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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Expanding Our Understanding of What Constitutes a Safety-Relevant Occurrence

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. The study of human contributions to safety represents a vast and largely unexplored opportunity to learn. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform system design, safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors.

Jon B Holbrook↗
NASA NTRS1992

Telerobotic system performance measurement - Motivation and methods

A systems performance-based strategy for modeling and conducting experiments relevant to the design and performance characterization of telerobotic systems is described. A developmental testbed consisting of a distributed telerobotics network and initial efforts to implement the strategy described is presented. Consideration is given to the general systems performance theory (GSPT) to tackle human performance problems as a basis for: measurement of overall telerobotic system (TRS) performance; task decomposition; development of a generic TRS model; and the characterization of performance of subsystems comprising the generic model. GSPT employs a resource construct to model performance and resource economic principles to govern the interface of systems to tasks. It provides a comprehensive modeling/measurement strategy applicable to complex systems including both human and artificial components. Application is presented within the framework of a distributed telerobotics network as a testbed. Insight into the design of test protocols which elicit application-independent data is described.

Kondraske, George V.↗
DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 2: Advanced Power Flow Control and Transmission Topology Optimization

The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Human Systems Integration (HSI) Framework and Training - Shifting the View of HSI for Better Implementation

The Implementation of Human Systems Integration (HSI) presents challenges within the acquisition community for two reasons. The first is that misconceptions of HSI still exist, with many Program Managers (PMs) and leadership uncertain of the value or where to begin. The second is due to an unbalanced approach to HSI in its own framework. These implementation challenges lead to barriers in the early prevention of mishaps. Understanding HSI practices and how they should be implemented in the Acquisition Product Life Cycle (PLC) has been a challenge across the government, leaving the value of HSI unknown and misunderstood with Program Managers. In the case for many acquisition programs, HSI is not implemented in early design, losing the perspective on human capabilities and limitations, creating impacts on human-centered design. Expectations in human performance are not clearly set and operations are baselined with no margin for changes in technology and processes that will affect system performance. The HSI framework addresses total system performance holistically using collaboration as the primary tool. The goal is to create a system with efficiencies while minimizing risk to the operators, maintainers, and support personnel, as well as any collateral personnel and systems. To accomplish this, HSI should be implemented as part of preemptive measures to minimize potential human error and mishaps during the operation phase. Investigative and assessment tools exist that consider events, issues, and other outside influences of a system that may not fall under the current construct of the HSI domains, leaving gaps in early HSI implementation and affecting the prevention of human errors and mishaps. This presentation will outline what NASA HSI is doing to support Early HSI implementation and Operational Performance shifts that affect human performance.

Anthony T Thomas↗
DOE OSTI2017

Technical Requirements for Application of Operator Support Technology across Multiple Plant Systems

Argonne National Laboratory (ANL) and Idaho National Laboratory (INL) are jointly developing and evaluating operator aids to: (i) facilitate more timely response to plant faults and grid disturbances; (ii) achieve better management of plant upsets and improved operator performance; and (iii) ultimately improve nuclear plant safety, production, and cost management. The overall objective of the collaborative effort in this third and final year of the project was to conduct human performance tests using nuclear plant operators in the Human Systems Simulation Laboratory (HSSL) control room at INL to assess the value of a computerized operator support system (COSS) as an aid to the operator. This report describes the work performed by ANL. A companion report describes the work performed by INL and the results of the human performance tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Study on main control room and human-system interface of generation III in China - 313

