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

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DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

National Fire Protection Association Spurs the Safe Adoption of EVs Through Education and Outreach

As electric vehicles (EV) and their charging infrastructure steadily increase in popularity across the U.S., growing pains still exist, impeding their true potential for growth. The barriers include a general lack of knowledge around EV systems, range anxiety, electrical workplace safety practices, code compliance and inspection for charging stations, maintenance garages and workers, insurance concerns, and the potential risks associated with damaged lithium-ion batteries (fires, re-ignition, water immersion, and stranded energy).NFPA partnered with SMEs, training development organizations, and Clean City Coalitions to create a series of common online trainings for each EV ecosystem sector, educating communities on the realities of EV proliferation, myths, facts, statistics, codes, and basic information for each sector to become versed in EV knowledge. We also constructed a workshop program for the many EV stakeholders who should be involved in setting up a strategic, cohesive outlook for the participating towns and cities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Design Approach for Circulating Fluidized Bed Riser Reactors such as NETL ASURE: A Review for Clustering Flow

Circulating fluidized bed riser reactors have been a commercial reality for some 80 to 90 years. Through all this time and hundreds if not thousands of research papers on the topic, no clear understanding exists on what is required to design and build a process reactor. There are numerous reasons for this, but the most significant reason is likely that riser reactors are chaotic in nature and unless this chaotic behavior is accounted for, there will be wide discrepancies in the predicted performance and in attempts to scale these reactors without significant trial and error. This work puts forth a new methodology to design riser reactors based upon the desired operating range using the chaotic microstructure behavior. This is accomplished through the use of a nondimensional regime map that was developed using chaotic parameters of memory and order/disorder along with higher moments of skewness and kurtosis. Validation of the approach is provided with circulating fluidized bed combustors and applied to the design of the NETL ASURE facility.

42 ENGINEERING↗
DOE OSTI2025

Prototyping of 2D and 3D photonic nanodevices by Electron Beam Lithography (CRADA Final Report)

Team developed a large area and fast electron beam lithographic for patterning visible wavelength optical materials to create 2 or 3 dimensional gratings and metasurfaces. Metasurfaces,are arrays of nanoantennas or optical resonators, are dielectric or plasmonic structures that allow control of the amplitude, phase, and polarization of light by locally modulating its wavefront The team was able to write a 1cm2 pattern with resolution better than 30 nm without visible discontinuities in the pattern. Performance to this specification allowed prototyping of optical lenses for production applications such as augmented reality goggles (optical displays) and other applications for visible light manipulations

36 MATERIALS SCIENCE↗
DOE OSTI2025

Valuing the Future Electric Grid: A Bid-Based Approach

Energy storage resources (ESRs) and other zero marginal cost (ZMC) resources have unique characteristics that are not fully captured in today’s electricity planning and operations modeling tools. Because the modeling assumptions used in these tools are simplified approximations of how operations and investment decisions occur in the real-world, accurately representing cost and operational characteristics are key for determining how these resources impact price formation. Questions such as—Where should we build new transmission? Will a small modular reactor earn enough revenue to participate in the future electric grid? Is retrofitting a coal plant with carbon capture technology economically feasible?—all require accurate electricity prices, which aren’t available from today’s electricity planning and operations modeling tools. As an example, production cost models (PCMs) are heavily utilized tools that determine the cost and reliability of the electric system. However, as PCMs were developed to help thermal generators manage their fuel inventories, production cost modeling is largely based on fuel prices. Because ESRs do not incur fuel costs, they are often modeled as ZMC resources. In reality, ESRs incur opportunity costs as well as technology-specific (degradation) costs that are non-trivial to calculate but are important for price formation. In this research, we identify options to incorporate more realistic opportunity and degradation costs in ESR bidding algorithms. Expanding available bidding assumptions allows energy system modelers to develop more accurate economic valuations for ESRs, leading to more accurate price formation from leading energy system modeling tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

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

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

GCAM Regional Tuning: A framework to tune GCAM parameters

GCAM assumptions typically generate scenarios that are designed to be internally consistent and globally coherent. The gcamdata tool which facilitates the compilation of data sets and user assumptions is not well suited to tailoring to specific country or regional realities, sponsor requirements, or perform harmonization for model intercomparison needs. As described in this report, the GCAM Regional Tuning project develops a computational framework that enables users to adjust GCAM parameters, so model outputs match targeted outcomes at user-defined spatial, temporal, and sectoral resolutions. The framework integrates GCAM, gcamdata, and gcamwrapper with a set of flexible “tuning directives” and an iterative numerical solver. Users can define targets (e.g., technology shares in power generation, BEV uptake, sectoral service demands), select tuners that manipulate relevant GCAM parameters (e.g., share weights, cost adders, elasticities), and export tuned parameters as reusable GCAM XML inputs for future runs. We demonstrate the approach and document usage, diagnostics, and known limitations, and we outline potential future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cyber-Informed Engineering (CIE) – Engineered Controls Database and Use

Cyber-Informed Engineering (CIE) addresses the reality that cyber-attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This database is meant to establish clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design. The goal is to ensure that resilience is engineered into systems from the outset. Unlike cybersecurity protections that defend the digital layer, engineered controls act directly at the physical and algorithmic levels to guarantee that unacceptable consequences are prevented or limited. CIE keeps the consequences of a cyber attack from impacting the safety, reliability, and performance of engineered systems.

