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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗
DOE OSTI2026

Elevating Engagement: Insights for Energy Infrastructure Siting from Oregon Literature

Our study conducts a meta-synthesis of existing Oregon-based literature on stakeholder and community engagement methods across a variety of contexts to identify best practices and lessons learned to inform future engagement processes for Oregon’s energy siting. We analyze and synthesize these strategies to understand what has worked well across engagement practices and how we can integrate and learn from a variety of methods to develop more effective practices for engagement moving forward. We look at the successes and challenges for each method, applying lessons learned to the pre-permitting phase of energy development and infrastructure planning in Oregon. Our results synthesize key recommendations for engagement in the pre-permitting phase for energy infrastructure siting in Oregon, such as consensus and relationship building, local partnerships, and background research to understand community context.

energy infrastructure↗
DOE OSTI2025

Overview of Fish Passage Facilities at Hydropower Developments across the Conterminous United States

This dataset contains geo-referenced information on the presence and types of fish passage facilities at hydropower developments across the conterminous United States (CONUS) presented in the ORNL Hydropower Fish Passage Database Webmap (https://www.ornl.gov/project/quantifying-national-fish-passage-data/webmap). It was developed through collaborative partnerships with fish passage engineers and biologists at both the US Fish and Wildlife Service (USFWS) and the National Marine Fisheries Service (NMFS), and hydropower experts at the Low Impact Hydropower Institute (LIHI). Information contained within this dataset has been provided by many different sources, including State and Federal resource management agencies, non-governmental organizations, hydropower industry members, and published datasets=. This dataset is intended to provide a high-level overview of the distribution of fish passage infrastructure at hydropower developments across CONUS; a more comprehensive database is anticipated to be released later in 2025. This dataset contains one data file in comma-separate (*.csv) format and the information within it was last updated on 24 March 2025.

13 HYDRO ENERGY↗
DOE OSTI2021

Assessing Climate Risks to Midwest Infrastructure Workshop

Stakeholders in industry, agriculture, and city governments are struggling to understand how to manage climate change risks. Climate scientists, meanwhile, want to know what specific information their fellow citizens need from them to take action.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Multi-Sectoral Urban Interactions: Fundamental Science Needs to Inform Pathways to More Resilient Communities in a Changing Climate Multi-Sectoral Urban Interactions: Fundamental Science Needs to Inform Pathways to More Resilient Communities in a Changing Climate

Urban areas and the supply networks that support their resource use are inherently multisectoral systems composed of infrastructural, environmental, and socio-institutional components. These systems are vulnerable to accelerating and interacting stresses from climate change, population growth, resource scarcity, and land-use pressure at the same time as they have a major influence on regional and global systems. For instance, a majority of the world’s greenhouse gas emissions, food consumption, and economic activity can be attributed to urban areas. Urban areas are highly heterogenous, both across and within cities in terms of their socio-demographic, environmental, and infrastructural characteristics. This heterogeneity shapes how urban systems interact and co-evolve and contributes to different economic, environmental, and health outcomes for communities within urban areas. Urban heterogeneity also generates differential vulnerabilities to stressors and differential capacity for adapting to change. The evolution of urban space is thus critical in shaping how human societies respond to global change as they seek to improve resilience to stressors, support prosperous and equitable communities, and use natural resources in a sustainable manner. Developing fundamental scientific understanding of urban heterogeneity and system interactions across sectors and scales is critical for mapping the resilience, sustainability, and equity implications of alternative future pathways. On July 2021 the MSD Urban Systems Working Group organized this workshop to provide a venue for coordination and identification of shared objectives, research themes, and major knowledge gaps, as well as developing a shared strategy for addressing these gaps. This report summarizes the outcomes of this workshop.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Augmenting LLM-Based Agents for Improved Performance in Pentesting and Commissioning Operational Technology in Critical Infrastructure

Artificial intelligence (AI), and more specifically large language models (LLMs) have the potential for use in penetration testing (“pentesting”) against devices, networks, and computer systems in information technology (IT). We explore the possibility of extending pentesting from IT systems to operational technology (OT) systems, which are more obscure than IT systems in their protocols and design. A challenge therefore exists when applying pretrained LLMs to OT systems as corpora are likely to underrepresent OT systems in comparison to other more prevalent systems. We evaluate augmentations of LLMs with various methods, especially retrieval augmented generation (RAG), to improve performance of the LLMs in the OT domain. In addition to pentesting, some of the testing of these OT devices may include commissioning to ensure that the newly installed devices work correctly. Our framework may also be applied in such cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Community-Informed Urban Flood Modeling for Impact Mitigation

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.

