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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A SIMULATION BASED APPROACH TO IMPACT ASSESSMENT OF PHYSICAL FAULTS: LARGE COMMERCIAL BUILDING HVAC CASE STUDY

We develop a simulation-based approach to fault impact analysis for a single duct VAV system, and analyze the dynamic and steady-state impact of equipment physical faults on building operation. The simulation model, a virtual testbed, can capture physical faults in the HVAC system of a large commercial building. The model can be utilized for generating data that can then be analyzed to quantify the impact of single or multiple faults. We illustrate the implementation of three physical faults: supply air temperature sensor bias, outdoor air temperature sensor bias, and cooling coil valve stuck. We simulate each fault with several fault intensity levels and at different seasonal operating conditions. Then, we analyze the impact of the above-mentioned faults using the metrics. The results show that the impact of a given physical fault depends on implemented control logic sequence and the seasonal operating conditions.

Chen, Yan↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2018

Kinematics of the 2015 San Ramon, California earthquake swarm: Implications for fault zone structure and driving mechanisms

Earthquake swarms represent a sudden increase in seismicity that may indicate a heterogeneous fault-zone, the involvement of crustal fluids and/or slow fault slip. Swarms sometimes precede major earthquake ruptures. An earthquake swarm occurred in October 2015 near San Ramon, California in an extensional right step-over region between the northern Calaveras Fault and the Concord–Mt. Diablo fault zone, which has hosted ten major swarms since 1970. The 2015 San Ramon swarm is examined here from 11 October through 18 November using template matching analysis. The relocated seismicity catalog contains ~4000 events with magnitudes between -0.2 < M d < 3.6. The swarm illuminated three sub-parallel, southwest striking and northwest dipping fault segments of km-scale dimension and thickness of up to 200 m. The segments contain coexisting populations of different focal-mechanisms, suggesting a complex fault zone structure with several sets of en échelon fault orientations. The migration of events along the three planar structures indicates a complex fluid and faulting interaction processes. We searched for correlations between seismic activity and tidal stresses and found some suggestive features, but nothing that we can be confident is statistically significant.

58 GEOSCIENCES↗
DOE OSTI2019

Hydromechanical modelling to evaluate impact of fault structure on CO 2 migration in stacked storage system

Faulting processes produce a complex fault damage zone (FDZ) having permeability variations over several orders of magnitude within a very short distance. In this study we use an example normal fault system from a depleted oil field in southern Louisiana with multiple stacked sand beds along the fault structure. Geostatistical techniques are used to introduce heterogeneities of several orders of magnitude over very short distances that honor the across- and along-fault architecture of the system. A total of 15 cases are simulated to account for the impact of a fragmented core, an intact core and a naturally fractured core on inter-sand leakage rates. The presented results show the complex nature of CO 2 migration within the fault structure, where an interplay between overcoming the fault core’s high capillary entry pressure and stress propagation in the vertical direction dictates the leakage direction and rates. It is observed that hydromechanical evolution of fracture flow properties may result in significant leakage from the storage zone. In one case with small caprock thickness, a loss of 18% of the total injected mass is observed. The results presented highlight some unique features of representative fault related leakage in stacked sand systems and thus will be beneficial for storage integrity analysis in these systems.

58 GEOSCIENCES↗
DOE OSTI2022

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Study on fault diagnosis system of active magnetic bearing - 18549

The three main mechanical faults of the active magnetic bearing (AMB) system are put forward and distinguished from the electrical faults. Their mechanism is elaborately investigated and the quantitative relationship between failures and consequent extra forces acting on the rotors is identified. The principles for fault recognition are determined on the basis of the fault mechanism and operating experience in conventional rotating machinery. Fault features extraction methods including Poincare Map and Fast Fourier Transformation and other algorithm are adopted. A practical fault diagnosis system based on the LabVIEW platform is realized. The test result demonstrates the effectiveness of the fault diagnosis system in on-line detection and identification of mechanical faults in AMB systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Seismic velocity changes associated with aseismic deformations of a fault stimulated by fluid injection

