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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Atomic Structural Features of Stacking Faults and Domain Connections in the Li- and Mn-Rich Cathode

Li- and Mn-rich layered oxides (LMRs), a class of earth-abundant materials for rechargeable Li-ion battery cathodes, crystallize into layered structures of two different symmetries: C2/m represented by Li 2 MnO 3 and R$\overline{3}$m represented by LiMn 0.5 Ni 0.5 O 2 . Fundamental questions about how the C2/m and R$\overline{3}$m domains spatially correlate within the same oxide grain and how the C2/m stacking faults arrange themselves when this happens still remain. Here, by using integrated differential phase contrast imaging in scanning transmission electron microscopy (STEM-iDPC), we probe the structural and compositional details of a prototypical, cobalt-free LMR material, 0.3Li 2 MnO 3 •0.7LiMn 0.5 Ni 0.5 O 2 (Li 1.13 Mn 0.57 Ni 0.3 O 2 ). The connection between the C2/m and R$\overline{3}$m domains is found to be abrupt, facilitated by the small lattice mismatch between the two structures. Stacking faults in the C2/m domains feature atomic plane shifting that accommodates stacking sequence changes, which explains why the stacking faults form in a random manner. Furthermore, a local disordering mechanism was identified to correlate with the C2/m stacking faults. Chemically, it is found that Ni coexists with Mn at the transition metal sites within the nominal Li 2 MnO 3 domain. As a result, this study demonstrates that STEM-iDPC is a very useful tool for capturing all the elements in a single image, revealing atomic details on domain connections and stacking faults in the LMRs.

Li-rich and Mn-rich oxides↗
DOE OSTI2020

Evolution of Elastic and Mechanical Properties During Fault Shear: The Roles of Clay Content, Fabric Development, and Porosity

Phyllosilicates weaken faults due to the formation of shear fabrics. Although the impacts of clay abundance and fabric on frictional strength, sliding stability, and porosity of faults are well studied, their influence on elastic properties is less known, though they are key factors for fault stiffness. We document the role that fabric and consolidation play in elastic properties and show that smectite content is the most important factor determining whether fabric or porosity controls the elastic response of faults. We conducted a suite of shear experiments on synthetic smectite-quartz fault gouges (10–100 wt% smectite) and sediment incoming to the Sumatra subduction zone. We monitored Vp, Vs, friction, porosity, shear and bulk moduli. We find that mechanical and elastic properties for gouges with abundant smectite are almost entirely controlled by fabric formation (decreasing mechanical and elastic properties with shear). Though fabrics control the elastic response of smectite-poor gouges over intermediate shear strains, porosity is the primary control throughout the majority of shearing. Elastic properties vary systematically with smectite content: High smectite gouges have values of Vp ~ 1,300–1,800 m/s, Vs ~ 900–1,100 m/s, K ~ 1–4 GPa, and G ~ 1–2 GPa, and low smectite gouges have values of Vp ~ 2,300–2,500 m/s, Vs ~ 1,200–1,300 m/s, K ~ 5–8 GPa, and G ~ 2.5–3 GPa. We find that, even in smectite-poor gouges, shear fabric also affects stiffness and elastic moduli, implying that while smectite abundance plays a clear role in controlling gouge properties, other fine-grained and platy clay minerals may produce similar behavior through their control on the development of fabrics and thin shear surfaces.

58 GEOSCIENCES↗
DOE OSTI2020

Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys

Abstract The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault energies on the degree of chemical disorder in multicomponent alloys. We introduce the notion of a “configurationally-resolved planar fault” (CRPF) energy and extend the cluster expansion method from alloy theory to express the CRPF as a function of chemical occupation variables of sites surrounding the fault. We apply the approach to explore the composition and temperature dependence of the unstable stacking fault energy (USF) in binary Mo–Nb alloys. First-principles calculations are used to parameterize a formation energy and CRPF cluster expansion. Monte Carlo simulations show that the distribution of USF energies is significantly affected by chemical composition and temperature. The formalism is broadly applicable to arbitrary crystal structures and alloy chemistries and will enable the development of rigorous models for deformation mechanisms in high-entropy alloys.

