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DOE OSTI2023

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗
DOE OSTI2023

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

DC distribution↗
DOE OSTI2021

Stable and selective catalysts for propane dehydrogenation operating at thermodynamic limit

Hitting the limits on propene synthesis The greater abundance of propane from shale gas has spurred efforts to use it as a propylene feedstock. Direct dehydrogenation catalysts consisting of platinum–tin alloy nanoparticles supported on alumina often must run with hydrogen dilution to avoid carbon buildup and excess tin to avoid alloy segregation. Motagamwala et al. report that platinum–tin nanoparticles interact more weakly with a silica support and the metals thus do not segregate. The use of undiluted reactants allowed the reaction to run near the thermodynamically limit of about 67% conversion with a selectivity to propylene of more than 99%. This catalyst also does not build up carbon and could run up to 30 hours without deactivation. Science , abg7894, this issue p. 217

Science & Technology - Other Topics↗
DOE OSTI2020

SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Renewable Thermal Energy Systems: Systemic Challenges and Transformational Policies (Report 2)

Two important challenges for RTES are the need for significant further innovation and deployment, particularly in the United States. In the face of continued success of other renewable energy technologies, such as solar photovoltaics (PV) and land-based wind energy for electricity, it is worthwhile to evaluate the current state of select RTES technologies in the context of technology innovation systems and energy transitions. This report marks a departure from the existing body of research we are aware of and have contributed to relating to RTES applications for industrial process heat in the United States. Our objectives for this report are fourfold: 1) Begin considering the challenges of RTES deployment in the United States through application of socio-technical and transitions frameworks; 2) Begin applying the theory of RTES as configurational technology innovation systems in the United States and for industrial process heat specifically; 3) Review RTES innovation, policies, and market formation activities in the contexts of Objectives 1 and 2; 4) Identify future paths for RTES research. Ultimately, the challenges associated with scaling the deployment of RTES in the United States are related to the need to develop a well-functioning innovation system that can overcome resistance from a highly stable socio-technical regime within a shrinking window of time to address the climate crisis, U.S. industrial competitiveness, and volatile energy commodity price shocks. Current efforts toward scaling for RTES applications in industry and buildings emphasize the need for further cost reductions, but not for interventions that support actors and their interactions and that increase the legitimacy of the technologies. As a result, there are significant opportunities to analyze the social contexts of RTES that can then be used to inform the design of effective, transformational policy portfolios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Feasibility and strategic implications of deploying nuclear power reactors in Africa

