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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reimagining Disassembly Interfaces With Visualization: Combining Instruction Tracing and Control Flow With DisViz

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. Here, we design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code.

Computer science↗
DOE OSTI2023

A Review of Visualization Methods for Cyber-Physical Security: Smart Grid Case Study

Cyber-Physical Systems (CPSs) are becoming increasingly complex and interconnected as they attempt to meet the demands of evolving society. As a result, monitoring and maintaining them becomes a more complex and demanding task for control system operators and cyber defenders. While the literature on visualization techniques in the context of cybersecurity is extensive, the same cannot be said for studies on visualization for the security of cyber-physical systems. This paper aims to fill that gap by: 1) defining the main features of a visualizations workflow for security visualizations in cyber-physical systems. The workflow includes the acquisition of cyber and physical data, processing of data, selection, and configuration of both visualization tools and end-user interactions. 2) Providing an overview of cyber-physical security visualization systems, with a focus on smart grids as a case study. Finally, we use the perspectives gained from this analysis to provide insights and directions for future research and design of cyber-physical visualization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Computed Tomography Scanning and Geophysical Measurements of the CarbonSAFE Seal Integrity Wells in the Illinois Basin

The U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) researchers in Morgantown, West Virginia, utilized computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) to evaluate the integrity of three cores (Geophysical Monitor #2 (GM2), Verification Well #1 (VW #1), and Wabash #1 for the Illinois Basin Carbon Storage Assurance Facility Enterprise (CarbonSAFE) efforts. This work was carried out in collaboration with the Illinois State Geological Survey (ISGS) as part of their efforts to analyze cores from two field locations, including the Illinois Basin-Decatur Project (IBDP) and second stage Illinois Industrial Carbon Capture and Storage (IL-ICCS) sites at Decatur, Illinois, and the Wabash CarbonSAFE project at the Wabash Valley Resources Integrated Gasification Combined Cycle plant in Terre Haut, Indiana. The results of this study are presented in several formats and are available online on the Energy Data eXchange (EDX). The rock characterization was conducted using non-destructive techniques, allowing for future analysis of the cores. While the equipment used did not provide direct visualization of shale pore space, it allowed for detection of fractures and discontinuities. Low resolution CT imagery with the NETL medical CT scanner was performed on the entire core. Qualitative analysis of the medical CT images, coupled with x-ray fluorescence (XRF), P-wave, and magnetic susceptibility measurements from the MSCL were utilized to identify areas of interest for further study as well as fractured zones. The ability to quickly identify key areas for more detailed study with higher resolution will save time and resources in future studies. The combination of methods used provided a multi-scale analysis of this core and provided both a macro and micro description of the core that is relevant for many subsurface energy-related examinations traditionally performed at NETL.

58 GEOSCIENCES↗
DOE OSTI2021

Demonstrating SolarPILOT’s Python API Through Heliostat Optimal Aimpoint Strategy Use Case

SolarPILOT is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s1 System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This paper presents a new, full-featured, Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT’s capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instance with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layouts; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying solar field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating solar field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux distribution; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot’s functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize power tower CSP research. This enables CSP researchers to perform analysis that was previously not possible through SolarPILOT’s existing interfaces. This paper discusses the capabilities of CoPylot and presents a use case wherein we demonstrate optimal solar field aiming strategies.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Blue Keanu: A Scientific Visualization Tool For Network Data

This software allows the user to visualize complex PCAP-ng files captured from network capture software such as Wireshark. The visualization runs in a GUI window that can be zoomed or moved to areas of interest in a waterfall type display. The user then can see an area of interest that looks different than the typical traffic visually, such as a human interaction or non-repetitive area of data. The program will tell the user the packet number and byte offset of interest for fast analysis of discrete atomic or non-random events. This is particularly useful for visualization of unknown binary format data, such as in PLC or SCADA protocols that may have human or other non-repetitive activity for further analysis, reverse engineering, or fast forensic analysis.

