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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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DOE OSTI2023

Distributed Energy Resource Visual Emulator: Phase 1

To help federal energy managers assess, monitor, and manage cybersecurity while achieving decarbonization, the National Renewable Energy Laboratory's Distributed Energy Resource Cybersecurity Framework (DER-CF) offers a comprehensive, web-based assessment tool focusing on cyber governance or policies, technical management, and physical security. The DER-CF currently presents users with a series of pertinent cybersecurity questions, which are used to generate a site-specific report and recommendations. This paper outlines a technical approach to integrate the DER-CF with another key asset—NREL's Advanced Research on Integrated Energy Systems (ARIES) Cyber Range—to visualize cybersecurity resilience and compliance and to enhance the usability and accessibility of the DER-CF. The result is a new tool called the Distributed Energy Resource Visual Emulator (DER-VE). Its development will include regular conversations with stakeholders to assess the effectiveness of these efforts, refine the visualization capability, and ensure its value to our partners. Phase 0 of the integration project was concluded in 2021. Phase 1, completed in 2022, has two components: The first is developing a working visualization of system compliance using the DER-CF, and the second is planning the design of a server application that takes input data from the DER-CF and creates a personal emulated environment of the user's system or a selected reference architect. Major components that were addressed in this phase are the DER-CF output, compliance visualization, data model, and compliance server design.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

WNR Facility FP90L Visual Inspection Report

An external and internal visual inspection of FP90L was performed March 2020. This report presents the results from this visual inspection, detailing individual components and their relative location along FP90L within the WNR facility bulk shield. This report will present the data gathered from this visual inspection. Then, utilizing available facility drawings as well as LIDAR data from surveys performed in the spring/summer of 2019, this inspection data will be merged with previous efforts and studies regarding the WNR facility to document a more complete representation of the current state of WNR FP90L.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA’s GenomeTrakr network

ABSTRACT Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA‘s genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject,PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA’s GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA. IMPORTANCE This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA’s laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Microbiology↗
DOE OSTI2025

Novel Results Visualization for Dynamic PSA and New Modeling Features in EMRALD

The Event Modeling Risk Assessment Linked Diagram (EMRALD) tool, developed at the Idaho National Laboratory (INL), was designed to simplify the creation of dynamic models and support various research projects. One of the primary goals of EMRALD was to provide visual methods for modeling. EMRALD consists of two main components: a web-based user interface for model development and a solve engine for running model simulations. Over time, it has evolved to meet the diverse needs of its users. Initially, EMRALD's results were simple text outputs with final key state percentages and uncertainty bounds. However, because EMRALD utilizes a three-phase discrete event simulation and tracks the paths of each simulation run leading to a key state, there is significant potential to analyze large sets of path results data, including state paths, events, and timing. Visualizing this data meaningfully posed a challenge. To address this, a novel time-based Sankey diagram was developed. EMRALD exports results data in a format that can be opened in this Sankey viewer, allowing users to visualize paths, occurrences, events, and probability data for the entire simulation run in a single diagram. Moreover, when EMRALD was first created, there were limited tools capable of meeting its graphical requirements, many of which are no longer supported. In 2024, a new web-based interface was developed using modern graphing tools, enabling additional modeling features. This paper discusses the new dynamic PSA results visualization capability and the enhanced modeling tools available in EMRALD.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

TEAMER - Field Demonstration of MarineSitu’s Marine Energy Monitoring Tools - CRADA 664 (Abstract)

