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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Case Study on the Challenges and Opportunities for the Deployment of PHM Capabilities in Existing Engineering Systems

The field of Prognostics and Health Management (PHM) of engineering systems has experienced considerable growth over the last decade. From benefits associated with faster and more powerful hardware in the form of wireless sensors, edge devices, and general computing capabilities (GPU’s and cloud computing), to development of powerful algorithms for anomaly detection and remaining useful life (RUL) estimation, the number of engineering systems featuring advanced diagnostics and prognostics capabilities continues to grow at an increasingly faster pace. However, the deployment of PHM capabilities as part of the upgrade of existing engineering systems presents multiple challenges to the PHM practitioner charged with retrofitting such systems. Issues include a lack of specific instrumentation needed to capture the signals of interest; insufficient data and sampling rates required for fault detection and diagnosis, and for detection of failure/degradation indicators; and difficulties in the identification of a system’s nominal behavior as a result of age induced degradation. Today’s PHM practitioner must be able to quickly identify and assess these types of issues to effectively evaluate and select the optimal PHM strategies required to achieve the desired results. This paper presents results from the preliminary evaluation of the High-Pressure Gas Facility (HPGF) infrastructure at NASA’s Stennis Space Center in Hancock County, Mississippi. This evaluation is part of a feasibility study conducted prior to the deployment of prognostics and diagnostics capabilities in the pumps skids of the liquid nitrogen (LN2) system of the HPGF.

Condition Based Maintenance↗
NASA NTRSDate not supplied

Stochastic Verification by Analysis for Autonomous Systems Management Architecture (ASMA)

The Gateway Vehicle Systems Manager (VSM) is the top-level of a distributed, hierarchical software control system. VSM is data-driven and will make decisions related to mission, fault, resource management and vehicle control. These attributes combined with a high degree of autonomy make it susceptible to emergent behavior. In order to achieve the high level of confidence needed in this critical system, the VSM team has developed a multifaceted verification strategy employing traditional verification techniques, simulation, model checking, and runtime verification. Individual algorithms are verified using conventional testing and model checking using assume-guarantee contracts. A discrete event-based simulation approach is being developed to verify timelines. This presentation describes an enhancement to the verification approach using analysis to enhance system robustness by detecting and resolving the potential for emergent behavior. The verification by analysis employs a Software in the Loop (SITL) environment with real flight software executing on emulated processors, simulations of vehicle subsystems, flight dynamics, and human inputs. Since the possible input space and configuration data set are too large for exhaustive testing, a Monte Carlo approach is used to cover feasible scenarios, augmented with corner cases and known higher-risk scenarios. A key problem in using Monte Carlo-based system verification is evaluating test results to ensure that system behavior is correct. The presentation describes the approach the VSM team uses to monitor behavior for compliance with predetermined boundaries and to identify anomalous behavior for further analysis. This presentation describes the multi-level systems approach to verification, and the simulation-based layer that covers the feasible state space: 1. Overview of the Gateway VSM 2. Special challenges due to heterogeneous, hierarchical architecture 3. Modeling and simulation environment using flight software and system simulations 4. Developing input sets to ensure state-space coverage 5. Developing model and data configuration sets to ensure model coverage 6. Interpreting results without predetermined outcomes 7. Lessons learned and future work

Verification and Validation↗
NASA NTRS2010

Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Gay, Gregory↗
NASA NTRS1991

Knowledge-based Autonomous Test Engineer (KATE)

Mathematical models of system components have long been used to allow simulators to predict system behavior to various stimuli. Recent efforts to monitor, diagnose, and control real-time systems using component models have experienced similar success. NASA Kennedy is continuing the development of a tool for implementing real-time knowledge-based diagnostic and control systems called KATE (Knowledge based Autonomous Test Engineer). KATE is a model-based reasoning shell designed to provide autonomous control, monitoring, fault detection, and diagnostics for complex engineering systems by applying its reasoning techniques to an exchangeable quantitative model describing the structure and function of the various system components and their systemic behavior.

