Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sampling variability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Iron-reducing bacteria accumulate ferric oxyhydroxide nanoparticle aggregates that may support planktonic growth

Iron-reducing bacteria (FeRB) play key roles in anaerobic metal and carbon cycling and carry out biogeochemical transformations that can be harnessed for environmental bioremediation. A subset of FeRB require direct contact with Fe(III) bearing minerals for dissimilatory growth, yet these bacteria must move between mineral particles. Further, they proliferate in planktonic consortia during biostimulation experiments. Thus, a key question is how such organisms can sustain growth under these conditions. Here we characterized planktonic microbial communities sampled from an aquifer in Rifle, Colorado, USA close to the peak of iron reduction following in situ acetate amendment. Samples were cryo-plunged on site and subsequently examined using correlated 2- and 3- dimensional cryogenic transmission electron microscopy (cryo-TEM) and scanning transmission X-ray microscopy (STXM). Most cells had their outer membranes decorated with up to 150 nm diameter aggregates composed of a few nm wide amorphous, Fe-rich nanoparticles. Fluorescent in situ hybridization of lineage-specific probes applied to rRNA of cells subsequently imaged via cryo-TEM identified Geobacter spp., a well studied group of FeRB. STXM results at the Fe L2,3 absorption edges indicate that nanoparticle aggregates contain a variable mixture of Fe(II)-Fe(III), and are generally enriched in Fe(III). Geobacter bemidjiensis cultivated anaerobically in the laboratory on acetate and hydrous ferric oxyhydroxides also accumulated mixed valence nanoparticle aggregates. In field-collected samples, FeRB with a wide variety of morphologies were associated with nano-aggregates, indicating that cell-surface Fe(III) accumulation may be a general mechanism by which FeRB can grow while in planktonic suspension.

biomineralization↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

SrAgZn and EuAgZn with KHg{sub 2}-type structure—Structure, magnetic properties, and {sup 151}Eu Mössbauer spectroscopy

Samples of SrAgZn and EuAgZn were synthesized by reaction of the elements in sealed tantalum crucibles. Both structures were refined on the basis of single crystal X-ray diffractometer data: KHg{sub 2}-type, Imma, a=476.7(1), b=780.9(2), c=810.1(2) pm, R{sub 1}/wR{sub 2}=0.0189/0.0119, 381 F² values for SrAg{sub 1.12}Zn{sub 0.88} and a=474.43(9), b=760.8(2), c=799.0(2) pm, R{sub 1}/wR{sub 2}=0.0226/0.0483, 370 F² values for EuAg{sub 1.17}Zn{sub 0.83} with 13 variables per refinement. Silver and zinc are randomly distributed on the Hg position and build up three-dimensional networks. EuAgZn shows ferromagnetic ordering at 29(1) K. In the temperature range from 75 to 300 K the sample shows Curie–Weiss behaviour with μ{sub eff}=7.87(1) μ{sub B}/Eu atom and θ{sub P}=37.1(1) K, indicating divalent europium. {sup 151}Eu Mössbauer spectroscopic measurements confirmed the divalent state with an isomer shift of −9.31 mm/s at 78 K. Temperature dependent {sup 151}Eu data show first magnetic hyperfine field splitting at 25 K and a saturated magnetization of 17 T at 5.2 K. The temperature dependence can be described by an S=7/2 Brillouin function. - Graphical abstract: The near neighbor coordination of the strontium and europium atoms in SrAg{sub 1.12}Zn{sub 0.88}, EuAg{sub 1.17}Zn{sub 0.83}, and EuAuZn. - Highlights: • Synthesis of new intermetallic zinc compounds SrAgZn and EuAgZn. • Ferromagnetic ordering of EuAgZn at 29 K. • Magnetic hyperfine field splitting in the {sup 151}Eu Mössbauer spectrum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Growth-Mortality Relationships in Piñon Pine ( Pinus edulis ) during Severe Droughts of the Past Century: Shifting Processes in Space and Time

