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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Morphology-Based Building Use-Type Modeling: Learnability-First Schema Discovery

This technical memorandum documents an update to the building use-type classification workflow, used in LandScan Mosaic, that replaces a fixed, semantically defined class schema with a learnability-first schema discovery procedure. Historically, the target label schema was specified a priori (e.g., predicting a chosen set of use-type codes), and model training and evaluation were performed within that fixed label space. In the updated workflow, the pipeline first evaluates which non-residential distinctions are learnable under spatial generalization and then collapses ambiguous classes into data-driven groupings before finalizing the schema used for production training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗
DOE OSTI2017

Nevada National Security Site Environmental Report Summary 2016

This document is a summary of the full 2016 Nevada National Security Site Environmental Report (NNSSER) prepared by the U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/ NFO). This summary provides an abbreviated and more readable version of the full NNSSER. NNSA/NFO prepares the NNSSER to provide the public an understanding of the environmental monitoring and compliance activities that are conducted on the Nevada National Security Site (NNSS) to protect the public and the environment from radiation hazards and from potential nonradiological impacts. It is a comprehensive report of environmental activities performed at the NNSS and offsite facilities over the previous calendar year. The NNSS is currently the nation’s unique site for ongoing national security–related missions and high-risk operations. The NNSS is located about 65 miles northwest of Las Vegas. The approximately 1,360-square-mile site is one of the largest restricted access areas in the United States. It is surrounded by federal installations with strictly controlled access as well as by lands that are open to public entry. In 2016, National Security Technologies, LLC (NSTec), was the NNSS Management and Operations Contractor accountable for ensuring work was performed in compliance with environmental regulations. NNSS activities in 2016 continued to be diverse, with the primary goal to ensure that the existing U.S. stockpile of nuclear weapons remains safe and reliable. Other activities included weapons of mass destruction first responder training; the controlled release of hazardous material at the Nonproliferation Test and Evaluation Complex (NPTEC); remediation of legacy contamination sites; characterization of waste destined for the Waste Isolation Pilot Plant in Carlsbad, New Mexico, or the Idaho National Laboratory in Idaho Falls, Idaho; disposal of low-level and mixed low-level radioactive waste; and environmental research. Facilities and centers that support the National Security/Defense mission include the U1a Facility, Big Explosives Experimental Facility (BEEF), Device Assembly Facility (DAF), National Criticality Experiments Research Center (NCERC) located in the DAF, Joint Actinide Shock Physics Experimental Research (JASPER) Facility, Dense Plasma Focus (DPF) Facility located in the Los Alamos Technical Facility (LATF), and the Radiological/ Nuclear Countermeasures Test and Evaluation Complex (RNCTEC). Facilities that support the Environmental Management mission include the Area 5 Radioactive Waste Management Complex (RWMC) and the Area 3 Radioactive Waste Management Site (RWMS), which has been in cold standby since 2006.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

PUEBLO OF ZIA RENEWABLE ENERGY DEVELOPMENT FEASIBILITY STUDY

The Pueblo of Zia will conduct a comprehensive feasibility study for best-use application(s) for development of renewable energy resources on its tribally held TRUST lands (i.e., Trust Lands of Zia Indian Reservation). The feasibility study is essential for determining the technical and economic viability of a future renewable project(s) on Zia tribal lands, including the potential economic and environmental benefits for the Tribe. Project Objectives: The feasibility study is essential for determining the technical and economic viability of future renewable project(s) on Zia tribal lands, including the potential economic and environmental benefits for the Tribe to: 1. Provide a balanced local renewable power supply for Zia Pueblo, its members, tribal offices, schools and buildings, and businesses on tribal lands 2. Provide a firm power supply for export and commercial market distribution 3. Provide economic development for the Tribe and its members, including job training and creation, each in accordance with the goals and objectives as conveyed by the Pueblo of Zia Tribal Council, Tribal Administration, and outlined in The Pueblo of Zia Comprehensive Plan and Pueblo of Zia — Zia Enterprise Zone Master Plan. A key goal of the study is to analyze the integrated development of solar, geothermal, and wind renewable energy resources at Zia Pueblo, with added potential to combine gas-fired generation to accomplish energy firming. Geothermal offers a base load source of energy, providing power continuously for end users. Wind and solar offer intermediate and peaking sources of energy, which can be harvested throughout the day, with periods of variable but predicable output. Variability will be managed in an integrated manner, using Zia Pueblo's combined renewable resources to generate high-quality power. Tasks are intended to collect, catalog, map, and analyze existing data on Zia Pueblo's renewable energy resource base and then match resource attributes with the most suitable renewable technologies for tribal energy consumption and needs. Also, key impacts on cultural and social values of Zia Pueblo will be addressed. Selected most favorable uses and technologies will be further investigated to determine initial feasibility and market for business development for up to six sites located on tribal land. The anticipated result will be the recommended development of sites that have sufficient background study completed to warrant a business plan, detailed engineering feasibility, and acquisition of project financing. Valuable technical and economic information will accrue from this study that may be applied to scale up or down the various power technology potential on Zia Pueblo for maximum benefit and best area(s) of application, project phasing and potential for future replicability and expansion.