Fluid pressure plays an important role in the stability of tectonic faults. However, the in situ mechanical response of faults to fluid pressure variations is still poorly known. To address this question, we performed here a fluid injection experiment in a fault zone in shales while monitoring fault movements at the injection source and seismic velocity variations from a near-distance (<10 m) monitoring network. We measured and located the P and S wave velocity perturbations in and around the fault using repetitive active sources. We observed that seismic velocity perturbations dramatically increase above 1.5 MPa of injection pressure. This is consistent with an increase of fluid flow associated with an aseismic dilatant shearing of the fault as shown by numerical modeling. We find that seismic velocity changes are sensitive to both fault opening by fluid invasion and effective stress variations and can be an efficient measurement for monitoring fluid-driven aseismic deformations of faults.

58 GEOSCIENCES↗
DOE OSTI2018

Estimating Fault Friction From Seismic Signals in the Laboratory

Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing but in general cannot be directly measured. We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. Finally, these results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.

58 GEOSCIENCES↗
DOE OSTI2017

Pressure Monitoring to Detect Fault Rupture Due to CO 2 Injection

The capacity for fault systems to be reactivated by fluid injection is well-known. In the context of CO 2 sequestration, however, the consequence of reactivated faults with respect to leakage and monitoring is poorly understood. Using multi-phase fluid flow simulations, this study addresses key questions concerning the likelihood of ruptures, the timing of consequent upward leakage of CO 2 , and the effectiveness of pressure monitoring in the reservoir and overlying zones for rupture detection. A range of injection scenarios was simulated using random sampling of uncertain parameters. These include the assumed distance between the injector and the vulnerable fault zone, the critical overpressure required for the fault to rupture, reservoir permeability, and the CO 2 injection rate. We assumed a conservative scenario, in which if at any time during the five-year simulations the critical fault overpressure is exceeded, the fault permeability is assumed to instantaneously increase. For the purposes of conservatism we assume that CO 2 injection continues ‘blindly’ after fault rupture. We show that, despite this assumption, in most cases the CO 2 plume does not reach the base of the ruptured fault after 5 years. As a result, one possible implication of this result is that leak mitigation strategies such as pressure management have a reasonable chance of preventing a CO 2 leak.

58 GEOSCIENCES↗
DOE OSTI2021

Field-scale fault reactivation experiments by fluid injection highlight aseismic leakage in caprock analogs: Implications for CO 2 sequestration

Observations on tens-of-meter scale experiments of fault activation by fluid injection conducted in shales allow exploring how aseismic and seismic events may jeopardize the integrity of a sealing caprock overlying a CO 2 sequestration reservoir. We contrast the behavior of shale faults with another set of experiments conducted in carbonates. Significant fluid leakage occurs along the initially low-permeability shale faults when rupture is activated. Most of the leakage pathway closes when fluid injection ceases and fluid pressure drops. Dilatant slip on the fault plane alone does not explain the observed leakage behavior, which is also caused by fault opening favored by the softness of the shale, and by the structure of the fault zone that prevents fluids from diffusing into the adjacent damage zone. Experiments show a large amount of aseismic deformation. Small-magnitude seismicity (M w < -2.5) is observed outside the pressurized leakage patch. Stress transferred from this aseismic deformation patch can build up to stress-criticality and favor seismicity. Thus, in terms of fault activation in caprocks, aseismic fault slip leading to increased permeability and a loss of seal integrity is of great concern.

58 GEOSCIENCES↗
DOE OSTI2022

Evaluation of possible reactivation of undetected faults during CO 2 injection

Geologic storage of carbon dioxide can efficiently contribute to reduce greenhouse gas emissions to the atmosphere. Two major hazards of this technology are leakage towards the ground surface and fluid-induced seismicity. While major faults may be detected and avoided during site characterization, undetected subseismic faults could be encountered once injection has started. This paper investigates leakage and reactivation of undetected faults through coupled thermal–hydraulic–mechanical modeling. The simulations are performed using a recently developed sequential simulator, TOUGH-Pylith, that allows to accurately account for the thermodynamics of brine-CO 2 injection mixtures, and to model faults as surfaces of discontinuity using state-of-the-art fault friction laws. The simulator is benchmarked against well-known analytical solutions and subsequently applied to investigate two cases of CO 2 injection injection close to undetected faults under normal and strike-slip faulting regimes. Furthermore, although the scenarios are generic and represent unfavorable conditions, they suggest that leakage could occur and that undetected faults could trigger minor seismic events. Therefore, careful site characterization and continuous monitoring during operations should be always performed.

