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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ~ 82%) and physiological information (i.e. physiological SIF yield, Φ F , 17% ~ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Φ F indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that Φ F responded to the proxy for physiological stress (partial correlation coefficient, r p = 0.40, p < 0.001) while non-physiological signals of SIF did not respond ( p > 0.1). The field-scale Φ F data showed water deficit stress from the comparison between irrigated and rainfed fields, and Φ F was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r = 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed Φ F was more strongly correlated spatially with air temperature and VPD ( r = 0.23 and 0.39) than SIF ( r = 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that Φ F reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Φ F is spatially scalable. Utilizing Φ F for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

A Systematic Interpretation of Subsurface Proppant Concentration from Drilling Mud Returns: Case Study from Hydraulic Fracturing Test Site (HFTS-2) in Delaware Basin

The aim of this study is generation and validation of a proppant log using analysis of drilling mud returns for child wells. Proppant log provides qualitative as well as quantitative insights into spatial distribution of proppant sand particles from prior stimulation of parent wells. While the basic methodology was developed and formalized during analysis of material collected from through fracture cores at Hydraulic Fracturing Test Site in Midland Basin (HFTS – 1), the test wells at HFTS – 2 in the neighboring Delaware Basin allowed the opportunity to validate the workflow on actual mud return samples from subsurface. As a child well is being drilled, periodic mud return samples are collected at the rig site and preserved for analysis. The workflow involves systematic cleaning of the samples including various steps such as washing, drying and segregation of samples into relevant size fractions of interest (< Mesh 20) based on specifications of pumped sand during stimulation of the parent well. Clean samples are imaged using high resolution transparency scanning. Scan images are then systematically analyzed for particles of interest using computer vision techniques. Sample counts are further validated using elemental analysis of smaller sub-samples at various depths of interest. This step is necessary to isolate proppant versus other naturally occurring minerals such as sulphates and carbonates which show similar optical properties. We successfully correlated proppant distribution against the existing parent well and validated propped versus relatively un-propped zones for a child well at the test site. The advantage of testing the proppant log concept at the HFTS – 2 site is the plethora of additional diagnostic data that is available to validate our primary observations. We can correlate spatial proppant distribution against variability in stimulation response based on independent observations such as image logs, microseismic attributes as well as DAS response, all of which tend to corroborate one another. One of our significant successes was being able to describe varying degrees of impact of the parent well along the lateral length of a stimulated child well. Our workflow represents a systematic and one-of-a-kind interpretation of spatial proppant distribution while drilling child wells. This provides unique opportunities to better understand the current state of the Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R003/2477415/urtec-2021-5189-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5189 2 reservoir being targeted including zones which are likely more drained relative to others and how the planned completion of the child well can be improved. Lastly, this log can be useful is validating optimal well spacing in relatively new fields under development.

58 GEOSCIENCES↗
DOE OSTI2020

Three dimensional cluster analysis for atom probe tomography using Ripley’s K-function and machine learning

The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley’s K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K (r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K (r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. Here we find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K (r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deactivation of Cu-Exchanged Automotive-Emission NH 3 -SCR Catalysts Elucidated with Nanoscale Resolution Using Scanning Transmission X-ray Microscopy

To gain insight into the underlying mechanisms of catalyst durability for the selective catalytic reduction (SCR) of NO x with an ammonia reductant, we employed scanning transmission X-ray microscopy (STXM) to study Cu-exchanged zeolites with the CHA and MFI framework structures before and after simulated 135 000-mile aging. X-ray absorption near-edge structure (XANES) measurements were performed at the Al K- and Cu L-edges. The local environment of framework Al, the oxidation state of Cu, and geometric changes were analyzed, showing a multi-factor-induced catalytic deactivation. In Cu-exchanged MFI, a transformation of Cu II to Cu I and Cu x O y was observed. We also found a spatial correlation between extra-framework Al and deactivated Cu species near the surface of the zeolite as well as a weak positive correlation between the amount of CuI and tri-coordinated Al. By inspecting both Al and Cu in fresh and aged Cu-exchanged zeolites, we conclude that the importance of the preservation of isolated Cu II sites trumps that of Brønsted acid sites for NH 3 -SCR activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A low-input strategy for chromium removal from industrial stormwater using peat sorbent

Low-cost and low-input water treatment systems are important for industrial stormwater remediation. Here we examine a flow-through reactor treatment installation where water exceeds the allowable maximum concentration for drinking water in multiple metals (e.g., chromium [Cr], cadmium [Cd], zinc [Zn]) prior to treatment. Specifically, we seek to understand why Cr attenuated in the reactors is not leachable by identifying the specific chemical form of Cr and dominant mechanisms promoting sequestration in the reactors. Total solid-phase Cr concentration in the peat media ranged from 50 to 150 mg/kg after 1 yr of exposure to stormwater to 300 to 900 mg/kg after 3.5 yr. X-ray fluorescence mapping images show Cr, iron (Fe), and Zn spatially correlated over a scale of 10 μm to 5 mm. Chromium rinds form on the edges of peat particles as Cr accumulates. Chromium and Fe K-edge X-ray absorption near edge structure spectroscopy reveal chromium predominately in the 3+ oxidation state with lesser amounts of elemental Cr. We propose the primary means of chromium attenuation in the reactors is precipitation as Cr-Fe hydroxides combined with trivalent Cr adsorption onto peat surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spatially Resolved Raman Spectroscopy of Thin Carbon Interphase in SiC Ceramic Matrix Composites

