Breakdown Events Research Articles

Two-stage learning scatter search algorithm for the distributed hybrid flow shop scheduling problem with machine breakdown

The distributed hybrid flow shop scheduling problem with machine breakdown is investigated to reduce the negative impact on real production caused by machine breakdown events. (DHFSPMB). DHFSPMB comprises two subproblems: the maintenance problem with machine breakdown and the distributed hybrid flow shop scheduling problem (DHFSP). A rescheduling method is designed to address the maintenance problem. Subsequently, a two-stage learning scatter search (TLSS) algorithm is proposed for optimizing the DHFSP when the machines break down. Firstly, a mixed integer programming model for DHFSPMB is constructed. Secondly, TLSS employs an improved reinforcement learning approach to enhance the capability of exploration by guiding the direction of global search. A two-stage approach is designed to address the lack of knowledge in the early periods of learning. Finally, a hybrid search strategy is devised to enhance the development capability of TLSS. The experimental results demonstrate that the TLSS algorithm outperforms the comparison algorithms in effectively addressing the DHFSPMB.

Destructive dielectric breakdown of 2D muscovite mica

This study investigates the destructive breakdown (DBD) phenomenon in the van der Waals gate dielectric 2D muscovite mica (4–12 nm thick), focusing on its electrical reliability as a gate dielectric material. Capacitor test structures were electrically stressed, and the resulting impact on the physical structure was analyzed using atomic force microscopy. The volume of material removed in a DBD event is found, and the energy required (Ereq) to vaporize the volume was calculated. It is found that Ereq is proportional to the average electrical energy dissipated in the capacitor during breakdown (BD), indicating a direct correlation between damage caused during DBD and the current flow at BD location. In contrast to other thin film dielectrics, the 2D mica is highly susceptible to DBD even at very low current density (<1 A/cm2) and the abrupt, destructive BD more resembles that of thick film dielectric breakdown. An explanation for these finding is proposed in which intercalated K+ ions agglomerate around defects generated by the electrical stressing such that the defect density increases substantially in the local vicinity of BD locations, which leads to increased current and associated Joule heating after the BD event.

Robust Design of Electrical System for Nuclear Plants to Prevent a Nuclear Accident During an Emergency Situation

The Chernobyl accident taught us an important lesson regarding the significance of the emergency diesel generator (EDG), which is crucial for supplying a nuclear facility with emergency alternating-current power. A worst-case scenario would occur if the EDG failed to start functioning. In response to this problem, this research addresses the issue and suggests an optimal design for another alternative electrical source using solar energy. Until now, employing this technology presented a challenge as a solar energy system generates electrical harmonics that are transmitted from the photovoltaic (PV) grid to the electrical network. These harmonics can negatively impact the performance and operation of nuclear facilities. Consequently, this study proposes a comprehensive investigation that tackles this issue by implementing a LCL filter to mitigate the harmonics in the PV network. This paper not only provides an alternative solution in the event of a diesel generator breakdown, but also studies the effect of this solution on the station’s performance and how to improve it. A mathematical model based on the MATLAB program is developed to simulate the application of the LCL filter strategy. The proposed technique is tested through simulation with an actual case, demonstrating its reliability and high-quality outcomes.

Quantitative time-resolved diagnostics of electric field dynamics during individual plasma breakdown events using burst laser pulse electric field induced second harmonic generation

In plasma discharges, the acceleration of electrons by a fast varying electric field and the subsequent collisional electron energy transfer determines the plasma dynamics, chemical reactivity, and breakdown. Current in situ electric field measurements require reconstruction of the temporal profile over many observations. However, such methods are unsuitable for non-repetitive and unstable plasmas. Here, we present a method for creating “movies” of dynamic electric fields in a single acquisition at sample rates of 500 × 106 fps. This ultrafast diagnostic was demonstrated in radio frequency electric fields between two parallel plates in air, as well as in Ar nanosecond-pulsed single-sided dielectric barrier discharges.

