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

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Using computer graphics to enhance astronaut and systems safety

Computer graphics is being employed at the NASA Johnson Space Center as a tool to perform rapid, efficient and economical analyses for man-machine integration, flight operations development and systems engineering. The Operator Station Design System (OSDS), a computer-based facility featuring a highly flexible and versatile interactive software package, PLAID, is described. This unique evaluation tool, with its expanding data base of Space Shuttle elements, various payloads, experiments, crew equipment and man models, supports a multitude of technical evaluations, including spacecraft and workstation layout, definition of astronaut visual access, flight techniques development, cargo integration and crew training. As OSDS is being applied to the Space Shuttle, Orbiter payloads (including the European Space Agency's Spacelab) and future space vehicles and stations, astronaut and systems safety are being enhanced. Typical OSDS examples are presented. By performing physical and operational evaluations during early conceptual phases. supporting systems verification for flight readiness, and applying its capabilities to real-time mission support, the OSDS provides the wherewithal to satisfy a growing need of the current and future space programs for efficient, economical analyses.

Computer Simulation↗
NASA NTRS1995

Automation based on knowledge modeling theory and its applications in engine diagnostic systems using Space Shuttle Main Engine vibrational data

Humans can perform many complicated tasks without explicit rules. This inherent and advantageous capability becomes a hurdle when a task is to be automated. Modern computers and numerical calculations require explicit rules and discrete numerical values. In order to bridge the gap between human knowledge and automating tools, a knowledge model is proposed. Knowledge modeling techniques are discussed and utilized to automate a labor and time intensive task of detecting anomalous bearing wear patterns in the Space Shuttle Main Engine (SSME) High Pressure Oxygen Turbopump (HPOTP).

Kim, Jonnathan H.↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
NASA NTRS2017

Understanding the International Space Station Crew Perspective following Long-Duration Missions through Data Analytics & Visualization of Crew Feedback

The International Space Station (ISS) first became a home and research laboratory for NASA and International Partner crewmembers over 16 years ago. Each ISS mission lasts approximately 6 months and consists of three to six crewmembers. After returning to Earth, most crewmembers participate in an extensive series of 30+ debriefs intended to further understand life onboard ISS and allow crews to reflect on their experiences. Examples of debrief data collected include ISS crew feedback about sleep, dining, payload science, scheduling and time planning, health & safety, and maintenance. The Flight Crew Integration (FCI) Operational Habitability (OpsHab) team, based at Johnson Space Center (JSC), is a small group of Human Factors engineers and one stenographer that has worked collaboratively with the NASA Astronaut office and ISS Program to collect, maintain, disseminate and analyze this data. The database provides an exceptional and unique resource for understanding the "crew perspective" on long duration space missions. Data is formatted and categorized to allow for ease of search, reporting, and ultimately trending, in order to understand lessons learned, recurring issues and efficiencies gained over time. Recently, the FCI OpsHab team began collaborating with the NASA JSC Knowledge Management team to provide analytical analysis and visualization of these over 75,000 crew comments in order to better ascertain the crew's perspective on long duration spaceflight and gain insight on changes over time. In this initial phase of study, a text mining framework was used to cluster similar comments and develop measures of similarity useful for identifying relevant topics affecting crew health or performance, locating similar comments when a particular issue or item of operational interest is identified, and providing search capabilities to identify information pertinent to future spaceflight systems and processes for things like procedure development and training. In addition, the comments were scored for sentiment using a polarity scoring algorithm to identify both positive and negative comments for particular groups and clusters, allowing the team to make analytically informed decisions regarding future hardware and operating procedures. The use of polarity scoring with time series analysis was used to provide insight into how crew health and habitability is changing throughout various spaceflight increments or the station lifecycle as a whole. Finally, a visualization framework was developed to address the needs of the end users to search for and analyze comments by user, category or mission. This paper will discuss how the use of an analytical framework in conjunction with the current human interface, improved the understanding of crew perspective and shortened the time for analysis allowing for more informed decisions and rapid development of improvements. These methods are significantly optimizing the way that this valuable data can be assessed and applied to current and future spaceflight design and development. This collaboration allows the FCI OpsHab team to effectively analyze and share data in a more automated and timely fashion. Trends are no longer derived manually and can be illustrated effectively and accurately with these evolving techniques to an ever growing group of human spaceflight end users.

