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At least 145 records · Page 8

Analysis of Tasks in Autonomous Systems Using the EMRALD Dynamic Risk Assessment Tool

An autonomous system refers to the system that has the power and ability for self-governance in the performance of system functions. Autonomous systems have been actively pursued in a variety of domains such as automotive, aviation, maritime, medicine, and nuclear fields. As an unmanned concept employing the highest automation level, the autonomous system basically performs most of the work in normal operations or emergency situations. However, despite advances in technology, many researchers have noted these systems still require human actions. The nature of human actions on autonomous systems is different than the human actions that are considered in existing systems. Nevertheless, only a few studies have been conducted on 1) characterizing the different types of errors and risks associated with human actions interacting with autonomous systems and 2) how to evaluate human actions in the autonomous operations. As a starting point, this study aims to investigate differences of tasks in autonomous operation compared to those in existing nuclear power plant operation using the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software. In this paper, insights aspect of human error and time are derived out and discussed based on the output of the EMRALD models.

99 GENERAL AND MISCELLANEOUS↗

New Era Towards Autonomous Additive Manufacturing: A Review of Recent Trends and Future Perspectives

Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.

Fan, Haolin↗

Human Factors Considerations for Anticipated Uses of Autonomy in Advanced Reactor Technologies

The objective of this report is examine anticipated advanced reactor designs that incorporate high levels of autonomy in operation of advanced reactor systems or facilities, and to begin identifying the human factors engineering (HFE) considerations relevant to those designs. The insights gained will help identify areas where U.S. Nuclear Regulatory Commission (NRC) may need to develop or update review guidance to adequately address HFE considerations essential for safe operation. The adequacy of existing guidance, along with recommendations for updates or new guidance, may be addressed in future reports. We use the term “landscape analysis” to describe the review, analysis, and characterization of publicly available information on the anticipated future state of the industry regarding particular technologies or operational concepts. This landscape analysis considers the potential range of advanced reactor automation concepts that may be used for monitoring and controlling nuclear systems and facilities, including the potential for highly automated, near autonomous, or fully autonomous operation of advanced nuclear reactors.

99 - GENERAL AND MISCELLANEOUS↗

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design

With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.

97 MATHEMATICS AND COMPUTING↗

Market Barriers and Drivers for the Next Generation Fault Detection and Diagnostic Tools

Commercial buildings in the U.S. consume as much as 30% excess energy compared to buildings that operate fault free and efficiently. Fault detection and diagnostic (FDD) platforms help to continually identify operational inefficiencies and maintain low-carbon performance. However, the recommendations generated by FDD tools need to be implemented by technicians, resulting in delays or lost savings opportunities. Recent research advances showed fault AUTOcorrection integrating with commercial FDD offerings filled this gap. Seven innovative AUTOcorrection algorithms were integrated into two FDD platforms and deployed across four buildings. The enhanced tools successfully correct faults focusing on incorrectly programmed schedules, override not released, control hunting, rogue zone, and suboptimal setpoints. Although its technical efficacy has been proven in the field, fault AUTO-correction is still early in the deployment cycle and opportunities and barriers need to be understood to reach its full potential in market transformation. This paper broadly introduces the new technology that automatically corrects HVAC faults. The authors describe in detail technology potential, market barriers, and enablers for scalability based on field testing results and interviews with the FDD providers and facility managers. The interviewees agreed that AUTO-correction can reduce the extent to which savings are dependent upon human intervention, scale building operators’ ability to act on FDD findings (especially for facilities with small operation teams), and achieve significant savings. To enable scalable deployment, future efforts are needed to overcome the barriers such as cybersecurity and accountability concerns from building operators and standardization of control parameters used in building automation systems.

