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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Robotics for Energy Infrastructure Construction on Earth and Beyond: Pilot Study Exploring Level 3-4 Semi-Autonomous Installation of a Reduced-Scale Photovoltaic Power Plant Prototype: Preprint

There is a growing need to build energy infrastructure at massive scale on both Earth and in space. On Earth, increasing electricity demand from applications such as AI data centers is driving rapid expansion of power systems, while in space, long-duration missions such as lunar operations require reliable and resilient energy infrastructure that can be deployed quickly. These needs outpace the capabilities of conventional construction methods. Robotic construction offers a path to faster, lower-cost, and more consistent deployment, yet most existing approaches focus on automating individual tasks rather than end-to-end assembly. The Industrialized Construction Innovation (ICI) team at the National Laboratory of the Rockies (NLR), in collaboration with the Colorado School of Mines, has been investigating robotics and automation strategies for rapid end-to-end energy infrastructure construction. This paper presents a pilot study demonstrating level 3-4 semi-autonomous, end-to-end robotic assembly of a reduced-scale photovoltaic power plant prototype under terrestrial conditions. The approach combines hardware redesign of photovoltaic components for robotic compatibility, discrete-event and physics-based robotic simulations in synthetic environments, and sim-to-real deployment to execute end-to-end robotic assembly of[NM1.1][NM1.2] tracking components, structural members, photovoltaic modules, and embedded electrical connections. The pilot identifies key limitations like tolerance management, perception and localization, end effector capability, fastening methods, and multi-physics simulation fidelity, that must be addressed to enable end-to-end robotic assembly of full-scale energy infrastructure. The study highlights how these insights inform scaling strategies for terrestrial energy infrastructure and adaptation to unstructured and extreme environments, including off-world scenarios such as robotic assembly of vertical tall lunar towers.

14 SOLAR ENERGY↗
DOE OSTI2021

Of Actors, Cities and Energy Systems: Advancing the Transformative Potential of Urban Electrification

The electrification of transportation and the integration of electric vehicles (EVs) with buildings connected to clean grids has been touted as one of the key solutions to the global decarbonization challenge. Cities are on the frontlines of current and future electrification, as they depend on and drive electricity generation, distribution, and use. City actors also occupy a central role in the actions to enable electrification to support energy transitions in efficient, equitable, environmentally sound, and resilient ways. Currently, however, research and development on the interactions between actors, cities and energy systems is predominantly conducted in disciplinary siloes. This topical review analyzes the transformational potential of urban electrification. It focuses on efforts to electrify transportation and integrate EVs with buildings connected to a clean grid. We find that actions in these area are driving change; they are adopted by wealthier populations and on an experimental basis by specific communities. Their larger-scale growth is constrained by institutional, behavioral, and infrastructural factors. We also find that existing siloed disciplinary approaches are often incompatible with advancing holistic research. To achieve that, divergent communities of scholars need to come together to integrate their research and create broader perspectives. Through incorporation of the social sciences, these perspectives need to consider the societal limits and potentials brought to bear by human behavior and decision making. Only then can urban electrification be understood as the empirically rich and socially complex topic that it is. And only with this understanding will innovations and smart policy actions be able to tap into the transformational potential of urban electrification.

cities↗
DOE OSTI2021

SNF Canister Coatings for Corrosion Prevention and Mitigation (FY21 Status Report)

