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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment of Different Application Grades of Post-Consumer Recycled (PCR) Polyolefins from Material Recovery Facilities (MRFs) in the United States

The recycling of plastics has been gaining traction in the following years as many companies, organizations, and governments are pushing toward a more circular economy. However, plastic recycling rates are still small compared to those of plastic production, especially for polyolefins, such as high-density polyethylene (HDPE) and polypropylene (PP). With the goal of finding applications for recycled plastic resin, recyclers can face some challenges associated with the heterogeneity in the polymer properties of plastic waste streams, which could hinder the full potential of recycling due to the lack of a consistent feedstock. Here, this study assessed how an additional sorting step of recycled HDPE and PP streams, separating them into two categories based on the previous application, impacts the recycled polymer properties, such as physical, molecular, and thermal. It was demonstrated that manually separating HDPE articles previously processed by extrusion blow molding (EBM) or injection molding (IM) and PP articles by thermoforming (TF) or IM could yield recycled resins with more consistent properties, considering the significant differences between them. This highlights that an additional sorting step would allow recyclers to target more premium or currently unattainable plastic processing applications with postconsumer recycled (PCR) resins, which could significantly promote an increase in plastic recycling rates. Nevertheless, the development of new technologies is needed to automate this extra sorting step, as with the increase in scale manual sortation could not be feasible and cost-effective, reducing the attractiveness of using PCR resins in comparison to virgin ones.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Semi-classical origin of the extreme magnetoresistance in PtSn 4

The so-called “extreme magnetoresistance” (XMR) found in few conductors poses interesting conceptual challenges which address needs in technology. In contrast to the more common XMR in semi-metals, PtSn 4 stands out as a rare example of a high carrier density multi-band metal exhibiting XMR, sparking an active debate about its microscopic origin. Here we report a sharp sensitivity of its XMR upon the field angle, with an almost complete collapse only for one specific current and field direction (B//b, I//a). Corroborated by band-structure calculations, we identify a singular open orbit on one of its Fermi surface sheets as the origin of this collapse. This remarkably switchable XMR resolves the puzzle in PtSn 4 as a semi-classical effect of an ultra-pure, compensated carrier metal. It further showcases the importance of Ockham’s razor in uncommon magnetotransport phenomena and demonstrates the remarkable physical properties conventional metals can exhibit given they are superbly clean.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identifying Downtime Sources in CEBAF SRF Linac Systems for Improving Its Reliability

In this work, we will present some recent analysis of the reliability statistics of the CEBAF SRF linac systems. Based on the data collected by the existing Down Time Manager (DTM), the year-to-year downtime evolution trend of linac zones over the six-year period from FY18 to FY23 is established. An in-depth downtime analysis at a resolution beyond the linac zone level was carried out by introducing a new system hierarchy, consisting of the SRF linac sub-systems and components. This new paradigm was implemented in a pilot downtime study over the two-month period of CEBAF operation from 9/13/23 to 11/13/23, enabling localization of the responsible sub-systems (SRF, HPRF, LLRF, Beamline vacuum, Cryogenics, etc.) and hardware components (cavity, tuner, RF coupler, etc.) in the CEBAF SRF linac systems. Pinpointing downtime sources over long operation periods at the sub-systems and component levels holds the key to improving the CEBAF SRF systems reliability. It also helps identify areas of SRF technology development needed by future high-power high-reliability CW SRF linacs.

