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This is a zipped ArcGIS shapefile containing faults mapped for the Tularosa Basin geothermal play fairway analysis project. The faults were interpolated from gravity and seismic (NASA area) data, and from geomorphic features on aerial photography. Field work was also done for validation of faults which had surface expressions.
These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.
Over the past several years, we have developed techniques to discriminate between fault-prone software modules and those that are not, to estimate a software system's residual fault content, to identify those portions of a software system having the highest estimated number of faults, and to estimate the effects of requirements changes on software quality.
Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.
We report that Multi-Agent Systems (MAS) are seen from different areas as one of the paramount trends for the next generation of power systems. Numerous published studies about MAS discuss its utilization in power distribution networks but none focuses on the prior step to restoration and self-healing that is fault section estimation. This paper aims to show how MAS can improve the utilities’ reliability indexes and consumer satisfaction by overcoming the multiple fault section estimation problem. In order to do this, the authors considered using MAS as a means of communication between smart meters. The purpose of smart meters usage is to employ devices that are already present in smart grids, mainly because of their reading and saving data capacity. The proposed method was tested on a radial feeder generated by the authors. The network was built on HYPERSIM, a software platform of OPAL-RT Technologies. The simulation results show that this MAS provides speed, efficiency, and automation for the process of fault section estimation.
Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.
Alloy 800H is a high nickel austenitic stainless steel with good high temperature mechanical properties which is considered for use in current and advanced nuclear reactor designs. The irradiation response of 800H was examined by characterizing samples that had been bulk ion irradiated at the Michigan Ion Beam Laboratory with 5 MeV Fe 2+ ions to 1, 10, and 20 dpa at 440 °C. Transmission electron microscopy was used to measure the size and density of both {111} faulted dislocation loops and cavities as functions of depth from the irradiated surface. The faulted loop density increased with dose from 1 dpa up to 10 dpa where it saturated and remained approximately the same until 20 dpa. The faulted loop average diameter decreased between 1 dpa and 10 dpa and again remained approximately constant from 10 dpa to 20 dpa. Cavities were observed after irradiation doses of 10 and 20 dpa, but not after 1 dpa. The average diameter of cavities increased with dose from 10 to 20 dpa, with a corresponding small decrease in density. Cavity denuded zones were observed near the irradiated surface and near the ion implantation peak. To further understand the microstructural evolution of this alloy, FIB lift-out samples from material irradiated in bulk to 1 and 10 dpa were re-irradiated in-situ in their thin-foil geometry with 1 MeV Kr 2+ ions at 440 °C at the Intermediate Voltage Electron Microscope. We observed that the cavities formed during bulk irradiation shrank under thin-foil irradiation in-situ while dislocation loops were observed to grow and incorporate into the dislocation network. The thin-foil geometry used for in-situ irradiation is believed to cause the cavities to shrink.
The reactivation of faults induced by natural/human induced fluid pressure increases is a major concern to explain subsurface fluid migration and to estimate the risk of losing the integrity of reservoir/seal systems. This study focusses on paleo-fluid migration in a strike slip fault with >100 m long, affecting a Toarcian shale (Causses Basin, France). A high calcite concentration is observed in a 5 cm thick zone at the boundary between the fault core and damage zone. Cumulated displacements in this zone are of millimeter-to-centimeter-scale offsets and different dilatant deformation textures are observed. The zone is affected by thin slip planes containing gouge. Cathodo-luminescence observations indicate that two phases of vein formation occurred. The first phase coincides with the fluid migration along this centimeter thick dilatant zone. The second one is associated to re-shear along the millimeter thick slip planes that results in more localized mineralization, but also in a better hydrologic connection through the shale formation. These results show that in shales fluids may migrate off a slipping surface in centimeter scale dilatant volumes, at first controlled by the intact shale anisotropy related to bedding and then favored by brecciating, structures re-orientation and strengthening processes induced by calcite sealing effects.
Commercial buildings often experience faults that waste energy, decrease occupant comfort, and increase operating costs. For medium and larger commercial buildings (buildings with more than approximately 1000 m 2 [approximately 10,000 ft 2 ] of floor area), studies have shown that automated fault detection and diagnosis (AFDD) tools can help building owners and operators identify and correct faults, improving building performance and producing up to 10% energy savings. However, the existing state of the art in AFDD tools and algorithms poorly serves the needs of commercial buildings less than approximately 1000 m2 (approximately 10,000 ft 2 ). Using the United States market and building stock as a case study, this article characterizes AFDD needs for small commercial buildings, surveys the types of AFDD tools presently available in the market, identifies gaps and barriers to widespread adoption of AFDD technology in small commercial buildings, and makes recommendations for the future research and development of small buildings AFDD technology.