Parrish, Carrie L.↗
DOE OSTI2018

Strain localization in dry sheared granular materials: A compactivity-based approach

Shear banding is widely observed in natural fault zones as well as in laboratory experiments on granular materials. Understanding the dynamics of strain localization under different loading conditions is essential for quantifying strength evolution of fault gouge, energy partitioning during earthquakes and characterizing rheological transitions and fault zone structure changes. To that end, we develop a physics-based continuum model for strain localization in sheared granular materials. The grain-scale dynamics is described by the Shear Transformation Zone (STZ) theory, a non-equilibrium statistical thermodynamic framework for viscoplastic deformation in amorphous materials. Using a finite strain computational framework, we investigate the initiation and growth of complex shear bands under a variety of loading conditions and identify implication for strength evolution and ductile to brittle transition. Our numerical results show similar localization patterns to field and lab observations and suggest that shear zones show more ductile response at higher confining pressures, lower dilatancy and loose initial conditions. Lower pressures, higher dilatancy and dense initial conditions favor a brittle response and larger strength drops. These findings shed light on a range of mechanisms for strength evolution in dry sheared granular materials and provide a critical input to physicsbased multiscale models of fault zone instabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Nonlinear dynamics induced in a structure by seismic and environmental loading

In this study,we show that under very weak dynamic and quasi-static deformation, that is orders of magnitude below the yield deformation of the equivalent stress strain curve (around 10 -3 ), the elastic parameters of a civil engineering structure (resonance frequency and damping) exhibit nonlinear softening and recovery. These observations bridge the gap between laboratory and seismic scales where elastic nonlinear behavior has been previously observed. Under weak seismic or atmospheric loading, modal frequencies are modified by around 1% and damping by more than 100% for strain levels between 10 -7 and 10 -4 . These observations support the concept of universal behavior of nonlinear elastic behavior in diverse systems, including granular materials and damaged solids that scale from millimeter dimensions to the scale of structures to fault dimensions in the Earth.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
DOE OSTI2018

Searching for Induced Seismicity at Punggye-ri Nuclear Test Site Using Subspace Detectors

The most recent declared nuclear test (DPRK-6) at the North Korean Punggye-ri nuclear test site was an order of magnitude larger than any of the previous explosions, and for the first time was accompanied by additional seismicity large enough to be observed at regional distances. The first such event was a collapse about 8.5 min after the explosion, and this was followed by three earthquakes over the next several weeks. Signals from these earthquakes recorded at four stations were used to build subspace detectors, which were then used to scan the available data for previously unobserved events near the test site. The time period scanned extends from early 2009 until March 2018, and in that time four explosions, the collapse event, the three template events, and 30–37 additional template-matched events were detected. (Seven of the detections are low-reliability single-station detections at station MDJ.) Most of the events occurred within the weeks following DPRK-6. Thirteen of the detected events have been located. These events form two clusters, both to the northwest of the test site. The first cluster is about 3 km from the test site and contains an event that occurred in 2016 (before DPRK-5) as well as two of the last events detected in this work. The second cluster is about 6 km northwest of the test site and contains only events that occurred after the last test. Both clusters appear to be quite compact. The north–south extent of the clusters is well constrained by the observations to be less than 1–2 km. Moreover, the relatively high correlation coefficients in the 2–8 Hz band also argue for small separations between events. These events are hypothesized to have occurred on a northwest-striking fault plane that passes near or under the Punggye-ri test site. High stress state (inferred from low b-values) supports the idea that each cluster is located at an asperity on the hypothesized fault plane. Prior to DPRK-3, no events were observed from either cluster, but since DPRK-3 there has been at least one event per year. This is consistent with stress change triggering behavior observed elsewhere and suggests that all these events were triggered by stress changes on the asperities due to the explosions.

58 GEOSCIENCES↗
NASA NTRS1983

Graphics enhanced computer emulation for improved timing-race and fault tolerance control system analysis

A computer simulation system has been developed for the Space Shuttle's advanced Centaur liquid fuel booster rocket, in order to conduct systems safety verification and flight operations training. This simulation utility is designed to analyze functional system behavior by integrating control avionics with mechanical and fluid elements, and is able to emulate any system operation, from simple relay logic to complex VLSI components, with wire-by-wire detail. A novel graphics data entry system offers a pseudo-wire wrap data base that can be easily updated. Visual subsystem operations can be selected and displayed in color on a six-monitor graphics processor. System timing and fault verification analyses are conducted by injecting component fault modes and min/max timing delays, and then observing system operation through a red line monitor.

