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DOE OSTI2024

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Architecture and Methods for Substation SCADA Cybersecurity: Best Practices

There are over 3000 electricity providers in the United States, encompassing investor and publicly owned utilities as well as electric cooperatives. There has been ongoing trends to increasingly automate and provide remote control and monitoring of electric energy delivery systems. The deployment of computer network technologies has increased the efficiency and reliability of electric power infrastructure. However, the increased use of digital communications has also increased the vulnerability to malicious cyber attacks [1]. In 2004 the National Research Councils (National Academies) formed a committee of specialists to address these vulnerabilities and propose possible solutions with an objective to prioritize the R&D needs for developing countermeasures. The committee addressed many potential concerns in the electric power delivery system and classified them based upon different criteria and presented recommendations to minimize the gap between the academic research directions and the needs of the electric utility industry. The complexity and diversity of the electric power delivery system in the U.S. has opened many ports for attackers and intruders [1]. This complexity and diversity is attributed to the fact that power delivery system is a network of substations, transmission and distribution lines, sub-networks of controlling, sensing and monitoring units, and human operator involvement for running the system [1]. Accordingly, any incident such as the occurrence of a fault or disturbance in this complex network cannot be deferred and should be resolved within an order of milliseconds, otherwise there is risk of large-scale outages similar to the occurrences in India and the U.S. in 2003 [2]. There are three main vulnerabilities in supervisory control and data acquisition (SCADA) systems commonly identified—physical vulnerability, cyber vulnerability and personal vulnerability [1]. In terms of cyber threats, SCADA systems are the most critical elements in the electric power grid in the U.S. Unauthorized access to a SCADA system could enable/disable unexpected equipment (such as disable the protection system or a circuit breaker) which could cause large scale disruptions of electric power delivery. This paper provides an overview of power system SCADA technologies in transmission substations (Section 2) and summarizes the best practices for implementing a cyber security program. After introducing SCADA system operations in Section 2, a description of the security challenges for SCADA systems is presented in Section 3. In Section 4, NECRC Critical Infrastructure Protection standards CIP-002 through CIP-009 are summarized. An overview of industry best practices is presented in Section 5.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

High-Throughput Study of Diffusion and Phase Transformation Kinetics of Magnesium-Based Systems for Automotive Cast Magnesium Alloys

The objective of the proposed study is to establish a scientific foundation on kinetic modeling of diffusion, phase precipitation, and casting/solidification, in order to accelerate the design and optimization of cast magnesium (Mg) alloys for weight reduction of U.S. automotive fleet. The team has performed the following tasks: 1) study diffusion kinetics of various Mg-containing binary systems using high-throughput diffusion multiples to establish reliable diffusivity and mobility databases for the Mg-aluminum (Al)-zinc (Zn)-tin (Sn)-calcium (Ca)-strontium (Sr)-manganese (Mn) systems; 2) study the precipitation kinetics (nucleation, growth and coarsening) using both innovative dual-anneal diffusion multiples and cast model alloys to provide large amounts of kinetic data (including interfacial energy) and microstructure atlases to enable implementation of the Kampmann-Wagner numerical model to simulate phase transformation kinetics of non-spherical/non-cuboidal precipitates in Mg alloys; 3) implement a micromodel to take into account back diffusion in the solid phase in order to predict microstructure and microsegregation in multicomponent Mg alloys during dendritic solidification especially under high pressure die-casting (HPDC) conditions; and, 4) widely disseminate the data, knowledge and information using the Materials Genome Initiative infrastructure (http://www.mgidata.org) as well as publications and digital data sharing to enable researchers to identify new pathways/routes to better cast Mg alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Inventory of Public Key Cryptography in US Electric Vehicle Charging

Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Dust-obscured galaxies in the local universe

