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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Damage Tolerance Behavior of Friction Stir Welds in Aluminum Alloys

Friction stir welding is a solid state welding process used in the fabrication of various aerospace structures. Self-reacting and conventional friction stir welding are variations of the friction stir weld process employed in the fabrication of cryogenic propellant tanks which are classified as pressurized structure in many spaceflight vehicle architectures. In order to address damage tolerance behavior associated with friction stir welds in these safety critical structures, nondestructive inspection and proof testing may be required to screen hardware for mission critical defects. The efficacy of the nondestructive evaluation or the proof test is based on an assessment of the critical flaw size. Test data describing fracture behavior, residual strength capability, and cyclic mission life capability of friction stir welds at ambient and cryogenic temperatures have been generated and will be presented in this paper. Fracture behavior will include fracture toughness and tearing (R-curve) response of the friction stir welds. Residual strength behavior will include an evaluation of the effects of lack of penetration on conventional friction stir welds, the effects of internal defects (wormholes) on self-reacting friction stir welds, and an evaluation of the effects of fatigue cycled surface cracks on both conventional and selfreacting welds. Cyclic mission life capability will demonstrate the effects of surface crack defects on service load cycle capability. The fracture data will be used to evaluate nondestructive inspection and proof test requirements for the welds.

McGill, Preston↗
NASA NTRS2014

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗
NASA NTRS1997

Control Synthesis for a Class of Hybrid Systems Subject to Configuration-Based Safety Constraints

We examine a class of hybrid systems which we call Composite Hybrid Machines (CHM's) that consists of the concurrent (and partially synchronized) operation of Elementary Hybrid Machines (EHM's). Legal behavior, specified by a set of illegal configurations that the CHM may not enter, is to be achieved by the concurrent operation of the CHM with a suitably designed legal controller. In the present paper we focus on the problem of synthesizing a legal controller, whenever such a controller exists. More specifically, we address the problem of synthesizing the minimally restrictive legal controller. A controller is minimally restrictive if, when composed to operate concurrently with another legal controller, it will never interfere with the operation of the other controller and, therefore, can be composed to operate concurrently with any other controller that may be designed to achieve liveness specifications or optimality requirements without the need to reinvestigate or reverify legality of the composite controller. We confine our attention to a special class of CHM's where system dynamics is rate-limited and legal guards are conjunctions or disjunctions of atomic formulas in the dynamic variables (of the type x less than or equal to x(sub 0), or x greater than or equal to x(sub 0)). We present an algorithm for synthesis of the minimally restrictive legal controller. We demonstrate our approach by synthesizing a minimally restrictive controller for a steam boiler (the verification of which recently received a great deal of attention).

Heymann, Michael↗
DOE OSTI2023

Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis: Tool Development and Method Evaluation

This report is an update to a prior report that describes progress and findings for a program of research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and throughout the plant, along with a greater interest in the use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human performance-related organizational and technical design issues are identified and addressed early in the design process. This report describes modeling tools and techniques, based on sociotechnical systems theory, to support these design goals and their application in the current research effort. The report is primarily intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control, feedback, and communication relationships amongst the system’s technical and organizational components. We have employed two STAMP-based tools in this effort. The first is Causal Analysis based on STAMP (CAST), an accident and incident analysis technique that was used to examine a performance- and safety-related incident at an industry partner’s plant involving the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. The second tool is Systems Theoretic Process Analysis (STPA) which is a proactive risk analysis tool used to examine existing and potential, planned sociotechnical systems. STPA was used to identify risk factors in the current design of a generic nuclear power plant (NPP) preventive maintenance system. Our analyses focused on identifying near-term system improvements and longer-term design requirements for an optimized IAE system. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived time and schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the eventual event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. STPA findings exposed several areas of concern in the design of current preventive maintenance systems. We also present two preliminary information automation models. The proactive issue resolution (PIR) model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system and represents an end-state vision for our work. From our results, we have generated an initial set of preliminary system-level requirements and safety constraints for these models. We have also focused on early development of easy to learn, easy to use “transportable” tools for sociotechnical systems analysis. We intend these to be used by NPP personnel as a means of gaining reliable and relatively quick insight into (1) sociotechnical systems factors impacting incidents and accidents, (2) potential sociotechnical risk factors in existing or planned system designs, and (3) potential weaknesses in a system’s safety and/or information control structure. We conclude the report with a set of summary recommendations, a discussion of planned and potential follow-on research and development, and a draft list of system-level requirements and safety constraints for optimized information automation systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗
NASA NTRS2022

