverage (about a factor of 60) and improved sensitivity (~2 mag), will be more powerful for finding such sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Biogeochemical cycling at the aquatic–terrestrial interface is linked to parafluvial hyporheic zone inundation history

Abstract. The parafluvial hyporheic zone combines the heightened biogeochemical and microbial interactions indicative of a hyporheic region with direct atmospheric/terrestrial inputs and the effects of wet–dry cycles. Therefore, understanding biogeochemical cycling and microbial interactions in this ecotone is fundamental to understanding biogeochemical cycling at the aquatic–terrestrial interface and to creating robust hydrobiogeochemical models of dynamic river corridors. We aimed to (i) characterize biogeochemical and microbial differences in the parafluvial hyporheic zone across a small spatial domain (6 lateral meters) that spans a breadth of inundation histories and (ii) examine how parafluvial hyporheic sediments respond to laboratory-simulated re-inundation. Surface sediment was collected at four elevations along transects perpendicular to flow of the Columbia River, eastern WA, USA. The sediments were inundated by the river 0, 13, 127, and 398 days prior to sampling. Spatial variation in environmental variables (organic matter, moisture, nitrate, glucose, % C, % N) and microbial communities (16S and internal transcribed spacer (ITS) rRNA gene sequencing, qPCR) were driven by differences in inundation history. Microbial respiration did not differ significantly across inundation histories prior to forced inundation in laboratory incubations. Forced inundation suppressed microbial respiration across all histories, but the degree of suppression was dramatically different between the sediments saturated and unsaturated at the time of sample collection, indicating a binary threshold response to re-inundation. We present a conceptual model in which irregular hydrologic fluctuations facilitate microbial communities adapted to local conditions and a relatively high flux of CO2. Upon rewetting, microbial communities are initially suppressed metabolically, which results in lower CO2 flux rates primarily due to suppression of fungal respiration. Following prolonged inundation, the microbial community adapts to saturation by shifting composition, and the CO2 flux rebounds to prior levels due to the subsequent change in respiration. Our results indicate that the time between inundation events can push the system into alternate states: we suggest (i) that, above some threshold of inundation interval, re-inundation suppresses respiration to a consistent, low rate and (ii) that, below some inundation interval, re-inundation has a minor effect on respiration. Extending reactive transport models to capture processes that govern such dynamics will provide more robust predictions of river corridor biogeochemical function under altered surface water flow regimes in both managed and natural watersheds.

58 GEOSCIENCES↗
DOE OSTI2016

The extreme ultraviolet variability of quasars

We study the extreme ultraviolet (EUV) variability (rest frame wavelengths 500–920 Å) of high-luminosity quasars using Hubble Space Telescope (HST) (low to intermediate redshift sample) and Sloan Digital sky Survey (SDSS) (high redshift sample) archives. The combined HST and SDSS data indicates a much more pronounced variability when the sampling time between observations in the quasar rest frame is >2×10{sup 7} s compared to <1.5×10{sup 7} s. Based on an excess variance analysis, for time intervals <2×10{sup 7} s in the quasar rest frame, 10% of the quasars (4/40) show evidence of EUV variability. Similarly, for time intervals >2×10{sup 7} s in the quasar rest frame, 55% of the quasars (21/38) show evidence of EUV variability. The propensity for variability does not show any statistically significant change between 2.5×10{sup 7} s and 3.16×10{sup 7} s (1 year). The temporal behavior is one of a threshold time interval for significant variability as opposed to a gradual increase on these timescales. A threshold timescale can indicate a characteristic spatial dimension of the EUV region. We explore this concept in the context of the slim disk models of accretion. We find that for rapidly spinning black holes, the radial infall time to the plunge region of the optically thin surface layer of the slim disk that is responsible for the preponderance of the EUV flux emission (primarily within 0–7 black hole radii from the inner edge of the disk) is consistent with the empirically determined variability timescale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties

This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). Here, the sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.

Aleatory uncertainty↗
DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗
DOE OSTI2023

X-Ray Emission of Ultraviolet Variable Active Galactic Nucleus Candidates

We analyze the X-ray properties of a sample of 23 high-probability active galactic nucleus (AGN) candidates with ultraviolet variability identified in Wasleske et al. Using data from the Chandra X-ray Observatory and XMM-Newton Observatory, we that find 11/23 nuclei are X-ray detected. We use spectral energy distribution modeling to compute star formation rates and show that the X-ray luminosities are typically in excess of the X-ray emission expected from star formation by at least an order of magnitude. Interestingly, this sample shows a diversity of optical spectroscopic properties. We explore possible reasons for why some objects lack optical spectroscopic signatures of black hole activity while still being UV-variable and X-ray bright. We find that host galaxy stellar emission and obscuration from gas and dust are potential factors. We study where this sample falls on relationships such as α OX - L 2500 and L X - L IR and find that some of the sample falls outside the typical scatter for these relations, indicating that they differ from the standard quasar population. With the diversity of optical spectroscopic signatures and varying impacts of dust and stellar emissions on our sample, these results emphasize the strength of variability in selecting the most complete set of AGN, regardless of other host galaxy properties.

79 ASTRONOMY AND ASTROPHYSICS↗