14 SOLAR ENERGY↗
DOE OSTI2020

Plant Summit 2019

The Plant Science Research Network organized the Plant Summit 2019, which took place February 10-13, 2019 at Biosphere 2 (Oracle, AZ). The Summit brought together plant scientists from many quarters, including stakeholders from academic institutions, government, nonprofits, and private industry with a broad range of experience in plant science research and education. Participants were guided through a facilitated workshop to review and assess the outcomes from previous PSRN workshops on cyberinfrastructure, postgraduate training, broadening participation, and future scenarios. An outcome of the Summit is the development of a report, “The Plant Science Decadal Vision (2020-2030): Reimagining the Potential of Plants for a Healthy Future.” The Decadal Vision frames societally vital, exciting and far-reaching research challenges as being necessary and deeply interwoven with human and technical resources. An Executive Summary of that report is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Nonproliferation and Arms Control Research and Development (NPAC R&D) [Slides]

NPAC R&D focus areas include: Advancing U.S. capabilities to detect and characterize nuclear weapon development activities globally, including material production, the movement of special nuclear materials and nuclear weapons, and the testing or use of nuclear weapons, Supporting the development and testing of policy options and technical capabilities for international nuclear safeguards, arms control / treaty verification, nuclear export controls, and nonproliferation initiatives consistent with U.S. Government goals and objectives to enable monitoring and verification, Utilizing uranium, lithium, and high explosives (HE) materials expertise to support national-level activities in nuclear forensics and nuclear detection technology testing and evaluation, Providing unique training and capacity-building programs, particularly with the Nuclear Detection and Sensor Testing Center (NDSTC), and Engaging internationally to promote nonproliferation and arms control norms and best practices through bilateral and multilateral work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗
DOE OSTI2025

Development and Validation of Smart Building Technology Modules for Academic and Professional Education (Final Technical Report)

Smart building technologies can improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both college curricula and building professionals’ continuing education, there is a lack of systematic instruction on smart building technologies. Slipstream, partnering with Texas A&M University (TAMU), the Society of Building Science Educators (SBSE), and the National Institute of Building Sciences (NIBS), developed a semester-long smart building curriculum for college students and 16 training videos for building professionals and the general public. The education and training cover the drivers and benefits of smart building technologies, key building energy systems, the latest sensor technologies and IoT devices, and focus on topics related to smart building controls (i.e., energy management information systems, smart building control platforms, cybersecurity, grid-interactive-efficient buildings [GEBs], smart building control methods, and occupant-centric control). The smart building curriculum for college students was taught at TAMU in the Spring semester of 2024 as part of the validation process. Student feedback was collected and summarized in a validation report by TAMU. The curriculum material was also reviewed by SBSE faculty who are interested in teaching smart building technology-related courses. Suggestions on revisions and better adoption of the materials by other faculty across the architectural, engineering, and construction (AEC) domains were compiled in a distinct validation report by SBSE. The SBSE validation report was used to create structured subsets of the curriculum material for adoption at different levels in different sub-disciplines. These subsets are categorized and offered on the SBSE website (https://www.sbse.org/courses/Smart-Building-Technologies). The 16 training videos for building professionals and the general public were previewed by 17 industry experts, and feedback and suggested changes were incorporated into the final version of these videos. The videos are organized into a smart building technology training course and published on the Whole Building Design Guide website (https://www.wbdg.org/ce/doe/bto/sbtt), which is hosted by the National Institute of Building Sciences (NIBS). Project team members created marketing materials to promote the awareness of these free, publicly available education and training resources. Outreach and marketing activities included creating short promotional videos, building project webpages, making project announcements on social media, conducting an email campaign, and directly reaching out to faculties and building professionals. This report describes the project approach, provides outlines of the training materials, along with links to resources, and identifies lessons learned in creating the content. We also suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies in the real world.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗
DOE OSTI2013

U.S. Department of Energy Pacific Region Clean Energy Application Center (PCEAC)