Ava, Spangler [Pennsylvania State University]↗
DOE OSTI2023

Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2023

Community Engagement and Equity in Renewable Energy Projects: A Literature Review

Projects to transition away from fossil fuels toward renewables can prompt opposition even if their proponents engage with the public and affected communities. Therefore, many scholars and practitioners call for a nuanced understanding of the challenges and opportunities of participation and engagement in decision-making processes. This literature review examines: (a) the nature of and links between engagement and equity in energy transition projects, and (b) the lessons learned, best practices and options in this emerging field. The goal of this report is to inform the U.S. Department of Energy (DOE) and proponents of renewable energy projects, such as the siting of bioenergy infrastructure, with knowledge on what engagement is, why and how it works on the ground, and how it relates to crucial societal elements of energy equity such as social acceptance, energy democracy, and equity outcomes.

09 BIOMASS FUELS↗
DOE OSTI2023

Energy Transitions Initiative Partnership Project: Alaska Longline Fishermen’s Association Alternative Fuels Review

This report provides information about additional options in electrifying the Alaska Longline Fishermen’s Association (ALFA) fishing fleet. Currently, the Energy Transitions Initiative Partnership Project is prioritizing electric hybrid and battery electric options for retrofit of select fishing vessels in the fleet. However, there are other options for reducing carbon emissions among the fleet, namely hydrogen, ammonia, and biofuels. The use of these alternative fuels may be an option for the fleet in the future as ALFA addresses its vulnerabilities. A literature review of available resources on these fuels was conducted to provide information on several topics associated with implementation of these technologies, including current applications, commercially available power modules, necessary components, infrastructure, and safety codes and standards. An additional aspect that this report investigates is general sizing and refueling schedule based on operating records of multiple ships in the fleet.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Energy Transitions Initiative Partnership Project: Alaska Longline Fishermen’s Association Alternative Fuels Review

This report provides information about additional options in electrifying the Alaska Longline Fishermen’s Association (ALFA) fishing fleet. Currently, the Energy Transitions Initiative Partnership Project is prioritizing electric hybrid and battery electric options for retrofit of select fishing vessels in the fleet. However, there are other options for reducing carbon emissions among the fleet, namely hydrogen, ammonia, and biofuels. The use of these alternative fuels may be an option for the fleet in the future as ALFA addresses its vulnerabilities. A literature review of available resources on these fuels was conducted to provide information on several topics associated with implementation of these technologies, including current applications, commercially available power modules, necessary components, infrastructure, and safety codes and standards. An additional aspect that this report investigates is general sizing and refueling schedule based on operating records of multiple ships in the fleet.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Focal Fisheries Requirements for Floating Offshore Wind Co-Design in the Gulf of Maine and the Mid-Atlantic Bight

Floating offshore wind is an emerging renewable energy technology in U.S. waters and globally. Floating offshore wind technology is more complex than fixed-bottom installations, requiring platforms, moorings, and power cables that take up a larger footprint, both in the water column and along the seafloor. This complexity affects opportunities for compatibility between floating offshore wind and other ocean users, particularly fishermen, whose fishing gear also takes up space in the water column and along the seafloor. This report describes the results of semi-structured interviews with key fishing informants from two focal fisheries: (1) Gulf of Maine lobster (fixed gear) and (2) Mid-Atlantic Bight pelagic longline and recreational tuna (mobile gear). A total of six fishermen were interviewed: four from the Gulf of Maine and two from the Mid-Atlantic Bight. The interviews were designed to elicit information and opinions on the co-design of marine space for particular fishing gear and floating offshore wind infrastructure. Here, we provide an overview of participant fishing experience, the spatial requirements of specific gear types, self-reported comfort levels in different fishing scenarios, and concerns about accessing and operating within floating offshore wind arrays. This summary of fishermen s concerns around floating offshore wind infrastructure can guide future co-existence conversations.

17 WIND ENERGY↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
DOE OSTI2024

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System: Preprint

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