This report assesses the feasibility and strategic implications of deploying nuclear power reactors, including large-scale plants, advanced small modular reactors (SMRs), and microreactors, in African countries. Case studies focus on South Africa, Egypt, Kenya, Ghana, and Nigeria, examining nuclear energy’s role in Africa’s rapidly evolving energy landscape, marked by fast-growing demand, significant electricity access gaps, increasing renewable penetration, and strong policy commitments to industrialization and energy security. Several U.S. reactor technologies and designs are considered based on their development status and readiness for deployment. The analysis finds that nuclear power can provide reliable, clean baseload and flexible generation, as well as high-temperature process heat for desalination, hydrogen production, and industrial applications. However, suitability is highly country-specific, depending on grid size and stability, transmission capacity, cooling water availability, regulatory readiness, and fuel supply chains. Near-term deployment opportunities are strongest for light-water reactors (such as NuScale, BWRX-300, AP300, and SMR-300) that use low-enriched uranium and build on proven technology. More advanced concepts, including gas-cooled, sodium-cooled, molten-salt cooled reactors, and microreactors, will likely be relevant for African deployment in the 2030s or later, contingent on demonstration projects, high-assay low-enriched uranium (HALEU) fuel availability, and mature international licensing frameworks. Economic analysis shows that SMRs are capital-intensive, with projected overnight costs for 300 MWe units in 2025 ranging from approximately 1.4 to 2.6 billion USD per module. The levelized cost of electricity (LCOE) is highly sensitive to the weighted average cost of capital (WACC). Given typically higher financing costs and utility balance-sheet weaknesses in many African countries, bankable project structures will require sovereign guarantees, robust offtake arrangements, and layered financing from export credit agencies, development finance institutions, and vendor nations. Comparisons with recent large nuclear projects in the United Arab Emirates (UAE) and Egypt underscore the central role of state-backed loans, long tenors, and concessional terms. Country case studies illustrate a spectrum of readiness and opportunity. South Africa operates two 920 MWe pressurized light water reactors (totaling 1,840 MWe) at Koeberg and has the most mature regulatory and industrial base, positioning it as a prime candidate for both large reactors and SMRs to replace coal, support desalination, and anchor industrial hubs. Egypt is constructing four VVER-1200 units at El Dabaa with strong state leadership and could later complement this fleet with SMRs for coastal and industrial applications. Kenya and Ghana are advancing through IAEA Milestones with growing institutional capacity and clear interest in SMRs that match their smaller grids and industrialization plans. Nigeria has the largest demand potential but faces acute constraints in grid reliability, project bankability, and regulatory capacity; targeted deployments of large reactors and SMRs near coastal or industrial sites could have high impact if accompanied by major grid upgrades and institutional reforms. The report identifies cross-cutting challenges such as financing, political continuity, public acceptance, nonproliferation and security, waste and back-end management, regulatory capacity, grid adequacy, and long deployment timelines for first-of-a-kind designs, and ANL/NSE-26/3 ii proposes broad directions for resolution. These include stronger multifaceted financing for nuclear, long-term national energy strategies that transcend electoral cycles, proactive stakeholder engagement, strengthened regional and national regulators, and systematic workforce development through centers of excellence and expanded training. The United States should develop partnerships with African countries and offer end-to-end nuclear package similar to those used effectively by competitors: coordinated project development, state-backed financing, long-term fuel services, and durable in-country support through regional offices and sustained workforce/regulatory training. With timely planning, sustained political commitment, and appropriate financing and institutional support, nuclear energy, both large reactors and advanced SMRs, can become a meaningful, though not dominant, pillar of Africa’s future power mix, enhancing energy security, enabling industrial growth, and supporting climate goals.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

High-Level Synthesis of Parallel Specifications Coupling Static and Dynamic Controllers

The increased need for efficient ways to implement domain-specific accelerators is driving design methodologies towards the use of abstractions higher than the Register Transfer Level (RTL). In this scenario, High Level Synthesis (HLS) plays a significant role by enabling the automatic generation of custom hardware accelerators starting from high level descriptions (e.g., C code). Conventional HLS tools exploit parallelism mostly at the Instruction Level (ILP). They statically schedule the input specifications, and build centralized Finite State Machine (FSM) controllers. However, aggressive exploitation of ILP in many applications has diminishing returns and, usually, centralized approaches do not efficiently exploit coarser parallelism because FSMs are inherently serial. In this paper we present a HLS framework able to synthesize applications that, beside ILP, also expose Task Level Parallelism (TLP). An application can expose TLP through annotations that identify the parallel functions (i.e., tasks). To generate accelerators that efficiently execute concur- rent tasks, we need to solve several issues: devise a mechanism to support concurrent execution flows, exploit memory parallelism, and manage synchronization. To support concurrent execution flows, we introduce a novel adaptive controller. The adaptive controller is composed of a set of interacting control elements that independently manage the execution of a single operation or function call. These control elements check dependencies and resource constraints at runtime, enabling as soon as possible execution. To support parallel access to shared memories and synchronization, we introduce a novel Hierarchical Memory Interface (HMI). With respect to previous solutions, the proposed interface supports multi-ported memories and atomic memory operations, which commonly occur in parallel programming. Our framework can generate the hardware implementation of C functions by employing two different approaches, depending on its characteristics. If a function exposes TLP, then the framework generates hardware implementations based on the adaptive controller. Otherwise, the framework implements the function by exploiting a more conventional FSM approach, which is optimized for ILP exploitation. We evaluate our framework on a set of parallel applications, and show substantial performance improvements (average speedup of 4.7) with limited area over- heads (average area increase of 5.48 times).