Szatkowski, G. P.↗
DOE OSTI2013

Coupled Flow and Deformation Modeling of Carbon Dioxide Migration in the Presence of a Caprock Fracture during Injection

Understanding the transport of carbon dioxide (CO{sub 2}) during long-term CO{sub 2} injection into a typical geologic reservoir, such as a saline aquifer, could be complicated because of changes in geochemical, hydrogeological, and hydromechanical behavior. While the caprock layer overlying the target aquifer is intended to provide a tight, impermeable seal in securing injected CO{sub 2}, the presence of geologic uncertainties, such as a caprock fracture or fault, may provide a channel for CO{sub 2} leakage. There could also be a possibility of the activation of a new or existing dormant fault or fracture, which could act as a leakage pathway. Such a leakage event during CO{sub 2} injection may lead to a different pressure and ground response over a period of time. In the present study, multiphase fluid flow simulations in porous media coupled with geomechanics were used to investigate the overburden geologic response and plume behavior during CO{sub 2} injection in the presence of a hypothetical permeable fractured zone in a caprock, existing or activated. Both single-phase and multiphase fluid flow simulations were performed. The CO{sub 2} migration through an existing fractured zone leads to changes in the fluid pressure in the overburden geologic layers and could have a significant impact on ground deformation behavior. Results of the study show that pressure signatures and displacement patterns are significantly different in the presence of a fractured zone in the caprock layer. The variation in pressure and displacement signatures because of the presence of a fractured zone in the caprock at different locations may be useful in identifying the presence of a fault/fractured zone in the caprock. The pressure signatures can also serve as a mechanism to identify the activation of leakage pathways through the caprock during CO{sub 2} injection. Pressure response and ground deformation behavior from sequestration modeling could be useful in the development of smart technologies to monitor safe CO{sub 2} storage and understand CO{sub 2} transport, with limited field instrumentation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Modular techniques for dynamic fault-tree analysis

It is noted that current approaches used to assess the dependability of complex systems such as Space Station Freedom and the Air Traffic Control System are incapable of handling the size and complexity of these highly integrated designs. A novel technique for modeling such systems which is built upon current techniques in Markov theory and combinatorial analysis is described. It enables the development of a hierarchical representation of system behavior which is more flexible than either technique alone. A solution strategy which is based on an object-oriented approach to model representation and evaluation is discussed. The technique is virtually transparent to the user since the fault tree models can be built graphically and the objects defined automatically. The tree modularization procedure allows the two model types, Markov and combinatoric, to coexist and does not require that the entire fault tree be translated to a Markov chain for evaluation. This effectively reduces the size of the Markov chain required and enables solutions with less truncation, making analysis of longer mission times possible. Using the fault-tolerant parallel processor as an example, a model is built and solved for a specific mission scenario and the solution approach is illustrated in detail.

Patterson-Hine, F. A.↗
DOE OSTI2018

Initial assessment of recharge areas for large karst springs: a case study from the central Zagros Mountains, Iran

Sousan Spring emerges from the Keyno Anticline, Zagros Mountains (Iran), and the mean annual discharge is ~24 m{sup 3}/s. Geological and hydrochemical evaluations suggest that the spring recharge is from the limestone Ilam-Sarvak Formation (Cretaceous) but the Mafaroon Fault, a major thrust feature, influences the regional groundwater flow path by juxtaposing other strata. Geological, geochemical, stable isotope and water balance studies were employed to interpret this behavior. Using the isotope data, the sources and elevations of the recharge area were found. Temporal variations of the isotopic data were compared with variations of electrical conductivity (EC). Unexpectedly, high EC was associated with a relative increase of discharge and depletion of δ{sup 18}O. Several hypotheses were investigated and approximate water balance studies employed for validation. It was found that an elongated catchment on the Keyno Anticline plus a lesser catchment on a pair of parallel anticlines recharge the aquifer. While the long groundwater flow path along the Keyno Anticline plus guidance by Mafaroon Fault and the adjacent Garou shaly strata lead to increased EC in the Sousan Spring at the end of the dry season, a flow pulse from two adjoining anticlines (Mahalbakh and Shirgoon) arrives at the same time to increase the discharge and deplete the δ{sup 18}O signal. Apparently the spring did not experience true base flow conditions during the recorded hydrological year. Although the spring response to specific precipitation events was similar to typical karst aquifers, standard interpretation of recession curves and related coefficients will not be practical at Sousan.

