Warning Time Research Articles

Thermal hazard comparison and assessment of Li-ion battery and Na-ion battery

Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries, due to the abundant Na resources and low cost. Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries, without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries. To address this issue, our work comprehensively compares commercial prismatic lithium iron phosphate (LFP) battery, lithium nickel cobalt manganese oxide (NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter. The thermal hazard of the three cells is then qualitatively assessed from thermal stability, early warning and thermal runaway severity perspectives by integrating eight characteristic parameters. The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity. However, the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway, allowing the generated gas to be released as early as possible and detected in a timely manner, providing sufficient time for early warning. Finally, a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment. Overall, LFP cells exhibit the lowest thermal hazard, followed by the Na-ion cells and NCM523 cells. This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells, providing crucial guidance for development and application of Na-ion cell.

Development of earthquake early warning algorithm magnitude scale for the 2023 Kahramanmaraş earthquake region

On 6 February 2023, Türkiye suffered two major earthquakes with magnitudes of Mw = 7.7 and Mw = 7.6 at 04:17 and 13:24 local time in Pazarcık and Elbistan districts of Kahramanmaraş, which were called as the disaster of the century. On 20 February 2023 at 20:04 local time, another major earthquake with Mw = 6.4 occurred in Yayladağı, Hatay. This disaster caused great loss of life and heavy destruction. The burden of the earthquake on the Turkish economy is extremely heavy. The purpose of earthquake early warning systems (EEWS) is to protect people and property, save time for precautions to be taken and prepare emergency response teams. An effective EEWS has a major role in reducing life, structural and economic losses. This study is an implementation of the original California optimized EPIC, an early warning algorithm, in the region of the 2023 Kahramanmaraş earthquakes. A total of 212 earthquakes in the region are replayed to derive magnitude scaling equations based on peak displacement amplitude (Pd) and predominant period (Tpmax). The performance of the modified equations is tested for three large earthquakes. By comparing the magnitudes estimated by Pd and Tpmax, it is observed that Pd gives more suitable results. The 2023 Kahramanmaras earthquakes real-time tests suggested a warning time of 32 s for the first earthquake in Antakya (Hatay), where the heaviest destruction and loss of life was observed. In the other earthquakes, the warning times were interpreted as quite useful to minimize the loss of life. The geography of the 2023 Kahramanmaraş earthquakes is a seismically active region and will be subject to earthquakes in the future. As a result of this study, a region-specific scaling of the magnitude, which is the most important parameter if an early warning system is established in the region, is presented. It is also a good example of how an effective earthquake early warning system can reduce potential hazards.

Development of Wood-Based functional composites for advanced sensing and energy conversion applications

With the low carbon-driven development of modern intelligent, research towards novel materials is concentrating on the sustainable biomass-based advanced functional composites. The advanced functionalized wood toward sensing, optoelectronic devices, and energy conversion has been contributed to fast electron transport upon temperature or photo stimulus. Fast electron transport has been highly depended on the interfacial adhesion between the functional layer and wood matrix as well as graphitization degree during carbonization, which promote the electronic excitation and jumps. Here, we develop wood-based functional composites for advanced fire warning and energy conversion by loading metal–insulator transition (MIT) function factor (Ti3O5) onto different woods via liquid crosslinkers, and systematically establish the interfacial adhesion-carbonization-electron transport processes and their relationships on the sensing and photothermal conversion properties. Firstly, balsa with high porosity (pore diameter ≈50 μm), low density (0.16 g/cm3) and extractives content (total extractives content 16.27 %) facilitates crosslinker penetration due to the more holding space and lower nonpolar components. A favorable permeation can promote better interfacial adhesion, and the formation of a continuous conductive network of char-layers during carbonization. Secondly, the crosslinker (chitosan) with excellent adhesion (1 level) and char-forming ability (char residues is 21.7 %), facilitates the formation of a compact network of highly graphitized char-layers (ID/IG=0.81), which increases the electrical conductivity (highest conductivity 5.52 × 10-4 S/m). We found that the most sensitive flame trigger time (0.86 s) for fire warning and the highest evaporation flux (1.63 kg m-2h−1) for desalination can be obtained by using balsa which coupled the chitosan as well as the Ti3O5.

