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DOE OSTI2025

FedCSpc: A Cross-Silo Federated Learning System With Error-Bounded Lossy Parameter Compression

Cross-Silo federated learning is widely used for scaling deep neural network (DNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. Here, to bridge this gap, we propose FedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designed FedCSpc proposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. Results show that FedCSpc can achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4 Gb size model, FedCSpc significantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality).

SZ3↗

DOE OSTI2025

FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression

Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. Here, to bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to 315×.

Cross-Silo Federated Learning Systems↗

DOE OSTI2024

From Silos to Synergy: Identifying a Roadmap for Cross-Sector Research to Accelerate the Clean Energy Transition

The U.S. Department of Energy's blueprints for the transportation, buildings, and electricity sectors call for substantial reductions in greenhouse gas (GHG) emissions by 2050. These plans focus on zero-emission vehicles, investments in transit, energy-efficient buildings, and the widespread adoption and deployment of renewable energy technologies like solar photovoltaics (PV), energy storage and energy-efficient appliances. However, these sectors are often studied and modeled in isolation, overlooking how household decisions to adopt clean technologies in one sector influence others. This study, led by an interdisciplinary team at the National Renewable Energy Laboratory (NREL), explores opportunities for cross-sector collaboration to drive more effective and equitable decarbonization. Through discussions with 22 NREL researchers across transportation, building, solar, and grid sectors, the study highlights the need for integrated tools and models that capture interactions between these sectors. Key insights include the need for data standardization and interoperability to enable cross-sector analysis and decision-making. Strengthening utility partnerships is also critical to align energy policies with decarbonization goals and manage the increased demand for renewable energy. The study also emphasizes the importance of equity in the clean energy transition, calling for targeted incentives and support to ensure that low-income and underserved communities benefit from clean technologies like electric vehicles and energy-efficient appliances. To support these efforts, innovative funding mechanisms must be expanded to facilitate interdisciplinary research, such as city-specific decarbonization plans and federal projects like DOE"s Standard Scenarios. By encouraging collaboration and integrating cross-sector insights, this study aims to provide a roadmap to accelerate the clean energy transition and ensure it is both sustainable and inclusive.

14 SOLAR ENERGY↗

DOE OSTI2026

Pilot Development Progress to Demonstrate Electric Thermal Energy Storage (ETES) Using Low-Cost Particles

The rapid growth of future energy demand increases the need to economically store electrical energy over durations up to several days in long-duration energy storage (LDES) applications. LDES can reduce the grid stress for improving the resilience of the grid. The integration of renewable power and storage has several significant and positive impacts including expanding the renewable energy portion of electricity generation, meeting peak-load demand, and coordinating supply and demand. Several energy storage approaches including mechanical, chemical and electrochemical methods are currently deployed or under development. However, LDES requirements pose unique challenges for scalability, energy capacity, and cost. Thermal energy storage (TES) has the ability to store a large capacity of energy with improved site flexibility compared to incumbent LDES technologies (i.e., pumped hydro) and has attracted significant interest. Our development in TES technology using solid particles as the storage medium has shown feasibility and potential in serving LDES purposes. Particle TES has various advantages over commercialized molten nitrate salt TES and has emerged as a promising storage technology originating from Generation 3 concentrating solar power (CSP) development. Stable, inexpensive silica sand produced in the Midwestern U.S. was identified as a suitable storage medium and has been validated through lab heating and cycling tests. We have successfully developed particle TES for bidirectional electric energy storage to broaden the applications for TES beyond CSP technologies and into standalone electric-thermal energy storage (ETES) systems. Key component designs and performance were verified with prototype testing and model simulations. The ETES system uses high-temperature, low-cost particle thermal energy storage coupled with a unique pressurized fluidized bed heat exchanger that supports a high-efficiency, air-Brayton combined power cycle. At times of low energy demand or high renewable production, an electric particle heater heats large quantities of solid particles to high temperatures in excess of 1100 degrees C. The particles are stored in internally insulated silos. At times of high electricity demand, the hot particles are gravity-fed through the heat exchanger where stored thermal energy is transferred to the power cycle working fluid to drive the gas turbine power generation system, thereby converting the thermal energy back into electricity for the grid. The cold particles leaving the heat exchanger are returned to the storage silos by a well-insulated particle conveyor. This ETES technology using particle TES can be sited anywhere and is predicted to be economic and efficient. Moreover, this ETES technology could be co-located with a retired coal or gas plant to leverage existing power generation and grid infrastructure. Our work has focused on the development of each of the key components (e.g., the electric particle heater, particle storage silos, and the particle-to-gas pressurized fluidized bed heat exchanger) through conceptual designs, prototype testing, and computational modeling. These developments demonstrate technological feasibility and are an important step towards the next phase of development of this promising LDES technology. This presentation will show the progress in developing a pilot demonstration of the ETES system on a commercially relevant scale.

