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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulating Distributed Energy Resource Responses to Transmission System-Level Faults Considering IEEE 1547 Performance Categories on Three Major WECC Transmission Paths

This study performs a large quantity of transmission and distribution simulations for three regions in the Western Interconnection along with final transmission simulations informed by distribution simulation results. Results find that the type of ride-through implemented by distributed energy resources (DERs) is a significant factor to the overall power system response to transmission-level faults. This impact is particularly high for transmission-level faults that depress voltage levels for high numbers of substations (i.e., for cases that experience large regional voltage sags). Because of the programmed nature of the ride-through of IEEE 1547-compliant DERs, the potential for large reductions in real power injection is possible based on IEEE 1547 interpretation and specific implementation. In this study, we made a first attempt to determine the quantity of DERs affected by a variety of transmission-level faults as well as a granular response of distribution-simulated DERs to a few types of IEEE 1547-2003 interpretations and IEEE 1547-2018 voltage ride-through categories. The distribution-informed transmission studies show that the category of IEEE 1547 adherence, particularly with the new 2018 standard, can have a significant impact on the amount of DER-based generation lost following transmission-level fault events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Nuclear Power Fault Diagnostics and Preventative Maintenance Optimization

Operation and maintenance costs for nuclear power plants are very large. Reactors are starting to shut down even after their operating licenses have been extended, because they are not price competitive compared to other energy sources. The nuclear industry is witnessing early closure of nuclear power plants due to economic reasons despite excellent safety records. Therefore, it is imperative to reduce costs to prevent these early closures. This paper showcases recent research into advanced fault diagnostics techniques and preventative maintenance optimization to reduce these maintenance costs. This report focuses on the condensate and feedwater system for both pressurized and boiling water reactor systems. The computerized maintenance management system, which contains the plant’s digital record of all the corrective- and preventative-maintenance work orders, was used as a ground truth to locate potential faults and label the process data as healthy or faulty. Various feature extraction techniques were utilized to further differentiate the faults from the healthy data. Support vectors machines were used to categorize other test sets of process data as healthy or faulty through a cross validation procedure. Similar faults were not found within this system leading to preventative maintenance optimization. Unnecessary amounts of preventative maintenance lead to inflated maintenance costs. This paper summarizes the steps for preventative maintenance optimization from component health determination to recommendation for action. This optimization was completed for condensate pumps, condensate booster pumps, and the respective motors that drive them.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Bringing Fault Detection and Diagnostics (FDD) Tools into the Mainstream: Retro Commissioning and Continuous Commissioning of HVAC and Refrigeration Systems

HVAC systems in the commercial building sector consume about 3.4 quads of energy annually. Of all the HVAC systems, packaged rooftop air–conditioning units (RTUs) provide cooling and heating for over 60 percent of the commercial building floorspace (about 90 billion ft2) in the U.S. and they are a significant source of energy consumption and peak demand. All HVAC systems suffer from faults that impact thermal comfort and increase energy consumption. There are several commercially available Automated Fault Detection and Diagnostic (AFDD) tools on the market that can detect and diagnose faults, and if those faults are corrected, can save significant national energy. However, there are multiple market barriers for these tools including lack of independent verification of their performance in the field in terms of their technical capabilities, ease of use and installation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A Field and Laboratory Study to Characterize Fault Prevalence in Residential Comfort Systems

This report describes a project whose goal was to determine the prevalence of residential comfort system faults. The focus is on air conditioners and heat pumps, and on key faults that can occur during installation and have significant impacts on performance: (1) incorrect refrigerant charge level (undercharge or overcharge); (2) indoor coil airflow rate; (3) liquid line restrictions; (4) non-condensable gas in the refrigerant; and (5) duct leakage. In addition to quantifying fault prevalence, we collected metadata that could be studied to determine whether there are correlations that suggest drivers of fault prevalence, such as regional variations in practice, economic factors, climatic impacts, and others.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Safety-assured, real-time neural active fault management for resilient microgrids integration

Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid's network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
DOE OSTI2023

Toward Statistical Real-Time Power Fault Detection

We propose statistical fault detection methodology based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The paper includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates.

bolted faults↗
DOE OSTI2022

Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data

Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Sequences of seismic and aseismic slip on bimaterial faults show dominant rupture asymmetry and potential for elevated seismic hazard