This report summarizes the current actives in FY21 related to the effort by Sandia National Laboratories to identify and test coating materials for the prevention, mitigation, and repair of spent nuclear fuel dry storage canisters against potential chloride-induced stress corrosion cracking. This work follows up on the details provided in Sandia National Laboratories FY20 report on the same topic, which provided a detailed description of the specific coating properties desired for application and implementation on spent nuclear fuel canisters, as well as provided detail into several different coatings and their applicability to coat spent nuclear fuel canisters. In FY21, Sandia National Laboratories has engaged with private industry to create a Memorandum of Understanding and established a collaborative R&D program building off the analytical and laboratory capabilities at Sandia National Laboratories and the material design and synthesis capabilities of private industry. The resulting Memorandum of Understanding included four companies to date (Oxford Performance Materials, White Horse R&D, Luna Innovations, and Flora Coating) proposing six different coating technologies (polyetherketoneketone, modified polyimide/polyurea, modified phenolic resin, silane-based polyurethane hybrid with and without a Znrich primer, and a quasi-ceramic sol-gel polyurethane hybrid) to be tested, evaluated, and optimized for their potential use for this application. This report provides a detailed description of each of the coating systems proposed by the participating industry partners. It also provides a description of the planned experimental actives to be performed by Sandia National Laboratories including physical tests, electrochemical tests, and characterization methods. These analyses will be used to identify specific ways to further improve coating technologies toward their application and implementation on spent nuclear fuel canisters. In FY21, Sandia National Laboratories began baseline testing of the base metal material in according with activities of the Memorandum of Understanding. In FY22, Sandia National Laboratories will receive coated coupons from each of the participating industry partners and begin characterization, physical, and electrochemical testing following the test plan described herein.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Clean Energy Education and Training Resources and Opportunities in New York's Southern Tier Region

New York's Southern Tier Region is experiencing high growth and investment in the clean energy sector and is anticipating more jobs to come in energy efficiency, renewable energy, and manufacturing in the coming years. The Network for a Sustainable Tomorrow (NEST) is a nonprofit network of programs working to develop a regional backbone system for education and training programs as well as curricula to support the workforce needed for the region's growing industries to succeed. Through its participation in the US Department of Energy's Better Buildings Workforce Accelerator, NEST requested technical assistance in conducting a landscape and needs assessment of the region's existing clean energy education and workforce development assets. This report supports NEST's efforts by providing a baseline of clean energy employment data, an inventory and gap analysis of the education and workforce development assets currently available and serving the Southern Tier Region, and case studies of innovative and successful regional clean energy education and workforce coalitions from around the county.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

2025 Advances in NekRS: Supporting improved performance for nuclear applications

This report presents several 2025 advancements in NekRS, a high-fidelity spectral element CFD code developed at Argonne National Laboratory to support the NEAMS thermal-hydraulics program. The forthcoming v25 release consolidates several of these advances, adding new features for portability across heterogeneous GPU architectures, real-time in situ visualization, improved turbulence modeling, and conjugate heat transfer coupling. Over the past year, NekRS has demonstrated strong scalability and performance on DOE’s leading exascale platforms, including Aurora and Frontier, confirming its readiness for some of the largest and most complex simulations attempted to date. These achievements provide a powerful new platform for high-fidelity data generation, which in turn supports the development and validation of advanced closure models critical for reactor safety and design. Significant algorithmic innovations have also been introduced. A new global runtime h-refinement capability simplifies workflows by reducing mesh preparation burdens and enabling coarse-to-fine restarts. Building on this, a novel multigrid strategy was implemented to accelerate pressure and transport solves at scale, addressing long-standing bottlenecks in exascale CFD. Together, these developments improve both the efficiency and accessibility of high-fidelity simulations for reactor-relevant problems. Collectively, these enhancements represent a major step forward in simulation technology, positioning NekRS as a cornerstone of NEAMS efforts to enable accurate, efficient, and scalable high-fidelity analysis of advanced nuclear systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Advanced control sequences and FDD technology. Just shiny objects, or ready for scale?