Pritoni, Marco↗

Integrating Cybersecurity with System Operations and Restoration

This presentation covers the interaction of the discipline of system operations with the discipline of cybersecurity. First is discussion of a number of fundamental concepts for system operators - organizational division of responsibilities, human and machine cooperation, goals, and priorities. The next section covers the importance of cybersecurity for a system operator organization and explains different risk management approaches based on the consequence and frequency of events. Finally the role of system operators in the security of the grid categorized by the NIST Cybersecurity Framework is discussed to present recommendations and ideas for future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Dynamic Human-in-the-Loop Simulated Nuclear Power Plant Thermal Dispatch System Demonstration and Evaluation Study

An Idaho National Laboratory research team performed a human-in-the-loop study with two formerly licensed retired operators to evaluate thermal power dispatch operations supported by the modified GSE Systems GPWR plant simulator. Virtual representations of the analog control panels were presented on touch-screen bays configured to mimic the control room layout in the newly renovated Human Systems Simulation Laboratory. The operators performed 15 scenarios covering normal evolutions to transition the plant from full turbine operation to joint turbine and thermal power dispatch operations in addition to transient response scenarios induced with simulated faults to evaluate the impact of thermal power dispatch system on operator and plant responses. A prototype human-system interface (HSI) was developed and displayed in tandem with the virtual analog panels to support the operators executing the procedurally drive evolutions and transient responses. An interdisciplinary team of operations experts, nuclear engineers, and human factors experts observed the operators performing the scenarios to evaluate the operations. Only a preliminary analysis of the results has been performed. The full analysis will be shared in a milestone report scheduled for release at the end of September; however, some high-level conclusions were readily available. Two high level findings for the system design were captured in the study. The manual control supported by the HSI to transition from standard operations to thermal power dispatch operation imposed a considerable amount of workload on the operators due to tedious manual valve manipulations and system monitoring required to verify their intended effect. An additional operator would be required in the control room to support the daily evolution. Automatic control for the transition was deemed a requirement for plant adoption without imposing additional staffing costs. The second finding was the necessity for an automatic thermal power dispatch system trip isolation function linked to a turbine and reactor trip signal. The operators completed scenarios with automatic isolation functionality and manually required actuation of the thermal power dispatch system. The operator response was sufficiently slower in the manual trip condition, such that operators were unable to manually actuate key post trip safety functions an indicate a degraded control capability that should be avoided. With an automatic trip signal little to no impact of the thermal power dispatch system was identified on the primary plant response and therefore the system could be readily and safely adopted. Together these two findings represent the need to support the adoption of thermal power dispatch capability into existing operations by leveraging automation to augment any additional operator tasking required to control and monitor an additional system beyond existing operations.

99 GENERAL AND MISCELLANEOUS↗

Disentangling natural and anthropogenic effects on benthic macroinvertebrate assemblages in western US streams

Abstract Stream macroinvertebrate assemblages are shaped by natural and human‐related factors that operate through complex hierarchical pathways. Quantifying these relationships can provide additional insights into stream ecological assessment. We applied a structural equation modeling framework to evaluate hypothesized pathways by which watershed, riparian, and in‐stream factors affect benthic macroinvertebrate condition in the Western Mountains (WMT) and Xeric (XER) ecoregions in the United States. We developed a conceptual model grounded in theory, empirical evidence, and expert opinion to evaluate the following hypotheses: (1) macroinvertebrate assemblages are primarily driven by proximal, in‐stream factors (e.g., water quality and physical habitat); (2) anthropogenic land uses affect macroinvertebrates indirectly by altering in‐stream characteristics; and (3) riparian vegetation cover attenuates land use effects. We tested our model separately on three measures of benthic macroinvertebrate assemblage condition: ratio of observed‐to‐expected taxonomic richness (O/E); a multimetric index (MMI); and richness of Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT). In the WMT, site‐level riparian cover, in‐stream physical habitat (relative bed stability), and water chemistry (total nitrogen) were the top three predictors of macroinvertebrate assemblages, each having over two times the magnitude of effect on macroinvertebrates compared with watershed‐level predictors. In the arid XER, annual precipitation and stream flow characteristics were top predictors of macroinvertebrate assemblages and had similar magnitudes of effect as in‐stream water chemistry. Path analyses revealed that land use activities in the watershed and at the stream site degraded macroinvertebrate assemblages indirectly by altering relative bed stability, water quality, and riparian cover/complexity. Increased riparian cover was associated with greater macroinvertebrate condition by reducing land use impacts on stream flow, streambed substrate, and water quality, but the pathways differed among ecoregions. In the WMT, site‐level riparian cover affected macroinvertebrate assemblages partly through indirect pathways associated with greater streambed stability and reduced total nitrogen concentrations. In contrast, in the XER, watershed‐level riparian cover affected macroinvertebrate assemblages through greater specific stream power. Identifying the relative effects of and pathways by which natural and anthropogenic factors affect macroinvertebrates can serve as a framework for prioritizing management and conservation efforts.