The processes leading to drought-associated tree mortality are poorly understood, particularly long-term predisposing factors, memory effects, and variability in mortality processes and thresholds in space and time. We use tree rings from four sites to investigate Pinus edulis mortality during two drought periods in the southwestern USA. We draw on recent sampling and archived collections to (1) analyze P. edulis growth patterns and mortality during the 1950s and 2000s droughts; (2) determine the influence of climate and competition on growth in trees that died and survived; and (3) derive regression models of growth-mortality risk and evaluate their performance across space and time. Recent growth was 53% higher in surviving vs. dying trees, with some sites exhibiting decades-long growth divergences associated with previous drought. Differential growth response to climate partly explained growth differences between live and dead trees, with responses wet/cool conditions most influencing eventual tree status. Competition constrained tree growth, and reduced trees’ ability to respond to favorable climate. The best predictors in growth-mortality models included long-term (15–30 year) average growth rate combined with a metric of growth variability and the number of abrupt growth increases over 15 and 10 years, respectively. The most parsimonious models had high discriminatory power (ROC >0.84) and correctly classified ~70% of trees, suggesting that aspects of tree growth, especially over decades, can be powerful predictors of widespread drought-associated die-off. However, model discrimination varied across sites and drought events. Weaker growth-mortality relationships and higher growth at lower survival probabilities for some sites during the 2000s event suggest a shift in mortality processes from longer-term growth-related constraints to shorter-term processes, such as rapid metabolic decline even in vigorous trees due to acute drought stress, and/or increases in the attack rate of both chronically stressed and more vigorous trees by bark beetles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

SLOAN DIGITAL SKY SURVEY OBSERVATIONS OF KUIPER BELT OBJECTS: COLORS AND VARIABILITY

Colors of trans-Neptunian objects (TNOs) are used to study the evolutionary processes of bodies in the outskirts of the solar system and to test theories regarding their origin. Here I describe a search for serendipitous Sloan Digital Sky Survey (SDSS) observations of known TNOs and Centaurs. I present a catalog of SDSS photometry, colors, and astrometry of 388 measurements of 42 outer solar system objects. I find weak evidence, at the Almost-Equal-To 2{sigma} level (per trial), for a correlation between the g - r color and inclination of scattered disk objects and hot classical Kuiper Belt objects. I find a correlation between the g - r color and the angular momentum in the z direction of all the objects in this sample. These findings should be verified using larger samples of TNOs. Light curves as a function of phase angle are constructed for 13 objects. The steepness of the slopes of these light curves suggests that the coherent backscatter mechanism plays a major role in the reflectivity of outer solar system small objects at small phase angles. I find weak evidence for an anticorrelation, significant at the 2{sigma} confidence level (per trial), between the g-band phase-angle slope parameter and the semimajor axis, as well as the aphelion distance, of these objects (i.e., they show a more prominent 'opposition effect' at smaller distances from the Sun). However, this plausible correlation should be verified using a larger sample. I discuss the origin of this possible correlation and argue that if this correlation is real it probably indicates that 'Sedna'-like objects have a different origin than other classes of TNOs. Finally, I identify several objects with large variability amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Near-infrared and gamma-ray monitoring of TANAMI gamma-ray bright sources

Context. We present that spectral energy distribution and its variability are basic tools for understanding the physical processes operating in active galactic nuclei (AGN). Aims. In this paper we report the results of a one-year near-infrared (NIR) and optical monitoring of a sample of 22 AGN known to be gamma-ray emitters, aimed at discovering correlations between optical and gamma-ray emission. Methods. We observed our objects with the Rapid Eye Mount (REM) telescope in J,H,K, and R bands nearly twice every month during their visibility window and derived light curves and spectral indexes. We also analyzed the gamma-ray data from the Fermi gamma-ray Space Telescope, making weekly averages. Results. Six sources were never detected during our monitoring, proving to be fainter than their historical Two micron all sky survey (2MASS) level. All of the sixteen detected sources showed marked flux density variability, while the spectral indexes remained unchanged within our sensitivity limits. Steeper sources showed, on average, a larger variability. From the NIR light curves we also computed a variability speed index for each detected source. Only one source (PKS 0208-512) underwent an NIR flare during our monitoring. Half of the sources showed a regular flux density trend on a one-year time scale, but do not show any other peculiar characteristic. The broadband spectral index α ro appears to be a good proxy of the NIR spectral index only for BL Lac objects. No clear correlation between NIR and gamma-ray data is evident in our data, save for PKS 0537-441, PKS 0521-360, PKS 2155-304, and PKS 1424-418. In conclusion, the gamma-ray/NIR flux ratio showed a large spread, QSO being generally gamma-louder than BL Lac, with a marked correlation with the estimated peak frequency (ν peak ) of the synchrotron emission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Statistical Treatment of Convolutional Neural Network Superresolution of Inland Surface Wind for Subgrid-Scale Variability Quantification