Research on thermal runaway characteristics and mechanism of NCM811 Lithium-ion batteries under cross-seasonal and wide-temperature condition at −10 °C ∼ 33 °C: A case study in Qingdao, China

Multiple heat sources and extreme ambient temperatures pose more severe challenges to the safety performance of lithium-ion batteries (LIBs), and the kinetic characterization of thermal runaway (TR) of LIBs at extreme temperatures has yet to be improved. Through the establishment of a dual heat source coupled stimulation experimental platform, this study investigates the TR mechanism and flame jet dynamics of LIBs under broad temperature ranges corresponding to spring, summer, autumn, and winter (−10 °C ∼ 30 °C). The results indicate that the increase in ambient temperature leads to a gradual reduction in the TR onset time (tonset), in the order of Tonset-Winter>Tonset-Spring>Tonset-Autumn>Tonset-Summer; for example, with 100% SOC (State of Charge), Tonset-Winter = 2528 s, Tonset-Summer = 2211 s, which was accelerated by 14.3%. However, TR maximum temperature (Tmax) showed the opposite trend, Tmax-summer = 687.2 °C, Tmax-winter = 796.5 °C, which decreased by 15.9%. When SOC ≥ 75%, the average warning time for TR disaster of LIB (from venting to the beginning of TR) is 254 s less than that of winter in the summer with higher temperature, which makes the adoption of emergency measures and escape more urgent. When SOC ≥ 50%, the TR process was accompanied by flame generation, and the flame eruption process was the most intense at 100% SOC, with a maximum flame area of about 4400 cm2, and its flame temperature was likewise the highest at 986.4 °C. Carbon and metal oxides were detected in the TR ejecta, and the lower the ambient temperature, the finer the ejecta particles were. The results of the study provide an important scientific reference for improving the theory of TR of LIBs, guiding the investigation of TR and fire in new energy vehicles, and formulating relevant standards.

Multi-dimensional early warning of the entire supply chain of power materials based on RFID technology

Early warning system needs to process a large amount of real-time data, and carry out in-depth analysis and mining of these data to identify potential risks and hidden dangers. However, existing data processing and analysis capabilities may not be able to meet this demand. To this end, a multi-dimensional early warning of the entire supply chain of power materials based on RFID technology is designed. According to the real-time failure probability of power materials, the probability of early warning accidents is calculated and classified, and the risk is graded based on these probabilities. Through the methods of bonus points, deduction points and grade evaluation, the risk early warning indicators of different stages in the whole supply chain of power materials were quantified. The objective and rationality of risk assessment can be ensured by means of comprehensive weight. A multi-dimensional early warning system based on RFID technology is established, combining multiple linear regression model and particle swarm optimization algorithm to determine the time window of multi-dimensional early warning, and carry out dynamic monitoring and early warning of supply chain. The experimental results show that the early warning effect of the design method can reach 95% and the highest early warning effect can reach 98% at 10 seconds. The average warning error is only 2.91%, and the average warning time is only 1.34 seconds, which is more accurate in identifying the number of first-level risks, second-level risks and third-level risks.

Wind turbine gearbox oil temperature feature extraction and condition monitoring based on energy flow

Supervisory Control and Data Acquisition (SCADA) data is widely used for wind turbine gearbox condition monitoring (WTGCM) due to its easy access and low cost, thus ensuring the safe and reliable operation of wind turbine gearboxes. However, existing WTGCM methods based on SCADA overlook the fundamental thermal physics and sensor failure. To effectively address the challenges of weak feature generalization and numerous false alarms, we propose a highly robust WTGCM method based on energy flow. Firstly, based on the principles of thermal balance and wind energy conversion, a feature extraction method considering the physical change process of oil temperature is proposed. Secondly, the performance of different feature extraction methods is quantitatively analyzed using data evaluation criteria. The reasons why different feature extraction methods have different effects are revealed through dimensionality reduction analysis. Finally, we compare the proposed method with other feature extraction methods using four models to validate the excellent performance of the proposed method in the early fault warning. The results demonstrate that the proposed method reduces the average root mean square error by 18.21% during the healthy operation of the wind turbine gearboxes. Moreover, the proposed method performs excellently in early warning of mechanical and sensor faults, significantly reducing the number of false alarms. The average warning time for mechanical faults is 32.56 h earlier.