The increase of temperature of a low-permeability, fluid-saturated media may trigger significant thermal pressurization, where the expansion of pore fluid cannot be accommodated by the thermal expansion of the pore space. With the aid of a coupled thermohydromechanical numerical simulator, we investigate the possible impact of thermal pressurization during the life of a deep geological repository (DGR) for high-level radioactive waste, characterized by the emplacement of radioactive material-filled canisters in a series of parallel tunnels, excavated in a low-permeability clay formation. We represent the fault as a planar structure embedded in a thermoporoelastic material and shear activation evaluated by a strain-softening Mohr-Coulomb failure criterion. The results show that stress changes caused by temperature and thermal pressurization of a rock mass around the emplacement tunnels may trigger a slip event on a fault plane in proximity of the geological disposal site: Rupture nucleates at depth, hundreds of meters below the DGR. Stress transfer plays a key role, while a direct hydraulic connection between the repository and the nucleation zone is not necessary in order to trigger rupture. A low stress ratio may favor the occurrence of slip up to a distance of 600 m of the fault from the outermost tunnel. Furthermore, these results highlight the need of investigating hydromechanical properties and local stress conditions at depth to characterize the geomechanical response of weak planes located in the surroundings of a high-level radioactive waste repository and to provide sufficient knowledge for the safe development of the DGR site.
Large-scale CO 2 sequestration into geological formations has been suggested to reduce CO 2 emissions from industrial activities. However, much like enhanced geothermal stimulation and wastewater injection, CO 2 sequestration has a potential to induce earthquake along weak faults, which can be considered a negative impact on safety and public opinion. This research shows the physical mechanisms of potential seismic hazards along basement faults driven by CO 2 sequestration under variation in geological and operational constraints. Specifically we compare the poroelastic behaviors between multiphase flow and single-phase flow cases, highlighting specific needs of evaluating induced seismicity associated with CO 2 sequestration. In contrast to single-phase injection scenario, slower migration of the CO 2 plume than pressure pulse may delay accumulation of pressure and stress along basement faults that may not be mitigated immediately by shut-in of injection. The impact of multiphase flow system, therefore, needs to be considered for proper monitoring and mitigation strategies.
Earthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real-time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR-GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine-Learning Assessed Rapid Geodetic Earthquake model (M-LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M-LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real-time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.
The electrical and optical properties of stacking faults (SFs) introduced by plastic deformation in 4H-SiC were studied by Electron Beam Induced Current (EBIC) and cathodoluminescence (CL) methods. Partial dislocations and stacking faults in the (0001) glide planes perpendicular to the surface were introduced in n-type 4H-SiC under a well-controlled state of stress by cantilever bending at 550°C. CL measurements allow determining the multiplicity of the SFs (single or double). It is observed that the overwhelming majority of stacking faults are double Shockley type SFs (CL emission at 504 nm) that correlates well with previously published high resolution transmission electron microscopy (HRTEM) investigations. However, single Shockley type SFs (CL emission at 422 nm) of much smaller lengths are also observed in some areas near the scratched region from where the defects are nucleated. This suggests that the velocity of partial dislocation pairs under a given applied stress could be higher than that of single partial dislocations. It is also shown that in the EBIC mode, SFs produce a strong bright contrast, which can be explained by considering the SFs in 4H-SiC to be quantum wells of II type.
We perform correlated studies of individual GaN nanowires in scanning electron microscopy combined to low temperature cathodoluminescence, microphotoluminescence, and scanning transmission electron microscopy. We show that some nanowires exhibit well localized regions emitting light at the energy of a stacking fault bound exciton (3.42 eV) and are able to observe the presence of a single stacking fault in these regions. Precise measurements of the cathodoluminescence signal in the vicinity of the stacking fault give access to the exciton diffusion length near this location.
In magnetic recording media, the grains or clusters reverse their magnetization over a range of reversal field, resulting in a switching field distribution. In order to achieve high areal densities, it is desirable to understand and minimize such a distribution. Clusters of grains which contain stacking faults (SF) or fcc phase have lower anisotropy, an order lower than those without them. It is believed that such low anisotropy regions reverse their magnetization at a much lower reversal field than the rest of the material with a larger anisotropy. Such clusters/grains cause recording performance deterioration, such as adjacent track erasure and dc noise. Therefore, the observation of clusters that reverse at very low reversal fields (nucleation sites, NS) could give information on the noise and the adjacent track erasure. Potentially, the observed clusters could also provide information on the SF. In this paper, we study the reversal of nucleation sites in granular perpendicular media based on a magnetic force microscope (MFM) methodology and validate the observations with high resolution cross-section transmission electron microscopy (HRTEM) measurements. Samples, wherein a high anisotropy CoPt layer was introduced to control the NS or SF in a systematic way, were evaluated by MFM, TEM, and magnetometry. The magnetic properties indicated that the thickness of the CoPt layer results in an increase of nucleation sites. TEM measurements indicated a correlation between the thickness of CoPt layer and the stacking fault density. A clear correlation was also observed between the MFM results, TEM observations, and the coercivity and nucleation field of the samples, validating the effectiveness of the proposed method in evaluating the nucleation sites which potentially arise from stacking faults.