Abstract Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between ∼50- and 100-km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of a type of superresolving convolutional neural network (SR-CNNs) to downscale surface wind speed data over land from different coarse resolutions (25-, 48-, and 100-km resolution) to 3 km. For each downscaling factor, we consider three convolutional neural network (CNN) configurations that generate superresolved predictions of fine-scale wind speed, which take between one and three input fields: coarse wind speed, fine-scale topography, and diurnal cycle. In addition to fine-scale wind speeds, probability density function parameters are generated through which sample wind speeds can be generated, accounting for the intrinsic stochasticity of wind speed. For assessing generalization to new data, CNN models are tested on regions with different topography and climate that are unseen during training. The evaluation of superresolved predictions focuses on subgrid-scale variability and the recovery of extremes. Models with coarse wind and fine topography as inputs exhibit the best performance when compared with other model configurations, operating across the same downscaling factor. Our diurnal cycle encoding results in lower out-of-sample generalizability when compared with other input configurations.

17 WIND ENERGY↗
DOE OSTI2021

Search for Magnetic Accretion in SW Sextantis Systems

SW Sextantis systems are nova-like cataclysmic variables that have unusual spectroscopic properties, which are thought to be caused by an accretion geometry having part of the mass flux trajectory out of the orbital plane. Accretion onto a magnetic white dwarf is one of the proposed scenarios for these systems. To verify this possibility, we analyzed photometric and polarimetric time-series data for a sample of six SW Sex stars. We report possible modulated circular polarization in BO Cet, SW Sex, and UU Aqr with periods of 11.1, 41.2, and 25.7 minutes, respectively, and less significant periodicities for V380 Oph at 22 minutes and V442 Oph at 19.4 minutes. We confirm previous results that LS Peg shows variable circular polarization. However, we determine a period of 18.8 minutes, which is different from the earlier reported value. We interpret these periods as the spin periods of the white dwarfs. Our polarimetric results indicate that 15% of the SW Sex systems have direct evidence of magnetic accretion. We also discuss SW Sex objects within the perspective of being magnetic systems, considering the latest findings about the demography, formation, and evolution of cataclysmic variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