Study on the characteristics of thermal runaway expansion force of LiNi0.5Co0.2Mn0.3O2/graphite lithium-ion batteries with different SOCs

In recent years, lithium-ion batteries have been widely adopted by the automotive industry because of their high energy density and environmentally friendly nature. However, the thermal runaway of lithium-ion batteries poses a significant risk of explosions, fires, and other hazards. Therefore, it is crucial to have effective thermal runaway warning systems to enhance the safety of battery applications. Currently, the main warning signals for thermal runaway include voltage, temperature, internal resistance, gas composition, and smoke. However, these signals suffer from issues such as low accuracy and delayed warnings. During thermal runaway, voltage, temperature, internal resistance, expansion force, and smoke undergo abnormal changes at varying times, with expansion force abnormalities detected notably earlier. To improve the accuracy and timeliness of thermal runaway warnings, it is crucial to quantitatively measure the changes in expansion force and signal progression related to voltage and temperature during thermal runaway through experiments. In this study, the 51 Ah LiNi0.5Co0.2Mn0.3O2/Graphite commercialized Li-ion batteries were used to study the characteristics of thermal runaway expansion force at different states of charge (SOCs) (25%, 50%, 100%, 110%). The variation patterns of thermal runaway parameters such as temperature, voltage, internal resistance, expansion force, and flame were analyzed. The test results indicate that the expansion force in lithium-ion batteries is related to the lithium-ion concentration in the negative electrode and remains below 2000 N with a rate of change under 1.8 N/s during normal charging and discharging. However, it surpasses 5000 N for thermal runaway. This paper suggests using a 2000 N expansion force as an early warning signal for thermal runaway, which precedes approximately 11.6 s earlier than the voltage signal and 10 s earlier than the internal resistance and temperature signals. Adopting a 1.8 N/s growth rate can further enhance warning time, issuing alerts 134.2 s before thermal runaway. Research confirms that using expansion force as the main signal significantly improves warning time and alarm accuracy in lithium-ion battery safety.

Are You Watching or Warning? the Role of Comprehension, Warning Lead Time and Prior Experience On Individual Preparation of Tornadic Events

This study examines how prior experience with tornados and predicted warning lead times of severe weather may influence one’s willingness to take protective action when a potential tornadic event is imminent. Using theoretical constructs from the Protective Action Decision Model and Risk Information Seeking and Processing model, the project examines how individuals make decisions about taking protective action and what factors motivate them during severe weather. The overall focus of the study is the impact of prior experience with tornados, geographic location and amount of warning lead time on an individual’s likelihood to prepare for potential severe weather. A survey of 679 mid-south residents provided insight into their perceptions of warning language and events. Results indicated that individuals who live in rural areas and those who have more prior experience with tornadic events are more likely to engage in protective behavior. Further, an interaction was noted, indicating those with more prior experience with tornados reporting that they needed less warning lead time to prepare when compared to those with less prior experience, who reported they wished for more lead warning time. Finally, definitions of “tornado warning” noted that more than half of participants did correctly identify its meaning.

A freeway vehicle early warning method based on risk map: Enhancing traffic safety through global perspective characterization of driving risk

In the era of rapid advancements in intelligent transportation, utilizing vehicle operating data to evaluate the risk of freeway vehicles and study on vehicle early warning methods not only lays a theoretical foundation for improving the active safety of vehicles, but also provides the technical support for reducing accident rate. This paper proposes a freeway vehicle early warning method based on risk map to enhance vehicle safety. Firstly, Modified Time-to-Collision (MTTC), a two-dimensional indicator that describes the risk of inter-vehicle travel, is used as an indicator of road traffic risk. This paper designs a transformation function to probabilistically transform MTTC into Risk Indicators (RI). The single-vehicle risk map is generated based on the mapping relationship between the risk values and the corresponding roadway segments. These single-vehicle risk maps of all vehicles on the road are superimposed to construct the risk map, which is used to describe the risk distribution in the freeway. Then, a vehicle early warning framework is built based on the risk map. The risk values in the risk map are compared with predefined early warning thresholds to alert the vehicle when it enters a high-risk state. Finally, VISSIM is used to carry out simulation experiments. The experiment simulates a freeway accident stopping situation. This scenario includes sub-scenarios such as unplanned stopping and lane-changing, continuous lane-changing, and adjacent lane overtaking. We analyze the risk map and vehicle warning results in different sub-scenarios, evaluate the risk changes of the vehicles before and after receiving the warning, and compare the warning results of the method in this paper with other alternative methods. The method is applied to 17 vehicles in the simulation to adjust their motion states. The results show that the total warning time is reduced by 29.6% and 73.3% of vehicles change lanes away from the accident vehicle. The overall results validate the effectiveness of the vehicle early warning method based on risk map proposed in this paper.