58 GEOSCIENCES↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A Thin Film Viscoplastic Theory for Calving Glaciers: Toward a Bound on the Calving Rate of Glaciers

Projections of the growth and demise of ice sheets and glaciers require physical models of the processes governing flow and fracture of ice. The flow of glacier ice has been treated using increasingly sophisticated models. By contrast, fracture, the process ultimately responsible for half of the mass lost from ice sheets through iceberg calving, is often included using ad hoc parameterizations. In this study we seek to bridge this gap by introducing a model where ice obeys a power law rheology appropriate for intact ice below a yield strength. Above the yield strength, we introduce a separate rheology appropriate for the flow of heavily fractured ice, where ice deformation occurs more readily along faults and fractures. We show that, provided the motion of fractured ice is sufficiently rapid compared to that of intact ice, the behavior of glaciers depends solely on the rheology of intact ice and the yield strength of ice and is insensitive to the precise rheology of fractured ice. Moreover, assuming that glacier ice is unyielded allows us to bound the long‐term average rate of terminus advance, providing a first principles estimate of rates of retreat associated with the marine ice cliff instability. We illustrate model behavior using idealized geometries and climate forcing and show that the model not only exhibits realistic patterns of advance and retreat but also has the potential to exhibit hysteresis. This hysteresis could provide an explanation for the sudden onset of rapid retreat observed in marine‐terminating glaciers.

Bassis, J. N.↗
DOE OSTI2014

Topological insulators/superconductors: Potential future electronic materials

A new material called topological insulator has been discovered and becomes one of the fastest growing field in condensed matter physics. Topological insulator is a new quantum phase of matter which has Dirac-like conductivity on its surface, but bulk insulator through its interior. It is considered a challenging problem for the surface transport measurements because of dominant internal conductance due to imperfections of the existing crystals of topological insulators. By a proper method, the internal bulk conduction can be suppressed in a topological insulator, and permit the detection of the surface currents which is necessary for future fault-tolerant quantum computing applications. Doped topological insulators have depicted a large variety of bulk physical properties ranging from magnetic to superconducting behaviors. By chemical doping, a TI can change into a bulk superconductor. Nb{sub x}Bi{sub 2}Se{sub 3} is shown to be a superconductor with T{sub c} ∼ 3.2 K, which could be a potential candidate for a topological superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Optical and Structural Properties of Microcrystalline GaN on an Amorphous Substrate Prepared by a Combination of Molecular Beam Epitaxy and Metal-Organic Chemical Vapor Deposition

Microscale platelet-shaped GaN grains were grown on amorphous substrates by a combined epitaxial growth method of molecular beam epitaxy (MBE) and metal-organic chemical vapor deposition (MOCVD). First, MBE GaN was grown on an amorphous substrate as a pre-orienting layer and its structural properties were investigated. Second, MOCVD grown GaN samples using the different growth techniques of planar and selective area growth (SAG) were comparatively investigated by transmission electron microscopy (TEM), cathodoluminescence (CL), and photoluminescence (PL). In MOCVD planar GaN, strong bound exciton peaks dominated despite the high density of the threading dislocations (TDs). In MOCVD SAG GaN, on the other hand, TDs were clearly reduced with bending, but basal stacking fault (BSF) PL peaks were observed at 3.42 eV. The combined epitaxial method not only provides a deep understanding of the growth behavior but also suggests an alternative approach for the growth of GaN on amorphous substances.

14 SOLAR ENERGY↗
DOE OSTI2025

Medium Voltage Testbed for Comparing Advanced Power Line Sensors vs. Transformers with Electrical Grid Events

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. These devices measure phase voltages and currents and are commissioned by electrical engineers. PTs/CTs can detect and react to various electrical anomalies that could adversely affect electrical grid operations. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. This study simulated a power grid model based on a utility circuit at the Riverside EPB of Chattanooga. The simulation circuit was created with MATLAB/Simulink software and was integrated into an RT-LAB project to run with the OP4510 real-time simulator at the OPLST. During the tests, the real-time simulations were run for 40 s, and the signal to record the test event with the power meter was set at 30 s for the event-trigger circuit. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. The results showed that this OPLS technology responded identically to the PT and CT under all conditions.

Piesciorovsky, Emilio [ORNL]↗
NASA NTRS2013

An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

Structural Decomposition↗