A SWIFT SURVEY OF ACCRETION ONTO STELLAR-MASS BLACK HOLES

We present a systemic analysis of all of the stellar-mass black hole binaries (confirmed and candidate) observed by the Swift observatory up to 2010 June. The broad Swift bandpass enables a trace of disk evolution over an unprecedented range in flux and temperature. The final data sample consists of 476 X-ray spectra containing greater than 100 counts, in the 0.6-10 keV band. This is the largest sample of high-quality CCD spectra of accreting black holes published to date. In addition, strictly simultaneous data at optical/UV wavelengths are available for 255 (54%) of these observations. The data are modeled with a combination of an accretion disk and a hard spectral component. For the hard component we consider both a simple power-law model and a thermal Comptonization model. An accretion disk is detected at greater than the 5{sigma} confidence level in 61% of the observations. Light curves and color-color diagrams are constructed for each system. Hardness-luminosity and disk fraction-luminosity diagrams are constructed and are observed to be consistent with those typically observed by RXTE, noting the sensitivity below 2 keV provided by Swift. The observed spectra have an average luminosity of {approx}1% Eddington, though we are sensitive to accretion disks down to a luminosity of 10{sup -3} L{sub Edd}. Thus, this is also the largest sample of such cool accretion disks studied to date. The accretion disk temperature distribution displays two peaks consistent with the classical hard and soft spectral states, with a smaller number of disks distributed between these. The distribution of inner disk radii is observed to be continuous regardless of which model is used to fit the hard continua. There is no evidence for large-scale truncation of the accretion disk in the hard state (at least for L{sub x} {approx}> 10{sup -3} L{sub Edd}), with all of the accretion disks having radii {approx}< 40 R{sub g} . Plots of the accretion disk inner radius versus hardness ratio reveal the disk radius to be decreasing as the spectrum hardens, i.e., enters the hard state. This is in contrast to expectations from the standard disk truncation paradigm and points toward a contribution from spectral hardening. The availability of simultaneous X-ray and optical/UV data for a subset of observations facilitates a critical examination of the role of disk irradiation via a modified disk model with a variable emissivity profile (i.e., T(r){proportional_to}r {sup -p}). The broadband spectra (X-ray-optical/UV) reveal irradiation of the accretion disk to be an important effect at all luminosities sampled herein, i.e., p {approx}< 0.75 for luminosities {approx}> 10{sup -3} L{sub Edd}. The accretion disk is found to dominate the UV emission irrespective of the assumed hard spectral component. Overall, we find the broadband soft-state spectra to be consistent with an irradiated accretion disk plus a corona, but we are unable to make conclusive statements regarding the nature of the hard-state accretion flow (e.g., ADAF/corona versus jet). Finally, the Swift data reveal a relation between the flux emitted by the accretion disk and that emitted by the corona, for this sample of stellar-mass black holes, that is found to be in broad agreement with the observed disk-corona relationship in Seyfert galaxies, suggesting a scale invariant coupling between the accretion disk and the corona.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

SPECTRAL ENERGY DISTRIBUTIONS OF TYPE 1 ACTIVE GALACTIC NUCLEI IN THE COSMOS SURVEY. I. THE XMM-COSMOS SAMPLE

The 'Cosmic Evolution Survey' (COSMOS) enables the study of the spectral energy distributions (SEDs) of active galactic nuclei (AGNs) because of the deep coverage and rich sampling of frequencies from X-ray to radio. Here we present an SED catalog of 413 X-ray (XMM-Newton)-selected type 1 (emission line FWHM > 2000 km s{sup -1}) AGNs with Magellan, SDSS, or VLT spectrum. The SEDs are corrected for Galactic extinction, broad emission line contributions, constrained variability, and host galaxy contribution. We present the mean SED and the dispersion SEDs after the above corrections in the rest-frame 1.4 GHz to 40 keV, and show examples of the variety of SEDs encountered. In the near-infrared to optical (rest frame {approx}8 {mu}m-4000 A), the photometry is complete for the whole sample and the mean SED is derived from detections only. Reddening and host galaxy contamination could account for a large fraction of the observed SED variety. The SEDs are all available online.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Generalized correlation integral vectors: A distance concept for chaotic dynamical systems

Several concepts of fractal dimension have been developed to characterise properties of attractors of chaotic dynamical systems. Numerical approximations of them must be calculated by finite samples of simulated trajectories. In principle, the quantities should not depend on the choice of the trajectory, as long as it provides properly distributed samples of the underlying attractor. In practice, however, the trajectories are sensitive with respect to varying initial values, small changes of the model parameters, to the choice of a solver, numeric tolerances, etc. The purpose of this paper is to present a statistically sound approach to quantify this variability. We modify the concept of correlation integral to produce a vector that summarises the variability at all selected scales. The distribution of this stochastic vector can be estimated, and it provides a statistical distance concept between trajectories. Here, we demonstrate the use of the distance for the purpose of estimating model parameters of a chaotic dynamic model. The methodology is illustrated using computational examples for the Lorenz 63 and Lorenz 95 systems, together with a framework for Markov chain Monte Carlo sampling to produce posterior distributions of model parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Self-organized criticality in multi-pulse gamma-ray bursts