Here we perform numerical simulations of sequences of earthquake and aseismic slip on planar rate and state faults separating dissimilar material within the 2-D plane strain approximation. We resolve all stages of the earthquake cycle from aseismic slip to fast ruptures while incorporating full inertia effects during seismic event propagation. We show that bimaterial coupling results in favorable nucleation site and subsequent asymmetric rupture propagation. We demonstrate that increasing the material contrast enhances this asymmetry leading to higher slip rates and normal stress drops in the preferred rupture propagation direction. The normal stress drop, induced by the bimaterial effect, leads to strong dynamic weakening of the fault and may destabilize the creeping region on a heterogeneous rate and state fault, resulting in extended rupture propagation. Such rupture penetration into creeping patches may lead to more frequent opening of earthquake gates, causing increased seismic hazard. Furthermore, bimaterial coupling may lead to irregular seismicity pattern in terms of event length, peak slip rates, and hypocenter location, depending on the properties of the creeping patches bordering the seismogenically active part of the fault. Our results highlight robust characteristics of bimaterial interfaces that persist over long sequence of events and suggest the need for further exploration of the role of material contrast in earthquake physics and models of seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2026

Stacking fault analysis for the early-stages of PVT growth of 4H-SiC crystals

Here, four types of stacking fault formation mechanisms are identified and discussed for early stage PVT growth of 4H-SiC crystals: Type 1: Shockley / double Shockley stacking fault formation inside the facet; Type 2: Stacking fault formation via 2D nucleation; Type 3: Frank + Shockley (S) stacking fault formation due to deflection of threading mixed dislocation (TMD) by macrosteps; Type 4: “Carrot” defect formation related to overgrowth of the terrace formed by separation of 3c/4 and c/4 step risers after deflection of a TMD by vicinal steps. The results help further reduction of defect generation in 4H-SiC substrates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Stacking Faults and Topological Properties in MnBi 2 Te 4 : Reconciling Gapped and Gapless States

Despite theoretical predictions of a gapped surface state for the magnetic topological insulator MnBi 2 Te 4 (MBT), there has been a series of experimental evidence pointing toward gapless states. Here, we theoretically explore how stacking faults could influence the topological characteristics of MBT. We envisage a scenario that a stacking fault exists at the surface of MBT, causing the uppermost layer to deviate from the ground state and its interlayer separation to be expanded. This stacking fault with modulated interlayer couplings hosts a nearly gapless state within the topmost layer due to charge redistribution as the outermost layer recedes. Furthermore, we find evidence of spin-momentum locking and preservation of weak band inversion in the gapless surface state, suggesting the nontrivial topological surface states in the presence of the stacking fault. In conclusion, our findings provide a plausible elucidation to the long-standing conundrum of reconciling the observation of gapped and gapless states on MBT surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Stacking Fault Induced Symmetry Breaking in van der Waals Nanowires

While traditional ferroelectrics are based on polar crystals in bulk or thin film form, two-dimensional and layered materials can support mechanisms for symmetry breaking between centrosymmetric building blocks, e.g., by creating low-symmetry interfaces in van der Waals stacks. Here, we introduce an approach toward symmetry breaking in van der Waals crystals that relies on the spontaneous incorporation of stacking faults in a nonpolar bulk layer sequence. The concept is realized in nanowires consisting of Se-rich group IV monochalcogenide (GeSe 1–x S x ) alloys, obtained by vapor–liquid–solid growth. The single crystalline wires adopt a layered structure in which the nonpolar A-B bulk stacking along the nanowire axis is interrupted by single-layer stacking faults with local A-A' stacking. Density functional theory explains this behavior by a reduced stacking fault formation energy in GeSe (or Se-rich GeSe 1–x S x alloys). Computations demonstrate that, similar to monochalcogenide monolayers, the inserted A-layers should show a spontaneous electric polarization with a switching barrier consistent with a Curie temperature above room temperature. Second-harmonic generation signals are consistent with a variable density of stacking faults along the wires. Here, our results point to possible routes for designing ferroelectrics via the layer stacking in van der Waals crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mitigating Injection‐Induced Seismicity Along Basement Faults by Extraction: Application to 2016–2018 Pohang Earthquakes

