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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Toward Practical Federal Spectrum Sharing for Advanced Wireless Technologies

Wireless communications have become critical to modern society as the foundation of vital services, business interactions, and social connections. As technology develops and new scenarios emerge, wireless communications become increasingly entrenched as a foundation for modern life. The materialization of 5G has been especially impactful as an enabler for a wide range of new scenarios and increased dependence. With increasing demand for wireless communications comes increasing demand for the radio frequency (RF) spectrum necessary to support these communications. This spectrum demand, largely driven by desire to realize ubiquitous 5G networks, both public and private, is currently driving a reconsideration of spectrum assignments and forcing legacy users of spectrum to update their use of spectrum. As one of the largest legacy users of spectrum, the U.S. Government (USG) is particularly impacted by the increasing demand for spectrum for non USG use. Unfortunately, there is no clear pathway for U.S. federal agencies to modernize their spectrum use and instead these agencies are forced to simply react to spectrum repurposing driven by overwhelming demand. Here we present preliminary work toward a roadmap for federal agencies to modernize their spectrum use through practical analysis and leveraging emerging spectrum sharing (SS) technologies. This effort utilizes the example of DOE spectrum use in the context of National Telecommunications and Information Administration (NTIA) and Federal Communications Commission (FCC) repurposing to examine the factors that must be considered to facilitate spectrum sharing by federal agencies. Furthermore, this work provides an initial survey of the SS models and SS technologies of highest relevance to federal use. We particularly examine the emerging capabilities of 5G itself to enable SS and unlicensed access through transformative capabilities such as new radio unlicensed (NR-U). This work is a step toward translating the large body of academic discussion on SS into a practical roadmap for federal agencies.

5G↗
DOE OSTI2022

SDSS IV MaNGA - star-formation driven biconical outflows in face-on galaxies

ABSTRACT We find 132 face-on and low inclination galaxies with central star formation driven biconical gas outflows (FSFB) in the SDSS MaNGA (Mapping Nearby Galaxies at APO) survey. The FSFB galaxies show either double peaked or broadened emission line profiles at their centres. The peak and maximum outflow velocities are 58 and 212 km s−1, respectively. The gas velocity dispersion reveals a mild dependence on the central star formation surface density compatible with models of gas dispersion powered by the Jeans instability in gas clumps or by gas turbulence dissipation. We estimate the gas outflow rate and conclude that the central gas depletion time does not depend on galactic mass. In turn, the ratio of the gas outflow rate to the gas consumption rate by the star formation is low in massive galaxies and high in low mass objects, while the star formation is a more rapid process of the gas consumption. We compare properties of the FSFB galaxies with a control sample of 375 comparison galaxies and find that the FSFB objects have high central concentration of star formation and also younger central stellar population with respect to their periphery. We analysed the environment of the galaxies and identified nearby satellites and elements of low surface brightness structure. We see that many tidal-enhanced features that can be assigned to early and intermediate stages of galactic interaction are much more frequent in the FSFB galaxies with respect to the comparison sample. We conclude that the gas should be replenished via the accretion from small satellites.

Bizyaev, D. (ORCID:000000023601133X)↗
DOE OSTI2021

The Hyperthermophilic Restriction-Modification Systems of Thermococcus kodakarensis Protect Genome Integrity

Thermococcus kodakarensis (T. kodakarensis), a hyperthermophilic, genetically accessible model archaeon, encodes two putative restriction modification (R-M) defense systems, TkoI and TkoII. TkoI is encoded by TK1460 while TkoII is encoded by TK1158. Bioinformative analysis suggests both R-M enzymes are large, fused methyltransferase (MTase)-endonuclease polypeptides that contain both restriction endonuclease (REase) activity to degrade foreign invading DNA and MTase activity to methylate host genomic DNA at specific recognition sites. In this work, we demonsrate T. kodakarensis strains deleted for either or both R-M enzymes grow more slowly but display significantly increased competency compared to strains with intact R-M systems, suggesting that both TkoI and TkoII assist in maintenance of genomic integrity in vivo and likely protect against viral- or plasmid-based DNA transfers. Pacific Biosciences single molecule real-time (SMRT) sequencing of T. kodakarensis strains containing both, one or neither R-M systems permitted assignment of the recognition sites for TkoI and TkoII and demonstrated that both R-M enzymes are TypeIIL; TkoI and TkoII methylate the N 6 position of adenine on one strand of the recognition sequences GTGAAG and TTCAAG, respectively. Further in vitro biochemical characterization of the REase activities reveal TkoI and TkoII cleave the DNA backbone GTGAAG(N) 20 /(N) 18 and TTCAAG(N) 10 /(N) 8 , respectively, away from the recognition sequences, while in vitro characterization of the MTase activities reveal transfer of tritiated S-adenosyl methionine by TkoI and TkoII to their respective recognition sites. Together these results demonstrate TkoI and TkoII restriction systems are important for protecting T. kodakarensis genome integrity from invading foreign DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