Hryhorenko, Oleksandr↗
DOE OSTI2024

Luminescent Materials for the Detection of Economically Critical Metals in Harsh Environments

Renewable energy technologies used for electric vehicles and wind turbines are heavily reliant upon metals, such as rare earth elements, cobalt, lithium, and nickel. Indeed, there are 50 minerals that are currently considered “economically critical” by the 2022 United States Geological Survey. With anticipated global adoption of renewable energy technologies, producing sufficient metals to meet this demand presents a significant challenge, particularly due to the current monopolistic market for many of these metals. The production of metals from unconventional sources, such as coal utilization byproducts, is one of many promising strategies to boost domestic supply. However, sensitive, rapid, and inexpensive characterization technologies are needed to minimize production costs associated with metals prospecting and processing. Photoluminescence-based sensing techniques are particularly intriguing due to their potential for low cost and portability, coupled with high sensitivity and selectivity. This presentation focuses on the development of high-performance sensing materials for a range of critical metals, including metal-organic frameworks capable of sensitizing detection of parts-per-billion concentrations of six different rare earth elements, nanoparticles that can detect down to 600 parts-per-billion levels of cobalt, and thin films that sense aluminum down to 120 parts-per-billion. These materials are highly selective, capable of withstanding low pH conditions, and provide a response within minutes. Importantly, each sensing material is integrated with a custom-built, fully portable fiber-optic spectrometer for potential field deployment, providing significant cost savings over commercial instruments, along with potential advantages such as material regeneration for use across multiple sensing cycles and solvent removal for enhanced emission signal. These results highlight the exciting potential of luminescence platforms as cost-effective alternatives for metals characterization.

Crawford, Scott↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Biphasic response of human iPSC-derived neural network activity following exposure to a sarin-surrogate nerve agent

Organophosphorus nerve agents (OPNA) are hazardous environmental exposures to the civilian population and have been historically weaponized as chemical warfare agents (CWA). OPNA exposure can lead to several neurological, sensory, and motor symptoms that can manifest into chronic neurological illnesses later in life. There is still a large need for technological advancement to better understand changes in brain function following OPNA exposure. The human-relevant in vitro multi-electrode array (MEA) system, which combines the MEA technology with human stem cell technology, has the potential to monitor the acute, sub-chronic, and chronic consequences of OPNA exposure on brain activity. However, the application of this system to assess OPNA hazards and risks to human brain function remains to be investigated. In a concentration-response study, we have employed a human-relevant MEA system to monitor and detect changes in the electrical activity of engineered neural networks to increasing concentrations of the sarin surrogate 4-nitrophenyl isopropyl methylphosphonate (NIMP). We report a biphasic response in the spiking (but not bursting) activity of neurons exposed to low (i.e., 0.4 and 4 μM) versus high concentrations (i.e., 40 and 100 μM) of NIMP, which was monitored during the exposure period and up to 6 days post-exposure. Regardless of the NIMP concentration, at a network level, communication or coordination of neuronal activity decreased as early as 60 min and persisted at 24 h of NIMP exposure. Once NIMP was removed, coordinated activity was no different than control (0 μM of NIMP). Interestingly, only in the high concentration of NIMP did coordination of activity at a network level begin to decrease again at 2 days post-exposure and persisted on day 6 post-exposure. Notably, cell viability was not affected during or after NIMP exposure. Also, while the catalytic activity of AChE decreased during NIMP exposure, its activity recovered once NIMP was removed. Gene expression analysis suggests that human iPSC-derived neurons and primary human astrocytes resulted in altered genes related to the cell’s interaction with the extracellular environment, its intracellular calcium signaling pathways, and inflammation, which could have contributed to how neurons communicated at a network level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are 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. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Acoustic Monitoring of Pyroprocessing for Safeguards

As pyroprocessing continues to be an attractive option for the reprocessing of spent nuclear fuel worldwide, safeguards technologies are needed to address the monitoring capabilities that can help state level authorities, or the International Atomic Energy Agency (IAEA) maintain continuity of knowledge of the plant operations. Idaho National Laboratory (INL) is studying the possibility of using acoustic monitoring as a means to monitor a pyroprocessing facility for safeguards purposes. This paper discusses the experimental design and some preliminary results of tests conducted at the Fuel Conditioning Facility, a pyrochemical capable facility, at INL.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2025

Evaporator Temperature Transient Testing of a High-Performance Sodium Filled Heat Pipe

