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Catastrophic landslides characterized by runaway slope failures remain difficult to predict. Here, we develop a physics‐based framework to prospectively assess slope failure potential. Our method builds upon the physics of extreme events in natural systems: the extremes so‐called “dragon‐kings” (e.g., slope tertiary creeps prior to failure) exhibit statistically different properties than other smaller‐sized events (e.g., slope secondary creeps). We develop statistical tools to detect the emergence of dragon‐kings during landslide evolution, with the secondary‐to‐tertiary creep transition quantitatively captured. We construct a phase diagram characterizing the detectability of dragon‐kings against “black‐swans” and informing on whether the slope evolves toward a catastrophic or slow landslide. We test our method on synthetic and real data sets, demonstrating how it might have been used to forecast three representative historical landslides. Our method can in principle considerably reduce the number of false alarms and identify with high confidence the presence of true hazards of catastrophic landslides. Plain Language Summary Catastrophic slope failures that pose great threats to life and property remain difficult to predict due to the strong variability of slope behavior. As a result, only a limited number of large rock slope failures have been so far successfully forecasted with associated risks mitigated. Here, we propose a novel predictive framework to prospectively and quantitatively detect slope failure precursors with high confidence. Our research sheds light on one of the most challenging questions in landslide prediction: Would an active landslide slowly move or catastrophically fail in the future? Our method adds a new conceptual framework and operational methodology with a significant potential to support existing early warning systems and hence reduce landslide risks. Key Points Tertiary creeps of catastrophic landslides accommodate dragon‐kings showing statistically different properties than secondary slope creeps A predictive framework is developed to forecast catastrophic landslides by detecting signatures typical of the emergence of dragon‐kings A phase diagram characterizes the detectability of dragon‐kings against black‐swans and discriminates catastrophic and slow landslides

Identifying precursory trends in acoustic/seismic observations allows the forewarning/prediction of catastrophic events. However, rupturing across multiple scales leaves it unclear whether features of small events are applicable predictors of the larger ensemble final collapse. To resolve this issue, we present a multiscale heterogeneous model that straightforwardly characterizes the duration and mechanism of multiscale catastrophic failures. Our results identify four distinct classes of failure including random single breaks, small catastrophic failure (SCF) events, large catastrophic failure (LCF) events that consist of subordinate SCF and random break events, and a culminating macroscopic catastrophic failure (MCF) event resulting from the coalescence of subordinate LCF events. Only the local response quantities, recorded at their corresponding position, show an accelerating precursory trend to an SCF event. LCF events can appear in stages both before and after the maximum load in the system. Our findings highlight that although cumulative LCF event and deformation rates for the entire system always exhibit singular accelerating precursors as MCF is approached, this is not true at all individual event points. This may explain why no clearly accelerating precursor is observed before some catastrophic events. Thus, these results suggest a methodology for recognizing and distinguishing effective precursory information from monitoring signals across scales and in eliminating false predictions.

C atastrophic tool failure (CTF) in milling process can cause damage to the product’s machined surface and the machine tools, leading to huge financial losses. It is therefore critical to detect CTF in advance and promptly respond to it. Because of the safety and quality requirements imposed in practice, there are far fewer failure samples than normal samples, and this disequilibrium makes it difficult to detect failures. The aim of this study is to develop a new, easy, and practical automatic system for tool breakage detection using the acoustic emission (AE) technique. Components of AE raw data are analysed to locate the moments of tool breakages and to screen the corresponding AE feature samples. A support vector machine-based cost-sensitive breakage detection model is established and optimized. The proposed model is applied and validated by experiments conducted on a factory’s milling machine. The model achieves an accuracy of 91.18% in the detection of breakages. The results show the practicability and validity of the proposed method.

The wind turbine gearbox, used as a multiplier, is one of the main components directly related to a wind turbine’s efficiency and lifespan. Therefore, strict control of the gearbox and its manufacturing processes and even minor improvements in this component strongly and positively impact energy production/generation over time. Since only some papers in the literature analyze the mechanical aspect of wind turbines, focusing on some parts in depth, this paper fills the gap by offering an analysis of the gearbox component under the highest amount of stress, namely relating to the sun shaft, as well as a more holistic analysis of the main gear drives, its components, and the lubrification system. Thus, this work diagnoses the fracture mechanics of a 1600 kW gearbox to identify the main reason for the fracture and how the chain of events took place, leading to catastrophic failure. The diagnoses involved numerical simulation (finite element analysis—FEA) and further analysis of the lubrication system, bearings, planetary stage gears, helical stage gears, and the high-speed shaft. In conclusion, although the numerical simulation showed high contact stresses on the sun shaft teeth, the region with the unexpectedly nucleated crack was the tip of the tooth. The most likely factors that led to premature failure were the missed lubrication for the planetary bearings, a lack of cleanliness in regard to the raw materials of the gears (voids found), and problems with the sun shaft heat treatment. With the sun gear’s shaft, planet bearings, and planet gears broken into pieces, those small and large pieces dropped into the oil, between the gears, and into the tooth ring, causing the premature and catastrophic gearbox failure.

