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Package Testing Program Process for Validation and Verification of Software

The purpose of this document is to define the process for validation and verification (V&V) of software used to test the thermal data acquisition equipment in the Oak Ridge National Laboratory (ORNL) Package Testing Program (PTP) as part of the testing configuration(s). This process adheres to all applicable ORNL Standards Based Management System requirements and correlates with the PTP Quality Management System and the Software Quality Assurance Plan.

97 MATHEMATICS AND COMPUTING↗

Buildings Sector Scenarios: Demand-side data to support energy system planning in the United States

The US energy system is in a period of high uncertainty about load growth, its implications for the energy generation mix, and downstream impacts on customer energy costs. In this context, there is a need for comprehensive, credible, and readily-customized projections of energy demand to ensure that planning decisions account for end-use management opportunities to improve system reliability and affordability. Here we introduce the Buildings Sector Scenarios (BSS) dataset, which includes a benchmark suite of such projections for the buildings sector — a key source of energy consumption, peak electricity demand, and consumer energy expenditures. The dataset contains projections through 2050 covering the contiguous United States (CONUS) resolved down to the county, hourly level by sector and end use for electricity demand and to the state, annual level by sector and end use for non-electric fuels. We summarize the BSS analysis workflow and the tools and datasets that support it, document key BSS scenario inputs and modeling assumptions, and outline BSS scenario outputs. We assess the technical quality of the dataset against historical surveys and projected estimates of buildings sector demand. Finally, we provide guidance on how stakeholders can access, use, and reproduce the dataset, and/or create new scenarios to explore their own analysis questions.

Langevin, Jared↗

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Conus-Wide Mapping of Water Availability and Cost

Background: New demands for water can be satisfied through a variety of source options. In some basins surface and/or groundwater may be available through permitting with the state water management agency (termed unappropriated water), alternatively water might be purchased and transferred out of its current use to another (termed appropriated water), or non-traditional water sources can be captured and treated (e.g., wastewater). The relative availability and cost of each source are key factors in the development decision. Unfortunately, these measures are location dependent with no consistent or comparable set of data available for evaluating competing water sources. With the help of water managers, water availability was mapped for over 2500 watersheds throughout CONUS. These values are unique in that they account for institutional factors (i.e., water rights, compacts, treaties, instream flows) that locally limit water use. Five water sources were individually examined, including unappropriated surface water, unappropriated groundwater, appropriated water, municipal wastewater and brackish groundwater. Associated costs to acquire, convey and treat the water, as necessary, for each of the five sources were estimated. These metrics were developed to support regional water planning and policy analysis with initial application to electric transmission planning in the western US. Problem: These data were originally developed in 20141 and updated in 20182. New datasets on which these data depend have been published. As such, there is need to update the water availability and cost data. There is also need to map this same data to a county resolution and to publish the data in a more accessible place. Organization of the dataset: Data are organized according to five primary water sources: • Unappropriated fresh surface water • Unappropriated fresh groundwater • Appropriated water • Recycled wastewater, and • Brackish water

Tidwell, Vincent [Pacific Northwest National Labor↗

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↗

torch-einshard v1.0

torch-einshard is a Python library for describing local and distributed PyTorch tensor computations with compact, einsum-like notation. Its expressions name logical axes, specify how they are sharded across a PyTorch DeviceMesh, and represent partial reductions. The library automatically performs contractions, permutations, reshaping, splitting, gathering, reduction, reduce-scatter, and repartitioning while preserving autograd. Additional features include sharding-aware FFTs, tensor rolls, halo exchange, sliding windows, 1D–3D convolutions, uneven-shard handling, parameter initialization and gradient management, and cost-based execution planning. It is designed for scientific machine learning and large-model workloads, including tensor-, sequence-, and spatial-parallel MLPs, attention, convolutions, and spectral operations. Compared with manually combining torch.einsum and distributed collectives, torch-einshard expresses both the mathematical operation and data placement in one readable formula. This reduces boilerplate and synchronization errors, keeps forward and backward communication consistent, and allows the library to select optimized collective strategies without changing model code.

Morozov, Dmitriy [Lawrence Berkeley National Labor↗

1994 SEMCOG Household-Based Person Trip Survey

The Southeast Michigan Council of Governments contracted with the Applied Management & Planning Group to conduct a travel behavior survey of 7,361 households from March 28, 1994, to June 10, 1994. The primary purpose of this study was to provide the council with a new database of travel behavior to assist in updating the region's transportation models. This data collection includes demographic, socioeconomic, travel, and mobile source emissions models for projecting future patterns of development, travel, congestion, and air pollution. Information about household characteristics and travel was collected using a one-day, activity-focused diary and a separate household survey. In an activity diary, respondents recorded 65,535 trips during the assigned the day.

