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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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All sources NASA NTRS DOE OSTI

DOE OSTI2014

Microbial Activity and Precipitation at Solution-Solution Mixing Zones in Porous Media -- Subsurface Biogeochemical Research

The goal for this research was to understand how best to add compounds to receptive microbial communities in porous media in order to achieve optimal calcite precipitation in a volumetrically significant space and to understand the physiological health of the cells that are responsible for the calcite precipitation. The specific objectives were to: (1) develop better tools for visually examining biofilms in porous media and calcium carbonate precipitation being mediated by microbes in porous media, and (2) demonstrate the effectiveness of using that tool within a flow cell model system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Complete Demonstration of a Prototype Version of FORCE User Interface and Conduct Analyst Survey Collecting Feedback on Interface Features and Usability

In 2024 the US Department of Energy (DOE) Office of Nuclear Energy (NE) Integrated Energy System (IES) program continued to develop the Framework for Optimization of Resources and Economics (FORCE) analysis ecosystem into a more traditional toolset with simplified software installation, automated workflows, and interactive results visualization. The DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench continued to be leveraged for user input, application workflow and runtime environment, and interactive results visualization capabilities. This report documents the demonstration of a FORCE User Interface (UI) prototype and the results of a survey of analysts’ using the Holistic Energy Resource Optimization Network (HERON) tool in FORCE with the prototype UI.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2016

The SAMI Galaxy Survey: galaxy interactions and kinematic anomalies in Abell 119

Galaxy mergers are important events that can determine the fate of a galaxy by changing its morphology, star formation activity and mass growth. Merger systems have commonly been identified from their disturbed morphologies, and we now can employ integral field spectroscopy to detect and analyze the impact of mergers on stellar kinematics as well. We visually classified galaxy morphology using deep images (μ{sub r}=28 mag arcsec{sup −2}) taken by the Blanco 4 m telescope at the Cerro Tololo Inter-American Observatory. In this paper we investigate 63 bright (M{sub r}<−19.3) spectroscopically selected galaxies in Abell 119, of which 53 are early type and 20 show a disturbed morphology by visual inspection. A misalignment between the major axes in the photometric image and the kinematic map is conspicuous in morphologically disturbed galaxies. Our sample is dominated by early-type galaxies, yet it shows a surprisingly tight Tully–Fisher relation except for the morphologically disturbed galaxies which show large deviations. Three out of the eight slow rotators in our sample are morphologically disturbed. The morphologically disturbed galaxies are generally more asymmetric, visually as well as kinematically. Our findings suggest that galaxy interactions, including mergers and perhaps fly-bys, play an important role in determining the orientation and magnitude of a galaxy’s angular momentum.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Scripts for Scalable Monitoring of Parallel Filesystem Infrastructure

Scripts for scalable monitoring of parallel filesystem infrastructure provide frameworks for monitoring the health of block storage arrays and large InfiniBand fabrics. The block storage framework uses Python multiprocessing to within scale the number monitored arrays to scale with the number of processors in the system. This enables live monitoring of HPC-scale filesystem with 10-50 storage arrays. For InfiniBand monitoring, there are scripts included that monitor InfiniBand health of each host along with visualization tools for mapping the topology of complex fabric topologies.

Caldwell, Blake↗

DOE OSTI2023

Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience

In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.

13 HYDRO ENERGY↗

DOE OSTI2023

Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience

13 HYDRO ENERGY↗

DOE OSTI2018

Toward All-Solid-State Lithium Batteries: Three-Dimensional Visualization of Lithium Migration in β-Li 3 PS 4 Ceramic Electrolyte

All-solid lithium batteries are an attractive next-generation technology that use ion-conducting solids such as ..beta..-Li3PS4 (LPS) to enable use of a lithium metal anode, which increases theoretical capacity and widens the stable voltage window over traditional lithium-ion systems. These ion-conductive solids also provide increased safety by replacing flammable liquid electrolytes. Although solid-state electrolytes are significantly more stable and dendrite-resistant than traditional liquid electrolytes, lithium anodes in all-solid systems may nevertheless grow dendrites under high stress or repeated cycling, leading to short circuits and premature battery breakdown. For this reason, we study the formation and propagation of Li metal features within solid electrolytes using synchrotron-based X-ray tomography with in-situ current-voltage cycling supported by our custom sample platform. Our results demonstrate the ability of this technique to delineate different layers of the Li/LPS/Li structure with spatial resolution approaching 1 um. At this resolution, we are able to detect expansion of voids, especially in early stages of cycling. This expansion of voids is observed throughout the volume of the symmetric cells and visually resembles propagation of cracks resulting from interactions between the Li metal and pre-existing voids in the LPS electrolyte.

