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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

femto-PIXAR: a self-supervised neural network method for reconstructing femtosecond X-ray free electron laser pulses

X-ray Free Electron Lasers (X-FELs) operate in a wide range of lasing configurations for a broad variety of scientific applications at ultrafast time-scales such as structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for analysis of many experiments as well as tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g. those needed for coherent, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here we show that a physics-based U-net model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from weak X-FEL pulse pairs, we demonstrate we can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Controlling Subsurface Fractures and Fluid Flow: A Basic Research Agenda

From beneath the surface of the earth, we currently obtain about 80-percent of the energy our nation consumes each year. In the future we have the potential to generate billions of watts of electrical power from clean, green, geothermal energy sources. Our planet’s subsurface can also serve as a reservoir for storing energy produced from intermittent sources such as wind and solar, and it could provide safe, long-term storage of excess carbon dioxide, energy waste products and other hazardous materials. However, it is impossible to underestimate the complexities of the subsurface world. These complexities challenge our ability to acquire the scientific knowledge needed for the efficient and safe exploitation of its resources. To more effectively harness subsurface resources while mitigating the impacts of developing and using these resources, the U.S. Department of Energy established SubTER – the Subsurface Technology and Engineering RD&D Crosscut team. This DOE multi-office team engaged scientists and engineers from the national laboratories to assess and make recommendations for improving energy-related subsurface engineering. The SubTER team produced a plan with the overall objective of “adaptive control of subsurface fractures and fluid flow.”This plan revolved around four core technological pillars—Intelligent Wellbore Systems that sustain the integrity of the wellbore environment; Subsurface Stress and Induced Seismicity programs that guide and optimize sustainable energy strategies while reducing the risks associated with subsurface injections; Permeability Manipulation studies that improve methods of enhancing, impeding and eliminating fluid flow; and New Subsurface Signals that transform our ability to see into and characterize subsurface systems. The SubTER team developed an extensive R&D plan for advancing technologies within these four core pillars and also identified several areas where new technologies would require additional basic research. In response, the Office of Science, through its Office of Basic Energy Science (BES), convened a roundtable consisting of 15 national lab, university and industry geoscience experts to brainstorm basic research areas that underpin the SubTER goals but are currently underrepresented in the BES research portfolio. Held in Germantown, Maryland on May 22, 2015, the round-table participants developed a basic research agenda that is detailed in this report. Highlights include the following: -A grand challenge calling for advanced imaging of stress and geological processes to help understand how stresses and chemical substances are distributed in the subsurface—knowledge that is critical to all aspects of subsurface engineering; -A priority research direction aimed at achieving control of fluid flow through fractured media; -A priority research direction aimed at better understanding how mechanical and geochemical perturbations to subsurface rock systems are coupled through fluid and mineral interactions; -A priority research direction aimed at studying the structure, permeability, reactivity and other properties of nanoporous rocks, like shale, which have become critical energy materials and exhibit important hallmarks of mesoscale materials; -A cross-cutting theme that would accelerate development of advanced computational methods to describe heterogeneous time-dependent geologic systems that could, among other potential benefits, provide new and vastly improved models of hydraulic fracturing and its environmental impacts; -A cross-cutting theme that would lead to the creation of “geo-architected materials” with controlled repeatable heterogeneity and structure that can be tested under a variety of thermal, hydraulic, chemical and mechanical conditions relevant to subsurface systems; -A cross-cutting theme calling for new laboratory studies on both natural and geo-architected subsurface materials that deploy advanced high-resolution 3D imaging and chemical analysis methods to determine the ;rates and mechanisms of fluid-rock processes, and to test predictive models of such phenomena. Many of the key energy challenges of the future demand a greater understanding of the subsurface world in all of its complexity. This greater under- standing will improve the ability to control and manipulate the subsurface world in ways that will benefit both the economy and the environment. This report provides specific basic research pathways to address some of the most fundamental issues of energy-related subsurface engineering.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Modeling Mesoscale Processes of Scalable Synthesis Final Scientific/Technical Report