We use Wide-field Infrared Survey Explorer (WISE), AKARI, and Galaxy Evolution Explorer (GALEX) data to select local analogs of high-redshift (z ∼ 2) dust obscured galaxies (DOGs). We identify 47 local DOGs with S {sub 12μm}/S {sub 0.22μm} ≥ 892 and S {sub 12μm} > 20 mJy at 0.05 < z < 0.08 in the Sloan Digital Sky Survey data release 7. The infrared (IR) luminosities of these DOGs are in the range 3.4 × 10{sup 10} (L {sub ☉}) ≲ L {sub IR} ≲ 7.0 × 10{sup 11} (L {sub ☉}) with a median L {sub IR} of 2.1 × 10{sup 11} (L {sub ☉}). We compare the physical properties of local DOGs with a control sample of galaxies that have lower S {sub 12μm}/S {sub 0.22μm} but have similar redshift, IR luminosity, and stellar mass distributions. Both WISE 12 μm and GALEX near-ultraviolet (NUV) flux densities of DOGs differ from the control sample of galaxies, but the difference is much larger in the NUV. Among the 47 DOGs, 36% ± 7% have small axis ratios in the optical (i.e., b/a < 0.6), larger than the fraction among the control sample (17% ± 3%). There is no obvious sign of interaction for many local DOGs. No local DOGs have companions with comparable optical magnitudes closer than ∼50 kpc. The large- and small-scale environments of DOGs are similar to the control sample. Many physical properties of local DOGs are similar to those of high-z DOGs, even though the IR luminosities of local objects are an order of magnitude lower than for the high-z objects: the presence of two classes (active galactic nuclei- and star formation-dominated) of DOGs, abnormal faintness in the UV rather than extreme brightness in the mid-IR, and diverse optical morphology. These results suggest a common underlying physical origin of local and high-z DOGs. Both seem to represent the high-end tail of the dust obscuration distribution resulting from various physical mechanisms rather than a unique phase of galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Enhancing disease surveillance with novel data streams: challenges and opportunities

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

STATISTICAL STUDY OF 2XMMi-DR3/SDSS-DR8 CROSS-CORRELATION SAMPLE

Cross-correlating the XMM-Newton 2XMMi-DR3 catalog with the Sloan Digital Sky Survey (SDSS) Data Release 8, we obtain one of the largest X-ray/optical catalogs and explore the distribution of various classes of X-ray emitters in the multidimensional photometric parameter space. Quasars and galaxies occupy different zones while stars scatter in them. However, X-ray active stars have a certain distributing rule according to spectral types. The earlier the type of stars, the stronger its X-ray emitting. X-ray active stars have a similar distribution to most stars in the g - r versus r - i diagram. Based on the identified samples with SDSS spectral classification, a random forest algorithm for automatic classification is performed. The result shows that the classification accuracy of quasars and galaxies adds up to more than 93.0% while that of X-ray emitting stars only amounts to 45.3%. In other words, it is easy to separate quasars and galaxies, but it is difficult to discriminate X-ray active stars from quasars and galaxies. If we want to improve the accuracy of automatic classification, it is necessary to increase the number of X-ray emitting stars, since the majority of X-ray emitting sources are quasars and galaxies. The results obtained here will be used for the optical spectral survey performed by the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST, also named the Guo Shou Jing Telescope), which is a Chinese national scientific research facility operated by the National Astronomical Observatories, Chinese Academy of Sciences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

THE TIME DOMAIN SPECTROSCOPIC SURVEY: VARIABLE SELECTION AND ANTICIPATED RESULTS

We present the selection algorithm and anticipated results for the Time Domain Spectroscopic Survey (TDSS). TDSS is an Sloan Digital Sky Survey (SDSS)-IV Extended Baryon Oscillation Spectroscopic Survey (eBOSS) subproject that will provide initial identification spectra of approximately 220,000 luminosity-variable objects (variable stars and active galactic nuclei across 7500 deg{sup 2} selected from a combination of SDSS and multi-epoch Pan-STARRS1 photometry. TDSS will be the largest spectroscopic survey to explicitly target variable objects, avoiding pre-selection on the basis of colors or detailed modeling of specific variability characteristics. Kernel Density Estimate analysis of our target population performed on SDSS Stripe 82 data suggests our target sample will be 95% pure (meaning 95% of objects we select have genuine luminosity variability of a few magnitudes or more). Our final spectroscopic sample will contain roughly 135,000 quasars and 85,000 stellar variables, approximately 4000 of which will be RR Lyrae stars which may be used as outer Milky Way probes. The variability-selected quasar population has a smoother redshift distribution than a color-selected sample, and variability measurements similar to those we develop here may be used to make more uniform quasar samples in large surveys. The stellar variable targets are distributed fairly uniformly across color space, indicating that TDSS will obtain spectra for a wide variety of stellar variables including pulsating variables, stars with significant chromospheric activity, cataclysmic variables, and eclipsing binaries. TDSS will serve as a pathfinder mission to identify and characterize the multitude of variable objects that will be detected photometrically in even larger variability surveys such as Large Synoptic Survey Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

