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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Simultaneous imaging and diffraction in the dynamic diamond anvil cell

The ability to visualize a sample undergoing a pressure-induced phase transition allows for the determination of kinetic parameters, such as the nucleation and growth rates of the high-pressure phase. For samples that are opaque to visible light (such as metallic systems), it is necessary to rely on x-ray imaging methods for sample visualization. Here, we present an experimental platform developed at beamline P02.2 at the PETRA III synchrotron radiation source, which is capable of performing simultaneous x-ray imaging and diffraction of samples that are dynamically compressed in piezo-driven diamond anvil cells. This setup utilizes a partially coherent monochromatic x-ray beam to perform lensless phase contrast imaging, which can be carried out using either a parallel- or focused-beam configuration. The capabilities of this platform are illustrated by experiments on dynamically compressed Ga and Ar. Melting and solidification were identified based on the observation of solid/liquid phase boundaries in the x-ray images and corresponding changes in the x-ray diffraction patterns collected during the transition, with significant edge enhancement observed in the x-ray images collected using the focused-beam. These results highlight the suitability of this technique for a variety of purposes, including melt curve determination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Basic Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Basic Energy Sciences, November 3-5, 2015, Rockville, Maryland

Computers have revolutionized every aspect of our lives. Yet in science, the most tantalizing applications of computing lie just beyond our reach. The current quest to build an exascale computer with one thousand times the capability of today’s fastest machines (and more than a million times that of a laptop) will take researchers over the next horizon. The field of materials, chemical reactions, and compounds is inherently complex. Imagine millions of new materials with new functionalities waiting to be discovered — while researchers also seek to extend those materials that are known to a dizzying number of new forms. We could translate massive amounts of data from high precision experiments into new understanding through data mining and analysis. We could have at our disposal the ability to predict the properties of these materials, to follow their transformations during reactions on an atom-by-atom basis, and to discover completely new chemical pathways or physical states of matter. Extending these predictions from the nanoscale to the mesoscale, from the ultrafast world of reactions to long-time simulations to predict the lifetime performance of materials, and to the discovery of new materials and processes will have a profound impact on energy technology. In addition, discovery of new materials is vital to move computing beyond Moore’s law. To realize this vision, more than hardware is needed. New algorithms to take advantage of the increase in computing power, new programming paradigms, and new ways of mining massive data sets are needed as well. This report summarizes the opportunities and the requisite computing ecosystem needed to realize the potential before us. In addition to pursuing new and more complete physical models and theoretical frameworks, this review found that the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) would directly affect the Basic Energy Sciences (BES) mission need. Simulation, visualization, and data analysis are crucial for advances in energy science and technology. Revolutionary mathematical, software, and algorithm developments are required in all areas of BES science to take advantage of exascale computing architectures and to meet data analysis, management, and workflow needs. In partnership with ASCR, BES has an emerging and pressing need to develop new and disruptive capabilities in data science. More capable and larger high-performance computing (HPC) and data ecosystems are required to support priority research in BES. Continued success in BES research requires developing the next-generation workforce through education and training and by providing sustained career opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Results of Ultrasonography-Guided Percutaneous Radiofrequency Ablation in Hyperparathyroid Patients in Whom Surgery Is Not Feasible

BackgroundThe aim of the study was to evaluate the results of ultrasonography (US)-guided percutaneous radiofrequency ablation (RFA) in hyperparathyroid patients who refused surgery or had high surgical risks.Patients and MethodsFive patients with hyperparathyroidism (HPT) underwent US-guided RFA for a single hyperfunctioning parathyroid lesion. Post-ablation serum calcium and parathormone (PTH) assays were performed. All patients underwent imaging studies 6 months after the ablation to visualize the post-ablation change in the size of the treated parathyroid lesions.ResultsAll patients were normocalcemic on the post-ablation 1st day and 6th month. The post-ablation PTH levels were normal in three patients but remained elevated in two patients. The size of the parathyroid lesion was ≥30 mm in the two patients with elevated PTH, whereas the lesion was smaller than 30 mm in those with normal post-ablation PTH values.ConclusionAlthough this is a limited case series, it demonstrates the potential feasibility of RFA for HPT. Benefits were achieved particularly in patients with small parathyroid lesions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

New insights into the shock tube ignition of H 2 /O 2 at low to moderate temperatures using high-speed end-wall imaging

