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Results for “Behavior Based Safety”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mechanical behavior of carbonated MgO-based Engineered Cementitious Composite (ECC) after high temperatures exposure

Carbonated MgO-based Engineered Cementitious Composite (ECC) has ultrahigh tensile ductility and tight crack width control behavior. However, the expectation of improved fire-resistant has not been confirmed. This study explored the alterations to mechanical and microstructure characteristics of this material after exposure to temperatures up to 500 °C. Material mass loss, compressive strength, tensile strength, strain capacity, and matrix fracture toughness were measured. Scanning electron microscopy, mercury intrusion porosimetry, thermogravimetric analysis, and X-ray diffraction were used to probe the degradation of the cement matrix and fibers. The effect of elevated temperature on carbonated MgO-based ECC was further assessed via examination of the micromechanical behavior of the fiber-matrix. The objective of this research was to assess the performance of carbonated MgO-based ECC exposed to fire hazards. The tensile ductility of carbonated MgO-based ECC was found to be enhanced when exposed to ~100 °C as compared with those at room temperature ~20 °C. Further increase in exposure temperature, however, posed a negative impact on the composite compressive strength, ultimate tensile strength, and ability to control crack width. Here the results provide a useful database for further investigations into carbonated MgO-based ECC for fire safety enhancement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanistic understanding of aging behaviors of critical-material-free Li 4 Ti 5 O 12 //LiNi 0.9 Mn 0.1 O 2 cells with fluorinated carbonate-based electrolytes for safe energy storage with ultra-long life span

Behind-the-meter storage (BTMS) systems - a viable method to minimize potential risk of blackout events and stabilize the grid - require a different type of cost-effective energy storage with excellent safety, ultra-long (>20 years) cycle life and reasonable energy density compared that of electric vehicles. To increase the energy density and reduce the cost of a long-term cyclable lithium-titanate-based cell, it is required to employ a critical-material-free high voltage cathode and an electrolyte with good electrochemical and transport properties. In this report the long-term electrochemical performance and behaviors of selected critical-material-free Li 4 Ti 5 O 12 (LTO)//LiNi 0.9 Mn 0.1 O 2 (LNMO) full cells for BTMS applications are evaluated and analyzed in the optimized voltage range of 1.4-2.7 V at 45 degrees C with different fluorinated carbonate-based electrolytes. The fluoroethylene carbonate (FEC)-based electrolyte cell shows the highest capacity retention of 57.9% and Coulombic efficiency (CE) of 99.96% after 1000 cycles, potentially attributed to a dense, homogenous and less resistive LiF-rich solid-electrolyte interphase (SEI) layer formed on the surface of LTO that may mitigate electrolyte decomposition and maintain relatively low cell impedance during cycling. The 3,3,3-fluoroethylmethyl carbonate (F-EMC)-based electrolyte cell, however, presents the worst performance with lower capacity and a sharp decrease of CE, due to unstable and non-uniform SEI formation and continuous oxidative electrolyte decomposition. This mechanistic understanding of cell aging behaviors and failure mechanisms with detailed analysis of surface chemistry and electrode morphology can guide design of new electrode chemistries and electrolyte formulations for the development of BTMS batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

Innovative mercury treatment technology options for the liquid waste system at the Savannah River Site: scoping studies

The Savannah River Site (SRS) Liquid Waste System (LWS) contains liquids, salts and sludges that are currently being processed into final wasteforms for disposition, specifically, waste glass for sludges, solids and liquids containing high levels of radioactivity, and saltstone grout for low activity decontaminated liquid solutions. The LWS also contains approximately 60,000 kg of mercury present in the following physical and chemical forms, specifically: a) ionic inorganic mercury, organomercury (e.g., methylmercury), and other minor components found in LWS fluids b) mercury solids such as oxides, hydroxides, amalgams sulfides and sorbed mercury, c) accumulations of dense liquid elemental mercury, and d) vapor phase elemental and organomercury mercury found in tank headspace gas and in evaporators. An effective and proactive management strategy of the mercury present in the LWS is needed to support processing of LWS wastes into glass and saltstone. Sustainable processing of the LAWS to completion requires mercury removal from the LWS at a rate of approximately 2,900 kg/yr. This removal can be accomplished through existing mercury treatment systems or newly implemented LWS “purge points”. The chemical speciation of mercury has emerged as the key factor that controls mercury behavior in the LWS. For example, past studies demonstrated that mercury speciation is adversely impacting the performance of existing removal systems so that significant levels of mercury are recycled from the Defense Waste Processing Facility (DWPF) back to the tank farm. Consequently, mercury concentrations have slowly increased in the LWS tank fluids over time. The presence of organo-mercury has also been identified as the cause of increased mercury leaching from saltstone. In response to these challenges, the Department of Energy (DOE) Office of Environmental Management (EM-TD) Technology Development Program has supported a series of scoping studies predicated on manipulating or controlling mercury speciation and mercury behaviors within the constraints of LWS waste chemistry and safety conditions. The intent of these studies was to rapidly triage potential technology options and develop a technically based go / no go recommendation for further work. This composite report presents the results of three scoping studies: 1) advanced photooxidation processes, 2) chemical reduction, and 3) mercury getters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2005

