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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

OpenSAMPL: An Open Source Library for Timing and Synchronization Measurements and Analytics

Today's power grid operators are implementing timing and synchronization solutions that provide resilience to Global Navigation Satellite System (GNSS) vulnerabilities. These vendor-specific solutions often come with additional software applications that are designed to monitor that vendor's synchronization performance data. However, resilient timing architectures often resulting in multi-vendor solutions, including approaches that blend terrestrial clocks with space-based subscription services. In such an environment, collecting, analyzing, and visualizing data from a variety of sources within a single platform was heretofore not possible. To address this need, the US Department of Energy's Center for Alternative Synchronization and Timing (CAST) developed OpenSAMPL, the Open Synchronized Analytics and Monitoring Platform, an open-source Python framework for processing, loading, and observing clock measurement data from distributed devices. OpenSAMPL enables the ingestion of diverse clock-probe sources into a scalable time-series database and applies robust analytics. OpenSAMPL currently supports two vendor data pipelines, and will be extended to more in the near future, enabling seamless monitoring of a variety of timing and synchronization devices in a common environment.

Grant, Josh [ORNL] (ORCID:0000000163475060)↗
DOE OSTI2023

Imaging Field–Driven Melting of a Molecular Solid at the Atomic Scale

Solid–liquid phase transitions are basic physical processes, but atomically resolved microscopy has yet to capture their full dynamics. A new technique is developed for controlling the melting and freezing of self–assembled molecular structures on a graphene field–effect transistor (FET) that allows phase–transition behavior to be imaged using atomically resolved scanning tunneling microscopy. This is achieved by applying electric fields to 2,3,5,6–tetrafluoro–7,7,8,8–tetracyanoquinodimethane–decorated FETs to induce reversible transitions between molecular solid and liquid phases at the FET surface. Nonequilibrium melting dynamics are visualized by rapidly heating the graphene substrate with an electrical current and imaging the resulting evolution toward new 2D equilibrium states. An analytical model is developed that explains observed mixed–state phases based on spectroscopic measurement of solid and liquid molecular energy levels. The observed nonequilibrium melting dynamics are consistent with Monte Carlo simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Additive friction stir deposition induced stress ripples in aluminum alloy

In additive manufacturing, modification of the viscosity of softened or molten metal plays a significant role in the development of mechanical properties distribution. Similar to soft polymers, molten or softened metal usually exhibits vastly different viscoelastic properties. During scratching tests, viscoelastic polymers undergo spatiotemporal stress development and relaxation, causing stress ripples to form on their surfaces. Here, it is observed that friction stir-based solid-state manufacturing can yield similar stress ripples on softened aluminum alloys due to the linear motion of the deposition tool. Softened aluminum alloys exhibit viscoelastic and viscoplastic responses similar to polymeric material during the solid-state deposition process. In the final product, residual stress ripples appear on the surface of the metal alloy due to periodic residual stress relaxation, leading to oscillatory behavior from the bulk mechanical properties of the material. The orientation of the in-built stress pattern formed in the bulk of the additively manufactured metal alloy underneath the visually evident onion ring-like surface pattern formation is diametrically in the opposite direction. The experimentally observed results are consistent with analytical calculation and numerical simulations in terms of behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dynamics of oil–water interface at the beginning of the ultrasonic emulsification process

A lot of effort has been dedicated in recent years towards understanding the basics of cavitation induced emulsification, mainly in the form of single cavitation bubbles. Regarding bulk acoustic emulsification, a lot less research has been done. In our here presented work we utilize advanced high-speed observation techniques in visible light and X-Rays to build upon that knowledge and advance the understanding of bulk emulsion preparation. During research we discovered that emulsion formation has an acute impact on the behavior of the interface and more importantly on its position relative to the horn, hence their interdependence must be carefully studied. We did this by observing bulk emulsification with 2 cameras simultaneously and corroborating these measurements with observation under X-Rays. Since the ultrasonic horns location also influences interface behavior, we shifted its initial position to different locations nearer to and further away from the oil–water interface in both phases. We found that a few millimeters distance between the horn and interface is not enough for fine emulsion formation, but that they must be completely adjacent to each other, with the horn being located inside the oil–water interface. We also observed some previously undiscovered phenomena, such as the splitting of the interface to preserve continuous emulsion formation, climbing of the interface up the horn and circular interface protrusions towards the horn forming vertical emulsion streams. Interestingly, no visible W/O emulsion was ever formed during our experiments, only O/W regardless of initial horn position.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
DOE OSTI2021