Adaptive IV&V for Increasingly Complex Software Systems

As NASA software systems continue to innovate, becoming more complex and nondeterministic, the need for the NASA IV&V Program to become systemically adaptive to ensure mission success is paramount. To ensure adaptability within resource constraints, IV&V has developed an agile, risk-based approach to identify, characterize, scope, focus, and prioritize mission assurance activities. This risk based adaptive framework has been applied to trends such as increased reliance on data driven algorithms for safety and mission critical software behavior, use of MBSE in system design, and application of agile principles to embedded software development. The framework is enabled by continuous innovation of new approaches such as software only test beds, assurance design tools, and initiatives that augment IV&V assurance methods with artificial intelligence and machine learning techniques. This presentation will highlight the trends the NASA IV&V Program is seeing, the innovative steps it is taking to address those challenges, and how it is postured to address evolving risk and constantly changing and new technologies.

Wesley W Deadrick↗
NASA NTRSDate not supplied

Preliminary Results from the Saffire VI Experiment

Preliminary results are presented for one test of the last flight of the Spacecraft Fire Experiment (Saffire VI) which was conducted on an orbiting Cygnus spacecraft. These experiments directly address the risks associated with our understanding of spacecraft fire behavior at practical length scales and geometries. The lack of this experimental data has forced spacecraft designers to base their designs and safety precautions on 1-g understanding of flame spread, flame self-extinguishment, fire detection, and suppression. The Saffire experiment was developed by an international team of investigators with the goal of addressing open issues in spacecraft fire safety. NASA’s Spacecraft Fire Safety Demonstration Project was formulated with the goal of conducting a series of large-scale experiments in spacecraft environments that represent practical spacecraft fires. These tests spanned 1.5 to 3.2 kW with free air volumes of 17 to 19 cubic meters. The final flight in the series of six experiments examined concurrent spread over large samples (all 41 cm wide) including a thin sheet of flammable fuel (cotton/fiberglass 50 cm long); 2-sided spread over 1 cm thick polymethyl methacrylate (PMMA) (18 cm long); 1-sided spread over 0.5 cm thick (18 cm long); and Nomex fabric (7 cm long). Results are presented for the PMMA samples, the SIBAL sample, and the thin cotton samples from Saffire IV and V. The flame heat release is determined and compared to the overall temperature rise in the spacecraft and the change in the concentration of carbon dioxide and carbon monoxide in the spacecraft. Overall, the temperature and pressure rise in the spacecraft were found to be less significant than the increase in carbon dioxide and carbon monoxide.

fire↗
NASA NTRSDate not supplied

Preliminary Results from the Saffire VI Experiment

The preliminary results are presented for one test of the last flight of the Spacecraft Fire Experiment (Saffire VI) which was conducted on an orbiting Cygnus spacecraft. These experiments directly address the risks associated with our understanding of spacecraft fire behavior at practical length scales and geometries. The lack of this experimental data has forced spacecraft designers to base their designs and safety precautions on 1-g understanding of flame spread, flame self-extinguishment, fire detection, and suppression. The Saffire experiment was developed by an international team of investigators with the goal of addressing open issues in spacecraft fire safety. NASA’s Spacecraft Fire Safety Demonstration Project was designed with the goal of conducting a series of large-scale experiments in spacecraft environments that represent practical spacecraft fires. The final flight examined concurrent spread over large samples (all 41 cm wide) including a thin sheet of flammable fuel (cotton/fiberglass 50 cm long); 2-sided spread over 1 cm thick polymethyl methacrylate (PMMA) (18 cm long) ; 1-sided spread over 0.5 cm thick (18 cm long); and Nomex fabric (7 cm long). This paper focusses on the two-sided PMMA sample. The flame heat release is determined and is compared to the overall temperature rise in the spacecraft and the change in the concentration of carbon dioxide and carbon monoxide in the spacecraft.

fire↗
NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation 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 health state 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 operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An 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, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗
NASA NTRS2013

Unmanned Vehicle Material Flammability Test

Microgravity combustion phenomena have been an active area of research for the past 3 decades however, there have been very few experiments directly studying spacecraft fire safety under low-gravity conditions. Furthermore, none of these experiments have studied sample and environment sizes typical of those expected in a spacecraft fire. All previous experiments have been limited to samples of the order of 10 cm in length and width or smaller. Terrestrial fire safety standards for all other habitable volumes on earth, e.g. mines, buildings, airplanes, ships, etc., are based upon testing conducted with full-scale fires. Given the large differences between fire behavior in normal and reduced gravity, this lack of an experimental data base at relevant length scales forces spacecraft designers to base their designs using 1-g understanding. To address this question a large scale spacecraft fire experiment has been proposed by an international team of investigators. This poster presents the objectives, status and concept of this collaborative international project to examine spacecraft material flammability at realistic scales. The concept behind this project is to utilize an unmanned spacecraft such as Orbital Cygnus vehicle after it has completed its delivery of cargo to the ISS and it has begun its return journey to earth. This 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. 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 examination of fire behavior on a scale that is relevant to spacecraft fire safety and will provide unique data for fire model validation. This will be the first opportunity to examine microgravity flame behavior at scales approximating a spacecraft fire.