This is the final scientific and technical report for this award. Molecular dynamics (MD) is a powerful tool to study biomolecules at the nanoscale. One of its main limitations however is that such simulations typically require a timestep of 1 femtosecond while reactions and conformational changes typically happen on the time scale of 1 microsecond or more. This discrepancy severely restricts the impact of this method. We have developed a series of algorithms to mitigate this problem and allow predicting long-time scale events using much shorter simulations. The method is based on the concurrent adaptive sampling method. It consists in using probabilistic weights for trajectories and biasing the sampling such that rare but important trajectories, e.g., those that go over energy barriers and lead to relevant conformational changes in the molecule, are observed with much higher frequency that in a standard MD simulation. We will demonstrate below that this approach has been very successful. In collaboration with Jay Grate at PNNL we have applied this novel method to the study the triazine polymers, which can be con gured using different types of side chains in order to adopt different 3-dimensional structures. We have found novel stable con- formations and have analyzed the impact of side chain modifcations on the native folded shape of the polymer. Direct MD simulations would have been untractable for this problem. The method of replica exchange was also used successfully to calculate free energy profiles, in particular for cis-trans torsional angle changes with high energy barriers. Studying molecules at the atomistic scale is essential to design new molecules in chemical engineering, such as the triazine polymers developed by Grate, in material science to study the strength of metals for examples (e.g., dislocation dynamics), or in biochemistry to understand how proteins fold and how they interact with ligands (a key step in designing drugs). Although new hardware makes these simulations increasingly faster (e.g., GPU, Anton from DE Shaw Research), new algorithms are key to making these methods more tractable and increase their impact. At this point, because of limitations in modeling, one of the main techniques for advancing science remains to a large extent numerous and costly experiments. For exam- ple, when designing drug, where multiple variants must be screened, the ability to run tests on a computer rather than experimentally testing with high throughput methods thousands of compounds would represent a signi cant advance with broad benefits for the public at large.

36 MATERIALS SCIENCE↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Applications of explainable artificial intelligence in renewable energy research

Researchers in renewable energy are applying deep learning (DL) to a variety of problems from diverse renewable energy domains, such as biofuels, wind, solar, power systems, buildings, vehicles, and transportation systems. Improvements in accuracy may be demonstrated using DL in laboratory settings. However, the lack of interpretability of DL models poses a practical limitation to their utility in advancing scientific knowledge and in the deployment of DL models in safety-critical energy systems. In this article, we discuss explainable artificial intelligence (XAI) as one pathway toward more interpretable DL models. We explore a brief timeline of U.S. national laboratory interest in XAI, an overview and taxonomy of methods in the field of XAI, and a selection of applications across renewable energy research domains. We conclude by highlighting pivotal areas where XAI can accelerate innovation in artificial intelligence for renewable energy research and other essential future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Analysis of heat transfer and AuNPs-mediated photo-thermal inactivation of E. coli at varying laser powers using single-phase CFD modeling