Natarajan, Anirudh Raju (ORCID:0000000202529480)↗
DOE OSTI2021

Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics

Abstract HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft 2 of electricity annually. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40%–60% of its total electricity usage and is equivalent to about 2%–3% of the total energy consumed by commercial buildings in the United States. Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Reducing refrigerant usage or using environmentally friendly alternatives can result in significant climate benefits. A challenge is the lack of publicly available data sets to benchmark the system performance and record the faulted performance. This paper identifies common faults of supermarket refrigeration systems and conducts an experimental study to collect the faulted performance data and analyze these faults. This work provides a foundation for future research on the development of FDD methods and field automated FDD implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Cooling-induced reactivation of distant faults during long-term geothermal energy production in hot sedimentary aquifers

Deep geothermal energy (DGE) represents an opportunity for a sustainable and carbon-free energy supply. One of the main concerns of DGE is induced seismicity that may produce damaging earthquakes, challenging its widespread exploitation. It is widely believed that the seismicity risk can be controlled by using doublet systems circulating water to minimize the injection-induced pressure changes. However, cold water reinjection may also give rise to thermal stresses within and beyond the cooled region, whose potential impacts on fault reactivation are less well understood. Here, we investigate by coupled thermo-hydro-mechanical modeling the processes that may lead to fault reactivation in a hot sedimentary aquifer (HSA) in which water is circulated through a doublet. We show that thermal stresses are transmitted much ahead of the cooled region and are likely to destabilize faults located far away from the doublet. Meanwhile, the fault permeability mainly controls the fault reactivation timing, which entails the importance of employing appropriate characterization methods. This investigation is crucial for understanding the mechanisms controlling induced seismicity associated with DGE in a HSA and allows the success of future DGE projects.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Fault localization in a microfabricated surface ion trap using diamond nitrogen-vacancy center magnetometry

Here, as quantum computing hardware becomes more complex with ongoing design innovations and growing capabilities, the quantum computing community needs increasingly powerful techniques for fabrication failure root-cause analysis. This is especially true for trapped-ion quantum computing. As trapped-ion quantum computing aims to scale to thousands of ions, the electrode numbers are growing to several hundred, with likely integrated photonic components also adding to the electrical and fabrication complexity, making faults even harder to locate. In this work, we used a high-resolution quantum magnetic imaging technique, based on nitrogen-vacancy centers in diamond, to investigate short-circuit faults in an ion trap chip. We imaged currents from these short-circuit faults to ground and compared them to intentionally created faults, finding that the root cause of the faults was failures in the on-chip trench capacitors. This work, where we exploited the performance advantages of a quantum magnetic sensing technique to troubleshoot a piece of quantum computing hardware, is a unique example of the evolving synergy between emerging quantum technologies to achieve capabilities that were previously inaccessible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Coincident learning for beam-based rf station fault identification using phase information at the SLAC linac coherent light source

Anomalies in radio-frequency (rf) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC’s Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-based detection, combining rf amplitude and beam position monitor data. Due to the simplicity of the rf amplitude data, classical methods are sufficient to identify faults, but the recall is constrained by the low-frequency and asynchronous characteristics of the data. In this work, we leverage high-frequency, time-synchronous rf phase data to enhance anomaly detection in the LCLS accelerator. Due to the complexity of phase data, classical methods fail, and we instead train deep neural networks within the Coincident Anomaly Detection (CoAD) framework. We find that applying CoAD to phase data detects nearly 3 times as many anomalies as when applied to amplitude data, while achieving broader coverage across rf stations. Furthermore, the rich structure of phase data enables us to cluster anomalies into distinct physical categories. Through the integration of auxiliary system status bits, we link clusters to specific fault signatures, providing additional granularity for uncovering the root cause of faults. We also investigate interpretability via Shapley values, confirming that the learned models focus on the most informative regions of the data and providing insight for cases where the model makes mistakes. This work demonstrates that phase-based anomaly detection for rf stations improves both diagnostic coverage and root cause analysis in accelerator systems and that deep neural networks are essential for effective analysis.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2023

High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach

This paper presents a model for detecting high impedance faults using parameter error modeling and a two step per-phase weighted-least squares state estimation process. The proposed scheme leverages the use of Phasor Measurement Units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through ?2 Hypothesis Testing applied to the composed measurement error. Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide area power flow and injection measurements, which are already inherent to the state estimation process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to high-impedance fault identification. Further, this error spread across measurement functions related to the fault will be clearly discerned from measurement error. Case studies are performed on the IEEE 33-Bus Distribution System along with the proposed model in Simulink.

Cooper, Austin↗
DOE OSTI2025

Early Fault Detection in Nuclear Systems: A Digital Engineering Approach

Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering, artificial intelligence, and immersive extended reality to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.