The ubiquitous deployment of Li-ion batteries (LIBs) in more demanding applications has reinforced the need to understand the root causes of thermal runaway. Herein, we perform a forensic simulation of a real-case failure scenario, using localised heating of Li(Ni0.5Mn0.3Co0.2)O2 versus graphite 18650 cylindrical cells. This study determined the localised temperatures that would lead to venting and thermal runaway of these cells, as well as correlating the gases produced as a function of the degradation pathway. Catastrophic failure, involving melting (with internal cell temperatures exceeding 1085 °C), deformation and ejection of the cell componentry, was induced by locally applying 200 °C and 250 °C to a fully charged cell. Conversely, catastrophic failure was not observed when the same temperatures were applied to the cells at a lower state of charge (SOC). This work highlights the importance of SOC, chemistry and heat in driving the thermal failure mode of Ni-rich LIB cells, allowing for a better understanding of battery safety and the associated design improvements.

This paper presents a spring-fiber bundle model used to describe the failure process induced by energy release in heterogeneous materials. The conditions that induce catastrophic failure are determined by geometric conditions and energy equilibrium. It is revealed that the relative rates of deformation of, and damage to the fiber bundle with respect to the boundary controlling displacement ε0 exhibit universal power law behavior near the catastrophic point, with a critical exponent of −1/2. The proportion of the rate of response with respect to acceleration exhibits a linear relationship with increasing displacement in the vicinity of the catastrophic point. This allows for the prediction of catastrophic failure immediately prior to failure by extrapolating the trajectory of this relationship as it asymptotes to zero. Monte Carlo simulations are completed and these two critical scaling laws are confirmed.

Catastrophic and major disasters in real-world systems, such as blackouts in power grids or global failures in critical infrastructures, are often triggered by minor events which originate a cascading failure in interdependent graphs. We present here a self-consistent theory enabling the systematic analysis of cascading failures in such networks and encompassing a broad range of dynamical systems, from epidemic spreading, to birth–death processes, to biochemical and regulatory dynamics. We offer testable predictions on breakdown scenarios, and, in particular, we unveil the conditions under which the percolation transition is of the first-order or the second-order type, as well as prove that accounting for dynamics in the nodes always accelerates the cascading process. Besides applying directly to relevant real-world situations, our results give practical hints on how to engineer more robust networked systems.

Catastrophic accidents caused by fatigue failures often occur in engineering structures. Thus, a fundamental understanding of cyclic-deformation and fatigue-failure mechanisms is critical for the development of fatigue-resistant structural materials. Here we report a high-entropy alloy with enhanced fatigue life by ductile-transformable multicomponent B2 precipitates. Its cyclic-deformation mechanisms are revealed by real-time in-situ neutron diffraction, transmission-electron microscopy, crystal-plasticity modeling, and Monte-Carlo simulations. Multiple cyclic-deformation mechanisms, including dislocation slips, precipitation strengthening, deformation twinning, and reversible martensitic phase transformation, are observed in the studied high-entropy alloy. Its improved fatigue performance at low strain amplitudes, i.e., the high fatigue-crack-initiation resistance, is attributed to the high elasticity, plastic deformability, and martensitic transformation of the B2-strengthening phase. This study shows that fatigue-resistant alloys can be developed by incorporating strengthening ductile-transformable multicomponent intermetallic phases. A fundamental understanding of fatigue-failure mechanisms is key to develop robust structural materials. Here the authors report a high entropy alloy with enhanced fatigue life by ductile transformable multicomponent B2 precipitates, as revealed by combined experimental and simulation methods.

We use tensor network techniques to obtain high-order perturbative diagrammatic expansions for the quantum many-body problem at very high precision. The approach is based on a tensor train parsimonious representation of the sum of all Feynman diagrams, obtained in a controlled and accurate way with the tensor cross interpolation algorithm. It yields the full time evolution of physical quantities in the presence of any arbitrary time-dependent interaction. Our benchmarks on the Anderson quantum impurity problem, within the real-time nonequilibrium Schwinger-Keldysh formalism, demonstrate that this technique supersedes diagrammatic quantum Monte Carlo by orders of magnitude in precision and speed, with convergence rates1/N2or faster, whereNis the number of function evaluations. The method also works in parameter regimes characterized by strongly oscillatory integrals in high dimension, which suffer from a catastrophic sign problem in quantum Monte Carlo calculations. Finally, we also present two exploratory studies showing that the technique generalizes to more complex situations: a double quantum dot and a single impurity embedded in a two-dimensional lattice.

Materials can suffer mechanical fatigue when subjected to cyclic loading at stress levels much lower than the ultimate tensile strength, and understanding this behaviour is critical to evaluating long-term dynamic reliability. The fatigue life and damage mechanisms of two-dimensional (2D) materials, of interest for mechanical and electronic applications, are currently unknown. Here, we present a fatigue study of freestanding 2D materials, specifically graphene and graphene oxide (GO). Using atomic force microscopy, monolayer and few-layer graphene were found to exhibit a fatigue life of more than 10 9 cycles at a mean stress of 71 GPa and a stress range of 5.6 GPa, higher than any material reported so far. Fatigue failure in monolayer graphene is global and catastrophic without progressive damage, while molecular dynamics simulations reveal this is preceded by stress-mediated bond reconfigurations near defective sites. Conversely, functional groups in GO impart a local and progressive fatigue damage mechanism. This study not only provides fundamental insights into the fatigue enhancement behaviour of graphene-embedded nanocomposites, but also serves as a starting point for the dynamic reliability evaluation of other 2D materials. Mechanical fatigue occurs under cyclic stress much lower than the tensile strength, but this has not been investigated for 2D materials. Here, graphene is found to have a fatigue life of 10 9 cycles.