TEAL Output Visualization

Tool for Economic Analysis (TEAL) is an open-source economics calculation package written and maintained by Idaho National Laboratory (INL). As a plugin of the Risk Analysis Virtual ENvironment (RAVEN), TEAL provides economics analysis models to RAVEN workflow users. In addition, TEAL is used in other RAVEN plugins such as LOGOS and the Holistic Energy Resource Optimization Network (HERON) to complement other analyses with economic calculations. This report documents the efforts in developing the capability of TEAL output visualization for users to better understand and investigate TEAL simulation results. In the past, outputs (e.g., various cash flows) were generated and printed to screen. According to the need and purpose for visualization, users can select various bar charts and donut charts to visualize the cash flows including inflows, outflows, and net cash flows in each year or in a selected time range, in addition to observing data stored in comma-separated value files (CSVs). The visualization capability is flexible and user-friendly by introducing several user-defined parameters, for instance, the start and the end project year of interest and the type of colors to be assigned to the cash flows.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Non-parametric Lagrangian biasing from the insights of neural nets

We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field.By fitting the mass-weighted halo field in the AbacusSummit simulations at z = 0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2–5% underestimation of the large-scale power and can cause the neural net to overfit.We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space.Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales.Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Raccoon density estimation from camera traps for raccoon rabies management

Abstract Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons ( Procyon lotor ) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates ( r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (<5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.

Davis, Amy J.↗
DOE OSTI2025

Determination of the spin and parity of all-charm tetraquarks

The traditional quark model accounts for the existence of baryons, such as protons and neutrons, which consist of three quarks, as well as mesons, composed of a quark–antiquark pair. Only recently has substantial evidence started to accumulate for exotic states composed of four or five quarks and antiquarks. The exact nature of their internal structure remains uncertain. Here we report the first measurement of quantum numbers of the recently discovered family of three all-charm tetraquarks, using data collected by the CMS experiment at the Large Hadron Collider from 2016 to 2018 . The angular analysis techniques developed for the discovery and characterization of the Higgs boson have been applied to the new exotic states. Here we show that the quantum numbers for parity P and charge conjugation C symmetries are found to be +1. The spin J of these exotic states is determined to be consistent with 2ħ, while 0ħ and 1ħ are excluded at 95% and 99% confidence levels, respectively. The J PC = 2 ++ assignment implies particular configurations of constituent spins and orbital angular momenta, which constrain the possible internal structure of these tetraquarks.

Physics↗
DOE OSTI2023

Transforming our understanding of chloroplast-associated genes through comprehensive characterization of protein localizations and protein-protein interactions

