Search NASA⌕ Search

SEARCH · Search NASA

Results for “Visual data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Detection of a Space Capsule Entering Earth’s Atmosphere with Distributed Acoustic Sensing (DAS)

On 24 September 2023, the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer sample return capsule (SRC) entered Earth’s atmosphere after successfully collecting samples from an asteroid. The known trajectory and timing of this return provided a rare opportunity to strategically instrument sites to record geophysical signals produced by the capsule because it travelled at hypersonic speeds through the atmosphere. For this work, we deployed two distributed acoustic sensing (DAS) interrogators to sample over 12 km of surface-draped fiber-optic cables, as well as six collocated seismometer-infrasound sensor pairs, spread across two sites near Eureka, Nevada. This campaign-style rapid deployment is the first reported recording of an SRC re-entry with any distributed fiber-optic sensing technology. The DAS interrogators recorded an impulsive arrival with an extended coda which had features that were similar to recordings from both the seismometers and infrasound sensors. While the signal-to-noise ratio of the DAS data was lower than the seismic-infrasound data, the extremely dense spacing of fiber-optic sensors allowed for more phases to be clearly distinguished and the visualization of the continuous transformation of the wavefront as it impacted the ground. Unexpectedly, the DAS recordings contain less low-frequency content than is present in both the seismic and infrasound data. The deployment conditions strongly affected the recorded DAS data; in particular, we observed that fiber selection and placement exert strong controls on data quality.

58 GEOSCIENCES↗
DOE OSTI2026

Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting

Lyα emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrowband photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed z = 2–4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ$^{2}$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Visualization and Quantification of Wind Induced Variability in Hydrogen Clouds Following Releases of Liquid Hydrogen: Preprint

Well characterized experimental data for consequence model validation is important in progressing the use of liquid hydrogen as an energy carrier. In 2019, the Health and Safety Executive (HSE) undertook a series of liquid hydrogen dispersion and combustion experiments as a part of the Pre-normative Research into the Safe Use of Liquid Hydrogen (PRESLHY) project. In partnership between the National Renewable Energy Laboratory (NREL) and HSE, time and spatially varying hydrogen concentration measurements were made in 25 dispersion experiments and 23 congested ignition experiments associated with PRESLHY WP3 and WP5, respectively. These measurements were undertaken using the hydrogen wide area monitoring system developed by NREL. During the 23 congested ignition experiments, high variability was observed in the measured explosion severity during experiments with similar initial conditions. This led to the conclusion that wind, including localized gusts, had a large influence on the dispersion of the hydrogen, and therefore the quantity of hydrogen that was present in the congested region of the explosions. Using the hydrogen concentration measurements taken immediately prior to ignition, the hydrogen clouds were visualized in an attempt to rationalize the variability in overpressure between the tests. Gaussian process regression was applied to quantify the variability of the measured hydrogen concentrations. This analysis could also be used to guide modifications in experimental designs for future research on hydrogen combustion behavior.

HSR&D↗
DOE OSTI2022

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗
DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗
DOE OSTI2020

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog from Double-Difference Seismic Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog obtained for a double-difference seismic tomography study. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained), S-wave velocity (km/s), and S-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained). The Updated_MEQ_catalog text file contains event origin time, x(m), y(m), z(m), error in x (m), error in y (m), error in z (m), and RMS misfit (millisecond). The 3D_seismic_P-wave_velocity_model animation file shows slices of the 3D P-wave velocity model. The 3D_seismic_S-wave_velocity_model animation file shows slices of the 3D S-wave velocity model. The Interactive_MEQ_location_comparison API file is an interactive visualization of the updated microseismic event locations and the original seismic catalog. The visualization allows users to view and compare the event locations by dragging, rotating, and zooming in. An updated version of 3D_seismic_velocity_model and associated animations were included, which were calculated with a more strict assumption for quality indicators.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Integrated, Interoperable Software Environment for Fusion Simulation and Data Analysis Tools SBIR Phase II