In this study, the effects of pre-ignition energy releases on H 2 —O 2 mixtures were explored in a shock tube with the aid of high-speed imaging and conventional pressure and emission diagnostics. Ignition delay times and time-resolved camera image sequences were taken behind the reflected shockwaves for two hydrogen mixtures. High concentration experiments spanned temperatures between 858 and 1035 K and pressures between 2.74 and 3.91 atm for a 15% H 2 \18% O 2 \Ar mixture. Low concentration data were also taken at temperatures between 960 and 1131 K and pressures between 3.09 and 5.44 atm for a 4% H 2 \2% O 2 \Ar mixture. These two model mixtures were chosen as they were the focus of recent shock tube work conducted in the literature. Experiments were performed in both a clean and dirty shock tube facility; however, no deviations in ignition delay times between the two types of tests were apparent. The high-concentration mixture (15%H 2 \18%O 2 \Ar) experienced energy releases in the form of deflagration flames followed by local detonations at temperatures < 1000 K. Measured ignition delay times were compared to predictions by three chemical kinetic mechanisms: GRI-Mech 3.0, AramcoMech 2.0, and Burke's et al. (2012) mechanisms. It was found that when proper thermodynamic assumptions are used, all mechanisms were able to accurately predict the experiments with superior performance from the well-validated AramcoMech 2.0 and Burke et al. mechanisms. Current work provides better guidance in using available literature hydrogen shock tube measurements, which spanned more than 50 years but were conducted without the aid of high-speed visualization of the ignition process, and their modeling using combustion kinetic mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Viskores: Integrating Parallel Scientific Visualization Research into Applications

Viskores is a scientific visualization library that is the primary deployment of such algorithms to the parallel accelerated processors of modern DOE supercomputers. In this paper, we review the capabilities provided by Viskores and how these capabilities are leveraged by other software in the high-performance computing ecosystem. We discuss the Viskores data representation and pay particular attention to array management. Through this array management we describe how data is adapted between Viskores and other software along with strategies for converting dynamic, polymorphic objects to static representations better suited to GPU processing. We conclude with several examples of Viskores integrating with high-performance software that is used in production today.

Moreland, Ken [ORNL] (ORCID:0000000270513288)↗
DOE OSTI2016

Observation Technology for Remote Operation in Contaminated Turbid Water - 16113

Remote underwater work in contaminated tanks and pools is one of major decontamination and decommissioning works under high-dose radiation environment. Generally, in this kind of work, visual information obtained by a camera (an optical camera) is limited due to turbid water caused by suspended sludge particles in the water and it makes remote underwater work difficult to be performed safely and efficiently. Therefore, some alternative observation methods to optical cameras have been required. In order to satisfy this requirement, the alternative observation technology which can obtain visual information in contaminated turbid water has been developed since 2014. It is a technology using an acoustic imaging system in a designated airtight container. It provides the visual information in real time regardless of turbidity without significant contamination of any parts of the system. Also, it is able to provide the location information of underwater objects with contactless. This paper will present development details of this innovative observation technology and its effectiveness to various remote works in contaminated turbid water. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

AI/ML Expo Boosting Job Performance with AI: Innovative Approaches and Success Stories

Our technology leverages artificial intelligence (AI) to enhance the user experience in High Performance Computing (HPC) environments. By analyzing user behavior and providing personalized recommendations, our AI system helps HPC users optimize their workflows and improve productivity. Additionally, we offer an advanced image similarity search feature, which utilizes AI algorithms to identify and retrieve visually similar images, saving users valuable time and effort in their research and analysis.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage

The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Λ Baryon Production in ν¯µ Interactions in the MicroBooNE Detector

