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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Frictional and Lithological Controls on Shallow Slow Slip at the Northern Hikurangi Margin

Slow slip events (SSEs) have been identified at subduction zones globally as an important link in the continuum between elastodynamic ruptures and stable creep. The northern Hikurangi margin is home to shallow SSEs which propagate to within 2 km of the seafloor and possibly to the trench, providing insights into the physical conditions conducive to SSE behavior. We report on a suite of friction experiments performed on protolith material entering the SSE source region at the Hikurangi margin, collected during the International Ocean Discovery Program Expedition 375. We performed velocity stepping and slide-hold-slide experiments over a range of fault slip rates, from plate rate (5 cm/yr or 1.6 × 10 –9 m/s) to ~1 mm/s (10 –3 m/s) and quantified the frictional velocity dependence and healing rates for a range of lithologies atdifferent stresses. The frictional velocity dependence (a-b) and critical slip distance DC increase with fault slip rate in our experiments. We observe atransition from velocity weakening to strengthening at slip rates of ~0.3 µm/s. This velocity dependence of DC could be due to a combination of dilatant strengthening and a widening of the active shear zone at higher slip rates. We document low healing rates in the clay-rich volcaniclastic conglomerates, which lie above the incoming plate basement at least locally, and relatively higher healing rates in the chalk lithology. Finally, our experimental constraints on healing rates in different input lithologies extrapolated to timescales of 1–10 years are consistent with the geodetically inferred low stress drops and healing rates characteristic of the Hikurangi SSEs.

58 GEOSCIENCES↗
NASA NTRS2006

Drilling Automation Demonstrations in Subsurface Exploration for Astrobiology

This project proposes to study subsurface permafrost microbial habitats at a relevant Arctic Mars-analog site (Haughton Crater, Devon Island, Canada) while developing and maturing the subsurface drilling and drilling automation technologies that will be required by post-2010 missions. It builds on earlier drilling technology projects to add permafrost and ice-drilling capabilities to 5m with a lightweight drill that will be automatically monitored and controlled in-situ. Frozen cores obtained with this drill under sterilized protocols will be used in testing three hypotheses pertaining to near-surface physical geology and ground H2O ice distribution, viewed as a habitat for microbial life in subsurface ice and ice-consolidated sediments. Automation technologies employed will demonstrate hands-off diagnostics and drill control, using novel vibrational dynamical analysis methods and model-based reasoning to monitor and identify drilling fault states before and during faults. Three field deployments, to a Mars-analog site with frozen impact crater fallback breccia, will support science goals, provide a rigorous test of drilling automation and lightweight permafrost drilling, and leverage past experience with the field site s particular logistics.

Glass, Brian↗
NASA NTRS2013

Detectors Ensure Function, Safety of Aircraft Wiring

Pedro Medelius waited patiently in his lab at Kennedy Space Center. He had just received word that a colleague was bringing over a cable from a Space Shuttle solid rocket booster to test Medelius new invention. Medelius was calm until his colleague arrived, with about 30 other people. "Talk about testing under pressure," says Medelius. "There were people there from the Navy, the Air Force, and the Federal Aviation Administration." After the group s arrival, Medelius took a deep breath and connected his Standing Wave Reflectometer (SWR) to the cable. He wiggled the cable around, and the display showed a fault (a short or open circuit in wire) about an inch and a half inside the connector on the cable. His colleague questioned the results, because he had already checked that area on the cable. Medelius used the SWR to check again but got the same result. "That is when we took the cable apart and looked inside," Medelius says. "Lo and behold, that was exactly where the fault was." The impetus for Medelius new wire inspection technology came about in 1999 when one of the space shuttles lost power due to a fault somewhere in its more than 200 miles of electrical wiring. "The backup circuit was activated and prevented a major dysfunction, but nevertheless, there was a problem with the wiring," Medelius describes. Even though technicians used a device called a multimeter to measure the electrical current to find which wire had a fault, it could not pinpoint exactly where on the wire the fault was located. For that, technicians had to visually inspect the wire. "Sometimes they would have to remove the whole wire assembly and visually inspect every single wire. It was a very tedious operation because the wires are behind cabinets. They go all over the place in the shuttle," says Medelius. "NASA needed an instrument capable of telling them exactly where the faults were occurring." To meet NASA s needs for a highly precise device to inspect electrical power bundles, wires, and connectors, Medelius devised the SWR. "It came down to what was affected when a wire is short circuited or opened," he says. "We worked out a few equations based on physical principles." The SWR proved very sensitive, and the technology was patented.