Concrete Model Checking with Abstract Matching and Refinement

We propose an abstraction-based model checking method which relies on refinement of an under-approximation of the feasible behaviors of the system under analysis. The method preserves errors to safety properties, since all analyzed behaviors are feasible by definition. The method does not require an abstract transition relation to he generated, but instead executes the concrete transitions while storing abstract versions of the concrete states, as specified by a set of abstraction predicates. For each explored transition. the method checks, with the help of a theorem prover, whether there is any loss of precision introduced by abstraction. The results of these checks are used to decide termination or to refine the abstraction, by generating new abstraction predicates. If the (possibly infinite) concrete system under analysis has a finite bisimulation quotient, then the method is guaranteed to eventually explore an equivalent finite bisimilar structure. We illustrate the application of the approach for checking concurrent programs. We also show how a lightweight variant can be used for efficient software testing.

Pasareanu Corina S.↗
NASA NTRS2006

Behavioral Health Program Element

The project goal is to develop behavioral health prevention and maintenance system for continued crew health, safety, and performance for exploration missions. The basic scope includes a) Operationally-relevant research related to clinical cognitive and behavioral health of crewmembers; b) Ground-based studies using analog environments (Antarctic, NEEMO, simulations, and other testbeds; c) ISS studies (ISSMP) focusing on operational issues related to behavioral health outcomes and standards; d) Technology development activities for monitoring and diagnostic tools; and e) Cross-disciplinary research (e.g., human factors and habitability research, skeletal muscle, radiation).

Leveton, Lauren B.↗
NASA NTRS2017

Physics-of-Failure Approach to Prognostics

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Kulkarni, Chetan S.↗
DOE OSTI2018

Can Relaxor Ferroelectric Behavior Be Realized for Poly(vinylidene fluoride- co -chlorotrifluoroethylene) [P(VDF–CTFE)] Random Copolymers by Inclusion of CTFE Units in PVDF Crystals?

Relaxor ferroelectric (RFE) polymers are attractive for various electrical applications such as electrostrictive actuation, electromechanical sensors, electric energy storage, and electrocaloric cooling because of their high dielectric constants and low hysteresis loss. Current state-of-the-art RFE polymers include poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF–TrFE)]-based random copolymers and terpolymers. However, the high costs due to a safety concern of TrFE make their near-term commercialization difficult. It is highly desirable to explore the opportunity of TrFE-free PVDF copolymers [e.g., P(VDF–CTFE); CTFE is chlorotrifluoroethylene] to achieve the RFE behavior by inclusion of CTFE in PVDF crystals (i.e., isomorphism). In this work, two strategies were employed to include CTFE in PVDF crystals. First, high-pressure crystallization was used to obtain extended-chain crystals via the pseudohexagonal paraelectric phase. Structural analyses indicated that CTFE units were largely excluded from the γ unit cells and ferroelectric domains of PVDF but located as kinks inside the extended-chain lamellae. As a result, no RFE behavior was observed because of large ferroelectric γ domains. The second strategy utilized mechanical stretching at low temperatures (–20 to 0 °C) to obtain oriented small β crystallites (ca. 5–7 nm). Structural analyses indicated that CTFE units were excluded from the β unit cells, locating at the crystal–amorphous interfaces. Although the hysteresis loops became somewhat slimmer as a result of small crystallite sizes, the RFE behavior with slim hysteresis loops was still not achieved. Furthermore, this study demonstrated that CTFE units were too large to be included in the tightly packed PVDF unit cells, whether the α, γ, or β phase. In the future, it is desirable to explore other PVDF copolymers with a smaller comonomer such as 1-chloro-1-fluoroethylene.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In-situ strain behavior and BISON simulations of Zircaloy cladding subjected to temperature cycling separate-effects tests in a steam environment

