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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Aviation security screening optimizer for risk and throughput (ASSORT)

The increasing number of air travelers each year presents a challenge as many airports are near their capacity in terms of resources and space for passenger screening. Fortunately, advancements in technologies like next-generation millimeter wave scanning offer solutions to ease this strain. The focus remains on managing risk while enhancing the passenger experience for the traveling public. The risk model presented in this paper known as the Aviation Security Screening Optimizer for Risk and Throughput (ASSORT) is designed to assess risk-based approaches for passenger screening and checkpoint operations. Additionally, ASSORT is exploring various traveler categories — general, trusted, and trusted-plus — along with different checkpoint screening Concept of Operations tailored to each traveler type. For instance, travelers with a higher trust level may experience fewer screening technologies, resulting in quicker processing times at the checkpoint. The output of ASSORT provides a risk score for predefined threat scenarios, as well as the overall risk to the checkpoint, aircraft, and airport by traveler type. In conclusion, benefits of using this tool include assessing the trade-offs between the overall risk associated with checkpoints and the throughput rate of passengers screened. We show for example the impact that different passenger volumes at the checkpoint can have on risk.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Nuclear fuel irradiation testbed for nuclear security applications

The nuclear security community has long been interested in the identification and quantification of nuclear material signatures to understand a material’s provenance, use, and ultimate application. New forensics signatures and methods intended for non-traditional or advanced nuclear fuel applications require fuel irradiation experiments to demonstrate viability and validity. Integral fuel irradiations have historically required significant costs and long timelines to design, irradiate, and characterize. This paper describes how a recently developed nuclear fuel irradiation testbed can be used to provide a low cost, rapid turnaround, modular test environment for irradiation and evaluation of nuclear fuel specimens for nuclear security applications. The irradiation testbed houses six small ‘MiniFuel’ samples within hermetically sealed capsules inside targets that can be removed in between each ∼25-day operating cycle of the High Flux Isotope Reactor (HFIR). As many as nine targets can be irradiated using a single irradiation position (reflector region) in HFIR, allowing for varying irradiation temperatures and burnups. A suite of hot cell capabilities have been established to perform post-irradiation examination for measuring performance (e.g., fuel swelling, fission gas release) and facilitating experiment disassembly for subsequent property measurements, microstructural analysis, or chemical assay. This new testbed allows fuel irradiations to be conducted on an accelerated timeframe to enable rapid proof of concept testing and to provide reference material for nuclear fuel security applications. Recent applications using this testbed include the testing of isotopic taggants in UO 2 fuel (intentional forensics), testing of U-10Mo fuel for down-conversion of highly enriched uranium–fueled reactors, and the production of irradiated UO 2 fuel material for signature analysis of its isotopic composition (plutonium, fission gases, etc.).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Tibbetts, Jake↗
DOE OSTI2018

Constructing Convex Inner Approximations of Steady-State Security Regions

Here, we propose a scalable optimization framework for estimating convex inner approximations of the steady-state security sets. The framework is based on Brouwer fixed point theorem applied to a fixed-point form of the power flow equations. It establishes a certificate for the self-mapping of a polytope region constructed around a given feasible operating point. This certificate is based on the explicit bounds on the nonlinear terms that hold within the self-mapped polytope. The shape of the polytope is adapted to find the largest approximation of the steady-state security region. While the corresponding optimization problem is nonlinear and non-convex, every feasible solution found by local search defines a valid inner approximation. The number of variables scales linearly with the system size, and the general framework can naturally be applied to other nonlinear equations with affine dependence on inputs. Test cases, with the system sizes up to 1354 buses, are used to illustrate the scalability of the approach. The results show that the approximated regions are not unreasonably conservative and that they cover substantial fractions of the true steady-state security regions for most medium-sized test cases.

42 ENGINEERING↗
DOE OSTI2021

Hot-pressed 6 LiInSe 2 for use as a ceramic radiation detector

Lithium based chalcogenides have many uses in the nuclear science field. 6 LiInSe 2 (or LISe) has been researched extensively because of its ability to operate in both scintillator and semiconductor modes. Here, this makes it useful in applications including nuclear material detection for national security, space applications, and medical imaging. In some applications growth and fabrication costs are of concern, as it takes upwards of 40 days to synthesize a charge, grow a crystal and fabricate a detector. Ceramics are a solution for this long route of preparation. This research shows that using a 6 LiInSe 2 ceramic instead of a single crystal significantly decreases the time required to make a detector. In the present work, ceramic wafers can be formed using a mechanical press equipped with a heating attachment. The resulting ceramic wafer performs very similar to a slow-growth single crystal when used in the scintillator mode.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Opportunities for iron and steel industrial wastewater treatment and reuse in the United States