Source record↗
DOE OSTI2023

Distributed Wind-Hybrid Microgrids with Autonomous Controls and Forecasting

Distributed wind-hybrid microgrids have the potential to provide key resilience and economic benefits to both the customers they serve and the utility grids they are connected to. Such microgrids will likely be a key part of the grid of the future, whether connected to large utility grids or linked together in multi-microgrid systems. Through the hybridization of distributed wind and solar photovoltaics, autonomous device-level and system-level controls, battery energy storage systems with smart inverters, and forecasting, these microgrids could maintain local stability and provide grid services - all with renewable power. In the literature, these elements have been considered individually. However, they have not been combined and demonstrated at a high fidelity, which is essential to prove the concept's operation before moving to hardware-in-the-loop and physical demonstrations. In this work, we develop a high-fidelity MATLAB-Simulink model of a real distributed wind-hybrid microgrid that includes all these elements. We demonstrate the microgrid maintaining stability and production in a variety of islanded, grid-connected, and transition scenarios. This includes riding through faults and grid transitions, handling resource variability, and providing grid services. The results demonstrate, at a high fidelity, how distributed wind-hybrid microgrids can operate in an economic and resilient fashion. Finally, we provide recommendations for future research to move advanced distributed wind-hybrid microgrids toward deployment.

ancillary services↗
DOE OSTI2022

Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys

A descriptor-less machine learning (ML) model based only on charge density images extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques for dealing with complex images and data. Identification of correct descriptors is a key bottleneck to develop ML models for predicting materials properties. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used as descriptors which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. Here, we illustrate that, within the scope of DFT, the search for descriptors can be circumvented by electronic charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The performance of our model is demonstrated by predicting SFE of concentrated alloys with an RMSE and R 2 of 6.18 mJ/m 2 and 0.87, respectively, validating the accuracy of the proposed approach.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Chemical hazards database and detection system for Microgravity and Materials Processing Facility (MMPF)

The ability to identify contaminants associated with experiments and facilities is directly related to the safety of the Space Station. A means of identifying these contaminants has been developed through this contracting effort. The delivered system provides a listing of the materials and/or chemicals associated with each facility, information as to the contaminant's physical state, a list of the quantity and/or volume of each suspected contaminant, a database of the toxicological hazards associated with each contaminant, a recommended means of rapid identification of the contaminants under operational conditions, a method of identifying possible failure modes and effects analysis associated with each facility, and a fault tree-type analysis that will provide a means of identifying potential hazardous conditions related to future planned missions.

Steele, Jimmy↗
NASA NTRS2008

Sensor Selection and Data Validation for Reliable Integrated System Health Management

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Garg, Sanjay↗
NASA NTRS2008

GN&C fault protection

Addressing fault tolerance for spacecraft Guidance, Navigation, and Control has never been easy. Even under normal conditions, these systems confront a remarkable blend of complex issues across many disciplines, with primary implications for most essential system functions. Moreover, GN&C must deal with the peculiarities of spacecraft configurations, disturbances, environment, and other physical mission-unique constraints that are seldom under its full control, all while promising consistently high performance.

Rasmussen, Robert D.↗
DOE OSTI2022

Spatio-Temporal Clustering of Seismicity Enabled by Off-Fault Plasticity

While significant progress has been made in understanding earthquake source processes in linear elastic domains, the effect of more realistic rheologies including plasticity is poorly understood. Here, in this work, we simulate the sequence of earthquake and aseismic slip of a 2D antiplane rate-and-state fault embedded in a full-space elastic-plastic bulk. We show that off-fault plasticity may lead to partial ruptures as well as temporal clustering of seismic events. Furthermore, the interaction of fault slip and off-fault plasticity results in pockets of slip deficit. While the energy dissipated through plastic deformation remains a small fraction of the total energy budget, its impact on the source characteristics is disproportionally large through the redistribution of stresses and viscous relaxation. Our results suggest a new mechanism of dynamic heterogeneity in earthquake physics that may have important implications on earthquake size distribution and energy budget.

