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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Heat Pump Retrofits for Central Plant Hydronic Heating Systems: A Software Toolkit for Screening and Design

Retrofitting existing central plants with high-efficiency heat pump technologies can play a crucial role in achieving long-term planning goals. Modern heat pump technologies are able to use waste heat recovery to meet a building's heating demand, but there is a lack of accessible tools designed for non-HVAC experts, such as building owners, to quickly and easily conduct what-if analysis, e.g., estimating retrofit costs and payback period for their partial or full equipment replacement. This paper introduces an open-source software toolkit designed to facilitate the initial screening and decision-making of heat pump retrofits in existing central plants using a building's yearly load profile from metered or utility bill data. The toolkit evaluates the technical and economic viability of replacing traditional central plant equipment with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. The toolkit offers (1) a web-based tool designed for user-friendly access by a broad audience and (2) Python-based source code for researchers and engineers conducting parametric studies and design parameter optimization. The toolkit compares a typical central plant configuration to a configuration that uses a heat pump to supply hydronic heating and cooling. The output metrics include energy consumption and output of each equipment, life-cycle cost analyses and metrics, and environmental impact of the system.

Excell, L↗
DOE OSTI2026

Toward Unified Autonomous Scattering Experiments: A Cross-Facility Case Study at ALS and PETRA III

Autonomous experiments rely on the integration of control, data acquisition, analysis, and decision-making frameworks. While such systems have been demonstrated at individual facilities, adapting them to additional instruments remains challenging due to differences in local infrastructure. We present a modular workflow that connects existing open-source tools for data access (Tiled), workflow orchestration (Prefect), analysis and visualization (pyFAI, Plotly Dash), and Gaussian-process-based adaptive sampling (gpCAM) into a unified framework for autonomous scattering experiments. The same configuration operates across two synchrotron beamlines (ALS 7.3.3 and PETRA III P03) with only minimal facility-specific adjustments, as shown in proof-of-concept demonstrations. This validates that a consistent design emphasizing modularity and shared interfaces can ease deployment across diverse experimental environments. The resulting framework provides a flexible foundation for extending autonomous control and analysis capabilities beyond a single beamline or instrument.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Grid Operator Analytics and Assessment Tools for Inverter- Based Resources Dominated Grid (GOAAT-IBR) Project Update

This presentation provides an update on the OPTIMA GOAAT project, with emphasis on the cloud-native data platform developed in-house to ingest, manage, and operationalize high-resolution power system data. Since our last NASPI presentation, accessible via OSTI ID #2671437, the project team advanced the design and deployment of a scalable architecture capable of handling both synchronized and non-synchronized streams, including PMU, point-on-wave (POW), COMTRADE, and SCADA data. These materials review the project status, recent progress, and key lessons learned. The core of the presentation examines the architecture and engineering of our cloud-native ingestion and data management platform. We then explain how pipelines were designed to collect, normalize, time-align, store, and serve heterogeneous data at scale. We will discuss design choices such as data models, streaming versus batch ingestion, storage tiers, and interoperability with analytics applications. Practical experiences with cloud-native technologies were shared during the event, including benefits, limitations, and integration challenges in a utility environment, along with methods used to improve performance, reduce latency, and optimize resource usage. The presentation also showcases user interface designs and visualization tools that convert raw measurements and analytics results into intuitive, actionable insights for operators and engineers. During the presentation examples were provided demonstrating how visualization, event views, and summarized analytics enhance situational awareness and support operational decision-making. These use cases illustrate how a well-designed data infrastructure can bridge the gap between high-volume measurements and practical grid operations.

Aminifar, Farrokh↗
DOE OSTI2025

Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.

Environmental sciences↗
DOE OSTI2026

Powered by dGen Webinar [Slides]

NLR's Powered By Webinar Series featuring NLR's dGen Modeling Tool. The Distributed Generation Market Demand (dGenTM) model simulates customer adoption of distributed energy resources for residential, commercial, and industrial entities in the United States or other countries through 2050. The model enables analysis at multiple geographic levels (national, state, and utility, or below) and offers sophistication in representation of decision-making regarding economic and behavioral considerations. Analysts have used dGen to answer questions about load forecasting and integrated resource planning, policy analysis, locational value of distributed energy resources, and more. dGen is open source, and various energy organizations - including independent system operators, regional transmission organizations, and the California Energy Commission - use the model internally.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

American cities in a time of global environmental change: the case of the Baltimore Social-Environmental Collaborative

The Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory seeks a new paradigm for urban climate research. Motivated by deep uncertainties in urban climate and the future of urban systems, BSEC works collaboratively across institutions and stakeholder groups to co-generate the science needed to advance energy security and resilience to extreme events across the city of Baltimore, Maryland, USA, and to do so in a manner that can inform similar efforts in other cities. BSEC begins with stakeholder priorities (health, affordable energy, etc) and designs observation networks and models to deliver climate science to address them. This takes the form of an iterative collaborative cycle, in which an initial research strategy is repeatedly updated in conversation with community partners, and researchers and stakeholders learn from each other. To date, this cycle has included multiple rounds of collaborative deliberation on urban heat mitigation, in which a multicriteria decision tool has been updated with more community-relevant spatial structure and modified optimization metrics. The guiding objective of this cycle is to inform potential ‘secure and resilient pathways’ for energy and infrastructure. In doing so, BSEC addresses fundamental urban science questions in natural and social sciences. It also tests our ability to integrate this science in a manner that advances participatory decision-making for urban resilience.

climate↗
DOE OSTI2026

Measuring labor productivity dynamics in U.S. industrial and electric power sectors: a case study (2014–2023)

This study proposes a subsystem methodology for measuring labor productivity in the U.S. industrial and electric power sectors by leveraging public data available between 2014 and 2023. Building on Pasinetti’s framework and subsequent developments, the approach employs Vertically Integrated Sectors (VIS) to account for both direct and indirect productivity effects. The novelty of this work is twofold. First, it enables the estimation of productivity trends over time, providing a robust foundation for empirical analysis. Second, it applies the methodology to a case study of the electric generation sector, highlighting its practical relevance. Using data from the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Impact Analysis for Planning (IMPLAN) tool, the study reveals significant discrepancies between conventional productivity measures and those derived from the VIS approach. Furthermore, the proposed method aligns with the principles of Integrated Energy Systems by capturing the interrelations among generation, distribution, storage, and consumption. This alignment underscores its utility and applications for energy-related policy and planning. Overall, the findings contribute to more precise labor productivity assessments, supporting informed decision-making and future research. Additionally, the method highlights the importance of considering the whole supply chain, providing interrelated metrics for labor productivity which includes both, direct and indirect effects on the final labor productivity metric. By incorporating intersectoral dependencies, this method offers a more comprehensive and accurate measure of labor productivity compared to traditional metrics.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Hosting downscaled decision-relevant community data products in ESGF2-US

As regionally-relevant high-resolution Earth system data is increasingly relied upon across scientific, policy, and practitioner communities, there is an urgent need for coordinated and federated infrastructure to store, manage, standardize, and distribute decision-relevant community data products. Substantial effort is required to ensure that these products, which are often critical for regional impact assessments and decision-making, are findable, accessible, interoperable, and reusable. The Earth System Grid Federation US project (ESGF2-US) is addressing this challenge by expanding its open-source, distributed platform to support the hosting and dissemination of downscaled Earth system datasets. This expansion includes aligning new downscaled datasets with developing community standards for metadata and file structure, consistent with existing ESGF archives. This includes ensuring CF-compliance, applying CMORization where appropriate, and developing tools to streamline user access. In this paper, we highlight the technical and coordination work required to bring downscaled data into ESGF2-US and aim to inform the broader Earth system data user community about the growing availability and utility of these curated resources.

ESGF↗
NASA NTRSDate not supplied

Plume Impingement Software Module for Real-Time Proximity Operations

Successfully executing proximity operations in space, such as docking or in-orbit servicing, requires sophisticated spacecraft design that accounts for induced environments. As a chaser vehicle’s attitude control thrusters fire, they create rarefied plumes that can impact the target vehicle, with the potential to overload components, exceed thermal limits, and spin the target vehicle out of control. High-fidelity simulations of the thruster plume impingement environment require the direct simulation Monte Carlo (DSMC) method, but DSMC is too computationally expensive to simulate proximity operations that involve thousands of thruster firings. For this analysis to be tractable, engineering models of the plume flowfield and impingement events are used to simulate these trajectories [1]. Currently, on-orbit plume impingement environments are modeled through an inefficient open-loop analysis cycle where the vehicle’s flight controller and plume impingement teams iterate on the trajectories until they pass the target vehicle’s plume requirements. As complex on-orbit missions evolve and become more frequent, lengthy design cycles will become operational bottlenecks. To address this gap, this work develops an advanced plume impingement module capable of operating at real-time scale that can be integrated with existing mission planning tools and onboard flight systems. The plume module leverages state-of-the-art plume simulation techniques [2] to deliver fast, physics-based impingement predictions in a software architecture that can be tailored to diverse proximity operations scenarios. A prototype of this plume impingement module is built to demonstrate the feasibility of real-time performance. This prototype completes plume impingement calculations in microseconds per target geometry mesh point. The software serves as a foundational capability for plume-aware trajectory design, operational risk assessment, and future autonomous decision-making systems.

Plume Impingement↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2026

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.