Investigating the Impact of Hardness on Dielectric Breakdown Characteristics of Polyurethane.

Polymeric materials play a vital role in high-voltage insulation, but their insulating properties can deteriorate over time, leading to insulation failures. The presence of voids resulting from manufacturing defects or external stresses can create a highly divergent field, further contributing to this issue. However, certain polymers, such as polyurethane (PU), possess self-healing properties that enable them to repair these voids and restore a uniform electric field distribution, thereby ensuring the reliability of the insulation. Surprisingly, the potential of PU as an insulating material in high-voltage applications remains unexplored. However, the self-healing capability of PU decreases with an increase in the hardness of the material. Therefore, in this study, the dielectric breakdown properties of PU with different levels of hardness, rated on the Shore scale as 40° (soft), 70° (medium), and 90° (hard), were investigated. The AC and DC dielectric breakdown characteristics of these PU variants and dielectric spectra were examined. Additionally, the study explores the relationship between the dielectric properties and the hardness of the material. Our findings revealed that the dielectric breakdown strength of PU increases as the material's hardness is increased under both AC and DC electric stress. However, this may come at the cost of reduced self-healing capabilities of PU. Therefore, there is a need to balance the hardness of the material with its ability to recover from breakdown events. The findings from this study can be useful for researchers and engineers, as they offer valuable insights into the dielectric properties of PU at various hardness levels.

Thermal and electric field driven rf breakdown precursor formation on metal surfaces

The phenomenon of electric breakdown poses serious challenges to the design of devices that operate in high electric field environments. Experimental evidence points toward breakdown events that are accompanied by elevated temperatures and dark current spikes, which is attributed to high-asperity nanostructure formation that enhances the local electric field and triggers a runaway process. However, the exact mechanistic origin of such nanostructures under typical macroscopic operational conditions of electric field and magnetic-field-mediated heating remains poorly understood. In this work, we simulate the evolution of a copper surface under the combined action of the electric fields and elevated temperatures. Using a mesoscale curvature-driven surface evolution model, we show how a copper surface can undergo a type of dynamical instability that naturally leads to the formation of sharp asperities in realistic experimental conditions. Exploring the combined effect of fields and temperature rise, we identify the critical regimes that allow for the formation of breakdown precursors. The results show that thermoelastic stresses, while not essential, can significantly lower the critical electric field required for runaway surface instability, which is consistent with experimental observations that thermal effects can increase breakdown rates. Published by the American Physical Society 2024

Remote Midlatitude Control of Rainfall Onset at the Southern African Tropical Edge

Abstract Rainfall over the southern African tropical boundary is highly variable, particularly around the onset of the summer rains. However, understanding of the causes of variability in onset timing is poor. A lack of observational data in the region is compounded by the complexity of climate dynamics and variability at the interface of the tropics and the midlatitudes. The key to onset over the region is the Congo air boundary (CAB), a distinguishing circulation feature present in September and declining in frequency until November. The CAB is crucial to early season rainfall; precipitation is broadly inhibited when the CAB is present, while its breakdown allows large-scale rainfall and onset over central southern Africa. This work identifies a remote midlatitude influence on the timing of CAB breakdown using the high-resolution reanalysis dataset ERA5. Propagating and (upstream) breaking low-wavenumber Rossby waves in the Southern Hemisphere westerly jet are shown to be related to >70% of breakdown events. Breakdown is forced by 1) the replacement of subsidence over eastern southern Africa with synoptically forced ascent and 2) wind field modification that leads to positive 850-hPa continental convergence anomalies and enhanced moisture advection from the Congo basin and Indian Ocean. Upper-level anticyclonic vorticity tendency induced by enhanced convection slows Rossby wave propagation aloft, contributing to wave breaking and the persistence of conditions favorable to convection. The identification of this midlatitude influence on CAB breakdown reveals new potential sources of predictability for onset and demonstrates the significant equatorward extent of midlatitude influences to the tropical edge. Significance Statement This study aims to improve our understanding of the causes of variability in the start date of the summer rainy season in central southern Africa. The research reveals that certain upper-level waves in the Southern Hemisphere westerly jet are associated with patterns of weather that force a large-scale transition from dry pre-onset conditions to widespread rainfall in a time scale of 2–5 days, and that these waves account for a large proportion of such transitions. This finding is of interest to subseasonal weather forecasters and may help to improve the long-range forecasting of onset date.