25 ENERGY STORAGE↗

DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Transportation and System Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and waste custodian sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. An analyst using NGSAM can define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide the visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions at and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams have been working closely for several years before formalizing this collaboration. START provides the SNF transportation routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2014

Visualization of magnetic domains in sculpted three-dimensional cobalt nanospirals

In this work, we report on the direct visualization of magnetic structure in sculpted three- dimensional cobalt (Co) nanospirals with a wire diameter of 20 nm and outer spiral diameter of 115 nm and on the magnetic interactions between the nanospirals, using aberration-corrected Lorentz transmission electron microscopy. By analyzing the magnetic domains in three di- mensions at the nanoscale, we show that magnetic domain formation in the Co nanospirals is a result of the shape anisotropy dominating over the magnetocrystalline anisotropy of the system. We also show that the strong dipolar magnetic interactions between adjacent closely- packed nanospirals leads to their magnetization directions adopting alternating directions to minimize the total magnetostatic energy of the system. Deviations from such magnetization structure can only be explained by analyzing the complex three-dimensional structure of the nanospirals. These nanostructures possess an inherent chirality due to their growth conditions and are of significant importance as nanoscale building blocks in magneto-optical devices. Keywords: Nanomagnetic structure, Nanospiral, 3D nanomagnetism, 3D visualization, Lorentz transmission electron microscopy.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2016

Competing Pathways for Nucleation of the Double Perovskite Structure in the Epitaxial Synthesis of La 2 MnNiO 6

Over the past decades a confluence of advanced synthesis techniques, data-driven characterization, and rapid increases in computing power has sparked a renaissance in materials engineering. The emerging “materials-by-design” approach, while radically transforming the development of multicomponent systems, has tended to overlook the complex kinetic pathways that define materials synthesis. Although we are able to envision almost limitless materials combinations, we are unable to synthesize all of them in practice, since existing characterization and modeling approaches often fail to capture the inherent complexity of such systems. There is currently a disconnect between highly local structural characterization and macroscale properties measurements, resulting in oversimplified or incomplete structure-property models. Here we describe a multi-technique approach that combines aberration-corrected transmission electron microscopy with emerging oxide atom probe tomography to measure chemical ordering and extended defects in the model complex oxide, La2MnNiO6. We visualize cation ordering, as well as a three-dimensional network of secondary phases, which we describe in terms of ab initio structure calculations. We propose a defect model in which these phases alter their surrounding octahedral environment, severely disrupting cation superexchange. Through this array of experimental and theoretical techniques, we uncover fundamental structure-property relationships and illustrate a new approach to engineer complex, multicomponent systems.

atom probe tomography↗

DOE OSTI2023

Network Analysis of Academic Medical Center Websites in the United States

Healthcare resources are published annually in repositories such as the AHA Annual Survey Database TM . However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Using the Criticality Accident Alarm System modeling capabilities in SCALE [Slides]

The following is a summary of advice for CAAS modeling in SCALE. Refer to the SCALE Criticality Safety and Radiation Shielding training slides or to the SCALE manual for exact syntax. Use a mesh for the fission source that is the most adequate for the problem to solve (coarse/fine). Don’t spend unnecessary resources; simplify the model if it does not impact the final results of interest. Be careful to deactivate secondary fissions in MAVRIC or the calculation may never end. Check that k eff and $\overline{\upsilon}$ calculated results are logical. Between KENO and MAVRIC, cross section libraries, materials, geometry, and mesh grid can be the same or different. Iterative calculations are usually complex problems that need variance reduction. It will be hard to find the best solving parameters in the first attempt; expert judgement is needed. Check each step separately. Use Fulcrum to visualize fission source, mesh source, and spatial/energy distributions to find potential errors or impactful imprecisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

BioenergyKDF: Enabling Spatiotemporal Data Synthesis and Research Collaboration

The Bioenergy Knowledge Discovery Framework (BioenergyKDF) is a scalable, web-based collaborative environment for scientists working on bioenergy related research in which the connections between data, literature, and models can be explored and more clearly understood. The fully-operational and deployed system, built on multiple open source libraries and architectures, stores contributions from the community of practice and makes them easy to find, but that is just its base functionality. The BioenergyKDF provides a national spatiotemporal decision support capability that enables data sharing, analysis, modeling, and visualization as well as fosters the development and management of the U.S. bioenergy infrastructure, which is an essential component of the national energy infrastructure. The BioenergyKDF is built on a flexible, customizable platform that can be extended to support the requirements of any user community especially those that work with spatiotemporal data. While there are several community data-sharing software platforms available, some developed and distributed by national governments, none of them have the full suite of capabilities available in BioenergyKDF. For example, this component-based platform and database independent architecture allows it to be quickly deployed to existing infrastructure and to connect to existing data repositories (spatial or otherwise). As new data, analysis, and features are added; the BioenergyKDF will help lead research and support decisions concerning bioenergy into the future, but will also enable the development and growth of additional communities of practice both inside and outside of the Department of Energy. These communities will be able to leverage the substantial investment the agency has made in the KDF platform to quickly stand up systems that are customized to their data and research needs.

Bioenergy.↗

DOE OSTI2022

A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA

Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.

58 GEOSCIENCES↗