The Cabibbo suppressed production of $\Lambda$ baryons in anti-neutrino interactions with nuclei is a rare process that is yet to be measured with a modern neutrino detector with automated reconstruction. The cross section for this process is sensitive to a number of unique nuclear effects, most notably the secondary interactions of the produced hyperon while attempting to escape from the nucleus. Other interactions within the nuclear remnant can impact the estimation of neutrino energy in oscillation measurements, and thus an accurate description of the nuclear environment is required. The strangeness violating hyperon production process is only available to anti-neutrinos. The model of this interaction is implemented into the NuWro neutrino interaction Monte Carlo simulation, and some predictions are presented, focusing on the role of nuclear effects. This model introduces a hyperon-nucleus potential, which calculations from hypernuclear theory permit to be strongly repulsive in th e case of $\Sigma$ baryons. The presence of this potential is found to sculpt the shape of the differential cross section in some variables. The MicroBooNE detector will be described, followed by a description of a measurement of the flux averaged, restricted phase space cross section of Cabibbo suppressed $\Lambda$ baryon production. A sophisticated event selection is employed, as a very large quantity of background neutrino interactions must be removed to perform the measurement with any sensitivity. This selection introduces some novel techniques such as the island finding method, and achieves a background reduction of $\sim 10^6$, with an efficiency of around 7\%. The calculation of the systematic uncertainties will be explained, including two procedures explored to handle sources of background with extremely poor simulation statistics: an in-situ constraint using data from sidebands, and a visual inspection of the data and simulation to remove the troublesome background events. The sensitivity to the $\Lambda$ baryon production cross section is calculated in the form of Bayesian posterior probability distributions, combining the systematic uncertainties with data and simulation statistical uncertainties. As a rare process, the statistical uncertainties are highly non-Gaussian, and the Bayesian approach is applied to include the full shapes of these uncertainties. Data corresponding to $2.2 \times 10^{20}$ protons on target of neutrino mode running and $4.9 \times 10^{20}$ protons on target of anti-neutrino running is analysed. When the data was unblinded, five $\Lambda$ production candidates were selected from the data, consistent with the MC simulation prediction of $5.3 \pm 1.1$ events. The final estimated cross section is $1.8^{+2.0}_{-1.6} \times 10^{-40}$cm$^2/$Ar when employing the sideband constraint procedure. A similar result of $2.0^{+2.2}_{-1.8} \times 10^{-40}$cm$^2/$Ar is obtained when performing the visual scan instead. The methods used in t his analysis are intended to be easily exported to other LArTPC detectors such as the Short Baseline Near Detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

Development & Optimization of Materials and Processes for a Cost Effective Photoelectrochemical Hydrogen Production System. Final report

The overall project objective was to apply high throughput experimentation and combinatorial methods together with novel syntheses to discover and optimize efficient, practical, and economically sustainable materials for photoelectrochemical production of bulk hydrogen from water. Automated electrochemical synthesis and photoelectrochemical screening systems were designed and constructed and used to study a variety of new photoelectrocatalytic materials. We evaluated photocatalytic performance in the dark and under illumination with or without applied bias in a high-throughput manner and did detailed evaluation on many materials. Significant attention was given to -Fe2O3 based semiconductor materials and thin films with different dopants were synthesized by co-electrodeposition techniques. Approximately 30 dopants including Al, Zn, Cu, Ni, Co, Cr, Mo, Ti, Pt, etc. were investigated. Hematite thin films doped with Al, Ti, Pt, Cr, and Mo exhibited significant improvements in efficiency for photoelectrochemical water splitting compared with undoped hematite. In several cases we collaborated with theorists who used density functional theory to help explain performance trends and suggest new materials. The best materials were investigated in detail by X-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet-visual spectroscopy (UV-Vis), X-ray photoelectron spectroscopy (XPS). The photoelectrocatalytic performance of the thin films was evaluated and their incident photon

08 HYDROGEN↗
DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗
DOE OSTI2021

pnnl/DPVPROT

Distributed photovoltaics protection software toolkit The code package includes several important functions for design and analysis of new grid protection schemes, for scenarios of distributed photovoltaic (DPV) generation approaching 100% of the peak feeder load, i.e., high-penetration DPV. Convert distribution feeder models, used by planning engineers, to electromagnetic transient (EMT) models that produce waveforms for advanced relay algorithms. Visualize the results of protection scheme operation. Tools to determine distance relay settings on distribution systems. Tools to predict the performance of time-domain, incremental distance relay algorithms

McDermott, Tom↗
DOE OSTI2023

Adaptive optics for optical microscopy [Invited]

Optical microscopy is widely used to visualize fine structures. When applied to bioimaging, its performance is often degraded by sample-induced aberrations. In recent years, adaptive optics (AO), originally developed to correct for atmosphere-associated aberrations, has been applied to a wide range of microscopy modalities, enabling high- or super-resolution imaging of biological structure and function in complex tissues. Here, we review classic and recently developed AO techniques and their applications in optical microscopy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗
DOE OSTI2016