Innovations in commercial building control sequences (ASHRAE guideline 36) and fault detection and diagnostics (FDD) technology have the potential to transform existing building operational efficiency by realizing whole-building level savings on the order of 15% and higher. However, several technical and market barriers related to up-front estimation of achievable savings, verifying implementation, and ensuring persistence are slowing down their adoption as a core offering through diverse owner-funded initiatives and rate-payer funded programs. This paper describes a suite of solutions to overcome these barriers to program delivery of advanced HVAC controls integrated with FDD energy management and information system (EMIS) technology. First, the Guideline 36 conformance test offers an automated method to confirm correct sequence implementation by the manufacturer, before field deployment. A successful test of a controller was conducted using a new manufacturer-independent hardware-in-the-loop testbed demonstrating the viability and scalability of this approach. Second, the Guideline 36 energy savings calculator uses modeling paired with high-level user inputs to estimate building-specific savings potential, target cost effective implementation and minimize uncertainty. Initial results indicate the baseline control sequences significantly impact energy savings, however, as much as 50% savings may be possible for the worst-case baseline scenario. Third, a functional specification provides minimum recommended EMIS-FDD capabilities, and best practices to integrate the technology into organizational practice and ensure that advanced control sequences provide persistent savings.

Pritoni, Marco↗
DOE OSTI2025

Applications of explainable artificial intelligence in renewable energy research

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

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Highly Insulated Wall Systems with Exterior Insulation of Polyisocyanurate under Different Facer Materials: Material Characterization and Long-Term Hygrothermal Performance Assessment

The application of exterior insulation in both new construction and retrofits is a common practice to enhance the energy efficiency of buildings. In addition to increased thermal performance, the rigid insulation can serve to keep the sheathing board warm and serve as a water-resistive barrier to keep moisture-related problems due to condensation and wind-driven rain. Polyisocyanurate (PIR) rigid boards have a higher thermal resistance in comparison to other commonly used exterior insulation boards. However, because of its perceived lower permeance, its use as exterior insulation is not very common. In this study, the hygrothermal property of PIR boards with different facer types and thicknesses is characterized. The material data obtained through experimental test and extrapolation is used in a long term hygrothermal performance assessment of a wood frame wall with PIR boards as exterior insulation. Results show that PIR with no facer has the smallest accumulated moisture on the sheathing board in comparison to other insulation boards. Walls with a bigger thickness of exterior insulation perform better when no vapor barrier is used. The PIR exterior insulation supports the moisture control strategy well in colder climates in perfect wall scenarios, where there is no air leakage and moisture intrusion. In cases where there is trapped moisture, the sheathing board has a higher moisture content with PIR boards with both aluminum or fiberglass type facers. An innovative facer material development for PIR boards can help efforts targeting improved energy-efficient and durable wall systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations

High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.

17 WIND ENERGY↗
DOE OSTI2025

The DREAM approach to demand-side emissions reductions in Indonesia, 2020–2060

Indonesia’s current energy system modeling is heavily focused on the supply side, but emissions reductions in the demand sector have a significant impact on advancing Indonesia’s ambitious emissions reductions goals. To address the gap, we develop a new modeling tool DREAM Indonesia based on a bottom-up, technology-rich demand side framework and formulate projections of demand-side emissions reductions in Indonesia in 2020–2060. We find that demand-side energy efficiency and electrification can halve the growth rate of final energy demand to 1.4% annually over 2020–2060 and reverse the growing trend of emissions. The feasibility of full electrification by 2060, coupled with rapid adoption of existing technologies, positions the building sector as a model for achievable decarbonization and a cornerstone of Indonesia’s emissions reductions ambitions. In the industrial sector, extensive emissions reductions of 87% by 2060 (compared to business-as-usual) are achievable through energy efficiency improvements, alongside enhanced material efficiency measures including optimized material usage, low-carbon substitutions, innovative technologies, and increased circularity. In the transportation sector, balancing final energy demand by incorporating energy efficiency improvements across all transport modes, along with electrification particularly in road transportation, could decrease the emissions by up to 82% in 2060 compared to business-as-usual. This study provides insights and modeling approaches for rapidly growing Asian economies as well as other developing countries facing combined development and decarbonization challenges.

