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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 19 records

Human-Centered and Explainable Artificial Intelligence in Nuclear Operations

Nuclear power plants in the United States are critical to the nation’s energy security, accounting for 20% of all electricity produced for the power grid. As energy needs grow, 100 gigawatts of additional nuclear power will be necessary by 2050, more than double the current capacity. Realizing this target requires cutting-edge technology like artificial intelligence (AI) and machine learning (ML) that can bring about significant increases in the level of automation. Human-centered AI (HCAI) is a combination of human-centered design (human factors, human-in-the-loop, etc.) with AI/ML to help produce an efficient and reliable system with full consideration for human engagement. This paper provides a comprehensive and novel discussion of HCAI considerations in nuclear power, introducing unique applications for the existing fleet as well as new advanced reactor designs. We include real-life use cases of AI applications to work management processes at nuclear power sites and highlight lessons learned for HCAI.

Hall, Anna↗

Bridging the Gap Between Modern UX Design and Particle Accelerator Control Room Interfaces

Accelerator control systems often represent relatively complex and safety-sensitive human-machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates is their human-system interfaces (HSIs) which often lag behind modern usability and design standards. An additional challenge is that although there is a multitude of established human factors (HF), and user experience (UX) principles for everyday digital applications, there are very few (if any) established principles for complex and safety-critical applications for an accelerator. This paper argues for the importance of established HF and UX principles (herein referred to as human-centered design principles) into the development of accelerator HSIs, emphasizing the need for clarity, consistency, responsiveness, and cognitive accessibility. Drawing from HF/UX best practices and human-centered design, this paper discusses how these approaches can enhance operator performance, reduce human error, and improve accelerator personnel collaboration. Case studies from Accelerator Control Operations Research Network (ACORN) at Fermilab are explored to demonstrate how interfaces built with human-centered design principles can scale with system complexity while remaining intuitive and efficient for diverse user roles including operators, machine experts, and engineers. By bridging the gap between traditional control system design and modern human-centered design methods, this paper provides a roadmap for evolving accelerator HSIs into more usable, maintainable, and effective tools.

Hill, Rachael [Idaho Natl. Lab.]↗

Human readiness levels and Human Views as tools for user-centered design

The Human Readiness Level (HRL) scale is a simple nine-level scale that brings structure and consistency to the real-world application of user-centered design. It enables multidisciplinary consideration of human-focused elements during the system development process. Use of the standardized set of questions comprising the HRL scale results in a single human readiness number that communicates system readiness for human use. The Human Views (HVs) are part of an architecture framework that provides a repository for human-focused system information that can be used during system development to support the evaluation of HRL levels. Here, this paper illustrates how HRLs and HVs can be used in combination to support user-centered design processes. A real-world example for a U.S. Army software modernization program is described to demonstrate application of HRLs and HVs in the context of user-centered design.

42 ENGINEERING↗

Trustworthiness and Trust: Identifying Factors that Drive Successful Human-AI Interaction in Nuclear Power Plant Applications

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are rapidly evolving and considered a promising tool for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may support personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for tasks such as surveillances or completing work orders. This is a fundamental shift in the way operators currently perform their tasks today. The literature of human-automation interaction indicates that trust is a crucial factor that drives successful interaction between a human operator and an automated system, like an AI-infused NPP application. This work presents the results of a literature review on key factors that relate to trust in AI/LLM technologies for NPP applications. The relevant literature of human factors and cognitive engineering has identified various factors related to trust including trustworthiness, performance characteristics, operator skill and perceived risk. This preliminary literature review will guide development and evaluation of models involving the identified factors influencing trust in AI and develop a framework for human-centered design for interface between humans and AI. By addressing trust, this work supports developing a technical basis for designing key characteristics of AI/LLM to support calibrated trust, which will ultimately support wide-scale adoption of AI/LLM technologies, as well as ensure safe, effective, and reliable use.

