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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Bootstrap inversion technique for atmospheric trace gas source detection and quantification using long open-path laser measurements

Advances in natural gas extraction technology have led to increased activity in the production and transport sectors in the United States and, as a consequence, an increased need for reliable monitoring of methane leaks to the atmosphere. We present a statistical methodology in combination with an observing system for the detection and attribution of fugitive emissions of methane from distributed potential source location landscapes such as natural gas production sites. We measure long (> 500 m), integrated open-path concentrations of atmospheric methane using a dual frequency comb spectrometer and combine measurements with an atmospheric transport model to infer leak locations and strengths using a novel statistical method, the non-zero minimum bootstrap (NZMB). The new statistical method allows us to determine whether the empirical distribution of possible source strengths for a given location excludes zero. Using this information, we identify leaking source locations (i.e., natural gas wells) through rejection of the null hypothesis that the source is not leaking. The method is tested with a series of synthetic data inversions with varying measurement density and varying levels of model–data mismatch. It is also tested with field observations of (1) a non-leaking source location and (2) a source location where a controlled emission of 3.1 × 10 -5 kg s -1 of methane gas is released over a period of several hours. This series of synthetic data tests and outdoor field observations using a controlled methane release demonstrates the viability of the approach for the detection and sizing of very small leaks of methane across large distances (4 + km 2 in synthetic tests). The field tests demonstrate the ability to attribute small atmospheric enhancements of 17 ppb to the emitting source location against a background of combined atmospheric (e.g., background methane variability) and measurement uncertainty of 5 ppb (1 σ ), when measurements are averaged over 2 min. The results of the synthetic and field data testing show that the new observing system and statistical approach greatly decreases the incidence of false alarms (that is, wrongly identifying a well site to be leaking) compared with the same tests that do not use the NZMB approach and therefore offers increased leak detection and sizing capabilities.

03 NATURAL GAS↗
DOE OSTI2020

3D characterisation and quantification of an offshore freshened groundwater system in the Canterbury Bight

Although offshore freshened groundwater (OFG) systems have been documented in numerous continental margins worldwide, their geometry, controls and emplacement dynamics remain poorly constrained. Here we integrate controlled-source electromagnetic, seismic reflection and borehole data with hydrological modelling to quantitatively characterise a previously unknown OFG system near Canterbury, New Zealand. The OFG system consists of one main, and two smaller, low salinity groundwater bodies. The main body extends up to 60 km from the coast and a seawater depth of 110 m. We attribute along-shelf variability in salinity to permeability heterogeneity due to permeable conduits and normal faults, and to recharge from rivers during sea level lowstands. A meteoric origin of the OFG and active groundwater migration from onshore are inferred. However, modelling results suggest that the majority of the OFG was emplaced via topographically-driven flow during sea level lowstands in the last 300 ka. Global volumetric estimates of OFG will be significantly revised if active margins, with steep coastal topographies like the Canterbury margin, are considered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration

This article is concerned with the approximation of high-dimensional functions by kernel-based methods. Motivated by uncertainty quantification, which often necessitates the construction of approximations that are accurate with respect to a probability density function of random variables, we aim at minimizing the approximation error with respect to a weighted $L^p$-norm. We present a greedy procedure for designing computer experiments based upon a weighted modification of the pivoted Cholesky factorization. The method successively generates nested samples with the goal of minimizing error in regions of high probability. Numerical experiments validate that this new importance sampling strategy is superior to other sampling approaches, especially when used with non-product probability density functions. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media

