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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Direct Observations of Solute Dispersion in Rocks With Distinct Degree of Sub‐Micron Porosity

Abstract The transport of chemical species in rocks is affected by their structural heterogeneity to yield a wide spectrum of local solute concentrations. To quantify such imperfect mixing, advanced methodologies are needed that augment the traditional breakthrough curve analysis by probing solute concentration within the fluids locally. Here, we demonstrate the application of asynchronous, multimodality imaging by X‐ray computed tomography (XCT) and positron emission tomography (PET) to the study of passive tracer experiments in laboratory rock cores. The four‐dimensional concentration maps measured by PET reveal specific signatures of the transport process, which we have quantified using fundamental measures of mixing and spreading. We observe that the extent of solute spreading correlate strongly with the strength of subcore‐scale porosity heterogeneity measured by XCT, while dilution is enhanced in rocks containing substantial sub‐micron porosity. We observe that the analysis of different metrics is necessary, as they can differ in their sensitivity to the strength and forms of heterogeneity. The multimodality imaging approach is uniquely suited to probe the fundamental difference between spreading and mixing in heterogeneous media. We propose that when multi‐dimensional data is available, mixing and spreading can be independently quantified using the same metric. We also demonstrate that one‐dimensional transport models have limited predictive ability toward the internal evolution of the solute concentration, when the model is solely calibrated against the effluent breakthrough curves. The data set generated in this study can be used to build realistic digital rock models and to benchmark transport simulations that account deterministically for rock property heterogeneity.

Kurotori, Takeshi [Department of Chemical Engineer↗
DOE OSTI2024

A 32-Channel Cryo-CMOS ASIC for SNSPD Biasing and Readout with Picosecond

Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specific integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifiers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verified using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.

Fredenburg, Jeff↗
DOE OSTI2026

Timeseries Photos of a Variably Inundated Stream: Umtanum Creek, Washington, United States

This dataset is associated with a broader study using game camera timeseries photos collected to evaluate stream variable inundation via changes in width (i.e. wet fraction). Four game cameras were deployed along Umtanum Creek (Washington, United States) to track changes in stream inundation over time. Drone imagery was collected at the same location on October 18, 2024 which was used to construct a digital elevation model (DEM) of the streambed topography. The associated paper and data can be found at https://doi.org/10.1016/j.envsoft.2025.106715 (Bao et al., 2025a)) and https://doi.org/10.15485/2589885 (Bao et al., 2025b), respectively. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to this readme, this data package also includes a file-level metadata (FLMD) files that describes each file and a data dictionaries (DD) that describe all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) field protocol; and (5) folders containing game camera photos. Game camera photos are organized into folders for each camera (CDL, CUL, CDR, CUR; see readme for information on camera naming) by the month photos were collected. All files are .csv, .jpg, or .pdf.

AI image segmentation↗
DOE OSTI2024

A 32-Channel Cryo-CMOS ASIC for SNSPD Biasing and Readout with Picosecond Timing

Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specifc integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verifed using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.

Fredenburg, Jeff↗
DOE OSTI2026

MARVEL Reactor Digital Engineering Developments

The MARVEL reactor project has served to introduce a new generation of engineers to the processes required to transform a reactor design from simply an idea on paper into what will be an approved, constructed, and operational nuclear power system. Much as there have been advances in materials, analysis, and evaluation methodologies over the 50 years since the last reactor was built at INL, so too has the technology for managing the engineering process itself advanced. Digital Engineering tools and methods provide improved coordination between previously siloed engineering disciplines, reduced burdens of non-value-added data transcription processes and bring forward insights and improvements that might otherwise fall later in the design stage, where changes are much more costly. While the tools and techniques to support the full digital engineering vision are not yet complete, the MARVEL design processes provide valuable demonstrations and validations of key aspects and illuminate further areas for implementation by subsequent projects.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

