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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2025

Pushing the limits of NAND technology scaling with ferroelectrics

Artificial intelligence (AI) continues to drive transformative advancements across various industries. The data-intensive nature of AI training (and inferencing) has resulted in the generation of unprecedented volumes of data with machine-generated content surpassing human-generated data by more than 100-fold in 2025. Efficiently managing this data influx necessitates advanced digital storage technologies. However, traditional NAND flash memory, which is critical for supporting data flows in AI systems—alongside high-bandwidth memory, for AI training—faces fundamental scaling limitations as it approaches the 1000-layer milestone, encompassing more than 40 trillion transistors. This article delves into the potential of hafnia-based ferroelectric materials as a breakthrough solution to these challenges. Recent advancements indicate that the intrinsic limitations of ferroelectric field-effect transistors (FEFETs) can be mitigated through material and device-level engineering. These advancements enable FEFETs to meet the stringent density, reliability, and scalability requirements of future three-dimensional NAND technology. The role of ferroelectrics in addressing NAND scaling challenges and expanding storage capabilities presents a promising avenue for meeting the storage demands of the AI-driven era.

3D NAND↗
DOE OSTI2025

Alpha Decay Chains as Thermal Power Sources: Analysis and Applications for RTGs

Radioactive sources can provide power in remote and environmentally harsh locations such as the arctic or space. The generators powered by such sources are rugged and can withstand extreme temperatures, lack of sunlight, and require no human intervention for multiple years. Radioisotopes are used in thermoelectric generators to provide power at remote sites and deep in space. Isotopes like Pu-238, Cm-244, and Am-241 are used in these generators by NASA for power in space probes and spacecrafts. These power sources deliver a steady supply of energy over extended periods of time. Alpha particles created during decay do not travel far in a material. Their kinetic energy is transferred to heat that we can then convert into energy. Unlike beta and gamma decay, the slower-moving alpha particles stop in the material, making their energy available for use. Energy from these natural decay processes provides a reliable source of power. Spontaneous fission is rare and unreliable, and unlike induced fission processes, alpha decay occurs naturally and does not require external management or ignition. The ideal properties of an isotope for use as a power source depend upon the intended use. For use in an Arctic research base over a period of several years, but less than a decade, an isotope that provides high power output over a shorter lifespan may be the most suitable option. Whereas, for deep space missions where a consistent power source for decades or perhaps more than 100 years is needed that would require a very different isotope. One with a much longer half-life that would provide consistent power throughout that time and survive in that state in for these extended periods of time. These examples represent two extreme sides in terms of time frames. By analyzing the power produced by different radioactive decay processes over time, we can evaluate the suitability of various isotope decay chains for specific uses. Some unstable isotopes undergo a series of radioactive decays, transforming into different isotopes at each step and resulting in a stable isotope. The lists of isotopes in these decay processes are known as decay chains. Some of these chains, illustrated in the figures below, are currently being investigated for use in radioisotope thermoelectric generators (RTGs) designed for a range of operational durations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗
DOE OSTI2023

Gridtrust: Electricity Grid Root-of-Trust Decentralized Supply Chain Cyber-Security (Final Scientific/Technical Report)

GridTrust represents a departure from reliance on a single organization or a single person to multiple organizations and therefore multiple people across organizational structures. The motivating idea behind involving multiple organizations is the increase in security due to human factors. More specifically, the requirement that distinct people in different organizations sign off on a change or an update makes a cyberattack much less likely due to the inherent requirement that both organizations be penetrated and fooled. GridTrust focuses on the software update process as the primary exemplar for the research and development work. A novel hardware-based technology referred to as a Physical Unclonable Function (PUF) provides a microchip Root-of-Trust (RoT), i.e., a starting point for verifying that the hardware being communicated with is the hardware the control center believes the hardware to be. As a result, staff at power grid control centers can ensure the accurate and reliable identification of hardware devices from the outset. The GridTrust protocol introduces two key innovations, as detailed in this report. Firstly, the utilization of a PUF as a root-of-trust in the initial phase of a software or firmware update. Secondly, the application of multiple cryptographic signatures from two or more organizations to the update binary. These signatures are verified before implementing the update on a power grid device in the field. In terms of GridTrust hardware design, this report outlines two main components. The first is the GridTrust Native Device, integrating PUF technology intrinsically into the hardware device itself. The second is the GridTrust Interfacing Device, which incorporates PUF technology and multiple cryptographic signatures. These signatures are cross-checked within a separate hardware positioned between the power grid control center and the legacy power grid device, functioning as an intermediary. While the GridTrust Interfacing Device offers the advantage of being applicable to existing power grid equipment, it may have reduced security if the intermediary component is targeted. On the other hand, the GridTrust Native Device boasts increased security due to protocol integration within a unified form factor. The effectiveness of GridTrust technology has been extensively demonstrated, with multiple external red-team attackers unable to breach GridTrust's security measures. This was observed both in controlled laboratory settings during Phase 1 of the project and in real-world conditions within a City of Marietta substation during Phase 2 of the project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Molecular Specificity and Proton Transfer Mechanisms in Aerosol Prenucleation Clusters Relevant to New Particle Formation

