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NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), is a platform built upon a database of radiation data relevant to space biology. RadLab provides visual and programmatic interfaces for interrogation of its database, as well as a submission process for inclusion of data from investigators. The RadLab application programming interface (API) implements a request syntax enabling users to retrieve data filtered by various combinations of parameters (detector type, location, direction, timespan, etc), which are delivered in machine-readable text formats, ready to be ingested by downstream analysis pipelines; while the graphical user interface (GUI) provides easy means to iteratively modify query parameters and incorporates a number of standard analyses and visualizations (time series plots, geospatial visualizations, detector comparison). Investigators from many countries, including US, Russia, Japan, Canada, the Czech Republic, Germany, Hungary, and Italy, have committed to provide data from their instruments located on the ISS; RadLab will also include data from other spacecraft in LEO (e.g., the Space Shuttle, the Mir space station), BLEO (e. g. BioSentinel, Mars Orbiter, among others), and on other celestial bodies (e. g. Chang’e 4, Curiosity). The first release of RadLab has been made available to the public. Once fully operational, RadLab will provide a comprehensive and ever-growing compendium of space radiation data, facilitating straightforward access to multiple types of readings and enabling space biology researchers to perform intercomparisons of detectors and to determine the radiation environment of research missions, both via programmatic retrieval of these data and via the graphical analysis toolkit; as well as a user-friendly submission portal for ingesting data from space agencies and research institutions. Radiation scientists will be able to use RadLab to gain a deeper understanding of the space radiation environment for future human space exploration. The RadLab Working Group has been formed to foster close collaborations among data contributors and users, to identify data sources, to put in place standards for data normalization, to guide the development of features of the analysis toolkit, to establish the use of RadLab in space radiation biology research, and eventually to provide a forum for discussing relevant research issues that can take advantage of RadLab's capabilities.

radiation↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Analysis Toolkit for Space Radiation Measurements Relevant to Space Radiation Biology

RadLab, a new addition to the NASA Open Science Data Repository (OSDR), is a public platform for space radiation data relevant to human space exploration. RadLab consists of a database, a submission portal, and user-friendly visualization and data analysis tools, including a graphical user interface (GUI) and an application programming interface (API). Investigators from ISS partners including Germany, Italy, Canada, Hungary, the Czech Republic, Russia, Japan have committed to providing data from their instruments. RadLab will also include data from other spacecraft in LEO: the Space Shuttle, the Mir space station, biosatellites; and beyond LEO: the lunar and the Martian surface, the heliocentric orbit at 1 AU, Mars orbit, and Earth-Mars space. Once fully operational, RadLab will provide open, centralized access to space radiation physics data relevant to human space exploration; a platform for submission of data by agencies and research institutions responsible for radiation detectors deployed in space; analysis tools to facilitate detector and dataset intercomparison to better understand space habitat radiation environments; capabilities for space biology investigators to determine the radiation environment to which samples were exposed. A RadLab Working Group (RLWG) has been formed, modeled on the GeneLab Analysis Working Groups and comprised of data contributors and users. RLWG tasks include identifying data sources, normalizing data from diverse detectors, expanding the analysis toolkit and, perhaps most importantly, sharing ideas for research exploiting capabilities of RadLab. We will provide an overview of RadLab data and capabilities and discuss examples of its potential as a resource for open science.

radiation↗

NASA NTRS2023

A General Procedure to Formulate 3D Elements for Finite Element Applications

This paper presents a general procedure to formulate and implement 3D elements of arbitrary order in meshes with multiple element types. This procedure includes obtaining shape functions and integration quadrature and establishing an approach for checking the generated element’s compatibility with adjacent elements’ surfaces. This procedure was implemented in Matlab, using its symbolic and graphics toolbox, and complied as a GUI interface named ShapeGen3D to provide finite element users with a tool to tailor elements according to their analysis needs. ShapeGen3D also outputs files with the element formulation needed to enable users to implement the generated elements in other programming languages or through user elements in commercial finite element software. Currently, finite element (FE) users are limited to employing element formulation available in the literature, commercial software, or existing element libraries. Thus, the developed procedure implemented in ShapeGen3D offers FEM users the possibility to employ elements beyond those readily available. The procedure was tested by generating the formulation for a brick element, a brick transition element, and higher-order hexahedron and tetrahedron elements that can be used in a spectral finite element analysis. The formulation obtained for the 20-node element was in perfect agreement with the formulation available in the literature. In addition, the results showed that the interpolation condition was met for all the generated elements, which provides confidence in the implementation of the process. Researchers and educators can use this procedure to efficiently develop and illustrate three-dimensional elements.

