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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Color-octet scalars in Dirac gaugino models with broken R symmetry

In this work we study the collider phenomenology of color-octet scalars ( sgluons ) in supersymmetric models with Dirac gaugino masses that feature an explicitly broken R symmetry ( R - broken models ). We construct such models by augmenting minimal R -symmetric models with a fairly general set of supersymmetric and softly supersymmetry-breaking operators that explicitly break R symmetry. We then compute the rates of all significant two-body decays and highlight new features that appear as a result of R symmetry breaking, including enhancements to extant decay rates, novel tree- and loop-level decays, and improved cross sections of single sgluon production. We demonstrate in some detail how the familiar results from minimal R -symmetric models can be obtained by restoring R symmetry. In parallel to this discussion, we explore constraints on these models from the Large Hadron Collider. We find that, in general, R symmetry breaking quantitatively affects existing limits on color-octet scalars, perhaps closing loopholes for light CP-odd (pseudoscalar) sgluons while opening one for a light CP-even (scalar) particle. Qualitatively, however, we find that — much as for minimal R -symmetric models, despite stark differences in phenomenology — scenarios with broken R symmetry and two sgluons below the TeV scale can be accommodated by existing searches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2021

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Abstract Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Morphology and particle size (MaPS) exercise: testing the applications of image analysis and morphology descriptions for nuclear forensics

Image analysis techniques have been applied and shown to be a valuable tool in nuclear forensics analysis. The interlaboratory exercise reported here has tested quantitative and qualitative approaches for characterizing nuclear materials. Particle size, surface features and morphology descriptions were compared by four laboratories on a common image set generated by Scanning Electron Microscopy and Digital Light Microscopy. Quantitative analysis of the image sets through the Morphological Analysis for MAterials software highlighted the strength of image analysis, but also that the application of the software alone can introduce significant bias in the analysis. Qualitative morphology descriptions following the process outlined by Tamasi et al. (J Radioanal Nuclear Chem 307:1611–1619, 2015) were compared with a discussion on the robustness and reproducibility of the results. Finally, future work should continue to focus on proficiency and standardization of image analysis through continued exercises within the extended nuclear forensics community.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A Review of the Influence of Processing Parameters on ODS Steels Produced via Additive Manufacturing Techniques

Abstract This paper reviews current observations regarding processing conditions for oxide dispersion-strengthened steels consolidated through additive manufacturing techniques. Variations in ODS steels observed across process parameters include changes in grain size, grain texture, oxide size, density of oxides, porosity, melt pool characteristics, and mechanical properties. These properties were then compared across techniques to understand which techniques and processing conditions lead to the highest strength, ductility, and oxide density. Current literature suggests that a mix of grain types, in the form of either morphology or phase, can significantly increase the strength of printed ODS steels. Meanwhile, the most ductile samples, regardless of consolidation technique or matrix material, were made from feedstock with oxide additions located on the powder surface. Reported grain and oxide sizes were plotted against the ratio of laser power to scan speed, volumetric energy density, and normalized enthalpy. No strong correlation between these values and microstructural features was observed. The plots that were made suggest that a larger data set, more in-depth representative equations, and more defined material properties as a function of specific feedstock used are necessary to determine a value that can be correlated to the printed ODS steel microstructure.

deJong, Matthew↗
DOE OSTI2026

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗
DOE OSTI2021

A Global High‐Resolution Mesoscale Convective System Database Using Satellite‐Derived Cloud Tops, Surface Precipitation, and Tracking

Abstract A new methodology is developed to construct a global (60°S–60°N) long‐term (2000–2019) high‐resolution (∼10‐km h) mesoscale convective system (MCS) database by tracking MCS jointly using geostationary satellite infrared brightness temperature ( T b ) and precipitation feature (PF) characteristics from the Integrated Multi‐satellitE Retrievals for GPM precipitation data sets. Independent validation shows that the satellite‐based MCS data set is able to reproduce important MCS statistics derived from ground‐based radar network observations in the United States and China. We show that by carefully considering key PF characteristics in addition to T b signatures, the new method significantly improves upon previous T b ‐only methods in detecting MCSs in the midlatitudes for all seasons. Results show that MCSs account for over 50% of annual total rainfall across most of the tropical belt and in selected regions of the midlatitudes, with a strong seasonality over many regions of the globe. The tracking database allows Lagrangian aspects such as MCS lifetime and translational speed and direction to be analyzed. The longest‐lived MCSs preferentially occur over the subtropical oceans. The land MCSs have higher cloud‐tops associated with more intense convection, and oceanic MCSs have much higher rainfall production. While MCSs are observed in many regions of the globe, there are fundamental differences in their dynamic and thermodynamic structures that warrant a better understanding of processes that control their evolution. This global database provides significant opportunities for observational and modeling studies of MCSs, their characteristics, and roles in regional and global water and energy cycles, as well as their hydrologic and other impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deep Multimodal Networks for M-type Star Classification with Paired Spectrum and Photometric Image