Environmental Sciences & Ecology↗

Enantioselective ecotoxicity of promethazine in two freshwater organisms: daphnia (Daphnia magna) and zebrafish ( Danio rerio )

Abstract Chiral pharmaceuticals, racemic or enantiomerically pure forms and their metabolites, can reach aquatic ecosystems via wastewater effluents (inefficient treatment operations) or by direct human disposal. They may negatively affect nontarget organisms even at low environmental concentrations. To make an accurate risk evaluation, the (eco)toxicity of both enantiomers needs to be assessed. Promethazine (PMZ) is a chiral antihistamine that has been detected in aquatic ecosystems owing to its high consumption. Promethazine undergoes metabolism in the liver, producing chiral metabolites such as promethazine sulfoxide (PMZSO) and N-desmethylpromethazine (DMPMZ) that reach water bodies. However, knowledge regarding the enantioselective toxicity of PMZ and its metabolites on aquatic organisms is missing. This study aimed to explore the potential enantioselective toxicity of PMZ and its metabolites on two relevant freshwater organisms, daphniid and fish, representing different trophic levels. The half maximal effect concentrations (EC50s) in Daphnia magna of PMZ, DMPMZ, and PMZSO were 2.33, 2.31, > 4 mg L−1, respectively, > 4 and 2.50 mg L−1 for (R) and (S)-PMZ, respectively, and > 4 mg L−1 for the enantiomers of DMPMZ and PMZSO. In studies involving zebrafish, Danio rerio, (R, S)-PMZ showed a median lethal concentration (LC50) of .72 mg L−1, and specific assays revealed that (R)-PMZ exhibited more pronounced adverse effects on larvae at the embryonic, morphological, and biochemical level than the racemate and (S)-PMZ. Toxicity and potential bioaccumulation of these compounds in daphniids and fish were also conducted using in silico tests through proprietary software. The results revealed a concordance between the experimental and predicted EC50 and LC50 values in both species.

Coelho, Maria Miguel↗

Sentinel Devices LLC (CRADA Final Report)

Industrial equipment is a critical component of virtually all at-scale manufacturing and infrastructure. Broadly speaking, modern equipment is predominantly controlled using hardened digital controllers – computers designed to be able to operate continuously for sometimes extremely long periods of time, with little to no maintenance. Due to the nature of how these digital controllers have evolved, they are solely optimized to execute a single task, and do not have the capabilities or resources to monitor or analyze their internal state beyond simple execution of their program. As a result, for many “common sense” situations where individual data points can be easily determined to be out-of-normal, the controllers are unable to identify these incorrect operational modes unless a human has explicitly programmed in detection of this degradation. This project seeks to develop an AI/ML system which can identify incorrect or anomalous trends in industrial data streams, of exactly the kind that would be produced and seen by these digital controllers, with a minimal amount of computing resources. The benefits produced by developing this system would ultimately be self-monitoring and self-reporting infrastructure, capable of identifying and alerting humans to issues as soon as they happen, potentially long before they have the chance to impact the industrial process.