Progress in research of rash and fever syndrome surveillance and early warning

Objective: To introduce the progress in research of rash and fever syndrome (RFS) surveillance and early warning both at home and abroad, and provide reference for surveillance and prevention of RFS in China. Methods: The keywords "fever" "rash" and "surveillance" and others were used for a literature retrieval by using China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, PubMed and Web of Science. The languages of literatures were limited in Chinese and English. The key information of the literatures were collected and analyzed with Excel. Results: A total of 36 study papers (21 in Chinese and 15 in English) were included. The studies mainly focused on the pathogen surveillance of RFS (n=19). The pathogens included measles virus, varicella-zoster virus, rubella virus, enterovirus, human B19 virus, dengue virus, streptococcus group A, Salmonella typhi and Salmonella paratyphoid,human herpesvirus, mumps virus and adenovirus. Eight studies were about the surveillance in major events, such as sport game, World Expo and religious gathering, or sudden natural disasters, such as earthquake and tropical storm, during 2010-2015. Eight studies focused on case or epidemic surveillance, most of which were studies from other counties. The surveillance sites were medical institutions. RFS was diagnosed according to the International Classification of Diseases, 9th (ICD-9) and symptoms descripted in chief-complaint. Only one study in Mongolia conducted RFS epidemic prediction. The analysis methods of 36 papers included simple descriptive analysis, time-based early warning models (such as regression analysis, fixed threshold method, Hugh Hart control chart method and cumulative sum control chart method) and time series analysis method. Conclusions: In the future, RFS surveillance system should cover both known pathogens and emerging pathogens. Automatic surveillance using information capture and intelligent modelling can be applied to improve the sensitivity and specificity of RFS surveillance and early warning.

AI monitoring and warning system for low visibility of freeways using variable weight combination model

AbstractIn intelligent vehicles, road environment perception technology is a key component of autonomous driving assistance systems. This component is the foundation for vehicle decision‐making and control, and is a guarantee of safety during the driving of the vehicle. The existing environment perception technology mainly targets well‐lit environments and requires visible light imaging equipment. Therefore, in low visibility environments, this technology cannot make good judgments about the external environment. Many existing perception systems mainly rely on sensors. Under low visibility conditions, these sensors weaken their effectiveness due to signal transmission, reflection, or absorption, resulting in incomplete or distorted data collection. Reduced visibility often affects the sensing range of various sensors, hindering the system's ability to detect and recognize distant objects, thereby limiting the necessary advance warning and response time for safe navigation. In response to this issue, this study proposed a combined method of infrared imaging and polarized imaging to collect feature data on road conditions in low visibility environments. Then, the obtained images were denoised and enhanced. The processed images were input into the system for recognition, and the images were analyzed and recognized using a low visibility road situation semantic segmentation algorithm based on deep learning. The research outcomes denoted that the pixel accuracy, average pixel accuracy, and average intersection ratio of the variable weight combination model in polarized degree images were 91.2%, 89.1%, and 71.6%, respectively. Those in infrared images were 83.6%, 90.6%, and 62.1%, respectively. The various indicators of the variable weight combination model were higher than those of the U‐shaped neural network model, indicating its performance is relatively excellent. The research results indicated that infrared imaging helps to acquire information at night or in low light conditions, while polarized imaging can provide better adaptation to cluttered light and reflections, enabling the system to provide more robust environmental sensing in complex weather conditions. It fills a critical gap in perception for autonomous driving systems in adverse weather conditions.

Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals.