Heat pipes are two-phase heat transfer devices that enable passive removal of heat from the reactor core to the power conversion system in heat pipe-cooled microreactor designs. Experimental investigations of heat pipe transients are needed for technology demonstration, verification and validation of numerical codes, and the establishment of regulatory requirements. The Single Primary Heat Extraction and Removal Emulator (SPHERE) facility at Idaho National Laboratory (INL) serves as a platform for evaluating the dynamic response of high-temperature heat pipes under a variety of operating conditions. The present work details the experimental investigation of a high-performance, defined as over 2 kW sodium heat pipe subjected to rapid input power fluctuations induced by sudden changes in the evaporator temperature setpoint. In addition, the heat pipe was subjected to an asymmetrical heat load where a subset of heaters operated at 30% and 70% below their nominal power. These experimental conditions were chosen to simulate thermal and operational stresses expected to be encountered in microreactors to provide data on heat pipe behavior during such important transient events. Key data and performance metrics, including time series of temperatures and strains, axial temperature profiles, thermal response times, and heat transfer capabilities, the thermal output over thermal input, were reported and discussed. The results highlight the resilience of heat pipes, revealing their potential to maintain thermal stability and efficiency under varying power loads. Lastly, the paper concludes with a discussion on the significance of the results and their implications for future research.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Efficient Routing of Quantum LDPC Codes on Programmable 2D Toric Architectures

Quantum low-density parity-check codes are promising candidates towards scalable fault-tolerant quantum computation. Among these, bivariate bicycle (BB) codes offer superior encoding rates and large code distance compared to surface codes. However, their requirement on long-range stabilizer measurements poses significant challenges for implementation on realistic hardware with limited connectivity, such as superconducting circuit platforms. In this work, we introduce a novel hardware-software co-design that leverages a programmable communication network architecture to address these limitations. Our approach utilizes a 2D toric network of oscillators as a flexible communication fabric linking qubits at each site. Such architecture significantly reduces the number of long-range couplers required from O ( n ) to O (√ n ). Dual-rail qubits, along with native gates including Swap-Wait-Swap gates and beamsplitter SWAPs, ensure that long-range two-qubit gates can be executed with high fidelity and low latency. To further enhance performance, our qubit layout and routing algorithm utilize symmetries of the codes and enable maximum parallelism for long-range two-qubit gates, maintaining a low syndrome extraction cycle duration and scalability over the code length. We perform circuit-level simulation with realistic noise modeling based on experimental hardware parameters, observing an logical error rate per logical qubit per cycle of 3.06% for [[18,4,4]] BB code, 2.6× less than the existing experimental result. These findings provide a practical roadmap and identify key technological advancements needed to achieve low-overhead fault-tolerant quantum computing at scale.

Liu, Kun [Yale Univ., New Haven, CT (United States↗
DOE OSTI2024

Enabling the Next Generation of Smart Sensors in Coal Fired Power Plants using Cellular 5G Technology

An important need for coal fired power plants is the ability to monitor multiple systems with ease and accuracy. Common implementations of these monitoring systems come with drawbacks due to the nature of coal fired power plants. Harsh environments, High Temperatures, and lots of RF (Radio Frequency) noise can create issues for accurately recording and transmitting data across wireless signals. In addition, as renewable energy sources come online, existing fossil fueled plants will need to operate more flexibly with their maintenance schedules outside of standard conditions. Therefore, additional sensing and control mechanisms need placed in existing plants to provide operators with more information such that maintenance decisions can be made well in advance of failures. A solution to this problem is the Next Generation of Smart Sensors, which leverages the power of 5G cellular signals and machine learning to overcome the myriad of problems with current implementations

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Nine Canyon Long-Duration Energy Storage: A Feasibility Study