A Review of the Beirut Ammonium Nitrate Explosion [Slides]

In September 2013, the cargo ship MV Rhosus, an 87 m, single-deck cargo ship with two cargo holds totaling around 4000 m 3 , was chartered to transport 2750 metric tonnes of bagged (in 1000 kg sacks) explosives-grade ammonium nitrate from a Georgian fertilizer manufacturer (Rustavi Azot LLC) to an explosives manufacturer (Fábrica de Explosivos Moçambique) in Mozambique. Visual details on the subsequent explosion are provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Visualization of Simulation and TAP Interaction

The Temporal Analysis of Products (TAP) Reactor applies transient kinetics to analyze sophisticated chemical reaction mechanisms, rapidly changing temperature, flow rate, pressure or concentration and then monitoring the system’s time dependent response. A simulation tool exists for the TAP reactor, but it requires a visualization platform to interface effectively with researchers. This graphical user interface remedies this problem, fixing many of the difficulties caused by users interacting directly with the simulation code by providing an intuitive web application that satisfies the simulation input parameters for fast generation of data. This tool will ensure robust and repeatable simulation results, expedite the data generating process and enhance the overall ability of TAP to produce significant findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. Here, we present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Electron Microscopy Studies of Soft Nanomaterials

This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. Here, the even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

Imaging↗
DOE OSTI2018

Analysis of 2H-Evaporator Acid Cleaning Samples

The 2H-Evaporator acid cleaning solution samples were analyzed by SRNL to determine a composition for the scale present in the evaporator before recent acid cleaning. Composite samples were formed from the solution samples from the two acid cleaning cycles. The solution composition was converted to a weight percent scale solids basis under an assumed chemical composition. The scale composition produced from the acid cleaning solution samples indicates a concentration of 6.85 wt% uranium. An upper bound, onesided 95% confidence interval on the weight percent uranium value may be given as 6.9 wt% + 1.645 × 0.596 wt% = 7.9 wt%. The comparison of the composition from the current acid cleaning solutions with the composition of recent scale samples along with the thermodynamic modeling results provides reasonable assurance that the sample results provide a good representation of the overall scale composition in the evaporator prior to acid cleaning. The small amount of scale solids dissolved in the 1.5 M nitric acid during the evaporator cleaning process likely produced only a small amount of precipitation based on modeling results and the visual appearance of the samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

In Operando Visualization of the Electrochemical Formation of Liquid Polybromide Microdroplets

Zinc–bromine flow batteries are promising for stationary energy storage, and bromine-complexing agents have been used to form phase-separated liquid polybromide products. However, an understanding of the dynamics of polybromide nucleation is limited due to the beam sensitivity and complexity of polybromides. Here we report an in operando platform composed of dark-field light microscopy and a transparent electrochemical cell to reveal the dynamics of polybromide formation in their native environment. Using our platform, we confirm and reveal the liquid nature, chemical composition, pinning effect (strong interaction with Pt), residual effect (residual charge products on the surface), self-discharging, and over-oxidation of the polybromide products. Overall, the results provide insights into the role of complexing agents and guide the future design of zinc–bromine flow batteries. Furthermore, our in operando platform can potentially be used to study sensitive species and phases in other electrochemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Ambient Metabolic Profiling and Imaging of Biological Samples with Ultrahigh Molecular Resolution using Laser Ablation Electrospray Ionization 21 Tesla FTICR Mass Spectrometry