A dedicated analysis method is presented to extract the Raman spectrum of an interphase layer thinner than the laser spot size. We focused on spatial correlations between the contrast of optical micrographs and Raman hyperspectral data to predict the constituents of the mixed spectra measured near the interphase. By employing a mapping step size of 0.1 μm, the Raman spectrum of approximately 0.3-μm-thick carbon interphase in a SiC fiber-reinforced SiC matrix composite was extracted from data acquired with a theoretical spot size of about 0.7 μm. Notably, conventional chemometrics procedures were unable to isolate the interphase signal, instead producing a spectrum representing a mixture of interphase and matrix. This study used another composite with approximately 0.9-μm-thick interphase to validate the analysis method, enabling direct measurement of the interphase spectrum. The proposed Raman analysis method has advantages in specimen volume and turnaround time compared to traditional characterization methods, such as transmission electron microscopy. In conclusion, this study also evaluates the applicability of the analysis method to different composite materials and identifies key requirements of the measurements, including the ratio of interphase thickness to spot size and the homogeneity of the surrounding matrix.

ceramic matrix composite↗
DOE OSTI2023

Coherent Random Lasing in Subwavelength Quasi‐2D Perovskites

Abstract Quasi‐2D lead halide perovskites have garnered increasing interest as lasing gain media. Relatively simple fabrication, high refractive index, and unique quantum well structure encourage their use in traditional cavity lasers and cavity‐free systems called random lasers (RLs). Despite tremendous advances reported thus far, coherent random lasing in quasi‐2D perovskite subwavelength films has not been reported. Consequently, coherent optical feedback mechanisms in quasi‐2D perovskite systems are still unexplored. Here, this work reports the observation of coherent random lasing in subwavelength quasi‐2D perovskite films. Statistical analysis of spectral measurements reveals Lévy‐like intensity fluctuations, replica symmetry breaking confirms random lasing, and the coherent modes are studied with spectral and spatial correlation techniques. The observed coherent lasing modes are found to be extended states that arise from the random crystal grain structure during fabrication and span the entire pump volume. These modes out‐compete diffusive lasing due to their coherence.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Estimating posterior quantity of interest expectations in a multilevel scalable framework

Scalable approaches for uncertainty quantification are necessary for characterizing prediction confidence in large-scale subsurface flow simulations with uncertain permeability. To this end we explore a multilevel Monte Carlo approach for estimating posterior moments of a particular quantity of interest, where we employ an element-agglomerated algebraic multigrid (AMG) technique to generate the hierarchy of coarse spaces with guaranteed approximation properties for both the generation of spatially correlated random fields and the forward simulation of Darcy's law to model subsurface flow. In both these components (sampling and forward solves), we exploit solvers that rely on state-of-the-art scalable AMG. To illustrate the applicability of this approach, numerical tests are performed on two 3D examples-a unit cube and an egg-shaped domain with an irregular boundary-where the scalability of each simulation as well as the scalability of the overall algorithm are demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2020

A spatial-statistical investigation of surface expressions associated with cyclic steaming in the Midway-Sunset Oil Field, California

In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to enhance heavy oil recovery through imbibition, wettability alteration, and viscosity reduction, among other mechanisms. The injected steam, however, does not always remain in the reservoir or return through the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called “surface expressions,” pose safety and environmental hazards. Even though these surface expressions are a widespread problem in Central California, they are not well documented and understood. Possible causes of the surface expressions include: high injection pressure, structurally controlled flow patterns, leakage of steam through old improperly abandoned wells, high injection volumes, or flow along naturally occurring faults, among other possible factors. This work examines attributes of the zones with surface expressions in order to determine factors that may contribute to their occurrence. Spatial statistical analysis using logistic regression, random forests, and classification trees is used to explore the relationship between the surface expressions and geological and production-related attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and geologic seal thickness. The results guide follow-up studies to further investigate the role of well abandonment and seal thickness in the occurrence of surface expressions.

02 PETROLEUM↗
DOE OSTI2026

Ammonia in northeast Colorado is increasing, rising most quickly in regions close to confined animal feeding operations