Bioenergy crops are a renewable source of fuels and are a critical base for building a carbon-neutral economy. Rational engineering of bioenergy crops has the potential to enhance the yields. However, our ability to engineer plants is limited because the functions of most genes remain unknown. Systematic characterization of gene function in plants thus has the potential to greatly accelerate bioenergy research. Here, we focus on the chloroplast, an underexplored energy-producing organelle that is a hallmark of plants. The chloroplast is one of the promising targets of biofuel crop engineering efforts because of its central role in photosynthesis, metabolism, and intracellular signaling. However, the protein composition of the chloroplast and the functions of most of its proteins remain poorly characterized. At the core of this project, we sought to comprehensively determine the localization of chloroplast-associated proteins and generate a spatially defined protein-protein interaction network for chloroplast. For this purpose, we used the leading model alga Chlamydomonas reinhardtii, which greatly increased experimental speed and throughput. We illustrated the value of our findings to land plants by determining the localization of Arabidopsis thaliana land plant homologs of the Chlamydomonas proteins. Altogether, we were successful in determining the localization of 1,034 chloroplast-associated proteins in Chlamydomonas. The localizations provide numerous insights into the spatial organization of chloroplasts and how they function to support photosynthesis. The localization patterns of distinct proteins revealed new chloroplast structures and revealed new spatial organization inside the chloroplast. We also identified new components of known chloroplast structures, such as the chloroplast envelope, nucleoid, plastoglobuli, and pyrenoid. We identified these new components by investigating the interacting partners of known proteins. Many proteins localized in both the chloroplast and other cellular structures, thereby hinting at new functions and communication between cellular structures. We also applied machine learning on the atlas to generate predictions for the location of all of the proteins in Chlamydomonas. This enabled us to assign putative functions to many uncharacterized proteins based on their cellular location. Altogether, this research establishes a rich resource that opens new avenues of investigation and guides future work in deciphering and manipulating chloroplast function. Next, we developed an extensive protein-protein interaction network for the chloroplast by performing affinity purification-mass spectrometry on ~1,150 tagged chloroplast-associated proteins, the first such large-scale study in any photosynthetic organism. This dataset reveals 4,694 high-confidence protein-protein interactions, offering insights into the functions of thousands of conserved poorly-characterized chloroplast proteins. This systematic identification of protein-protein interactions in the chloroplast also provides multiple exciting new research directions and a detailed blueprint of the chloroplast's operation. This research lays the groundwork to decipher the inner workings of the chloroplast, the cell structure at the heart of photosynthesis. The spatial atlas and protein-protein interactions reveal chloroplast organizational features that would not have been accessible with traditional approaches. The localization mapping, insights into the function, and research materials generated further provide a rich resource for the research community to advance the understanding of how the chloroplast is organized to enable engineering of enhanced photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Shape evolution of neutron-rich 106,108,110 Mo isotopes in the triaxial degree of freedom

Background: Neutron-rich nuclei with mass number between 100 and 110 attract much attention, since several kinds of shapes, such as spherical, prolate, oblate, and triaxial shapes, are predicted. In particular, for neutron-rich Mo isotopes, different models predict different magnitudes and rigidity of triaxial deformation. Previous interpretations of experimental results based solely on low-lying $2^+_2$ states are insufficient to distinguish between the rigid triaxial shape, $\gamma$ vibration, or $\gamma$-soft rotor. Purpose: The shape evolution of 106 Mo, 108 Mo, and 110 Mo is investigated through their $2^+_1$-state lifetimes, decay-branching ratios of the $2^+_2$ state, and energies of the low-lying collective excited states with $K^π = 0^+, 2^+$, and $4^+$. Method: $\beta$-delayed $\gamma$-ray spectroscopy was employed for neutron-rich Nb and Zr isotopes produced at the RIKEN RI Beam Factory to populate excited states in 106 Mo, 108 Mo, and 110 Mo. The EUroball-RIKEN Cluster Array was used for high-resolution $\gamma$-ray detection and lifetimes of the $2^+_1$ states were determined using the UK fast-timing array of LaBr 3 (Ce) detectors. Results: New $\gamma$-ray transitions and levels are reported, including newly assigned $0^+_2$ states in 108,110 Mo. Quadrupole deformations were obtained for 106,108,110 Mo from their $2^+_1$ energies and lifetimes. The $\beta$-delayed neutron-emission probabilities of 108 Nb and 110 Nb were determined by examining the $\gamma$ rays of their respective daughter decays. Conclusions: In this work, the even-odd energy staggering in the $2^+_2$ band was compared with typical patterns of the $\gamma$-vibrational band, rigid triaxial rotor, and $\gamma$-soft rotor. The very small even-odd staggering of 106 Mo, 108 Mo, and 110 Mo favors a $\gamma$-vibrational band assignment. The kinematic moment of inertia for the $2^+_2$ band showed a trend similar to the ground-state band, which is as expected for the $\gamma$-vibrational band. Beyond-mean-field calculations employing the constrained Hartree-Fock-Bogoliubov and local quasiparticle-random-phase approximation method using the SLy5 + T interaction reproduced the ground and $2^+_2$ bands in 106 Mo and 108 Mo. The collective wave functions are consistent with the interpretation of the $2^+_2$ band as the $\gamma$-vibrational band of the prolate shape. However, the staggering pattern observed in 110 Mo differs from the one suggested in the calculations which predict a $\gamma$-soft rotor. There was no experimental indication of the oblate shape or the $\gamma$-soft rotor predicted in these Mo isotopes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Metabolite discovery through global annotation of untargeted metabolomics data

Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak–peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Furthermore, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Investigation of mode activity in NBI-heated experiments of Wendelstein 7-X

The 2018 operation phase (OP 1.2b) of the stellarator Wendelstein 7-X (W7-X) included, for the first time, neutral beam injection (NBI) to heat the plasma. Since the injection geometry at W7-X is not parallel, this generates both passing and trapped fast particles. During longer phases of NBI injection, with the primary purpose to study the heating efficiency of this system, Alfvén eigenmodes (AEs) were observed by a number of diagnostics, including the phase contrast imaging (PCI) system, the magnetic pick-up coils (Mirnov coils), and the soft x-ray multi-camera tomography system (XMCTS). Alfvén eigenmodes are of great interest for future fusion reactors as it has been shown that the resonant interaction of fast ions with self-excited AEs can lead to enhanced transport of fast ions and potentially to energy losses. This is especially true for so-called gap-modes, Alfvén eigenmodes with frequencies in gaps of the continuous spectrum, since they lack continuum damping. These modes are commonly known to be excited by fast ions, but other destabilizing mechanisms, e.g. the electron-pressure gradient are also possible. In this article we present a first analysis of the experimentally observed frequencies from the theoretical side. The calculation of shear Alfvén wave continua for selected cases and the assignment of observed frequencies to the gaps of the continuous spectra are presented. Using the ideal-MHD code CKA (Könies A. 2007 10th IAEA TM on Energetic Particles in Magnetic Confinement System), we find gap modes that match the experimental measurements in terms of the observed frequencies. We emphasize the crucial roles played by the coupling of sound and Alfvén waves as well as of the Doppler shift arising as a consequence of the radial electric field in W7-X. We employ the perturbative gyrokinetic code CKA-EUTERPE (Feh´er 2013 Simulation of the interaction between Alfv´en waves and fast particles), using a slowing-down distribution function for the fast ions as calculated by the Monte-Carlo particle following code ASCOT (Hirvijoki et al 2014 Comput. Phys. Commun. 185 1310–21) to assess the fast-ion drive. Here we find that the fast-ion drive is insufficient to overcome the background-plasma damping. The fact that unstable modes were observed experimentally may point to problems with the modelling or indicate the existence of other destabilizing mechanisms, e.g. associated with the electron-pressure gradient (Windisch et al 2017 Plasma Phys. Control. Fusion 59 105002) that sensitively depend on the profiles of the background plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Self-Organizing Map-Based Resilience Quantification and Resilient Control of Distribution Systems Under Extreme Events

Due to climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution system operators (DSO) to ensure that there is uninterrupted power supply to critical loads in their networks. To embed resilience into DSO's decision-making, resilience needs to be first quantified and then integrated into the system-level optimization. Therefore, this paper first develops a novel self-organizing map (SOM) based method (called SomRes) to quantify the time-varying resilience index of a system that can leverage the powerful classification property of SOMs and removes some of the disadvantages of subjective weight assignment methods. Using SomRes, a resilient resource allocation and operational dispatch algorithm is further developed to enhance system resilience against extreme events by considering the SomRes resilience index directly as the feedback. Here, the proposed resilience quantification approach is benchmarked with a state-of-the-art approach and the efficacy of the proposed resilient dispatch algorithm is demonstrated through several deterministic and statistical case studies on the IEEE 123-bus distribution system. Simulation studies show that the proposed SomRes quantification method is an appropriate indicator of system resilience, and the resilient resource allocation and dispatch strategy can significantly reduce critical load shedding under varying event propagation scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