Castellana, Vito G.↗
DOE OSTI2021

High-Level Synthesis of Parallel Specifications Coupling Static and Dynamic Controllers

The increased need for efficient ways to implement domain-specific accelerators is driving design methodologies towards the use of abstractions higher than the Register Transfer Level (RTL). In this scenario, High Level Synthesis (HLS) plays a significant role by enabling the automatic generation of custom hardware accelerators starting from high level descriptions (e.g., C code). Conventional HLS tools exploit parallelism mostly at the Instruction Level (ILP). They statically schedule the input specifications, and build centralized Finite State Machine (FSM) controllers. However, aggressive exploitation of ILP in many applications has diminishing returns and, usually, centralized approaches do not efficiently exploit coarser parallelism because FSMs are inherently serial. In this paper we present a HLS framework able to synthesize applications that, beside ILP, also expose Task Level Parallelism (TLP). An application can expose TLP through annotations that identify the parallel functions (i.e., tasks). To generate accelerators that efficiently execute concur- rent tasks, we need to solve several issues: devise a mechanism to support concurrent execution flows, exploit memory parallelism, and manage synchronization. To support concurrent execution flows, we introduce a novel adaptive controller. The adaptive controller is composed of a set of interacting control elements that independently manage the execution of a single operation or function call. These control elements check dependencies and resource constraints at runtime, enabling as soon as possible execution. To support parallel access to shared memories and synchronization, we introduce a novel Hierarchical Memory Interface (HMI). With respect to previous solutions, the proposed interface supports multi-ported memories and atomic memory operations, which commonly occur in parallel programming. Our framework can generate the hardware implementation of C functions by employing two different approaches, depending on its characteristics. If a function exposes TLP, then the framework generates hardware implementations based on the adaptive controller. Otherwise, the framework implements the function by exploiting a more conventional FSM approach, which is optimized for ILP exploitation. We evaluate our framework on a set of parallel applications, and show substantial performance improvements (average speedup of 4.7) with limited area over- heads (average area increase of 5.48 times).

Castellana, Vito G.↗
DOE OSTI2024

Mortality of older construction and craft workers employed at Department of Energy (DOE) nuclear sites: Follow‐up through 2021

Background To determine if construction and trades workers formerly employed at US Department of Energy (DOE) nuclear weapons sites are at significant risk for occupational diseases, we studied the mortality experience of participants in the Building Trades National Medical Screening Program (BTMed). Methods The cohort included 26,922 participants enrolled between 1998 and 2021 and 8367 deaths. Standardized mortality ratios were calculated based on US death rates. Cox models compared construction workers (n = 22,747; 7487 deaths) to two nonconstruction subpopulations: administrative, scientific and security workers (n = 1894; 330 deaths), and all other nonconstruction workers (n = 2218; 550 deaths). Results Mortality was elevated for all causes, all cancers, cancers of the trachea, bronchus, lung, kidneys, and lymphatic and hematopoietic system, mesothelioma, chronic obstructive pulmonary disease (COPD), asbestosis, transportation injuries, and other injuries, particularly accidental poisonings. There were 167 deaths from coronavirus disease 2019 (COVID-19), which was lower than expected using US death rates. Overall cause-specific mortality was significantly higher among construction workers than for internal comparison groups. Conclusions Construction workers employed at DOE sites have a significantly increased risk for occupational illnesses. Apart from COVID-19 deaths, this update: (1) found that mortality among construction workers is significantly elevated compared to the US population and significantly higher than in the internal comparison populations, and (2) confirmed excess risk for these workers for first employment after 1990. Cancer mortality risks are similar to the cancers identified for DOE compensation from radiation exposures. In conclusion, the high lung cancer risk supports the value of early lung cancer detection. Continued medical surveillance is important.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Expedited Walkdowns and Preparation of Cost Estimates for Decontamination and Demolition of Excess Facilities - 20335

