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Automated Glovebox Work-cell Design - 18370

Los Alamos National Laboratory (LANL) is the primary plutonium science and manufacturing facility in the United States supporting multiple national security programs. It is responsible for completing a high variety of small-batch manufacturing tasks requiring the handling and precision machining of Special Nuclear Materials (SNM). Given the myriad of tasks that must be completed in a limited workspace, LANL intends to utilize flexible, multi-use robotic work-cells capable of completing a variety of mission critical tasks in a single glovebox with no re-tooling needed between batch jobs. This approach would allow LANL more flexibility in its production schedule and to meet its future production and research objectives. In this paper, we present a system that integrates multiple sensors and other manufacturing peripherals into existing glovebox infrastructure. Currently, a technician can place an object in the work area where vision sensors and supervisory software identify the object and its pose and automatically determine which manufacturing task to complete. Pose uncertainty is addressed in real-time by the task planner, eliminating the need for fixtures. Grasps are validated using pressure sensors, payload sensors, and grasp quality metrics to ensure that objects are not dropped or damaged during manipulation. The robot, sensors, and peripherals are controlled using open-source software built utilizing ROS (Robot Operating System), which minimizes redevelopment and allows for re-use and extensibility. Established capabilities support vision, collision-free path planning, and more, minimizing development time. For this effort, particular attention was given to the integration of general peripheral devices (drill, chuck, etc.), which is critical to efficiently utilizing ROS in manufacturing environments. This integration is achieved with an in-development framework that allows for quick integration of peripherals, including I/O devices and motors. Contact tasks utilize the Generalized Contact Control Framework (GCCF) previously developed at UT Austin. GCCF allows application developers to easily define multiple contact control tasks for a single robot. This paper summarizes the key software components necessary to develop the multi-use work-cell including existing ROS components. UT-developed software for grasp validation, peripheral integration, and contact control is presented in more depth. Finally the glovebox system is demonstrated completing a set of tasks with no fixturing necessary. Primary tasks include surface preparation/ablation, drilling, press-fitting, and sorting, as well as supporting material transfer and inspection tasks. The glovebox work-cell reduces cost, operator dose, ergonomic issues, and time loss due to material transfer between gloveboxes that only support one task. If multiple glovebox work-cells are in use, they have the potential to eliminate bottlenecks and scale production capabilities beyond current limits. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Compact video content representation for video coding using low multi-linear tensor rank approximation with dynamic core tensor order

Tensor is a multi-way representation of an entity that is widely used in many applications of mathematics and physics. The wide range of tensor algebra operations attract the researchers of graphical and vision field to identify unique methods for various applications such as segmentation, compression, indexing and so on. In this paper, low multi-linear rank approximation of a tensor is used for decomposition so as to perform compression on a video that is represented as a 4D tensor. Thus, the encoder has blocks for (1) multilinear rank approximation method so as to aid decomposition, (2) sparsity removal of residual data to reduce the required memory storage to a small level, (3) quantization on the sparse less residual data and (4) LZ77 dictionary coding for the video. The decoder uses a tensor reconstruction block along with residual error correction block that reduces loss/error in the reconstructed video. The size of core tensor is an important factor in rank approximation. It is identified at run time depending on the video contents. This size is determined adaptively using Tikhonov’s regularization method. The best value of core tensor size is identified with the corner of L-curve which preserves the video contents from heavy loss/error. Experimental results of the proposed work on comparison with H.264/AVC compression method indicate that the proposed method achieves up to 10% improvement in compression ratio with the least amount of loss and a maximum signal to noise ratio of 0.14. The proposed system gives better compression ratio than H.265 for videos with more number of frames without compromising quality.