Deceptive information in online news and social media has had dramatic effect on our society in recent years. This study is the first to gain deeper insights into writers' intent behind digital misinformation by analyzing psycholinguistic signals: moral foundations and connotations extracted from different types of deceptive news ranging from strategic disinformation to propaganda and hoaxes. To ensure consistency of our findings and generalizability across domains, we experiment with data from: (1) confirmed cases of disinformation in news summaries, (2) {propaganda}, hoax, and disinformation news pages, and (3) social media news. We first contrast lexical markers of biased language, syntactic and stylistic signals, and connotations across deceptive news types including disinformation, propaganda, and hoaxes, and {deceptive} strategies including misleading or falsification. We then incorporate these insights to build machine learning and deep learning predictive models to infer deception strategies and deceptive news types. Our experimental results demonstrate that unlike earlier work on deception detection, content combined with biased language markers, moral foundations, and connotations leads to better predictive performance of deception strategies compared to syntactic and stylistic signals (as reported in earlier work on deceptive reviews). Falsification strategy is easier to identify than misleading strategy. Disinformation is more difficult to predict than to propaganda or hoaxes. Deceptive news types (disinformation, propaganda, and hoaxes), unlike deceptive strategies (falsification and misleading), are more salient, and thus easier to identify in tweets than in news reports. Finally, our novel connotation analysis across deception types provides deeper understanding of writers' perspectives and therefore reveals the intentions behind digital misinformation.

deception↗
DOE OSTI2023

Organic matter sulfurization and organic carbon burial in the Mesoproterozoic

Throughout the Proterozoic Era, sedimentary organic carbon burial helped set the pace of global oxygenation and acted as a major modulator of atmospheric CO 2 and climate. Although Proterozoic rocks generally contain low concentrations of organic matter (OM), there are key exceptions to this rule, including the relatively OM-rich Arctic Bay shales from Baffin Island, Canada (Bylot Supergroup, Borden Basin, ~1.05 Ga). The mechanisms driving elevated OM concentrations in these and other Proterozoic shales remain poorly understood. In the Mesozoic and Cenozoic, organic matter sulfurization can be a major driver of enhanced OM burial across a range of redox conditions comparable to those inferred for many Proterozoic environments. Therefore, in this study, we evaluate the role of sulfurization in driving OM preservation in the Mesoproterozoic Borden Basin and discuss its relevance to Proterozoic systems in general. We present the first evidence for syngenetic-to-early-diagenetic OM sulfurization in a Proterozoic basin, which begins to fill a several-billion-year gap in our record of organic S across Earth history. We find that OM sulfurization was particularly extensive in shales from a relatively shallow-water section (Alpha River) but less extensive in shales deposited in deeper water (Shale Valley), which is consistent with models that infer sulfidic ‘wedges’ or O 2 -minimum-zone-type structures on shelf margins at least intermittently at this time. At the shallower site, organic S and pyrite are similarly 34 S-depleted and thus likely formed at roughly the same time near the sediment–water interface under conditions previously interpreted to have been ferruginous to intermittently sulfidic. In contrast, at the deeper-water site, large S-isotope differences between pyrite and organic S along with low apparent OM sulfurization intensities indicate that pyrite formation was favored over OM sulfurization during early sedimentation under variable but primarily ferruginous conditions. Although Mesoproterozoic biomass can be substantially sulfurized, indicators of sulfurization intensity are not correlated with OM concentrations, and therefore sulfurization does not appear to have been the primary driver of enhanced OM concentrations in Arctic Bay Formation shales. The link between sulfurization and total OM preservation may have been modulated during the deposition of Arctic Bay Formation shales by differences in iron availability, nutrient cycling, and particle dynamics in the Mesoproterozoic.