Lawrence Livermore National Security, LLC (LLNS) is the prime contractor responsible for managing and operating Lawrence Livermore National Laboratory (LLNL). The mission of the LLNL is to strengthen security of the United States through development and application of world-class science and technology to enhance the nation's defense, reduce the global threat from terrorism and weapons of mass destruction, and respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. To accomplish its mission, LLNL must plan and manage its campus space and facilities to optimize use of its relatively small one-square-mile footprint. LLNL currently has numerous excess facilities that require decontamination and demolition (D and D). The LLNL Legacy Facility Program is responsible for stewardship and risk reduction programs to effectively manage these excess assets at LLNL. The Legacy Facility Program identified 10 excess facilities at LLNL that were candidates for demolition based on risks from further degradation of the facilities, potential exposure to hazardous materials, or need for removal to make way for new program facilities. The buildings range from a structure built in 1943 to serve as a US Navy drill hall during World War II, to a facility that provided neutrons to study material properties for the fusion energy program. The National Nuclear Security Administration (NNSA) appropriated FY19 funding for the development of Class 3 estimates for these 10 excess facilities that would be used to develop a Program Management Plan for funding consideration. Class 3 estimates are not conceptual, nor overly detailed, but adequate to be used for budget and appropriation purposes. Using existing strategic sourcing agreements, LLNL was able to organize a team of senior subject matter experts (SMEs) with diverse technical backgrounds across the Department of Energy (DOE) Complex to complete extensive process knowledge (PK) reviews and walk-downs of each excess facility and to deliver accurate and complete estimates under an expedited schedule (i.e., within eight weeks). To facilitate the expedited schedule, LLNL was fully prepared for the arrival of the JGMS team, which consisted of the following companies: J.G. Management Systems, Inc. (JGMS); Strata-G, LLC; and Michael Baker International. LLNL provided background and PK information on the excess facilities, pictures, supporting historical documentation, and radiological survey history as well as escorted access to the facilities. The JGMS team has previous experience supporting Program Management Plans for excess facilities at the Y-12 National Security Complex. The team mobilized to LLNL and performed the facility walk-downs from January 22-25, 2019. Using a methodical evaluation process, expertise, and experience, the team was able to deliver walk-down draft reports and estimates to LLNL before their February 8, 2019 deadline. LLNL then in turn reviewed, completed the estimates by adding waste management costs and other LLNL costs, and delivered the estimates to NNSA by February 15, 2019. The completed final reports, including draft D and D estimates, were delivered to LLNL before their March 6, 2019 deadline. The work was successfully completed ahead of the expedited schedule required by LLNL, resulting in a commendation from the client. This paper describes how the partnership and collaboration as a fully integrated team comprised of the prime contractor, LLNS, and the JGMS team successfully met the needs of the US government, resulting in the performance of expedited site walk-downs, the development of summary evaluation reports, and the preparation of draft Class 3 estimates and preliminary schedules for the D and D of 10 excess facilities at LLNL. The efforts of the team will enable the D and D of the first excess facility in the 2020-2021 time frame. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Model predictive control for optimal dispatch of chillers and thermal energy storage tank in airports

Cost of energy consumption is one of the biggest operational cost for airports, and it is increasing from time to time as airports expand to support growing number of passengers. Various factors affect the energy consumption including efficiency of airport Heating Ventilation and Air conditioning (HVAC) systems, which in turn depends on the efficiency of individual subsystems. Here, in this paper, we present an optimal scheduling method for the central plant system at Dallas Fort Worth airport, involving chillers, pumps, and a thermal energy storage (TES) system. A model predictive control (MPC) problem is formulated to minimize both energy and demand charge costs while satisfying the cooling needs of the airport. The proposed Mixed-Integer Nonlinear Programming (MINLP) formulation includes performance curve based models for chillers and pumps and a simplified state of charge model for TES. The formulation also includes predictions of cooling load and chilled water return temperature. Simulation results for a month in summer show savings around 10% compared to the baseline. Initial recommendations based on insights from simulation results to the manual operation procedures resulted in significant savings. Field test results show a 7% chiller efficiency improvement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Controlling Structural, Electronic, and Energy Flow Dynamics of Catalytic Processes through Tailored Nanostructures