In order to effectively monitor for marine life around marine energy devices and thus minimize the risk of collision, multiple sensors working in coordination and augmented with around-the-clock automated monitoring algorithms need to be installed in challenging high-energy tidal and wave environments. Such systems are often too expensive for widespread adoption, or lack sufficient sensors or smarts to enable around-the-clock, real-time monitoring without human involvement. MarineSitu has been working to tackle this problem by developing a low-cost, combined sonar and stereo camera sensor array with connected real-time AI-based algorithms for automatically detecting marine life in these marine energy suitable environments. In this TEAMER project with Pacific Northwest National Lab (PNNL), MarineSitu will be testing this novel sensor system for the first time in the high-energy tidal channel environment at PNNL’s Marine and Coastal Research Lab. Throughout this deployment, MarineSitu will be monitoring their system and running analytics on the sensor’s data in real-time. Meanwhile, PNNL Data Scientists and Ocean Engineers, will be evaluating the system’s effectiveness and ease of use both as a tool for plug-and-play environmental monitoring and novel environmental monitoring research. In doing so, the team will improve MarineSitu’s system and software, produce insightful data products, and develop novel visualizations and AI algorithms for combining and analyzing the data produced by systems like MarineSitu’s.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

TEAMER – Field Demonstration of MarineSitu’s Marine Energy Monitoring (Abstract)

In order to effectively monitor for marine life around marine energy devices and thus minimize the risk of collision, multiple sensors working in coordination and augmented with around-the-clock automated monitoring algorithms need to be installed in challenging high-energy tidal and wave environments. Such systems are often too expensive for widespread adoption, or lack sufficient sensors or smarts to enable around-the-clock, real-time monitoring without human involvement. MarineSitu has been working to tackle this problem by developing a low-cost, combined sonar and stereo camera sensor array with connected real-time AI-based algorithms for automatically detecting marine life in these marine energy suitable environments. In this TEAMER project with Pacific Northwest National Lab (PNNL), MarineSitu will be testing this novel sensor system for the first time in the high-energy tidal channel environment at PNNL’s Marine and Coastal Research Lab. Throughout this deployment, MarineSitu will be monitoring their system and running analytics on the sensor’s data in real-time. Meanwhile, PNNL Data Scientists and Ocean Engineers, will be evaluating the system’s effectiveness and ease of use both as a tool for plug-and-play environmental monitoring and novel environmental monitoring research. In doing so, the team will improve MarineSitu’s system and software, produce insightful data products, and develop novel visualizations and AI algorithms for combining and analyzing the data produced by systems like MarineSitu’s.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling

Residential and commercial buildings in the United States accounted for 40% of total energy in 2020. Building energy modeling (BEM) is a useful tool that allows individuals, researchers, companies, or utilities to save energy by optimizing buildings through estimation of building technology savings and performance projection of building energy under various environmental conditions. Urban building energy modeling (UBEM) expands the scope beyond individual buildings to the buildings in a neighborhood, city, utility and more. Yet there is a knowledge gap in the literature as to how these models compare to measured data on an individual and aggregated basis. As UBEM data and methods continue to develop, it is important to consider the accuracy, bias, and limitations of the models. Here, nation-scale data and UBEM software suite named Automatic Building Energy Modeling (AutoBEM) was used to model 50,843 buildings in Chattanooga, Tennessee. The uncalibrated simulation results were compared to aggregated 15-minute electricity data for the year 2019 with visualizations highlighting sources of bias in building data and the AutoBEM framework while considering how they relate to other UBEM methods. Estimation of building type and year of constructions are found to be the major sources of bias. Accounting for the amount of conditioned area per building significantly improves the overall fit of the simulated energy use intensity. it was found that inherent variation in building energy use contributes to R 2 values between 0.008 and 0.095 across building types but slope values near 1 for the total number of buildings. This indicates the need for building aggregation for representative building energy modeling with data sources available at an urban scale while illustrating the need for additional individual building data and model improvement beyond the originally produced UBEM models for individual building analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Model Assessment Wizard (MAW)

SAND2026-18710O The Model Assessment Wizard (MAW) is a tool for evaluating ontologies and provides users with a comprehensive workbench for analysis. MAW features sub-modules for visualization, alignment, Shapes Constraint Language (SHACL) and Web Ontology Language (OWL) constraints, and simplification. Users can upload data, identify missing information, visualize ontologies, and update constraints. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Murdock, Jaimie [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2024

Spatially Resolved Raman Spectroscopic Investigation of Uranyl Fluoride: A Case Study in the Importance of Instrument Optimization