97 MATHEMATICS AND COMPUTING↗

Evaluation of Roadmap to Achieve Energy Delivery Systems Cybersecurity

The Department of Energy/Office of Electricity Delivery and Energy Reliability (DOE/OE) Cybersecurity for Energy Delivery Systems (CEDS) program is currently evaluating the Roadmap to Achieve Energy Delivery Systems Cybersecurity document that sets a vision and outlines a set of milestones. The milestones are divided into five strategic focus areas that include: 1. Build a Culture of Security; 2. Assess and Monitor Risk; 3. Develop and Implement New Protective Measures to Reduce Risk; 4. Manage Incidents; and 5. Sustain Security Improvements. The most current version of the roadmap was last updated in September of 2016. Sandia National Laboratories (SNL) has been tasked with revisiting the roadmap to update the current state of energy delivery systems cybersecurity protections. SNL is currently working with previous and current partners to provide feedback on which of the roadmap milestones have been met and to identify any preexisting or new gaps that are not addressed by the roadmap. The specific focus areas SNL was asked to evaluate are: 1. Develop and Implement New Protective Measures to Reduce Risk and 2. Sustain Security Improvements. SNL has formed an Industry Advisory Board (IAB) to assist in answering these questions. The IAB consists of previous partners on past CEDS funded efforts as well as new collaborators that have unique insights into the current state of cybersecurity within energy delivery systems. The IAB includes asset owners, utilities and vendors of control systems. SNL will continue to maintain regular communications with the IAB to provide various perspectives on potential future updates to further improve the breadth of cybersecurity coverage of the roadmap.

24 POWER TRANSMISSION AND DISTRIBUTION↗

IER-539 CED-1: Preliminary Design of a New Horizontal Split Table

This report presents the preliminary design (CED-1) of IER-539, focused on a new Horizontal Split Table (HST) critical assembly machine. The Department of Energy (DOE) National Nuclear Criticality Safety Program (NCSP) funds critical experiment R&D and its Mission and Vision document identifies a long-standing technical gap of a general purpose HST. HSTs have historically been used for experiments that are impractical to assemble on a vertical lift machine (VLM), usually due to their large physical size or weight. While many HSTs were historically employed in the US (including at LLNL), there are currently no surviving HSTs in the US. While there are two critical experiments facilities remaining in the US, the National Critical Experiments Research Center (NCERC) operated by LANL and the Critical Experiments Facility (CX) operated by SNL, these facilities are not equipped with a general purpose HST. There are a number of experiment designs that require an HST, including needs for Defense Programs and Nuclear Energy. Lack of an HST constraints the national ability to perform large and heavy experiments. Large footprints are needed to successfully create mockup experiments for advanced reactors (mock-up cold critical reactor cores, which is a urgent need for new reactor designs using novel materials and longer cycle lengths), to create solution experiments in lattices, or to test nuclear data of heavy metals. A new HST is a vital piece of criticality experiment infrastructure that would allow for the conduct of integral critical experiments to address needs in the nuclear criticality safety, nuclear data, and other DOE stakeholder communities.

42 ENGINEERING↗

Fusion Energy Research at Idaho National Laboratory: Experimentation and Simulation to Support Safety and Rapid Technology Development

Research into fusion energy is growing rapidly, responding to a call for sustainable sources of energy to replace fossil fuels and mitigate climate change. Within the United States, at least, researchers are also responding to the “Bold Decadal Vision” proposed by the White House, seeking to have a commercially relevant fusion pilot plant deployed within a decade. Before this can become a reality, many Fusion Science & Technology (FS&T) gaps remain. For over 45 years, Idaho National Laboratory has been at the forefront of addressing these FS&T gaps in the context of fusion safety and technology via the operation of world-leading experimental facilities within the Safety and Tritium Applied Research (STAR) Facility. Here, INL focuses on the tritium fuel cycle, conceptual system design studies, risk assessment, waste management, and materials safety. Modeling and Simulation (M&S) has also been a component of this portfolio of research, but, early on, focused on individual systems. Since 2019, active development and research on integrated whole device modeling tools based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework has been undertaken. This has culminated in a MOOSE-based version of the Tritium Migration and Analysis Program (TMAP), an INL code historically focused on tritium permeation and trapping within fusion systems. More recently, INL Laboratory Directed Research and Development funds have been used to create the Fusion ENergy Integrated multiphys-X (FENIX) code focused on scrape-off layer plasma physics and the first wall of a magnetically confined fusion device. This talk will focus on an overview of INL activities in the FS&T research area, with a particular focus on recent M&S activities and results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

