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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Development of a compact E ? B microchannel plate detector for beam imaging

A beam imaging detector was developed by coupling a multi-strip anode with delay line readout to an E×B microchannel plate (MCP) detector. This detector is capable of measuring the incident position of the beam particles in one-dimension. To assess the spatial resolution, the detector was illuminated by an α-source with an intervening mask that consists of a series of precisely-machined slits. The measured spatial resolution was 520 um source FWHM, which was improved to 413 um FWHM by performing an FFT of the signals, rejecting spurious signals on the delay line, and requiring a minimum signal amplitude. This measured spatial resolution of 413 um FWHM corresponds to an intrinsic resolution of 334 um FWHM when the effect of the finite slit width is de-convoluted. To understand the measured resolution, the performance of the detector is simulated with the ion-trajectory code SIMION.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling

The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Scattering-type scanning near-field optical microscopy with Akiyama piezo-probes

Recent developments of the scattering-type scanning near-field optical microscope at cryogenic temperatures (cryogenic s-SNOM or cryo-SNOM) have led to many breakthroughs in the studies of low energy excitations in quantum materials. However, the simultaneous demands on vibration isolation, low base temperature, precise nano-positioning, and optical access make the construction of a cryo-SNOM a daunting task. Adding to the overhead space required for a cryo-SNOM is the atomic force microscopy control, which predominantly utilizes a laser-based detection scheme for determining the cantilever tapping motion. In this work, we provide an alternative and straightforward route to performing s-SNOM using metal-coated Akiyama probes, where the cantilever tapping motion is detected through a piezoelectric signal. We show that the Akiyama-based cryo-SNOM attains high spatial resolution, good near-field contrast, and is able to perform imaging with a significantly more compact system compared to other cryo-SNOM implementations. Our results firmly establish the potential of s-SNOM based on self-sensing piezo-probes, which can easily accommodate far-infrared wavelengths and high magnetic fields in the future.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Argonne Pixel Tracking Telescope at the Fermilab Test Beam Facility

The Argonne Pixel Tracking Telescope is installed at the Fermilab Test Beam Facility. The telescope consists of six planar n + -in-n silicon sensors with a pixel size of 250 × 50 μm 2 . The instrumentation of the telescope is described including the electrical and mechanical setups. A 120 GeV proton beam is used to evaluate the telescope performance using criteria such as cluster size, pixel tracking efficiency, and spatial resolution. The spatial resolution of the telescope is measured to be 72 μm × 13 μm and is consistent with the resolution determined from the simulation. In conclusion, this telescope will be used to test the performance of the various silicon pixel technology as well as to study the effects and the performance of the detectors after being irradiated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Apparatus, method and system for imaging and utilization of SEM charged particles

A scanning electron microscope (SEM) system includes an SEM objective that emits an electron beam toward a sample, causing emission of charged particles including secondary electrons, Auger electrons, backscattered electrons, anions and cations. The SEM system includes electron optics elements that are configured to establish electric fields around the sample that accelerate charged particles toward a detector. A two-dimensional distribution of locations of incidence of the charged particles on the detector is indicative of energies of the charged particles and their emission angles from the sample. A three-dimensional spatial distribution of charged particles emitted from the sample is recovered by performing an Abel transform over the distribution on the detector. The energies and emission angles of the charged particles are then determined from the three-dimensional spatial distribution.

42 ENGINEERING↗
DOE OSTI2017

Transport and spatial energy deposition of relativistic electrons in copper-doped fast ignition plasmas

Fast electron transport and spatial energy deposition are investigated in integrated cone-guided Fast Ignition experiments by measuring fast electron induced copper K-shell emission using a copper tracer added to deuterated plastic shells with a geometrically reentrant gold cone. Experiments were carried out at the Laboratory for Laser Energetics on the OMEGA/OMEGA-EP Laser where the plastic shells were imploded using 54 of the 60 OMEGA60 beams (3ω, 20 kJ), while the high intensity OMEGA-EP (BL2) beam (1 ω, 10 ps, 500 J, I peak > 10 19 W/cm 2 ) was focused onto the inner cone tip. Here, a retrograde analysis using the hybrid-PIC electron transport code, ZUMA, is performed to examine the sensitivity of the copper Kα spatial profile on the laser-produced fast electrons, facilitating the optimization of new target point designs and laser configurations to improve the compressed core areal density by a factor of 4 and the fast electron energy coupling by a factor of 3.5.