The variability in multi-pulse gamma-ray bursts (GRBs) may help to reveal the mechanism of underlying processes from the central engine. To investigate whether the self-organized criticality (SOC) phenomena exist in the prompt phase of GRBs, we statistically study the properties of GRBs with more than 3 pulses in each burst by fitting the distributions of several observed physical variables with a Markov Chain Monte Carlo approach, including the isotropic energy E iso , the duration time T, and the peak count rate P of each pulse. Our sample consists of 454 pulses in 93 GRBs observed by the CGRO/BATSE satellite. The best-fitting values and uncertainties for these power-law indices of the differential frequency distributions are: α$^{d}_{E}$ = 1.54±0.09, α$^{d}_{T}$ = 1.82±$^{+0.14}_{0.15}$ and α$^{d}_{P}$ = 2.09$^{+0.18}_{–0.19}$, while the power-law indices in the cumulative frequency distributions are: α$^{c}_{E}$ = 1.44$^{+0.08}_{–0.10}$, α$^{c}_{T}$ = 1.75$^{+0.141}_{–0.13}$ and α$^{c}_{P}$ = 1.99$^{+0.16}_{–0.19}$. We find that these distributions are roughly consistent with the physical framework of a Fractal-Diffusive, Self-Organized Criticality (FD-SOC) system with the spatial dimension S = 3 and the classical diffusion β=1. The results of this study support that the jet responsible for the GRBs should be magnetically dominated and magnetic instabilities (e.g., kink model, or tearing-model instability) lead the GRB emission region into the SOC state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
DOE OSTI2023

Faraday detector uranium isotope ratio measurement: Insights from solution- and laser ablation-based sampling methodologies on the Neoma MC-ICP-MS

In this study we utilize an all-faraday cup detector configuration on the Neoma MC-ICP-MS to perform uranium isotope ratio determinations from certified reference materials and a nuclear fuel precursor material via solution and laser ablation based sampling methodologies. The goal in performing these measurements is primarily to demonstrate that the all-faraday cup method can produce highly precise and accurate isotope ratios even at relatively low signal intensity levels while also developing a preliminary understanding of how the complex interplay between different amplifier resistor levels (e.g. 10 11 vs 10 13 Ω) and integration times impacts the precision and accuracy of different types of measurements. Our results show that the faraday cups with 1013 Ω resistors in their amplifier feedback loops can be used to produce accurate and relatively precise isotope ratios for ion beams down to ~5 K cps. However, at the lower end of the signal intensities observed in this study, longer integration times are necessary to mitigate the impact of ion beam instability that can occur during laser ablation based sampling. Despite this potential limitation, the all-faraday cup method is highly versatile as exemplified by the analysis of a collection of uranium reference materials with highly variable uranium isotopic compositions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Gaussian wave packet transform based numerical scheme for the semi-classical Schrödinger equation with random inputs

In this work, we study the semi-classical limit of the Schrödinger equation with random inputs, and show that the semi-classical Schrödinger equation produces O(ε) oscillations in the random variable space. With the Gaussian wave packet transform, the original Schrödinger equation is mapped to an ordinary differential equation (ODE) system for the wave packet parameters coupled with a partial differential equation (PDE) for the quantity w in rescaled variables. Further, we show that the w equation does not produce ε dependent oscillations, and thus it is more amenable for numerical simulations. We propose multi-level sampling strategy in implementing the Gaussian wave packet transform, where in the most costly part, i.e. simulating the w equation, it is sufficient to use ε independent samples. We also provide extensive numerical tests as well as meaningful numerical experiments to justify the properties of the numerical algorithm, and hopefully shed light on possible future directions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Adsorbate Partition Functions via Phase Space Integration: Quantifying the Effect of Translational Anharmonicity on Thermodynamic Properties

A new method for computing anharmonic thermophysical properties for adsorbates on metal surfaces is presented. Classical Monte Carlo phase space integration is performed to calculate the partition function for the motion of a hydrogen atom on Cu(111). Here, a minima-preserving neural network potential energy surface is used within the integration routine. Two different sampling schema for generating the training data are presented, and two different density functionals are used. The results are benchmarked against direct state counting results by using discrete variable representation. The phase space integration results are in excellent quantitative agreement with the benchmark results. Additionally, both the discrete variable representation and the phase space integration results confirm that the motion of H on Cu(111) is highly anharmonic. The results were applied to calculate the free energy of dissociative adsorption of H 2 and the resulting Langmuir isotherms at 400, 800, and 1200 K in a partial pressure range of 0–1 bar. It shows that the anharmonic effects lead to significantly higher predicted surface site fractions of hydrogen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