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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 OSTI2022

A Critical Review of the Circular Economy for Lithium-Ion Batteries and Photovoltaic Modules: Status, Challenges, and Opportunities

To meet net-zero emissions and cost targets for power production, recent analysis indicates that photovoltaic (PV) capacity in the United States could exceed 1 TW by 2050 alongside comparable levels of energy storage capacity, mostly from batteries. For comparison, the total U.S. utility-scale power capacity from all energy sources in 2020 was 1.2 TW (EIA 2022), of which solar satisfied approximately 3% (DOE 2021). With such massive scales of deployment, questions have arisen regarding issues of material supply for manufacturing, end-of-life management of technologies, environmental impacts across the life cycle, and economic costs to both individual consumers and society at large. A set of solutions to address these issues center on the development of a circular economy - shifting from a take-make-waste linear economic model to one that retains the value of materials and products as long as possible, recovering materials at end of life to recirculate back into the economy. With limited global experience, scholars and practitioners have begun to investigate circular economy pathways, focusing on applying novel technologies and analytical methods to fast-growing sectors like renewable energy. This critical review aims to synthesize the growing literature to identify key insights, gaps, and opportunities for research and implementation of a circular economy for two of the leading technologies that enable the transition to a renewable energy economy: solar PV and lithium-ion batteries (LIBs). We apply state-of-the-science systematic literature review procedures to critically analyze over 3,000 publications on the circular economy of solar PV and LIBs, categorizing those that pass a series of objective screens in ways that can illuminate the current state of the art, highlight existing impediments to a circular economy, and recommend future technological and analytical research. We conclude that while neither PV nor LIB industries have reached a circular economy, they are both on a path towards increased circularity. Based on our assessment of the state of current literature and scientific understanding, we recommend research move beyond its prior emphasis on recycling technology development to more comprehensively investigate other CE strategies, more holistically consider economic, environmental and policy aspects of CE strategies, increase leveraging of digital information systems that can support acceleration towards a CE, and to continue to study CE-related aspects of LIB and PV markets.

circular economy↗
DOE OSTI2021

A catalogue of white dwarfs in Gaia EDR3

ABSTRACT We present a catalogue of white dwarf candidates selected from Gaia Early Data Release 3 (EDR3). We applied several selection criteria in absolute magnitude, colour, and Gaia quality flags to remove objects with unreliable measurements while preserving most stars compatible with the white dwarf locus in the Hertzsprung–Russell diagram. We then used a sample of over 30 000 spectroscopically confirmed white dwarfs and contaminants from the Sloan Digital Sky Survey (SDSS) to map the distribution of these objects in the Gaia absolute magnitude–colour space. Finally, we adopt the same method presented in our previous work on Gaia Data Release 2 (DR2) to calculate a probability of being a white dwarf (PWD) for ≃1.3 million sources that passed our quality selection. The PWD values can be used to select a sample of ${\simeq} 359\,000$ high-confidence white dwarf candidates. We calculated stellar parameters (effective temperature, surface gravity, and mass) for all these stars by fitting Gaia astrometry and photometry with synthetic pure-H, pure-He, and mixed H–He atmospheric models. We estimate an upper limit of 93 per cent for the overall completeness of our catalogue for white dwarfs with G ≤ 20 mag and effective temperature (Teff) > 7000 K, at high Galactic latitudes (|b| > 20°). Alongside the main catalogue we include a reduced proper motion extension containing ${\simeq} 10\,200$ white dwarf candidates with unreliable parallax measurements that could, however, be identified on the basis of their proper motion. We also performed a cross-match of our catalogues with SDSS Data Release 16 (DR16) spectroscopy and provide spectral classification based on visual inspection for all resulting matches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Statistical analysis of Al III and C III ] emission lines as virial black hole mass estimators in quasars

We test the usefulness of the intermediate ionisation lines Al IIIλ1860 and C III]λ1909 as reliable virial mass estimators for quasars. We identified a sample of 309 quasars from the Sloan Digital Sky Survey Data Release 16 (SDSS DR16) in the redshift range 1.2 ≤ z ≤ 1.4 that have [O II]λ3728 recorded on the same spectrum as Al IIIλ1860, Si III]λ1892, and C III]λ1909. We set the systemic quasar redshift using careful measurements of [O II]λ3728. We then classified the sources as Population A (Pop. A), extreme Population A (Pop. xA), and Population B (Pop. B), and analysed the 1900 Å blend using multi-component models to look for systematic line shifts of Al IIIλ1860 and C III]λ1909 along the quasar main sequence. We do not find significant shifts of the Al IIIλ1860 line peak in Pop. B or in the vast majority of Pop. A. For Pop. xA, a small median blueshift of -250 km s -1 was observed, motivating a decomposition of the Al III line profile into a virialised component centred at rest frame and a blueshifted component for an outflow emission. For Pop. B objects, we proved the empirical necessity to fit a redshifted very broad component, clearly seen in C III], and analysed the physical implications on a Pop. B composite spectrum using CLOUDY simulations. We find consistent black hole mass estimations using Al III and C III] as virial estimators for the bulk of Pop. A. Al III (and even C III]) is a reliable virial black hole mass estimator for Pop. A and Pop. B objects. The Pop. xA sources deserve special attention due to the significant blueshifted excess observed in the line profile of Al III, although it is not as large as the excess observed in C IVλ1549.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i – z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Catalog of Planetary Nebulae Detected by GALEX and Corollary Optical Surveys