The U.S. Department of Energy Pacific Region Clean Energy Application Center (PCEAC) was formed in 2009 by the U.S. Department of Energy (DOE) and the California Energy Commission to provide education, outreach, and technical support to promote clean energy -- combined heat and power (CHP), district energy, and waste energy recovery (WHP) -- development in the Pacific Region. The region includes California, Nevada, Hawaii, and the Pacific territories. The PCEAC was operated as one of nine regional clean energy application centers, originally established in 2003/2004 as Regional Application Centers for combined heat and power (CHP). Under the Energy Independence and Security Act of 2007, these centers received an expanded charter to also promote district energy and waste energy recovery, where economically and environmentally advantageous. The centers are working in a coordinated fashion to provide objective information on clean energy system technical and economic performance, direct technical assistance for clean energy projects and additional outreach activities to end users, policy, utility, and industry stakeholders. A key goal of the CEACs is to assist the U.S. in achieving the DOE goal to ramp up the implementation of CHP to account for 20% of U.S. generating capacity by 2030, which is estimated at a requirement for an additional 241 GW of installed clean technologies. Additional goals include meeting the Obama Administration goal of 40 GW of new CHP by 2020, key statewide goals such as renewable portfolio standards (RPS) in each state, California’s greenhouse gas emission reduction goals under AB32, and Governor Brown’s “Clean Energy Jobs Plan” goal of 6.5 GW of additional CHP over the next twenty years. The primary partners in the PCEAC are the Department of Civil and Environmental Engineering and the Energy and Resources Group (ERG) at UC Berkeley, the Advanced Power and Energy Program (APEP) at UC Irvine, and the Industrial Assessment Centers (IAC) at San Diego State University and San Francisco State University. The center also worked with a wide range of affiliated groups and industry, government, NGO, and academic stakeholders to conduct a series of CHP education and outreach, project technical support, and related activities for the Pacific region. Key PCEAC tasks have included: - Preparing, organizing and conducting educational seminars on various aspects of CHP - Conducting state baseline assessments for CHP - Working with state energy offices to prepare state CHP action plans - Providing technical support services including CHP/district energy project feasibility screenings - Working with state agencies on CHP policy development - Developing additional CHP educational materials The primary specific services that PCEAC has offered include: - A CHP “information clearinghouse “ website: http://www.pacificcleanenergy.org - Site evaluations and potential projects screenings - Assessment of CHP status, potential, and key issues for each state - Information and training workshops - Policy and regulatory guidance documents and other interactions These services were generally offered at no cost to client groups based on the DOE funding and additional activities supported by the California Energy Commission, except for the in-kind staff resources needed to provide input data and support to PCEAC assessments at host sites. Through these efforts, the PCEAC reached thousands of end-users and directly worked with several dozen organizations and potential CHP “host sites” from 2009-2013. The major activities and outcomes of PCEAC project work are described.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

West Virginia Residential Codes Study

The U.S. Department of Energy (DOE) Building Energy Codes Program launched a series of research studies in 2014 across several U.S. states to investigate typical energy efficiency practices in residential buildings and identify opportunities to reduce household utility bills. A primary goal of the study was to help document baseline practices in new single-family construction, target areas for improvement relative to the state energy code, and quantify related savings potential. This information was intended to assist states in measuring energy compliance with their codes and to identify areas of focus for future education and training initiatives. Following the original baseline studies, project teams in each state conducted a series of education, training and outreach activities aimed at improving compliance with the state residential energy code. A second baseline study was then implemented in several states with the objective of determining whether the investment in education, training and outreach programs had produced a significant and measurable change in statewide residential energy use. Through the program, DOE planned to establish a sufficient data set to represent baseline construction trends across states and detect significant changes in energy use resulting from education and training activities. The three basic phases employed were as follows: I. Pre-Study: A baseline study to identify the energy use in typical single-family residential buildings in a given state and opportunities for improving energy efficiency II. Education & Training: Education, training and outreach activities targeting issues identified through the baseline study III. Post-Study : A second study to identify the change in energy use following the education and training activities This report outlines the application of the study in the state of West Virginia with a focus on the experiences of the project team across the multi-phased approach. Data gathered through phase I has been submitted to Pacific Northwest National Laboratory (PNNL) for analysis. Findings available to date are published as PNNL technical reports on the DOE Building Energy Codes Program website with final analysis pending publication later in 2018.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