42 - ENGINEERING↗
DOE OSTI2020

3D fault architecture controls the dynamism of earthquake swarms

The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. Here we leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.

58 GEOSCIENCES↗
DOE OSTI2020

Analysis of Microseismicity and Reactivated Fault Size to Assess the Potential for Felt Events by CO 2 Injection in the Illinois Basin

The results of monitoring of carbon dioxide (CO 2 ) injection at the Illinois Basin—Decatur Project (IBDP) and the companion Illinois Industrial Carbon Capture and Sequestration Sources (IL-ICCS) project—have shown that reservoir response to fluid pressure changes can vary significantly at different injection locations within the same reservoir. Predrill reservoir characterization is important to identify potentially seismogenic faults. However, interpretations of newly reprocessed 3D seismic reflection data illustrate the challenges related to their identification in a region dominated by faulting with small vertical offsets. Faults interpreted in the 3D seismic volume range from ~300 to 1200 m wide and are in the same size range as faults that could have been the source of historical events up to Mw 2.7 in central Illinois. The array of monitoring sensors that was installed for the IBDP continues to collect data, as injection operates in IL-ICCS, the second injection well. CO 2 injection rates for the IL-ICCS well are on average 1.7 times the rates injected in the IBDP well, but a significantly reduced rate of induced seismicity is observed. This article presents results of passive seismic monitoring for the duration of the project to date, integrating active and passive seismic data to develop a new interpretation of the subsurface structure at the Decatur site that explicitly identifies pathways for fluid flow into the basement leading to induced seismicity, and provides a geological explanation for the sharp reduction of induced seismicity during injection at higher rates into the second well. The use of seismic moment to estimate the length of seismogenic slip planes in the local subsurface suggests that faults large enough to produce felt seismicity are unlikely to be present at or near the Decatur site.

Geochemistry & Geophysics↗
DOE OSTI2022

Development of a Annual Air Handling Unit Fault Dataset for FDD Tools: Lessons Learned and Considerations for FDD Developers

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault data for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling units and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for a single duct air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, a detailed AHU model was employed to carry out annual simulations of numerous common sensor and mechanical faults, which were then validated by comparing their effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2020

Rupture Dynamics Simulations of Shallow Crustal Earthquakes on Reverse Slip Faults

The main goal of this study was to perform dynamic rupture simulations in order constrain the characterization of kinematic rupture models implemented in techniques for simulating strong ground motion for crustal earthquakes. First, we investigated the rupture process of the 2008 Iwate-Miyagi Nairiku earthquake by developing a dynamic rupture model on a reverse fault using a trial-and-error technique that produced a slip distribution and near-fault ground motion that matched the recorded ones. The simulations were performed in the frequency range 0-2 Hz, using a 3D staggered grid finite-difference method and a linear slip weakening friction law. Constrained by the observed slip distribution and consistent with dynamic rupture models, the derived kinematic rupture model of the Iwate-Miyagi Nairiku earthquake contains areas with relatively large slip rate, representing strong motion generation areas, set against lower amplitude heterogeneous background slip, and a relatively low slip rate in the weak zone of the top-most crust (upper 3km). Second, we performed rupture dynamics modeling to constrain shallow slip characterization in rupture models for strike-slip crustal earthquakes. The objective was to establish general rules about the characterization of slip rate function and slip at shallow depths and in the strong motion generation areas (SMGAs). The simulations of spontaneous rupture were performed in the frequency range 0-2.0Hz, using a 3D staggered-grid finite-difference method and a layer over half-space 1D crustal velocity model with a minimum shear-wave velocity of 2.8 km/s. In order to account for changes in material ductility and reduction of stress drop, observed in the shallow crust (upper 3-5 km), and the transition from ductile state to brittle state in the upper seismogenic zone, in our stress models we included a shallow weak zone (<4km). In this zone the stress drop was set to zero at the free surface and gradually increased with depth, while the slip weakening distance was set to 75 cm at the free surface and decreased to 50 cm at the base of the weak zone. As in the case of reverse faulting during the Iwate-Miyagi Nairiku earthquake, from these computations we found a systematic change in the shape of the slip-rate function, from Kostrov-type in the deeper part of the fault to a more symmetric cosine-type in the upper few kilometers, near the free-surface. Moreover, the average slip duration in the weak zone, with respect to slip duration in the deeper parts of the fault, increases by at most a factor of 1.5. We found a systematic and gradual change in the shape of the slip-rate function from Kostrov-type in the asperity areas (SMGAs), to more symmetric cosine-type in the upper few km near the free-surface, on the long period motion generation area (LMGA). The effective rise time in the LMGA, with respect to that in the SMGAs, increases by at most a factor of 2. Effective rise time is the time difference between the time at which the slip rate drops to a level that is equal to 25% of its peak and the onset time of the signal. In addition, the slip in the LMGA located above the SMGAs is almost the same as the one in the SMGAs, and about 1.5 times larger than the average slip.