The Nine Canyon Long Duration Energy Storage (LDES) Feasibility Study explores the technical and economic viability of deploying advanced energy storage technologies at Energy Northwest's (EN) Nine Canyon (9C) Wind Project site in Benton County, Washington. Supported by the Washington State Department of Commerce and the U.S. Department of Energy’s Office of Electricity under its LDES Voucher Program, the study represents a collaborative effort between EN, Pacific Northwest National Laboratory (PNNL), and ARES North America. At the core of this effort is the development of a generalized techno-economic modeling framework and evaluation tool designed to assess the value proposition of LDES projects across a variety of contexts. The modeling tool is technology-agnostic and accommodates user-defined parameters such as rated power, energy duration, round-trip efficiency, capital and operational costs, and dispatch constraints. It also integrates economic inputs, including market prices, energy revenue structures, and financing parameters to evaluate performance through key metrics. The tool provides utilities with a transparent, adaptable platform to support decision-making, investment prioritization, and portfolio planning for various storage technologies. To guide scenario design and interpretation, the study first surveyed the LDES technology landscape, including lithium-ion batteries, flow batteries, non-hydro gravity storage, and thermo-mechanical systems, comparing cost trajectories, technical performance, safety and hazards, materials sourcing and recyclability, and spatial/siting considerations. This literature-grounded review highlights technology trade-offs and reinforces the need to align technology choice with site characteristics, use cases, and project objectives. A companion chapter examines ownership structures (EN ownership, third-party ownership, shared models) and offtake options (energy marketing, capacity/energy PPAs, time-of-use PPAs, block-delivery PPAs, and tolling), where PPAs (power purchase agreements) represent contractual arrangements for buying and selling electricity. The chapter also highlights implications for risk allocation, capital access, operational control, and revenue certainty. The study also evaluates supervisory control and data acquisition (SCADA) and transmission interconnection pathways, options include upgrading the existing SCADA or deploying a dedicated LDES controller, with attention to protection schemes, data telemetry, cybersecurity, and regulatory coordination with BPA. In addition, an ARES-specific geotechnical and hydrology assessment presented in the appendix screens multiple corridors for slope stability, bearing capacity, cut-and-fill magnitude, and stormwater behavior.

25 ENERGY STORAGE↗
DOE OSTI2025

Arms Control Development Criteria (ACDCs)

The Arms Control Development Criteria (ACDC) have been developed to provide a consistent framework for understanding the readiness of a technology or capability for use in arms control as poten>al components of a verifica>on system. More importantly, they are also intended to serve as a guide for technology developers to beCer address the development needs of a technology from the start of the development process, rather than trying to adapt a technology to arms control aDer it is developed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Additively Manufactured Fe-3Si Stator for a High-Performance Electrical Motor

Additive manufacturing (AM) has the potential to produce novel high-performance electrical machines, enabling the direct printing of complex shapes and the simultaneous processing of multiple feedstocks in a single build. We examined the properties and functional performance of Fe-3 wt.% Si materials that were printed via selective laser melting, machined down to thin laminates, and stacked to form a stator core of a prototype brushless permanent-magnet electrical motor. Big Area Additive Manufacturing of Nd2Fe14B (NdFeB)–polyphenylene sulfide (PPS) bonded magnets was performed, with them then being magnetized and used for the rotor. The magnetic, mechanical, and electrical properties of the as-printed and various heat-treated thin laminates and the back electromotive force (EMF) of the electrical motors at different rotational speeds were measured. The thin laminates exhibit a maximum relative permeability of 7494 at an applied field of 0.8 Oe and a core loss of about 20 W/lb at 60 Hz with the maximum induction of 15 kg. In addition to the demonstration of AM printing, motor assembly, and complete characterization of printed Fe-3 wt.% Si, this report highlights the areas of improvement needed in printing technologies to achieve AM built electrical motors and the need for isotropic microstructure refinements to make the laminates appropriate for high-mechanical-strength and low-loss rotational electrical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Utilizing Time Reversal Ultrasonics to Detect the Removal of Nuclear Materials from Geological Repositories (FY26 Mid-Year)