Concerns around water security in the United States have heightened the interest in industrial water treatment and reuse to improve water efficiency and operational reliability. Alongside nationwide efforts to expand industrial capacity, primary manufacturing sectors are adopting more resource-efficient technologies. This transition is expected to shift industrial water consumption patterns, driving the need for improved treatment and reuse practices. This study investigates opportunities for water use, treatment, and reuse in the iron and steel sector through a review of academic and industry literature and interviews with industry representatives. It identifies key challenges in water and wastewater management and outlines the conditions under which innovative treatment technologies could be deployed. Based on these insights, the study presents a practical water management action plan. Furthermore, it assesses water quality targets across different process operations, evaluates existing treatment technologies, and highlights challenges and opportunities for improvement relative to future performance expectations. Although water is often perceived as a low-cost commodity, industry feedback suggests that improvements in water use and treatment efficiency are typically prioritized only when they also reduce energy use, carbon emissions, or costs. This study advocates for a direct two-way partnership between industry and research audiences to bring their attention toward sustainable industrial water use, treatment, and reuse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Neutron detection performance of gallium nitride based semiconductors

Neutron detection is crucial for particle physics experiments, nuclear power, space and international security. Solid state neutron detectors are of great interest due to their superior mechanical robustness, smaller size and lower voltage operation compared to gas detectors. Gallium nitride (GaN), a mature wide bandgap optoelectronic and electronic semiconductor, is attracting research interest for neutron detection due to its radiation hardness and thermal stability. This work investigated thermal neutron scintillation detectors composed of GaN thin films with and without conversion layers or rare-earth doping. Intrinsic GaN-based neutron scintillators are demonstrated via the intrinsic 14 N(n, p) reaction, which has a small thermal neutron cross-section at low neutron energies, but is comparable to other reactions at high neutron energies (>1 MeV). Gamma discrimination is shown to be possible with pulse-height in intrinsic GaN-based scintillation detectors. Additionally, GaN-based scintillation detector with a 6 LiF neutron conversion layer and Gd-doped GaN detector are compared with intrinsic GaN detectors. These results indicate GaN scintillator is a suitable candidate neutron detector in high-flux applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

IEEE TRANSACTIONS ON CYBERNETICS

MODERN societies depend on complex and critical infrastructures for energy, transportation, sustenance, medical care, emergency response, communications security. As computers, automation, and information technology (IT) have advanced, these technologies have been exploited to enhance the efficiency of operating the processes that make up these infrastructures

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

CyberPetri at CDX 2016: Real-time Network Situation Awareness

CyberPetri is a novel visualization technique that provides a flexible map of the network based on available characteristics, such as IP address, operating system, or service. Previous work introduced CyberPetri as a visualization feature in Ocelot, a network defense tool that helped security analysts understand and respond to an active defense scenario. In this paper we present a case study in which we use the CyberPetri visualization technique to support real-time situation awareness during the 2016 Cyber Defense Exercise.

Cybersecurity↗
DOE OSTI2017

Combustion energy frontier research center (CEFRC) final report (August 1, 2009 – July 31, 2016)