CHESS 2025: Crown polygons and extracted reflectance for field sampling sites

This dataset contains (1) crown polygons for each tree, meadow, and shrub site sampled in the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign (in geojson format, .geojson) and (2) extracted reflectance, uncertainty, and shade estimates for each crown polygon from the 2018 National Ecological Observatory Network (NEON) and 2025 CHESS campaigns. (in CSV format, .csv). Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). Crown polygons were manually delineated for each site in the 2025 campaign using a combination of field-collected GPS data (doi:10.15485/3022418), RGB (red, green, blue) and false color reflectance mosaics (doi:10.15485/3013535), and LiDAR-derived (Light Detection and Ranging) canopy height (CHM) and digital surface (DSM) models (DOI and citation to be added upon publication). Where there was misalignment between the spectrometer- and LiDAR-derived data products, polygons prioritized alignment with the spectrometer-derived data products. Polygons were delineated conservatively to only select pixels representative of vegetation samples collected in the field. Crown polygons for 2018 are published at (doi:10.15485/1618130) and were developed using the same protocol. For each polygon, all pixels from all flightlines were extracted where the pixel centroid was contained within the polygon. For each pixel, we extracted the surface reflectance, uncertainty, and shade estimates. Details on the extracted datasets are available at (doi:10.15485/3013527, doi:10.15485/3013535). 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 Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
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 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 OSTI2024

Efficient Extraction Of Building Elevation Attributes For Flood Risk Management Using Airborne LiDAR Data

In this paper, we address the need for extracting two key building elevation attributes—Lowest Adjacent Grade (LAG) and Highest Adjacent Grade (HAG)—which are crucial for effective flood risk management. Conventional methods, involving onsite surveying or the use of optical imagery-derived building footprints combined with Digital Elevation Models (DEMs), often face misalignment and time discrepancy issues due to varied remote sensing sources. We introduce a new, scalable method that exclusively relies on airborne LiDAR data to overcome these challenges. Our approach employs an object-based ground filtering technique, and the results were evaluated using two different DEMs and building footprint sets. The findings demonstrate that our single-source method, utilizing only airborne LiDAR data, significantly improves the accuracy of LAG and HAG calculations compared to traditional methods that use hand-digitized building footprints. The proposed approach offers a solution for comprehensive flood risk management endeavors.

Song, Hunsoo↗
DOE OSTI2026

Gravitational Lenses in UNIONS and Euclid (GLUE). I. A Search for Strong Gravitational Lenses in UNIONS with Subaru, CFHT, and Pan-STARRS Data

We present the results of our pipeline for discovering strong gravitational lenses in the ongoing Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We successfully train a deep residual convolutional neural network based on CMU-Deeplens architecture, which is designed to detect strong lenses in ground-based imaging surveys. We train on images of real strong lenses and deploy on a sample of 8 million galaxies in areas with full coverage in the g, r, and i filters—the first multiband search for strong gravitational lenses in UNIONS. Following human inspection and grading, we report the discovery of a total of 1346 new strong-lens candidates, of which 146 are Grade A, 199 are Grade B, and 1001 are Grade C. Of these candidates, 283 have lens galaxy spectroscopic redshifts from the Sloan Digital Sky Survey, and an additional 297 have them from the Dark Energy Spectroscopic Instrument Data Release 1. We find 15 of these systems display evidence of both lens and source galaxy redshifts in spectral superposition. We also report the spectroscopic confirmation of seven lensed sources in high-quality systems, all with z > 2.1, using the Keck Near Infrared Echellette Spectrograph and the Gemini Near-Infrared Spectrograph.

Storfer, Christopher J. [University of Hawaii, Hon↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2026

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National ↗
DOE OSTI2024

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonlinear encoding in diffractive information processing using linear optical materials

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

42 ENGINEERING↗
DOE OSTI2025

Establishing Models for Digital Twin of Hydropower Systems Using Probability Density Function Shaping

This paper introduces a digital twin modeling method for hydropower systems with Kaplan turbines using probability density function (PDF) shaping. We first use multilayer perceptron (MLP) model to build the discretized openloop Kaplan unit, where the MLP is trained by historical data. Then we use a proportional integral double derivative (PIDD) controller and a lead-lag exciter to test the obtained digital twin model in a closed-loop fashion. Simulation results show that the proposed digital twin modeling method can accurately capture the dynamics of the Kaplan hydropower unit. Finally, we show that the obtained digital twin can help to optimize the PIDD parameters. Compared with the original PIDD controller, the optimized one can achieve an over 90% improvement on the mean square tracking error.

Yin, Zhun [New York University]↗