Attenuation correction is an essential component of the long chain of data correction techniques required to achieve the full potential of quantitative positron emission tomography (PET) imaging. The development of combined PET/magnetic resonance imaging (MRI) systems mandated the widespread interest in developing novel strategies for deriving accurate attenuation maps with the aim to improve the quantitative accuracy of these emerging hybrid imaging systems. The attenuation map in PET/MRI should ideally be derived from anatomical MR images; however, MRI intensities reflect proton density and relaxation time properties of biological tissues rather than their electron density and photon attenuation properties. Therefore, in contrast to PET/computed tomography, there is a lack of standardized global mapping between the intensities of MRI signal and linear attenuation coefficients at 511 keV. Moreover, in standard MRI sequences, bones and lung tissues do not produce measurable signals owing to their low proton density and short transverse relaxation times. MR images are also inevitably subject to artifacts that degrade their quality, thus compromising their applicability for the task of attenuation correction in PET/MRI. MRI-guided attenuation correction strategies can be classified in three broad categories: (i) segmentation-based approaches, (ii) atlas-registration and machine learning methods, and (iii) emission/transmission-based approaches. This paper summarizes past and current state-of-the-art developments and latest advances in PET/MRI attenuation correction. The advantages and drawbacks of each approach for addressing the challenges of MR-based attenuation correction are comprehensively described. The opportunities brought by both MRI and PET imaging modalities for deriving accurate attenuation maps and improving PET quantification will be elaborated. Future prospects and potential clinical applications of these techniques and their integration in commercial systems will also be discussed.

61 RADIATION PROTECTION AND DOSIMETRY↗

Vision 20/20: Positron emission tomography in radiation therapy planning, delivery, and monitoring

Positron emission tomography (PET) is increasingly considered as an effective imaging method to support several stages of radiation therapy. The combined usage of functional and morphological imaging in state-of-the-art PET/CT scanners is rapidly emerging to support the treatment planning process in terms of improved tumor delineation, and to assess the tumor response in follow-up investigations after or even during the course of fractionated therapy. Moreover, active research is being pursued on new tracers capable of providing different insights into tumor function, in order to identify areas of the planning volume which may require additional dosage for improved probability of tumor control. In this respect, major progresses in the next years will likely concern the development and clinical investigation of novel tracers and image processing techniques for reliable thresholding and segmentation, of treatment planning and beam delivery approaches integrating the PET imaging information, as well as improved multimodal clinical instrumentation such as PET/MR. But especially in the rapidly emerging case of ion beam therapy, the usage of PET is not only limited to the imaging of external tracers injected to the patient. In fact, a minor amount of positron emitters is formed in nuclear fragmentation reactions between the impinging ions and the tissue, bearing useful information for confirmation of the delivered treatment during or after therapeutic irradiation. Different implementations of unconventional PET imaging for therapy monitoring are currently being investigated clinically, and major ongoing research aims at new dedicated detector technologies and at challenging applications such as real-time imaging and time-resolved in vivo verification of motion compensated beam delivery. This paper provides an overview of the different areas of application of PET in radiation oncology and discusses the most promising perspectives in the years to come for radiation therapy planning, delivery, and monitoring.

60 APPLIED LIFE SCIENCES↗

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

Institutional Transformation Version 2.5 Modeling and Planning.