MoS 2 (molybdenum disulfide) is a highly-versatile catalyst material for support of numerous reactions from hydrodesulfurization and denitrogenation to the focus of this renewal proposal: hydrogenation of CO/CO 2 towards (higher) alcohols. At the same time, MoS 2 is a non-toxic, environmentally-benign and rather inert material- which under ambient conditions for some time has served as a lubricant and, more recently, as next-generation electronic material. The apparent contrast between inertness and stability in ambient, and catalytic activity under reactive conditions is puzzling and calls for a synergistic theoretical and experimental investigation with the long-term objective of enabling the rational design of MoS 2 -based catalysts for alcohol-formation reactions by providing a microscopic understanding of the environmental factors that determines site activity and selectivity. Our research project seeks answers to the questions (a) what conformation does MoS 2 adopt under reaction conditions (as opposed to that under ultrahigh vacuum and low temperatures)?; (b) what reaction pathways exist on such a material?; (c) how can the local environment of the active sites be manipulated so as to make MoS 2 an efficient catalyst for production of higher alcohol from syngas? In particular, research strategies will explore how the basal plane composed of sulfur atoms can be activated so as to exhibit a reactivity of its own, by addressing three research targets and building on extensive preliminary and enabling work: (1) vacancies and vacancy aggregates on the basal plane; (2) non-local catalyst transformation through alkali doping, hydrogenation and phase transition; (3) fabrication of a metal-nanoparticle-activated MoS 2 system, in which particle anchoring, reactive sites and pathways as well as selectivity are controlled by design, as an example of predictive development of a catalyst material,. All strategies are directed to improve the efficacy of the key reactive sites and selectivity of chemical pathways by design, to replace the inefficient methodology of trial and error in catalyst development. This research project represents a synergistic combination of computational guidance, foundational surface-science-based experiments and validation under reactive conditions that aims at transformative new insights into the working of MoS 2 -based hydrogenation-catalysts, a topic squarely at the center of the interest of DOE BES. Guided and led by Talat Rahman, a computational physicist, this project will apply density functional theory to understand structure, reaction pathways and chemical potentials associated with MoS 2 -based CO/CO 2 hydrogenation, augmented by kinetic Monte Carlo methods for reaction rates and prefactors as well as ab-initio molecular dynamics for evaluation of thermal stability. Complementary experimental input and validation will originate from co-PI Ludwig Bartels, a physical chemist and materials scientist, whose group focuses on local imaging and preparation of MoS 2 materials, from co-PI Peter Dowben, an experimental physicist, whose group is expert in the spectroscopy of occupied and unoccupied electronic states, and from senior collaborator Michael White of Brookhaven National Laboratory, whose group generates high-resolution electronic and activity information on size-selected well-defined metal chalcogenide clusters. This collaborative effort will enable a comprehensive understanding of the correlation of structural integrity and catalytic activity of MoS 2 in forms ranging from extended films to individual particles with known geometries and binding sites. Alcohol formation from syngas is a rapidly emerging application that has great potential through facile, economic, and decentralized biomass gasification. CO 2 activation is one of the most pressing concerns of our time: increasing CO 2 levels in the atmosphere change the climate and expose the globe to environmental transformations with the potential for enormous economic and societal impact. We will investigate CO/CO 2 hydrogenation via an interdisciplinary research collaboration with established synergy – one that, in accordance with the mission of the DOE, involves accredited Hispanic-Serving Institutions and that, through student exchange with international collaborators and National Labs, will directly benefit a broad spectrum of communities and generate human resources in sciences essential to their future.

2D materials↗
DOE OSTI2025

Microstructure-Based Understanding of High-Temperature Deformation Behaviors in Laser Powder Bed Fusion (LPBF) 316H Stainless Steel

This report presents the results of high-temperature mechanical property testing and microstructural analysis of laser powder bed fusion (LPBF) 316H stainless steel (SS) conducted during Fiscal Year 2025 (FY25) under the U.S. Department of Energy, Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program. The study builds upon prior work by expanding the mechanical property test matrix and advancing a microstructure-based mechanistic understanding of LPBF 316H SS performance, with a focus on the solution annealed (SA, 1100°C for 1 hour) materials. High-temperature tension, creep, fatigue, and creep-fatigue tests were performed. Thermal aging studies were conducted. Advanced characterization techniques, including scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), and high-energy synchrotron X-ray diffraction, were employed to investigate the microstructural evolution during thermal aging and mechanical testing. It was discovered that the as-built (AB) LPBF 316H SS exhibited precipitation kinetics during thermal aging that are 10–100 times faster than its wrought counterpart due to the presence of high densities of preferred nucleation sites, while the precipitation kinetics in SA LPBF 316H was under investigation. The accelerated precipitation of embrittling phases such as sigma phase in LPBF 316H SS compared to its wrought counterpart led to significant impacts on creep ductility. The influence of laser printing parameters and build orientations on fatigue and creep-fatigue performance was observed. In assessing the creep-fatigue performance, it was discovered that the SA LPBF 316H samples failed in less than 200 cycles under 595°C, 0.5% (strain amplitude) with a 60-minute hold time. This work establishes a mechanistic framework for predicting the long-term behavior of LPBF 316H SS, supporting its rapid qualification under the ASME BPVC. The findings contribute to the broader goal of enabling the deployment of advanced materials in next-generation nuclear energy systems. An outlook for FY26 work, including continued aging studies, mechanical testing, and microstructural characterization, is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Characterizing IHE Response to Multiple Shock Loading