Understanding fuel system performance during anticipated transients without scram (ATWSs) in boiling water reactors (BWRs) is necessary for refining current and future safety limits. High-fidelity material models and simulations are fundamental to rigorous assessment of zirconium-based cladding performance. However, experimental thermomechanical data during simulated ATWSs to validate these modes are limited. To provide relevant in-situ data, Zircaloy-4 cladding was subjected to cyclic heating in a steam environment to simulate an out-of-pile BWR ATWS. Digital image correlation was used to capture the cladding strain behavior in-situ for comparison against simulations using the BISON finite element code. Conventional high-temperature models were compared using multiple schemes to gain a better understanding of the applicability of three BISON models to BWR ATWS: (1) the default combination of creep models in BISON, (2) the high-temperature Erbacher model alone, and (3) the low-temperature Limback-Andersson model alone. The cases run with the Limback-Andersson model alone produced the lowest root mean square error (RMSE). The lowest RMSE for the Limback-Andersson model alone was 0.659%, and the highest RMSE reported was 4.22%. A data gap within the model in the temperature regime of interest was also identified, and to account for this gap, the current model in BISON is linearly interpolated between two separate datasets. In conclusion, this evaluation highlights the need to either develop a new model or to improve the existing model to capture transient creep effects resulting from a cyclic temperature transient.

Anticipated transient without SCRAM↗
NASA NTRSDate not supplied

Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System

While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. These reports can be searched in a variety of ways. This paper describes methods for systematically examining ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), various approaches and tools for such inquiries are described. The approach, process, challenges, and suggestions to building an operator-based description of resilient performance are discussed, and tools to facilitate data analysis to maximize learning from these reports are described.

ASRS narratives↗
DOE OSTI2018

Next Generation Anodes for Lithium-ion Batteries: Thermodynamic Understanding and Abuse Performance

As we develop new materials to increase performance of lithium ion batteries for electric vehicles, the impact of potential safety and reliability issues become increasingly important. In addition to electrochemical performance increases (capacity, energy, cycle life, etc.), there are a variety of materials advancements that can be made to improve lithium-ion battery safety. Issues including energetic thermal runaway, electrolyte decomposition and flammability, anode SEI stability, and cell-level abuse tolerance behavior. Introduction of a next generation materials, such as silicon based anode, requires a full understanding of the abuse response and degradation mechanisms for these anodes. This work aims to understand the breakdown of these materials during abuse conditions in order to develop an inherently safe power source for our next generation electric vehicles.

25 ENERGY STORAGE↗
NASA NTRS2014

Large-Scale Spacecraft Fire Safety Tests

An international collaborative program is underway to address open issues in spacecraft fire safety. Because of limited access to long-term low-gravity conditions and the small volume generally allotted for these experiments, there have been relatively few experiments that directly study spacecraft fire safety under low-gravity conditions. Furthermore, none of these experiments have studied sample sizes and environment conditions typical of those expected in a spacecraft fire. The major constraint has been the size of the sample, with prior experiments limited to samples of the order of 10 cm in length and width or smaller. This lack of experimental data forces spacecraft designers to base their designs and safety precautions on 1-g understanding of flame spread, fire detection, and suppression. However, low-gravity combustion research has demonstrated substantial differences in flame behavior in low-gravity. This, combined with the differences caused by the confined spacecraft environment, necessitates practical scale spacecraft fire safety research to mitigate risks for future space missions. To address this issue, a large-scale spacecraft fire experiment is under development by NASA and an international team of investigators. This poster presents the objectives, status, and concept of this collaborative international project (Saffire). The project plan is to conduct fire safety experiments on three sequential flights of an unmanned ISS re-supply spacecraft (the Orbital Cygnus vehicle) after they have completed their delivery of cargo to the ISS and have begun their return journeys to earth. On two flights (Saffire-1 and Saffire-3), the experiment will consist of a flame spread test involving a meter-scale sample ignited in the pressurized volume of the spacecraft and allowed to burn to completion while measurements are made. On one of the flights (Saffire-2), 9 smaller (5 x 30 cm) samples will be tested to evaluate NASAs material flammability screening tests. The first flight (Saffire-1) is scheduled for July 2015 with the other two following at six-month intervals. A computer modeling effort will complement the experimental effort. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. This will facilitate the first examination of fire behavior on a scale that is relevant to spacecraft fire safety and will provide unique data for fire model validation.