higher-order elements↗

NASA NTRSDate not supplied

The State of NOS3

The NASA Operational Simulator for Small Satellites (NOS3) showcases some of the Jon McBride Software Testing and Research (JSTAR) laboratories technologies on an open-source platform. NOS3 is a software digital twin providing a virtualized platform inside which you have your traditional flight software, ground software, environmental simulators, and middleware to keep all pieces in sync. NOS3 leverages the core Flight System (cFS), OpenC3 COSMOS, and NASA GSFC’s 42 software as the baseline to which additional research technologies can be developed. Current technologies to be demonstrated include NOS3 Igniter, constellation support, NASA JPL’s SYNOPSIS integration, and NASA GSFC’s OnAir. NOS3 Igniter is a GUI in which you can configure, build, and run your simulation. This along with improvements to the documentation and training available open source aims to reduce the ramp up time with new users and improve accessibility. As constellations introduce another level of complexity, it is important to ensure the baseline design reference mission covers all the basics required and allows users to experiment, understand, and test at all levels of the system. The Science Yield improvement via Onboard Prioritization and Summary of Information Systems (SYNOPSIS) is an open-source tool developed by NASA JPL to enable data prioritization and planning. GSFC’s Onboard Artificial Intelligence Research (OnAIR) enables custom algorithm development written in python to interface with the flight software allowing scientists to develop what they need for the next generation of missions and easily interface back to the traditional flight software. During the presentation, a review and demonstration of the above technologies is planned along with a roadmap.

NOS3↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), comprises a database of radiation measurements relevant to space biology, and visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. modules of the ISS), associated celestial bodies, trajectories, and spacecraft coordinates. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations, such as spacecraft schematics, time series plots, geospatial visualizations, and provides easy means to iteratively refine search parameters, inspect the data on the fly, and download target subsets of these data. The release of RadLab currently available to the public contains datasets provided by US and international collaborators and focuses on data recorded on the ISS. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit; RadLab will also soon expand to include past (e.g. Shuttle and Mir) and future (e.g. Artemis) data. RadLab will provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments, both via programmatic retrieval of these data and through the graphical analysis toolkit. The RadLab Working Group has been formed to foster collaborations among data contributors and users, to identify data sources, to put in place standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the space radiation environment in human habitats.

radiation↗

NASA NTRSDate not supplied

The Environmental Data Application for Analysis of Space Telemetry Data

Sensors on the International Space Station (ISS) and multiple spacecraft elsewhere in Earth orbit and in deep space continuously monitor and collect environmental data, transmitting this information back to Earth. These data include ionizing radiation and, on the ISS and spacecrafts, CO2, relative humidity levels, and temperature, and are of great importance to space biology research. Looking ahead to future long duration crewed missions beyond low Earth orbit, the ability to study how factors including CO2 levels, light cycle, temperature modulate the response to ionizing radiation and microgravity is essential. To date, access to these data has been fragmented across space agencies, spacecraft, and databases. To address this issue, NASA’s Open Science Data Repository (OSDR) has developed a user interface for interrogation of telemetry data: the Environmental Data Application (EDA). The EDA provides the capability to visualize telemetry and radiation data collected on the International Space Station and corresponding ground platforms during the Rodent Research missions. Telemetry data includes temperature, relative humidity, and CO2 levels. Radiation data includes galactic cosmic rays, the contribution of the South Atlantic Anomaly, total radiation dose rate, and accumulated radiation dose. The application allows users to view single missions, compare multiple missions, and view and download summary or full data tables. In summary, the EDA provides GUIs for data visualization and exploration, as well as means for data export, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

telemetry↗

NASA NTRSDate not supplied

Py MILab: Capturing, Analyzing and Storing Test Data

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.