Abstract Traditional stellar classification methods include spectral and photometric classification separately. Although satisfactory results can be achieved, the accuracy could be improved. In this paper, we pioneer a novel approach to deeply fuse the spectra and photometric images of the sources in an advanced multimodal network to enhance the model’s discriminatory ability. We use Transformer as the fusion module and apply a spectrum–image contrastive loss function to enhance the consistency of the spectrum and photometric image of the same source in two different feature spaces. We perform M-type stellar subtype classification on two data sets with high and low signal-to-noise ratio (S/N) spectra and corresponding photometric images, and the F1-score achieves 95.65% and 90.84%, respectively. In our experiments, we prove that our model effectively utilizes the information from photometric images and is more accurate than advanced spectrum and photometric image classifiers. Our contributions can be summarized as follows: (1) We propose an innovative idea for stellar classification that allows the model to simultaneously consider information from spectra and photometric images. (2) We discover the challenge of fusing low-S/N spectra and photometric images in the Transformer and provide a solution. (3) The effectiveness of Transformer for spectral classification is discussed for the first time and will inspire more Transformer-based spectral classification models.

Astronomy & Astrophysics↗
DOE OSTI2020

Unraveling the Topological Phase of ZrTe 5 via Magnetoinfrared Spectroscopy

For materials near the phase boundary between weak and strong topological insulators (TIs), their band topology depends on the band alignment, with the inverted (normal) band corresponding to the strong (weak) TI phase. Here, taking the anisotropic transition-metal pentatelluride ZrTe 5 as an example, we show that the band inversion manifests itself as a second extremum (band gap) in the layer stacking direction, which can be probed experimentally via magnetoinfrared spectroscopy. Specifically, we find that the band anisotropy of ZrTe 5 features a slow dispersion in the layer stacking direction, along with an additional set of optical transitions from a band gap next to the Brillouin zone center. Furthermore, our work identifies ZrTe 5 as a strong TI at liquid helium temperature and provides a new perspective in determining band inversion in layered topological materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Desert truffle genomes reveal their reproductive modes and new insights into plant–fungal interaction and ectendomycorrhizal lifestyle

Desert truffles are edible hypogeous fungi forming ectendomycorrhizal symbiosis with plants of Cistaceae family. Knowledge about the reproductive modes of these fungi and the molecular mechanisms driving the ectendomycorrhizal interaction is lacking. Genomes of the highly appreciated edible desert truffles Terfezia claveryi Chatin and Tirmania nivea Trappe have been sequenced and compared with other Pezizomycetes. Transcriptomes of T. claveryi × Helianthemum almeriense mycorrhiza from well-watered and drought-stressed plants, when intracellular colonizations is promoted, were investigated. We have identified the fungal genes related to sexual reproduction in desert truffles and desert-truffles-specific genomic and secretomic features with respect to other Pezizomycetes, such as the expansion of a large set of gene families with unknown Pfam domains and a number of species or desert-truffle-specific small secreted proteins differentially regulated in symbiosis. A core set of plant genes, including carbohydrate, lipid-metabolism, and defence-related genes, differentially expressed in mycorrhiza under both conditions was found. Our results highlight the singularities of desert truffles with respect to other mycorrhizal fungi while providing a first glimpse on plant and fungal determinants involved in ecto to endo symbiotic switch that occurs in desert truffle under dry conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Spinbox: tools for many-body quantum systems in a Monte Carlo context