99 - GENERAL AND MISCELLANEOUS↗

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗

Reimagining How Flood Warnings Can Inform Decision‐Making and Community Actions

Society faces increasingly severe flood hazards, intensifying demand for flood early warning systems (FEWS) that deliver accurate and actionable information. However, most existing FEWS remain prediction‐centric, treating decision‐making as a downstream consumer of hazard forecasts while offering limited support for uncertainty interpretation, risk communication, and real‐world response. This Perspective presents a vision and blueprint for a novel inland FEWS‐decision‐making (FEWS‐DM) framework that repositions decision‐making as an equal partner in the forecasting process—not a passive recipient of its outputs. The framework is built on three tightly coupled, co‐evolving thrusts: Physical Science (T1), which advances flood prediction with quantified uncertainty informed by decision relevance; Human Science (T2), which incorporates psychology, behavior, and cultural and institutional context; and Decision Science (T3), which unifies physical predictions and human factors through principled, utility‐based decision support with end‐to‐end uncertainty management. Rather than treating T1 as a solved problem, FEWS‐DM recognizes that forecast development itself must be shaped by decision needs through continuous bidirectional feedback. We identify key scientific, behavioral, and operational challenges limiting such integration and discuss the enabling role of AI, while emphasizing human‐centered design and community feedback as essential for building trust and improving flood risk management.

54 ENVIRONMENTAL SCIENCES↗

Journey Mapping Distributed Wind Deployment: Installer Perspectives

This work uses journey maps to assess the deployment of distributed wind technologies through the perspective of installers. Journey mapping is a human-centered design method that chronologically traces processes from the perspective of those who participate in them. The journey map will be leveraged to identify deployment pain points (i.e., manifestations of generic deployment barriers) that the Strategize, Engage, Network and Deploy (SEND) Distributed Wind project team will seek to address in future work.

17 WIND ENERGY↗

PowerAnalytics.jl: User-Centric Power Systems Analysis in Julia

The National Laboratory of the Rockies recently released version 1 of PowerAnalytics.jl, an analysis module for the outputs of its popular open-source electrical power systems modeling platform Sienna. It features an extensible framework - based on the flexible selecting of components, the execution of arbitrary metrics on them, and a familiar DataFrames-based output interface with embedded metadata - to process results in the Sienna style while keeping the interface as simple as possible for non-Julia experts. Here, I describe the package and where it fits into the Sienna ecosystem, how I harnessed user-centered design and Julia features to achieve beginner friendliness without sacrificing performance and expressibility, and what lessons might be drawn from the package's design and implementation.

97 MATHEMATICS AND COMPUTING↗

Are humans still necessary? Expanding the discussion

The rise of automation, artificial intelligence (AI), and autonomous systems raises important questions about the future role of humans and the field of human factors/ergonomics in workplaces. This paper builds on Dr. Peter Hancock’s 2023 ‘Are Humans Still Necessary?’ article published in the Ergonomics journal. Using a multi-method approach that included a debate, opinion polling, roundtable discussions, and AI queries, the current effort examined the necessity of human involvement in future work environments. Debate team members presented arguments for and against the need for human workers, considering human factors, technology, and socioeconomic factors. Observations indicate that while AI may handle routine tasks, humans will likely remain essential for complex decision making, creativity, and ethical considerations. The paper advocates for viewing workplace dynamics as collaborative human-AI partnerships rather than competition, highlighting the need for a transdisciplinary approach in which human factors/ergonomics professionals play a vital role in enhancing these relationships.

99 - GENERAL AND MISCELLANEOUS↗

Advancing Small Business Solar Equity: Final Technical Insights Report

The Advancing Small Business Solar Equity: Final Technical Insights Report represents the culmination of two years of work in participation with Round 3 of the National Renewable Energy Laboratory's Solar Energy Innovation Network (SEIN). SEIN Round 3, titled "Equitable Solar in Underserved Communities," supported eight underserved communities across the United States in "exploring new approaches to the equitable adoption of solar energy in residential and commercial-scale settings" and in "confronting the solar barriers they face and unlocking the solar benefits most relevant to their own contexts" (National Renewable Energy Laboratory 2023). This project was one of four selected to support solar access for commercial entities in underserved communities. The research conducted for this report was done within the Minnesota Twin Cities geographic context, but certain findings and the proposed Solar Hub Network model may be found applicable nationally. Community-based organizations that serve small businesses, chambers of commerce, community development finance institutions and other community lenders, municipal governments, solar incentive providers, solar industry professionals, and others may find elements of this report useful.