42 ENGINEERING↗

MSD CoP Webinar: "Generative agents: A new frontier for representing human actors and their behavior in MSD models"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Talk #1: Behavioral Generative Agents for Energy Operations Presenter: Dr. Cong Chen (Thayer School of Engineering, Dartmouth College) Abstract: Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This talk introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. Talk #2: Simulating multiple human perspectives in socio-ecological systems using large language models Presenter: Dr. Yongchao Zeng (Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) of the Karlsruhe Institute of Technology in Germany) Abstract: Understanding socio-ecological systems requires insights from diverse stakeholder perspectives. This talk describes a novel simulation system called HoPeS (Human-oriented Perspective Shifting). HoPeS enables model users to not only explore simulated socio-ecological systems (SESs) from a third-person observer's perspective but also take any of the simulated stakeholder roles, like playing an RPG game. By shifting multiple perspectives, model users can reflect and integrate the situated knowledge learned through the participatory simulation, approximating a more holistic and less biased understanding of SESs. Moderators: Jim Yoon (MSD CoP Human Systems Modeling Working Group Co-Chair); Stefano Galelli (MSD CoP Using AI to Enhance MSD Research Working Group Co-Chair); Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: November 13th, 2025 from 12-1 PM EST.

Artificial Intelligence↗

Development and Demonstration of a Reduced-Order PWR Power Dispatch Simulator

This report describes the development, modeling, and results of a reduced-order thermal power dispatch pressurized water reactor (RO-TPD-PWR) power plant simulator that incorporates coupled electrical and thermal power dispatch to an industrial process. The simulator is built on models that have been developed in previous work, including a Rancor Microworld model previously developed by INL and the University of Idaho [Ref] and reactor core and secondary system models that have been published previously [Ref]. The simulator also includes a scalable model of a high temperature electrolysis (HTE) system that has been developed at INL. A particular benefit of the Rancor Microworld model is that it is compatible with a human-system interface (HMI) that has already been developed by the Human System Simulation Laboratory (HSSL) at INL that can emulate the control room of a PWR for realistic human-in-the-loop studies with mock nuclear power plant operating procedures. The results of these human-in-the-loop tests will be compared with similar tests using a commercial full-scope generic PWR (GPWR) simulator that has been modified to include thermal power dispatch, referred to as the TPD-GPWR [ , ]. The simplicity of the RO-TPD-PWR helps to better understand and verify the results of the TPD-GPWR, which employs >1,000 variables and has much greater complexity. A second key purpose of the RO-TPD-PWR simulator is that its relative simplicity allows it scale between lab-size equipment (~100 kW) and full-scale nuclear power plants (~1 GW) by adjusting only a few parameters in the models, such as fluid masses and heat exchanger areas. The RO-TPD-PWR simulator will be used to guide the controls of lab tests, so that equipment in those tests, such as the Thermal Energy Distribution System (TEDS) and pilot-scale high temperature electrolysis (HTE) units at scales of 50-250 kW, may operate in a manner that is relevant to full-scale PWR operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Applying Cyber-Informed Engineering to Power System Operations

This presentation covers the interaction of the discipline of system operations with the growing body of knowledge around Cyber-Informed Engineering (CIE). First is discussion of a number of fundamental concepts for system operators - organizational division of responsibilities, human and machine cooperation, goals, and priorities. The next section covers the reasons why CIE was developed, what it is, and the key design and operational, and organizational principles of CIE. Finally, some thoughts on how CIE can be applied to power system operations are offered, along with some examples of how an organization can approach applying CIE principles in their particular circumstances.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cognitive Aging as a Human Factor: Effects of Age on Human Performance