In the wake of the COVID-19 pandemics, the demand for innovative and effective methods of bacterial inactivation has become a critical area of research, providing the impetus for this study. The purpose of this research is to analyze the AuNPs-mediated photothermal inactivation of E. coli. Gold nanoparticles irradiated by laser represent a promising technique for combating bacterial infection that combines high-tech and scientific progress. The intermediate aim of the work was to present the calibration of the model with respect to the gold nanorods experiment. The purpose of this work is to study the effect of initial concentration of E. coli bacteria, the design of the chamber and the laser power on heat transfer and inactivation of E. coli bacteria. Using the CFD simulation, the work combines three main concepts. 1. The conversion of laser light to heat has been described by a combination of three distinctive approximations: a- Discrete particle integration to take into account every nanoparticle within the system, b- Rayleigh-Drude approximation to determine the scattering and extinction coefficients and c- Lambert–Beer–Bourger law to describe the decrease in laser intensity across the AuNPs. 2. The contribution of the presence of E. coli bacteria to the thermal and fluid-dynamic fields in the microdevice was modeled by single-phase approach by determining the effective thermophysical properties of the water-bacteria mixture. 3. An approach based on a temperature threshold attained at which bacteria will be inactivated, has been used to predict bacterial response to temperature increases. The comparison of the thermal fields and temporal temperature changes obtained by the CFD simulation with those obtained experimentally confirms the accuracy of the light-heat conversion model derived from the aforementioned approximations. The results show a linear relationship between maximum temperature and variation in laser power over the range studied, which is in line with previous experimental results. It was also found that the temperature inside the microchamber can exceed 55 °C only when a laser power higher than 0.8 W is used, so bacterial inactivation begins. The experimental data allows to determinate the concentration of nanoparticles. This parameter is introduced into the mathematical model obtaining the same number of AuNPs. However, this assumption introduces a certain simplification, as in the mathematical model the distribution of nanoparticles is uniform. This work is directly connected to the use of gold nanoparticles for energy conversion, as well as the field of bacterial inactivation in microfluidic systems such as lab-on-a-chip. Presented mathematical and numerical models can be extended to the entire spectrum of wavelengths with particular use of white light in the inactivation of bacteria. This work represents a significant advancement in the field, as to the best of the authors’ knowledge, it is the first to employ a single-phase computational fluid dynamics (CFD) approach specifically combined with the thermal inactivation of bacteria. Moreover, this research pioneers the use of a numerical simulation to analyze the temperature threshold of photothermal inactivation of E. coli mediated by gold nanorods (AuNRs). The integration of these methodologies offers a new perspective on optimizing bacterial inactivation techniques, making this study a valuable contribution to both computational modeling and biomedical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2021

Simple, Secure, Internet Delivery of MOOSE-based Applications

Application packaging and distribution are the final steps for delivering software to end-users; both are frequently neglected when creating scientific software. Commercial businesses rely on electronic distribution systems that have rendered disk drives obsolete. Still, national laboratories continue to rely heavily on removable media to distribute and limit access to controlled applications. With increasing concerns of unauthorized copying of sensitive applications, a modern distribution system that utilizes cryptographically secure communication and authentication protocols has been developed. This new distribution system will secure the chain of custody for nuclear software while simultaneously simplifying access to these tools. This report summarizes four primary advancements made toward the secure distribution of Nuclear Energy Advanced Modeling and Simulation (NEAMS)-developed, Multiphysics Object Oriented Simulation Environment (MOOSE)-based applications: application installation, package distribution, automated package building, and distribution of documentation. NEAMS is currently developing more than ten separate applications based on the open-source MOOSE Framework. Distribution of these applications has primarily been accomplished by distributing source code, with end-users compiling the applications themselves. This work created a mechanism where MOOSE applications can be installed in a similar way to any other software. This allows both administrators and end-users simplified access to runnable executables. With this new installation capability, it was then possible to rethink distribution. A new, secure capability for delivering MOOSE-based applications over the internet has been created. This system requires unique cryptographic tokens for authentication, greatly securing the custody chain for software. Once granted access, installation of any NEAMS code can be accomplished with these terminal commands: "conda install ncrc" "ncrc install ncrc-bison." After these two commands (and authenticating) the BISON application will be securely down- loaded from Idaho National Laboratory (INL)’s servers, installed, and ready to use. To enable this new distribution capability to be successful, the open-source Continuous Integration, Verification, Enhancement, and Testing (CIVET) Continuous Integration (CI) capability was augmented to add Continuous Delivery (CD). CD enables the automated building and packaging of MOOSE-based applications as they are modified by development teams, ensuring that our customers can obtain up-to-date versions of the software at any time. The need for instruction on how to use these applications was addressed through modifications to the MOOSE documentation system. The MooseDocs capability, which enables robust documentation of MOOSE-based applications, has been extended to allow both for the installation of documentation and the packaging of documentation with installed applications. Together, these enhancements form the core of a new, secure distribution mechanism for nuclear simulation tools. In concert with the Nuclear Computational Resource Center (NCRC), NEAMS- developed applications will now be straightforward to obtain securely.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Final Scientific Findings Report- Hydro Research Foundation- Research Awards Program