Planetary nebulae (PNs) consist of an ionized envelope surrounding a hot central star (CSPN) that emits mostly at ultraviolet (UV) wavelengths. UV observations, therefore, provide important information on both the CSPN and the nebula. We have matched the PNs in the Hong Kong/Australian Astronomical Observatory/Strasbourg Hα catalog with the Galaxy Evolution Explorer (GALEX) UV sky surveys, the Sloan Digital Sky Survey (SDSS) data release 16, and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) PS1 second release. A total of 671 PNs were observed by GALEX with the far-UV (FUV; 1344 Å–1786 Å) and/or the near-UV (NUV; 1771 Å–2831 Å) detectors on (GUVPNcat); 83 were observed by SDSS (PNcatxSDSSDR16) and 1819 by Pan-STARRS (PNcatxPS1MDS). We merged a distilled version of these matched catalogs into GUVPNcatxSDSSDR16xPS1MDS, which contains a total of 375 PNs with both UV and optical photometry over a total spectral coverage of ~1540 Å–9610 Å. We analyzed separately 170 PNs resolved in GALEX images and determined their UV radius by applying a flux profile analysis. The CSPN flux could be extracted separately from the PN emission for 8 and 50 objects with SDSS and Pan-STARRS counterparts, respectively. The multiband photometry was used to distinguish between compact and extended PNs and CSPNs (binary CSPNs) by color–color diagram analysis. We found that compact PN candidates could be identified by using the r - i < - 0.4 and -1 < FUV-NUV < 1 colors, whereas binary CSPN candidates in given T eff ranges (all with color r-i > -0.4) can be identified in the color region (FUV-NUV) ≤ 6(r-i)+1.3, -0.8 < FUV-NUV < 0.4, and r-i < 0.75.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Black hole mass estimation for active galactic nuclei from a new angle

Abstract The scaling relations between supermassive black holes and their host galaxy properties are of fundamental importance in the context black hole-host galaxy co-evolution throughout cosmic time. In this work, we use a novel algorithm that identifies smooth trends in complex data sets and apply it to a sample of 2000 type 1 active galactic nuclei (AGNs) spectra. We detect a sequence in emission line shapes and strengths which reveals a correlation between the narrow L([O iii])/L(H β) line ratio and the width of the broad H α. This scaling relation ties the kinematics of the gas clouds in the broad line region to the ionization state of the narrow line region, connecting the properties of gas clouds kiloparsecs away from the black hole to material gravitationally bound to it on sub-parsec scales. This relation can be used to estimate black hole masses from narrow emission lines only. It therefore enables black hole mass estimation for obscured type 2 AGNs and allows us to explore the connection between black holes and host galaxy properties for thousands of objects, well beyond the local Universe. Using this technique, we present the MBH–σ and MBH–M* scaling relations for a sample of about 10 000 type 2 AGNs from Sloan Digital Sky Survey. These relations are remarkably consistent with those observed for type 1 AGNs, suggesting that this new method may perform as reliably as the classical estimate used in non-obscured type 1 AGNs. These findings open a new window for studies of black hole-host galaxy co-evolution throughout cosmic time.

Baron, Dalya↗
DOE OSTI2019

Comparative Assessment of Experimental Testing of Instrument with an Embedded Digital Device Using Model-Based and Conventional Methods