Detecting unauthorized nuclear material removal from storage environments, such as geological repositories, is a critical safeguards task essential to ensuring the integrity and non-diversion of nuclear materials. However, this process is fraught with significant technical challenges. Storage configurations often involve tightly packed nuclear material containers or obstructed environments, making detection of removal events exceedingly difficult. Optical surveillance cameras, which are commonly used for monitoring, suffer from substantial limitations, including restricted coverage, reliance on line-of-sight measurements, and vulnerability to environmental conditions in certain storage scenarios. As the global inventory of monitored nuclear materials increases and storage configurations become more complex— such as deep geological repositories, inaccessible storage vaults, and tightly packed containers—there is an urgent need for innovative detection technologies that can reliably identify unauthorized diversion events in these challenging environments. The challenge of detecting nuclear material removal in complex storage environments is both significant and urgent. Preventing unauthorized access, diversion, or tampering with nuclear materials is a cornerstone of global nuclear safeguards and nonproliferation efforts. Current detection methods are increasingly inadequate as storage configurations become more intricate and inaccessible. The limitations of existing technologies—such as their inability to detect changes behind obstructions, reliance on costly and labor-intensive processes, and vulnerability to environmental conditions—pose risks to the effectiveness of safeguards systems. Addressing this challenge is critical to maintaining international trust in nuclear safeguards frameworks and ensuring compliance with nonproliferation agreements. Our project builds on the proven concept of TRU technology that can address this unmet need. TRU has demonstrated exceptional spatial sensitivity and change detection capabilities in complex non-line-ofsight environments, making it uniquely suited for detecting unauthorized nuclear material removal in challenging storage configurations. Unlike optical methods, TRU is not limited by line-of-sight constraints or environmental conditions, enabling reliable detection of subtle alterations even behind obstructions. By leveraging TRU’s ability to identify removal or tampering events, we aim to develop a robust detection system that enhances safeguards in geological repositories, storage vaults, and other complex environments.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Development of Human and Technology Integration Guidance for Work Optimization and Effective Use of Information

Existing nuclear power contributes to roughly 20% of the total electricity generation, and consistently provides the highest capacity factor of any other electricity generating resource in the United States. Despite these advantages, the existing nuclear power plant fleet in the United States has been enduring significant challenges in providing electricity in a cost-competitive manner, which has ultimately threatened the long-term economic viability of these plants. A major contributor to these increased operating and maintenance costs has been the continued reliance of a large workforce who perform their work under an operating model that has largely remained unchanged since the commissioning of these nuclear power plants. Unfortunately, while this operating model has provided safe and reliable electricity, other industries have already began transforming their workforce through the use of advanced digital technology and automation that has reduced their operating and maintenance cost significantly. In order for the United States nuclear industry to remain economically viable, a similar transformation must be considered in which digital technologies and automation capabilities are brought in to support key plant functions across all work functions across plant operation, maintenance, and support. To effectively integrate digital technology and automation in the existing nuclear power plant operating model, a multidisciplinary approach is needed that addresses technological and sociotechnical (i.e., human and technology integration) considerations. This report describes an extension to the human and technology integration methodology, herein referred to as Human Integration and Technology Task Force for Work Management Optimization (HITT), to support the safe, reliable, and efficient use of proposed innovations with the intended users in their intended environment to perform their intended tasks. By effectively incorporating human and technology integration into a plant modernization effort by using HITT, we believe that a utility can significantly improve work performance and overall workforce quality of life. For instance, if HITT is performed to optimize work management, scheduling performance and scope stability can be improved by up to 10%. HITT enables these benefits through developing a rich understanding of the work being performed, the utility’s vision, and the opportunities that provide greatest value to optimize performance through a 10-step process illustrated below as a quick guide. The quick guide contains links in the righthand side of each step that allows for navigation to the detailed section (Section 4) of HITT in this technical report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