The Research Awards Program was established in 2014 by the Hydro Research Foundation (Foundation) to support research efforts for the hydropower industry. Using the previously funded Fellowship Program that ran from 2010-2015, the Research Awards Program (RAP or program) captured the lessons learned from the Fellowship program and fine-tuned a nation-wide graduate level research program to support innovation, research and workforce development. The RAP was funded through a $2 million-dollar grant through the Department of Energy’s Water Power Program within the Energy Efficiency and Renewable Energy office (DOE). This award made possible, 33 research projects by 32 students from 21 universities in 11 states. The grant widely engaged the industry by providing the opportunity to work with 25 organizations as well as 63 students and advisors. The funded research focused on a topic list selected by industry and worked to cover a breadth of research needs across many disciplines, basins and types of hydropower issues. The funded work, answered pressing research needs in the fields of the following disciplines; civil engineering, mechanical engineering, environmental engineering, electrical engineering, geography, fisheries, wildlife, water resources, geology, energy science and materials innovation. The Foundation estimates that 116 individuals benefited from this research through either direct mentorship (42), research (32), overseeing the research (31), partnerships (7) and the day-to-day management of the program (4). This investment by DOE showcases how private-public partnerships can leverage limited funds to create a wide range of research products. The project objective was to build on the successful Fellowship Program to develop and execute the RAP on a national level. The program sought to provide competitively awarded hydropower-related graduate-level research awards. The intent of the RAP was to support beneficial, innovative research related to conventional or pumped-storage hydropower. It also sought to stimulate interest among students and universities in this research arena and provide students with a foundation for productive careers within the hydropower industry. The outcome was 33 research findings with 100% of proposed research completed. In addition, 87% of students chose to either join the hydropower industry or continue their hydropower related research at their universities. These research findings advanced the understanding of pressing research topics within the hydropower industry and met the objective of the funding. An ancillary benefit the program identified through this funding was, not only did 63 students and advisors work on hydropower within the academia space, but now there are more professors and students teaching hydropower at the university level. The program saw an addition of 12 new professors, teaching assistants, and graduate researchers continuing to simulate interest in hydropower. Another interesting outcome is that during this grant funding, the program saw alumni of the predecessor program, who are now professors, applying for the RAP with their students. A unique element of this funding is there was significant cost-share provided by host universities, who did not charge facilities and administration overhead to the student’s research awards. In addition, each university provided laboratories, computing capacity and research facilities to support the research which is extraordinarily unique to grant funding at the university level. The total cost share exceeded 30% of the total grant cost. The funding clearly reached many sectors of the industry to provide helpful work and continue building hydropower’s next generation of innovation and workforce. The Foundation has established a broad network of universities interested and engaged in hydropower. The work was supported by the hydropower industry through donations of meeting space, conference registrations, in-kind contributions, mentorship and support of students. The Foundation would recommend finding a vehicle to further advance programs like the graduate level research through competitions, competitively funded awards and student research. It will take many universities to advance hydropower’s most pressing challenges. To let a program like the RAP dwindle away would incapacitate research at universities.

13 HYDRO ENERGY↗
DOE OSTI2022

Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Furthermore, our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Oh! I Slipped the Surly Bounds of Earth....and Ran into Space Weather!