The use of digital equipment has not been widespread in U.S. nuclear facilities. A major contributing factor to this situation arises from regulatory concerns about common cause failures (CCFs) stemming from the use of identical software in redundant systems, questions as to how to quantify the reliability of software-based products, validity of tools that are used for software verification and validation (V&V), and the effectiveness of software QA procedures. Compounding the concerns are sporadic incidents of digital equipment malfunctions in nuclear power plants, including lockups resulting from software memory leaks, feedwater control malfunctions due to excessive traffic on Ethernet networks, software logic errors, and other problems leading to plant trips. Additionally, the limited amount of demonstrated research and a lack of industry consensus on quantitative software reliability methodologies has hindered the widespread adoption of digital I&C in the nuclear industry. In a recent Regulatory Issue Summary (RIS), the U.S. Nuclear Regulatory Commission (NRC) identified concerns about the impact of greater use of instrumentation and control (I&C) equipment with an embedded digital device (EDD) [1]. Specifically, the NRC staff states that increased use of such devices “may increase a facility’s vulnerability to a CCF” (i.e., common-cause failure). The prevailing NRC guidance regarding software CCF (SCCF) identifies two design attributes that are acceptable for eliminating CCF concerns: (1) diversity or (2) testability (specifically, 100% testability). Either solution can result in high costs and remaining licensing uncertainty (i.e., how much diversity is enough? how to ensure test coverage of every possible sequence of device states?). Consequently, many utilities and reactor designers have limited or avoided more extensive use of digital technology to minimize licensing, scheduling, and financial risk. Without development of cost effective qualification methods to satisfy regulatory requirements and address the potential for CCF vulnerability associated with EDDs, the nuclear power industry may not be able to realize the benefits of digital technology achieved by other industries. In order to address these challenges, the U.S. Department of Energy (DOE) is sponsoring this research to assess the prospect of CCF in digital I&C equipment. The purpose of the current research is to develop an effective approach employing science-based methods to resolve concerns about CCF vulnerability that serve to inhibit deployment of advanced instrumentation (e.g., sensors, actuators, microcontrollers) with EDDs in nuclear power applications. The research objectives address the challenge of establishing high levels of safety and reliability assurance needed for the qualification of EDDs (e.g., microprocessors, programmable logic devices) that are subject to software design faults, complex failure modes, and CCF vulnerability. Specific objectives are: (1) assess the regulatory context for treatment of CCF vulnerability a graded approach to their qualification, (3) develop and extend model-based testing methods to enable effective demonstration of whether devices are subject to CCF, which may arise from vulnerabilities introduced at any stage of the design lifecycle, (4) establish a cost-effective testing framework that incorporates automation and test scenario prioritization, and (5) demonstrate the qualification approach through selection and testing of candidate digital device(s). This report documents an assessment of the results from experimental testing of a representative instrument with an embedded digital device. The experiment serves as a proof-of-concept demonstration of a model-based testing (MBT) framework. The objective of MBT is to ensure that an EDD undergoes suitably comprehensive testing to establish that it is not subject to SCCF. The approach by which MBT accomplishes this objective is through development and application of an effective test suite that provides the appropriate coverage to detect the full range of postulated faults that may arise at the requirements, design, and code (implementation) level of the EDD software. The findings of the experiment involve the application of the MBT framework by Ohio State University (OSU) through testing of the selected demonstration instrument (i.e., the prototype smart pressure sensor developed by Virginia Commonwealth University, VCU). In addition, the experiment findings also involve application of a more conventional black-box testing approach that serves as a baseline against which the MBT methodology can be compared. The primary outcome documented in this report is the comparative analysis of the capabilities demonstrated for each method. The results of the analysis demonstrate clear benefits that arise from the MBT methodology. The comparison indicates greater effectiveness from the more comprehensive evidence available through MBT and enhanced efficiency achieved through automation within the MBT framework. The structure of the report addresses the approach and application of each test method as well as the comparative analysis of the experimental results. Chapter 2 gives a summary overview of the demonstration approach. Chapter 3 covers the baseline testing and Chapter 4 documents the model-based testing. Chapter 5 describes the findings of the comparative analysis and presents the conclusions that can be drawn about the effectiveness and efficiency of the MBT methodology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Digital Breast Tomosynthesis for Intraoperative Margin Assessment during Breast-Conserving Surgery

Background: Intraoperative margin assessment for breast cancer patients undergoing segmental mastectomy (SM) enables identification of positive margins, with immediate excision of additional tissue to obtain negative margins. Objective: The aim of this study was to determine the ability of digital breast tomosynthesis (DBT) to detect positive margins compared with an institution’s standard extensive processing (SEP). Methods: SM specimens underwent intraoperative SEP with two-dimensional (2D) imaging of the intact and sliced specimen, with review by a breast radiologist and gross assessment by a breast pathologist. Findings guided the surgeon to excise additional tissue. DBT images of intact specimens were prospectively obtained and retrospectively reviewed by a breast radiologist. A positive margin was defined as tumor at ink. Results: Ninety-eight patients underwent 99 SMs. With SEP, 14 (14%) SM specimens had 19 positive margins. SEP did not detect 3 of the 19 positive margins, for a sensitivity of 84%, specificity of 78%, positive predictive value (PPV) of 11%, and negative predictive value (NPV) of 99%. Moreover, DBT did not detect 5 of the 19 positive margins, for a sensitivity of 74% (p > 0.05), specificity of 91% (p < 0.05), PPV of 21.5%, and NPV of 99%. With SEP guidance to excise additional tissue, six cases had final positive margins, with SEP not identifying three of these cases and DBT not identifying two. Pathology from the second surgery of these patients showed either no additional malignancy or only focal ductal carcinoma in situ. Conclusions: DBT is an accurate method for detecting positive margins in breast cancer patients undergoing SM, performing similar to institutional labor-intensive, intraoperative standard processing.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