Durller, MichaelGeorge↗
DOE OSTI2024

The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters

Secondary metabolites are small molecules produced by all corners of life, often with specialized bioactive functions with clinical and environmental relevance. Secondary metabolite biosynthetic gene clusters (BGCs) can often be identified within DNA sequences by various sequence similarity tools, but determining the exact functions of genes in the pathway and predicting their chemical products can often only be done by careful, manual comparative analysis. To facilitate this, we report the first release of the secondary metabolism collaboratory (SMC), which aims to provide a comprehensive, tool-agnostic repository of BGC sequence data drawn from all publicly available and user-submitted bacterial and archaeal genome and contig sources. On the website, users are provided a searchable catalog of putative BGCs identified from each source, along with visualizations of gene and domain annotations derived from multiple sequence analysis tools. SMC’s data is also available through publicly-accessible application programming interface (API) endpoints to facilitate programmatic access. Users are encouraged to share their findings (and search for others’) through comment posts on BGC and source pages. At the time of writing, SMC is the largest repository of BGC information, holding 13.1M BGC regions from 1.3M source sequences and growing, and can be found at https://smc.jgi.doe.gov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enhanced dynamic contingency analysis for power systems

The present disclosure describes systems and techniques that enhance effectiveness and efficiency of a contingency analysis tool that is used for studying the magnitude and likelihood of extreme contingencies and potential cascading events across a power system. The described systems and techniques include deploying the contingency analysis tool in a high-performance computing (HPC) environment and incorporating visual situational awareness approaches to allow power system engineers to quickly and efficiently evaluate multiple power system simulation models. Furthermore, the described systems and techniques include the power system contingency-analysis tool calculating and coordinating protection element settings, as well as assessing controls of the power system using small-signal nomograms, allowing power system engineers to more effectively comprehend, evaluate, and analyze causes and effects of cascading events against a topology of a power system.

Samaan, Nader A.↗
DOE OSTI2020

A cross-sectional aeroelastic analysis and structural optimization tool for slender composite structures

A fully open-source available framework for the parametric cross-sectional analysis and design optimization of slender composite structures, such as helicopter or wind turbine blades, is presented. The framework—Structural Optimization and Aeroelastic Analysis (SONATA)—incorporates two structural solvers, the commercial tool VABS, and the novel open-source code ANBA4. SONATA also parameterizes the design inputs, postprocesses and visualizes the results, and generates the structural inputs to a variety of aeroelastic analysis tools. It is linked to the optimization library OpenMDAO. This work presents the methodology and explains the fundamental approaches of SONATA. Structural characteristics were successfully verified for both VABS and ANBA4 using box beam examples from literature, thereby verifying the parametric approach to generating the topology and mesh in a cross section as well as the solver integration. The framework was furthermore exercised by analyzing and evaluating a fully resolved highly flexible wind turbine blade. Computed structural characteristics correlated between VABS and ANBA4, including off-diagonal terms. Stresses, strains, and deformations were recovered from loads derived through coupling with aeroelastic analysis. The framework, therefore, proves effective in accurately analyzing and optimizing slender composite structures on a high-fidelity level that is close to a three-dimensional finite element model.

42 ENGINEERING↗
DOE OSTI2024

Focused‐beam X‐ray fluorescence and diffraction microtomographies for mineralogical and chemical characterization of unsectioned extraterrestrial samples

Abstract This study describes the application of new synchrotron X‐ray fluorescence (XRF) and diffraction (XRD) microtomographies for the 3‐D visualization of chemical and mineralogical variations in unsectioned extraterrestrial samples. These improved methods have been applied to three compositionally diverse chondritic meteorite samples that were between 300 and 400 μm in diameter, including samples prepared from fragments of the CR2 chondrite LaPaz Icefield (LAP) 02342, H5 chondrite MacAlpine Hills (MAC) 88203, and the CM2 chondrite Murchison. The synchrotron‐based XRF and XRD tomographies used are focused‐beam techniques that measure the intensities of fluorescent and diffracted X‐rays in a sample simultaneously during irradiation by a high‐energy microfocused incident X‐ray beam. Measured sinograms of the emitted and diffracted intensities were then tomographically reconstructed to generate 2‐D slices of XRF and XRD intensity through the sample, with reconstructed pixel resolution of 1–2 μm, defined by the resolution of the focused incident X‐ray beam. For sample LAP 02342, primary mineral phases that were visualized in reconstructed slices using these techniques included isolated grains of α‐Fe, orthopyroxene, and olivine. For our sample of MAC 88203, XRF/XRD tomography allowed visualization of forsteritic olivine as a primary mineral phase, a vitrified fusion crust at the sample surface, identification of localized Cr‐rich spinels at spatial resolutions of several micrometers, and imaging of a plagioclase‐rich glassy matrix. In the sample of Murchison, major identifiable phases include clinoenstatite‐ and olivine‐rich chondrules, variable serpentine matrix minerals and small Cr‐rich spinels. Most notable in the tomographic analysis of Murchison is the ability to quantitatively distinguish and visualize the complex mixture of serpentine‐group minerals and associated tochilinite–cronstedtite intergrowths. These methods provide new opportunities for spatially resolved characterization of sample texture, mineralogy, crystal structure, and chemical state in unsectioned samples. This provides researchers an ability to characterize such samples internally with minimal disruption of sample micro‐structures and chemistry, possibly without the need for sample extraction from some types of sampling and capture media.