The Combustion Energy Frontier Research Center (CEFRC) was established to tackle the single overarching grand challenge of energy sustainability, energy security and global warming: to develop a “validated, predictive, multi-scale, combustion modeling capability to optimize the design and operation of evolving fuels in advanced engines for transportation applications,” as identified in the DOE report on “Basic Energy Needs for Clean and Efficient Combustion of 21st Century Transportation Fuels”. The challenge is particularly daunting since energy conversion efficiencies and exhaust emissions are governed by coupled chemical and transport processes at multiple length scales ranging from electron excitation to molecular rearrangements to nanoscale particulate formation to turbulent fuel/air mixing. To tackle this challenge, the CEFRC assembled a world-class team of 15 principal investigators, with the objectives to: 1) develop and test theoretical models to predict elementary reaction rates, molecule thermalization rates, chemical bond dissociation energies, and nonequilibrium transport properties using quantum chemistry calculations that account for strong electron correlation and multiple electronic potential energy surfaces; 2) develop automated kinetic mechanism generation, reduction, and error control methods for predicting alternative fuel including biofuel oxidation, fuel droplet decomposition, and NOx and particulate formation; 3) validate and improve the predictions of these models by measuring ignition delay times, species profiles, flame structures, burning limits, turbulence-kinetic coupling, and NOx and soot emissions at high-pressures and near-limit conditions, by using advanced experimental diagnostic techniques including multiple laser techniques, molecular beam sampling and synchrotron photoionization, and by conducting the measurements in high-pressure shock tubes, jet-stirred and flow reactors, flame bombs, counterflow flames, and advanced-design rapid compression ignition instruments; and 4) develop a suite of validated petascale high-fidelity simulation and modeling capabilities to understand and predict chemistry-turbulence-radiation coupling for new fuels in new regimes, including the high pressure, low-temperature combustion in advanced engine and turbine designs, and 5) establish a knowledge highway between researchers and engineers in academia, national laboratories, and industry to facilitate the dissemination and exchange of knowledge on national and international levels, and enrich the talent pool and capabilities of the next generation of combustion scientists and engineers. The technical activities of the CEFRC were conducted through three Disciplinary Working Groups – Chemistry Theory, Experiment and Mechanism, and Reacting Flows, which coordinated the Center’s research on the development of combustion chemistry of Foundation Fuels (C0–C4 hydrocarbons), Alcohols, and Biodiesel through three corresponding Mechanism Thrust Groups. Such a two-dimensional coordinated and tightly interwoven research structure has been proven to be highly effective in assuring the interplay between the developments of the fundamentals of combustion science and the utilization of the various categories of fuels. The Center has accomplished the above goals over the five year period (August 1, 2009 – July 31, 2014) with appropriated funding, followed by two additional no-cost-extension (NCE) years. The research results are documented in 230 journal publications, with six legacy review papers on the study of combustion chemistry using shock tubes, flow reactors, rapid compression machines, and flames, on uncertainty quantification, and on theoretical reaction dynamics and chemical modeling of combustion. A robust outreach program complemented these PI-led research activities, consisting of: 1) a roving post-doc program comprised of a corps of Center-appointed, co- or multi-sponsored post-doctoral fellows with rotating assignments to conduct seed projects initiated by at least two PIs, in residence with these sponsoring PIs, to rapidly pursue new and high-risk, high-payoff interdisciplinary ideas; 2) an annual summer school on combustion heavily attended (~200) by senior graduate students and practicing researchers covering advanced topics on chemical kinetics, fluid mechanics, turbulent combustion, engine combustion, new technologies, etc.; 3) a robust open web-site providing Center and community information as well as the lecture videos and notes of the summer school; and 4) widely distributed biannual newsletters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Biologically Inspired Ammonia Production with Immobilized Nitrogenase Final Report

Ammonia is a critical component of the US economy as US agricultural exports exceeded $100BB in 2015 and depend heavily on ammonia fertilizers. Ammonia production consumes 1% of the world’s total energy supply and millions of tons are imported each year. An innovative bioelectrocatalyzed process is being developed using immobilized mutated nitrogenase enzymes to directly convert atmospheric nitrogen to ammonia at standard conditions, 25° C and 1 atm, and without the need for natural gas. This technology utilizes an enhanced enzyme and advanced enzyme immobilization method for increased production efficiency. The Phase I project developed a new enzyme and polymer support structure which enables direct conversion of nitrogen to ammonia under mild conditions without the need for added chemicals; only electricity and water are required. The system exhibited significantly higher efficiency than competing technologies. The Phase II project will develop a commercial process for the enzyme/polymer system. This will include scaling up production of the enzyme and determining process conditions for maximum productivity. This technology is intended for the commercial fertilizer industry, which currently uses large, expensive plants for centralized production, consuming vast amounts of natural gas and generating significant CO 2 pollution. This technology could also be used to generate alternative ammonia-based fuel and ammonia-based commodity chemicals. The Federal Government would benefit from this technology by reducing dependence on foreign imports, and increasing domestic production of a commodity chemical. Ammonia is used as key component in explosives and propellants and is essential for national security. Distributed production will lower the vulnerability of this resource. The key benefit for the customer is lower capital and operating cost. Building new ammonia production plants cost >$2BB with considerable infrastructure costs to ship the ammonia. The primary method of transportation is pipe to truck distribution, although some is still carried via railway. The feature that set our technology apart from the current Haber-Bosch process is the ability for low-cost distributed production.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Physical Sciences Vistas: Perspectives on Simultaneous Excellence (Issue 1, 2023)