SLOAN DIGITAL SKY SURVEY OBSERVATIONS OF KUIPER BELT OBJECTS: COLORS AND VARIABILITY

Colors of trans-Neptunian objects (TNOs) are used to study the evolutionary processes of bodies in the outskirts of the solar system and to test theories regarding their origin. Here I describe a search for serendipitous Sloan Digital Sky Survey (SDSS) observations of known TNOs and Centaurs. I present a catalog of SDSS photometry, colors, and astrometry of 388 measurements of 42 outer solar system objects. I find weak evidence, at the Almost-Equal-To 2{sigma} level (per trial), for a correlation between the g - r color and inclination of scattered disk objects and hot classical Kuiper Belt objects. I find a correlation between the g - r color and the angular momentum in the z direction of all the objects in this sample. These findings should be verified using larger samples of TNOs. Light curves as a function of phase angle are constructed for 13 objects. The steepness of the slopes of these light curves suggests that the coherent backscatter mechanism plays a major role in the reflectivity of outer solar system small objects at small phase angles. I find weak evidence for an anticorrelation, significant at the 2{sigma} confidence level (per trial), between the g-band phase-angle slope parameter and the semimajor axis, as well as the aphelion distance, of these objects (i.e., they show a more prominent 'opposition effect' at smaller distances from the Sun). However, this plausible correlation should be verified using a larger sample. I discuss the origin of this possible correlation and argue that if this correlation is real it probably indicates that 'Sedna'-like objects have a different origin than other classes of TNOs. Finally, I identify several objects with large variability amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

mJIVE-20: A survey for compact mJy radio objects with the very long baseline array

We present the description and early results of the mJy Imaging VLBA Exploration at 20 cm (mJIVE-20). mJIVE-20 is a large project on the Very Long Baseline Array which is systematically inspecting a large sample of mJy radio sources, pre-selected from the Faint Images of the Radio Sky at Twenty cm (FIRST) survey made with the Very Large Array, to identify any compact emission that may be present. The survey is being undertaken using filler time on the VLBA, which utilizes short segments scheduled in bad weather and/or with a reduced number of antennas, during which no highly rated science projects can be scheduled. The newly available multifield capability of the VLBA makes it possible for us to inspect of the order of 100 sources per hour of observing time with a 6.75σ detection sensitivity of approximately 1 mJy beam{sup –1}. The results of the mJIVE-20 survey are made publicly available as soon as the data are calibrated. After 18 months of observing, over 20,000 FIRST sources have been inspected, with 4336 very long baseline interferometry detections. These initial results suggest that within the range 1-200 mJy, fainter sources are somewhat more likely to be dominated by a very compact component than brighter sources. Over half of all arcsecond-scale mJy radio sources contain a compact component, although the fraction of sources that are dominated by milliarcsecond scale structure (where the majority of the arcsecond scale flux is recovered in the mJIVE-20 image) is smaller at around 30%-35%, increasing toward lower flux densities. Significant differences are seen depending on the optical classification of the source. Radio sources with a stellar/point-like counterpart in the Sloan Digital Sky Survey (SDSS) are more likely to be detected overall, but this detection likelihood appears to be independent of the arcsecond-scale radio flux density. The trend toward higher radio compactness for fainter sources is confined to sources that are not detected in SDSS or that have counterparts classified as galaxies. These results are consistent with a unification model of active galactic nuclei in which less luminous sources have on average slower radio jets, with lower Doppler suppression of compact core emission over a wider range of viewing angles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Pilot Project Technology Business Case: Mobile Work Packages

Performance advantages of the new pilot project technologies are widely acknowledged, but it has proven difficult for utilities to derive business cases for justifying investment in these new capabilities. Lack of a business case is often cited by utilities as a barrier to pursuing wide-scale application of digital technologies to nuclear plant work activities. The decision to move forward with funding usually hinges on demonstrating actual cost reductions that can be credited to budgets and thereby truly reduce O&M or capital costs. Technology enhancements, while enhancing work methods and making work more efficient, often fail to eliminate workload such that it changes overall staffing and material cost requirements. It is critical to demonstrate cost reductions or impacts on non-cost performance objectives in order for the business case to justify investment by nuclear operators. The Business Case Methodology (BCM) was developed in September of 2015 to frame the benefit side of II&C technologies to address the “benefit” side of the analysis—as opposed to the cost side—and how the organization evaluates discretionary projects (net present value (NPV), accounting effects of taxes, discount rates, etc.). The cost and analysis side is not particularly difficult for the organization and can usually be determined with a fair amount of precision (not withstanding implementation project cost overruns). It is in determining the “benefits” side of the analysis that utilities have more difficulty in technology projects and that is the focus of this methodology. The methodology is presented in the context of the entire process, but the tool provided is limited to determining the organizational benefits only. This report describes a the use of the BCM in building a business case for mobile work packages, which includes computer-based procedures and other automated elements of a work package. Key to those impacts will be identifying where the savings are “harvestable,” meaning they result in an actual reduction in headcount and/or cost. The report describes the specific activities conducted with a partner utility to examine the various work activities associated with mobile work packages to determine what time savings and error rate reductions are available. The report summarizes these findings in the form of a business case for the technology.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRA's of nuclear power plants (NPPs), and, (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware , development of a philosophical basis for defining software failure , and identification of desirable attributes of quantitative software reliability methods . Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. It is widely recognized that software failures are due to the triggering of pre-existing defects by the software's operational environment. Software defects can arise from errors made in user requirements or coding errors introduced during the developmental process. The software's operational environment includes factors such as the time history of digital system inputs, communication interfaces, the internal state of the digital system, and external conditions. Thus, software reliability is a function of both the number of pre-existing defects and the presence of a triggering condition caused by the manner in which the software is used. In this paper, we describe a statistical software-testing approach for quantifying software reliability and applied it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The work involved collaboration between BNL and INL. The objectives of the study include: (1) Development of a statistical testing approach for estimating software failure probability on demand, the results of which are suitable for including in a probabilistic risk assessment (PRA); and, (2) Application of the approach to the LOCS to estimate its failure probability, and obtain insights into the feasibility, practicality, and usefulness of the estimation in models of digital systems for inclusion in nuclear power plants' PRAs. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