Data management↗

NASA NTRS2024

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing ‘fit-for-purpose’ materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of judicious automation in the material decision process; that is implementing some optimization algorithms to truly enable the benefits of ICME, particularly when considering materials at multiple length and time scales. Furthermore, the ability to effectively store developed material models, experimental data used for validation, and link models at multiple length and time scales must be implemented to ensure traceability across the material design process, such that the data gathered can be leveraged towards efficient material design. To enable such an optimization scheme a robust framework must exist: (1) that can capture changes made at a given length scale, (2) automatically propagate changes upstream to the highest scale, and (3) evaluate the material’s performance at the structural level. In this work, a developed framework for tracking material changes, automatically running the necessary simulations to determine the properties at the next highest scale, and saving each iteration of the design process to maintain the application’s digital thread is presented for polymer matrix composites (PMCs). The Automated Information Management Across Organizations and Scales (AIMAOS) program offers users an interactive graphical user interface (GUI) for defining constituent materials, building lamina and laminates, and applying effective laminate properties to finite element and composite optimization third party software. At each length scale, the necessary input files are automatically written, and subsequent analysis tools are called to solve for effective properties at the next scale, which are then read by the AIMAOS tool and displayed to the user. As changes are made to the material at lower length scales, information is automatically propagated upstream to higher length scales, and changes made are automatically tracked and versioned to maintain traceability during the design process. The AIMAOS tools serves as the first step in enabling optimized design of composites from the nano to the macroscale for a given application.

Brandon L Hearley↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.

Kirill Grigorev↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), comprises a database of radiation measurements relevant to space biology, and visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. modules of the ISS), associated celestial bodies, trajectories, and spacecraft coordinates. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations, such as spacecraft schematics, time series plots, geospatial visualizations, and provides easy means to iteratively refine search parameters, inspect the data on the fly, and download target subsets. The release of RadLab currently available to the public contains datasets provided by US and international collaborators and focuses on data recorded on the ISS. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit; RadLab will also soon expand to include past (e.g. Shuttle and Mir) and future (e.g. Artemis) data. RadLab will provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. The RadLab Working Group has been formed to foster collaborations among data contributors and users, to identify data sources, to put in place standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the space radiation environment in human habitats.

database↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

Kirill Grigorev↗

NASA NTRSDate not supplied

STARscan: Spatial Targeting and Alignment Rig for Scanning

The Spatial Targeting and Alignment Rig for Scanning (STARScan) is a 3D photogrammetry system developed at NASA Ames Research Center to address bottlenecks in pre/post-test scanning of arcjet test articles. It reduces scan time from 15 minutes with handheld laser scanners to under 2 minutes, while maintaining high accuracy (±0.2-0.5 mm). STARScan integrates an array of cameras, a 3D-printed rack, turntable, and LED light panels, all controlled via a user-friendly graphical user interface (GUI). The system offers tools for scan visualization, mesh analysis, and data export, automating tasks such as alignment of pre/post-test scans, material recession measurements, surface roughness assessment, and curvature analysis. By integrating scanning, imaging, and post-processing into one application, STARScan significantly improves efficiency in scanning and analyzing arcjet test samples.