42 ENGINEERING↗
DOE OSTI2020

Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab project

Microseismic monitoring plays an important role in many energy-related and environmental industries.The microseismic event catalog and seismic structure of the subsurface are two of the primary outputsof the microseismic monitoring system. Though rough locations of microseismic events can be estimated automatically, obtaining high-resolution microseismic event locations requires a significant amount of human laborespecially on seismic phase picking. Unlike traditional automatic pickers that are usually less precise than human analysts, a fewrecently proposed algorithms based on deepneural networks(DNN)were able to match or surpass human performance for earthquake signals. Due to differences in the spatial scale of the study area, sensor sampling rate, and geometry of the monitoring system, it is not clear whether these deepneural networkmodels can be used to speed up microseismic data processing. In this paper, we adapted the DNN based technique for automatic phase picking of microseismic signals. We usedmicroseismic data recorded at the experiment 1 site of the enhancedgeothermal system (EGS) Collab project anddesigneda workflowthat we call transfer-learning aided double-difference tomography (TADT),thatcombines transfer learning and seismic tomography. We re-train an existing DNN with our data to obtain a newmodel using around 3500 seismogramsand associated manual phase picks. Thistransfer learnedmodel is able to reach human performance but muchfaster than human analysts. The transfer-learning-derived phase pickswereused to improve microseismic event locations and imagethesubsurface. The results are similar to or slightly better than those obtained with manual phase picks.

Chai, Chengping↗
DOE OSTI2022

Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities

To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized energy source in urban systems, the overall performance can vary by levels of urban nutrients, solar and CO 2 resources, and the transportation cost when considering its application to different urban densities, urban form, and the spatial scale of urban settings. This research explores three potential impacts on the algal system’s energy performance: (1) urban density, (2) urban form in different contexts, and (3) spatial scale. The research examines the impacts by testing urban settings given in actual contexts in Atlanta, Georgia, USA. Four neighborhoods representing the high-density urban, mid-density urban, mixed suburban, and typical suburban areas are investigated. The density-scale–performance relationships are explored through testing different urban forms of neighborhoods in both hypothetical and actual neighborhood settings. A GIS-based model is developed to estimate the overall energy performance of the decentralized renewable energy system in urban environments. Results show that the energy performance is positive mainly for high-density urban neighborhoods with small-to-medium scales, up to 0.36 MJ per ton of municipal solid wastes for actual settings and 0.37 MJ for hypothetical cases. Neighborhoods with higher density have higher energy performance while up scaling has negative effects on the energy performance with a low degree of significance. Optimal scales are found as a 1-km radius in real test beds and 1.3 km in hypothetical settings, in which the results show trade-offs between scaling effects in the system efficiency gain and the transportation cost increase.

Quan, Steven Jige↗
DOE OSTI2020

Measurements of the Flow in the Vicinity of an Additively Manufactured Turbine Leading-Edge Using X-Ray Particle Tracking Velocimetry

