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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

What Happened, and Why: Toward an Understanding of Human Error Based on Automated Analyses of Incident Reports

The objective of the Aviation System Monitoring and Modeling (ASMM) project of NASA s Aviation Safety and Security Program was to develop technologies that will enable proactive management of safety risk, which entails identifying the precursor events and conditions that foreshadow most accidents. This presents a particular challenge in the aviation system where people are key components and human error is frequently cited as a major contributing factor or cause of incidents and accidents. In the aviation "world", information about what happened can be extracted from quantitative data sources, but the experiential account of the incident reporter is the best available source of information about why an incident happened. This report describes a conceptual model and an approach to automated analyses of textual data sources for the subjective perspective of the reporter of the incident to aid in understanding why an incident occurred. It explores a first-generation process for routinely searching large databases of textual reports of aviation incident or accidents, and reliably analyzing them for causal factors of human behavior (the why of an incident). We have defined a generic structure of information that is postulated to be a sound basis for defining similarities between aviation incidents. Based on this structure, we have introduced the simplifying structure, which we call the Scenario as a pragmatic guide for identifying similarities of what happened based on the objective parameters that define the Context and the Outcome of a Scenario. We believe that it will be possible to design an automated analysis process guided by the structure of the Scenario that will aid aviation-safety experts to understand the systemic issues that are conducive to human error.

Maille, Nicolas P.↗
NASA NTRS1994

Safety aspects of spacecraft commanding

The commanding of spacecraft is a potentially hazardous activity for the safety of the spacecraft. Present day control systems contain safety features in their commanding subsystem and in addition, strict procedures are also followed by operations staff. However, problems have occurred on a number of missions as a result of erroneous commanding leading in some cases to spacecraft contingencies and even to near loss of the spacecraft. The problems of checking commands in advance are increased by the tendency in modern spacecraft to use blocked/time-tagged commands and the increased usage of on-board computers, for which commands changing on-board software tables can radically change spacecraft or subsystem behavior. This paper reports on an on-going study. The study aims to improve the approach to safety of spacecraft commanding. It will show how ensuring 'safe' commanding can be carried out more efficiently, and with greater reliability, with the help of knowledge based systems and/or fast simulators. The whole concept will be developed based on the Object-Oriented approach.

Peccia, N.↗
NASA NTRS2017

Automated Analysis of Stateflow Models

Stateflow is a widely used modeling framework for embedded and cyber physical systems where control software interacts with physical processes. In this work, we present a framework a fully automated safety verification technique for Stateflow models. Our approach is two-folded: (i) we faithfully compile Stateflow models into hierarchical state machines, and (ii) we use automated logic-based verification engine to decide the validity of safety properties. The starting point of our approach is a denotational semantics of State flow. We propose a compilation process using continuation-passing style (CPS) denotational semantics. Our compilation technique preserves the structural and modal behavior of the system. The overall approach is implemented as an open source toolbox that can be integrated into the existing Mathworks Simulink Stateflow modeling framework. We present preliminary experimental evaluations that illustrate the effectiveness of our approach in code generation and safety verification of industrial scale Stateflow models.

Stateflow↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
DOE OSTI2022

Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation

Geologic Disposal Safety Assessment Framework is a state-of-the-art simulation software toolkit for probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste developed by the United States Department of Energy. This paper presents a generic reference case and shows how it is being used to develop and demonstrate performance assessment methods within the Geologic Disposal Safety Assessment Framework that mitigate some of the challenges posed by high uncertainty and limited computational resources. Variance-based global sensitivity analysis is applied to assess the effects of spatial heterogeneity using graph-based summary measures for scalar and time-varying quantities of interest. Behavior of the system with respect to spatial heterogeneity is further investigated using ratios of water fluxes. This analysis shows that spatial heterogeneity is a dominant uncertainty in predictions of repository performance which can be identified in global sensitivity analysis using proxy variables derived from graph descriptions of discrete fracture networks. New quantities of interest defined using water fluxes proved useful for better understanding overall system behavior.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Complete Initial Studies in Microstructural Studies of Nuclear Graphite