Multifunctional HfAlO thin film: Ferroelectric tunnel junction and resistive random access memory.

This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current-voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole-Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology.

Frequency dependence of wake-up and fatigue characteristics in ferroelectric Al0.93B0.07N thin films

B-doped AlN ferroelectric thin films are promising candidates for non-volatile memory device applications, due to their relatively low deposition temperatures, large remanent polarizations (up to 120 μC/cm2), and outstanding polarization retention properties. In this research, the polarization switching fatigue characteristics in epitaxial B-doped AlN thin films were investigated. Films were deposited via reactive RF magnetron sputtering onto W-coated c-plane sapphire substrates. Polarization switching fatigue measurements were conducted with triangular waveforms for frequencies between 50 and 1000 Hz from room temperature to 200°C. On repeated cycling, the films undergo wake-up; the wake-up process was characterized by an activation energy of 0.15 ± 0.05 eV. After a period of normal switching, the film leakage current increased with additional cycling, and finally the films underwent dielectric breakdown. Unpatterned Al0.93B0.07N films with 100 nm thick Pt top electrodes survived ∼104 bipolar cycles, whereas films with 1000 nm W top electrodes survived ∼105 cycles before dielectric breakdown. Modeling was used to design a field plate, which improved the performance to ∼106 fatigue cycles. It was found that dielectric failure during fatigue was not due to surface flashover but was associated with hard breakdown events in the dielectric. Failure for 100 Hz cycling was not associated with device self-heating but was exacerbated by field concentrations; at higher frequencies, the instantaneous temperature rise during the switching becomes prominent. Based on the fatigue process under 100 Hz under various ambient temperatures, the activation energy of the polarization switching fatigue of B-doped AlN thin films was found to be Ea = 0.10 ± 0.03 eV.

Unintended gas breakdowns in narrow gaps of advanced plasma sources for semiconductor fabrication industry

Occurrence of unintended gas breakdown in the narrow gaps of plasma processing chambers is one of the critical challenges in developing advanced plasma sources. We present a combined experimental and theoretical study of unintended discharges in the narrow gaps of plasma processing chambers and report significant drop of the gas breakdown voltage in the presence of a background plasma facing the gap. Experimentally measured breakdown voltages decrease in subsequent breakdown events due to wall erosion caused by the discharge. Therefore, preventing and mitigating the first discharge is of paramount importance. An analysis of kinetic simulation results indicates that the charged particle influx from the background plasma in the processing chamber into the gap is responsible for the onset of early breakdown: higher charged particle density within the gap modifies the electric field profile, allowing unintended breakdowns to occur at a significantly reduced threshold voltage.

Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing

The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for “point” events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for “phase” events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.

Estimating emergency department crowding with stochastic population models.

Environments such as shopping malls, airports, or hospital emergency-departments often experience crowding, with many people simultaneously requesting service. Crowding highly fluctuates, with sudden overcrowding "spikes". Past research has either focused on average behavior, used context-specific models with a large number of parameters, or machine-learning models that are hard to interpret. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding. We test the model using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) cuts the probability of severe overcrowding events by 50%. Such forecasting is critical in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many systems in nature.

Various Types of Lightning Observed in Mountainous Region Recorded from Kathmandu, Nepal

Vertical electric fields of lightning, generated by thunderstorms manifest on the rugged terrain in the mountainous vicinity of Kathmandu, Nepal. Diverse forms of lightning occurrences have been meticulously documented from a hill station in Kathmandu spanning a three-year interval, commencing from 2015 through 2017. Throughout this duration, cloud-to-cloud lightning discharges constituted 62.8% of the occurrences, while cloud-to-ground lightning discharges accounted for 20.4%. Additionally, 9.5% of the observed events were characterized as unusual lightning phenomena, and 2.9% were attributed to breakdown events. The elevated hills and tall structures inherent to the mountainous landscape significantly influence lightning activity, contributing to a heightened frequency of ground flashes in contrast to other areas.

