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DOE OSTI2024

Decentralized Microgrid Protection Through Relative Fault Direction Classification: Preprint

Protection in inverter-based resources (IBRs) dominated microgrids generally face significant challenges due to the low fault current and inconsistent fault behaviors from IBRs. Recently, machine learning-based approaches have attracted considerable attention to address these challenges. This paper introduces a novel decentralized protection strategy for microgrids. The proposed method decomposes the protection challenge into several distributed learning tasks, enabling individual relays to autonomously determine the direction of faults using a binary classification framework based on support vector machine (SVM) algorithms. Following the distributed fault direction estimation, classifier outcomes are shared among neighboring relays, facilitating a local decision-making process to ascertain the presence of faults within the neighborhood. Finally, a tripping signal is generated based on the classifier results of each relay to operate the circuit breaker. To test and validate this approach, a 100% renewable microgrid model is simulated in MATLAB/Simulink. In the numerical analysis, the application of SVM classifiers in our approach yields impressive results: an average relay classification accuracy of 98%, and a 96% accuracy in circuit breaker control. These findings highlight the potential of machine-learning-based approaches in enhancing the efficiency and reliability of microgrid protection systems.

decentralized algorithm↗

DOE OSTI2021

Information Leakage Analysis using a Co-design-Based Fault Injection Technique on a RISC-V Microprocessor

The RISC-V instruction set architecture open licensing policy has spawned a hive of development activity, making a range of implementations publicly available. The environments in which RISC-V operates have expanded correspondingly, driving the need for a generalized approach to evaluating the reliability of RISC-V implementations under adverse operating conditions or after normal wear-out periods. Fault injection (FI) refers to the process of changing the state of registers or wires, either permanently or momentarily, and then observing execution behavior. The analysis provides insight into the development of countermeasures that protect against the leakage or corruption of sensitive information which might occur because of unexpected execution behavior. In this paper, we develop a hardware-software co-design architecture that enables fast, configurable fault emulation and utilize it for information leakage and data corruption analysis. Modern System-on-chip FPGAs enable building an evaluation platform where control elements run on a processor(s) (PS) simultaneously with the target design running in the programmable logic (PL). Software components of the FI system introduce faults and report execution behavior. A pair of RISC-V FI-instrumented implementations are created and configured to execute the Advanced Encryption Standard and Twister algorithms. Key and plaintext information leakage and degraded pseudo-random sequences are both observed in the output for a subset of the emulated faults.

42 ENGINEERING↗

DOE OSTI2018

Simulations of Brady's-Type Fault Undergoing CO2 Push-Pull: Pressure-Transient and Sensitivity Analysis

Input and output files used for fault characterization through numerical simulation using iTOUGH2. The synthetic data for the push period are generated by running a forward simulation (input parameters are provided in iTOUGH2 Brady GF6 Input Parameters.txt [InvExt6i.txt]). In general, the permeability of the fault gouge, damage zone, and matrix are assumed to be unknown. The input and output files are for the inversion scenario where only pressure transients are available at the monitoring well located 200 m above the injection well and only the fault gouge permeability is estimated. The input files are named InvExt6i, INPUT.tpl, FOFT.ins, CO2TAB, and the output files are InvExt6i.out, pest.fof, and pest.sav (names below are display names). The table graphic in the data files below summarizes the inversion results, and indicates the fault gouge permeability can be estimated even if imperfect guesses are used for matrix and damage zone permeabilities, and permeability anisotropy is not taken into account.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Constraints on Friction, Dilatancy, Diffusivity, and Effective Stress From Low-Frequency Earthquake Rates on the Deep San Andreas Fault

Families of recurring low-frequency earthquakes (LFEs) within nonvolcanic tremor on the San Andreas Fault in central California are sensitive to tidal stresses. LFEs occur at all levels of the tides, are strongly correlated and in phase with the ~200 Pa shear stresses, and weakly and not systematically correlated with the ~2 kPa tidal normal stresses. Here, we assume that LFEs are small sources that repeatedly fail during shear within a much larger scale, aseismically slipping fault zone and consider two different models of the fault slip: (1) modulation of the fault slip rate by the tidal stresses or (2) episodic slip, triggered by the tides. LFEs are strongly clustered with duration much shorter than the semidiurnal tide; they cannot be significantly modulated on that time scale. The recurrence times of clusters, however, are many times longer than the semidiurnal, leading to an appearance of tidal triggering. In this context we examine the predictions of laboratory-observed triggered frictional (dilatant) fault slip. The undrained end-member model produces no sensitivity to the tidal normal stress, and slip onsets are in phase with the tidal shear stress. The tidal correlation constrains the diffusivity to be less than ~1 × 10 -6 /s and the product of the friction and dilatancy coefficients to be at most 5 × 10 -7 , orders of magnitude smaller than observed at room temperature. In the absence of dilatancy the effective normal stress at failure would be about ~55 kPa. Finally, for this model the observations require intrinsic weakness, low dilatancy, and lithostatic pore fluid.