Nuclear graphite is a critical material for the performance and safety of high-temperature reactors, making its study essential for the successful deployment of next-generation reactors in the United States. During the past year, Oak Ridge National Laboratory (ORNL) has contributed extensively to advancing the understanding of nuclear graphite’s properties, behavior, and applications; particular focus has been given to irradiation effects, oxidation mechanisms, thermal and mechanical performance, and long-term stability under reactor conditions. This report provides an update on the ongoing publications being prepared at ORNL. These studies are collectively intended to build a comprehensive knowledge base on nuclear graphite. The studies focus on key aspects such as microstructural characterization, oxidation behavior, advanced analytical techniques, molten salt infiltration, technique development, and billet-to-billet variability. By summarizing the progress of these publications, this report outlines ORNL’s contributions to the field, identifies knowledge gaps, and proposes future research directions. The findings presented in these studies will help enhance the understanding of nuclear graphite’s behavior, support the qualification of nuclear graphite materials, further the development of new techniques and guidelines for using microscopy techniques, and improve reactor safety and performance.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2024

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Conservative buffering of approximate nonlinear constraints

In engineering design practice behavior is usually predicted based on some known nominal design. However, when the design is fabricated it will differ from the nominal design because of manufacturing tolerances. In order to generate nominal designs that will still satisfy behavior constraints in the presence of manufacturing tolerances, engineers resort to the use of safety factors, over and above those introduced to account for other uncertainties (e.g., in load conditions, material properties, analysis modeling). The accurate selection of the values of these manufacturing tolerances safety factors is dependent on the capability of the engineer to determine the sensitivity of the critical constraints to changes in the design variables. This process usually leads to overly conservative designs. The task of choosing safety factors is much more difficult in structural synthesis because: (1) it is not known which constraints will be active at the final design, (2) as the design changes during the synthesis process the sensitivities of the constraints with respect to the design variables also change, and (3) the imposition of the safety factors themselves may change the set of critical constraints. These difficulties can be overcome with the approximation concepts approach to structural synthesis by buffering the approximate constraints with quantities that are related to the design variable tolerances and the accurate sensitivities of the constraints with respect to the design variable. Designs generated by this approach tend to be feasible but not overly conservative.

Thomas, H. L.↗
NASA NTRS2019

Structural Analysis and Performance-Based Validation of a Composite Wing Spar

Electric-motor powered aircraft possess the ability to operate with efficient energy delivery, but lack the operational range of internal combustion engine powered aircraft. This range limitation requires the use of high aspect ratio, thin-chord wings to minimize aerodynamic drag losses, which results in highly loaded composite spar structures. High aspect ratio wings are required to increase mission durations for a NASA-developed experimental multi-rotor electric powered aircraft denoted as the Scalable Convergent Electric Propulsion Technology and Operations Research (SCEPTOR) or “X-57”. This paper examines the structural performance of the composite main wing spars to validate spar strength using ply-based laminate finite element methods. Geometric scaling of a main spar test-section was initially proposed for proof-testing but sacrificed stability. Ply-based structures modeling with local structural features was implemented as a risk-reduction methodology. Ply-based modeling was selected to augment the conventional “building block” approach to reduce risk, and leverage a “performance-based” approval processes encouraged in Federal Aviation Administration (FAA) design guidance. Therefore, ply-based laminate modeling of the full-scale main spar and forward spar shear-web attachments were subsequently undertaken to determine load path complexity with predicted flight loads. Ply-based modeling included stress concentrations and interlaminate behavior at interface locations that can be obscured in traditional finite element sizing models. Analysis of the wing spar laminate ply-based models compared with bearing test coupon performance was used to reduce future wing assembly proof-testing burden and facilitate performance-based flight hardware safety for the X-57 experimental aircraft.