Combustion↗
DOE OSTI2021

Strength and rupture geometry of un-irradiated C26M FeCrAl under LOCA burst testing conditions

Ferritic iron-chromium-aluminum (FeCrAl) alloys are an accident tolerant fuel candidate to replace the incumbent Zr-based claddings. Nuclear grade FeCrAl alloys are marked by superior high temperature mechanical behavior and exceptional steam oxidation resistance, both of which increase safety margins during accident scenarios. In the present study, the loss of coolant accident (LOCA) burst behaviors of three un-irradiated commercially fabricated cladding materials in a simulated LOCA environment were compared: (1) T35Y2, a 1 st generation nuclear grade FeCrAl, (2) C26M, a 2 nd generation nuclear grade FeCrAl, and (3) Zircaloy-2. Both FeCrAl alloys showed improved mechanical strength and steam oxidation resistance compared to Zircaloy-2. C26M claddings burst at significantly higher temperatures for all tested engineering hoop stresses, had limited ballooning, and demonstrated preferential fuel retention behavior in terms of burst opening area and length. High temperature tensile data for C26M is also presented. For both un-irradiated FeCrAl alloys, it was found that a distinct “threshold” burst stress signified the transition between small and large openings. Finally, higher threshold hoop stresses were associated with higher uniaxial strength for the FeCrAl alloys, indicating that tensile data, rather than creep data, could be useful for predicting rupture size and assessing fuel dispersal concerns.

36 MATERIALS SCIENCE↗
DOE OSTI2024

International fuel performance study of fresh fuel experiments for PCMI effects during RIA experiments

This paper presents the results of High-burnup Experiments for Reactivity-initiated Accident (HERA) Modeling & Simulation (M&S) exercise. The HERA project under the Nuclear Energy Agency (NEA) Second Framework for Irradiation Experiments (FIDES-II) program is focused on studying Light Water Reactor (LWR) fuel behavior during Reactivity-Initiated Accident (RIA) conditions. The Part I M&S cases are based on a series of tests in the Transient Reactor Test (TREAT) facility in the United States and the Nuclear Safety Research Reactor (NSRR) in Japan. The purpose of this work is to evaluate the test design to accomplish its goals in establishing clearer understanding of the effects of power pulse width during RIA conditions. Further, the blind predictions using various computational tools have been performed and compared amongst to interpret the behaviors of high burnup fuels during RIA. While many international participants evaluate the thermal–mechanical behavior of fuel rod under different conditions, a considerable scatter of outputs comes out for the cases due to the disparity between codes in predicting mechanical behaviors. In general, however, the results of thermal–mechanical analysis elaborate that nominal design conditions the shorter pulse width tests in NSRR should cause cladding failures while the TREAT tests appear to have more split prediction of failure or not. Furthermore, the sensitivity analysis varying key testing parameters reveals the considerable effect of power pulse width and total energy deposition on prediction of fuel rod failure.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1986

Engineering evaluation of SSME dynamic data from engine tests and SSV flights

An engineering evaluation of dynamic data from SSME hot firing tests and SSV flights is summarized. The basic objective of the study is to provide analyses of vibration, strain and dynamic pressure measurements in support of MSFC performance and reliability improvement programs. A brief description of the SSME test program is given and a typical test evaluation cycle reviewed. Data banks generated to characterize SSME component dynamic characteristics are described and statistical analyses performed on these data base measurements are discussed. Analytical models applied to define the dynamic behavior of SSME components (such as turbopump bearing elements and the flight accelerometer safety cut-off system) are also summarized. Appendices are included to illustrate some typical tasks performed under this study.