This SBIR effort was focused on developing a production ready system to address the integration and interoperability challenges with analysis and visualization in fusion simulations. Our overarching technical objective was to minimize the code development simulation scientists incur when coupling their simulation codes with different analysis frameworks. To this end, we developed an open-source software library to make data exchange between application easier and a Web application to manage and display analysis extracts from simulations. We have also augmented existing libraries funded by DOE such as ADIOS and VTK-m. When used together, these make it significantly easier to integrate simulation and analysis capability. We demonstrated the flexibility of our approach using two common simulation codes in the fusion community, XGC1 and GTC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Modularized Urban Scale Building Energy Modeling Framework Designed with An Open Mind

In recent years, physics-based building energy modeling (BEM) has started being used to evaluate the performance of buildings in the context of connected communities and on an urban scale to study their aggregated energy use, interactions, and impacts on the energy supply infrastructure and environment. The development of urban-scale BEM solutions needs extensive effort. Existing attempts tend to focus on different aspects of BEM on an urban scale, such as collecting as-built building data from different information sources, integrating geometry modeling with geographic information systems (GISs), representing operational and occupancy profiles, automating workflow, processing and visualizing the results, and conducting large-scale simulations. Urban-scale BEM development would benefit from multi-disciplinary research areas and from an open platform to adopt advancements on data sources and tools. For these purposes, this research proposes a modularized bottom-up model creation and simulation framework that is built on the state-of-the-art BEM tools and can accommodate different building stock data. This framework uses a standardized schema to describe building design and operational characteristics, and it can be instantiated from different building survey datasets with heterogeneous structures. The paper demonstrates how thousands of surveyed buildings from the 2012 U.S. Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) were one-to-one converted to EnergyPlus models through the schema and the model generation process, then simulated with distributed computing, and their results are summarized.

Lei, Xuechen↗
DOE OSTI2021

Post-Closure Inspection Report for the Tonopah Test Range and Nevada Test and Training Range, Nevada for Calendar Year 2020 (Rev. 0)

This report provides the results of the annual post-closure use restriction (UR) inspections at corrective action sites (CASs) located on the Tonopah Test Range (TTR) and Nevada Test and Training Range (NTTR). This report covers post-closure UR inspections for calendar year 2020, and includes visual inspections and repair activities completed at the following corrective action units (CAUs): • CAU 407, Roller Coaster RadSafe Area (TTR) • CAU 424, Area 3 Landfill Complexes (TTR) • CAU 453, Area 9 UXO Landfill (TTR) • CAU 487, Thunderwell Site (TTR). Note: CAU 400, Bomblet Pit and Five Points Landfill (TTR) (CASs TA-19-001-05PT, Ordnance Disposal Pit; and TA-55-00-TAB2, Ordnance Disposal Pit) no longer requires inspection and reporting, as described in the Post-Closure Inspection Report for the Tonopah Test Range and Nevada Test and Training Range, Nevada for Calendar Year 2019. Site figures are included in Appendix A. The Post-Closure Inspection Plans and Record of Technical Change modifying the requirements for each UR are included in Appendix B. The Post-Closure Inspection Checklists are included in Appendix C. Field Notes are included in Appendix D. Appendix E, Inspection Photographs, does not include photographs because none were taken during the 2020 post-closure inspections. Appendix F, Post-Closure Vegetation Monitoring Report, does not include any data because vegetation monitoring was not conducted or required at any CAU in 2020. Visual inspections were conducted according to the post-closure requirements outlined in specific CAU and CAS URs, and approved by NDEP. The annual post-closure inspections for CASs associated with CAUs 407, 424, 453, and 487 were conducted on May 20, 2020. No maintenance or repair issues were noted at CAUs 487, or at CASs associated with CAU 424 (except CASs 03-08-002-A304 and 03-08-002-A308). Maintenance items and subsequent repairs include the following: • CAU 407, CAS TA-23-001-TARC (Roller Coaster RadSafe Area): A UR sign was loose and was rehung during the inspection. • CAU 424, CAS 03-08-002-A304 (Landfill Cell A3-4): The northeast monument needed a UR sign. The UR sign was attached to the monument on August 11, 2020. • CAU 424, CAS 03-08-002-A308 (Landfill Cell A3-8): The southeast monument needed additional lava rock. The lava rock was added during the inspection. • CAU 453, CAS 09-55-001-0952 (Area 9 Landfill): Two animal burrows were noted in the western portion of the northern and southern trenches (one burrow per trench). The animal burrows were repaired on August 11, 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dual particle imaging: Applications in security and environmental imaging

