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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 OSTI2019

Composite boson signature in the interference pattern of atomic dimer condensates

We predict the existence of high frequency modes in the interference pattern of two condensates made of fermionic-atom dimers. These modes, which result from fermion exchanges between condensates, constitute a striking signature of the dimer composite nature. From the 2-coboson spatial correlation function, that we derive analytically, and the Shiva diagrams that visualize many-body effects specific to composite bosons, we identify the physical origin of these high frequency modes and determine the conditions to see them experimentally by using bound fermionic-atom pairs trapped on optical lattice sites. The dimer granularity which appears in these modes comes from Pauli blocking that prevents two dimers to be located at the same lattice site.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

WebGIobe - A cloud-based geospatial analysis framework for interacting with climate data

While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running compute-intensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.

Sharma, Arun↗
DOE OSTI2019

Exploration and Analysis of Legacy Data at the Hanford Site Using PHOENIX - 19044

Radioactive contaminants pose risks to human health and the environment. These risks are influenced by factors such as disposal/release and transport mechanisms, location in the environment, potential exposure receptors, and the presence of comingled contaminants. Understanding the behavior, transport, and distribution of radionuclides in the environment is essential to facilitating risk-informed decision-making related to remedial design, action, and monitoring. These decisions are based on significant amounts of complex data that must be accessed, integrated, and analyzed to effectively evaluate data and formulate appropriate conclusions and recommendations. Often, data volume, complexity, and legacy access issues pose severe challenges for analysts, regulators, and stakeholders interested in evaluating scientific data to support decision-making. To address these challenges at the U.S. Department of Energy Hanford Site, Pacific Northwest National Laboratory (PNNL) has developed PHOENIX (PNNL-Hanford Online Environmental Information exchange) to facilitate risk-informed decision-making by streamlining investigative and analytical processes through the integration and visualization of previously isolated datasets created under distinct regulatory or operational regimes, each serving a specific purpose. With the large volume of data dispersed across many Hanford environmental databases, finding the correct sources of relevant data, accessing them, and relating them in a meaningful way can prove challenging and time consuming. By analyzing Hanford databases, performing substantial optimization using modern data processing methods and developing extensive queries on the refactored data, PHOENIX enables users to access, integrate, display, visualize, and retrieve data that would have been historically unfeasible to examine. The data and information accessible through PHOENIX help inform remediation-related assessments and evaluations, predict and quantify the risks that radioactive and chemical contaminants pose to human health and the environment, and communicate this information to decision makers responsible for risk-based remediation actions. (authors)

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

Energy Management Information Systems Technical Resources Report

Guide supports federal facility staff in understanding, designing, procuring, and implementing Energy Management Information Systems (EMIS) as a valuable component of their portfolio-level energy and water planning and management strategies. As a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, EMIS tools present significant opportunities for federal sector energy savings and improved operational performance. EMIS are at the forefront of transforming energy management best practices by providing building owners and operators with well-organized building performance and energy consumption data, enabling a host of analytic capabilities. These capabilities include portfolio-wide energy benchmarking, data visualization, and key performance indicator tracking; automated fault detection and diagnostics (AFDD); artificial intelligence for predictive analytics and control; automated measurement and verification of energy conservation measures; and supervisory control enabling automated system optimization and demand management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

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

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

Chinthavali, Supriya↗
DOE OSTI2021

Data Analysis Approach for Large Data Volumes in a Connected Community

Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems are providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, and in-memory processing of large volumes of streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive and predictive data analysis for understanding live operation of the neighborhood, fault identification, and future opportunities for further optimization of load curves. We then demonstrate how we achieve live situational awareness of the connected neighborhood through a suite of visualization components. Finally, we discuss a few analytic dashboards that address questions such as peak load reductions obtained due to optimization, customer preference for automatic control of appliances (do they override the automatic control of HVAC?, etc.). 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Chinthavali, Supriya↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

COTS Data Analytics Software User Manual: Version 1.0

Large volumes of data are being collected by Sandia National Laboratories as part of an active commercial-off-the-shelf (COTS) part testing and surveillance program. This user manual documents Python-based COTS Data Analytics software that has been developed for standardizing, displaying, visualizing, and analyzing the resulting COTS part testing and surveillance data. It is the objective of these software tools to streamline the analysis of COTS testing and surveillance data and improve the efficiency with which test engineers and data analytics experts can pinpoint possible performance and reliability problems in COTS parts.