Over the past decade the concept of space weather has been introduced and matured in both the scientific community and popular press. Likewise the concept of space climatology recently also is being advanced. Closely linked to these concepts are their impacts on ground- and space-based technological systems; one such system commonly mentioned is manned space flight exemplified by the Space Shuttle and International Space Station (ISS). From a manned space flight perspective, space weather and space climatology have significant effects on the amount of radiation exposure received by humans in space from the ambient high-energy charged particles present in interplanetary space and trapped in the geomagnetosphere. Whereas the impact of space weather for most technological systems is usually discrete and well correlated in time, the principle impact of space weather and space climatology is to increase the probability of latent cancer formation in thetraveler cohort. In this regard, while space weather may be the dominating factor for a given mission, over the life of a long-term program such as the Space Shuttle or ISS space climatology is the controlling factor of latent cancer risk. Human radiation exposure enhancements associated with space weather disturbances has been a concern among scientist and mission controllers since the inception of manned spaceflight nearly forty years ago. This led NASA to develop, in conjunction with the Environmental Science Services Administration s Space Disturbance Forecast Center and the USAF/AWS, the Solar Particle Alert Network (SPAN)-the foundation of an initial U.S. space weather monitoring and forecasting service. Since Apollo, routine space flight operations have evolved to include the use of space weather and climatology data provided through a world-wide network of operational space weather data services to predict and recommend actions to minimize astronaut radiation exposures. NASA Space Radiation Analysis Group (SRAG) flight controllers use real-time space weather data to detect and assess the impact of solar particle events, outer electron belt enhancements, the formation of pseudo-stable additional trapped radiation belts, and the solar cycle modulation of trapped radiation belts and galactic cosmic rays. Energetic particle data from GOES spacecraft are automatically ingested from NOAA Space Environment Center data servers and used to drive a model for the estimating the exposure to astronauts from solar particle events. While adequate for current manned space flight support, the existing operational space weather support system requires improvements to address the anticipated evolution in both the character of manned missions as well as space flight operations management. Necessary space weather data improvements include: reliably available (near) real-time space weather data on a fixed schedule via redundant access methods that support autonomous data acquisition by computer systems behind enterprise firewalls; and rapid transition of promising research sensors into operational systems.

Golightly, Michael J.↗
DOE OSTI2024

Extending deterministic transport capabilities for very-high and ultra-high energy electron beams

Focused Very-High Energy Electron (VHEE, 50–300 MeV) and Ultra-High Energy Electron (UHEE, > 300 MeV) beams can accurately target both large and deeply seated human tumors with high sparing properties, while avoiding the spatial requirements and cost of proton and heavy ion facilities. Advanced testing phases are underway at the CLEAR facilities at CERN (Switzerland), NLCTA at Stanford (USA), and SPARC at INFN (Italy), aiming to accelerate the transition to clinical application. Currently, Monte Carlo (MC) transport is the sole paradigm supporting preclinical trials and imminent clinical deployment. In this paper, we propose an alternative: the first extension of the nuclear-reactor deterministic chain Njoy-Dragon for VHEE and UHEE applications. We have extended the Boltzmann-Fokker-Planck (BFP) multigroup formalism and validated it using standard radio-oncology benchmarks, complex assemblies with a wide range of atomic numbers, and comprehensive irradiation of the entire periodic table. We report that 99% of water voxels exhibit a BFP-MC deviation below 2% for electron energies under 1.5 GeV. Additionally, we demonstrate that at least 97% of voxels of bone, lung, adipose tissue, muscle, soft tissue, tumor, steel, and aluminum meet the same criterion between 50 MeV and 1.5 GeV. For water, the thorax, and the breast intra-operative benchmark, typical average BFP-MC deviations of 0.3% and 0.4% were observed at 300 MeV and 1 GeV, respectively. By irradiating the entire periodic table, we observed similar performance between lithium (Z = 3) and cerium (Z = 58). Deficiencies observed between praseodymium (Z = 59) and einsteinium (Z = 99) have been reported, analyzed, and quantified, offering critical insights for the ongoing development of the Evaluated Nuclear Data File mode in NJOY.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

17 WIND ENERGY↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

TU-CD-BRA-11: Application of Bone Suppression Technique to Inspiratory/expiratory Chest Radiography