Moore, James B.↗
NASA NTRSDate not supplied

A Safety-Driven Approach to Exploring and Comparing Air Traffic Management Concepts for Enabling Urban Air Mobility

There is broad recognition that the high tempo and density of Urban Air Mobility (UAM) operations will require identifying new Air Traffic Management (ATM) concepts to safely integrate UAM air traffic into the airspace alongside existing air traffic. However, the simulation models used to compare ATM concepts today are difficult to apply during the early stages of concept development and do not offer enough support in identifying potential new concepts. In addition, they have limited ability to evaluate ATM concepts in terms of safety, security, and other key emergent properties. Instead of using simulation to evaluate ATM concepts, this paper demonstrates how a safety-driven systems engineering approach based on Systems-Theoretic Accident Model and Processes (STAMP) can be used to design properties such as safety into an ATM system from the earliest stages of development. Using a hazard analysis technique called Systems-Theoretic Process Analysis (STPA), system requirements and the desired ATM behavior are derived. As an example, two possible ATM concepts to implement that behavior are compared to identify their safety-related benefits and tradeoffs. This new approach enables (1) systematic exploration of alternative ATM concepts and (2) identification of the safety-related tradeoffs between concepts as early as possible in the development process.

system safety↗
DOE OSTI2023

Relationship of the Molecular Structure and Transport Properties of Imide-Based Lithium Salts of “Acetonitrile/Water-in-Salt” Electrolytes

“Water-in-salt” (WIS) electrolytes exhibit excellent safety and electrochemical performance. However, they possess high concentrations, relatively low diffusion coefficient, and high viscosity. “Acetonitrile/water in salt” (AWIS) electrolytes can overcome the disadvantages of WIS electrolytes. Under relatively low concentrations, AWIS electrolytes show good electrochemical performance comparable to WIS electrolytes and low conductivity. Herein, we investigate the relationship between the solvation structures and the transport properties using small-angle X-ray scattering and molecular dynamics simulation. We observed two solvation behaviors of AWIS: anions dissolved in acetonitrile forming small acetonitrile/anion clusters and additional water further dissolving the small acetonitrile/anion clusters. The introduction of acetonitrile weakens the water–solute interaction and enhances the cation–anion interaction, which results in an enhanced dynamical slowdown as the concentration increases. In conclusion, this work provides molecular-level understanding of the connection between two-stage solvation structures and transport properties for imide-based lithium salt solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Mechanistic Analysis of Microstructural Attributes to Lithium Plating in Fast Charging

Metallic lithium deposition on graphite anodes is a critical degradation mode in lithium-ion batteries, which limits safety and fast charge capability. A conclusive strategy to mitigate lithium deposition under fast charging yet remains elusive. Herein, we examine the role of electrode microstructure in mitigating lithium plating behavior under various operating conditions, including fast charging. The multilength scale characteristics of the electrode microstructure lead to a complex interaction of transport and kinetic limitations that significantly governs the cell performance and the occurrence of Li plating. We demonstrate, based on a comprehensive mesoscale analysis, that the performance and degradation can be significantly modulated via systematic design improvements at the hierarchy of length scales. It is found that the improvement in kinetic and transport characteristics achievable at disparate scales can dramatically affect Li plating propensity.

25 ENERGY STORAGE↗
DOE OSTI2020

Hydrogen wide area monitoring of LH2 releases

The characterization of liquid hydrogen (LH2) releases has been identified as an international research priority to expand the safe use of hydrogen as an energy carrier. The elucidation of LH2 release behavior will require the development of dispersion and other models, guided and validated by empirical field measurements such as those afforded by Hydrogen Wide Area Monitoring (HyWAM). HyWAM can be defined as the quantitative spatial and temporal three-dimensional monitoring of planned or unintentional hydrogen releases. With support provided through the FCH JU Prenormative Research for the Safe Use of Liquid Hydrogen (PRESLHY) program, HSE performed a series of LH2 releases to characterize the dispersion and pooling behavior of cold hydrogen releases. The NREL Sensor Laboratory developed a HyWAM system based upon a distributed array of point sensors that is amenable for profiling cold hydrogen plumes. The NREL Sensor Laboratory and HSE formally committed to collaborate on profiling the LH2 releases. This collaboration included the integration of the NREL HyWAM into the HSE LH2 release hardware. This was achieved through a deployment plan jointly developed by the NREL and HSE personnel. Under this plan, the NREL Sensor Laboratory provided multiple HyWAM modules that accommodated 32 sampling points for near-field hydrogen profiling during the HSE PRESLHY LH2 releases. The NREL HyWAM would be utilized throughout the LH2 release study performed under PRESLHY by HSE, including Work Package 3 (WP3—Release and Mixing--Rainout) and subsequent work packages (WP4—Ignition and WP5—Combustion). Under the auspices of the PRESLHY WP6 (Implementation), data and findings from the HSE LH2 Releases are to be made available to stakeholders in the hydrogen community. Comprehensive data analysis and dissemination is ongoing, but the integration of the NREL HyWAM into the HSE LH2 Release Apparatus and its performance as well as some key outcomes of the LH2 releases in WP3 are presented.