High resolution electronic spectroscopy of uranium mononitride, UN

The isoelectronic molecules UN and UO + are known to have Ω = 3.5 and Ω = 4.5 ground states, respectively (where Ω is the unsigned projection of the electronic angular momentum along the internuclear axis). A ligand field theory model has been proposed to account for the difference [Matthew and Morse, J. Chem. Phys. 138, 184303 (2013)]. The ground state of UO + arises from the U 3+ (5f 3 ( 4 I 4.5 ))O 2− configuration. Owing to the higher nominal charge of the N 3− ligand, the U 3+ ion in UN is stabilized by promoting one of the 5f electrons to the more polarizable 7s orbital, reducing the repulsive interaction with the ligand and rendering U 3+ (5f 2 7s( 4 H 3.5 ))N 3− the lowest energy configuration. In the present work, we have advanced the characterization of the UN ground state through studies of two electronic transitions, [18.35]4.5-X(1)3.5 and [18.63]4.5-X(1)3.5, using sub-Doppler laser excitation techniques with fluorescence detection. Further, spectra were recorded under field-free conditions and in the presence of static electric or magnetic fields. The ground state electric dipole moment [μ = 4.30(2) D] and magnetic g e -factor [2.160(9)] were determined from these data. These values were both consistent with the 5f 2 7s configurational assignment. Dispersed fluorescence measurements were used to determine vibrational constants for the ground and first electronically excited states. Electric dipole moments and magnetic g e -factors are also reported for the higher-energy electronically excited states.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Waste Glass Property Database and Data Qualification Plan

The U.S. Department of Energy vitrification facilities currently employ glass property-composition models to ensure processability of waste streams and acceptability of the final glass products. The efficiency (e.g., process flexibility, reduced down time, and reduced cost) of waste vitrification is directly tied to the size of the waste processing envelope. To increase the size of the processing envelope, a larger database was developed. The database will improve the prediction accuracy of glass properties, leading to higher waste loadings, higher waste throughput, broader process flexibility, and reduced mission life. Some of the data in this expanded database has insufficient quality assurance (QA) rigor for use in nuclear facility operation and glass qualification. A plan was developed to qualify high-value data under the appropriate QA rigor for use at the Hanford Waste Treatment and Immobilization Plant’s High-Level Waste (HLW) Facility. This qualification plan first compares the data coverage over the composition region of interest to Hanford HLW by property. Those studies that significantly improve the data coverage in the Hanford HLW glass composition region were prioritized for qualification activities. Qualification methods including QA equivalence, peer review, data corroboration, and confirmatory testing have been assigned to each high-priority study.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Spatially Averaged Ice Contents of Ice-Wedge Polygon Cross-Sections to 3-m Depth, July 2013, Utqiagvik, Alaska

This dataset contains spatially averaged estimates of ice content (volume percent) for two-dimensional cross-sectional profiles (from trough center to trough center to a depth of 3 meters) of low- and flat-centered ice-wedge polygons (three of each type) located near Utqiagvik, Alaska. A combination of soil pits, trenches, and cores were used to describe, sample, and map the cross-section stratigraphy of soil horizons and ice wedges for each polygon at 6 depth intervals. Observed soil horizons were assigned to four types with increasing amounts of organic components (mineral, mineral/organic, organic/mineral, and organic). The average ice contents of each soil horizon type below the permafrost boundary and wedge ice were weighted by their cross-sectional area fractions to calculate spatially averaged estimates of ice content for each polygon. In the active layer, spatially averaged estimates of volumetric water contents were similarly determined and reported here as “ice” content to enable estimates of the soil’s structurally competent porosity and excess ice in permafrost layers. In this dataset, the file AK13_ice_contents.csv includes cross-sectional area fractions and spatially averaged ice contents for soil layers and horizon types at the six depth intervals. The file AK13_permafrost_ice_fractions.csv contains the calculated partitioning of ice contents into pore ice and excess ice fractions for the three permafrost-dominated depth intervals. In addition, there is a data dictionary file for each of these data files and a file-level metadata file. These data were generated by the Department of Energy’s Soil Carbon Response to Environmental Change Scientific Focus Area and were used as inputs to model simulations examining the consequences of thaw-affected subsidence and microtopography change on active layer thickness of low-relief polygonal tundra landscapes in a warming Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Novel machine-learning method for spin classification of neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonant structure whose shape is determined in part by the angular-momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is, therefore, paramount. In this project, we apply machine learning to automate the quantum number assignments using only the resonances' energies and widths and not relying on detailed transmission or capture measurements. The classifier used for quantum number assignment is trained using stochastically generated resonance sequences whose distributions mimic those of real data. Here we explore the use of several physics-motivated features for training our classifier. These features amount to out-of-distribution tests of a given resonance's widths and resonance-pair spacings. We pay special attention to situations where either capture widths cannot be trusted for classification purposes or where there is insufficient information to classify resonances by the total spin J. We demonstrate the efficacy of our classification approach using simulated and actual 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