X-ray particle tracking velocimetry (PTV) is performed, for the first time, to measure the velocity field inside a leading-edge of a turbine blade made by laser-additive-manufacturing (LAM) process. The traditional showerhead holes were replaced by a porous matrix in the leading-edge. The flow through such a leading-edge piece cannot be faithfully recreated by traditional prototype testing methods due to the surface roughness and imperfections caused by LAM process. Hence, direct measurement is the only option. However, it is difficult to measure flow inside such pieces with traditional velocimetry measurements due to the existence of metallic walls. Moreover, small internal size and high flow speeds call for a measurement technique with high spatial and temporal resolutions. To address these issues, we performed time-resolved X-ray PTV using the Advanced Photon Source (APS) synchrotron facility at the Argonne National Laboratory (ANL). A hydraulic system was constructed to run water, mixed with seeding particles, through the leading-edge piece. A high-speed camera captured the images of the seeding particles, which were later processed to create particle tracks. The time-averaged velocity field showed distinct pairs of vortices located in front of the porous outlet inside the leading-edge piece. The inlet channel showed reversed flow due to partial obstruction by the porous inlet of the test piece. Such knowledge of the flow field inside a leading-edge of a turbine blade will help us to design better cooling paths leading to higher cooling efficiency and increased life-span of a turbine blade.

42 ENGINEERING↗
DOE OSTI2012

A soft x-ray transmission grating imaging-spectrometer for the National Ignition Facility

A soft x-ray transmission grating spectrometer has been designed for use on high energy-density physics experiments at the National Ignition Facility (NIF); coupled to one of the NIF gated x-ray detectors it records 16 time-gated spectra between 250 and 1000 eV with 100 ps temporal resolution. The trade-off between spectral and spatial resolution leads to an optimized design for measurement of emission around the peak of a 100-300 eV blackbody spectrum. Performance qualification results from the NIF, the Trident Laser Facility and vacuum ultraviolet beamline at the National Synchrotron Light Source, evidence a <100 {mu}m spatial resolution in combination with a source-size limited spectral resolution that is <10 eV at photon energies of 300 eV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

THREE-DIMENSIONAL CORE-COLLAPSE SUPERNOVA SIMULATED USING A 15 M{sub ⊙} PROGENITOR

We have performed ab initio neutrino radiation hydrodynamics simulations in three and two spatial dimensions (3D and 2D) of core-collapse supernovae from the same 15 M{sub ☉} progenitor through 440 ms after core bounce. Both 3D and 2D models achieve explosions; however, the onset of explosion (shock revival) is delayed by ∼100 ms in 3D relative to the 2D counterpart and the growth of the diagnostic explosion energy is slower. This is consistent with previously reported 3D simulations utilizing iron-core progenitors with dense mantles. In the ∼100 ms before the onset of explosion, diagnostics of neutrino heating and turbulent kinetic energy favor earlier explosion in 2D. During the delay, the angular scale of convective plumes reaching the shock surface grows and explosion in 3D is ultimately lead by a single, large-angle plume, giving the expanding shock a directional orientation not dissimilar from those imposed by axial symmetry in 2D simulations. We posit that shock revival and explosion in the 3D simulation may be delayed until sufficiently large plumes form, whereas such plumes form more rapidly in 2D, permitting earlier explosions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring

Using a semi-supervised machine learning approach we present a real-time anomaly detection system based on an autoencoder used for online data quality monitoring of the CMS electromagnetic calorimeter operating at the CERN LHC. We introduce a novel method that maximizes the anomaly detection performance making use of the time-dependence of anomalies and the spatial variations in the detector response. The autoencoder-based system efficiently detects anomalies in real time and maintains a very low false discovery rate. We validate the performance of this novel system with anomalies from LHC collision data taken in 2018 and 2022. In addition, results are presented after deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow at the beginning of LHC Run 3 resulting in the system to detect issues that were missed by the existing system.