Source record↗
NASA NTRS2018

Buoyancy Effects on Concurrent Flame Spread Over Thick PMMA

The flammability of combustible materials in a spacecraft is important for fire safety applications because the conditions in spacecraft environments differ from those on earth. Experimental testing in space is difficult and expensive. However, reducing buoyancy by decreasing ambient pressure is a possible approach to simulate on-earth the burning behavior inside spacecraft environments. The objective of this work is to determine that possibility by studying the effect of pressure on concurrent flame spread, and by comparison with microgravity data, observe up to what point low-pressure can be used to replicate flame spread characteristics observed in microgravity. Specifically, this work studies the effect of pressure and microgravity on upward/concurrent flame spread over 10 mm thick polymethyl methacrylate (PMMA) slabs. Experiments in normal gravity were conducted over pressures ranging between 100 and 40 kPa and a forced flow velocity of 200 mm/s. Microgravity experiments were conducted during NASA’s Spacecraft Fire Experiment (Saffire II), on board the Cygnus spacecraft at 100 kPa with an air flow velocity of 200 mm/s. Results show that reductions of pressure slow down the flame spread over the PMMA surface approaching that in microgravity. The data is correlated in terms of a non-dimensional mixed convection analysis that describes the convective heat transferred from the flame to the solid, and the primary mechanism controlling the spread of the flame. The extrapolation of the correlation to low pressures predicts well the flame spread rate obtained in microgravity in the Saffire II experiments. Similar results were obtained by the authors with similar experiments with a thin composite cotton/fiberglass fabric (published elsewhere). Both results suggest that reduced pressure can be used to approximately replicate flame behavior of untested gravity conditions for the burning of thick and thin solids. This work could provide guidance for potential ground-based testing for fire safety design in spacecraft and space habitats.

Environmental conditions↗
DOE OSTI2021

Hydrogen Wide Area Monitoring of LH2 Releases at HSE for the PRESHLY Project

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. Empirical field measurements such as those afforded by Hydrogen Wide Area Monitoring (HyWAM) can elucidate the behavior of LH2 releases, which can then be used to support and validate dispersion models. HyWAM can be defined as the quantitative three-dimensional spatial and temporal profiling of planned or unintentional hydrogen releases. The NREL Sensor Laboratory developed a HyWAM based upon a distributed array of hydrogen Sensors. The NREL Sensor Laboratory and Health and Safety Executive (HSE) formally committed to collaborate on profiling GH2 and LH2 releases, which allowed for the integration of the NREL HyWAM into the HSE LH2 release behavior investigation supported by the FCH JU Prenormative Research for the Safe Use of Liquid Hydrogen (PRESLHY) program. A HyWAM system consisting of 32 hydrogen measurement points and co-located temperature sensors were distributed downstream of the LH2 release apparatus developed by HSE. In addition, there were proximal wind and weather monitors. In a separate presentation at this conference "HSE SD Experimental Summary for the Characterisation, Dispersion and Electrostatic Hazards of LH2 for the PRESLHY Project" HSE researchers summarize the experimental apparatus and experimental protocols utilized in the HSE LH2 releases that were performed under the auspices of PRESLHY. To supplement the HSE presentation, this presentation will focus on the spatial and temporal behavior LH2 releases as measured by the NREL HyWAM. Correlations to ambient conditions, such as wind speed and direction, plume temperature and hydrogen concentrations will be discussed in addition to the design and performance of the NREL HyWAM, and its potential for improving hydrogen facility safety.

HYDROGEN↗
DOE OSTI2023

Review and Assessment of Available Data Regarding the Behavior of Sodium Aerosols

Recently, there has been a resurgence of interest in advanced (non-light water) reactor designs, including sodium fast reactors (SFRs). In parallel, multiple efforts are underway to develop risk informed, performance-based licensing pathways for advanced reactors. However, such licensing approaches depend on the accuracy of the associated safety assessments, including mechanistic source term (MST) analyses. Unlike historical source term assessments, which utilizes bounding estimates of radionuclide release, MSTs attempt to realistically estimate the transport and retention of radionuclides for specific transient scenarios. For SFR MST analyses, the behavior of radionuclide aerosols is a key factor, as noted in recent studies. Due to the use of sodium as the primary coolant, many potential transient scenarios involve the release of radionuclide aerosols in conjunction with sodium aerosols. For such scenarios, sodium may be the dominant aerosol species, compared to that of released radionuclides. Therefore, understanding the behavior of sodium aerosols is particularly important to an accurate assessment of aerosol transport. Although many approaches to aerosol modeling exist, it is vital to establish their capabilities in evaluating sodium aerosol behavior for SFR analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2020

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health 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 accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. 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. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. 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.

Banerjee, Portia↗
DOE OSTI2024

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

99 GENERAL AND MISCELLANEOUS↗