42 ENGINEERING↗
DOE OSTI2019

A Design Document: Custom Analytical Tools and Automation for the Industrial Hygienist

This report outlines an approach for developing a modular and scalable web-based application suite, Internal Data Access and Visualization (I-DAV), that would be developed to meet the specific analytic requirements for Industrial Hygienist (IH) analysts and tank vapors Subject Matter Expert’s (SME) at the Hanford site. Through interviews and requirements elicitation with IH analyst, and on-site in depth review of workflow patterns and expected outcomes, PNNL has developed a framework for proposed collection of interdependent goal oriented software applications. Commercial off the shelf applications, which require only minimal custom code, were deployed this year as a mechanism of rapid prototyping and providing proof of concept. This report describes these efforts and accomplishments to date, as well as concepts for future applications and general enhancement of the DAV and core concepts for the I-DAV. Additional content was integrated into this report as of March 2019. Updated content was added to reflect the inclusion of I-DAV concepts and ideas, and refined versions of original concepts set forth as the work commenced and continued through the first half of FY19. New and refined concepts have been the product of ongoing weekly meetings, outreach, and elicitation with WRPS.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Design Document: Custom Analytical Tools and Automation for the Industrial Hygienist

This report outlines an approach for developing a modular and scalable web-based application suite, Internal Data Access and Visualization (I-DAV), that would be developed to meet the specific analytic requirements for Industrial Hygienist (IH) analysts and tank vapors Subject Matter Expert’s (SME) at the Hanford site. Through interviews and requirements elicitation with IH analyst, and on-site in depth review of workflow patterns and expected outcomes, PNNL has developed a framework for proposed collection of interdependent goal oriented software applications. Commercial off the shelf applications, which require only minimal custom code, were deployed this year as a mechanism of rapid prototyping and providing proof of concept. This report describes these efforts and accomplishments to date, as well as concepts for future applications and general enhancement of the DAV and core concepts for the I-DAV. Additional content was integrated into this report as of March 2019. Updated content was added to reflect the inclusion of I-DAV concepts and ideas, and refined versions of original concepts set forth as the work commenced and continued through the first half of FY19. New and refined concepts have been the product of ongoing weekly meetings, outreach, and elicitation with WRPS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

SCALE Newsletter (Number 52, Spring 2020)

SCALE Newsletter is published by the Reactor and Nuclear Systems Division of the Oak Ridge National Laboratory. This issue, number 52 from Spring of 2020, holds information about the current state, planned updates, and previews of the SCALE system designed and maintained by ORNL staff. This issue contains employee spotlights for Dr. Kursat Bekar as well as Dr. Andrew M. Holcomb and displays data and charts from new/updated features of SCALE such as new data libraries, Fulcrum 3D visualizations, and interactions with ENDF/B-VIII.0 data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Extreme-Scale Distribution-Based Data Analysis

Scientists overview and identify regions of interest by transforming data into compact information descriptors that characterize simulation results and allow detailed analysis on demand. Among many existing feature descriptors, statistical information derived from data samples is a promising approach to taming the big data avalanche because data distributionscomputed from a population can compactly describe the presence and characteristics of salient data features with minimal data movement. The ability to computationally summarize and process data using distributions also provides an efficient and representative capture of information that can adjust to size and resource constraints, with the added benefit that uncertainty associated with results can be quantified and communicated. In this project, we have developed a distribution-based data analysis and visualization framework for optimizing in situ processing and analysis of extreme-scale scientific data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

AIMSim : An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.