Purpose: The bone suppression technique based on advanced image processing can suppress the conspicuity of bones on chest radiographs, creating soft tissue images normally obtained by the dual-energy subtraction technique. This study was performed to investigate the usefulness of bone suppression technique in quantitative analysis of pulmonary function in inspiratory/expiratory chest radiography. Methods: Commercial bone suppression image processing software (ClearRead; Riverain Technologies) was applied to paired inspiratory/expiratory chest radiographs of 107 patients (normal, 33; abnormal, 74) to create corresponding bone suppression images. The abnormal subjects had been diagnosed with pulmonary diseases, such as pneumothorax, pneumonia, emphysema, asthma, and lung cancer. After recognition of the lung area, the vectors of respiratory displacement were measured in all local lung areas using a cross-correlation technique. The measured displacement in each area was visualized as displacement color maps. The distribution pattern of respiratory displacement was assessed by comparison with the findings of lung scintigraphy. Results: Respiratory displacement of pulmonary markings (soft tissues) was able to be quantified separately from the rib movements on bone suppression images. The resulting displacement map showed a left-right symmetric distribution increasing from the lung apex to the bottom region of the lung in many cases. However, patients with ventilatory impairments showed a nonuniform distribution caused by decreased displacement of pulmonary markings, which were confirmed to correspond to area with ventilatory impairments found on the lung scintigrams. Conclusion: The bone suppression technique was useful for quantitative analysis of respiratory displacement of pulmonary markings without any interruption of the rib shadows. Abnormal areas could be detected as decreased displacement of pulmonary markings. Inspiratory/expiratory chest radiography combined with the bone suppression technique has potential for predicting local lung function on the basis of dynamic analysis of pulmonary markings. This work was partially supported by Nakatani Foundation, Grant-in-aid for Scientific Research (C) of Ministry of Education, Culture, Sports, Science and Technology, JAPAN (Grant number : 24601007), and Nakatani Foundation, Mitsubishi Foundation, and the he Mitani Foundation for Research and Development. Yasushi Kishitani is a staff of TOYO corporation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

An Integrated Risk Assessment Process for Digital Instrumentation and Control Upgrades of Nuclear Power Plants

Most of the existing nuclear power plants (NPPs) in the world rely on traditional analog instrumentation and control (I&C) systems for monitoring, control, and protection functions. With the industrial base largely moving to digital systems, the operation and maintenance of plants involves managing issues including lack of needed analog spare parts, increasing maintenance costs, and the loss of vendor support. Compared with existing analog I&C systems, digital I&C systems have significant functional advantages, such as reliable system performance in terms of accuracy and computational capability, high data handling and storage capabilities to fully measure and display operating conditions, and improved capabilities (e.g., fault tolerance, self-testing, signal validation, process system diagnostics). Therefore, the U.S. nuclear power industry has initiated the replacement of existing, aging analog systems with digital I&C technology, and is developing new designs for advanced plants using digital systems in integrated control rooms to provide modern control and protection systems. However, the qualification of digital I&C systems remains a challenge, especially the issue of software common cause failure (CCF), which has been difficult to address. A CCF is the malfunction of two or more plant components or functions due to a single failure source. CCFs have the potential to generate unanalyzed events or sequences that may not be bounded by previous plant accident analyses, therefore, to challenge the plant safety. Existing analyses on CCF in I&C systems are mainly focusing on hardware failures. With the application and upgrades of new digital I&C systems, software CCFs due to design flaws have become a potential threat to plant safety considering most redundancy designs are using the similar digital platforms or software in the operating and application systems. With complex multi-layer redundancy designs to meet the single failure criterion, these I&C safety systems are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. Therefore, there is a need to develop an integrated risk assessment strategy with digital CCF and plant transient responses considered to assure the long-term safety and reliability of vital digital systems and reduce uncertainties in costs, time, and support integration of digital systems in the plant. The overall goal of this project is to deliver a strong technical basis to support effective, licensable, and secure digital I&C technologies for the digital upgrades to existing NPPs. To deal with the expensive licensing justifications from regulatory insights, this technical basis is instructive for nuclear vendors and utilities to effectively lower the costs associated with digital compliance and speed-up industry advances by: (1) defining an integrated risk-informed analysis process for digital I&C upgrades including hazard analysis, reliability analysis, and consequence analysis; (2) applying systematic and risk-informed tools to address CCFs and quantify responding failure probabilities for digital I&C technologies; (3) evaluating the impact of digital failures at the individual level, system level, and plant level; and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced digital I&C technologies on NPPs. Upgrading digital I&C (safety and non-safety related) systems in existing NPPs within a cost-effective and regulatory acceptable way offers the foremost means of performance improvements and cost-reductions for existing NPPs. One key outcome of this project is to perform plant-specific risk assessment to provide a sustainable scientific support for enabling industry to balance the digital-related risks, costs, reliability, and safety.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