Spinbox is a piece of software that facilitates quantum mechanical calculations relevant to Monte Carlo simulation of atomic nuclei. At the front lines of research on the nuclear many-body problem are a large number of supercomputer-scale simulation codes. These codes produce valuable results but can be hard to understand, especially for those without intimate knowledge of the relevant theoretical methods. Thus, tools that fill pedagogical roles are extremely valuable. Spinbox makes it easy for one to replicate and analyze the computational processes relevant to a Quantum Monte Carlo (QMC) simulation that may be difficult to understand/debug/analyze due to the scale of the corresponding simulation software. Spinbox is written in Python using other state-of-the-art Python modules for numerical calculations. While a number of Python libraries exist that are suited to general quantum many-body calculations, the motivation of Spinbox is quite particular. In Diffusion Monte Carlo methods (DMC, GFMC, AFDMC), the central calculation is the imaginary-time propagation of individual samples of the many-body wavefunction. Although quantum wavefunctions generally must be described by a probability distribution over a basis, DMC imbues particles (within one sample) with classical spatial coordinates. This method is unusual, so other Python packages are typically not set up to do this easily. Furthermore, the software has built-in options for nuclear systems assuming isospin symmetry, which can be set up with other libraries but is a nontrivial process to do so. Features: - numerical representation of samples of the many-body wavefunctions, including tensor-product states (used in AFDMC) - numerical representation of many-body operators, including tensor-product operators: general, spin, imaginary-time propagation, etc. - the correct associated arithmetic and algebra, implemented as class methods - classes for representing realistic nuclear two- and three-body Hamiltonians (e.g. Argonne V18, Illinois NNN) - large-scale parallel integration over random variables, crucial for the AFDMC method My goal is to make this package open source so that anyone may use it and contribute to it, particularly other researchers doing AFDMC calculations

Fox, Jordan↗
DOE OSTI2020

Random Forest Optimization for Radionuclide Identification

Radionuclide identification through gamma-ray spectroscopy is an indispensable tool in combatting the illicit smuggling of nuclear material. The radionuclide identification devices used in the field need to provide ready-made answers to non-experts, and therefore require sophisticated algorithms that can interpret the underlying data. We investigated the Random Forest classifier as a tool for identifying the radionuclide that is consistent with the data. We were provided with training and validations data sets and used them to optimize the two hyperparameters of the classifiers: maximum features required, and minimum samples used to split each node. The F1 score, a harmonic mean of precision and recall, was used to evaluate the performance of each built classifier. We found the optimal performance with minimum samples of 5 and maximum features of 50, with the F1 score of 0.95.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

The CanBikeCO Mini Pilot: Preliminary Results and Lessons Learned

In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Classification of Orbits in Poincare Maps Using Machine Learning

Poincare plots, also called Poincare maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plots are created by the intersection of field lines with a two-dimensional poloidal plane that is perpendicular to the axis of the torus representing the tokamak. A plot is composed of multiple orbits, each created by a different field line as it goes around the torus. Each orbit can have one of four distinct shapes, or classes, that indicate changes in the topology of the magnetic fields confining the plasma. Given the (x, y) coordinates of the points that form an orbit, the analysis task is to assign a class to the orbit, a task that appears ideally suited for a machine learning approach. In this paper, we describe how we overcame two major challenges in solving this problem - creating a high-quality training set, with few mislabeled orbits, and converting the coordinates of the points into features that are discriminating, despite the variation within the orbits of a class and the apparent similarities between orbits of different classes. Our automated approach is not only more objective and accurate than visual classification, but is also less tedious, making it easier for plasma physicists to analyze the topology of magnetic fields from numerical simulations of the tokamak.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measuring Young Stars in Space and Time. I. The Photometric Catalog and Extinction Properties of N44

In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program Measuring Young Stars in Space and Time (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to m {sub F555W} ≃ 29 mag and m {sub F814W} ≃ 28 mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of ≈ 0.09 M {sub ☉} at the LMC distance. In this first paper we describe the observing strategy of MYSST and the data reduction procedure and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44's superbubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump (RC) feature by applying the machine-learning algorithm RANSAC, and we select a set of upper-main-sequence stars as primary probes to build an extinction map, deriving a relatively modest median extinction A {sub F555W} ≃ 0.77 mag. The same procedure applied to the RC provides A {sub F555W} ≃ 0.68 mag.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

The CanBikeCO Mini Pilot: Procedure and Preliminary Results

In fall 2020, the Colorado Energy Office, as part of the State of Colorado's "Can Do Colorado" initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, start in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The non-motorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO2. Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

In-situ digital image correlation and thermal monitoring in directed energy deposition

A method and a system for digital image correlation and thermal monitoring during directed energy deposition are provided. The method and the system include global off-axis 3D mapping of surfaces features at high frame rates using the natural surface roughness of the additive build. Infrared thermography is projected onto these surface features to record the thermo-mechanical history of the finished component. As set forth herein, the method and the system provide a low-cost solution to monitoring and optimizing the unique temporal artifacts induced by complex scan strategies.

Haley, James↗