58 GEOSCIENCES↗
DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Advanced Physics-Based Fluid System Performance Monitoring to Support Nuclear Power Plant Operations. Final CRADA report

This project targeted performance monitoring requirements for nuclear power plants with the goal of improving plant economics. We introduced a new and innovative technology to the industry that can reduce operations staffing levels by automating surveillance and can reduce maintenance staffing levels by providing early detection of degrading equipment. A beta-stage release of the PRO-AID power plant equipment fault diagnostic software ready for deployment in nuclear power plants will be delivered and demonstrated in a utility pilot project. To provide for efficient use of DOE funds the aim is to generate a minimum viable product for which we can prove the value of the product. The end state product will be serviceable for use in either a simulator environment or plant environment. The former provides a means for demonstration of the diagnostic capability for faults unlikely to be encountered in day-to-day operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Towards stacking fault energy engineering in FCC high entropy alloys

Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for many years as a key intrinsic mechanical property, its inference via experimental observations or prediction using, for example, computationally intensive first-principles methods is challenging. This difficulty precludes the explicit use of SFE as an alloy design parameter. In this work, we combine DFT calculations (with necessary configurational averaging), machine-learning (ML) and physics-based models to predict the SFE in the fcc CoCrFeMnNiV-Al high-entropy alloy space. The best-performing ML model is capable of accurately predicting the SFE of arbitrary compositions within this 7-element system. Finally, this efficient model along with a recently developed model to estimate intrinsic strength of fcc HEAs is used to explore the strength–SFE Pareto front, predicting new-candidate alloys with particularly interesting mechanical behavior.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Representing functions/procedures and processes/structures for analysis of effects of failures on functions and operations

Current qualitative device and process models represent only the structure and behavior of physical systems. However, systems in the real world include goal-oriented activities that generally cannot be easily represented using current modeling techniques. An extension of a qualitative modeling system, known as functional modeling, which captures goal-oriented activities explicitly is proposed and how they may be used to support intelligent automation and fault management is shown.

Malin, Jane T.↗
NASA NTRSDate not supplied

Enabling in-time Prognostics with Surrogate Modeling through Physics-enhanced Dynamic Mode Decomposition Method

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the PHM domain, where far-ahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. The Koopman theory, however, provides a mechanism to tradeoff low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. In this way, DMD allows for configurable simulation accuracy dependent on the dimensionality of the Koopman operator. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the prog models python package.

prognostics and health management↗
DOE OSTI2020

Modeling Time to Failure in Potential-Induced Degradation of Silicon Solar Modules based on Quantitative Sodium Kinetics

We present a physics-based simulation environment for the prediction of potential-induced-degradation (PID) failure time in silicon solar modules. Our model is based on experimental sodium migration kinetics obtained from our recently developed trap-corrected bias-stress-temperature method and from secondary ion mass spectrometry measurements. Furthermore, we model sodium migration to stacking faults and subsequent formation of conductive pathways through the p-n junction, which causes a decrease of the shunt resistance and loss of efficiency.

42 ENGINEERING↗
DOE OSTI2021

Dynamic Rupture Modeling of the M7.1, 2019 Ridgecrest, California, Earthquake

An important part of the simulation technique implemented in the strong motion prediction recipe developed in Japan, originally proposed by Irikura and Miyake (2011) is the earthquake rupture characterization that is based on established empirical scaling relationships between source parameters and seismic moment. These scaling relationships and rupture characterization are derived from recorded data and are tested by simulations. Recent analysis of results obtained with the Recipe for the 2016 M7.1 Kumamoto earthquake performed by several researchers, indicate that in order to reproduce near-fault long-period ground motions with permanent displacements, typical for earthquakes with surface rupturing, the recipe needs modifications. In particular, improvements are required in characterizing the rupture kinematics at shallow depths, above the seismogenic zone (Irikura et al., 2019). Modeling of rupture dynamics for recorded earthquakes, and numerical simulations for different rupture scenarios, provides physical constrains on slip evolution in time and space, which can be used in adjustments of empirical source scaling relationships for large crustal earthquakes. In this study we mainly focused on rupture dynamics modeling of the 2019 M7 Ridgecrest, California earthquake. The characterization of the kinematic rupture model derived from the dynamic rupture models of this earthquake can inform further refinements of rupture modeling proposed by the Recipe. Through different rupture scenarios we investigated potential relationships between characteristics of Long Period Ground Motion Generation Area (LMGA), used in the Recipe, and asperity depth and rupture initiation.

58 GEOSCIENCES↗