58 GEOSCIENCES↗
DOE OSTI2024

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

58 GEOSCIENCES↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Role of Well Operations and Multiphase Geomechanics in Controlling Fault Stability During CO 2 Storage and Enhanced Oil Recovery

CO 2 injection in active oil fields is a technology proposed for industrializing carbon storage operations. However, monitoring CO 2 migration, oil recovery, and mechanical deformation within caprock and on faults is a challenging problem in large-scale storage-enhanced oil recovery operations due to intersecting faults, multiphase flow effects, and wells with complex production-injection schedule. We develop and demonstrate a methodology based on our coupled modeling framework to monitor the movement of CO 2 , hydrocarbons and water, and the associated evolution in mechanical stability of faults during CO 2 storage-enhanced oil recovery in the Farnsworth Unit oil field in Texas, United States. The methodology honors geological, geophysical, and production-injection data acquired in the field over six decades. Differential depletion from hydraulically isolated fault compartments followed by water and CO 2 injection-induced overpressure causes volumetric contraction and expansion of the reservoir, and changes in the total and effective stresses in the overburden-reservoir-underburden complex. CO 2 migrates upward to accumulate near top of the geologic structure, and water migrates downward to pressurize the faults. Three-dimensional changes in the pressure and stress fields in the system lead to changes in the shear and effective normal tractions on three major faults compartmentalizing the field. Evolution in fault tractions is used to compute the evolution in the Coulomb failure function of the faults to quantify induced-slip tendency under production and injection. We explain the spatial heterogeneity and time variability of Coulomb failure function in terms of well location heterogeneity and well rate variability.

58 GEOSCIENCES↗
DOE OSTI2019

Fault Characteristics of Distributed Solar Generation

Inverter-based distributed energy resources (DERs) are characterized with low fault current and negligible amount of negative and zero sequence currents. Understanding DER's fault characteristics is important for fault analysis and protective relay setting. Despite the abundant work on DER modeling, few research studies have been done to analyze DER's fault behaviors during actual fault events. Our work explores recorded fault events collected by Dominion Energy. Fault magnitude, angle, and sequence components are analyzed to show that actual DER fault response may differ from previous understandings.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence

The 2016–2017 central Italy seismic sequence occurred on an 80 km long normal-fault system. The sequence initiated with the Mw 6.0 Amatrice event on 24 August 2016, followed by the Mw 5.9 Visso event on 26 October and the Mw 6.5 Norcia event on 30 October. We analyze continuous data from a dense network of 139 seismic stations to build a high-precision catalog of ~900,000 earthquakes spanning a 1 yr period, based on arrival times derived using a deep-neural-network-based picker. Our catalog contains an order of magnitude more events than the catalog routinely produced by the local earthquake monitoring agency. Aftershock activity reveals the geometry of complex fault structures activated during the earthquake sequence and provides additional insights into the potential factors controlling the development of the largest events. Activated fault structures in the northern and southern regions appear complementary to faults activated during the 1997 Colfiorito and 2009 L’Aquila sequences, suggesting that earthquake triggering primarily occurs on critically stressed faults. Delineated major fault zones are relatively thick compared to estimated earthquake location uncertainties, and a large number of kilometer-long faults and diffuse seismicity were activated during the sequence. These properties might be related to fault age, roughness, and the complexity of inherited structures. The rich details resolvable in this catalog will facilitate continued investigation of this energetic and well-recorded earthquake sequence.

58 GEOSCIENCES↗
DOE OSTI2022

Node Monitoring as a Fault Detection Countermeasure against Information Leakage within a RISC-V Microprocessor

Advanced, superscalar microprocessors (μP) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the μP. Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The μP designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the μPs.

42 ENGINEERING↗