Next-Generation Factory-Produced Cool Asphalt Shingles: Phase 1 Final Report

As the least expensive category of high-slope roofing in the U.S., shingles are found on the roofs of about 80% of U.S. homes, and constitute about 80% (by product area) of this market. Shingles are also among the least reflective high-slope roofing products, with few cool options on the market. The widespread use of cool roofs in the two warmest U.S. climate zones could reduce annual residential cooling energy use in these zones by over 7%. This project targets the development of high-performance cool shingles with initial solar reflectance at least 0.40 and a cost premium not exceeding US$0.50/ft². Phase 1 of the current study explored three approaches to increasing shingle reflectance. Method A replaces dark bare granules by white bare granules to enhance the near-infrared reflectance attained with cool pigments. Method B applies a white basecoat and a cool-color topcoat to a shingle surfaced with dark bare granules. Method C applies a visually clear, NIR-reflecting surface treatment to a conventionally colored shingle. Method A was the most successful, but our investigation of Method B identified roller coating as a promising top-coating technique, and our study of Method C developed a novel approach based on a nanowire mesh. Method A yielded red, green, brown, and black faux shingles with solar reflectance up to 0.39 with volumetric coloration. Since the base material is white, these reflectances can readily be increased by using less pigment. The expected cost premium for Method A shingles is less than our target limit of $0.50/ft², and would represent less than a 10% increase in the installed cost of a shingle roof. Using inexpensive but cool (spectrally selective) iron oxide pigments to volumetrically color white limestone synthesized from sequestered carbon and seawater appears to offer high albedo at low cost. In Phase 2, we plan to refine the cool shingle prototypes, manufacture cool granules, and manufacture and market high-performance cool shingles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Scalable shear-exfoliation of high-quality phosphorene nanoflakes with reliable electrochemical cycleability in nano batteries

Atomically thin black phosphorus (called phosphorene) holds great promise as an alternative to graphene and other two-dimensional transition-metal dichalcogenides as an anode material for lithium-ion batteries (LIBs). But, bulk black phosphorus (BP) suffers from rapid capacity fading and poor rechargeable performance. This work reports for the first time the use of in situ transmission electron microscopy (TEM) to construct nanoscale phosphorene LIBs. This enables direct visualization of the mechanisms underlying capacity fading in thick multilayer phosphorene through real-time capture of delithiation-induced structural decomposition, which serves to reduce electrical conductivity thus causing irreversibility of the lithiated phases. Furthermore, we demonstrate that few-layer-thick phosphorene successfully circumvents the structural decomposition and holds superior structural restorability, even when subject to multi-cycle lithiation/delithiation processes and concomitant huge volume expansion. This finding provides breakthrough insights into thickness-dependent lithium diffusion kinetics in phosphorene. More importantly, a scalable liquid-phase shear exfoliation route has been developed to produce high-quality ultrathin phosphorene using simple means such as a high-speed shear mixer or even a household kitchen blender with the shear rate threshold of ~1.25 × 10 4 s -1 . Our results reported here will pave the way for industrial-scale applications of rechargeable phosphorene LIBs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2010

OVIS 3.2 user's guide.

This document describes how to obtain, install, use, and enjoy a better life with OVIS version 3.2. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect meaningful conditions and anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 3.2 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It provides an interactive 3-D physical view in which the cluster elements can be colored by raw or derived element values (e.g., temperatures, memory errors). The visual display allows the user to easily determine abnormal or outlier behaviors. Additionally, it provides search capabilities for certain scheduler logs. The OVIS capabilities were designed to be highly interactive - for example, the job search may drive an analysis which in turn may drive the user generation of a derived value which would then be examined on the physical display. The OVIS project envisions the capabilities of its tools applied to compute cluster monitoring. In the future, integration with the scheduler or resource manager will be included in a release to enable intelligent resource utilization. For example, nodes that are deemed less healthy (i.e., nodes that exhibit outlier behavior with respect to some set of variables shown to be correlated with future failure) can be discovered and assigned to shorter duration or less important jobs. Further, HPC applications with fault-tolerant capabilities would respond to changes in resource health and other OVIS notifications as needed, rather than undertaking preventative measures (e.g. checkpointing) at regular intervals unnecessarily.

97 MATHEMATICS AND COMPUTING↗