42 - ENGINEERING↗
DOE OSTI2025

R.U.N. Motion VR Boots: Realistic Unimpeded Natural (RUN) Motion Boots for VR gaming in the comfort of your home

Virtual reality (VR) gaming has experienced increasingly widespread adoption in recent years largely due to the development of affordable, consumer-grade headsets equipped with high quality, miniature displays and compact sensors for motion tracking. These devices are now able to provide users with immersive experiences that far surpass what traditional video games can achieve. However, despite these technological strides, a fundamental limitation remains: VR users are unable to move freely in their physical environment without the risk of colliding with real-world obstacles and hazards. This safety concern significantly restricts the level of immersion and natural movement possible in VR gaming.

42 ENGINEERING↗
DOE OSTI2025

Synthesis Roadmap for Actinide Chloride Salts

Molten salt reactors (MSRs) are among the main advanced nuclear reactor types at the forefront of development by industry, with the support of the US government, for the next fleet of nuclear reactors to support the demand for energy in the coming decades. MSRs are highly unique because they are cooled and typically also fueled by molten salt. This quality brings about safety benefits such as low-pressure operation and self-stabilization of the neutron flux, operational benefits such as high-temperature operation and the ability for online refueling, and fuel and waste management benefits thanks to the flexibility of post-processing of molten salts and reprocessing options that involve removal of fission products and actinide separation. In fact, domestic deployment of molten salt (or molten salt–cooled) reactors is an approaching reality: a handful of molten salt reactor developers are planning to operate demonstration-scale reactors, as a step toward commercial-scale power reactors, within the decade. For example, Natura Resources received a construction permit in September 2024 for the deployment of MSR-1, which is a graphite-moderated thermal spectrum reactor, at Abilene Christian University. Also, TerraPower, in a collaborative effort with Southern Company and Idaho National Laboratory (INL), received approval from DOE in 2023 to proceed with the construction of the Molten Chloride Reactor Experiment (MCRE), a homogeneous chloride fast reactor at INL, and has begun assembly of system components. There are several other examples of developers at different stages of development of their own unique MSR designs (Jenet et al., 2025). As developers are in the process of obtaining approvals for their designs, deploying demonstration-scale reactors, and planning for their commercial-scale power reactors, there is a critical supply chain need for the synthesis of fuel for these reactors that must be addressed. The challenge generally is three-fold: (1) the quantity of fueled salt needed for these reactors is extraordinarily high (>100s of kilograms), but demonstrations of scaled-up techniques for fueled salt synthesis are significantly more limited than demonstrations of lab-scale syntheses; (2) each developer has a unique reactor design, which means different actinide halide elements in different carrier salts must be synthesized; and (3) there is a need for particularly high-purity salt so as to ensure the long-term operability of these reactors with minimal degradation to salt-wetted components, which necessitates synthesis techniques with high levels of quality control, repeatability, and well-characterized precursor and reactant materials. It is crucial that this supply chain challenge be addressed by demonstrating synthesis techniques that are scalable, de-risked, and well-documented so that these technologies may be adopted and utilized by industry to support the fueling needs of MSRs that are to come online within the next 10 years. With this supply chain challenge clearly defined for the developing MSR industry, it is important to consider that addressing such a challenge is oftentimes complex and context-dependent. There is not necessarily a single synthesis technique that can be scaled up and adopted to address the needs of all MSR developers; the synthesis approach that may be viable for producing a desired fuel salt will be entirely dependent on the exact salt composition needed, the quantity needed, purity needed, the refueling and waste plans, and the availability of a carrier salt for the fuel. Therefore, it is important to consider more broadly what synthesis techniques are available and have been demonstrated to understand the benefits, challenges, and general nuances that should be considered when evaluating a particular route for efficient production of high-purity fuel salts at scale.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees

This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.

42 ENGINEERING↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2025

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Outcomes of PAX sapiens-Supported Global Wildlife Data Sharing Conferences for Enhanced One Health Security (GWDSC)

Across two consecutive Global Wildlife Data Sharing Conferences supported by PAX sapiens—Year 1 (May 2024) at Pacific Northwest National Laboratory and Year 2 (2025) in Ciudad Real, Spain—the initiative converted wildlife data sharing from aspiration into operational reality, producing measurable impacts in platform development, data mobilization, standards harmonization, and international partnership formation. The conferences addressed a critical gap in global health security: while 75% of emerging infectious diseases affect both humans and animals and over 60% originate in wildlife, wildlife health surveillance has historically lagged behind human and agricultural sectors due to fragmented databases, inconsistent terminology, uneven capacity, and limited cross-border coordination. By convening practitioners, government agencies, international organizations, academic institutions, and NGOs, the GWDSC catalyzed trust-based relationships and practical workflows that enable earlier detection, better risk assessment, and more effective prevention of threats at the wildlife–domestic animal–human–environment interface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Calculations Document

