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At least 829 records · Page 46

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DOE OSTI2018

Novel thin/tunable gas diffusion electrodes with ultra-low catalyst loading for hydrogen evolution reactions in proton exchange membrane electrolyzer cells

Proton exchange membrane electrolyzer cells (PEMECs) have received great attention for hydrogen/oxygen production due to their high efficiencies even at low-temperature operation. Because of the high cost of noble platinum-group metal (PGM) catalysts (Ir, Ru, Pt, etc.) that are widely used in water splitting, a PEMEC with low catalyst loadings and high catalyst utilizations is strongly desired for its wide commercialization. In this study, the ultrafast and multiscale hydrogen evolution reaction (HER) phenomena in an operating PEMEC is in-situ observed for the first time. The visualization results reveal that the HER and hydrogen bubble nucleation mainly occur on catalyst layers at the rim of the pores of the thin/tunable liquid/gas diffusion layers (TT-LGDLs). This indicates that the catalyst material of the conventional catalyst-coated membrane (CCM) that is located in the middle area of the LGDL pore is underutilized/inactive. Based on this discovery, a novel thin and tunable gas diffusion electrode (GDE) with a Pt catalyst thickness of 15 nm and a total thickness of about 25 um has been proposed and developed by taking advantage of advanced micro/nano manufacturing. The novel thin GDEs are comprehensively characterized both ex-situ and in-situ, and exhibit excellent PEMEC performance. More importantly, they achieve catalyst mass activity of up to 58 times higher than conventional CCM at 1.6 V under the operating conditions of 80 degrees C and 1 atm. This study demonstrates a promising concept for PEMEC electrode development, and provides a direction of future catalyst designs and fabrications for electrochemical devices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels

Here, heritage data for the class of 9 to 12 wt pct Cr steels are studied using data science to quantify the statistically significant relationships among multiple processing/microstructure and performance variables. The effort is undertaken to find new martensitic steels for creep life of 10 5 hours or greater at 650 °C and 100 MPa using machine learning. Linear regression and lasso regression were utilized to identify alloying elements that contribute towards better creep strength. Visualization techniques such as t-distributed stochastic neighbor embedding and pair-wire element specific comparisons were utilized to explore information gaps that exist within the data and are in conflict with existing domain knowledge. Lastly, cfombining all results suggest that the next alloy design to be explored should be 9 wt pct Cr with high W (2 to 3 wt pct) and high Co (2 to 3 wt pct) for creep life of 10 5 hours or greater at 650 °C, 100 MPa.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Impact of grain boundaries on efficiency and stability of organic-inorganic trihalide perovskites

Organic-inorganic perovskite solar cells have attracted tremendous attention because of their remarkably high power conversion efficiencies. To further improve device performance, it is imperative to obtain fundamental understandings on the photo-response and long-term stability down to the microscopic level. Here, we report the quantitative nanoscale photoconductivity imaging on two methylammonium lead triiodide thin films with different efficiencies by light-stimulated microwave impedance microscopy. The microwave signals are largely uniform across grains and grain boundaries, suggesting that microstructures do not lead to strong spatial variations of the intrinsic photo-response. In contrast, the measured photoconductivity and lifetime are strongly affected by bulk properties such as the sample crystallinity. As visualized by the spatial evolution of local photoconductivity, the degradation process begins with the disintegration of grains rather than nucleation and propagation from visible boundaries between grains. In conclusion, our findings provide insights to improve the electro-optical properties of perovskite thin films towards large-scale commercialization.

14 SOLAR ENERGY↗

DOE OSTI2013

Visual investigation on the heat dissipation process of a heat sink by using digital holographic interferometry

We present a method for visually and quantitatively investigating the heat dissipation process of plate-fin heat sinks by using digital holographic interferometry. A series of phase change maps reflecting the temperature distribution and variation trend of the air field surrounding heat sink during the heat dissipation process are numerically reconstructed based on double-exposure holographic interferometry. According to the phase unwrapping algorithm and the derived relationship between temperature and phase change of the detection beam, the full-field temperature distributions are quantitatively obtained with a reasonably high measurement accuracy. And then the impact of heat sink's channel width on the heat dissipation performance in the case of natural convection is analyzed. In addition, a comparison between simulation and experiment results is given to verify the reliability of this method. The experiment results certify the feasibility and validity of the presented method in full-field, dynamical, and quantitative measurement of the air field temperature distribution, which provides a basis for analyzing the heat dissipation performance of plate-fin heat sinks.