25 ENERGY STORAGE↗

DOE OSTI2026

Integrating three plan evaluation approaches for coordinated heat resilience in cities across the Arizona urban corridor

Increasing heat poses a growing threat to cities worldwide due to both climate change and the urban heat island effect. While heat planning and governance are still emergent, research suggests that silos and conflicts within cities' networks of plans often impede heat resilience. Integrated heat resilience planning, therefore, requires a systematic and comprehensive analysis of the silos and conflicts relevant to heat resilience within networks of plans. This study is the first to combine three complementary plan evaluation methods to assess how cities' networks of plans address heat resilience. We applied 1) plan cross-referencing, 2) Plan Quality Evaluation for Heat Resilience, and 3) Plan Integration for Resilience Scorecard™ (PIRS™) for Heat to 19 plans from seven Arizona cities. We find similarities and differences in how these cities' networks of plans address heat hazards. The plans have consistently high-quality participation and coordination principles but lack details on vulnerability and climate change uncertainty, suggesting a need to move beyond immediate heat risks. We also identify opportunities to diversify policy mechanisms, spatially target high heat risk areas, and enhance the connection between planning efforts. These results validate that plan elements are interlinked and the importance of integrative plan development processes to improve heat resilience.

Extreme heat↗

DOE OSTI2024

Advancing community-engaged research for offshore wind on the West Coast

The Biden Administration has called for 30 GW of offshore wind (OSW) to be implemented by 2030 and 15 GW of floating OSW by 2035. Because floating OSW is a novel technology for the West Coast, the United States, and the world, there is limited information about the implications of floating OSW for coastal communities. Floating OSW faces a complex regulatory landscape and a wide set of interested parties that stand to gain or lose based on the development process and outcomes. Key agencies are often siloed in their mission space, with little attention or resources for innovation in planning. Furthermore, many communities along the West Coast of the U.S. have experienced the boom-and-bust cycles of large, extractive industries that use coastal resources to benefit consumers in other locations, while leaving behind few long-lasting benefits at the local level. These siloes in government and civic society, and the lack of trust born out of past failures, make it difficult for communities, government agencies, scientists, and industry to plan for OSW based on community values and concerns. There is a need to develop projects that more equitably distribute benefits while safeguarding the ocean ecosystems upon which coastal populations depend.

17 WIND ENERGY↗

DOE OSTI2025

Historic and modern nuclear graphite impurities: Pathways to improved waste strategies

Graphite has been used in large volumes as a structural material and neutron moderator since the earliest days of nuclear fission. However, no international consensus exists on the disposal of irradiated graphite, leaving much of the historic radioactive graphite inventory in interim vault or silo storage. With several new graphite-moderated reactors planned or under construction, the issue of graphite waste management is becoming increasingly urgent. This paper reviews and quantifies impurities in both historic and modern nuclear graphite, with emphasis on nitrogen—responsible for much of the 14 C inventory—and chlorine, which plays a critical role in repository performance and design. Modern graphites, benefitting from stringent quality-control measures developed for non-nuclear industries, meet or exceed the ASTM Ultra-High Purity nuclear standards, even without halide purification. Both chlorine and nitrogen concentrations have declined over time. For chlorine, identified as a key impurity influencing U.S. waste repository design, we propose a target of 0.1 appm in as-fabricated billets as a reasonable benchmark. Nitrogen sources are traced throughout the graphite production process, with surface and bulk concentrations characterized for all materials studied. Modern graphites commonly exhibit nitrogen levels below 5 appm, with values approaching 1 appm achievable. Using such reduced-nitrogen grades is critical to keeping graphite-induced radioactivity below the greater-than-Class-C waste threshold, thereby avoiding disposal cost penalties of nearly an order of magnitude.

Chlorine↗

DOE OSTI2025

Fungal endophytes

Organisms are commonly grouped into ecological guilds that reflect their shared resource use and similar ecological roles. The guild concept has been used to categorize the vast diversity of the fungal kingdom (estimated at 2.2–5 million species) into groups of fungi with common lifestyles, such as mycorrhizal symbionts, pathogens of animals and plants, and the group that is the focus of this primer — fungal endophytes of plants. Fungal endophytes have resisted scientists’ attempts to silo organisms into neat assemblages. In conclusion, scattered across the fungal tree of life and lacking few diagnostic characteristics, they are defined less by what they are than by what they are not.