1Hz data↗

Concrete Thermal Energy Storage Enabling Flexible Operation without Coal Plant Cycling

The work described in this report is responsive to the Office of Fossil Energy program “Energy Storage for Fossil Power Generation.” The pilot plant built as a result of this project demonstrated the feasibility and performance of a concrete thermal energy storage (CTES) system integrated with a supercritical coal power plant. The 10 MWh electrical (>25 MWh thermal) CTES unit, developed by Storworks Power, was designed to enable flexible operation of coal plants without cycling damage. The project's key technical achievements showcase a significant advancement in energy storage technology. A modular CTES system using 42 “Bolderblocs” units was successfully designed and constructed at Alabama Power’s Plant Gaston Unit 5, with each block containing embedded stainless-steel coils in specialized, cost-effective high-temperature concrete. The system interfaced seamlessly with the plant's 3500 psig (241 barg), 1000°F (538°C) supercritical steam, demonstrating operational flexibility. Over 86 full cycles, the CTES exhibited rapid charging and discharging capabilities, effectively mimicking steam turbine feed conditions and handling varying load profiles and storage durations. Performance validation confirmed the system's ability to consistently meet design target steam conditions of 75 bar-a and ~400°C for nominal baseline discharge. The concrete material withstood repeated thermal cycling without degradation, validating earlier lab-scale tests. Integration of balance of plant components, including a condensate management system with storage tank and air-cooled condenser, minimized plant interfaces and water consumption. A robust control scheme ensured safe, automated operation across various scenarios. Key learnings from the project were invaluable: 1. Initial concrete drying and commissioning procedures were refined for future deployments, enhancing efficiency in subsequent installations. 2. System flexibility exceeded expectations, with rapid response to changing conditions. 3. Design improvements were identified including optimized insulation and piping that will enhance overall system efficiency in future deployments 4. Full cycle thermal roundtrip efficiencies exceeded 88%. While the roundtrip electrical efficiency was somewhat limited by known challenges using input steam, such constraints may be mitigated by swapping steam for hot air as thermal input. 5. A summary of key performance parameters for the pilot test and predicted performance of a full scale commercial system with specified improvements determined from the pilot are shown in Section 8. The project faced challenges, including COVID-19 delays and host plant availability constraints. However, these were overcome through adaptive planning and execution. The successful management of these obstacles demonstrated the resilience and adaptability of the project team and the robustness of the CTES technology. This successful pilot demonstrates the potential for CTES to enhance coal plant flexibility, supporting grid stability as renewable penetration increases. The validated design and operational data provide a solid foundation for scaling up to utility-scale implementations, potentially transforming how thermal plants operate in evolving energy landscapes. The system's ability to rapidly respond to changing grid conditions while maintaining high efficiency makes it a promising solution for balancing intermittent renewable energy sources. Furthermore, the project highlighted the potential for even greater efficiencies in future iterations. The use of air as an input medium could potentially eliminate the limitations observed with steam input, opening new possibilities for energy storage applications beyond coal plant integration. In conclusion, this pilot project not only achieved its primary goals but also uncovered additional benefits and potential applications of the CTES technology. It represents a significant step forward in addressing the challenges of grid stability and flexibility in an increasingly renewable-driven energy landscape.

01 COAL, LIGNITE, AND PEAT↗

FY25 Ion Exchange Processing for the West Area Risk Management (WARM) Project

The West Area Risk Management (WARM) Project was established to enable near-term retrieval and pretreatment of tank waste from Hanford’s 200 West Area and to support deployment of an ion exchange (IX) system capable of producing cesium-depleted supernate suitable for offsite treatment and disposal. The WARM experimental campaign was designed to provide key data for design and operational planning of the 200 W IX system, which plans to utilize crystalline silicotitanate (CST) as the active media for cesium removal. This report documents a comprehensive FY25 experimental campaign designed to produce key technical data required for design and operational planning of the WARM IX system. Testing includes assessing Cs and Sr breakthrough performance in a 4-column system, Cs capacity batch contact testing, phosphate precipitation assessments, reduced-hydroxide feed displacement evaluation, and Sr speciation impacts on Sr removal. The experimental results provide detailed trends in Cs and Sr loading behavior, breakthrough performance across a 4-column staged IX system, distribution profiles within CST beds, and projections of operational flowrates relative to Waste Acceptance Criteria (WAC) limits. Batch contact testing established equilibrium partitioning behavior for the S1–S5 simulants and characterized how matrix chemistry influenced Cs sorption onto CST. Additionally, further evaluations on waste matrix as it pertains to precipitation potential were also determined. Collectively, these datasets support engineering design choices for WARM system throughput and pretreatment planning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Multi-Entity Simulation with CoSim Toolbox

Co-simulation is an analysis technique for linking multiple software models during runtime by facilitating data exchange and simulation time synchronization. There are numerous challenges when constructing an effective co-simulation including simulation tool installation, data management, and writing new models in a manner compatible with the co-simulation framework of choice. CoSim Toolbox is an integration of multiple pieces of software designed to make assembling such a co-simulation in HELICS easier. This report summarizes the existing capabilities of CoSim Toolbox and outlines future development plans.