WE-G-204-09: Medical Physics 2.0 in Practice: Automated QC Assessment of Clinical Chest Images

Purpose: To determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in our clinical operation. Methods: Seventeen gridless AP Chest radiographs from a GE Optima portable digital radiography (DR) unit (Group 1), seventeen (routine) PA Chest radiographs from a GE Discovery DR unit (Group 2), and sixteen gridless (non-routine) PA Chest radiographs from the same Discovery DR unit (Group 3) were chosen for analysis. Groups were selected to represent “sub-standard” (Group 1), “standard-of-care” (Group 2), and images with a gross technical error (Group 3). Group 1 images were acquired with lower kVp (90 vs. 125), shorter source-to-image distance (127cm vs 183cm) and were expected to have lower quality than images in Group 2. Group 3 was expected to have degraded contrast versus Group 2.This evaluation was approved by the institutional Quality Improvement Assurance Board (QIAB). Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software previously described{sup 1} and validated{sup 2}. Image quality for individual images was reported in terms of lung grey level(Lgl); lung noise(Ln); rib-lung contrast(RLc); rib sharpness(Rs); mediastinum detail(Md), noise(Mn), and alignment(Ma); subdiaphragm-lung contrast(SLc); and subdiaphragm area(Sa). Metrics were compared across groups. Results: Metrics agreed with published Quality Consistency Ranges with three exceptions: higher Lgl, lower RLc, and SDc. Higher bit depth (16 vs 12) accounted for higher Lgl values in our images. Values were most internally consistent for Group 2. The most sensitive metric for distinguishing between groups was Mn followed closely by Ln. The least sensitive metrics were Md and RLc. Conclusion: The software appears promising for objectively and automatically identifying substandard images in our operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Google Health Trends performance reflecting dengue incidence for the Brazilian states

Abstract Background Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. Methods We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. Results From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. Conclusions The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2015

Light Water Reactor Sustainability Program: Digital Architecture Requirements

The Digital Architecture effort is a part of the Department of Energy (DOE) sponsored Light-Water Reactor Sustainability (LWRS) Program conducted at Idaho National Laboratory (INL). The LWRS program is performed in close collaboration with industry research and development (R&D) programs that provides the technical foundations for licensing and managing the long-term, safe, and economical operation of current nuclear power plants (NPPs). One of the primary missions of the LWRS program is to help the U.S. nuclear industry adopt new technologies and engineering solutions that facilitate the continued safe operation of the plants and extension of the current operating licenses. Therefore, a major objective of the LWRS program is the development of a seamless digital environment for plant operations and support by integrating information from plant systems with plant processes for nuclear workers through an array of interconnected technologies. In order to get the most benefits of the advanced technology suggested by the different research activities in the LWRS program, the nuclear utilities need a digital architecture in place to support the technology. A digital architecture can be defined as a collection of information technology (IT) capabilities needed to support and integrate a wide-spectrum of real-time digital capabilities for nuclear power plant performance improvements. It is not hard to imagine that many processes within the plant can be largely improved from both a system and human performance perspective by utilizing a plant wide (or near plant wide) wireless network. For example, a plant wide wireless network allows for real time plant status information to easily be accessed in the control room, field workers’ computer-based procedures can be updated based on the real time plant status, and status on ongoing procedures can be incorporated into smart schedules in the outage command center to allow for more accurate planning of critical tasks. The goal of the digital architecture project is to provide a long-term strategy to integrate plant systems, plant processes, and plant workers. This include technologies to improve nuclear worker efficiency and human performance; to offset a range of plant surveillance and testing activities with new on-line monitoring technologies; improve command, control, and collaboration in settings such as outage control centers and work execution centers; and finally to improve operator performance with new operator aid technologies for the control room. The requirements identified through the activities in the Digital Architecture project will be used to estimate the amount of traffic on the network and hence estimating the minimal bandwidth needed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