14 SOLAR ENERGY↗

Form Follows Function (F3): A framework for community-based energy resilience planning in the Midwest

This is the final technical report for the Form Follows Function (F3) grant project led by the City of Duluth, MN. F3 was an innovative two-year initiative that created a replicable, human-centered energy resilience planning process specifically designed for smaller, cold-climate communities in the Midwest. F3 demonstrated both systems-level government planning and community-based research techniques to inform renewable investments and community preparedness strategies.

14 SOLAR ENERGY↗

Stabilization of Preternatural Barium Oxidation States as an Unexpected Byproduct of β-Decay: Discovery of a New Halide Semiconductor Alloy

137 Cs has a wide range of roles in the nuclear industry. The solid material, safely encapsulated in CsCl as 137 CsCl, is stored as fission product waste from nuclear power production and legacy waste from nuclear weapons production; it has also served as a radiation source in food and sewage irradiators as well as medical devices. However, because of the solubility of the chloride salt and the relatively high specific activity of 137 Cs, damaged or broken capsules can lead to severe radiological accidents. Safe capsule design and material recycling are complicated by the unclear structural evolution during β-decay, which remains ambiguous due to the differing oxidation states of Cs (1+) and Ba (2+). Here, in this study, we use first-principles calculations to investigate the evolving structure–property relationships of Cs 1–x Ba x Cl during β-decay. Despite the well-established 2+ formal oxidation state of alkali-earth metals, we find that Ba 1+ can be stabilized in the form of a mixed-valence alloy at low concentrations. Specifically, we identify three regimes for the β-decay of 137 Cs into CsCl: Ba-doped CsCl (Ba ≤ 14%), wherein Ba has the expected 2+ oxidation state; Cs–Ba–Cl alloys, where Ba has a mix of the usual Ba 2+ and highly unusual Ba 1+ oxidation state in the form of a quasi-disordered mixed-valence alloy (Ba = 25%); and phase separation into a CsCl + BaCl 2 + Ba (m) mechanical mixture, where Ba reverts to its expected 2+ oxidation state (Ba > 25%). Surprisingly, the Cs 0.75 Ba 0.25 Cl mixed-valence alloy is a narrow indirect band gap semiconductor (1.05 eV) despite the insulating nature of both CsCl and BaCl 2 . It also exhibits strongly excitonic polarized optical properties, has glass-like ultralow thermal conductivity (directional average of 0.21 W/mK at 300 K), and shows greater resistance to deformation under both tensile and volumetric strengths compared with the original CsCl structure (e.g., shear and Young’s modulus of 9.04 and 31.62 GPa, respectively). These findings imply that transmutation of 137 Cs leads to highly unusual chemical bonding that stabilizes Ba 1+ in local regions of the quasi-disordered Cs 0.25 Ba 0.75 Cl, resulting in anomalous physical properties. Moreover, this discovery provides valuable insight for safe nuclear waste capsule design, which can aid in preventing environmental or human exposure to radioactive materials.

Fuhr, Addis S. [Oak Ridge National Laboratory (ORN↗

Human Factors Considerations in Artificial Intelligence Applications for Nuclear Power Plants

In recent years, there has been a wave of artificial intelligence (AI) technologies that offer to solve problems from shopping habits to mortgage approvals to critical systems operations. The rapidity of the development of these systems has led to both excitement and apprehension about the roles these systems should play in our modern societies. Furthermore, this paper focuses on the critical infrastructure industry, in general, and nuclear power generation, in particular, and seeks to scrutinize how we can leverage these novel technologies in human-centered ways to maintain or enhance the established high levels of reliability and resilience in these industries. First, we discuss the broader aspects of cognitive systems and activities that are critical to understanding the human-AI space. Then we explore different approaches to explainability in AI and the notions of trust. We then move on to discuss several human factors concepts and methods and how they can support the design of human-AI teams. We then explore recent research related to nuclear power that has been undertaken and evaluate the current industry and regulatory landscapes. Finally, we discuss identified research gaps and recommendations for solving these for the critical infrastructure space.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗

Equity-Centered Engagement Through Climate Resilience Policy in Massachusetts

Communities who experience disproportionate climate change impacts tend to be excluded from resilience planning (Vale, 2014). Those efforts typically follow top-down processes within established governance practices that are inaccessible to marginalized folks and reinforce inequalities (Malloy & Ashcraft, 2020; Adger, 2003). Such participatory planning processes may offer the public little opportunity to influence the process itself or the outcomes (Smith & McDonough, 2001). They might ignore important public values or alternative ways of knowing which can be critical assets in resilience (Few et al., 2007). Designing communities for climate action and resilience means creating opportunities for everyone to meaningfully shape those decisions and experience related benefits. Having the opportunity to shape one’s community is necessary for human flourishing (Allen, 2016). Resilience planning that shifts power into communities and focuses on social vulnerability can affect how people survive and thrive in a climate changed world. The Massachusetts Municipal Vulnerability Preparedness (MVP) 2.0 program is an attempt to change the status quo in resilience planning by bringing new voices into decision-making power, recognizing their labor, addressing root causes of vulnerability, and investing in social infrastructure. It aspires to build capacity for equity-focused community engagement within teams of municipal staff and community liaisons, and ultimately build social capital and community cohesion. My mixed methods research investigates implementation of this state grant program in several western Massachusetts towns. I am using document review, participant observation, and interviews to understand the MVP 2.0 process as written, how different towns navigate it, and how individuals make sense of their experiences in it. I seek to understand how those experiences explain relationships between engagement approaches, mediating factors, and process outcomes. I am interested in the conditions that allow for community empowerment and how a model like MVP 2.0 can shift conditions that hold systems in place. In a practical sense, our findings will help municipalities reflect on their work during MVP 2.0 and plan for future community engagement. They may be informative for designing future iterations of the MVP program and for other municipalities, offices of community engagement, and practitioners. The findings will also contribute to the participation, resilience, and climate justice literatures, by adding perspectives on equity-centered resilience and community engagement approaches in smaller towns and rural settings. References: Adger, W. N. (2003). Social capital, collective action, and adaptation to climate change. Economic Geography, 79, 387-404. Allen, D. (2016). Toward a connected society. Our compelling interests: The value of diversity for democracy and a prosperous society, 71-105. Few, R., Brown, K., & Tompkins, E. L. (2007). Public participation and climate change adaptation: avoiding the illusion of inclusion. Climate Policy, 7(1), 46–59. Malloy, J. T., & Ashcraft, C. M. (2020). A framework for implementing socially just climate adaptation. Climatic Change, 160(1), 1–14. Smith, P. D., & McDonough, M. H. (2001). Beyond public participation: Fairness in natural resource decision making. Society & natural resources, 14(3), 239-249. Vale, L. J. (2014). The politics of resilient cities: whose resilience and whose city? Building Research & Information, 42(2), 191–201.

Callaham, Shannon↗

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗

mRNA-LNP HIV-1 trimer boosters elicit precursors to broad neutralizing antibodies

Germline-targeting (GT) HIV vaccine strategies are predicated on deriving broadly neutralizing antibodies (bnAbs) through multiple boost immunogens. However, as the recruitment of memory B cells (MBCs) to germinal centers (GCs) is inefficient and may be derailed by serum antibody–induced epitope masking, driving further B cell receptor (BCR) modification in GC-experienced B cells after boosting poses a challenge. Using humanized immunoglobulin knockin mice, we found that GT protein trimer immunogen N332-GT5 could prime inferred-germline precursors to the V3-glycan–targeted bnAb BG18 and that B cells primed by N332-GT5 were effectively boosted by either of two novel protein immunogens designed to have minimum cross-reactivity with the off-target V1-binding responses. The delivery of the prime and boost immunogens as messenger RNA lipid nanoparticles (mRNA-LNPs) generated long-lasting GCs, somatic hypermutation, and affinity maturation and may be an effective tool in HIV vaccine development.

Science & Technology - Other Topics↗