58 GEOSCIENCES↗
DOE OSTI2020

Machine Learning Based Cavity Fault Classification and Prediction

We report on the development of machine learning models for classifying C100 superconducting radio frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the eight cavities in the cryomodule. Subject matter experts (SME) can analyze the collected time-series data, identify which of the eight cavities faulted first, and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the ML models during a recent physics run.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Blockchain for Fault-Tolerant Grid Operations

Distribution system fault/failure has a direct impact on customers. Distribution systems are not designed with the contingency of one or more elements failing as the transmission system, however, distribution systems are more likely to have faults in comparison to transmission and generation systems. Most of the distribution systems have a radial design with the protection system assumption of unidirectional power flow. The radial design results in the disconnection of customers for any given component failure. The rapid deployment of distributed energy resources conflicts with the assumption of unidirectional power flow. To maintain/improve the distribution system fault tolerance for a grid with high penetration of distributed energy resources requires improvement. Blockchain can add value to improve fault-tolerant grid operations. That can be achieved using blockchain’s core features of distributed consensus-based decision-making process and immutability. In the process of preparing for an event or system restoration, there is a need for reliable data and system situational awareness. The system situational awareness enables accurate planning for possible scenarios and system restoration after an event, an area where blockchain’s distributed architecture can help. The value of blockchain for a fault-tolerant grid operation is elaborated with three use cases: 1) fault location, isolation, and service restoration; 2) topology identification; and 3) data configuration. These use cases demonstrate how blockchain-based architecture can facilitate bridging the vulnerabilities of the existing implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Activating a Natural Fault Zone in the Swiss Alps

One major hurdle for understanding earthquake mechanics are observational limitations. Important phenomena like strain localisation, fault dilation, and fault healing are readily studied in rock mechanical laboratory experiments and with numerical models. At the scale of natural earthquakes, however, these phenomena are often unresolvable, even by state-of-the-art observatories. To overcome this limitation, we are currently building the Earthquake Physics Testbed at the Bedretto Underground Laboratory for Geosciences and Geoenergies (BedrettoLab), an experimental testbed where we can activate an extensively instrumented natural fault zone via hydraulic stimulation. The goal of the Fault Activation and Earthquake Rupture (FEAR) project is to induce earthquakes of up to Mw~1.0 on this exceptionally well characterised and instrumented fault zone. Here we summarize the main scientific goals and current FEAR project status, and present first results from conducted experiments. We discuss how this large-scale experimental approach may allow us to tackle both fundamental science as well as practical questions on earthquake physics, induced seismicity and seismic hazard.

Meier, Men-Andrin↗
DOE OSTI2026

A Systematic Framework for Tuning Open-Source Multifunctional IBR Models To Emulate OEM Black-Box Fault Dynamics

This paper presents a systematic framework to tune a generic IBR EMT model to match with an OEM provided balckbox inverter model based on the fault current responses. The key learnings and findings are summarized as follows: The tunable key parameters include inner control loops and current limiters to align the fault current magnitude, sequence content, and phase trajectories with the OEM models across diverse fault type and locations. The tuned model's fidelity is validated through comparative analysis with an OEM blackbox model, assessing both the fault current response and the responses of multiple relay elements. The results demonstrate the tuned generic model can trigger relay decision logic that is identical or near identical to that of the OEM model, thus generating very good match model for fault studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Fault-tolerant controller for modular multi-level converters

Various examples are provided related to fault-tolerant controller architectures for multi-level converters. In one example, a multi-level converter includes an array of power modules. The power modules can include a controller communicatively coupled to controllers of adjacent power modules in the array of power modules. Circuitry of the controllers can receive operational data from the adjacent power modules; identify a fault condition in an adjacent power module using the operational data; and initiate reconfiguration of the array of power modules in response to an indication of the fault condition, where the reconfiguration bypasses the adjacent power module. In another example, a method includes identifying a fault condition in an adjacent power module in an array of power modules based upon operational data from one or more adjacent power modules of the array; and initiating reconfiguration of the array of power modules in response to an indication of the fault condition.

42 ENGINEERING↗