Geochemistry & Geophysics↗
DOE OSTI2024

Automating Log Synthesis and Visualization with Python and Splunk

The goal of this project is to automate log analysis by utilizing Splunk, Bash, and Python together. Simplifying the monitoring and analysis of network traffic was the main goal. In order to accomplish this, a Bash script was created to use 'tcpdump' to automate network sniffing. It also included a 24-hour file rotation mechanism to effectively manage the pcap files that were generated. After that, a Python script was written to read these pcap files and retrieve pertinent data about network traffic. After processing the collected data, Splunk is used to summarize the important metrics and visualize said information with relevant graphs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.

AI↗
DOE OSTI2025

Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy

Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Vis-SAGA: Visual Analytics for Situational Awareness of Grid Anomalies: Preprint

We describe supporting near real-time situational awareness of the electric distribution system by visualizing novel data from voltage sensors deployed on existing broadband cable television network equipment. Our scalable web-based visual analytics platform supports interactive geospatial exploration, time-series analysis, and summarization of grid behavior during potentially anomalous events. The broadband cable television sensor network provides observability of the electrical distribution system at a higher local spatial resolution than is typically available to most utilities, revealing the operational state of the network and aiding in the detection of abnormal behaviors or deviations from expected patterns, particularly across electric utility service areas. We outline the design and development of interactive geospatial and time-series visualization components and the scalable data services that supply metadata, historical, and real-time streams of sensor data across the network. We evaluate our platform during periods of extreme weather, demonstrating its ability to assist in detecting patterns of operation that affect power availability, quality, resiliency, and service restoration.

cable television↗
DOE OSTI2021

Online Interactive Platform for COVID-19 Literature Visual Analytics: Platform Development Study

Background Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. Objective We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. Methods We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher’s query. Results PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. Conclusions The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.</:p>

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

In situ feature analysis for large-scale multiphase flow simulations

The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. Herein, to address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2023

MacroSheds: A synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies

The US Federal Government supports hundreds of watershed monitoring efforts from which solute fluxes can be calculated. Although instrumentation and methods vary between studies, the data collected and their motivating questions are remarkably similar. Nevertheless, little effort toward their compilation has previously been made. The MacroSheds project has developed a future-friendly system for harmonizing daily time series of streamflow, precipitation, and solute chemistry from 169+ watersheds, and supplementing each with watershed attributes. Here, we describe the breadth of MacroSheds data, and detail the steps involved in rendering each data product. We provide recommendations for usage and discuss when other datasets might be more suitable. The MacroSheds dataset is an unprecedented resource for watershed science, and for hydrology, as a small-watershed supplement to existing collections of streamflow predictors, like CAMELS and GAGES-II. The MacroSheds platform includes a web dashboard for visualization and an R package for data access and analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ionospheric Disturbances in GNSS TEC Data: SpaceX Falcon 9 Deorbit Maneuvers Over CONUS in April–May 2024

Traveling ionospheric disturbances (TIDs) driven by a large number of internal and external sources are detectable with dense networks of ground‐based Global Navigation Satellite System (GNSS) receivers' measurements of total electron content (TEC). We present the newly developed System for Rapid Analysis of Ionospheric Dynamics (S‐RAID), providing data and visual GNSS TEC products of TIDs of periods ~2–120 min and horizontal resolution up to tens of kilometers for years 2017–2024. The S‐RAID data reveal myriad natural and anthropogenic TIDs from meteorology and space weather, and from human spaceflight activities. In this report, we focus on new prominent disturbances found during SpaceX second stage deorbit maneuvers in April–May 2024. Signatures include waves emanating from the Falcon's trajectory above California and depletions following its deorbit and passage over Arizona. These findings suggest further opportunities to detect and quantify small‐scale events in the ionosphere as well as to understand the responses of the atmosphere‐ionosphere system to known inputs.

58 GEOSCIENCES↗