DOE Award No.: DE-FE0023919 Phase 3 Scientific/Technical Report

This is the Phase 3 Report for the ‘Deepwater Methane Hydrate Characterization and Scientific Assessment or Genesis of Methane Hydrates in the Gulf of Mexico (GOM2)’ research project (DOE Award No. DE-FE0023919). The report summarizes activities from January 16, 2018 to September 30, 2019. The project is led by the University of Texas at Austin (UT). The objective is to gain insight into the nature, formation, occurrence and physical properties of methane hydrate-bearing sediments for the purpose of methane hydrate resource appraisal through the planning and execution of drilling, coring, logging, testing and analytical activities that assess the geologic occurrence, regional context, and characteristics of marine methane hydrate deposits in the Gulf of Mexico outer continental shelf (OCS). We determined that it would not be possible to pursue the project with the International Ocean Discovery Program (IODP) and then developed a revised science and operations plan to maximize the science using a commercial vessel within the budget that is available. We improved the scientific capability of the UT pressure core center by adding the ability to X-ray pressure cores, and the ability to cut and store multiple core samples for experimental analysis. We transferred pressure cores to peer institutions, including the National Energy Technology Lab (NETL) the United States Geological Survey (USGS Woods Hole). We made advances in understanding the composition and source of gasses locked in the methane hydrate from Green Canyon 955 (GC-955). We developed approaches to determine the in-situ salinity of hydrate-bearing samples, revealing that the in situ salinity of the GC 955 reservoir is just above that of seawater. We determined that the GC-955 hydrate reservoir is composed of sandy silt with a high concentration of hydrate interbedded with clayey silt with no hydrate present. We determined that some or all of the intervals bounding the hydrate reservoir are composed of material similar to the reservoir (sandy silt with hydrate and clayey silt with no hydrate), but with a lower net to gross. We determined that the reservoir effective permeability at a hydrate saturation of ~80-90 % is ~0.1 mD (or 1.0×10-16 m2) to ~0.5 mD (or 5.0×10-16 m2) and the intrinsic permeability is ~12 mD (or 1.2×10-14 m2). The GC-955 reservoir in-situ porosity of sandy silt is 0.38 to 0.40 and is largely independent of effective stress, and that the porosity of clayey silt is 0.33 at an in situ effective stress of 3.8 MPa to ~0.37 at zero effective stress. We developed a systematic, repeatable approach to studying hydrate reservoir properties by reconstituting individual lithofacies from dissociated pressure cores. We determined index properties of GC-955 reservoir, including liquid limit and plasticity, porosity, capillary behavior, and particle size distribution. We developed a more robust pressure coring technology. We finalized and published the UT-GOM2-1 Expedition Volume and we finalized a dedicated volume on the UT-GOM2-1 expedition that will be published in the American Association of Petroleum Geologists Bulletin (AAPG) in spring 2020.

03 NATURAL GAS↗