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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measurement of the inclusive and differential $t\overline{t}γ$ cross sections in the single-lepton channel and EFT interpretation at $\sqrt{s}$ = 13 TeV

The production cross section of a top quark pair in association with a photon is measured in proton-proton collisions at a center-of-mass energy of 13 TeV. The data set, corresponding to an integrated luminosity of 137 fb -1 , was recorded by the CMS experiment during the 2016–2018 data taking of the LHC. The measurements are performed in a fiducial volume defined at the particle level. Events with an isolated, highly energetic lepton, at least three jets from the hadronization of quarks, among which at least one is b tagged, and one isolated photon are selected. The inclusive fiducial $t\overline{t}γ$ cross section, for a photon with transverse momentum greater than 20 GeV and pseudorapidity |η| < 1.4442, is measured to be 798 ± 7(stat) ± 48(syst) fb, in good agreement with the prediction from the standard model at next-to-leading order in quantum chromodynamics. The differential cross sections are also measured as a function of several kinematic observables and interpreted in the framework of the standard model effective field theory (EFT), leading to the most stringent direct limits to date on anomalous electromagnetic dipole moment interactions of the top quark and the photon.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Trajectory-Based Interpretation of Laser Light Diffraction by a Sharp Edge

In the diffraction pattern produced by a half-plane sharp edge when it obstructs the passage of a laser beam, two characteristic regions are noticeable. There is a central region, where the diffraction of laser light appears in the region of geometric shadow, while intensity oscillations are observed in the non-obstructed area. On both sides of the edge, there are also very long light traces along the normal to the edge of the obstacle. The theoretical explanation of this phenomenon is based on the Fresnel–Kirchhoff diffraction theory applied to the Gaussian beam propagation behind the obstacle. In this paper, we supplement this explanation by considering electromagnetic flow lines, which provide a more complete interpretation of the phenomenon in terms of electric and magnetic fields and flux lines; at the same time, that can be related to average photon paths. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Published Tunneling Results of Binnig et al Interpreted as Related to Surface Superconductivity in SrTiO{sub 3}

In 1980, Binnig et al. reported tunneling measurements on Nb-doped SrTiO{sub 3}, and interpreted their results as indicating two-band superconductivity in the bulk of SrTiO{sub 3}. However, (1) effective masses determined from tunneling results in the normal state by Sroubek in 1969 and 1970 are much smaller than those determined by most other methods. The much smaller masses were attributed to properties of a surface layer by the present author in 1971. (2) The only other reports of two-band superconductivity in bulk SrTiO{sub 3} can be used to infer much smaller values for the band separations than found by Binnig et al. In this paper, we give an alternative explanation of the results of Binnig et al. in terms of superconductivity in a surface layer. We obtain fair fits to the band gaps versus Fermi energy for the two bands in the three samples where two surface subbands are occupied and to the temperature dependence of the gaps in one crystal, using a model with three adjustable interaction parameters, an adjustable energy for the phonons which dominate the pairing, and an adjustable ratio of the mean-field T{sub c} to the actual T{sub c}. We show results for a combined fit to the low-temperature band gaps and to the T-dependence in one crystal. The phonon energy which gives the best fit is 21 meV. This is probably an appropriate average over the three longitudinal polar modes and acoustic modes in the material. A large value of about two is found for the ratio T{sub cmf}/T{sub c}, and we conjecture that this arises because a band with a small Fermi energy, not seen in the tunneling results, plays a part in increasing T{sub cmf}/T{sub c}.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Physical Interpretation of Strict Solutions of Diffraction Problems by Heuristic Relations

We propose a new approach to construct heuristic relations describing solutions of diffraction problems. Those relations are based on physical principles and allow one to interpret mathematically strict solutions. Since the heuristic relations possess high performance and accuracy, they can also be used along with any strict approach or experimental results for a significant improvement of efficiency of solutions of practical problems related to applications of the diffraction theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling

A recent acceleration of model-data synthesis activities has leveraged many terrestrial carbon (C) datasets, but utilization of soil respiration (RS) data has not kept pace with other types such as eddy covariance (EC) fluxes and soil C stocks. Here we argue that RS data, including non-continuous measurements from survey sampling campaigns, have unrealized value and should be utilized more extensively and creatively in data synthesis and modeling activities. We identify three major challenges in interpreting RS data, and discuss opportunities to address them. The first challenge is that when RS is compared to ecosystem respiration (RECO) measured from EC towers, it is not uncommon to find substantial mismatch, indicating one or both flux methodologies are unreliable. We argue the most likely cause of mismatch is unreliable EC data, and there is an unrecognized opportunity to utilize RS for EC quality control. The second challenge is that RS integrates belowground heterotrophic (RH) and autotrophic (RA) activity, whereas modelers generally prefer partitioned fluxes, and few models include an explicit RS output. Opportunities exist to use the total RS flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. Pushing for more experiments that not only partition RS but also monitor the age of RA and RH, as well as for the development of belowground RA components in models, would allow for more direct comparison between measured and modeled values. The third challenge is that soil respiration is generally measured at a very different resolution than that needed for comparison to EC or ecosystem- to global-scale models. Measuring soil fluxes with finer spatial resolution and more extensive coverage, and downscaling EC fluxes to match the scale of RS, will improve chamber and tower comparisons. Opportunities also exist to estimate RH at regional scales by implementing decomposition functional types, akin to plant functional types. We conclude by discussing the benefits that wide use of RS data will bring to model development, and database developments that will make RS data more robust, useful, and broadly available to the research community.

carbon cycle↗
DOE OSTI2018

UV-POSIT: Web-Based Tools for Rapid and Facile Structural Interpretation of Ultraviolet Photodissociation (UVPD) Mass Spectra

UV-POSIT (Ultraviolet Photodissociation Online Structure Interrogation Tools) is a suite of web-based tools designed to facilitate the rapid interpretation of data from native mass spectrometry experiments making use of 193 nm ultraviolet photodissociation (UVPD). The suite includes four separate utilities which assist in the calculation of fragment ion abundances as a function of backbone cleavage sites and sequence position; the localization of charge sites in intact proteins; the calculation of hydrogen elimination propensity for a-type fragment ions; and mass-offset searching of UVPD spectra to identify unknown modifications and assess false positive fragment identifications. UV-POSIT is implemented as a Python/Flask web application hosted at http://uv-posit.cm.utexas.edu . UV-POSIT is available under the MIT license, and the source code is available at https://github.com/jarosenb/UV-POSIT . .

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

An atomistic survey of shear coupling in asymmetric tilt grain boundaries and interpretation using the disconnections framework

Grain Boundaries (GB) play an important role in determining the behavior of polycrystalline materials. While the mechanisms of motion and associated shear response for symmetric tilt grain boundaries (STGBs) are well studied, the same is not true for asymmetric tilt grain boundaries (ATGBs) despite their greater prevalence in polycrystals. Here, this study aims to investigate the shear response of a large collection of asymmetric tilt grain boundaries (ATGBs) using molecular dynamics (MD) simulations and interpret the data using a discrete disconnections model that works remarkably well for STGBs. MD simulations of shear-driven ATGBs show that the plastic shear (shear coupling factor) in the region swept by a GB exhibits a complex dependence on the inclination angle, and this dependence changes with the misorientation of the GB. In addition, the shear response was observed to be highly sensitive to the applied shear rate and temperature. Recognizing the spatial and temporal scale limitations of MD simulations, we extended the discrete disconnections mesoscale model of Khateret al. (2012) to calculate the nucleation barriers of disconnection modes and predict the effective shear coupling of an ATGB. We observed that the mesoscale model’s predictions of the shear coupling factor of ATGBs do not agree with those observed in MD simulations. Finally, we examine the hypotheses of our mesoscale model that contribute to disagreements between MD simulations and the mesoscale model and propose improvements to the mesoscale model for future work.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Concerning Mössbauer experiments in a rotating system and their physical interpretation

