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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Approach to Automate tools for the Risk Assessment of Digital Instrumentation and Control Systems

Reliable digital instrumentation and control systems (DI&C) are integral for sustaining the continued operation of nuclear power plants. These systems ensure that nuclear reactors operate safely, efficiently, and within regulatory requirements. Yet, the cost of designing and licensing new nuclear DI&C can be prohibitively expensive. Under the U.S. Department of Energy Light Water Reactor Sustainability Program, Idaho National Laboratory has developed a framework for supporting the risk-informed design of DI&C systems by offering methods to support the identification, quantification, and evaluation of risks for various DI&C design architectures. The framework indicates potential software failure modes and provides pathways for quantifying the potential for these software failures, including common cause failures. Using the framework’s systematic approach, challenges for assessing risks within new and existing nuclear DI&C systems can be reduced. Nevertheless, the current framework can be further improved using the convenience of automation. This paper introduces the development of Software for the Hazard Identification and Evaluation of Digital Systems (SHIELDS). SHIELDS is an engineering software package that enables the identification, elimination, and mitigation of potential risks and reduces the burden of deploying reliable DI&C systems. This work introduces plans and techniques to digitize and improve the manual risk assessment modules of the framework. These improvements will save time and increase the repeatability and usability of the framework, making it more accessible to a wider range of users. Ultimately, this introduces SHIELDS and how its modules support efficient development of safe and reliable DI&C systems.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

exadigitUE5

This project provides the AR/VR interface to ORNL's exascale digital twin. The main functionality is implemented using Unreal Engine 5.1 for Desktop or Microsoft Hololens2 based visualization and interation with the system. The digital twin provides data ingestion from telemetry, as well as triggering and interacting with simulations developed for the wider ExaDigiT project at ORNL, as well as for the LUMI system at CSC and other CrayEX Supercomputers. For the overarching project, see ExaDigiT at https://exadigit.github.io, with the code repositories at https://code.ornl.gov/exadigit.

Maiterth, Matthias [Oak Ridge National Laboratory ↗
DOE OSTI2025

WRPS Hanford Update [Slides]

The purpose of this report is to coordinate with the staff from WRPS, the current contract holder for operations at the Hanford site to better inform design decisions for the Autonomous systems for Hanford waste tank handling project. The project goals are to support risk reduction associated with monitoring, inspection and mapping of Hanford tank underground pits and includes: 1. A pit mock-up demonstration of Idaho National Laboratory’s (INL) Autonomous Pit Exploration System (APES) technology, which has two configurations: A. The Autonomous Robotic Arm, which is a proposed collaboration with Florida International University (FIU) and has capabilities to visually inspect, monitor, map and conduct simple tool manipulation tasks in the tank pits, and B. The robotic crawler configuration, which has capabilities to deploy to the bottom of the pits and conduct closer and bottom-up visual inspections. 2. An Implementation Plan that identifies Hanford site-wide application of the INL remote robotics technologies to enhance the performance of the tank farm systems and the characterization of the waste they contain to mitigate risk and optimize the overall waste mission. 3. A first-of-a-kind environmental digital twin will be produced. Digital twins to date require permanently installed sensors in order to produce asset specific predictions. This project will use the intermittent signals during inspections from deployed sensors on robotic systems to produce the data sets used by AI/ML to enable predictions of issues on tanks.

42 - ENGINEERING↗
DOE OSTI2025

Radio Frequency Field Programable Gate Array Implementation of Reflectometry Cable Monitoring

PNNL has developed an innovative cable testing system that leverages the Xilinx RF System-on-Chip (RFSoC) technology to create a more flexible and capable measurement tool than traditional approaches. The architecture is designed on the ZCU111 development board and takes advantage of the high-speed digital to analog converters (DAC) and analog to digital converters (ADC) to generate and capture digitally synthesized waveforms. The platform allows engineers to easily adjust power, frequency, duration and modulation on the fly rather than being locked into fixed hardware configurations

Tedeschi, Jonathan [Pacific Northwest National Lab↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Data and Code for: Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits

This repository contains the simulation outputs and processing scripts associated with the study of winter wheat traits across the United States, utilizing the Ecosys agroecosystem model. The dataset includes model results for both rainfed and irrigated winter wheat systems, supporting the findings presented in the manuscript titled "Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits." Data includes the original Ecosys simulation outputs (archived in .db format within the compressed .zip files) and extracted analysis data (stored in .pkl files for efficient processing). Python code for data processing and figure generation is provided in a Jupyter notebook. External Observational Datasets should refer to the following official repositories for the input and validation data used in this study. The eddy covariance data from the AmeriFlux network (https://ameriflux.lbl.gov/). Climate-forcing data of NLDAS-2 from NASA LDAS (https://ldas.gsfc.nasa.gov/nldas/nldas-2-forcing-data). Soil data from the Gridded Soil Survey Geographic Database (gSSURGO), available at (https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database). Crop yields, planting and harvest dates from the USDA public databases (https://quickstats.nass.usda.gov/; https://webapp.rma.usda.gov/apps/actuarialinformationbrowser/CropCriteria.aspx). Satellite-derived SLOPE GPP data from ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1786). Land use and crop progress information from the USDA Crop Data Layer and Crop Progress and Condition Gridded Layers (https://www.nass.usda.gov/Research_and_Science/). The Ecosys model code is available online at https://github.com/jinyun1tang/ECOSYS.