Bryant, Cody↗
DOE OSTI2018

Bridging the simulator gap: Measuring motivational bias in digital nuclear power plant environments

Using digital NPP simulator facilities for research in Human Reliability Analysis (HRA) and nuclear power research is increasingly popular. For this work, we propose a method for characterizing and quantifying the gap between data collected in a simulator and data that reflect NPP operations. Using novice operators, we demonstrate how to manipulate and measure the impact of the simulator environment on operator actions. A set of biases are proposed to characterize factors introduced by the simulator environment. There are two categories of simulator bias: Environmental Biases (physical differences between the simulator and the control room), and Motivational Biases (cognitive differences between training in a simulator and operating a NPP). This study examines Motivational Bias. A preliminary causal model of Motivational Biases is introduced and tested in a demonstration experiment using 30 student operators. Data from 41 simulator sessions are analyzed. Data include crew characteristics, operator surveys, and time to recognize and diagnose the accident in the scenario. Quantitative models of the Motivational Biases using Structural Equation Modeling (SEM) are proposed. With these models, we estimate how the effects of the scenario conditions are mediated by simulator bias, and we demonstrate how to quantify the strength of these effects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

The commercial U.S. light-water reactor fleet has been operating at historical efficiency, reliability, and safety over the last decade. Nuclear power has the highest capacity factor of any other power generation technology while also serving as the largest baseload source for carbon-free energy. Despite this remarkable achievement, continued operations for many plants are threatened due to fierce electricity market competition and rising operations and maintenance costs of which continued maintenance of obsolete analog equipment is a contributor. The digital age and associated technologies are where the future lies in process control, and nuclear has yet to take full advantage of the capabilities offered therein. The Light Water Reactor Sustainability Program (LWRS) at Idaho National Laboratory (INL), sponsored by the Department of Energy, has a mission to help the light-water reactor fleet manage its foundational capabilities to continue providing safe and reliable carbon-free power. LWRS helps support that mission by providing scientific, technology-based solutions for advanced concepts of operations with a more viable business model that will allow the fleet to continue to operate at peak levels through extended plant operation. The LWRS Digitalization Project at INL seeks to leverage digital technologies to synthesize and transform work processes. We provide a state-of-the-art analysis of digitalized work processes in nuclear power and investigate ways in which researchers at INL and the nuclear industry can work together to identify what data to access, how to access it, what to do with the data, and most importantly, how to use the insights for decision-making across all levels within the business. Borne from these considerations, we present four guiding principles for digitalization: develop a coherent digitalization plan, apply human factors engineering, establish data governance, and anticipate unintended consequences. Together, these principles form a method that plants can use to effectively to digitalize nuclear industry work processes. Our guiding principles are informed by multiple knowledge sources. First, we document activities from the Work Digitalization Initiative, which was conceived as a means for nuclear organizations to help define and standardize the industry’s approach to digitalizing work. Second, we detail primary research conducted with industry professionals regarding drivers and barriers to digitalization adoption. We present survey results that demonstrate what the industry hopes to get out of digitalization and the ways that INL can continue to support the industry’s digital transformation. Third, we present a digitalization use case with industry partners NextAxiom Technology and Xcel Energy. The project objective was to transform the current condition report work process from paper to digital, incorporating digitalized principles. We report the development of the application and lessons learned. The accomplishments achieved by this research and development serve to identify critical needs for plant guidance in support of digitalization implementation and contribute to the knowledge and strategies available for utilities considering or undertaking digitalization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
DOE OSTI2023

Dependence of the kinetic energy absorption capacity of bistable mechanical metamaterials on impactor mass and velocity

Using an alternative mechanism to dissipation or scattering, bistable structures and mechanical metamaterials have shown promise for mitigating the detrimental effects of impact by reversibly locking energy into strained material. Herein, we extend prior works on impact absorption via bistable metamaterials to computationally explore the dependence of kinetic energy transmission on the velocity and mass of the impactor, with strain rates exceeding 10 2 s –1 . We observe a large dependence on both impactor parameters, ranging from significantly better to worse performance than a comparative linear material. We then correlate the variability in performance to solitary wave formation in the system and give analytical estimates of idealized energy absorption capacity under dynamic loading. In addition, we find a significant dependence on damping accompanied by a qualitative difference in solitary wave propagation within the system. The complex dynamics revealed in this study offer potential future guidance for the application of bistable metamaterials to applications including human and engineered system shock and impact protection devices.