42 ENGINEERING↗

DOE OSTI2018

Automated map sharpening by maximization of detail and connectivity

An algorithm for automatic map sharpening is presented that is based on optimization of the detail and connectivity of the sharpened map. The detail in the map is reflected in the surface area of an iso-contour surface that contains a fixed fraction of the volume of the map, where a map with high level of detail has a high surface area. The connectivity of the sharpened map is reflected in the number of connected regions defined by the same iso-contour surfaces, where a map with high connectivity has a small number of connected regions. By combining these two measures in a metric termed the `adjusted surface area', map quality can be evaluated in an automated fashion. This metric was used to choose optimal map-sharpening parameters without reference to a model or other interpretations of the map. Map sharpening by optimization of the adjusted surface area can be carried out for a map as a whole or it can be carried out locally, yielding a locally sharpened map. To evaluate the performance of various approaches, a simple metric based on map–model correlation that can reproduce visual choices of optimally sharpened maps was used. The map–model correlation is calculated using a model with B factors (atomic displacement factors; ADPs) set to zero. This model-based metric was used to evaluate map sharpening and to evaluate map-sharpening approaches, and it was found that optimization of the adjusted surface area can be an effective tool for map sharpening.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Wildfire Risk Evaluation Framework for Grid Operations and Planning

The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.

Wildfire, Resilience, Extreme Events, Grid Reslien↗

DOE OSTI2014

SU-E-I-78: Improving Prostatic Delineation Using Dual-Energy CT

Purpose: Visual prostatic definition is difficult using conventional CT. This is because the prostate is surrounded closely with tissue of similar electron density. Definition is further hindered when the region contains high-Z material (such as fiducial markers). Dual-energy CT (DECT) is a technique where images are rendered using two tube voltages during a single scan session. This study evaluates DECT as a means of improving prostatic volume delineation for radiation oncology. Methods: The patients were scanned using a Definition AS20 (Siemens Healthcare, Malvern, PA). This device uses a single-tube configuration, where two scans of differing energies are performed in serial. The scans are acquired with tube voltage of 80kVp and 140kVp. Following acquisition, these scan data were used to generate effective monoenergetic scans ranging from 40keV to 190keV. In the current study, the data were presented to observers using a novel program, which allows real-time adjustment of window, level, and effective keV; all while scrolling through volumetric slices. Three patients were scanned, each with a different high-contrast material in or around the prostate: I-125 seeds, gold fiducial markers, and prostatic calcifications. These images are compared to a weighted average of the 80kVp and 140kVP scans, which yield a scan similar to that of a 120 kVp scan, which is a common tube voltage in radiation oncology. Results: Prostatic definition improved in each case. Differentiation of soft tissue from surrounding adipose improved with lower keV, while higher keV provided a reduction of high-z artifacts. Furthermore, the dynamic adjustment of the keV allowed observers to better recognize regions of differing tissue composition within this relatively homogeneous area. Conclusion: By simultaneously providing the observer with the benefits of high-energy images and low-energy images, and allowing adjustment in real-time, improved imaging in highly homogeneous regions such as the male pelvis is achievable.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Characterization of Build Parameters and Microstructure in Low Heat Input WAAM of Ni-Based Superalloy Haynes 282

Ni-based superalloy Haynes® 282® is being targeted for various applications in advanced power generation systems for its superior fabricability, weldability, and excellent high temperature creep and corrosion performance. This process optimization study aims to use a low heat-input, high deposition rate, controlled Gas Metal Arc Welding (GMAW) process, Cold Metal Transfer (CMT) by Fronius, attempting to achieve fully dense fabrication and possibly avoid the need for HIP. Twenty-one multilayer blocks (~25x100x40 mm3) were deposited to explore a large set of build parameters variations that focused on varying the travel speed from 14 to 42 inches per minute (ipm) and wire feed speed from 150 to 450 ipm. A strong correlation has been observed between arc energy – controlled primarily by travel and wire feed speed. Initial visual inspection, internal microstructural examination, and computed tomography (CT) have been used to determine the effects of built parameters on evolution of internal porosity and defects. Scanning electron microscopy techniques enabled structural and compositional imaging of heterogeneity and changes in microstructural properties.

additive manufacturing↗

DOE OSTI2025

Characterization of Build Parameters and Microstructure in Low Heat Input WAAM of Ni-Based Superalloy Haynes 282

additive manufacturing↗

DOE OSTI2022

Gamma-Ray Imaging to Monitor Accumulating Holdup

Over time, nuclear materials can accumulate in process equipment and keeping track of such material is of interest to many stakeholder communities including safeguards, process monitoring, criticality safety, and radiological safety. Passive gamma-ray imaging provides a visual and quantitative tool that can be applied to this problem, allowing one to determine the amount of material present from the images. In this work we emulate the accumulation of highly enriched uranium (HEU) in a large rectangular stainless-steel duct by stacking 5, 27 × 50 cm2 cards, each of which has approximately 11 g of 235U in oxide form distributed across its surface, behind each other. A series of images were collected using a coded-aperture gamma-ray imager, starting with a single card, adding a second card, etc. Long integrations were collected to allow evaluating the performance of the approach as a function of integration time. The images are analyzed using first principles to determine the amount of material in each image. The results are compared to the known amount of material in each configuration, and as a function of time. The analysis includes a first-principles error propagation and to further evaluate the uncertainty, a boot-strap sampling approach is also applied to the data. The work offers a proof of concept for unattended monitoring of uranium holdup with a quantitative gamma-ray imaging technique.