Figure 1 shows a simplified model schematic of the Tetracage target design. In this figure the green lines represent the Tetracage yarns, which consist of two parallel yarns on the top and two parallel yarns on the bottom with the pairs in a cross hatched pattern. For simplicity, as we are only considering vertical forces, we will perform calculations without consideration for the two pairs of yarns being perpendicular to each other. Also worth noting is the model shows the yarns extending directly to the capsule then closely hugging it – in reality, the yarns behave as you would expect where they hug the capsule only as much as the geometry of their deflection allows, similarly to if you held a tennis ball with a pair of parallel strings anchored at opposing ends. The dashed black lines show the path the yarns take straight across from slot glue point to slot glue point before the target is closed and the capsule deforms them. The variable r corresponds to the capsule outer radius. The variable L corresponds to the hohlraum inner radius. The variable a corresponds to the yarn offset from the center of the diagnostic band. The variable d corresponds to the wire deflection vertically.

42 ENGINEERING↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗
DOE OSTI2026

Cluster Dynamics Simulations of Intra-Granular Fission Gas Bubble Size and Pressure Evolution in UO 2

Fission gases such as xenon (Xe) play a critical role in determining the behavior and response of nuclear fuel. Given that Xe has little solubility in UO 2 , it accumulates and forms bubbles, which significantly impact fuel performance. Intra- and inter-granular bubble nucleation and growth can lead to fuel swelling, and once bubbles interconnect at grain boundaries, fission gas can be released into the plenum. At low temperatures, limited uranium vacancy mobility can restrict swelling, therefore causing the bubbles to become highly pressurized. Consequently, this can induce micro-cracking, promote fission gas release (increasing the likelihood of cladding failure), and even lead to fuel pulverization under accident conditions such as a loss of coolant accident. As bubble evolution is strongly influenced by local temperature and fission rate, markedly different behavior occurs across the radial profile of the fuel pellet. Capturing the mechanisms that underpin bubble evolution is therefore important to predict these behaviors in the fuel. Previous models describing important mechanisms informed by lower length scale simulations have been developed under the NEAMS program. These can describe the evolution of a single bubble type (i.e., single value for radius and pressure) at each position in the pellet, for instance using the Centipede cluster dynamic code. However, in reality, a full distribution in bubble sizes and pressures exists within the microstructure at a given position in the pellet. To address this the cluster dynamics code Xolotl, which can predict Xe and vacancy phase space (i.e., bubble distributions) for intra-granular bubbles, has been used before. Prior work benchmarked the Xolotl code against the Centipede cluster dynamics code to ensure compatibility and to verify that mobile defect properties are adequately transferred between the two codes, along with some physics improvements. In this work, we go further by introducing a physics-based set of improvements that will allow us to accurately predict bubble size distributions and internal bubble pressures under representative UO 2 irradiation conditions. The improvements include (i) coupling bubble-defect reaction energies to a virial equation of state (EOS), (ii) including a bubble surface tension contribution, (iii) incorporating radiation-induced re-solution of Xe and vacancies, (iv) enabling pressure-driven dislocation loop punching through an effective emission of interstitial clusters informed by interstitial loop energetics, (v) accounting for radiation induced athermal diffusion of Xe, and (vi) implementing a Booth-type grain boundary sink representation for all mobile defects and defect clusters. After these modifications, we observe good agreement of Xolotl fission gas bubble size and concentration predictions with legacy experimental measurements. Additionally, it allows the distribution of Xe bubble pressures and radius to also be predicted and compared to data produced through the Advanced Fuels Campaign (AFC) program. Here, we have done this by running simulations under conditions similar to the AFC post-irradiation examination (PIE) samples irradiated at North Anna 2 light water reactor (LWR). Our results shows excellent agreement with these experimental measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Sim2Real Autonomous Robotic Exploration [Poster]

Autonomous robots offer promising solutions for exploration in environments that are inaccessible or hazardous to humans. Despite this, physical training of such robots is often constrained by safety risks, high cost or limited accessibility. This project presents an end-to-end simulation to reality pipeline leveraging Nvidia Isaac Sim and Boston Dynamics' Spot to enable autonomous navigation in indoor environments. A reinforcement learning policy is first trained using Nvidia Isaac Lab to establish Spot's locomotion pattern. Virtual LiDAR sensors are then integrated to perform SLAM-based navigation using simulated odometry. Finally, the simulated navigation scheme is transferred to a physical Spot robot to inspect and record images of a real-world room by repeating the learnt trajectory. The proposed framework highlights the potential of scalable training in simulation and reliable deployment in physical environments. Future directions include dynamic trajectory generation in unseen and challenging environments and integration of environmental sensing like temperature, radiation or humidity via sensor and material simulation.

97 - MATHEMATICS AND COMPUTING↗