97 MATHEMATICS AND COMPUTING↗

Edge AI-Enhanced Traffic Monitoring and Anomaly Detection Using Multimodal Large Language Models

This paper addresses the challenge of traffic monitoring and incident detection in remote areas, utilizing multimodal large language models (LLMs) deployed on edge AI devices. The key novelty of the LLM is to convert real-time video streams into descriptive texts, enabling low-bandwidth transmissions and reliable detection of anomalies and incidents in environments of intermittent connectivity. The model is developed based on fine-tuning open-source LLMs and extending it with multi-modal capabilities to analyze video frames. Our work also involves deploying this model on edge devices such as Nvidia IGX Orin and is planned to be tested in realistic environments in future work. The methodology includes data set curation, iterative model fine-tuning and compression, and hardware-based optimization. This approach aims to enhance traffic safety and response speed in remote areas, marking a significant advancement in the application of AI for traffic monitoring and safety management.

Peruski, Ryan [University of Tennessee, Knoxville ↗

Soil Gas Survey Results Supporting Groundwater Correction Action Plan (GCAP) Development for the Moab Site

A soil gas survey was performed at the Moab Uranium Mill Tailings Remedial Action (UMTRA) Project Site during the week of November 6, 2023. Soil gas surveys are used to characterize residual subsurface sources of volatile contaminants, such as volatile organic compounds, as well as contaminants that generate a surrogate indicator gas or otherwise influence soil gas composition. The primary objective of the Moab soil gas survey was to confirm, identify, quantify, and refine secondary contaminant source area locations for uranium and ammonium/ammonia (NH 4 + /NH 3 ) in the vadose zone and shallow groundwater. The overarching goal was to provide information to assist in developing the technical basis for the Groundwater Compliance Action Plan (GCAP). Specifically, the soil gas data will support the deployment of source control technologies; e.g., where supplementary capping, removal actions, or amendments might be beneficial.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Illinois Storage Corridor CarbonSAFE Phase III: Stakeholder Engagement and Outreach Plan

The Stakeholder Engagement and Outreach Plan provides a comprehensive framework for engaging stakeholders of the Illinois Storage Corridor (ISC) project. The ISC project is a CarbonSAFE Phase III project designed to facilitate commercial deployment of carbon capture, utilization, and storage (CCUS) in Illinois. The project aims to establish a multi-industry carbon storage corridor through development of storage sites near the One Earth Energy (OEE) ethanol production facility in north-central Illinois and the Prairie State Generating Company (PSGC) coal-fired power plant in south-central Illinois, with combined annual CO 2 capture ultimately exceeding 8.6 million tons per year. Stakeholder engagement is recognized as a critical component for successful CCUS deployment, alongside technical and economic considerations. As an emerging technology, CCUS may not be well understood by the general population, and lack of public awareness can lead to opposition that poses significant barriers to project development. This plan addresses this challenge through systematic stakeholder identification, analysis, planning, and implementation of engagement actions. The plan is structured around four main sections: Communication, Stakeholder Analysis, Stakeholder Engagement, and Environmental Justice. Activities will be conducted under Tasks 1 and 4 of the project's Statement of Project Objectives, with two key subtasks: (1) developing a stakeholder analysis and engagement plan through face-to-face meetings, facilitated discussions, and surveys; and (2) implementing stakeholder engagement and public outreach activities including meetings, open houses, and permit hearings. The Illinois State Geological Survey (ISGS) will manage engagement activities following DOE-NETL best practices, focusing on providing objective, fact-based information about CCUS and the ISC project. A comprehensive Communication Plan establishes protocols for media contacts, site visits, and crisis communications. The stakeholder analysis follows a structured workflow process divided into Pre-feasibility and Feasibility phases, incorporating contextual understanding, assessment, data collection, and analysis. Key stakeholder groups include government bodies, educational organizations, conservation and environmental groups, agricultural communities, and religious organizations. The plan addresses common stakeholder questions regarding project risks, benefits, safety, property values, liability, and environmental impacts. Recommendations emphasize developing clear messaging, creating informational materials, and preparing to address both project-specific and broader environmental concerns to ensure transparent communication and build stakeholder support throughout project implementation.

25 ENERGY STORAGE↗

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗

Digital Twins for Data Centers

Fueled by an unprecedented adoption of AI (Artificial Intelligence), data centers are becoming the largest growing consumers of energy. Digital Twins provide living digital models of physical systems that enable data-driven analysis and application of AI to better manage selective aspects of the data center and drive efficiency for sustainability. Digital twins have emerged as a way to create virtual prototypes of physical artifacts, which may be used in a variety of contexts. Physical artifacts include airplanes, factories, or even static objects, such as bridges or dams. Digital twin helps monitor changes and assist in predicting planned or unplanned behaviors of physical objects. In this paper, we discuss digital twins for data centers.

97 MATHEMATICS AND COMPUTING↗

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Advance Distribution Management System (ADMS)

This presentation explores the transition from traditional distribution management systems to Advanced Distribution Management Systems (ADMS) as a foundation for smart grid development. It highlights the key benefits of ADMS, including enhanced reliability, improved operational efficiency, and increased situational awareness. The presentation also addresses common implementation challenges such as system integration, data management, and organizational readiness. It concludes with a forward-looking perspective on the evolving role of ADMS and essential takeaways for utilities and stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