Atmospheric aerosol particles influence Earth’s radiative energy balance and cloud properties either directly through interacting with the solar radiation or indirectly through acting as cloud condensation nuclei (CCN), thus impacting Earth’s climate change, air quality, and human health. Regarding both the important scientific and overarching practical implications of aerosols, the past two decades have seen an extensive research effort, with emphasis on the chemical compositions and underlying mechanisms of aerosol formation. New particle formation (NPF) has been recognized as an important pathway, contributing up to 50% of atmospheric aerosols. Nowadays, the general consensus in NPF is that it proceeds via two distinct stages—the nucleation from gaseous precursors to form critical nuclei of sub-1–2 nanometer size, and the subsequent growth into large particles. However, a fundamental understanding of both the NPF process and the molecular-level characterization of the critical size aerosol clusters is still largely missing, hampering the development of reliable and predictive aerosol nucleation and climate models.

Hou, Gao-Lei↗
DOE OSTI2025

Sim2Real Autonomous Robotic Exploration [Poster]

Autonomous robots offer promising solutions for exploration in environments that are inaccessible or hazardous to humans. Despite this, physical training of such robots is often constrained by safety risks, high cost or limited accessibility. This project presents an end-to-end simulation to reality pipeline leveraging Nvidia Isaac Sim and Boston Dynamics' Spot to enable autonomous navigation in indoor environments. A reinforcement learning policy is first trained using Nvidia Isaac Lab to establish Spot's locomotion pattern. Virtual LiDAR sensors are then integrated to perform SLAM-based navigation using simulated odometry. Finally, the simulated navigation scheme is transferred to a physical Spot robot to inspect and record images of a real-world room by repeating the learnt trajectory. The proposed framework highlights the potential of scalable training in simulation and reliable deployment in physical environments. Future directions include dynamic trajectory generation in unseen and challenging environments and integration of environmental sensing like temperature, radiation or humidity via sensor and material simulation.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Power System Resilience Metrics Augmentation for Critical Load Prioritization

One of the major goals of new grid operation regimes, such as transactive energy systems (TESs), is to make the power grid more resilient to withstand natural or man-made disasters and potential reliability events, and to continue to serve the maximum number of its customers. But it is a well-known fact to system operators that not all customers are the same. This implies that any discussion of TESs’ impacts on the resilience of the power system should consider the needs of its critical customers (such as the power system operation centers, fire and police stations, and hospitals) over those of other customers. When evaluating the resilience of the system, bonus points must be awarded to any system that could maintain its power supply to critical customers during a disturbance that may cause an outage. This report discusses critical infrastructure (CI) as found in the literature and then categorizes it based on the field to which the operations belong (such as human life/safety-related, operations management, necessary city operation, industrial customers, etc.). Each of these CI categories is further divided into types of critical customers (e.g., the human life/safety-related category has different types of customers like hospitals, fire and police stations, etc.). The entire demand of each of the critical customer types is not categorized as critical load (CL); instead, only a portion of the total load of these critical customers is characterized as critical load. This is done based on the categories of equipment, the function of which is crucial in the operation of the overall facility. CL categorization is performed to provide the ratio of the critical load portion to the overall load , so that it can serve as a parameter in the resilience evaluation of the grid through a metrics-based approach. Such categorization is important as it helps to augment the existing quantifiable resilience metrics with CL categorization. The metrics for a power system need to not only consider how well a system performed during a disturbance event, but also how it reduced strain and supplied power to its CLs. The first step in this process is characterize CLs in the system. After CL characterization, the next step is the inclusion of these loads in the resilience metrics. To that end, in this report weight-based augmentation of resilience metrics is proposed, where certain customers (the ones that are categorized as critical) are assigned higher weights than others. Though an overview of assigning weights to customers is discussed, there is no one-size-fits-all approach for every power system. The decisions made about assigning such weights to customers vary greatly from one operator to another, based on their unique systems and the current and predicted states of critical customers. This decision-making can include the type of disturbance event, which might only affect certain parts of the system. In general, analyzing critical customers before an event helps understand system vulnerabilities. It also helps in planning and conducting operations during the event, evaluating system performance after the event, and supporting better planning for future events. An alternative to the current practices of managing the grid for outages is an innovative TES, which has the potential to provide a platform for including distributed energy resources for managing CLs. This report also describes how TES qualities can help (1) to maintain power supply to critical customers for uninterrupted operations and (2) to restore lost power supply to the critical customers rapidly.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