DREAM Indonesia↗
DOE OSTI2026

Examples of State and Utility Actions on Proactive Planning and Investments

As states across the U.S. confront rising electricity demand, clean energy deployment, grid modernization imperatives, and the integration of large new loads, some regulators and utilities are shifting away from reactive, “just-in-time” investment approaches toward more proactive planning and investment frameworks. This report compiles examples of jurisdictional and utility actions that reshape planning processes, cost recovery mechanisms, and performance oversight to anticipate—rather than simply respond to—future grid needs. Several themes emerge from state actions examined in this report. First, legislatures and commissions are increasingly directing utilities to proactively upgrade their distribution and transmission systems, reflecting a shift toward a forward-looking system that aligns planning with state policy goals. Second, states are establishing long-term, iterative planning frameworks that often feature multi-year horizons, biannual or annual compliance reporting, structured opportunities for stakeholder engagement, and emphasis on collaboration among utilities, regulators, and stakeholders. Third, states are actively investigating innovative cost recovery mechanisms designed to support accelerated electrification and grid modernization, while balancing consumer advocates’ concerns regarding the ratepayer financial risks of premature investments. Fourth, performance metrics and reporting requirements are being developed to ensure transparency and accountability for proactive investments. Fifth, methodological improvements in planning—such as aligning load forecasting assumptions, incorporating sensitivities, and considering load management potential across building, vehicles, storage, and demand response—are recurring areas of stakeholder focus across jurisdictions. Overall, these developments signify a growing recognition among state regulators, utilities, and stakeholders that proactive planning—supported by clear definitions, consistent and transparent methodologies, robust performance metrics, and innovative cost recovery mechanisms—is a tool that can be used to address the scale and urgency of contemporary grid needs.

electricity market↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict their performance in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies, have come on the market over the years, with claims of improved performance compared to their predecessors; yet in practice, their performance has led to neither high deployment nor high user satisfaction in any significant application, nor has it differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them have typically been loosely described and incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a well-described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, also additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recent published, well-described test methods for characterizing occupancy sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates the best techniques for characterizing sensor performance in indoor spaces and identifies test method gaps that need to be filled in order to evaluate emerging technologies and products.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

RENEWABLE MARINE-SOURCE INTEGRATED HEAT PUMP FOR SPACE CONDITIONING, WATER HEATING, AND FRESH WATER HARVESTING IN REMOTE COASTAL AND ISLAND COMMUNITIES

US coastal and island communities have vulnerable energy infrastructure and high energy costs, which are exacerbated by climate change. A unique opportunity exists to use thermal energy from year-round mild temperature variations to satisfy the broad energy needs of these communities. The US Department of Energy’s Oak Ridge National Laboratory designed a small-scale marine-source integrated heat pump (MS-IHP) that, as a single appliance, combines space conditioning, dehumidification (including fresh water harvesting from humid air), and water heating for home and commercial building applications in remote coastal and island communities. The unique, innovative features of the MS-IHP offer improved efficiency and substantially reduced costs for space conditioning, dehumidification, and water heating by seamlessly integrating a heat pump, heat pump water heater, and dehumidifier by using a single compressor; optimal year-round space cooling and heating, dehumidification, water harvesting, and water heating; and improved thermal comfort and reliable operation without frosting and defrosting because of the ocean’s warm temperatures and high heat capacity. Preliminary testing was conducted by retrofitting a commercial heat pump, demonstrating an outperformed performance compared with traditional air-source heat pumps. The MS-IHP technology can help decarbonize energy systems in remote coastal and island communities.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
DOE OSTI2022

Evaluation of integrated daylighting and electric lighting design projects: Lessons learned from international case studies

This article presents and discusses the lessons learned from the monitoring of 25 integrated daylighting and electric lighting international case study projects. The case studies consist of real occupied buildings that have been monitored as part of the International Energy Agency (IEA) SHC Task 61/EBC Annex 77 Programme. The general goal of the case studies was to balance lighting energy use with occupants’ visual and non-visual requirements. This was achieved using innovative solutions for daylighting and electric lighting with advanced controls, but also implementing simple and out-of-the-box strategies. The findings suggest that energy demands for lighting can significantly be reduced by combining sensible daylight provision, efficient lighting sources, and advances in controls. Yet, the effective achievement of project goals requires adequate monitoring, fine-tuning, and verification. The findings also suggest that the adoption of “integrative” lighting – that is, lighting systems that address both visual and non-visual responses – is getting increasingly popular. Catering to non-visual requirements will likely drive further innovation in lighting technology. Currently, there is limited investment available for developing daylighting systems for integrative lighting, and the current related electric strategies often come at the risk of energy rebound effects. Overall, providing daylighting and understanding user requirements are fundamental steps towards achieving quality projects, with potential benefits beyond saving energy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Multifunctional Heat Pump with Energy Storage Capability