This paper focuses on the study on Main Control Room (MCR) and Human-System Interface (HSI) of Generation III in China. An intelligent HSI has been developed to enhance the safety and availability of Nuclear Power Plant (NPP) by improving operational reliability. The key elements of the proposed HSI such as Advanced Alarm System, State Oriented Procedures (SOP), Large Display Panel (LDP), Computerized-base procedures..., that affects what information the operator sees or the system's response to a control input must be empirically evaluated to ensure that the new design does not compromise HSI effectiveness. Based on CPR1000's experience in China, this paper presents the approach used as well as the most relevant aspects of this kind of project for the next generation. This approach will be used in new Generation III nuclear power plant, and also used in modernizing I and C system in currently operative nuclear power plants, in addition to meeting safety requirements and the plant's operational requirements, to improve cost-effective plant and human performance and to reduce likelihood of human errors, to gain maximum benefit of the implemented technology and to increase the performance, resulting in improved plant safety, availability, reliability, and cost-effective operation. The proposed HSI has been validated and demonstrated using the CPR1000 simulator of National Key Laboratory. After sufficient validation, the characteristic design features of the proposed HSI will be reflected in the design of the main control room of the next generation reactors in China. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2017

Playbook Data Analysis Tool: Collecting Interaction Data from Extremely Remote Users

Typically, user tests for software tools are conducted in person. At NASA, the users may be located at the bottom of the ocean in a pressurized habitat, above the atmosphere in the International Space Station, or in an isolated capsule on a simulated asteroid mission. The Playbook Data Analysis Tool (P-DAT) is a human-computer interaction (HCI) evaluation tool that the NASA Ames HCI Group has developed to record user interactions with Playbook, the group's existing planning-and-execution software application. Once the remotely collected user interaction data makes its way back to Earth, researchers can use P-DAT for in-depth analysis. Since a critical component of the Playbook project is to understand how to develop more intuitive software tools for astronauts to plan in space, P-DAT helps guide us in the development of additional easy-to-use features for Playbook, informing the design of future crew autonomy tools.P-DAT has demonstrated the capability of discreetly capturing usability data in amanner that is transparent to Playbook’s end-users. In our experience, P-DAT data hasalready shown its utility, revealing potential usability patterns, helping diagnose softwarebugs, and identifying metrics and events that are pertinent to Playbook usage aswell as spaceflight operations. As we continue to develop this analysis tool, P-DATmay yet provide a method for long-duration, unobtrusive human performance collectionand evaluation for mission controllers back on Earth and researchers investigatingthe effects and mitigations related to future human spaceflight performance.

in-flight monitoring↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety-producing behaviors in addition to safety-reducing behaviors. Opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors are discussed.

Aviation Safety↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors. The panel will discuss opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors.

aviation safety↗
NASA NTRS1978

Human problem solving performance in a fault diagnosis task

It is proposed that humans in automated systems will be asked to assume the role of troubleshooter or problem solver and that the problems which they will be asked to solve in such systems will not be amenable to rote solution. The design of visual displays for problem solving in such situations is considered, and the results of two experimental investigations of human problem solving performance in the diagnosis of faults in graphically displayed network problems are discussed. The effects of problem size, forced-pacing, computer aiding, and training are considered. Results indicate that human performance deviates from optimality as problem size increases. Forced-pacing appears to cause the human to adopt fairly brute force strategies, as compared to those adopted in self-paced situations. Computer aiding substantially lessens the number of mistaken diagnoses by performing the bookkeeping portions of the task.

Rouse, W. B.↗
NASA NTRSDate not supplied

Predicting Cognitive States Using Machine Learning Fusion Paradigms to Reduce Model Uncertainty

The development of a synergetic system between humans and technology is a challenge that the scientific community has been facing for many years. Our aeronautic research aims to enhance this synergy between humans and machines through predictive human performance modeling for systems to mitigate high-risk situations. By being able to predict and anticipate human states, the crew monitoring system should be able to adjust and support the pilot for aviation safety. Our work focusing on attention-related human performance-limiting states (AHPLS) that impact a pilot’s performance and introduce high-risk catastrophic situations [1]. For example, AHPLS has been cited as a causal factor in more than 50% of all loss control in flight and thus contributes significantly toward commercial aviation fatalities [1, 2]. Cognitive state and its physiological fingerprint can be valuable information for this detecting AHPLS, but human cognitive state detection is still a major limitation for these crew monitoring systems.

machine learning↗