Sun, Ruixiao [ORNL] (ORCID:0000000341768676)↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2026

ARM FY2026 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility maintains a suite of advanced atmospheric radar systems that serve as critical tools in ARM’s mission to provide continuous, high-quality observations for advancing the understanding and modeling of atmospheric processes. These radar systems enable detailed characterization of clouds, precipitation, and dynamic structures in the atmosphere, supporting a broad range of scientific applications. The number of deployed systems exceeds what current staffing levels can fully support for continuous 24/7/365 operation. As such, it is essential to have a clearly defined and community-informed plan that prioritizes radar operations and communicates ARM’s strategy for sustaining and evolving these observational assets. This FY2026 Radar Plan outlines ARM’s approach to managing its radar portfolio—balancing scientific impact, operational feasibility, and long-term sustainability. It reflects ARM’s continued commitment to delivering calibrated, well-documented radar data products that enable process-level studies and support the development and evaluation of weather and climate models. Through this plan, ARM aims to ensure transparency in decision-making, alignment with user needs, and support for innovative science across the facility’s fixed and mobile observatories. Given uncertainties around the Fiscal Year (FY) 2026 budget, this plan was developed to assume business as usual and will be updated as budgets and plans may change. It should be noted that, given the limited timeframe involved, this plan will be more succinct than previous plans.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Visual Systems Mapping to Define and Compare Woody Biomass LCAs for Sustainable Systems

The challenge addressed in this research centres on the need to choose between several biomass sources and energy production processes, while supporting rural economies and resilience of forest systems. A key barrier to effective decision-making for strategies using biomass is the lack of standardized and transparent life cycle assessment (LCA) baselines. These baselines are critical for assessing the impacts of biomass strategies but often vary due to regional factors and chosen simplifying assumptions of the LCAs. However, omitting key variables can mean the LCA omits key feedback and balancing loops relevant to fully assessing impacts of the change or test scenario. To address these complexities, this project employs a systems engineering approach: visual systems mapping. This technique is used to define the boundaries and dynamic behaviours of LCA baselines, enhancing transparency. By examining five literature sources and their documented baseline scenarios, the systems mapping case-studies demonstrates an approach to documenting and archiving these baselines. Recommendations are that visual systems mapping should be used to document key assumptions, such as baselines, of LCAs. Further, where possible open data repositories should hold key information about LCA baselines and reproducible workflows (e.g., using open-source tools) should be used to improve transparency and comparability in LCAs. Given the consensus within the broader scientific community on the importance of replicable data practices, this research reinforces the need for standardized frameworks and systems engineering tools in LCAs. This research demonstrates a pathway to more transparent, standardized, and comparable LCAs, that may bolster decisions for biomass systems.

Davis, Maggie [ORNL] (ORCID:0000000181319328)↗
NASA NTRSDate not supplied

Standards Roadmapping for Mission Assurance in Commercial Spaceflight

This paper explores the application of established roadmapping approaches in standards planning and roadmap development to support mission assurance objectives. Mission assurance requires proactive planning across multiple domains including technical, schedule, cost, organizational, and policy. The intersection of these domains happens at the enterprise level, and enterprise systems engineering processes can systematically guide mission assurance activities, including development of standards roadmaps. Standards planning and development is a critical contributor to an effective mission assurance strategy, although standards are often developed and adopted reactively. Technology and standards planning is a key Enterprise Process Management activity, as identified by the Systems Engineering Body of Knowledge, and technology and standards roadmaps are strategic tools used to enable that process. Technology roadmapping has proven effective in guiding enterprise architecture and concept design, and standards planning supports enterprise requirements definition and management. However, there are limited methodologies to guide such standards planning activities. The application of technology roadmapping principles to standards planning formalizes the process and establishes a repeatable framework for standards development and integration. The framework development described in this paper fills a methodological gap by adapting proven roadmapping techniques to standards planning. Through a comparison of roadmapping methodologies and a case study analysis, this approach outlines a systematic process for anticipating and planning standards needs. In the case of developing a standards integration roadmap for NASA’s Office of Safety and Mission Assurance (OSMA), technology roadmapping principles were adapted to develop a roadmap framework to allow NASA and OSMA to define an agency-wide standards integration plan for commercial and industry safety and mission assurance standards. The outcome highlights key adaptations required for roadmapping in a standards context and describes the process steps to formalize a standards planning activity. The roadmapping framework adapted for standards development and integration enables more strategic, forward-looking standards planning and allows for better alignment between evolving capabilities and mission assurance requirements. Beyond its application to mission assurance, this methodology can be transferred to other domains requiring standards development, providing a systematic approach that integrates systems engineering principles into policy strategy and decision-making processes.

Enterprise Systems Engineering↗
DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making

High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents—retrieval, description refiner, classifier, validator, and feedback logger—coordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an "Item to Evidence to Decision" loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts—illustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