Toward an Interpretable CNN Model for the Classification of Lightning‐Produced VLF/LF Signals

AbstractAn interpretable convolutional neural network model is proposed for the classification of very low frequency and low frequency lightning electric field waveforms. This model adopts multi‐scale convolutional kernels and shortcut connections to enhance the ability of lightning waveform classification. Based on the data recorded from five provinces in China, the proposed model achieves an accuracy of 98.56% for a four‐type classification task including return strokes, the intra‐cloud lightning, preliminary breakdown, and narrow bipolar events. The proposed model is validated with another open‐source data set from Argentina with an accuracy of 98.45%, which shows good robustness. To ensure the classification, the features learned by the model are visualized. The class activation mapping (CAM) method is adopted to visualize the class‐specific contribution of different waveform parts by using the feature maps of the final convolutional layer. It is highlighted by the CAM method that the proposed model focuses on waveform parts that align with those areas of interests identified by human experts. The high‐contribution waveform parts are furtherly analyzed, which indicate that the proposed model possesses the capability to associate waveform features with the corresponding lightning discharge processes.

Scaling laws of failure dynamics on complex networks

AbstractThe topology of the network of load transmitting connections plays an essential role in the cascading failure dynamics of complex systems driven by the redistribution of load after local breakdown events. In particular, as the network structure is gradually tuned from regular to completely random a transition occurs from the localized to mean field behavior of failure spreading. Based on finite size scaling in the fiber bundle model of failure phenomena, here we demonstrate that outside the localized regime, the load bearing capacity and damage tolerance on the macro-scale, and the statistics of clusters of failed nodes on the micro-scale obey scaling laws with exponents which depend on the topology of the load transmission network and on the degree of disorder of the strength of nodes. Most notably, we show that the spatial structure of damage governs the emergence of the localized to mean field transition: as the network gets gradually randomized failed clusters formed on locally regular patches merge through long range links generating a percolation like transition which reduces the load concentration on the network. The results may help to design network structures with an improved robustness against cascading failure.

Modeling and simulation of successive breakdown events in thin gate dielectrics using standard reliability growth models

The application of constant electrical stress to a metal–insulator-semiconductor (MOS) or metal–insulator-metal (MIM) structure can generate multiple breakdown events in the dielectric film. Very often, these events are detected as small jumps in the current–time characteristic of the device under test and can be treated from the stochastic viewpoint as a counting process. In this letter, a wide variety of standard reliability growth models for this process are assessed in order to determine which option provides the best simulation results compatible with the experimental observations. For the generation of the breakdown event arrivals, two alternative stochastic methods for the power-law Poisson process are investigated: first, the inversion algorithm for the cumulative distribution function and second, an on-the-fly method based on the so-called rejection algorithm. Though both methods are equivalent, the first one is more appropriate for data analysis using spreadsheet calculations while the second one is highly suitable for circuit simulation environments like LTSpice. The connection of the selected nonhomogeneous Poisson process with the Weibull model for dielectric breakdown is also discussed.

Dynamic Distributed Collaborative Control for Equitable Current Distribution and Voltage Recovery in DC Microgrids