13 HYDRO ENERGY↗
DOE OSTI2023

Evaluating various composite sampling modes for detecting pathogenic SARS-CoV-2 virus in raw sewage

Inadequate sampling approaches to wastewater analyses can introduce biases, leading to inaccurate results such as false negatives and significant over- or underestimation of average daily viral concentrations, due to the sporadic nature of viral input. To address this challenge, we conducted a field trial within the University of Tennessee residence halls, employing different composite sampling modes that encompassed different time intervals (1 h, 2 h, 4 h, 6 h, and 24 h) across various time windows (morning, afternoon, evening, and late-night). Our primary objective was to identify the optimal approach for generating representative composite samples of SARS-CoV-2 from raw wastewater. Utilizing reverse transcription-quantitative polymerase chain reaction, we quantified the levels of SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA in raw sewage. Our findings consistently demonstrated that PMMoV RNA, an indicator virus of human fecal contamination in water environment, exhibited higher abundance and lower variability compared to pathogenic SARS-CoV-2 RNA. Significantly, both SARS-CoV-2 and PMMoV RNA exhibited greater variability in 1 h individual composite samples throughout the entire sampling period, contrasting with the stability observed in other time-based composite samples. Through a comprehensive analysis of various composite sampling modes using the Quade Nonparametric ANCOVA test with date, PMMoV concentration and site as covariates, we concluded that employing a composite sampler during a focused 6 h morning window for pathogenic SARS-CoV-2 RNA is a pragmatic and cost-effective strategy for achieving representative composite samples within a single day in wastewater-based epidemiology applications. This method has the potential to significantly enhance the accuracy and reliability of data collected at the community level, thereby contributing to more informed public health decision-making during a pandemic.

sampling timing↗
DOE OSTI2023

Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning

Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices have been developed to characterize tree-crown architecture. However, the required branching data (BD) to develop these indices are difficult to collect. Traditionally, BD have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial laser scanning (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected 16 young peach trees scanned in 2021 and 2022. These 16 trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico BD, biometric data, and quantitative structural model branch uncertainty data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation when comparing young tree (YT) height was approx. 5.93%, with crown volume was approx. 13.26% across both 2021 and 2022. All point clouds of the YTs in 2022 showed residuals lower than 12 mm to cylinders fitted to all branches, and mean surface coverage greater than 40% for both the trunk and primary branching orders.

09 BIOMASS FUELS↗
DOE OSTI2022

Assessment of human nuclear and mitochondrial DNA qPCR assays for quantification accuracy utilizing NIST SRM 2372a

In forensic DNA casework, a highly accurate real-time quantitative polymerase chain reaction (qPCR) assay is recommended per the Scientific Working Group on DNA Analysis Methods (SWGDAM) (SWGDAM Validation Guidelines for DNA Analysis Methods [1]) to determine whether a DNA sample is of sufficient quantity and robust quality to move forward with downstream short tandem repeats (STR) or sequencing analyses. Most of these assays rely on a standard curve, referred to herein and traditionally as absolute qPCR, in which an unknown is compared, relative to that curve. However, one fundamental issue with absolute qPCR is the quantifiable concentration of commercial assay standards can vary depending on (1) origin, i.e., whether from a cell line or a human subject, (2) supplier, (3) lot number, (4) shipping method, etc. In 2018, the National Institute for Standards and Technology (NIST) released a human DNA standard reference material for evaluating qPCR quantification standards, Standard Reference Material (SRM) 2372a, Romsos et al. (2018) [2] which contains three well-characterized human genomic DNA samples: Component A) a single male1 donor, Component B) a single female 1 donor, and Component C) a 1:3 male 2 :female 2 donor, each with certification data for nDNA and informational mitochondrial DNA(mtDNA)/nuclear DNA (nDNA) ratio data. The SRM 2372a was used to assess four qPCR assays: (1) Quantifiler Trio (Thermo Fisher Scientific, Waltham, MA) for nDNA quantification, (2) NovaQUANT (EMD Millipore Corporation, San Diego, CA) for nDNA and mtDNA quantification, (3) a custom duplex mtDNA assay, and (4) a custom triplex mtDNA assay. Additionally, extracts from eighteen (18) skeletal remains were tested with the latter three assays for concordance of DNA concentration and with assays (2) and (3), for the degradation state. Our assessment revealed that an accurate, efficient, and reproducible qPCR assay is dependent on (1) the quality and reliability of the DNA standard, (2) the qPCR chemistry, and (3) the specific primers, and probes (if applicable), used in an assay. Finally, our findings indicate qPCR assays may not always quantify as expected and that performance of each lot should be verified using a well-characterized DNA standard such as the NIST SRM 2372a and adjusted if warranted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Large-scale deep learning for metastasis detection in pathology reports