Wheat↗
DOE OSTI2025

Cyber-Informed Engineering (CIE) Workbook: End-of-Train (EoT) / Head-of-Train (HoT) Communications

This workbook presents a vulnerability (CVE-2025-1727 ) found in train applications and guides a digital risk assessment and mitigation analysis and application of Cyber-Informed Engineering principles to mitigate the potential consequences and ultimately the hazard through the engineering discipline because of exploiting this vulnerability. Workshop participants are encouraged to use the workbook to capture insights and lessons learned. The workbook guides the participant to: • Understand the HE communication vulnerability • Map digital threats to physical consequences • Use bowtie analysis to illustrate both “security” and “engineering” barriers • Apply CIE principles to ensure that even if communications are compromised, the physical engineered system still behaves safely. • Produce an actionable set of engineered and infosec controls for implementation

42 - ENGINEERING↗
DOE OSTI2025

Data Centers and Digital Assurance Introduction to Supply Chain and Cybersecurity for Data Centers, Session 1

The first session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort Workshop, held on October 30, 2025, introduced foundational concepts of Digital Assurance in the context of data center and grid integration. Sponsored by the U.S. Department of Energy, the workshop brought together utilities, data center operators, developers, and vendors to address cybersecurity and supply chain vulnerabilities. The session emphasized the growing criticality of data centers within the electric grid and the need for secure, real-time, bidirectional communication. Participants explored the principles of Digital Assurance, including cybersecurity, cyber-informed engineering (CIE), and lifecycle security, and applied a threat-vulnerability-consequence framework to identify and mitigate risks at the data center–grid interface. Discussions covered a range of threats such as spoofed dispatch signals and insider threats, architectural vulnerabilities like SCADA interfaces and insecure protocols, and potential consequences including cascading grid failures. The session also raised strategic questions about business value, vendor assurance, and defining cyber boundaries and responsibilities. This foundational workshop set the stage for deeper technical analysis and the development of actionable frameworks in subsequent sessions. Session 1 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Prototype Development for MBSE-Driven Digital Environment at Fermilab

Complex projects like Fermilab s accelerators and detectors involve thousands of interdependent components and requirements, making traditional documentation hard to keep consistent and often causing rework. Model-Based Systems Engineering (MBSE) tackles this by representing the system as a digital, queryable model. While widely used in aerospace and safety-critical industries, MBSE adoption has been limited elsewhere due to steep learning curves and high costs. This project investigates how a web-first, low-code MBSE stack can reduce those barriers and offer an accessible, unified source of truth for engineers and physicists.

Valle, Diego Pedro (ORCID:0009000865900663)↗
DOE OSTI2025

Synopsis of NREL's Automated Mobility District (AMD) Research Program and Associated Publications

An automated mobility district (AMD) envisions a system of integrated mobility options that serves major activity centers such as campuses, central business districts, and large medical facilities. The National Renewable Energy Laboratory (NREL) has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings. This document provides a synopsis of findings revealed over the last three phases of work, which have yielded insights into the creation and management of AMDs anticipated to use automated vehicle (AV) technology over the next decade. Phase I and Phase II tracked the deployment and lessons learned from 10 early-stage demonstrations of automated shuttle deployments, and their associated insights into the challenges for automated driving systems to achieve safe operations within district-scale deployments. Phase III began in-depth investigations of critical subsystem components, as automation, electrification, and on-demand service continue to converge within initial AMD operations. These directed studies focus on elements of electrification, curbfront/station management, the role of infrastructure sensing, and overall integration of AMD safety management in central, simultaneous coordination of multiple AMD fleets. Future research in AMDs includes systems engineering methodology (more frequently referred to as "digital twins") for planning, design, testing, and ongoing operation of AMDs; location (or co-location) of management functions; and human supervision and passenger communications for safety and security in unattended vehicles. The synopsis references the foundational research products (papers and presentations) that have been published through conference proceedings, journal articles, and NREL reports.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗
DOE OSTI2026

Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models

This research introduces a comprehensive framework for creating and deploying a digital twin platform for continuous monitoring and predictive maintenance within industrial settings. Through utilizing advanced technologies, including Unreal Engine 5, Unity 3D, the Message Queue Telemetry Transport protocol, Random Forest machine learning algorithms, and Large Language Models (LLMs), we establish a platform that digitally reproduces physical equipment and translates digital controls into real-world actions. This facilitates preventive maintenance approaches and improves operational effectiveness. The digital twin platform gathers sensor data from operational equipment, analyzes it using machine learning, and delivers practical insights to prevent potential malfunctions and enhance equipment performance. Furthermore, the incorporation of a web portal enables efficient monitoring and access to historical data, educational materials, and equipment status information. Preliminary findings indicate that digital twins can transform industrial equipment management and maintenance methodologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Detuned synchronized waveform averaging for reducing distortions due to sample interleaving

This invention introduces a method for optimally detuning the ratio of analog-to-digital (ADC) sample rate and trigger rate during waveform averaging to minimize the deleterious effects from sample interleaving. A prescription is described for determining the optimal detuning factor for the ADC or trigger rate in any scenario, depending on the number of ADC cores used and the limited tuning resolution of the clock rates for a specific implementation.

42 ENGINEERING↗
DOE OSTI2025

Data Centers and Digital Assurance Workshop 3 – Mitigations for Digital Assurance Risks

The third session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort, held on November 18, 2025, focused on developing mitigation strategies for digital assurance risks identified in previous workshops. Hosted by Idaho National Laboratory (INL) and ScottMadden, the session emphasized the application of Cyber-Informed Engineering (CIE) to data center infrastructure, particularly at the utility–data center interface. Participants revisited and ranked key digital assurance risks, including architecture and interface weaknesses, governance gaps, and AI-enabled threats. The workshop introduced the 12 principles of CIE, advocating for consequence-focused design, engineered controls, and secure information architecture to proactively reduce cyber-physical vulnerabilities. These principles were applied to critical data center systems such as power distribution, UPS, cooling, SCADA/BMS, and grid-forming batteries. The session also addressed governance challenges at the interconnection boundary, highlighting the need for clear roles in telemetry sharing, firmware management, and trip settings. Special attention was given to emerging risks from behind-the-meter (BTM) generation, including reverse-power flow and the integration of small modular reactors (SMRs), which shift data centers from large loads to complex generation nodes. Participants explored how interconnection agreements can serve as enforceable instruments for digital assurance, and reviewed gaps in current standards such as NERC CIP, IEC 62443, and IEEE 1547. The workshop concluded with pathways to standardization, including model agreement language, state-level programs, and expanded NERC guidance. INL also presented tools and frameworks for secure procurement and supplier risk management, reinforcing the need for integrated engineering and policy solutions to secure the evolving data center–grid ecosystem. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Framework for a Digital Documented Safety Analysis

This framework is developed to progress the digital implementation of digital tools applied to the DOE authorization process, with future applications to NRC SAR development/review, to accelerate the design and review processes of advanced nuclear reactors. The engineering design and licensing process for nuclear reactors is currently burdened by a document-based approach that leads to duplications and errors due to a lack of traceability among numerous static documents. Changes to design information require labor-intensive manual tracing through these documents, creating a high potential for human error. The adoption of a digital ecosystem, utilizing a digital thread to link various aspects of project design and analysis, promises dynamic documentation generation, automatic updates, and error reduction. Model Based Definition (MBD) and Product Lifecycle Management (PLM) tools are central to this digital transformation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Digital Droplet PCR and Mesocosm-Based Methods to Evaluate Biocontainment Strategies in a Native Soil Ecosystem

Genetically modified industrial production microbes and their associated bioproducts have emerged as an integral component of a sustainable bioeconomy. However, the rapid development of these innovative technologies raises biosecurity concerns, namely, the risk of environmental escape. Thus, the realization of a bioeconomy hinges not only on the development and deployment of microbial production hosts, but also on the development of secure biosystems and biocontainment designs. Current laboratory-based biocontainment testing systems do not accurately reflect the complexities found in natural environments, necessitating an environmentally relevant analysis pipeline that allows for the detection of rare escapees within a complex soil microbiome and differentiation between closely related strains. To this end, we have developed an approach that utilizes soil mesocosms and integrated digital droplet PCR (ddPCR) system to evaluate the efficacy of novel biocontainment strategies. We demonstrate the utility of this approach by modeling contamination with industrial microbial chasses versus their biocontained counterparts. Here we demonstrate the broad utility of this system by highlighting findings from strains of Saccharomyces cerevisiae that are contained with an inducible toxin anti-toxin system, strains of Synechocystis sp. PCC 6803 contained via gene knockout or toxin anti-toxin system, and strains of Escherichia coli that are contained via genomic recoding. We also show that ddPCR can be used to detect gene copies from E. coli equal to those counted by traditional spot plating assays. The resultant data demonstrates that this system has broad utility across diverse microbial chassis and biocontainment strategies and enables researchers to track the fate of our contaminating microbe with high sensitivity in the soil. The findings presented here support the use of this mesocosm-based approach to assess the environmental impact of industrial microbes and to validate biocontainment strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