How galaxies populate halos in extreme density environments: An analysis of the halo occupation distribution in SDSS

Recent works have shown that the properties of galaxy populations in simulated dark matter halos vary with large-scale environments. These results suggest a variation in the halo occupation distribution (HOD) in extreme density environments since the dynamical and astrophysical conditions prevailing in these regions may significantly affect the formation and evolution of their halos and residing galaxies, influencing the mean number of satellite galaxies. To analyse these effects, we identify cosmic voids and future virialised structures (FVS) in the Sloan Digital Sky Server Data Release 12 (SDSS-DR12) and estimate the HOD within these super-structures using group catalogues as dark matter halo proxies. Our goal is to use observational galaxy data to characterise the HOD within cosmic voids and FVS, explore the different properties of these galaxies populations and compare them with the general results outside these super-structures. We use a publicly available observational galaxy catalogue with information on redshifts, positions, magnitudes and other astrophysical features to build a volume complete galaxy sample and identify cosmic voids and FVS. Using a publicly available galaxy group catalogue as a proxy to dark matter halos, we compute the HOD within both types of super-structures for different absolute magnitude thresholds. We also study the dependence on the results on the main void and FVS properties, density and volume. We also analysed the main characteristics of the stellar content of galaxies inside these extreme-density regions such as the mean stellar age and the galaxy light concentration index. In all cases, we compare the results with those derived from the Field sample, defined by objects outside both types of environments. Inside cosmic voids, we find a strong decrease in HOD concerning the Field results. In the most extreme cases, the mean number of satellites fall to ~ 50%. Inside FVS, the HOD shows a significant increase to the Field, with a ~ 40% excess in the mean number of satellites. These results are present for the different galaxy luminosity ranges explored. In both environments, the differences with respect to the Field increases for the extreme values of the density environments. However, we obtain no signs of variations related to intrinsic characteristics of the super-structures, indicating that the effects depend mainly on the density of the large-scale environment. In addition, we find that the cumulative distribution of the mean age of stars of the central galaxy also varies in the different regions, this suggests that the history of the formation of the dark matter halos may be different. Finally, we explore the HOD for the 25% youngest (oldest) galaxies, based on the mean age of their stars. We find that for the low-mass groups the youngest galaxies are only present inside voids and are generally central galaxies. On the other hand, for the high-mass groups the FVS environments show the same increase in the HOD concerning the Field as previously mentioned. We find that cosmic voids lack a significant fraction of galaxies with the oldest stellar population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

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

The 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. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing 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 microstructural variability 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 models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s 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 additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation 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 helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Composite Spectral Energy Distributions and Infrared–Optical Colors of Type 1 and Type 2 Quasars

We present observed mid-infrared and optical colors and composite spectral energy distributions (SEDs) of type 1 (broad-line) and 2 (narrow-line) quasars selected from Sloan Digital Sky Survey (SDSS) spectroscopy. A significant fraction of powerful quasars are obscured by dust and are difficult to detect in optical photometric or spectroscopic surveys. However, these may be more easily identified on the basis of mid-infrared (MIR) colors and SEDs. Using samples of SDSS type 1 and 2 matched in redshift and [O iii] luminosity, we produce composite rest-frame 0.2–15 μm SEDs based on SDSS, UKIDSS, and Wide-field Infrared Survey Explorer photometry and perform model fits using simple galaxy and quasar SED templates. The SEDs of type 1 and 2 quasars are remarkably similar, with the differences explained primarily by the extinction of the quasar component in the type 2 systems. For both types of quasar, the flux of the active galactic nucleus (AGN) relative to the host galaxy increases with AGN luminosity (L{sub [OIII]}) and redder observed MIR color, but we find only weak dependencies of the composite SEDs on mechanical jet power as determined through radio luminosity. We conclude that luminous quasars can be effectively selected using simple MIR color criteria similar to those identified previously (W1--W2>0.7; Vega), although these criteria miss many heavily obscured objects. Obscured quasars can be further identified based on optical–IR colors (for example, (u--W3 [AB])>1.4(W1--W2 [Vega])+3.2). These results illustrate the power of large statistical studies of obscured quasars selected on the basis of MIR and optical photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
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 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↗