In a stand-alone DC microgrid featuring several distributed energy resources (DERs), droop control is adopted to achieve a proportional distribution of current among the DERs within the microgrid. The operation of the droop control mechanism leads to a variation in bus voltage, which is further amplified by the line impedance between the DC bus and DERs. This paper proposes an enhanced distributed secondary control technique aimed at achieving equitable current sharing and voltage regulation simultaneously within a DC microgrid. The proposed distributed secondary control is incorporated into the cyber layer of the microgrid, facilitating the exchange of information among the controllers. In the event of a communication link breakdown, this technique upholds the reliability of the entire system. The control loop utilizes a type-II fuzzy logic control framework for the adaptive selection of the control parameters to improve the control response. Furthermore, the proposed technique can handle both resistive and constant power loads without any particular prerequisites. Utilizing the Lyapunov method, appropriate stability criteria for the proposed controller have been formulated. Various tests were performed across a range of operational scenarios to assess the robustness of the proposed control technique through MATLAB/Simulink® models, which have been validated with real-time experiments. The outcomes revealed that the proposed control effectively achieves its control objectives within a DC microgrid, showcasing rapid responsiveness and minimal oscillation.

Probing passivity of corroding metals using scanning electrochemical probe microscopy

AbstractPassive films are essential for the longevity of metals and alloys in corrosive environments. A great deal of research has been devoted to understanding and characterizing passive films, including their chemical composition, uniformity, thickness, porosity, and conductivity. Many characterization techniques are conducted under vacuum, which do not portray the true in‐service environments passive films will endure. Scanning electrochemical probe microscopy (SEPM) techniques have emerged as necessary tools to complement research on characterizing passive films to enable the in situ extraction of passive film parameters and monitoring of local breakdown events of compromised films. Herein, we review the current research efforts using scanning electrochemical microscopy, scanning electrochemical cell microscopy (or droplet cell measurements), and local electrochemical impedance spectroscopy techniques to advance the knowledge of local properties of passivated metals. The future use of SEPM for quantitative extraction of local film characteristics within in‐service environments (i.e., with varying pH, solution composition, and applied potential) is promising, which can be correlated to nanostructural and microstructural features of the passive film and underlying metal using complementary microscopy and spectroscopy methods. The outlook on this topic is highlighted, including exciting avenues and challenges of these methods in characterizing advanced alloy systems and protective surface films.

Classification of Vacuum Breakdown During Conditioning Based on Deep Learning

Conditioning is an efficient technology to improve vacuum gap insulation, which is a collection of a series of breakdown events. Each breakdown event contains and be represented by one breakdown waveform with voltage and current. This paper aims to classify the vacuum breakdown during the conditioning based on deep learning with its advantages on image feature extraction and recognition. The differences among pulsed current induced breakdown (PB), field emission current induced breakdown (FEBD) and particle induced breakdown (PBD) during the pre-breakdown period and breakdown numbers during breakdown period in voltage and current could be extracted and recognited by deep learning. Thus, four kinds of breakdown waveforms PB, PBD1 (PBD with one or several breakdowns), PBD2 (PBD with continuous breakdowns) and FEBD are separated for deep learning to recognize and classify. The classification takes about 10 seconds for each breakdown waveform and its accuracy is above 84.7%. The validation is confirmed from different perspectives.

Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown

Motivated by the service-oriented intelligent maintenance activity under the development of the digital economy, we study the operation and maintenance strategies of the Maintenance Scheduling and Technician Routing Problem (MSTRP) for medical equipment implemented by multi-skilled technicians. We consider the maintenance services provided for both preventive maintenance (PM, e.g., periodic maintenance based on the contract schedule) and corrective maintenance (CM, e.g., repair action in the event of a sudden breakdown). As these activities are performed on equipment with various types of requirements at different geographically distributed systems, technicians with diverse qualifications need to build teams and visit the matching maintenance requests. Meanwhile, we consider the maintenance actions concerning the dynamic change in equipment failure rate and establish a flexible maintenance mechanism by balancing the total cost. This paper develops an efficient hybrid heuristic based on a genetic algorithm, variable neighborhood search, and other optimization strategies to solve this multi-period equipment maintenance scheduling and multi-skilled technician routing and scheduling problem. Based on the settings of the studied MSTRP, this paper designs different sizes of testing instances and multi-period explanatory cases before conducting extensive comparative experiments and sensitivity analysis to demonstrate the effectiveness of our proposed dynamic maintenance strategy and optimization method.