Radiation imaging detectors have been applied in a variety of fields for detection, localization and characterization of radioactive sources. In this work, we describe a handheld detection system (H2DPI) that can simultaneously image neutrons and gamma rays, and reconstruct the energy spectra of the incident particles. This system was applied to the detection and characterization of a plutonium metal source in experiments performed at the National Criticality Experiments Research Center, where the 4.5 kg plutonium source was placed approximately 60 cm from the H2DPI. Neutron and gamma-ray images were measured that show good agreement with the known location of the source. Furthermore, the H2DPI was also used to measure the neutron spectrum, which shows the expected shape of a Watt fission spectrum, and the gamma-ray energy spectrum, which shows the energies common to plutonium metal. Finally, we describe the application of the H2DPI in augmented reality, whereby real-time neutron and gamma-ray imaging data can be streamed to the Microsoft HoloLens to allow the user to visualize the source locations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

TListSpectrum

TListSpectrum is a C++ class developed inside the CERN high-energy physics analysis C++ framework ROOT. This class structure was developed to assist in the processing, visualization, and analysis of list-mode or time-stamped radiation spectroscopy data. The class structure currently contains parsing and functionality to synthesize list-mode data from CAEN and Mirion Lynx radiation spectroscopy digital acquisition systems along with feature functionality to post-process data sets and build coincident data sets from the instrument.

Pierson, Bruce↗
DOE OSTI2025

mvBayesR

SAND2025-11559O The mvBayesR tool performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis, and visualization. The tool compiles state-of-the-art methodology into one easy-to-use package. 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.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

mvBayesPy

SAND2025-11476O The mvBayesPy tool is a Python package that performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis and visualization. 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.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2023

Staffing Analyses Tool

The Staffing Analyses Tool is an interactive resource planning tool used for visualizing programmatic staffing financial information. This dashboard provides historical and current staff charging data under organizations’ and/or programs’ views. This tool is used to manage staffing cost and inform outyear planning. It was developed in collaboration with Associate Laboratory Directorate for Infrastructure and Capital Projects (ALDICP) Planning Office.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

EVI-LOCATE User Manual

One of the longest stages in the deployment of electric vehicle supply equipment (EVSE) is the initial planning of the infrastructure itself. Engineers and fleet experts from the National Renewable Energy Laboratory (NREL) have supported dozens of charging infrastructure site plans over the past couple decades, including the generation of site schematics, determinations of electric capacity, and estimates for likely costs. As the market for electric vehicles (EVs) has matured, this approach should no longer require a time and personnel intensive process. In order to shorten the time taken to develop site plans and cost estimates, NREL developed a tool that fleet managers, facility managers, electricians, EVSE installers, and members of the public can use to develop initial schematics and ballpark pricing for charging station installations. The Electric Vehicle Infrastructure - Locally Optimized Charger Assessment Tool and Estimator (EVI-LOCATE) provides a structured and consistent way for users to enter information about their planned EVSE project in a relatively simple web-based format. EVI-LOCATE then calculates electrical equipment capacity, wiring runs, and project costs. It produces a site diagram optimized around surface characteristics with differential trenching costs for softscape such as grass compared to hardscape such as asphalt that can be adjusted by users in the tool. It also stores the resulting site plans and costs in a dashboard for access at a later date, including plan revisions if necessary. This document guides users through the EVI-LOCATE screens and associated questions. It contains tip text boxes throughout on how best to interface with the tool and find additional information or context. The appendices contain the assumptions and calculations underpinning the tool. Much of the information for EVI-LOCATE was gathered through industry engagements with EVSE installers, invoices from completed EVSE installations, Gordian's RS Means construction data, and the General Services Administration blanket purchase agreement for EVSE. For a visual tutorial of the tool, users can watch EVI-LOCATE Step-by-Step Video. The tool itself is available at https://evi-locate.nrel.gov.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