Harilal, Abhirami [Carnegie Mellon University, Pit↗
DOE OSTI2021

High-Resolution 3D Acoustic Borehole Integrity Monitoring (Research Performance Progress Report Q2 2021)

Real-time, in-situ, high spatial resolution (sub-cm) imaging of the near-borehole environment would revolutionize wellbore diagnostics and integrity assessment by direct observation of defects. It is becoming increasingly apparent, that better understanding of the near-wellbore environment is required to meet the safety and operational needs in challenging environments such as those present in subsurface energy extraction (geothermal) and storage (CO2 sequestration) applications. Therefore, it is important to have a more robust ability to image the near-borehole and reliably detect defects. It was proposed to further develop and improve our advanced 3D imaging system to evaluate casing defects (e.g. corrosion) and cement quality in either open- or cased-borehole with the ultimate goal to develop a commercially deployable technology. The system consists of a unique acoustic source (LANL) and advanced inversion techniques for image processing (LANL, ORNL). This system will provide comprehensive borehole integrity monitoring with improved resolution over existing techniques. As an application of this imaging system, we will characterize the effectiveness of next-generation wellbore completion technology (NETL, SNL), and will demonstrate that, unlike current technology, the proposed approach can successfully characterize foamed cements.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

DARHT Radiographic Analysis of Spatially Modulated Objects

This document describes some analysis methods that can be performed using the Kaleidoscope test object (KTO) in order to assess DARHT radiography performance. The goal of these methods is to provide a simple metric of radiographic performance to compare different system configurations at DARHT. Specifically, these methods can be used to assess the Tiffany configurations to determine relative radiographic performance. To demonstrate the methods, we show results analysis performed on the ‘Tiffany A’ shot conducted in July 2021.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Quantifying the Impacts of Grain-scale Heterogeneity on Mechanical Response

Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Simultaneous three-dimensional elemental mapping of Hollandite and Pyrochlore material phases in ceramic waste form materials

In this study, differential X-ray absorption contrast tomography was performed on model ceramic composite systems to image the 3D spatial distributions of Ga and Nd containing phases. The model material systems were fabricated such that they contained two constituent phases. Ga–doped Ba hollandite (Ba 1.33 Ga 2.66 Ti 5.34 O 16 ), and pyrochlore (Nd 2 Ti 2 O 7 ) which are under consideration as nuclear waste immobilization matrices. Two complementary techniques have been used to characterize the 3D distribution of phases. The analysis suggests that the 3D spatial distributions of hollandite and pyrochlore can be readily identified through the distributions Ga and Nd, respectively. These results represent a critical development towards characterizing the complex microstructure inherent to practical materials such as multiphase ceramic composite waste forms. Furthermore, these methods can be applied to elucidate microstructural features that fundamentally determine the performance and properties of complex systems, which is central to the design of durable waste forms as well as many other real materials such fuel cells, batteries, ultra-high temperature ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Land quality, sustainable development and environmental degradation in agricultural districts: A computational approach based on entropy indexes

Land Degradation (LD) in socio-environmental systems negatively impacts sustainable development paths. This study proposes a framework to LD evaluation based on indicators of diversification in the spatial distribution of sensitive land. We hypothesize that conditions for spatial heterogeneity in a composite index of land sensitivity are more frequently associated to areas prone to LD than spatial homogeneity. Spatial heterogeneity is supposed to be associated with degraded areas that act as hotspots for future degradation processes. A diachronic analysis (1960–2010) was performed at the Italian agricultural district scale to identify environmental factors associated with spatial heterogeneity in the degree of land sensitivity to degradation based on the Environmentally Sensitive Area Index (ESAI). In 1960, diversification in the level of land sensitivity measured using two common indexes of entropy (Shannon's diversity and Pielou's evenness) increased significantly with the ESAI, indicating a high level of land sensitivity to degradation. In 2010, surface area classified as “critical” to LD was the highest in districts with diversification in the spatial distribution of ESAI values, confirming the hypothesis formulated above. Entropy indexes, based on observed alignment with the concept of LD, constitute a valuable base to inform mitigation strategies against desertification. - Highlights: • Spatial heterogeneity is supposed to be associated with degraded areas. • Entropy indexes can inform mitigation strategies against desertification. • Assessing spatial diversification in the degree of land sensitivity to degradation. • Mediterranean rural areas have an evident diversity in agricultural systems. • A diachronic analysis carried out at the Italian agricultural district scale.

54 ENVIRONMENTAL SCIENCES↗