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

Mobility Pattern Analysis for Power Restoration Activities Using Geo-Tagged Tweets

In this study, we analyzed mobility patterns of at-risk populations affected by an extreme event using geotagged tweets to geo-target power restoration efforts. Unlike other studies that have used tweets to facilitate emergency management activities, we used 1.5 million geotagged tweets generated during Hurricane Sandy (2012) to determine the mobility patterns and geospatial distribution of impacted populations who experienced power outage before, during and after the hurricane. We implemented a three-step analytical framework to: (i) analyze tweet contents with visual methods, including dendrograms, word clouds to identify common keywords pertaining to power outage; (ii) identify target users whose tweets contained information about power outages; and (iii) create a user-tweet locations matrix and an origin-destination matrix to examine clusters of target users and their mobility patterns. Preliminary results indicate that potential clusters were present in and around New York city, Philadelphia, Washington D.C. and Baltimore, which were used as potential evacuation destination cities after hurricane Sandy. The travel pattern and destination information can be used to (i) mobilize restoration efforts by utility companies and (ii) address resource allocation needs both in impacted and destination cities. Future work will focus on analyzing potential destinations for different origins and travel-time to identify evacuation routing patterns.

Kar, Bandana↗
DOE OSTI2024

gcamfaostat: An R package to prepare, process, and synthesize FAOSTAT data for global agroeconomic and multisector dynamic modeling

The gcamfaostat R package is designed for the preparation, processing, and synthesis of the Food and Agriculture Organization (FAO) Statistics (FAOSTAT) agroeconomic data. The primary purpose is to facilitate FAOSTAT data use in global economic and multisector dynamic models while ensuring transparency, traceability, and reproducibility. Here, we provide an overview of the development of gcamfaostat (v1.0) and demonstrate its capabilities in generating and maintaining agroeconomic data required for the Global Change Analysis Model (GCAM). Our initiative seeks to enhance the quality and accessibility of data for the global agroeconomic modeling community, with the aim of fostering more robust and harmonized outcomes in a collaborative, efficient, and open-source framework. One of the important features of the package is the possibility to construct the FAO Food Balance Sheets at the disaggregated commodity level (with over 500 commodities), which provides a comprehensive and detailed data input for a variety of analytical and modeling applications. The processed data and visualizations offered by gcamfaostat can also be valuable to a broader audience interested in gaining insights into the intricacies of global agriculture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

On Improving Analytical Models of Cosmic Reionization for Matching Numerical Simulations

The methods for studying the epoch of cosmic reionization vary from full radiative transfer simulations to purely analytical models. While numerical approaches are computationally expensive and are not suitable for generating many mock catalogs, analytical methods are based on assumptions and approximations. We explore the interconnection between both methods. First, we ask how the analytical framework of excursion set formalism can be used for statistical analysis of numerical simulations and visual representation of the morphology of ionization fronts. Second, we explore the methods of training the analytical model on a given numerical simulation. We present a new code which emerged from this study. Its main application is to match the analytical model with a numerical simulation. Then, it allows one to generate mock reionization catalogs with volumes exceeding the original simulation quickly and computationally inexpensively, meanwhile reproducing large scale statistical properties. These mock catalogs are particularly useful for CMB polarization and 21cm experiments, where large volumes are required to simulate the observed signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

ON IMPROVING ANALYTICAL MODELS OF COSMIC REIONIZATION FOR MATCHING NUMERICAL SIMULATION

The methods for studying the epoch of cosmic reionization vary from full radiative transfer simulations to purely analytical models. While numerical approaches are computationally expensive and are not suitable for generating many mock catalogs, analytical methods are based on assumptions and approximations. We explore the interconnection between both methods. First, we ask how the analytical framework of excursion set formalism can be used for statistical analysis of numerical simulations and visual representation of the morphology of ionization fronts. Second, we explore the methods of training the analytical model on a given numerical simulation. We present a new code which emerged from this study. Its main application is to match the analytical model with a numerical simulation. Then, it allows one to generate mock reionization catalogs with volumes exceeding the original simulation quickly and computationally inexpensively, meanwhile reproducing large-scale statistical properties. These mock catalogs are particularly useful for cosmic microwave background polarization and 21 cm experiments, where large volumes are required to simulate the observed signal.

79 ASTRONOMY AND ASTROPHYSICS↗