Objectives No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. Materials and Methods We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM). Results Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. Discussion A smaller deep learning architecture trained on task-specific data outperforms a general LLM. Equally critical to model performance is the incorporation of uncertainty quantification, achieved here through an abstention mechanism. Conclusions This study’s finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.

machine learning↗
DOE OSTI2021

Fault Detection on Seismic Structural Images Using a Nested Residual U-Net

Automatic identification of faults on seismic structural images is a challenging yet crucial task in quantitative seismic interpretation. Human picking or attribute-based fault detection methods may misidentify faults on noisy, complex seismic images. In this work, we develop a new automatic fault detection method using a nested residual U-shaped convolutional neural network. Each of the encoders and decoders in this neural network is a residual U-Net, leading to a nested architecture. The final fault map results from the fusion of three fault maps with low, medium, and high fault resolutions. We demonstrate the excellent fault-detection capability of our nested neural network using a series of synthetic and field seismic images. We find that our approach produces clearer and more interpretable fault maps than the current state-of-the-art U-Net fault detection method, particularly on noisy seismic images. Our new automatic fault detection method can facilitate reliable quantitative seismic interpretation on field seismic images.

58 GEOSCIENCES↗
DOE OSTI2022

Oak Ridge National Laboratory Pilot Demonstration of an Attestation and Anomaly Detection Framework using Distributed Ledger Technology for Power Grid Infrastructure

This report summarizes the design and pilot demonstration of a framework called Grid Guard that was created to provide increased data and device trustworthiness to electric grid devices by leveraging distributed ledger technology (DLT), specifically blockchain. Grid Guard contains a combination of core cryptographic methods such as the secure hash algorithm (SHA), and asymmetric cryptography, private permissioned blockchain, baselining configuration data, consensus algorithm (Raft) and the Hyperledger Fabric (HLF) framework. The system implements a low energy, fast, and robust enhancement to system trustworthiness within and across electric grid systems such as substations, control centers and metering infrastructures. Blockchain is a distributed database structured that provides a practically unalterable (immutable) timeline of stored transactions. By relying on hashing and the Raft consensus algorithm, if an entity tries to illegitimately alter a record at one instance of the database the other ledger nodes are not altered. They work to cross-reference each other and easily locate any incorrectly added data and remove it. The bulk raw data is stored in an off-chain storage (outside of the blockchain ledger) and a hash of this baseline data is stored in the Blockchain ledger via hashing windows of time-series and configuration data, after aggregation and filtering. The bulk off-chain data repository is then considered to be trust-anchored using the hashes stored in the blockchain. To secure the electric grid testbed devices and data, device configuration baselines were compared to those baselines that had been previously stored in the ledger. Statistical baselines for device configurations, network communication patterns, and high-speed sensor data are calculated and then stored off-chain and hashes stored in the ledger. Measurements such as three-phase voltage and current, frequency, breaker status, protection scheme settings, network configuration settings (and other device configuration artifacts) and network traffic features (packet interarrival times) are compared every minute or other selected time windows. During phase 1 of the Grid Guard DLT project different DLT technologies were studies, and an assessment was performed on DLT technology vulnerabilities, uses, and key characteristics. DLT consensus protocols were studies (e.g., RAFT, named after Reliable, Replicated, Redundant, And Fault-Tolerant). Also, cryptography, public, private and permissioned or permissionless systems were assessed. Grid Guard implements a permissioned private DLT. Consensus algorithm selection and choice of DLT implementation depended heavily on the use-case. For this use-case, parameters were selected to measure performance and existing tools for assessment. Benchmarking was performed theoretically and practically. During phase 2 hashed transactions/blocks were inserted into the ledger every second. During phase 2 of the Grid Guard DLT project, a prototype framework was developed and demonstrated for attestation of critical substation devices and data using precision timing systems that use PTP and IRIG-B protocols) on a testbed of operational devices that emulated a distribution substation, control center, and power metering infrastructure using real Operational Technology (OT). The testbed includes OT devices such as protective relays, human machine interfaces (HMI), and power meters. To determine when to collect and compare system and network baselines, an initial examination of an anomaly detection capability to identify malicious manipulation of data streams was conducted. The resulting anomaly detection was demonstrated in a set of experiments and leveraged to trigger device artifact attestation checks. Attestation checks occur against device configuration baselines when compared with the immutable blockchain-stored baselines, which provided a cryptographically supported means by which to store baselines. The electrical substation-grid testbed was created to test the Grid Guard framework. The testbed emulates the operations of a portion of a power grid and SCADA systems as closely as possible. The testbed integrates real protocols, mainly IEC 61850 standard protocols, such as the Sampled Value (SV) and the GOOSE protocols. The testbed also supports DNP3 and other layer 2 and layer 3 protocols such as Telnet, SSH, SFTP/FTP and other proprietary protocols needed to connect to industrial control system equipment. The testbed emulates real power conditions using the OpalRT hardware-in-the-loop (HIL) device which can create fault situations that cannot be easily tested on real systems. The electrical substation-grid testbed was created using real measurement, communication, and protection devices that electrical utilities commonly use.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