58 GEOSCIENCES↗
DOE OSTI2023

Cannabis monoterpene synthases: evaluating structure–function relationships

Terpene synthases catalyze the first committed step in the biosynthesis of terpenes, a structurally diverse class of natural products that also encompasses volatiles derived from precursors in the C10 to C15 range (termed monoterpenes and sesquiterpenes, respectively). In the review section of this article, we are providing information about all functionally characterized monoterpene synthases (MTSs) and sesquiterpene synthases (STSs) of Cannabis sativa L. We are also exploring the locations of MTSs and STSs in the chromosome-level assembly of the reference chemovar CBDRx. A follow-up computational structure–function analysis focuses on MTSs, as there is already a rich literature available on the topic. More specifically, by employing sequence comparisons and homology structural modeling, we infer which amino acid residues are likely to constrain the available space in the active site of cannabis MTSs. The emphasis of these studies was to investigate why some MTSs accept only a C10 diphosphate as substrate, while mixed MTS/STS enzymes also accommodate a C15 diphosphate. Here, by combining a literature review and computational analyses in a hybrid format, we are laying the foundation for future studies to better understand the determinants of substrate and product specificity in these fascinating enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multiple RGB ortho-mosaics and digital surface models in 2017 and 2018 across the Lower Montane site in the East River Watershed, Colorado

Aerial imagery was collected at the Lower Montane site (Pumphouse) in the East River Watershed, Colorado during the spring, summer, and fall seasons of 2017 and 2018 to improve the understanding of seasonal vegetation dynamics and their drivers. The datasets include Red-Green-Blue (RGB) ortho-mosaics and digital surface models (DSMs) inferred from the Unoccupied Aerial System (UAS) acquired aerial RGB imagery for June 3, June 19, July 7, and August 14, 2017, and for March 14, April 26, June 1, June 18, July 6, and August 7, 2018. Real-Time Kinematic Global Positioning System (RTK-GPS) surveyed Ground control points (GCPs) were used to increase the reconstruction accuracy. The reconstructed RGB mosaics and DSMs have been trimmed to cover a similar spatial domain. The accuracy of the RGB mosaics is considered high (~10 cm). DSM accuracy is highest (~10 cm) where sufficient GCPS are available, and more difficult to assess elsewhere (see reconstruction reports for uncertainty estimates). The dataset includes a total of 20 GeoTIFF (.tif) files, 10 PDF (.pdf) files, 3 data CSV (.csv) files, and 2 metadata CSV (.csv) files. Feel free to contact the authors with any questions or collaboration interests.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tunable Microwave Conductance of Nanodomains in Ferroelectric PbZr 0.2 Ti 0.8 O 3 Thin Film

Ferroelectric materials exhibit spontaneous polarization that can be switched by electric field. Beyond traditional applications as nonvolatile capacitive elements, the interplay between polarization and electronic transport in ferroelectric thin films has enabled a path to neuromorphic device applications involving resistive switching. A fundamental challenge, however, is that finite electronic conductivity may introduce considerable power dissipation and perhaps destabilize ferroelectricity itself. In this work, tunable microwave frequency electronic response of domain walls injected into ferroelectric lead zirconate titanate (PbZr 0.2 Ti 0.8 O 3 ) on the level of a single nanodomain is revealed. Tunable microwave response is detected through first-order reversal curve spectroscopy combined with scanning microwave impedance microscopy measurements taken near 3 GHz. Contributions of film interfaces to the measured AC conduction through subtractive milling, where the film exhibited improved conduction properties after removal of surface layers, are investigated. Using statistical analysis and finite element modeling, we inferred that the mechanism of tunable microwave conductance is the variable area of the domain wall in the switching volume. These observations open the possibilities for ferroelectric memristors or volatile resistive switches, localized to several tens of nanometers and operating according to well-defined dynamics under an applied field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bedrock architecture, soil texture, and hyporheic zone characterization combining electrical resistivity and induced polarization imaging

The shallow (<15 m depth) subsurface environment of a short reach along a bedrock stream was investigated with electrical resistivity and induced polarization (IP) to map details of bedrock and soil sediments. The bedrock is mostly comprised of limestone, and is generally resistive compared to the overlying soil. The soil-bedrock interface was determined through a trial and error approach using a sharp boundary feature in the inversion model. The inferred bedrock surface determined from the inversions exhibited undulating patterns with troughs and ridges. A near continuous trough ran alongside the stream within the floodplain, and is suggestive of a paleochannel among other interpretations of this feature. The structure of the electrical resistivity above the bedrock showed small-scale elongated features. The chargeability from the IP method showed larger scale features. High values of chargeability were associated with the sediments in the floodplain, and low values were associated with bedrock, stream, and soil on the elevated banks above the stream. If the chargeability is associated with membrane polarization characteristic of clayey soils, then IP seems to highlight the mere existence of clay, while the resistivity may be more discernable of the relative proportion of clay. Vegetation differences may also explain the chargeability distribution, where parts of the survey with high chargeability had dense pine with no understory making the soils more organically rich.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Inference of phase field fracture models