Replacing gas furnaces with heat pumps on a large scale can pose challenges to the electrical grid. It will significantly increase the demand, strain the electrical grid, especially during peak usage times. To ensure that heat pumps contribute to reducing emissions, it's essential to increase the share of renewable energy in the grid. However, the supply of renewable energy is unstable. Large scale of electrification must address the concern of grid resilience to meet necessary heating/cooling demands and shift peak electric load. All these require future heat pumps having energy storage capability. These systems can store excess energy during low-demand periods and release it during high-demand periods, reducing stress on the grid. This paper will introduce an innovative configuration, control, and laboratory investigations of a multifunctional packaged heat pump. In addition to the original indoor and outdoor air coils, the heat pump has a parallel hydronic loop with a brazed plate heat exchanger (BHP). The hydronic loop circulates hot/chilled water to store heating/cooling energy in phase change material (PCM). It is capable of space cooling, space heating, cooling energy storage/defrosting, water heating/heating energy storage with outdoor air source or indoor air source, and cooling/heating energy free discharge. The laboratory studies and breadboard unit are presented. Additionally, building energy simulation demonstrates utility cost reduction potential via charging PCM panels/ceilings during off-peak hours and releasing the energy during peak hours to mitigate the peak power consumption.

Shen, Bo↗
DOE OSTI2026

Utilizing Time Reversal Ultrasonics to Detect the Removal of Nuclear Materials from Geological Repositories (FY26 Mid-Year)

Detecting unauthorized nuclear material removal from storage environments, such as geological repositories, is a critical safeguards task essential to ensuring the integrity and non-diversion of nuclear materials. However, this process is fraught with significant technical challenges. Storage configurations often involve tightly packed nuclear material containers or obstructed environments, making detection of removal events exceedingly difficult. Optical surveillance cameras, which are commonly used for monitoring, suffer from substantial limitations, including restricted coverage, reliance on line-of-sight measurements, and vulnerability to environmental conditions in certain storage scenarios. As the global inventory of monitored nuclear materials increases and storage configurations become more complex— such as deep geological repositories, inaccessible storage vaults, and tightly packed containers—there is an urgent need for innovative detection technologies that can reliably identify unauthorized diversion events in these challenging environments. The challenge of detecting nuclear material removal in complex storage environments is both significant and urgent. Preventing unauthorized access, diversion, or tampering with nuclear materials is a cornerstone of global nuclear safeguards and nonproliferation efforts. Current detection methods are increasingly inadequate as storage configurations become more intricate and inaccessible. The limitations of existing technologies—such as their inability to detect changes behind obstructions, reliance on costly and labor-intensive processes, and vulnerability to environmental conditions—pose risks to the effectiveness of safeguards systems. Addressing this challenge is critical to maintaining international trust in nuclear safeguards frameworks and ensuring compliance with nonproliferation agreements. Our project builds on the proven concept of TRU technology that can address this unmet need. TRU has demonstrated exceptional spatial sensitivity and change detection capabilities in complex non-line-ofsight environments, making it uniquely suited for detecting unauthorized nuclear material removal in challenging storage configurations. Unlike optical methods, TRU is not limited by line-of-sight constraints or environmental conditions, enabling reliable detection of subtle alterations even behind obstructions. By leveraging TRU’s ability to identify removal or tampering events, we aim to develop a robust detection system that enhances safeguards in geological repositories, storage vaults, and other complex environments.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