Mass spectrometry (MS) has been an indispensable analytical tool in capturing the complexity and array of biomolecules within biological systems. However, conventional MS-based analytical workflows are limited in both throughput and measuring the native molecular state these systems, due in large part to sample handling and preparation. The development of ambient ionization methods capable of sampling directly from tissues circumvent many of these issues but requires the use of high-performance MS to resolve the molecular complexity within these samples. Herein, we demonstrate the unique combination of laser ablation electrospray ionization (LAESI) coupled with a 21 Tesla Fourier transform ion cyclotron resonance (21T-FTICR-MS) for MS imaging applications. This analytical platform provides isotopic fine structure information directly from native tissues, delivering a higher degree of confidence for molecular identification. The ability to capture and visualize the complex metabolic behavior of living organisms in situ is a bioanalytical grand challenge that has yet to be fully realized. Due to their excellent sensitivity and selectivity, mass spectrometry (MS)-based platforms offer a unique opportunity to characterize an array of biomolecules from a variety of biological samples.[1] One method in particular, MS imaging (MSI), has demonstrated the ability to simultaneously determine the spatial distributions for hundreds of biomolecules, e. g., metabolites, lipids, peptides, and xenobiotics, within complex biological systems simultaneously.[2-3] Matrix-assisted laser desorption/ionization (MALDI)-MSI remains the most common of these methods.[4] However, the application of an exogenous matrix, and the limited availability of commercial sources that operate under atmospheric pressure,[5] has significantly prohibited the ability of MALDI to be useful for biomolecular mapping of living systems. Ambient ionization methods that require minimal to no sample preparation, like desorption electrospray ionization (DESI and nano-DESI) and laser ablation electrospray ionization (LAESI), demonstrated the ability to spatially map biomolecule distributions within live organisms.

Mass Spectometry, fourier transform ion cyclostron↗
DOE OSTI2019

Suite of Comprehensive Rapid Analysis Tools for Estimation (SOCRATES) - 19076

Long-term monitoring and remedy implementation require ongoing assessment and analytics, much of which relies on intensive and time-consuming data integration and engineering, data manipulation, and 'big-data' analytics. Many of these analyses, such as groundwater flow direction, contaminant rate attenuation, and non-parametric statistics, are spatial-temporal in nature and involve coalescing disparate data sets from a variety of sources with measurement frequencies from milliseconds to years, as well as total data volumes often exceeding the petabyte scale. Web-based analytical tools are advantageous for ongoing real-time or near-real-time ingestion of data from various sources for general data access and visualization, and also allow for complex, data rich calculations by leveraging expansive networks and virtually unlimited compute power. A web-based framework offers a natural architecture for such endeavors, as access to compute nodes through auto-scaling and dynamic frameworks enables speedy data manipulation and unlimited access to any data set that is made available via an http request. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Reactive Flows in Porous Media: Challenges in Theoretical and Numerical Methods

We review theoretical and computational research, primarily from the past 10 years, addressing the flow of reactive fluids in porous media. The focus is on systems where chemical reactions at the solid–fluid interface cause dissolution of the surrounding porous matrix, creating nonlinear feedback mechanisms that can often lead to greatly enhanced permeability. Here, we discuss insights into the evolution of geological forms that can be inferred from these feedback mechanisms, as well as some geotechnical applications such as enhanced oil recovery, hydraulic fracturing, and carbon sequestration. Until recently, most practical applications of reactive transport have been based on Darcy-scale modeling, where averaged equations for the flow and reactant transport are solved. We summarize the successes and limitations of volume averaging, which leads to Darcy-scale equations, as an introduction to pore-scale modeling. Pore-scale modeling is computationally intensive but offers new insights as well as tests of averaging theories and pore-network models. We include recent research devoted to validation of pore-scale simulations, particularly the use of visual observations from microfluidic experiments.

wormhole formation↗
DOE OSTI2019

Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Lazar, Alina↗