Nuclear power plant (NPP) control room operators must make ongoing computations and decisions that maximize production and ensure safety, which places a high cognitive burden on the operators. How cognitions such as attention, visuospatial ability, and working memory interact with socio-technical systems to achieve optimal operations is well studied. However, there is an absence of research that examines how cognitive functioning within the NPP control room environment is moderated by developmental aging processes. This is of critical importance because different types of cognitive actions are known to develop and peak at different times across the adult life span, and it is becoming increasingly clear that there is no age at which all cognitive faculties operate at maximum capacity. Thus, given that NPPs are experiencing an aging workforce, it is vital to identify how mission critical cognitions change with age. This paper reviews implications of aging on reactor operators in the current and new fleet. We highlight lessons that can be learned from state-of-the-art human factors research that considers aging, lessons from the large cognitive aging literature, and lessons from aging workers in other industries that use sophisticated socio-technical systems, such as aviation. We also consider the important subject of aging effects versus expertise and present preliminary data that support the premise that age of operator is linked to effective and efficient operations but that this relationship may be moderated by level of operations expertise. In conclusion, we apply these lessons to future considerations for aging research in current nuclear operations and with the advent of advanced modernized control rooms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

An Investigation of Time Distributions for Task Primitives to Support the HUNTER Dynamic Human Reliability Analysis

As an effort to support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method. GOMS-HRA was developed to provide cognition-based time and human error probability information for dynamic HRA calculation in the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework. HUNTER is a framework to support the dynamic modelling of human error in conjunction with other modelling tools. In this paper, we investigate time distributions using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which suggests an HRA data collection framework to complement full-scope simulator research as well as collect input data for dynamic HRA using simplified simulators such as the Rancor Microworld Simulator. In this study, time required for GOMS-HRA task primitives to satisfy thirteen statistical distributions is investigated. Then, the time distributions from student operators and professional operators are compared and discussed. As a result, this study identified several time distributions on five GOMS-HRA task primitives at a statistically significant level. According to analyses to date, a greater number of significant time distributions was found in abnormal or emergency operating procedures rather than standard operating procedures. In the future, it is expected that the result of this study can provide objective reference on elapsed time data for task primitives as well as help to realistically simulate scenarios within dynamic HRA.

99 GENERAL AND MISCELLANEOUS↗

Time Distribution Analysis for Task Primitives to Support Dynamic Human Reliability Analysis

To support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method and Human Reliability data EXtraction (HuREX). GOMS-HRA was developed to provide cognition-based time and human error probability (HEP) information for dynamic HRA calculations within the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework, while HuREX is a comprehensive HRA data collection method developed by the Korea Atomic Energy Research Institute (KAERI). In this paper, we examine time distributions by using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which proposes an HRA data collection framework to complement full-scope simulator research and gather input data for dynamic HRA by using simplified simulators such as the Rancor Microworld simulator. This paper investigates whether the time required for GOMS-HRA and HuREX task primitives fits 13 statistical distributions. Additionally, we compare and discuss the time distributions obtained from both student operators and professional operators. The result was that this study identified several time distributions for five GOMS-HRA and four HuREX task primitives. In the future, the results of this study are expected to provide objective reference data on the elapsed time for task primitives and aid in realistically simulating scenarios within dynamic HRA.

Dynamic Human Reliability Analysis↗

A Typology of Quantum-Classical Faults

This paper introduces an extended taxonomy of faults specific to hybrid quantum-classical systems, addressing the unique challenges that arise from integrating quantum accelerators into high-performance computing (HPC) infrastructures. Building on the foundational fault classification by Avizienis et al., we incorporate fault types unique to quantum computing-such as qubit decoherence, spontaneous gate errors, and photon loss-alongside traditional and human-induced faults including development errors, operational mistakes, and malicious attacks. Our taxonomy classifies faults by their origin (natural vs. human-made), intent (accidental, deliberate non-malicious, or malicious), system boundaries (internal vs. external), and persistence (transient to permanent). We also explore how different architectural integration patterns-ranging from tight coupling to loose on-premise and cloud-based configurations-shape the manifestation and propagation of faults. These scenarios are analyzed in terms of timing mismatches, interface inconsistencies, and security threats such as data tampering and denial-of-service attacks. Through this fault-centric lens, we aim to support the co-design of dependable quantum-classical systems and highlight the critical role that integration strategies play in ensuring reproducibility, resilience, and security across hybrid computing platforms.

Giusto, Edorado [University of Naples Federico II,↗