Ablation↗

DOE OSTI2024

Improving the User Interface of the DeepLynx Data Warehouse

DeepLynx is an open-source ontology-based data warehouse created by INL to support the creation and life cycle of digital engineering projects, with a particular emphasis on digital twins [1]. Digital twins are systems that represent physical assets and process in a real-time digital environment [1]. Most well-known commercial data warehouses use Graphical User Interfaces (GUIs) for users to interact with their systems [3]. Limited publications have addressed the design of these interfaces and understanding of their target users. The current users and development team acknowledge the need to improve the current UI, not just for aesthetics but to improve functionality and workflow of DeepLynx. Traditional data warehouse users are developers, data scientists and business analysts [2]. DeepLynx users have a vast range of experience using data warehouses, and diverse roles, including engineers, scientists and management positions. Because there is a broader audience of target users for DeepLynx than a typical data warehouse, it is essential that DeepLynx has a useable and intuitive user interface. To achieve this the team performed human-computer interaction methods, including a Heuristic Evaluation of current UI using Neilsen’s Usability Heuristic, create personas based on current users by designing a user survey, data analysis and develop of personas. Followed by a redesign of the UI following using Neilsen’s Usability Heuristic and Norman’s Principles of Interactive Design in industry standard software Figma. Lastly a Heuristic Evaluation of new UI design, using Neilsen’s Usability Heuristic and User testing of redesign UI and have a group of users complete a Thinking Aloud Test of the new UI. Preliminary results of the Heuristic Evaluation of current UI arise issue with Consistency and Standards, Visibility of System Status, Match System and Real World and Recognition Rather than Recall. These issues were addressed in the proposed redesign by applying Neilsen’s Usability Heuristic and Norman’s Principles of Interactive Design. Next steps include formalized list of lessons learned and design implications for future publications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Automated Detection of Qubit Structures on Quantum Chips

In this study, YOLO (You Only Look Once), a well-known object detection and image segmentation model, is used to detect qubits on a quantum chip. The model is trained by exposing it to various images of objects of interest and by tweaking various training parameters to allow the model to learn from a limited and highly specialized dataset of SEM imagery. The model was provided with numerous images of qubits and various prominent features of note located on a qubit. These detections were then integrated into a simple GUI, allowing for real-time feedback alongside SEM usage. This will allow us to create various functions enabling automated functions like chip-wide scanning, auto-focusing, and image correction.

Perjuste, Ruth [Wellesley Coll.]↗

DOE OSTI2026

Natural Language Processing to Inform Agent-Based Modeling: With Application to Modeling Adoption of Medium-Duty Electric Vehicles

Agent-based socio-technical modeling of medium- and heavy-duty (MDHD) electric vehicle (EV) adoption has the potential to provide analysis, prediction, and gui. This paper describes new applications of text analysis developed through machine learning (ML) to build and understand relevant topics and their saliency in the published discourse on adoption of MDHD EVs. This work contributes to the state of the art in topic mining models by defining a new metric of topic ranking (START) that quantifies the importance of predefined topics within the corpus using weighted results for predefined topics from two topic modeling approaches: Latent Dirichlet Allocation (LDA) and BERTopic. The START metric is then demonstrated in practice to model how academia and industry view the EV adoption process based on the respective texts published by these groups. Results show that academic literature places more emphasis on categories of interests such as norms/attitudes and adopter knowledge, while trade journals tend to emphasize long-term cost more than academia. The two bodies of literature agree on the importance of policy and incentives in MDHD EV adoption. Together these results illustrate the potential to use ML-based text analysis to populate the characteristics of agent-based socio-technical models.

Electric vehicle adoption, fleet electrification, ↗

DOE OSTI2025

Work updates for Prof. Lee

I want to update my work which I have done so far in INL, including matlab GUI for neutron radiography and some fuel EBSD works.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2025

Developing Digital Twin Visualizations: A Methodology and Case Study on Chemical Separation Processing

As advances in digital engineering continue to push the technological boundaries, digital twin (DT) visualizations for diagnostics and safeguards advancement become much more feasible and practical. DTs generate large and complex data streams that require effective user interfaces to provide monitoring and diagnostic capabilities. Unfortunately, while these frameworks exist, there is not much research on the systematic documentation of human–computer interaction (HCI) for DT visualization. This work presents a dual-mode visualization methodology (two dimensional [2D] graphical user interface dashboard and 3D mixed reality) designed to support diagnostic tasks in DT systems and building on a validated framework and applying established HCI principles. The methodology is demonstrated through a case study of aqueous processing at Idaho National Laboratory, using experimental data from the chemical solvent extraction runs. Our interfaces display real-time alerts and monitoring to inform users of safeguards anomalies. The interfaces use immersive 3D mixed-reality visualization for further system and experiment investigation. This work demonstrates how the systematic application of HCI principles can inform DT visualization design for diagnostic and safeguards applications. While formal user evaluation studies remain as future work, this paper documents the systematic design methodology and demonstrates a proof-of-concept implementation.

3D visualization↗