Raman spectroscopy is an emerging technique for rapid and nondestructive analysis of nuclear materials for forensic and nonproliferation applications as it is a powerful tool for distinguishing multiple chemical forms of materials with similar stoichiometries. Recent developments in spectroscopic software have enabled rapid data collection with high-speed Raman spectroscopic mapping capabilities. However, some uranium-rich materials are susceptible to degradation in humid air and/or laser-induced phase transformations. To mitigate environmental or measurement-related sample degradation of potential samples of interest, we have taken a systematic approach to define optimized data collection parameters for high-throughput measurements of uranyl fluoride (UO 2 F 2 ), which is an important intermediate material in the nuclear fuel cycle. First, we systematically describe the influence of optical magnification (5× to 100×), laser power, and exposure time on obtained signal for identical particles of UO 2 F 2 and find that at low laser power and exposure times, comparable signal is obtained regardless of optical magnification. Second, we ensure sample integrity during data collection, and third, collect spectroscopic maps that employ optimized parameters to reduce the time required to obtain spatially resolved spectroscopic information. Reductions of 90% and 99% in measurement times are discussed as they relate to differences in resolving spectroscopic features of particles in identical mapping areas. Finally, during this work, we found that additional data processing options were needed and thus developed a customized Python script for importing, processing, analyzing, and visualizing Raman spectroscopic map data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Pulse: An Outlier Sensitive Downsampling Algorithm For Timeseries Data

Pulse is a downsampling algorithm for timeseries data. Frequently datasets become so large that visualization tools and web browsers cannot effectively render graphics due to memory constraints. Downsampling algorithms are commonly applied to minimize the quantity of data required to visualize important features or trends in the data, but some datasets are composed by distinct enough features and trends that most existing downsampling algorithms fail to preserve them. Pule was developed to downsample timeseries data for galvanostatic stack test data at the Idaho National Laboratory. These datasets were composed by approximately 4 million records, most of them being extremely uniform. However, during relatively brief time periods when the stack test changes state, for example when the test article is powered on, or a load is added, the data produce sparse asymptotes. No existing downsampling algorithm was capable of preserving the sparse asymptotes in electrolysis stack test data. Instead, we develop a downsampling algorithm that preserves important outliers in data, and otherwise aggressively downsamples uniform data. The algorithm has applications in other domains like seismology, in the measurement of earthquakes, or astronomy, in the measurement of quasars or transit photometry.

Woodruff, Nathan [Idaho National Laboratory (INL),↗
DOE OSTI2021

PyKrev: A Python Library for the Analysis of Complex Mixture FT-MS Data

In this study, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. Additionally, the results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D

We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool

In this paper, we report the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package to process data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step, such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data stack, including DataShader and Param, to create an interactive visualization of mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS, and the visualization methods could be useful for other open-source mass spectrometry tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

An overview of visualization and visual analytics applications in water resources management

Recent advances in information, communication, and environmental monitoring technologies have increased the availability, spatiotemporal resolution, and quality of water-related data, thereby leading to the emergence of many innovative big data applications. Among these applications, visualization and visual analytics, also known as the visual computing techniques, empower the synergy of computational methods (e.g., machine learning and statistical models) with human reasoning to improve the understanding and solution toward complex science and engineering problems. These approaches are frequently integrated with geographic information systems and cyberinfrastructure to provide new opportunities and methods for enhancing water resources management. Here, we present a comprehensive review of recent hydroinformatics applications that employ visual computing techniques to (1) support complex data-driven research problems, and (2) support the communication and decision-makings in the water resources management sector. Then, we conduct a technical review of the state-of-the-art web-based visualization technologies and libraries to share our experiences on developing shareable, adaptive, and interactive visualizations and visual interfaces for water resources management applications. We close with a vision that applies the emerging visual computing technologies and paradigms to develop the next generation of hydroinformatics applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Rubin Observatory LSST Tutorials

A collection of tutorials -- both Jupyter notebooks and documentation-based --- demonstrating data access, analysis, and visualization techniques for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). These tutorials are intended for astronomers, educators, and data scientists working with LSST data products.

Vera C. Rubin Observatory, NSF-DOE↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