Combustion↗
NASA NTRS2024

Aeroelastic Stability Assessment Methodology and Application to Slat Noise Treatments on the High-Lift Common Research Model

This work supports a larger effort at NASA to reduce airframe noise and, thus, environmental noise around airports. The leading-edge slat of conventional high-lift systems is a prominent source of airframe noise, and two technologies were previously identified as promising for noise reduction without significant aerodynamic or weight penalty: the slat cove filler (SCF) and the slat gap filler (SGF). NASA fabricated a 10%-scale semispan aircraft model for wind tunnel testing based upon the High-Lift Common Research Model (CRM-HL). NASA embarked on a test campaign with the 10% CRM-HL to determine the noise reduction effectiveness and other performance metrics of 3D SCF and SGF treatments in flow conditions representative of flight. The highly flexible SCF and SGF treatments posed a risk to the model and aeroelastic instability would compromise research integrity, so aeroelastic stability assessment was required. This work briefly describes the 10% CRM-HL, design of the SCF and SGF treatments to the model, and supporting fluid structure interaction (FSI) research. Relations for static and dynamic aeroelastic similitude are then developed for convenience of reference, and simplifications are introduced of particular utility to the cases considered in this study. The similitude relations are employed to assess the stability of the treatments under wind tunnel flow conditions based upon known behaviors from the supporting research. It is shown that the treatments are safe for test under the anticipated wind tunnel flow conditions with significant margins/factors of safety. It is furthermore demonstrated that the SCF is more susceptible to aeroelastic instability than the SGF, which has implications for flight feasibility.

Similitude↗
DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗
NASA NTRS2017

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Prognostics↗
NASA NTRSDate not supplied

Statistical learning framework for safety and failure analysis of a DNN-based autonomous aircraft system

Deep Neural Networks (DNNs) and Machine Learning technology is increasingly used for safety-critical applications in the Aerospace domain. To ensure safe operations, the DNN and the system must undergo rigorous verification and validation, including advanced statistical analyses. Performance and safety of the DNN and system behavior must not only be analyzed for the nominal case, but under numerous off-nominal and failure cases. In this paper we will describe how our statistical learning framework SYSAI can efficiently perform such analyses using the tool’s unique combination of advanced learning modeling and statistical analysis techniques. SYSAI can effectively explore the high-dimensional state and failure space of the system under test; geometrical shape detection of safety regions and boundaries support explainability of the results to the designer. In this paper, we report experiments and results obtained with a vision-based DNN control system (ACT) that is capable of autonomously steering an aircraft down a runway.

Yuning He↗
DOE OSTI2022

A Robust Numerical Treatment of Solid-Phase Diffusion in Pseudo Two-Dimensional Lithium-Ion Battery Models

Solid-phase diffusion in active materials of lithium-ion batteries significantly affects charging and safety-related behavior of lithium-ion batteries. Therefore, it is essential to develop an efficient and robust numerical algorithm for solving solid-phase diffusion equations in physics-based battery models. In this work, we discuss the origins of numerical instabilities that can occur when solving the solid-phase diffusion equations using iterative methods. Then, in order to resolve such issues, we propose a simple numerical treatment to the surface flux term of discretized solid-phase diffusion equations. To demonstrate its numerical robustness, the proposed method is implemented into a pseudo two-dimensional (P2D) physics-based battery model and simulations are conducted at wide ranges of operating conditions. Even with extremely poor initial guesses for the Li+ concentrations of the active materials, computations using the proposed method do not diverge and the their computational speeds are comparable to those with conventional initial guesses. Comprehensive tests of the proposed method are also performed with a dynamic current profile based on US06 driving profile and a multi-stage charging profile with very high initial C-rate (12C).

battery modeling↗
NASA NTRS1979

Performability evaluation of the SIFT computer

Performability modeling and evaluation techniques are applied to the SIFT computer as it might operate in the computational evironment of an air transport mission. User-visible performance of the total system (SIFT plus its environment) is modeled as a random variable taking values in a set of levels of accomplishment. These levels are defined in terms of four attributes of total system behavior: safety, no change in mission profile, no operational penalties, and no economic process whose states describe the internal structure of SIFT as well as relavant conditions of the environment. Base model state trajectories are related to accomplishment levels via a capability function which is formulated in terms of a 3-level model hierarchy. Performability evaluation algorithms are then applied to determine the performability of the total system for various choices of computer and environment parameter values. Numerical results of those evaluations are presented and, in conclusion, some implications of this effort are discussed.

Meyer, J. F.↗