42 ENGINEERING↗
DOE OSTI2024

CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration

Abstract The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Gene sharing networks to automate genome-based prokaryotic viral taxonomy

Viruses of bacteria and archaea impact natural, engineered and human ecosystems, but their study is hampered by the lack of a universal or scalable taxonomic framework. Furthermore we introduce vConTACT v2.0, a network-based application to establish prokaryotic virus taxonomy that scales to thousands of uncultivated virus genomes, and integrates distance-based hierarchical clustering and confidence scores for all taxonomic predictions. Performance tests demonstrated significant improvements over the original tool and near-identical (96%) correspondence to current International Committee on Taxonomy of Viruses (ICTV) viral taxonomy where genus-level assignments are available. Beyond these “known viruses”, vConTACT v2.0 suggested automatic genus assignments for 1,364 previously unclassified reference viruses, with perfectly scoring assignments submitted as new taxonomic proposals to ICTV. Scaling experiments with 15,280 global ocean large viral genome fragments demonstrated that the reference network was rapidly scalable and robust to adding large-scale viral metagenomic datasets. Together these efforts provide a critically-needed, systematically classified reference network and an accurate, scalable, and automatable taxonomic analysis tool.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Temperature Profile of the Solution Vessel of an Accelerator-Driven Subcritical Fissile Solution System

Dynamic System Simulation (DSS) models of fissile solution systems have been developed and verified against a variety of historical configurations. DSS techniques have been applied specifically to subcritical accelerator-driven systems using fissile solution fuels of uranium. Initial DSS models were developed in DESIRE, a specialized simulation scripting language. In order to tailor the DSS models to specifically meet needs of system designers they were converted to a Visual Studio implementation, and one of these subsequently to National Instrument’s LabVIEW for human factors engineering and operator training. Specific operational characteristics of subcritical accelerator-driven systems have been examined using a DSS model tailored to this particular class using fissile fuel.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

First-of-A-Kind Control Room Modernization Project Plan

This project plan describes a comprehensive approach to the design of an end-state concept for a modernized control room for Palo Verde. It describes the collaboration arrangement between the DOE LWRS Program Control Room Modernization Project and the APS Palo Verde Nuclear Generating Station. It further describes the role of other collaborators, including the Institute for Energy Technology (IFE) and the Electric Power Research Institute (EPRI). It combines advanced tools, methodologies, and facilities to enable a science-based approach to the validation of applicable engineering and human factors principles for nuclear plant control rooms. It addresses the required project results and documentation to demonstrate compliance with regulatory requirements. It describes the project tasks that will be conducted in the project, and the deliverable reports that will be developed through these tasks. This project plan will be updated as new tasks are added and as project milestones are completed. It will serve as an ongoing description on the project both for project participants and for industry stakeholders.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Human Factors Considerations for Automating Microreactors

The U.S. Nuclear Regulatory Commission (NRC) has interacted with vendors pursuing the commercialization of microreactors ( <10 MW th ). It is envisioned that microreactors could be assembled and fueled in a factory and shipped to a site. Many of the sites are expected to be remote locations requiring off-grid power or in some cases military bases. However, before this new class of nuclear reactor can be fully developed and implemented by designers, an effort must be made to explore the technical issues and provide reasonable assurance to the public regarding health and safety impacts centered on various technical issues. Prior works have examined many of these issues. One issue not yet fully explored is the possible change in role of the operations and support personnel. Due to the passive safety features of microreactors and their low level of nuclear material, the microreactor facilities may automate more functions and rely on inherent safety features more than its predecessor nuclear power plants. In some instances, human operators may not be located onsite and may instead be operating or monitoring the facility from a remote location. Some designs also call for operators to supervise and control multiple microreactors from the control room. This report explores the issues around reduced staffing of microreactors, highlights the historical safety functions associated with human operators, assesses current licensing requirements for appropriateness to varying levels of personnel support, and describes a recommended regulatory approach for reviewing the impact of reduced staff to the operation of microreactors. The report evaluates these issues through an extensive literature survey and Subject Matter Expert interviews. The literature covers research topics related to Human Factors Engineering as well as regulatory guidance given by the Nuclear Regulatory Commission.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Improved Creep Testing Approach for Bentonite EBS