Early Detection of Li-Ion Battery Thermal Runaway Using Commercial Diagnostic Technologies

The rate of electric vehicle (EV) adoption, powered by the Li-ion battery, has grown exponentially; largely driven by technological advancements, consumer demand, and global initiatives to reduce carbon emissions. As a result, it is imperative to understand the state of stability (SoS) of the cells inside an EV battery pack. That understanding will enable the warning of or prevention against catastrophic failures that can lead to serious injury or even, loss of life. The present work explores rapid electrochemical impedance spectroscopy (EIS) coupled with gas sensing technology as diagnostics to monitor cells and packs for failure markers. These failure markers can then be used for onboard assessment of SoS. Experimental results explore key changes in single cells and packs undergoing thermal or electrical abuse. Rapid EIS showed longer warning times, followed by VOC sensors, and then H2 sensors. While rapid EIS gives the longest warning time, with the failure marker often appearing before the cell vents, the reliability of identifying impedance changes in single cells within a pack decreases as the pack complexity increases. This provides empirical evidence to support the significant role that cell packaging and battery engineering intricacies play in monitoring the SoS.

Bayesian Estimation of Advanced Warning Time of Precipitation Emergence

AbstractClimate models disagree on the direction of precipitation change over about half of the Earth. Current characterizations of expected change use the ensemble mean, which systematically underestimates the magnitude and overestimates the time of emergence (ToE) of precipitation change in regions of high uncertainty. We develop a new approach to estimate both ToE and the potential to update uncertainty in precipitation over time with new observations. Further, we develop two new metrics that increase the usefulness of ToE for adaptation planning. The time of confidence estimates when projections of precipitation emergence will have high confidence. Second, the advance warning time (AWT) indicates how long policymakers will have to prepare for a new precipitation regime after they know change is likely to occur. Our approach uses individual model projections that show change before averaging across models to calculate ToE. It then applies a Bayesian method to constrain uncertainty from climate model ensembles using a perfect model approach. Results demonstrate the potential for widespread and decades‐earlier precipitation emergence, with potential for end‐of‐century emergence to occur across 99% of the Earth compared to 60% in previous estimates. Our method reduces uncertainty in the direction of change across 8% of the globe. We find positive estimates of AWT across most of the Earth; however, in 34% of regions there is potential for no advanced warning before new precipitation regimes emerge. These estimates can guide adaptation planning, reducing the risk that policymakers are unprepared for precipitation changes that occur earlier than expected.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions. To study the influence of unloading rate on rockburst, gneiss rockburst experiments were conducted under three groups of unloading rates. A high-speed photography system and acoustic emission (AE) system were used to monitor the entire process of rockburst process in real-time. The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate, which is manifested as the reduction of AE energy and fragments ejection velocity. The mechanisms are proposed to explain this effect: (i) The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst. This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate, and more energy released in the form of shear crack propagation. Then, less strain energy is converted into kinetic energy of fragments ejection. (ii) Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate, resulting in the destruction of rockburst incubation process. The enlightenments of reducing the unloading rate for the project are also described quantitatively. The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s, which was judged by the ejection velocity.

Early Warning of Credit Risk of Internet Financial Enterprises Based on CNN-LSTM Model

The importance of enterprise credit risk management is increasingly prominent. Under the background of financial technology and big data, this paper studies a model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) by comprehensively analyzing multi-dimensional data such as corporate financial statements, credit scores and transaction records, so as to capture and learn the complex characteristics of credit risk of Internet financial enterprises. Firstly, this paper collects the relevant data of Internet finance enterprises from multiple data sources, and carries out standardization processing and missing value filling. Then, CNN-LSTM model is constructed based on these data, and model training and hyperparametric optimization are carried out by adjusting convolution layer and LSTM layer. In addition, this study also designed a comparative experiment to evaluate the performance of CNN-LSTM model and CNN and LSTM models alone in prediction time, prediction accuracy and interpretability. The results show that CNN-LSTM model has significant advantages in predicting the credit risk of Internet finance enterprises. The model also shows a faster response speed, and the maximum warning time is only 701ms, which is much lower than the LSTM and CNN models. The highest accuracy of the model is 94.1%, which is significantly higher than that of LSTM and CNN models. In addition, the model also has high confidence in interpretability, which provides a solid basis for financial decision-making.