The Colorado Front Range urban corridor and nearby agricultural operations are important source regions of atmospheric ammonia (NH 3 ). Upslope flows periodically transport these emissions into Rocky Mountain National Park (RMNP), located 50 km west of the urban corridor, where wet and dry deposition of excess reactive nitrogen (N) impacts ecosystems. Here, we use a combination of in situ passive NH 3 measurements and NH 3 vertical column density retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) to assess variability and changes in NH 3 across three land use categories in the northeast Colorado source region (agricultural, urban, and remote) during the period 2013-2023. A strong seasonal cycle is present across the region with increased NH 3 during summer months. Elevated NH 3 is spatially correlated with the number of permitted animal units in confined animal feeding operations (CAFOs) within 12 km. Ground-level NH 3 concentrations are strongly positively correlated with monthly gridded IASI satellite column densities. Satellite retrievals reveal an increasing trend in NH 3 column amounts of ∼3% per year in agricultural and ∼2% per year in urban sub-regions. The magnitude of the trend observed in NH 3 columns averaged over the agricultural sub-region is > 3 times larger than observed near and over Denver. The largest increases in NH 3 are closely aligned with the distribution of CAFOs. Reductions in particle sulfate associated with declining sulfur dioxide (SO 2 ) emissions could account for only ∼0.1% per year increase in gaseous NH 3 . Wildfire smoke across the region has increased but appears unlikely to explain the majority of the observed NH 3 increase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis of structure-property gradients in orthogonally machined chips and workpiece subsurface

Production of machined surfaces with optimized mechanical properties requires fundamental process-structure-property knowledge. In this paper, we focus on structure-property gradients in orthogonally cut OFHC Copper chips and the workpiece subsurface. We utilize orientation imaging microscopy and statistical spatial correlation metrics to quantify the microstructure. Constitutive elastic-plastic properties of the workpiece subsurface and chips are measured using spherical nanoindentation. Analysis of the data reveals significant structure and property gradients in both regions. Differences and similarities in structure and property gradients near the workpiece surface and tool-chip interface, and in the chip middle are discussed and explained.

42 ENGINEERING↗
DOE OSTI2023

Quantifying the effect of late bombardment on terrestrial zircons

The first 500 million years of Earth history is thought to be a period of intense planetary bombardment, but the timing and flux of this meteorite bombardment is poorly understood. In particular, on the basis of an inferred lunar impact history, some workers have hypothesized a ~3.9 Ga terminal cataclysm (TC) in which there was marked increase in the impact flux affecting the Moon, the Earth and possibly other terrestrial planets. Minerals that survived this enigmatic period offer a way to test early planetary bombardment models as they may contain telltale micro- to nanoscale shock features. Here, we present results from a numerical modeling calculation that assesses the probability that a zircon residing in the crust would escape shock melting or shock deformation during a TC bombardment event. Even with conservative pressure estimates for zircon shock deformation and intermediate bombardment intensities, we find that only ~6% of ≥4.0 Ga crust would be expected to survive a 3.9 Ga cataclysm without experiencing either complete melting or zircon shock metamorphism. In this study, we couple this modeling with a search for shock effects in the oldest zircons from the Acasta Gneiss Complex, which would have been present in the Earth's crust during a putative 3.9 Ga TC. Spatially correlated electron and NanoSIMS ion microscopy of 4.02 Ga igneous zircons from Acasta reveals no evidence of ancient shock. These data, together with similar results from other Hadean zircon suites, confirm that a post-Hadean TC is unlikely to have occurred. We suggest that the dearth of pre-3.9 Ga terrestrial crust and zircons is instead best explained by endogenic processes related to the mechanisms of early crust formation. Our modeling allows us to evaluate bombardment scenarios from the terrestrial zircon record by applying probabilistic interpretations to zircon shock deformation data. This approach will be valuable for other planetary bodies, allowing broader conclusions to be drawn from geographically limited datasets.

58 GEOSCIENCES↗
DOE OSTI2025

The role of Ca-bridged organic matter in an alkaline soil, as revealed by multimodal chemical imaging

Mineral–organic matter (OM) studies have predominantly focused on acidic soils that are abundant in iron (Fe) oxides and aluminum (Al) oxides. We have probed mineral–OM interactions in an alkaline or calcareous soil of the Aridisols class. Unlike the role of Fe and Al, the role of Ca-minerals (particularly calcite), which are ubiquitous in alkaline soils, in OM sequestration is not well understood. Multiple recent model studies with aqueous Ca2+ or synthetic calcite and a suite of OM compounds have shown Ca-OM assemblages to be spatially correlated with calcite at the microscale. To study the chemical state of both Ca and Fe and their competing role in soil organic matter (SOM) stabilization, we performed laboratory characterization using x-ray diffraction, Mössbauer spectroscopy, x-ray photoelectron spectroscopy, scanning electron microscopy, and scanning transmission electron microscopy, alongside synchrotron-based microscale chemical imaging using scanning transmission x-ray microscopy combined with near-edge x-ray absorption fine structure. Ca mineral–organic associations were found to be ubiquitous in this system and are likely critical for understanding SOM stabilization/degradation in alkaline soils. From our findings on mineralogy, speciation, and the nature of Ca-OM bridging, we identified differences in C and Ca chemistry based on the relative location of OM to Ca minerals. The OM near the calcite crystal was enriched in lipid and protein moieties, Ca-OM next to Fe minerals displayed a strong contribution from aromatic compounds, while on the surface of microbes, the carbonate was believed to be of microbial in origin, as also suggested by preliminary works reporting on the formation of amorphous calcite or nano-calcite. In Ca-OM admixed with carbonate, it was difficult to distinguish Ca-associated OM from amorphous calcite or nano-calcite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