In the United States, the nuclear reactor contributes 18.7 percent of the nation’s total electricity generation and produces a significant amount of spent nuclear fuel (SNF) and high-level waste. Because SNF is being stored for longer periods than initially envisioned, the U.S. Department of Energy Office of Nuclear Energy, Office of Spent Fuel and Waste Science and Technology is assessing the technical performance of the SNF storage systems after extended durations. The concept of a sustainable geologic repository for SNF disposal relies heavily on the safety provided by multiple barriers, such as repository host rock, overlying rock formation, and human-made engineered barrier systems (EBS). One of the major components of EBS is bentonite-based buffer materials that isolate the nuclear-waste canisters from the host rock and fill the void left in the horizontally drilled boreholes. The resaturated bentonite has a swelling characteristic, which is useful for self-sealing the microcracks in the host rock and supporting the canister’s heavy weight. However, factors such as nonuniformity in the saturation, swelling pressure of bentonite, and the high temperature at the canister-bentonite interface may lead to instability of the waste canister inside the borehole. Therefore, evaluating the long-term deformation of bentonite-based material is necessary. RESPEC Company, LLC recently completed a preliminary study for the U.S. Department of Energy (DE SC0022804) that is attempting to understand the time-dependent deformation in the consolidated sand/bentonite (SB) specimens through triaxial creep experiments and improve the understanding of EBS performance under anticipated repository conditions. The project included developing a standard testing procedure for preparing the consolidated core specimen from a mixture of sand and bentonite in a 50:50 ratio by weight, fabricating a new pressure vessel in-house, modifying existing creep equipment to perform triaxial creep experiments on consolidated SB specimens, and performing six long-term triaxial creep experiments under low-deviatoric stress at two different temperature conditions, which is representative of hypothetical conditions that might be encountered in a geologic repository within a reasonable rate of success in completing creep experiments. The consolidated SB specimen was prepared using the novel specimen preparation procedure and had physical characteristics (e.g., moisture content and bulk density) reasonably comparable with the properties of bentonite-based buffer, which is proposed to be used in the EBS in the Swedish KBS-3H design and the Swiss design of SNF disposal in the horizontal drifts. The newly fabricated pressure vessel could withstand the confining pressure of up to 15 megapascals (MPa) while maintaining the test temperature of up to 90 degrees Celsius (°C) for long-term creep experiments. The preliminary results from the triaxial creep experiments suggested that the consolidated SB specimen may experience time-dependent deformation at a low-deviatoric stress state and room temperature condition. Based on the deformation recorded in the SB specimen from axial and radial linear variable differential transformers (LVDTs) and physical characteristic changes, it is apparent that the time-dependent deformation is a combination of consolidation and creep, which is influenced by the level of deviatoric stresses and temperature. The systematic creep testing of bentonite-based specimens helped develop a standard testing procedure applicable for analyzing the similar characteristics of shale or clay-like materials in different geologic conditions outside repository science, including in civil engineering and infrastructure projects, underground and open pit mining, and deep drilling.

58 GEOSCIENCES↗
DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Human Factors Considerations for Hydrogen Rail Refueling and Maintenance

The Federal Railroad Administration (FRA) contracted Sandia National Laboratories to review the risks introduced specifically through human interactions within hydrogen rail refueling and maintenance activities and present recommendations for future system development and implementation. Due to the lack of domestic hydrogen-powered trains at the time of review, observational data was collected on analogous existing systems to consider various ways hydrogen might be implemented within future designs. The team developed 50 human factors design guidelines through analysis of human risk factors. Because hydrogen rail systems are still in the early design stage, the resulting human factors guidelines represent general best practices and do not include specific design features. The research team recommends design and regulatory agencies include a Human Factors Engineer in the design and review process to ensure the guidelines are appropriately implemented and verified.

08 HYDROGEN↗
DOE OSTI2025

Embracing Uncertainty and Perseverance. A Brief Perspective on Conducting On-Site NDT Research

Dr. Judi E. See, a Systems Analyst and Human Factors Engineer at Sandia National Laboratories, reflects on her experience conducting NDT research in a male-dominated environment. She emphasizes the importance of persistence, flexibility, and persuasive skills in overcoming challenges, ranging from gaining access to test sites and equipment to building trust with inspectors. She shares her personal experience of navigating professional situations where gender disparities were evident, highlighting the need for women to adapt and overcome obstacles in traditionally male-dominated settings. See's journey demonstrates that perseverance and ingenuity can lead to significant contributions, process improvements, and recognition in the NDT field.

42 ENGINEERING↗