Reducing the resource consumption and emissions of large institutions is an important step toward a sustainable future. Sandia National Laboratories' (SNL) Institutional Transformation (IX) project vision is to provide tools that enable planners to make well-informed decisions concerning sustainability, resource conservation, and emissions reduction across multiple sectors. The building sector has been the primary focus so far because it is the largest consumer of resources for SNL. The IX building module allows users to define the evolution of many buildings over time. The module has been created so that it can be generally applied to any set of DOE-2 ( http://doe2.com ) building models that have been altered to include parameters and expressions required by energy conservation measures (ECM). Once building models have been appropriately prepared, they are checked into a Microsoft Access (r) database. Each building can be represented by many models. This enables the capability to keep a continuous record of models in the past, which are replaced with different models as changes occur to the building. In addition to this, the building module has the capability to apply climate scenarios through applying different weather files to each simulation year. Once the database has been configured, a user interface in Microsoft Excel (r) is used to create scenarios with one or more ECMs. The capability to include central utility buildings (CUBs) that service more than one building with chilled water has been developed. A utility has been created that joins multiple building models into a single model. After using the utility, several manual steps are required to complete the process. Once this CUB model has been created, the individual contributions of each building are still tracked through meters. Currently, 120 building models from SNL's New Mexico and California campuses have been created. This includes all buildings at SNL greater than 10,000 sq. ft., representing 80% of the energy consumption at SNL. SNL has been able to leverage this model to estimate energy savings potential of many competing ECMs. The results helped high level decision makers to create energy reduction goals for SNL. These resources also have multiple applications for use of the models as individual buildings. In addition to the building module, a solar module built in Powersim Studio (r) allows planners to evaluate the potential photovoltaic (PV) energy generation potential for flat plate PV, concentrating solar PV, and concentration solar thermal technologies at multiple sites across SNL's New Mexico campus. Development of the IX modeling framework was a unique collaborative effort among planners and engineers in SNL's facilities division; scientists and computer modelers in SNL's research and development division; faculty from Arizona State University; and energy modelers from Bridger and Paxton Consulting Engineers Incorporated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Artificial Intelligence, the Final Piece to the Counterforce Puzzle?

It is widely expected that artificial intelligence (AI) will disrupt and fundamentally transform many aspects of social, economic, and political life over the coming decades. As we are in the early stages of AI adoption in most fields, many discussions of its potential necessarily impact remain vague and superficial, including where national security is concerned. Nevertheless, some scholars have productively explored AI’s impact on the character of warfare, the balance of power among states, and human society itself. This article contributes to this growing body of literature by examining how this new technology might interact with one of the most prominent features of the post-World War II strategic environment: nuclear weapons. Specifically, it asks whether AI-driven improvements to intelligence, surveillance, and reconnaissance (ISR) capabilities, i.e., automated image recognition/computer vision, could enable an effective counterforce capability and thereby imperil nuclear deterrence and weaken first-strike stability. Since the advent of the nuclear age, analysts have been divided over the meaning of nuclear weapons for international politics. Proponents of the “nuclear revolution” argue that nuclear weapons, particularly a secure second-strike capability, which would allow a state to retaliate with nuclear force after suffering a first strike, satisfy a state’s security needs because they present a potential for unacceptable damage that would deter any potential challenger from even considering aggression. In their view, international politics in the shadow of nuclear weapons would be largely peaceful, competition would be relegated to the margins, and nuclear superiority would be meaningless. However, recent research suggests that this may not be the case. In fact, nuclear weapon states have sought to hold at risk the nuclear arsenals of their adversaries throughout the nuclear age. Still, no country has attacked another’s operational nuclear arsenal. That is because the requirements for a disarming strike are considerably higher than for damage limitation or retaliation against nuclear attack. A state trying to disarm its opponent would have to be certain to destroy, or at least disable, all of its adversary’s nuclear weapons. Even one operational nuclear warhead could inflict unacceptable damage in a retaliatory blow. The inability to be certain that a first strike could find and eliminate all enemy nuclear weapons has been the main roadblock for effective counterforce. As the number of battlefield sensors grows and ISR capabilities improve, however, this roadblock is crumbling. Some expect it to be fully overcome with the help of AI.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗

Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)

RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗

Project DarkStar: Vision for LLNL in 2030

DarkStar was a Strategic Initiative (FY2021-FY2024) to investigate applications of Artificial Intelligence (AI) and Machine Learning (ML) to scientific problems of complex hydrodynamics, shockwave physics and energetic materials. The research focused on physics and engineering design as a process that can be tremendously accelerated through merging AI with advanced physics simulation on exascale-class platforms, and to experimentally validate this revolutionary new approach through dynamic materials campaigns. A central thread of scientific inquiry was in the application of AI to enable human understanding of how to control hydrodynamic instability (which has impacts to areas such as inertial confinement fusion) via engineering features and time-dependent sources. Motivated by an unfinished line of research started by Dr. Johnny von Neumann, AI-enabled simulation approaches were developed that allowed DarkStar researchers to uncover several ground-breaking discoveries regarding hydrodynamic instability, including how to completely suppress Richtmyer-Meshkov instability (RMI). These S&T discoveries, along with other advances, have shown the way for an entirely new approach to time-dependent problems known as inverse design – the idea that complex systems can be developed directly from a final state that is to be achieved and resolve the initial design via satisfying several constraints simultaneously via AI/ML. Through experimental campaigns conducted across a wide range of facilities in the NNSA complex (the High Explosive Application Facility at LLNL, the Dynamic Compression Sector/Advanced Photon Source at Argonne National Lab, and Special Technologies Laboratory at MSTS) the radical new AI/ML approach to engineering complex material dynamics was verified, establishing a new field of study within the realm of shock physics. As advanced manufacturing capabilities continue to develop, the great importance of inverse design as a means to apply that technology effectively for NNSA missions will feature prominently over this decade. DarkStar has positioned NNSA as a world-leader in this newly emerging cross-disciplinary area of AI methods for advanced physics simulation and pioneered multiple novel approaches that have enabled the broader scientific community. By allowing us to see past the horizon, to 2030 and beyond, DarkStar has illuminated the vast potential of AI/ML to impact a wide range of new national security missions and, consequently, multiple areas of further research have already emerged across the NNSA and DOD complex.

42 ENGINEERING↗

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Understanding Structures of Cyber Competition in an Era of Major Power Rivalry

Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

North Dakota Integrated Carbon Storage Complex Feasibility Study. Final report

In spring 2017, the Energy & Environmental Research Center (EERC) initiated an effort to determine the feasibility of developing a commercial-scale CO 2 geologic storage complex able to store 50+ million tonnes (Mt) of CO 2 in central North Dakota safely, permanently, and economically. The objective was to fulfill the goals of the U.S. Department of Energy (DOE) Carbon Storage Assurance Facility Enterprise (CarbonSAFE) Initiative and address technical and nontechnical challenges specific to commercial-scale deployment of a CO2 storage project. The findings clearly show that the concept of capturing CO 2 from a lignite-fired electrical generation facility in central North Dakota and safely and permanently storing the CO 2 in the deep subsurface is indeed technically, economically, and socially feasible. This project evaluated two study areas and their respective geologic storage complexes located adjacent to separate coal-fired facilities in North Dakota: the Basin Electric Power Cooperative (BEPC)-owned Great Plains Synfuels Plant (GPSP) and the Minnkota Power Cooperative (Minnkota)-owned Milton R. Young Station (MRYS). These locations, one with CO 2 capture in place and an existing CO 2 pipeline, are bolstered by progressive North Dakota pore space ownership and long-term liability laws. These elements and a motivated team created an ideal synergistic scenario for ensuring success of the CarbonSAFE Initiative and promoting North Dakota’s statewide vision for carbon management. The project included drilling two new geologic characterization wells, integrating an existing 3-D seismic survey, creating a geologic model subsequently used for injection simulation, a risk assessment, public outreach, and generating a site development plan based on results. In addition, the performance of select National Risk Assessment Partnership tools was evaluated. The geologic characterization wells were drilled ~5600 feet deep to the Broom Creek Formation; ~350 feet of core was retrieved from each well. The core included the Broom Creek (targeted injection zone) and a portion of the overlying Opeche Shale (seal). The Flemmer-1 well, west of Beulah, North Dakota, yielded 169 feet of sandstone. The BNI-1 well located south of Center, North Dakota, yielded 124 feet of sandstone. In each case, laboratory analysis of the sandstone showed permeability in the 300–1000-mD range, with porosity of 20%–30%. The Flemmer-1 well was sited within the boundaries of an existing 3-D seismic survey. Colocating the well with the seismic survey maximized the relationship between new and legacy data and developed a first-of-its-kind interpretation of the geologic fabric of the Broom Creek. Geologic characterization data were integrated into a 5544-mi 2 geocellular model that encompassed both new wells and stratigraphy from the surface to the Amsden Formation (underlying the Broom Creek). The model was later expanded vertically to include the deeper Black Island–Deadwood interval to examine its potential viability as a storage target. The geocellular model provided the foundation for dynamic simulation of CO 2 into the Broom Creek. Results of the simulation suggest that the Broom Creek could accept the DOE target rate of 2 Mt/yr of CO 2 into as few as two wells. To bracket the expected capture from MRYS, simulations were also investigated for a 4-Mt/yr rate near MRYS. Although more wells are needed (two additional), the Broom Creek still has the storage resource to accept the CO 2 at the increased rate. A risk assessment exercise was conducted to identify and assess technical and nontechnical risks that could prevent potential candidate storage complexes within the study area from serving as commercial storage sites. The assessment identified and evaluated six technical risk categories: 1) CO 2 injectivity, 2) storage capacity, 3) lateral migration of CO 2 , 4) lateral pressure propagation, 5) vertical migration of CO 2 or formation brine, and 6) induced seismicity. Following two rounds of analysis and scoring, no risks were determined to preclude continued efforts to develop carbon capture, utilization, and storage (CCUS) in central North Dakota. The risk assessment results will be used to guide future site characterization, modeling and simulation, and monitoring activities. A specific nontechnical strategic risk based on challenges that may be realized in amalgamation of pore space resulted in vertically expanding the geologic model to incorporate the potential for stacked storage in multiple saline reservoirs. By including the Black Island–Deadwood interval (the basal sedimentary reservoir in this region), the amalgamated areal extent could be reduced by as much as 45%. Working with a smaller geographic area reduces risks and costs associated with monitoring and pore space leasing. An economic evaluation incorporating capture; transport (<5 mi); Class VI wells; permitting; and monitoring, verification, and accounting suggests implementing commercial-scale CCUS is economically attractive if the federal tax benefits of 45Q are included. This is validated by Minnkota’s continued pursuit of CO 2 capture and geologic storage at MRYS through its Project Tundra initiative, indicating that there is a business case for CCUS in central North Dakota. Currently, North Dakota is the only state with underground injection control (UIC) Class VI primacy. Built into the North Dakota Century Code is a series of regulatory requirements that guide the process to obtain a Class VI CO 2 storage facility permit. As part of this project, a site development plan was compiled to assure compliance with North Dakota’s requirements to permit a commercial-scale CO 2 storage operation and includes a prospective time line encompassing a general breakdown of activities. In total, an estimated 30 months would be needed to execute the necessary steps to attain a North Dakota CO 2 storage facility permit. Outreach was an integral part of the project and encompassed any project-related activity that had contact or exposure beyond the project team. The goals of outreach were to foster an environment from which stakeholders could make informed decisions about the project and gauge community receptiveness to a CCUS project. A consistent set of messages and outreach products were developed in conjunction with an outreach advisory board that integrated project partners and team members. To gauge public acceptability of geologically storing CO 2 , 5611 households in the project area were invited to participate in an online survey. The survey results indicate that the public attitude regarding CCUS is neutral to positive, with strong sentiment that CO 2 capture and storage may be an approach to maintain the economic vitality of the region. To achieve project objectives, critical support in the form of financial backing, engineering evaluations, site access, outreach collaboration, operations data, risk assessment/evaluation, and software access was provided by BEPC, the North Dakota Industrial Commission Lignite Research Council, ALLETE Clean Energy, BNI Energy, North American Coal Corporation, Minnkota, Prairie Public Broadcasting, Computer Modelling Group Ltd., and Schlumberger.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