Ziock, Klaus-Peter↗

DOE OSTI2024

High-Speed OH-PLIF Diagnostics of Flame Flashback in Low Swirl Hydrogen-Enriched Flames

Hydrogen-enrichment is a potential strategy to reduce carbon emissions for the development of next-generation lean premixed combustors. However, the higher flashback propensity with hydrogen enrichment owing to the faster chemical kinetics is one of the key challenges in existing natural gas turbine combustors. The fundamentals of stabilized flame dynamics and flashback events in a premixed low swirl burner (LSB) configuration are visualized experimentally using nanosecond (ns)-based high-speed hydroxyl radical planar laser-induced fluorescence (OH-PLIF). Swirlers with two different turning angles, 26 and 33 and three different center-body hole diameters, 1.08, 1.12 and 1.16 mm with measured swirl numbers varying from 0.43 to 0.49 are tested in this study at atmospheric temperature and pressure inlet conditions. The inlet conditions are varied systematically with respect to pre-mixer velocities (V), flame equivalence ratio (ϕ) and hydrogen content (X_(H_2 )) for 50%-90% (H2, by mole) H2/ CH4 reactant mixtures and OH-PLIF characterization is performed in a stable flame at 20 kHz repetition rate. An increase in flashback propensity is observed with increasing ϕ, X_(H_2 ) and decreasing V via detailed investigation of flame lift-off length (L) above the burner rim.

Parajuli, Pradeep↗

DOE OSTI2023

The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human ‘expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 percent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ~25 or Einstein radii less than ~1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

A Structural Basis for 129 Xe Hyper-CEST Signal in TEM-1 β-Lactamase

Genetically encoded (GE) contrast agents detectable by magnetic resonance imaging (MRI) enable non-invasive visualization of gene expression and cell proliferation at virtually unlimited penetration depths. Using hyperpolarized 129 Xe in combination with chemical exchange saturation transfer, an MR contrast approach known as hyper-CEST, enables ultrasensitive protein detection and biomolecular imaging. GE MRI contrast agents developed to date include nanoscale proteinaceous gas vesicles as well as the monomeric bacterial proteins TEM-1 β-lactamase (bla) and maltose binding protein (MBP). To improve understanding of hyper-CEST NMR with proteins, we performed structural and computational studies to further characterize the Xe-bla interaction. X-ray crystallography validated the location of a high-occupancy Xe binding site predicted by MD simulations, and mutagenesis experiments confirmed this Xe site as the origin of the observed CEST contrast. Structural studies and MD simulations with representative bla mutants offered additional insight regarding the relationship between local protein structure and CEST contrast.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Data-driven selection and spectral classification of white dwarf stars

The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection, and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios ≥9. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with ≥ 90 % accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

Development and testing of improved statistical wind power forecasting methods.

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

17 WIND ENERGY↗

DOE OSTI2023

Thermo-Catalytic Decomposition Comparisons: Carbon Catalyst Structure, Hydrocarbon Feed and Regeneration

Thermo-catalytic decomposition (TCD) activity and stability depend upon the initial carbon catalyst structure. However, further transitions in the carbon structure depend on the carbon material (structure and composition) originating from the TCD process. In this article, reaction data are presented that illustrates the time-dependent TCD activity as TCD-formed carbon contributes and then dominates conversion. A variety of initial carbon catalysts are compared, including sugar char, a conductive carbon black (AkzoNobel Ketjenblack), a rubber-grade carbon black (Cabot R250), and its graphitized analogue as formed and partially oxidized. Regeneration of carbon catalysts by partial oxidation is evaluated using nascent carbon black as a model, coupled with subsequent comparative TCD performance relative to the nascent, non-oxidized carbon black. Activation energies for TCD with nascent and oxidized carbons are evaluated by a leading-edge analysis method applied to TCD. Given the correlation between nanostructure and active sites, two additional carbons, engine soots, are evaluated for regeneration and dependence upon nanostructure. Active sites are quantified by oxygen chemisorption, followed by X-ray photoelectron spectroscopy (XPS). The structure of carbon catalysts is assessed pre- and post-TCD by high-resolution transmission electron microscopy (HRTEM). Last, energy dispersive X-ray analysis mapping (EDS) is carried out for its potential to visualize oxygen chemisorption.

03 NATURAL GAS↗

DOE OSTI2016

Utilizing Semantic Big Data for realizing a National-scale Infrastructure Vulnerability Analysis System

Critical Infrastructure systems(CIs) such as energy, water, transportation and communication are highly interconnected and mutually dependent in complex ways. Robust modeling of CIs interconnections is crucial to identify vulnerabilities in the CIs. We present here a national-scale Infrastructure Vulnerability Analysis System (IVAS) vision leveraging Se- mantic Big Data (SBD) tools, Big Data, and Geographical Information Systems (GIS) tools. We survey existing ap- proaches on vulnerability analysis of critical infrastructures and discuss relevant systems and tools aligned with our vi- sion. Next, we present a generic system architecture and discuss challenges including: (1) Constructing and manag- ing a CI network-of-networks graph, (2) Performing analytic operations at scale, and (3) Interactive visualization of ana- lytic output to generate meaningful insights. We argue that this architecture acts as a baseline to realize a national-scale network based vulnerability analysis system.

Chinthavali, Supriya↗