08 HYDROGEN↗
DOE OSTI2021

Nanostructured Block Polymer Electrolytes: Tailoring Self-Assembly to Unlock the Potential in Lithium-Ion Batteries

Ion-containing solid block polymer (BP) electrolytes can self-assemble into microphase-separated domains to facilitate the independent optimization of ion conduction and mechanical stability; this assembly behavior has the potential to improve the functionality and safety of lithium-ion batteries over liquid electrolytes to meet future demands (e.g., large capacities and long lifetimes) in various applications. However, significant enhancements in the ionic conductivity and processability of BPs must be realized for BP-based electrolytes to become robust alternatives in commercial devices. Toward this end, the controlled modification of BP electrolytes’ intra-domain (nanometer-scale) and multi-grain (micrometer-scale) structure is one viable approach; intra-domain ion transport and segmental compatibility (related to the effective Flory–Huggins parameter, χ eff ) can be increased by tuning the ion and monomer-segment distributions, and the morphology can be selected such that the multi-grain transport is less sensitive to grain size and orientation.

25 ENERGY STORAGE↗
DOE OSTI2026

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↗
NASA NTRS2023

Compliant Behaviors for Supervised and Autonomous Robotic Operations

The demands of traditional industrial robotics differ significantly from those of remote, supervised operations in hostile environments, such as operations in space. While industry requires robots that can perform repetitive tasks with precision and speed, the space environment needs robots to cope with uncertainties, dynamics, and communication delays or interruptions, similar to human astronauts. These demands make a well-suited application for compliant robotics and behavior-based programming. Pose Target Wrench Limiting (PTWL) is a compliant behavior paradigm developed specifically to meet these demands. PTWL controls a robot by moving a virtual attractor to a target pose. The attractor applies virtual forces, based on stiffness and damping presets, to an underlying admittance controller. Guided by virtual forces, the robot will follow the attractor until safety conditions are violated or success criteria are met. PTWL was tested on a variety of quasi-static and dynamic tasks that may be useful for future space operations. Results demonstrate that PTWL is a powerful tool. It makes teleoperation easy and safe for a wide range of quasi-static tasks. It also facilitates the creation of semi-autonomous state machines that can reliably complete complex tasks with minimal human intervention.

Joseph D. Cressman↗
DOE OSTI2025

Hungary 908 Event - Risk Based Graded Approach to ITM

This presentation, Risk-Based, Graded Approach to Insider Threat Mitigation: Human Measures, introduces a structured framework for managing insider threat risk using internationally recognized guidance from the International Atomic Energy Agency (IAEA) Nuclear Security Series No. 8-G (Rev. 1) and the Joint Statement on Mitigating Insider Threats (INFCIRC/908). The presentation emphasizes that effective insider threat mitigation (ITM) depends on both positional controls, which manage inherent risk based on access, authority, and knowledge, and human measures, which address residual risk reflected in behavior, motivation, and reliability. Using a risk-informed and graded approach, the presentation outlines methods for identifying and prioritizing high-risk positions, applying layered organizational controls, and integrating human reliability mechanisms such as the Behavior Observation Program (BOP), Fitness-for-Duty (FFD) evaluations, Employee Assistance Programs (EAP), and Nuclear Security Culture (NSC). The human-focused portion examines behavioral and organizational indicators of opportunity, vulnerability, motivation, and crisis, demonstrating how early detection, deterrence, and response can prevent insider events. The session concludes with a case review of the Millstone Nuclear Power Station incident involving engineer George Galatis. The case illustrates how weak leadership and a poor safety culture can create conditions for failure and how a comprehensive ITM framework could have altered the outcome. The objective of this presentation is to help practitioners apply a risk-based, graded philosophy to human factors and promote a culture of accountability, communication, and resilience within nuclear organizations.

99 - GENERAL AND MISCELLANEOUS↗