The response of high explosives to shock loading is traditionally measured with a steady loading pressure. In many accident scenarios involving fragment impact, however, a loading duration that is shorter than the build up to detonation may occur. Fragments passing through multiple materials before reaching a high explosive charge may produce loading that is comprised of more than one shock wave. Additionally, the build up to detonation in high explosive corner turning loads the explosive a short duration pressure pulse, since rarefactions can often rapidly overtake the reactive wave. For these reasons, we have studied the response of the insensitive high explosive (IHE) materials PBX 9502 and LX-17 to complex loadings of varied intensity and duration. We refer to a single loading of limited duration as a “thin pulse”, whereas more complex scenarios were studied with an impactor that produces a double shock in the explosive. The following report presents experimental data and analyses of thin pulse shock initiation and double shock experiments designed to guide development of models of Insensitive High Explosives (IHEs) under controlled one-dimensional conditions relevant to accident scenarios and corner turning. Thin pulse shock initiation data on PBX 9502 and LX-17 were obtained under varied pulse duration, pressed density, and temperature conditions in order to probe various parameters essential for the development of a physics-based Cheetah reactive flow hotspot model. In situ pressure gauges provide insight into the degree of reaction in the explosive that are not obtainable with optical PDV measurements or distance measurements such as run to detonation. Double shock data was obtained to inform a Composition Aware Cheetah model which can be applied to any TATB-based IHEs. This model supports efforts to find a new IHE formulation and potentially incorporate new binders into IHE formulations. Simulations of each experiment are included to demonstrate the utility of these focused experiments to developing models of HE behavior. One-dimensional gas gun experiments are essential for characterizing shocked HE behavior and informing HE models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Resilient Information Architecture Platform for Smart Grid (RIAPS)

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Assessing electrification readiness in U.S. single-family homes based on a nationwide survey of electrical panel capacities

Electrification of residential buildings is a key strategy for increasing the use of renewable energy sources. Central to this transition is understanding the capacity of existing electrical infrastructure—specifically electrical panels—to safely and effectively manage increased electricity demands from electrification technologies. However, comprehensive nationwide data on electrical panel capacities in U.S. single-family homes is currently lacking. To address this gap, we conducted a nationwide survey of single-family homes, collecting detailed data on electrical panel capacities, breaker slot availability, major electric and gas appliances, electrical panel models, and home characteristics such as construction year and floor area. Photographic documentation was used to verify electrical panel data and appliance information. Results show that approximately 60% of surveyed homes have electrical panels rated at ≥200 amperes (A), indicating that a significant portion of the existing housing stock can accommodate additional electric loads. However, 31% of homes possess panels rated at ≤100 A, potentially restricting their ability to adopt new electric appliances without significant upgrades. Panel capacities positively correlate with both home size and construction year, with newer and larger homes generally better suited for electrification. Homes with higher-capacity panels tend to have fewer gas appliances, reflecting a gradual shift toward electric technologies. Conversely, homes with lower-capacity panels frequently rely on multiple gas appliances, highlighting substantial electrification challenges. Additionally, approximately 3% of surveyed homes had potentially hazardous electrical panel models, emphasizing important safety considerations in the residential electrification process. Our findings underscore the need for targeted policies, financial incentives, and infrastructure investments designed specifically to address infrastructural and safety barriers, particularly in older and smaller homes, to support equitable and efficient electrification across the U.S. residential sector.

Gul, Sadia↗