The issue begins with an article highlighting Physics Division’s involvement in diagnostic development that led to the measurement of ignition at the National Ignition Facility at Lawrence Livermore National Laboratory. Subsequent articles transition to some of the materials development occurring in Sigma, Materials Science and Technology, and Materials Physics and Applications divisions. This is inclusive of the development of next generation moderator materials as an enabling technology of small modular reactors. These materials will most likely be qualified at a facility at Idaho National Laboratory. The issue also highlights development of novel, additively manufactured foams for next generation weapons. This work is being done in close collaboration with the Kansas City National Security Campus to enable transition of materials development to production in a more agile way. Finally, an article describes Accelerator Operations and Technology Division’s hard work to replace the legacy remote instrumentation and control equipment at the Los Alamos Neutron Science Center (LANSCE) with a modern control system. The story discusses how that engineering success will lead to improved sustainability of the beam during operation and thus a better experience for all of our collaborators and partners in the LANSCE user program.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Demonstration of acoustic monitoring for structural health of microreactors: Through use of neural networks and resonant ultrasound spectroscopy

Nuclear microreactors prioritize modularity and portability and are intended to be a cost-effective technology for non-conventional nuclear markets. As such, the development of microreactors into a safe and feasible solution for energy security applications will necessitate the development of non-destructive technologies to monitor the integrity of inaccessible reactors components during operation. This demonstration applies linear and nonlinear acoustic techniques, in combination with machine learning, to detect and classify mechanical changes (stress and damage) in a test article which are broadly representative of potential operating challenges within a functioning microreactor. All necessary data has been collected for this demonstration, with minor experimental issues identified that can be addressed in follow-on work. Motivated by the expected conditions within a functioning microreactor, we have demonstrated our monitoring techniques on a core-block-like test article using an unstructured excitation source that approximates the noisy acoustic environment expected during reactor operation. At all stress states mechanically applied to the test article, a machine learning model using an artificial neural network was able to classify with 100% accuracy whether a 3D laser vibrometry point measurement was made on an intact or artificially defective test article. Further, model predictions about whether the defect interface was rough or smooth were 95% accurate, indicating the ability of acoustic techniques to recover defect characteristics. Resonant ultrasound spectroscopy (RUS) was also applied to the dataset to provide further quantitative insights about material properties. RUS analysis was ultimately hampered by several minor experimental and data issues, limiting results to certain cases for this demonstration. Last, analysis using nonlinear RUS exhibited sensitivity to changing levels of applied stresses for each intact and defective state. As presented in this demonstration, acoustic monitoring exhibits sensitivity to stress changes, which are of concern due to high thermal gradients expected during startup and operation. Further, our techniques distinguish between measurements made on intact and damaged test articles.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Cyber100 Compass User Guide

This document provides an overview of the Cyber100 Compass tool and serves as a guide to users interested in understanding the cybersecurity risks facing their clean energy transition. The National Renewable Energy Laboratory (NREL) developed the Cyber100 Compass tool for cyber risk assessment at the system-of-systems level for system planners trying to reach high levels of renewables. Two offices of the U.S. Department of Energy (DOE) - the Office of Electricity and the Office of Cybersecurity, Energy Security, and Emergency Response-funded NREL to develop this framework that will enable grid system planners to understand and mitigate cybersecurity risk for grids transitioning to high levels of renewable generation, including 100%. Cyber100 Compass can help systems planners apply the principle of security-by-design at scale. Investing in an upfront understanding of system-of-systems (where the constituent systems are operating entities of the different renewable resources) risks from high-renewable grids will result in a more resilient grid at a lower long-term cost.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Safety Basis Compliant Flexible Operations [Capstone Project]

Compliant nuclear operations must be weighed against the radiological risk to the worker, public, and the environment. This project set out to look at one facility at Los Alamos National Laboratory (LANL or the Laboratory) and compare the risk with the operational restrictions associated with the Technical Safety Requirements (TSRs). The hope was to identify controls that could be reduced or simplified while maintaining compliance with 10 CFR 830, Nuclear Safety Management. This would result in increasing facility operational flexibility and efficiency. Controls were identified to be simplified or eliminated based on risk. This identification was initially envisioned from the perspective of reanalysis of the accident and hazard analysis consequences. Additionally, it was envisioned that fire testing could show that the consequences of a fire are not as dramatic as analyzed. Specific facility controls were identified that cause operational inefficiencies. Based on data collection, two controls were identified for further evaluation. The first control was combustible loading within the waste storage buildings, and the second control is related to the seismic power cutoff switch. In evaluating the combustible loading control, the thought is that the fire suppression system (FSS) provides adequate protection and the combustible loading controls do not need elevation to a TSR control. The FSS, along with the Fire Protection Program, would provide protection to prevent a fire from impacting large amounts of stored waste material. The seismic switch was more of a challenge. When reviewing the facility operation, it was determined that the facility does not operate anywhere near the established material-at-risk (MAR – radioactive material) limits. By adjusting these limits, a reduced consequence could be achieved and hence, a significant risk reduction achieved. A new operating mode would be used to achieve this reduction. A reduced MAR limit was successfully used as a test case for snow removal during this project. This project expands on that trial case as related to combustible loading and the seismic power cutoff switch TSRs. This will increase the efficiency of operations with reduced TSR controls while reducing overall facility risk. This is a win-win situation for Triad National Security, LLC (Triad) and the U.S. Department of Energy (DOE). Due to schedule and budgetary constraints, not all envisioned work scope was accomplished. However, it is maintained in this report for future efforts.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments

There is an increasing aspiration to utilize machine learning (ML) for various tasks of relevance to national security. ML models have thus far been mostly applied to tasks and domains that, while impactful, have sufficient volume of data. For predictive tasks of national security relevance, ML models of great capacity (ability to approximate nonlinear trends in input-output maps) are often needed to capture the complex underlying physics. However, scientific problems of relevance to national security are often accompanied by various sources of sparse and/or incomplete data, including experiments and simulations, across different regimes of operation, of varying degrees of fidelity, and include noise with different characteristics and/or intensity. State-of-the-art ML models, despite exhibiting superior performance on the task and domain they were trained on, may suffer detrimental loss in performance in such sparse data environments. This report summarizes the results of the Laboratory Directed Research and Development project entitled Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments. The objective of the project was to develop a new transfer learning (TL) framework that aims to adaptively blend the data across different sources in tackling one task of interest, resulting in enhanced trustworthiness of ML models for mission- and safety-critical systems. The proposed framework determines when it is worth applying TL and how much knowledge is to be transferred, despite uncontrollable uncertainties. The framework accomplishes this by leveraging concepts and techniques from the fields of Bayesian inverse modeling and uncertainty quantification, relying on strong mathematical foundations of probability and measure theories to devise new uncertainty-aware TL workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Synopsis of NREL's Automated Mobility District (AMD) Research Program and Associated Publications

An automated mobility district (AMD) envisions a system of integrated mobility options that serves major activity centers such as campuses, central business districts, and large medical facilities. The National Renewable Energy Laboratory (NREL) has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings. This document provides a synopsis of findings revealed over the last three phases of work, which have yielded insights into the creation and management of AMDs anticipated to use automated vehicle (AV) technology over the next decade. Phase I and Phase II tracked the deployment and lessons learned from 10 early-stage demonstrations of automated shuttle deployments, and their associated insights into the challenges for automated driving systems to achieve safe operations within district-scale deployments. Phase III began in-depth investigations of critical subsystem components, as automation, electrification, and on-demand service continue to converge within initial AMD operations. These directed studies focus on elements of electrification, curbfront/station management, the role of infrastructure sensing, and overall integration of AMD safety management in central, simultaneous coordination of multiple AMD fleets. Future research in AMDs includes systems engineering methodology (more frequently referred to as "digital twins") for planning, design, testing, and ongoing operation of AMDs; location (or co-location) of management functions; and human supervision and passenger communications for safety and security in unattended vehicles. The synopsis references the foundational research products (papers and presentations) that have been published through conference proceedings, journal articles, and NREL reports.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Control Room of the Future Testbed Workshop – After-Action Report

The U.S. Department of Energy’s Office of Electricity is supporting a one-year, multi-laboratory effort to define the needs and requirements for a Control Room of the Future testbed, or CROFT. The effort responds to increasing grid complexity driven by large new loads, dynamic generation resources, and the growing adoption of advanced technologies and tools, including artificial intelligence (AI) and machine learning (ML). To support safe, secure, and effective grid modernization, CROFT will focus on how emerging technologies and tools can be rigorously evaluated in realistic operational settings, with attention to human-machine interaction, cognitive load, and workforce readiness. The project team includes Argonne National Laboratory, Idaho National Laboratory, National Laboratory of the Rockies, and Pacific Northwest National Laboratory. As part of the scoping effort, the team conducted two industry-focused workshops: one at DTECH on February 5, 2026, informed by prior industry interviews, and a second on May 4, 2026, adjacent to IEEE T&D. These engagements brought together utilities, vendors, consultants, national laboratories, academia, and government stakeholders to identify and prioritize use cases, barriers, validation needs, data-sharing constraints, and near- and longer-term requirements. This feedback will directly inform CROFT’s architecture and research focus areas, ensuring the testbed is grounded in real-world operational needs and designed to evaluate emerging technologies and tools in realistic control-room environments.

artificial intelligence↗