We shortly review different attempts to interpret the results of Mössbauer rotor experiments in a rotating system and particularly we show that the latest work on this subject by J. Iovane and E. Benedetto (2019), which claims that the outcomes of these experiments can supposedly be explained via “desynchronization of clocks” in the rotating frame and in the laboratory frame, is inapplicable to all of the Mössbauer rotor experiments performed up to date and thus does not have any significance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

With the rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most viable platforms for improving building design and management. Researchers investigated the possibility of integrating energy management systems with cyber-physical systems to form energy-cyber-physical systems in order to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is a major source of uncertainty for energy management, ignoring it often results in both energy waste caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposes an occupancy-linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilizes ensemble classification algorithms to extract three forms of occupancy information. It creates a data interface to link energy management systems and cyber-physical systems and allows for automated occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy-linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy data type, the proposed model can potentially save about 26.4% of energy consumption in cooling and ventilation demands.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Establishing nationwide power system vulnerability index across US counties using interpretable machine learning

Power outages have become increasingly frequent, intense, and prolonged in the US due to climate change, aging electrical grids, and rising energy demand. However, largely due to the absence of granular spatiotemporal outage data, we lack data-driven evidence and analytics-based metrics to quantify power system vulnerability. This limitation has hindered the ability to effectively evaluate and address vulnerability to power outages in US communities. Here, in this work, we collected ∼179 million power outage records at 15-min intervals across 3022 US contiguous counties (96.15 % of the area) from 2014 to 2023. We developed a power system vulnerability assessment framework based on three dimensions (intensity, frequency, and duration) and applied interpretable machine learning models (XGBoost and SHAP) to compute Power System Vulnerability Index (PSVI) at the county level. Our analysis reveals a consistent increase in power system vulnerability across the US counties over the past decade. We identified 318 counties across 45 states as hotspots for high power system vulnerability, particularly in the West Coast (California and Washington), the East Coast (Florida and the Northeast area), the Great Lakes megalopolis (Chicago-Detroit metropolitan areas), and the Gulf of Mexico (Texas). Our heterogeneity analysis indicates that urban counties and those located along regional transmission boundaries tend to exhibit significantly higher vulnerability. Our results highlight the significance of the proposed PSVI for evaluating the vulnerability of communities to power outages. The findings underscore the widespread and pervasive impact of power outages across the country and offer crucial insights to support infrastructure operators, policymakers, and emergency managers in formulating policies and programs aimed at enhancing the resilience of the US power infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Interpretation of wide-angle x-ray diffraction patterns from mesophase pitch-based carbon fibers – a simulation and experimental study

Carbon fiber is a critical material in a wide range of industries, where it is highly valued for its high specific strength/stiffness, excellent wear resistance, efficient electrical and thermal transport properties, chemical resistance, and low coefficient of thermal expansion. The properties of a specific carbon fiber are closely tied to its structural characteristics at all length scales. Here, in this work, we applied wide-angle x-ray diffraction to a set of heat-treated mesophase pitch-based carbon fibers, with the goal of elucidating the crystalline structures as a function of fiber orientation. To assist with analysis and interpretation of the experimental data, we employed diffraction pattern simulations using the scalar and vector forms of the Debye scattering equation to determine the influence of basal plane orientation, crystalline ordering (turbostratic-graphitic), and basal plane asymmetry on the diffraction patterns. The results presented here suggest that growth of the transverse crystallites in mesophase pitch-based carbon fiber is fixed until graphitization temperatures are reached. The work completed here provides a framework for the analysis of carbon fiber and other oriented carbon-based materials via diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. Finally, a corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.