Cosner, Julian [Oak Ridge National Laboratory (ORN↗

DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗

DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗

DOE OSTI2026

MARVEL Reactor Digital Engineering Developments

The MARVEL reactor project has served to introduce a new generation of engineers to the processes required to transform a reactor design from simply an idea on paper into what will be an approved, constructed, and operational nuclear power system. Much as there have been advances in materials, analysis, and evaluation methodologies over the 50 years since the last reactor was built at INL, so too has the technology for managing the engineering process itself advanced. Digital Engineering tools and methods provide improved coordination between previously siloed engineering disciplines, reduced burdens of non-value-added data transcription processes and bring forward insights and improvements that might otherwise fall later in the design stage, where changes are much more costly. While the tools and techniques to support the full digital engineering vision are not yet complete, the MARVEL design processes provide valuable demonstrations and validations of key aspects and illuminate further areas for implementation by subsequent projects.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2026

Assessing heat resilience coordination in networks of plans

Networks of plans coordinating on hazard mitigation can limit losses. We offer a novel network analysis methodology to investigate how networks of plans explicitly coordinate, and the purpose and nature of coordination. We illustrate the method using networks of plans shaping heat resilience in seven Arizona cities. The network analysis can help planners to identify influential plans that need to be high quality, peripheral plans, and potential governance silos. Furthermore, investigation into plan roles offers an ontological lens into how plans network, consult, and share information. The nature of coordination varies by purpose. General plans are cited for goals, while hazard mitigation plans are referenced for heat fact base. Transportation plans cite goals and fact base in other transportation plans, but rarely cite other plan types. Furthermore, these findings will help planners to consider the roles and merits of different plans while integrating hazards across the next generation of networks of plans.

coordination↗

DOE OSTI2025

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Purohit, Sumit [BATTELLE (PACIFIC NW LAB)] (ORCID:↗

DOE OSTI2026

“One Table to Rule Them All”: How a Single Table can Enable Extensive Insights, Analytics and Assessment on Human Mobility Data

While much research has been conducted in Human Mobility Science, most studies on the analytics/insights part generally focus on one of the following: processing and analytics on human stop-trip behavior, design of individual mobility metrics (often in silos), calculation and characterization of only a handful (typically 5-6) of human mobility metrics on geospatial-temporal human mobility data of interest. Although human mobility research offers a vast and diverse array of available metrics, most individual studies typically compute only a small subset of five or six metrics at a time when analyzing trajectory datasets of human mobility across different areas of interest. This paper is motivated by the critical need to repeatedly compute an extensive array of human mobility metrics across several trajectory datasets and perform individual metric-level benchmarking to establish a new, standardized Test and Evaluation (T&E) suite for the field of Human Mobility Science. We first present our findings on the minimal yet sufficient pre-processing required to reliably and efficiently compute a wide range of human mobility metrics. The key findings are specifically related to the proposed Composite Stop Locations table, which serves as a core pre-processing data layer. Subsequently, we present a case study demonstrating how the Composite Stop Locations table facilitates computation of at least 14 distinct human mobility metrics (unlike 5-6 different set of metrics used for studies in the literature) using the popular and open-source OpenPFLOW dataset. Finally, we have also presented an example of our benchmarking methodology to evaluate the quality and performance of the trajectory dataset of interest, assessed across multiple human mobility metrics.

De, Debraj [ORNL] (ORCID:0000000233630020)↗

DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Plan It Together: Optimizing Across Generation, Transmission, Distribution, and Distributed Energy Resources

Integrated planning holds the promise of unlocking lower total system cost solutions by connecting previously siloed planning processes. This article summarizes the state of bulk and local grid planning today and details multiple new analytical approaches that can enable more holistic planning methods to develop comprehensive solutions to generation, transmission, distribution, and distributed energy resource needs. These include both iterative approaches as well as cooptimization techniques. In addition to the benefits of these methods, the technical and institutional challenges and associated solutions are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Enabling DER visibility using a distributed dissemination network

The electrical grid is currently undergoing a series of rapid transformational changes that have resulted in the introduction of new actors and operational schemes that have fragmented the data and control planes. To help address the issue, this paper describes the implementation of a sensor-oriented, distributed data dissemination network that seeks to eliminate data silos. The implementation is based on the DGSS architecture previously described in [1]. The developed product seeks to facilitate the seamless integration of multi-operator, multi-origin, multi-domain sensor data by using a distributed systems approach. The proposed solution decouples the sensor’s data streams from the application-specific infrastructure and migrates them into a software-defined databus that can be configured to suit the end application’s demands. To further validate DGSS capabilities, a DER oriented use case has been developed.

Sensor Dissemination Networks, Enhanced DER visibi↗