The commercial U.S. light-water reactor fleet has been operating at historical efficiency, reliability, and safety over the last decade. Nuclear power has the highest capacity factor of any other power generation technology while also serving as the largest baseload source for carbon-free energy. Despite this remarkable achievement, continued operations for many plants are threatened due to fierce electricity market competition and rising operations and maintenance costs of which continued maintenance of obsolete analog equipment is a contributor. The digital age and associated technologies are where the future lies in process control, and nuclear has yet to take full advantage of the capabilities offered therein. The Light Water Reactor Sustainability Program (LWRS) at Idaho National Laboratory (INL), sponsored by the Department of Energy, has a mission to help the light-water reactor fleet manage its foundational capabilities to continue providing safe and reliable carbon-free power. LWRS helps support that mission by providing scientific, technology-based solutions for advanced concepts of operations with a more viable business model that will allow the fleet to continue to operate at peak levels through extended plant operation. The LWRS Digitalization Project at INL seeks to leverage digital technologies to synthesize and transform work processes. We provide a state-of-the-art analysis of digitalized work processes in nuclear power and investigate ways in which researchers at INL and the nuclear industry can work together to identify what data to access, how to access it, what to do with the data, and most importantly, how to use the insights for decision-making across all levels within the business. Borne from these considerations, we present four guiding principles for digitalization: develop a coherent digitalization plan, apply human factors engineering, establish data governance, and anticipate unintended consequences. Together, these principles form a method that plants can use to effectively to digitalize nuclear industry work processes. Our guiding principles are informed by multiple knowledge sources. First, we document activities from the Work Digitalization Initiative, which was conceived as a means for nuclear organizations to help define and standardize the industry’s approach to digitalizing work. Second, we detail primary research conducted with industry professionals regarding drivers and barriers to digitalization adoption. We present survey results that demonstrate what the industry hopes to get out of digitalization and the ways that INL can continue to support the industry’s digital transformation. Third, we present a digitalization use case with industry partners NextAxiom Technology and Xcel Energy. The project objective was to transform the current condition report work process from paper to digital, incorporating digitalized principles. We report the development of the application and lessons learned. The accomplishments achieved by this research and development serve to identify critical needs for plant guidance in support of digitalization implementation and contribute to the knowledge and strategies available for utilities considering or undertaking digitalization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Ensuring Safe, Effective, and Reliable Use of Artificial Intelligence-Based Applications for Nuclear Electricity Generation through a Systems Approach

There is a significant potential to reduce operating and maintenance cost at nuclear power plants using artificial intelligence (AI) and machine learning (ML). For instance, AI/ML has the potential to significant improve work management processes, condition reporting, and plant surveillance activities. However, the nuclear industry has been slow in adopting AI/ML due to several multifaceted barriers discussed in this paper. This work presents such multidisciplinary approach intended to 1) accelerate industry adoption of AI/ML-based applications at nuclear power plants and 2) ensure their safe, reliable, and effective use. This approach is discussed further in this work and will be used to address overarching challenges associated with AI deployment at scale to provide industry guidance that support accelerated adoption of AI/ML technologies throughout the industry.

99 - GENERAL AND MISCELLANEOUS↗