Abstract The risk of induced earthquake is considered as the major obstacle for sustainable subsurface energy activities. As a mitigation approach, mechanistic risk assessment can be essential to minimize the potential of seismic hazard. Coupled hydro‐mechanical modeling approach is integrated with geomechanical analysis to better assess the occurrence of seismicity induced by injection and/or extraction, which will provide a basis to update operational conditions (e.g., number and location of mitigating wells, rate and duration of operations) for mitigating earthquake nucleation. The Coulomb stability analysis and seismicity rate estimates from this generic study indicate that simultaneous extraction can relieve injection‐induced pore‐pressure buildup, but also transfer and accumulate elastic energy along the faults. Field‐scale simulation with variation in well design for the Pohang EGS site, South Korea, show that multiple wells aligned across a preexisting fault form gradients of pressure and stress fields normal to the fault, which can enhance the direct impact of diffusion process and poroelastic stressing on the fault stability perturbation. Our mechanistic study suggests multiphysics‐oriented mitigation strategies that consider (1) possible mechanical deformation, caused by hydro‐mechanical coupling effects, and (2) proper configuration of well operations based on site‐specific geological characteristics.

Chang, K. W.↗
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 OSTI2023

Lateral Variations Across the Southern San Andreas Fault Zone Revealed From Analysis of Traffic Signals at a Dense Seismic Array

Abstract We image the shallow seismic structure across the Southern San Andreas Fault (SSAF) using signals from freight trains and trucks recorded by a dense nodal array, with a linear component perpendicular to SSAF and two 2D subarrays centered on the Banning Fault and Mission Creek Fault (MCF). Particle motion analysis in the frequency band 2–5 Hz shows that the examined traffic sources can be approximated as moving single‐ or multi‐point sources that primarily induce Rayleigh waves. Using several techniques, we resolve strong lateral variations of Rayleigh wave velocities and Q ‐values across the SSAF, including 35% velocity reduction across MCF toward the northeast and strong attenuation around the two fault strands. We further resolve 10% mass density reduction and 45% shear modulus decrease across the MCF. These findings suggest that the MCF is currently the main strand of the SSAF in the area with important implications for seismic hazard assessments.

58 GEOSCIENCES↗
DOE OSTI2024

Intermittent Criticality Multi‐Scale Processes Leading to Large Slip Events on Rough Laboratory Faults

Abstract We discuss data of three laboratory stick‐slip experiments on Westerly Granite samples performed at elevated confining pressure and constant displacement rate on rough fracture surfaces. The experiments produced complex slip patterns including fast and slow ruptures with large and small fault slips, as well as failure events on the fault surface producing acoustic emission bursts without externally‐detectable stress drop. Preparatory processes leading to large slips were tracked with an ensemble of ten seismo‐mechanical and statistical parameters characterizing local and global damage and stress evolution, localization and clustering processes, as well as event interactions. We decompose complex spatio‐temporal trends in the lab‐quake characteristics and identify persistent effects of evolving fault roughness and damage at different length scales, and local stress evolution approaching large events. The observed trends highlight labquake localization processes on different spatial and temporal scales. The preparatory process of large slip events includes smaller events marked by confined bursts of acoustic emission activity that collectively prepare the fault surface for a system‐wide failure by conditioning the large‐scale stress field. Our results are consistent overall with an evolving process of intermittent criticality leading to large failure events, and may contribute to improved forecasting of large natural earthquakes.

Geochemistry & Geophysics↗
DOE OSTI2024

Stress Field Dynamics and Fault Slip Potential in the Paradox Basin

Abstract The Paradox Basin, straddling Utah, Colorado, Arizona, and New Mexico is characterized by an intricate amalgamation of evaporites and clastic layers and is dominated by prominent salt walls and related subsurface structures. Our research offers a new examination of the stress distribution across the basin, deriving from continuous and discrete stress measurements conducted in boreholes in the region and focal mechanism analysis, emphasizing variations over salt structures. Integrating Coulomb failure criteria with probabilistic methods, we assess potential fault movements resulting from fluid pressure alterations. Our approach provides a comprehensive understanding of the Paradox Basin's state of stress, showing a continuous change of the maximum horizontal stress orientation from N‐S at the Wasatch Fault Zone to WNW‐ESE in the northern part of the Paradox Basin and to WSW‐ENE in the southern part of the basin. Further East, into the Colorado Plateau and the Uncompahgre Uplift, the S H max orientation becomes E‐W. Decoding stress orientation dynamics has enabled critical insights into fault slip potential, especially in the basin's northern region. The salt wall faults are less likely to slip, and the Paradox Formation's evaporite and clastic rock sequence can serve as a potential low seismic risk target for carbon storage and hydrocarbon extraction.

Geochemistry & Geophysics↗