FY2019 Technology Integration Annual Progress Report

The 2019 Technology Integration Annual Progress Report covers 30 multi-year projects funded by the Vehicle Technologies Office. The report includes information on 23 competitively awarded projects, ranging from safety training and design for maintenance facilities housing gaseous fuel vehicles, to electric vehicle community partner programs, to truck platooning with advanced driver assistance systems. It also includes seven projects conducted by several of VTO’s National Laboratory partners, Argonne National Laboratory, Oak Ridge National Laboratory and the National Renewable Energy Laboratory. These projects range from a Technical Assistance project for business, industry, government and individuals, to the EcoCar Advanced Vehicle Technology Competition, and the Fuel Economy Information Project.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Abstract. In Part 1, we created an ensemble based on spherical Fourier neural operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires orders-of-magnitude fewer computational resources. Here in Part 2, we generate a huge ensemble (HENS), with 7424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system. For extreme climate statistics, HENS samples events 4σ away from the ensemble mean. At each grid cell, HENS increases the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

Mahesh, Ankur↗
NASA NTRS1990

Software technology insertion: A study of success factors

Managing software development in large organizations has become increasingly difficult due to increasing technical complexity, stricter government standards, a shortage of experienced software engineers, competitive pressure for improved productivity and quality, the need to co-develop hardware and software together, and the rapid changes in both hardware and software technology. The 'software factory' approach to software development minimizes risks while maximizing productivity and quality through standardization, automation, and training. However, in practice, this approach is relatively inflexible when adopting new software technologies. The methods that a large multi-project software engineering organization can use to increase the likelihood of successful software technology insertion (STI), especially in a standardized engineering environment, are described.

Lydon, Tom↗
NASA NTRS1997

Neural Network Control of a Magnetically Suspended Rotor System

Abstract Magnetic bearings offer significant advantages because of their noncontact operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

Choi, Benjamin↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
DOE OSTI2015

Bridging the gap: adapting advanced display technologies for use in hybrid control rooms

The Institute for Energy Technology (IFE), runs the OECD Halden Reactor Project (HRP), featuring a state-of-the-art research simulator facility in Halden, Norway, called HAMMLAB. HAMMLAB serves two main purposes: the study of human behaviour in interaction with complex process systems; and the development, test and evaluation of prototype control centres and their individual systems. By studying operator performance in HAMMLAB and integrating the knowledge gained into new designs, the HRP contributes to improving operational safety, reliability, efficiency and productivity. The U.S. Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program has contracted IFE to assist DOE national laboratory staff at Idaho National Laboratory (INL) in adapting HAMMLAB design concepts for the purpose of control room modernization at nuclear power plants in the U.S. In support of this effort, the DOE has built a simulator research facility at INL called the Human Systems Simulation Laboratory (HSSL). The HSSL is centered on control room modernization, in which industry provided plant instrumentation and controls are modified for upgrade opportunities. The HSSL houses the LWRS simulator, which is a reconfigurable full-scale and full-scope control room simulator. Consisting of 45 large touchscreens on 15 panels, the LWRS simulator is currently using this glass top technology to digitally represent and replicate the functionality of the analog I&C systems in existing control rooms. The LWRS simulator is reconfigurable in that different plant training simulator models obtained from the utilities can be run on the panels, and the panels can be physically moved and arranged to mimic the layout of those control rooms. The glass top technology and reconfigurability capabilities allow the LWRS simulator to be the research platform that is necessary to design, prototype, and validate human-system interface (HSI) technologies that can replace existing analog I&C. IFE has recently assisted INL in establishing the technical infrastructure for implementation of HSI prototypes from HAMMLAB into the HSSL to demonstrate relevant control room replacement systems in support of the LWRS program. In March, 2014, IFE delivered the first HSI prototype utilizing this infrastructure — a large screen overview display for INL's simulator. The co-operation now continues by developing Procedure Support Displays targeted for operators in hybrid control room settings. These prototypes are being validated with U.S. reactor operators in the HSSL and optimized to enhance their performance. This research serves as a crucial stepping stone toward incorporation of advanced display technologies into conventional main control rooms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Integrated Methane Monitoring Platform Extension, Volume I: Final Technical Report

The IMMPE project, DE-FE0032284, was to enhance methane monitoring technologies and their applications across various natural gas asset classes. The scope included deploying advanced methane detection and monitoring technologies to identify and mitigate fugitive methane emissions, measuring emission rates, and assessing impacts. The findings included the successful mitigation of identified emissions and quantification of emission rates. A key outcome was the development of a comprehensive template and summary of recommendations for methane emissions monitoring, which is replicable for both upstream and downstream applications. Furthermore, the project emphasized the importance of education by providing training opportunities for technicians and regulators, thereby fostering awareness and promoting the adoption of cost-effective methane emissions monitoring and management techniques.

02 PETROLEUM↗