The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network

Although tsunamis occur less frequently compared to some other natural disasters, they can be extremely devastating in the nearshore environment if they occur. An earthquake of magnitude 6.9 Mw occurred on 30 October 2020 at 12:51 p.m. UTC (2:51 p.m. GMT+03:00) and its epicenter was approximately 23 km south of İzmir province of Turkey, off the Greek island of Samos. The tsunami event triggered by this earthquake is known as the 30 October 2020 İzmir-Samos (Aegean) tsunami, and in this paper, we study the hydrodynamics of this tsunami using some of these artificial intelligence (AI) techniques applied to observational data. More specifically, we use the tsunami time series acquired from the UNESCO data portal at different stations of Bodrum, Syros, Kos, and Kos Marina. Then, we investigate the usage and shortcomings of the Long Short Term Memory (LSTM) DL technique for the prediction of the tsunami time series and its Fourier spectra. More specifically we study the predictability of the offshore water surface elevation dynamics, their spectral frequency and amplitude features, possible prediction success and enhancement of the accurate early prediction time scales. The uses and applicability of our findings and possible research directions are also discussed.

Critical slowing down precursor information for the acoustic emission response characteristics of defective tuffs

Fracture characteristics of rock and rational structural design are necessary to ensure the stability of underground rock engineering. Early warning of rock damage is a hot and challenging issue for research in geotechnical engineering. In this paper, uniaxial compression tests and acoustic emission (AE) tests were conducted on different defective specimens. The AE characteristics during rock damage were statistically analyzed based on the critical slowing theory. The results showed that the stress fluctuation in the stress–strain curve appeared earlier with the increase of initial damage. The failure pattern shifted overall from tensile to shear cracks. With the load increased, the AE amplitude changed from low amplitude and low density to high amplitude and high-density characteristics. The AE RA/AF was more sensitive to tiny cracks and had better immunity to attenuated AE signals. Both cumulative AE energy and RA/AF showed a precursor characteristic of a sudden increase in slope before rock damage. The AE b-value experienced a period of rising, fluctuation, and decline, with a sudden drop before the large-scale crack extension, showing a decreasing trend overall. The magnitude of fluctuations and density of distribution of the critical slowing down parameters (variance and autocorrelation coefficients) reflected the severity of specimen failure. The early warning characteristic of the variance was more significant, while the autocorrelation coefficient had an apparent disorder, which interfered with the judgment of the system phase transition. The average warning time of different AE parameters' “critical precursor point” was greater than 42 s, and the stress level was lower than 91.50%. The warning time sequence of different parameters in descending order was as follows: variance of AE parameters > autocorrelation coefficient of AE parameters > traditional AE parameters. The overall warning effect of the critical slowing down phenomena was better than that of classic AE parameters.

Real‐Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR‐GNSS and Deep Learning

AbstractEarthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real‐time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR‐GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine‐Learning Assessed Rapid Geodetic Earthquake model (M‐LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M‐LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real‐time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.

X-Ray Energy Deposition Model for Simulating Asteroid Response to a Nuclear Planetary Defense Mitigation Mission

In the event of a potentially catastrophic asteroid impact, with sufficient warning time, deploying a nuclear device remains a powerful option for planetary defense if a kinetic impactor or other means of deflection proves insufficient. Predicting the effectiveness of a potential nuclear deflection or disruption mission depends on accurate multiphysics simulations of the device's X-ray energy deposition into the asteroid and the resulting material ablation. The relevant physics in these simulations span many orders of magnitude, require a variety of different complex physics packages, and are computationally expensive. Having an efficient and accurate way of modeling this system is necessary for exploring a mission's sensitivity to the asteroid's range of physical properties. To expedite future simulations, we present a completed X-ray energy deposition model developed using the radiation-hydrodynamics code Kull that can be used to initiate a nuclear mitigation mission calculation. The model spans a wide variety of possible mission initial conditions: four different asteroid-like materials at a range of porosities, two different source spectra, and a broad range of radiation fluences, source durations, and angles of incidence. Using blowoff momentum as the primary metric, the model-initiated simulation results match the full radiation-hydrodynamics results to within 10%.