42 ENGINEERING↗
DOE OSTI2024

Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. Furthermore, the results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Response to a comment on Dessert et al. “The dark matter interpretation of the 3.5 keV line is inconsistent with blank-sky observations”

The dark matter explanation of the 3.5 keV line is strongly disfavored by our work in Dessert et al. (2020). Boyarsky et al. (2004) questions that conclusion: modeling additional background lines is claimed to weaken the limit sufficiently to re-allow a dark matter interpretation. Here, we respond as follows. (1) A more conservative limit is obtained by modeling additional lines; this point appeared in its entirety in our work in Dessert et al. (2020), though we also showed that the inclusion of such lines is not necessary. (2) Despite suggestions in Boyarsky et al. (2004), even the more conservative limits strongly disfavor a decaying dark matter origin of the 3.5 keV line.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Unveiling stimulation fluid-driven alterations in shale pore architecture through combined interpretation of TD-NMR and multi-component gas adsorption

The hydraulic fracturing stimulation process can alter petrophysical and flow properties of the shale matrix in unconventional reservoirs. These alterations can result from interactions with native and exogenous fluids, and can be detrimental to reservoir productivity. Different experimental procedures have been proposed to study reactive processes and thereby understand the aforementioned interactions. Most published works do not consider the rock initial saturation with formation fluid, or the effect of organic matter (OM). To gain insights into these effects, two reactive experimental sets, differing in experimental conditions, were conducted. In the first set, at 125°C and 45 MPa, a stimulation fluid surrogate was put in contact with Baxter Shale samples, previously saturated and equilibrated with synthetic formation fluid. A baseline consisted of exposure to formation fluid exclusively. In the second set, at 0.08 MPa and 25°C, contact with stimulation fluid took place as in the first set. Two additional assays in this second set either skipped the initial formation fluid saturation or removed the OM, respectively. Petrophysical properties analyses, focused on the pore architecture, relied on the combined interpretation of gas adsorption and Time-Domain Nuclear Magnetic Resonance (TD-NMR). Results show that the presence of the formation fluid significantly restricts pore space accessibility to the stimulation fluid and mitigates the pore-architecture alteration to the macro-pore region. Mineral dissolution and precipitation dominate over surface or wettability alteration. Next, results suggest that the OM restricts accessibility to some pores. Ultimately, the equilibration process with synthetic formation fluid drives mainly surface alteration, unhindered by OM.

58 GEOSCIENCES↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2017

Formation of solid thorium monoxide at near-ambient conditions as observed by neutron reflectometry and interpreted by screened hybrid functional calculations

Oxidation of a ~1000 Å sputter-deposited thorium thin film at 150 °C in 100 ppm of flowing oxygen in argon produces the long-sought solid form of thorium monoxide. Changes in the scattering length density (SLD) distribution in the film over the 700-min experiment measured by in-situ, dynamic neutron reflectometry (NR) shows the densities, compositions and thickness of the various thorium oxides layers formed. Screened, hybrid density-functional theory calculations of potential thorium oxides aid interpretation, providing atomic-level picture and energetics for understanding oxygen migration. NR provided evidence of the formation of substoichiometric thorium oxide, ThO y (y < 1) at the interface between the unreacted thorium metal and its dioxide overcoat which grows inward, consuming the thorium at a rate of 2.1 Å/min while y increases until reaching 1:1 oxygen-to-thorium. Its presence indicates that kinetically-favored solid-phase ThO can be preferentially generated as a majority phase under the thermodynamically-favored ThO 2 top layer at conditions close to ambient.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Novel interpretation of the mean structure of feroxyhyte

The structure of the iron oxyhydroxide called feroxyhyte (δ-FeOOH), which shows an elusive X-ray powder diffraction pattern, has been represented so far using models describing a mean structure based on the crystalline network of the iron(III) oxide hematite (α-Fe{sub 2}O{sub 3}). In this paper, a novel description of the mean structure of feroxyhyte is presented, which is based on the structure of the thermodynamically stable iron oxyhydroxide goethite. Starting from different local arrangements present in the goethite network, a mean structural model is determined which shows an X-ray powder diffraction pattern almost coincident with previous studies. This outcome enables to integrate the structure of feroxyhyte among those of other well characterized iron oxyhydroxides. - Graphical abstract: The structure of the iron oxy-hydroxide feroxyhyte can be described by local arrangements present in the goethite network. - Highlights: • The structure of feroxyhyte (δ-FeOOH) proposed in literature is discussed. • The structure of goethite (α-FeOOH) is analyzed. • A structural relationship between feroxyhyte and goethite is found. • New interpretation of the mean structure of δ-FeOOH is given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