The phase field approach to modeling fracture uses a diffuse damage field to represent cracks. This representation mollifies singularities that arise in computations with sharp interface models and some of the resultant difficulties in the mathematical and numerical treatment of fracture. Phase field fracture models have proven effective at representing crack propagation, branching, and merging. Specific formulations, beginning with brittle fracture, have also been shown to converge to classical solutions. Extensions to cover the range of material failure, including ductile and cohesive fracture, lead to an array of possible models. There exists a large body of literature focusing on this class of models and on the impact of model form on the predicted crack evolution. However, there have not been systematic studies into how optimal models may be chosen. Here, we take a first step in this direction by developing formal methods for identification of the best parsimonious model of phase field fracture given full-field data on the damage and deformation fields. We consider some of the main models that have been used for the degradation of elastic response due to damage and its propagation. Our approach builds upon Variational System Identification (VSI), a weak form variant of the Sparse Identification of Nonlinear Dynamics (SINDy). Furthermore, in this first communication we focus on synthetically generated data but we also consider central issues associated with the use of experimental full-field data, such as data sparsity and noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Unfolded states under folding conditions accommodate sequence-specific conformational preferences with random coil-like dimensions

Proteins are marginally stable molecules that fluctuate between folded and unfolded states. Here, we provide a high-resolution description of unfolded states under refolding conditions for the N-terminal domain of the L9 protein (NTL9). We use a combination of time-resolved Förster resonance energy transfer (FRET) based on multiple pairs of minimally perturbing labels, time-resolved small-angle X-ray scattering (SAXS), all-atom simulations, and polymer theory. Upon dilution from high denaturant, the unfolded state undergoes rapid contraction. Although this contraction occurs before the folding transition, the unfolded state remains considerably more expanded than the folded state and accommodates a range of local and nonlocal contacts, including secondary structures and native and nonnative interactions. Paradoxically, despite discernible sequence-specific conformational preferences, the ensemble-averaged properties of unfolded states are consistent with those of canonical random coils, namely polymers in indifferent (theta) solvents. These findings are concordant with theoretical predictions based on coarse-grained models and inferences drawn from single-molecule experiments regarding the sequence-specific scaling behavior of unfolded proteins under folding conditions.

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

From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak

Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021–22 experimental campaign.

integrated data analysis↗
DOE OSTI2023

Search for photons above 10 19 eV with the surface detector of the Pierre Auger Observatory

We use the surface detector of the Pierre Auger Observatory to search for air showers initiated by photons with an energy above 10 19 eV. Photons in the zenith angle range from 30° to 60° can be identified in the overwhelming background of showers initiated by charged cosmic rays through the broader time structure of the signals induced in the water-Cherenkov detectors of the array and the steeper lateral distribution of shower particles reaching ground. Applying the search method to data collected between January 2004 and June 2020, upper limits at 95% CL are set to an E -2 diffuse flux of ultra-high energy photons above 10 19 eV, 2 × 10 19 eV and 4 × 10 19 eV amounting to 2.11 × 10 -3 , 3.12 × 10 -4 and 1.72 × 10 -4 km -2 sr -1 yr -1 , respectively. While the sensitivity of the present search around 2 × 10 19 eV approaches expectations of cosmogenic photon fluxes in the case of a pure-proton composition, it is one order of magnitude above those from more realistic mixed-composition models. The inferred limits have also implications for the search of super-heavy dark matter that are discussed and illustrated.

ultra high energy photons and neutrinos↗