A novel Micro-Raman technique was designed and used to detect extended defects in 4H-SiC homoepitaxy. The technique uses above band-gap high-power laser densities to induce a local increase of free carriers in undoped epitaxies (n < 10{sup 16} at/cm{sup −3}), creating an electronic plasma that couples with the longitudinal optical (LO) Raman mode. The Raman shift of the LO phonon-plasmon-coupled mode (LOPC) increases as the free carrier density increases. Crystallographic defects lead to scattering or recombination of the free carriers which results in a loss of coupling with the LOPC, and in a reduction of the Raman shift. Given that the LO phonon-plasmon coupling is obtained thanks to the free carriers generated by the high injection level induced by the laser, we named this technique induced-LOPC (i-LOPC). This technique allows the simultaneous determination of both the carrier lifetime and carrier mobility. Taking advantage of the modifications on the carrier lifetime induced by extended defects, we were able to determine the spatial morphology of stacking faults; the obtained morphologies were found to be in excellent agreement with those provided by standard photoluminescence techniques. The results show that the detection of defects via i-LOPC spectroscopy is totally independent from the stacking fault photoluminescence signals that cover a large energy range up to 0.7 eV, thus allowing for a single-scan simultaneous determination of any kind of stacking fault. Combining the i-LOPC method with the analysis of the transverse optical mode, the micro-Raman characterization can determine the most important properties of unintentionally doped film, including the stress status of the wafer, lattice impurities (point defects, polytype inclusions) and a detailed analysis of crystallographic defects, with a high spectral and spatial resolution.
Conductor-on-round-core (CORC®) cables composed of rare-earth-barium-copper-oxide high-temperature superconducting (HTS) tapes are of great interest for power transmission applications due to their many advantages such as high power density, light weight, and low loss. Closed circulation loops of cryogenic helium gas can be used to cool HTS cables down to low temperatures to significantly improve their current-carrying capacity. Coupled circuit-electromagnetic-thermal finite element simulations implemented in the COMSOL Multiphysics package were developed, validated, and then used for simulating the fault current limiting (FCL) performance and the cooling processes of an 8-layer CORC® cable cooled with a flow of cryogenic helium gas. In the simulations, the temperature dependence of the electrical and thermal properties of all component materials is implemented for improved accuracy. To overcome computational challenges caused by the considerable difference in geometrical scales (i.e. few-µm-thick HTS layers versus 10 m-long HTS cable), the model is divided into two separate simulations. The first simulation is performed on the transverse cross-section of the cable to calculate the electric field, heating power and temperature rise in each component of a CORC® cable during FCL operation. The heating power calculated in the first simulation is transferred to the second model to simulate the cooling of a 10 m-long cable after the fault is cleared. The effect of the helium gas flow rate on the cooling process is also investigated to develop strategic approaches for optimizing cooling systems for HTS cables with FCL capability. The simulations indicated that a 40 ms fault with a voltage drop of 20 V m −1 along the cable can result in a temperature increase from 60 K to about 165 K inside the cable, and it takes about 500 s to cool the cable back to nearly 60 K with a flow of cold helium gas at a rate of 5 g s −1 .
Deterministic replay of a parallel application is commonly used for discovering bugs or to recover from a hard fault with message-logging fault tolerance. For message passing programs, a major source of overhead during forward execution is recording the order in which messages are sent and received. During replay, this ordering must be used to deterministically reproduce the execution. Previous work in replay algorithms often makes minimal assumptions about the programming model and application in order to maintain generality. However, in many cases, only a partial order must be recorded due to determinism intrinsic in the code, ordering constraints imposed by the execution model, and events that are commutative (their relative execution order during replay does not need to be reproduced exactly). In this paper, we present a novel algebraic framework for reasoning about the minimum dependencies required to represent the partial order for different concurrent orderings and interleavings. By exploiting this theory, we improve on an existing scalable message-logging fault tolerance scheme. The improved scheme scales to 131,072 cores on an IBM BlueGene/P with up to 2x lower overhead than one that records a total order.