58 GEOSCIENCES↗

DOE OSTI2017

Effects of the distribution and evolution of the coefficient of friction along a fault on the assessment of the seismic activity associated with a hypothetical industrial-scale geologic CO 2 sequestration operation

Carbon capture and storage (CCS) in geological formations is considered as a promising option that could limit CO 2 emissions from human activities into the atmosphere. However, there is a risk that pressure buildup inside the storage formation can induce slip along preexisting faults and create seismic event felt by the population. To prevent this to happen a geomechanical fault stability analysis should be performed, considering uncertainties of input parameters. In this paper, we investigate how the distribution of the coefficient of friction and the applied frictional law could influence the assessment of fault stability and the characteristics of potential injection-induced seismic events. Our modelling study is based on a hypothetical industrial-scale carbon sequestration project located in the Southern San Joaquin Basin in California, USA, where the stability on a major (25 km long) fault that bounds the sequestration site is assessed during 50 years of CO 2 injection. We conduct nine simulations in which the distributions of the coefficients of static and dynamic friction are changed to simulate a hardening and softening phase before and during rupture. Here, our main findings are: (i) variations in friction along the fault have an important effect on the predicted seismic activity, with maximum magnitude ranging from 1.88 to 5.88 and number of seismic events ranging from 338 to 3272; (ii) the extreme values of the coefficient of friction (lowest and highest) present along the rupture area control how much stress is accumulated before rupture; and (iii) an argillaceous caprock can prevent the development of large magnitude seismic events but favor the occurrence of a large number of smaller events.

58 GEOSCIENCES↗

DOE OSTI2016

A novel Lagrangian approach for the stable numerical simulation of fault and fracture mechanics

The simulation of the mechanics of geological faults and fractures is of paramount importance in several applications, such as ensuring the safety of the underground storage of wastes and hydrocarbons or predicting the possible seismicity triggered by the production and injection of subsurface fluids. However, the stable numerical modeling of ground ruptures is still an open issue. The present work introduces a novel formulation based on the use of the Lagrange multipliers to prescribe the constraints on the contact surfaces. The variational formulation is modified in order to take into account the frictional work along the activated fault portion according to the principle of maximum plastic dissipation. The numerical model, developed in the framework of the Finite Element method, provides stable solutions with a fast convergence of the non-linear problem. The stabilizing properties of the proposed model are emphasized with the aid of a realistic numerical example dealing with the generation of ground fractures due to groundwater withdrawal in arid regions. - Highlights: • A numerical model is developed for the simulation of fault and fracture mechanics. • The model is implemented in the framework of the Finite Element method and with the aid of Lagrange multipliers. • The proposed formulation introduces a new contribution due to the frictional work on the portion of activated fault. • The resulting algorithm is highly non-linear as the portion of activated fault is itself unknown. • The numerical solution is validated against analytical results and proves to be stable also in realistic applications.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Machine-learning-aided density functional theory calculations of stacking fault energies in steel

A combined large-scale first principles approach with machine learning and materials informatics is proposed to quickly sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and manganese. A systematic exploration of the distribution of aluminum and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computational cost of exploring the composition space, this process is sped up using an automated machine learning tool: DeepHyper. Here our results predict that it is energetically favorable for Al to stay away from a stacking fault, but Mn atoms do not affect the stacking fault energy and can stay in the vicinity of the fault. The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their interactions on the equilibrium distribution are systematically analyzed.

36 MATERIALS SCIENCE↗

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 OSTI2019

Seismic Velocity Change Patterns Along the San Jacinto Fault Zone Following the 2010 M 7.2 El Mayor–Cucapah and M 5.4 Collins Valley Earthquakes

We study temporal changes of seismic velocity (d v /v ) in the crust around the central section of the San Jacinto fault zone (SJFZ), Southern California. Focusing on a 200-day-long period around April 2010, our analysis resolves two tens-of-days-long successive episodes of reduced velocities that are compatible with signals from the long base strainmeter at the Piñon Flat Observatory. The imaged d v /v sequences are proxies for evolving material properties in the crust surrounding the SJFZ. The temporal and the spatial coincidence of the observed d v /v patterns with the occurrence of two proposed creep episodes suggest that the relative velocity changes reflect the response to deep creep events that follow the M 7.2 El Mayor-Cucapah earthquake and the M 5.4 Collins Valley earthquake that occurred 94 days later on the San Jacinto fault. The main slip during the creep events was proposed to occur below 10-km depth. Wavefield properties suggest sensitivity to medium changes above this source zone, in the top 10 km. The distribution of the obtained d v /v reductions shows a strong difference between large values to the west of the SJFZ and significantly smaller amplitudes to the east. The similarity to the seasonal velocity change pattern implies that the results are likely controlled by the contrast of mechanical properties across the fault, such as fault-perpendicular shear modulus variations. Our analysis extends the spectrum of methods that can be used to study earthquake interaction, fault zone rheology and dynamics, triggering, and the interplay between creep episodes and earthquakes.

58 GEOSCIENCES↗

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 OSTI2015

Fast Fault Injection and Sensitivity Analysis for Collective Communications, In: 2015 IEEE International Conference on Cluster Computing

The collective communication operations, which are widely used in parallel applications for global communication and synchronization are critical for application's performance and scalability. However, how faulty collective communications impact the application and how errors propagate between the application processes is largely unexplored. One of the critical reasons for this situation is the lack of fast evaluation method to investigate the impacts of faulty collective operations. The traditional random fault injection methods relying on a large amount of fault injection tests to ensure statistical significance require a significant amount of resources and time. These methods result in prohibitive evaluation cost when applied to the collectives. In this paper, we introduce a novel tool named Fast Fault Injection and Sensitivity Analysis Tool (FastFIT) to conduct fast fault injection and characterize the application sensitivity to faulty collectives. The tool achieves fast exploration by reducing the exploration space and predicting the application sensitivity using Machine Learning (ML) techniques. A basis for these techniques are implicit correlations between MPI semantics, application context, critical application features, and application responses to faulty collective communications. The experimental results show that our approach reduces the fault injection points and tests by 97% for representative benchmarks (NAS Parallel Benchmarks (NPB)) and a realistic application (Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)) on a production supercomputer. Further, we statistically generalize the application sensitivity to faulty collective communications for these workloads, and present correlation between application features and the sensitivity.

Computer Science↗

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↗