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To study the mechanical deformation characteristics and anti-explosion mechanisms of steel-structure protective doors under chemical explosion shock wave loads, numerical simulations of loads and door damage were carried out using the AUTODYN and LS-DYNA software based on model tuning with actual field test results. The finite element simulation results were compared with the test results to verify the accuracy of the simulation model and material parameters. A parametric analysis was carried out on the influencing factors of the anti-explosion performance of the beam–plate steel structure protective door under typical shock wave loads. The impact of the material strength and geometry of each part of the protective door on its anti-explosion performance was studied. The results showed that the protective door sustained a uniform shock wave load and that increasing the steel strength of the skeleton could significantly reduce the maximum response displacement of the protective door. The steel strength increase of the inner and outer panels had little or a negligible effect on the anti-explosion performance of the protective door. The geometric dimensions of different parts of the protective door had different effects on the anti-explosion performance. Increasing the skeleton height had the most significant effect on the anti-explosion performance. The skeleton’s I-steel flange thickness and the inner and outer panel thicknesses had less significant effects.

In some forms of supernovae and chemical explosions, a flame moving at subsonic speeds (deflagration) spontaneously evolves into one driven by a supersonic shock (detonation), vastly increasing the power output. The mechanism of this deflagration-to-detonation transition (DDT) is poorly understood. Poludnenko et al. developed an analytical model to describe DDTs, then tested it with lab experiments and numerical simulations. Their model successfully reproduced the DDT seen in the experiments and predicted a DDT in type Ia supernovae, which is consistent with observational constraints. The same mechanism may apply to DDTs in any unconfined explosion. Science , this issue p. eaau7365 A detonation formation model is developed for chemical flames and supernovae by using lab experiments and numerical simulations. The nature of type Ia supernovae (SNIa)—thermonuclear explosions of white dwarf stars—is an open question in astrophysics. Virtually all existing theoretical models of normal, bright SNIa require the explosion to produce a detonation in order to consume all of stellar material, but the mechanism for the deflagration-to-detonation transition (DDT) remains unclear. We present a unified theory of turbulence-induced DDT that describes the mechanism and conditions for initiating detonation both in unconfined chemical and thermonuclear explosions. The model is validated by using experiments with chemical flames and numerical simulations of thermonuclear flames. We use the developed theory to determine criteria for detonation initiation in the single-degenerate Chandrasekhar-mass SNIa model and show that DDT is almost inevitable at densities of 10 7 to 10 8 grams per cubic centimeter.

Using P‐wave seismograms, we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P‐wave, underground nuclear explosion (UNE) P‐wave, and noise. With the current absence of nuclear testing by countries that have signed the Comprehensive Test Ban Treaty, high quality seismic data from UNEs is limited. Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. We applied the model on holdout datasets of the North Korean test explosions to evaluate the performance on unique region and station‐source pairs, with promising results. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical explosions to act as a surrogate for nuclear explosions. We anticipate that machine‐learning models like our classifier system can have broad application for other seismic signals including volcanic and non‐volcanic tremor, anomalous earthquakes, ice‐quakes or landslide‐quakes. Plain Language Summary We train a global seismic event classifier using machine learning on underground nuclear test explosion seismic data. Our classifier model can successfully discriminate (with over 95% accuracy) between underground nuclear explosion, earthquake, and noise signals from stations both regionally and far‐field. Since this model was trained on a relatively small data set (for machine learning applications) we expect that similar methods can be applied to event or discrimination of other unique seismic sources like those from volcanoes, landslides, or glaciers. Key Points We successfully discriminate underground nuclear explosions with a Convolutional Neural network (CNN) trained on P‐wave seismograms Robust global seismic event discrimination is possible with machine learning trained on regional and teleseismic data A CNN trained with historical nuclear explosion data can be applied with high accuracy to other regions, like the six Democratic People's Republic of Korea's test explosions

The dynamic responses of both underground structures and their surrounding geoformations induced by tamped spherical detonation have been recognised as one of the key topics in both defence engineering and civil engineering. Proper understanding and evaluation of tamped detonation-induced particle movement, spherical stress wave propagation/attenuation, and dynamic crack propagation in geoformations require effective experimental methods and numerical tools. To capture the main characteristics of the spherical shock waves, including the wave propagation and attenuation, a systematic tamped spherical detonation test technique on PMMA has been designed in this study. A mini-explosive sphere with a diameter of 4 mm is generated to produce a small-scale explosion within the PMMA specimen. To monitor the movement of particles during explosion, an electronic measurement system consisting of embedded particle velocity sensors and high-intensity magnetic field generators, has been developed. For the modelling of tamped spherical detonation, a modified multibody failure criterion, equation of state (EOS), and Johnson–Holmquist–Beissel (JHB) model have been implemented in a four-dimensional lattice spring model, thus forming an improved JHB-4DLSM model (M-JHB-4DLSM). It is capable of reproducing the effects of large Poisson’s ratios, the strain rate and the high ratio of uniaxial compressive strength to the uniaxial tensile strength values (UCS/T). The developed M-JHB-4DLSM model has been validated through modelling the dynamic responses of both granite and PMMA. Results indicate that the dynamic process and fracturing patterns reproduced by M-JHB-4DLSM are consistent with experimental observations. M-JHB-4DLSM model is then applied to investigate the impact effects of tunnels subjected to close-in buried blasting.HighlightsA systematic experimental technique has been developed for the tamped spherical denotation test.A M-JHB-4DLSM model has been proposed for more realistic modelling of brittle materials subjected to blasting load.The proposed model has been validated through modelling the dynamic responses of both PMMA and granite.

A helium‐filled mylar balloon carrying a smartphone and infrasound sensors ascended to a stratospheric height of 35 km over the surface detonation of a chemical explosive, with a total acoustic propagation distance of 127 km. The smartphone was configured to collect multi‐modal data at high rates from internal sensors. Analysis of the data shows successful collection of the explosion signal by both the smartphone's microphone and its accelerometers, the first from an ascending balloon. Comparison of the acoustic signal with that collected by other infrasound sensors, both airborne and ground‐based, provides insight into the possibilities and limitations of collecting acoustic data from the stratosphere. Plain Language Summary A surface chemical explosion was observed by pressure and acceleration sensors suspended from an ascending free‐floating balloon at a height of 35 km and a total propagation distance of 127 km from the blast. The balloon's sensor package included a smartphone collecting different types of data (including audio, acceleration, and position), as well as more traditional infrasound and location sensors. The explosion signal was observed by the airborne pressure and vertical accelerometer sensors, as well as by surface‐deployed traditional and smartphone pressure and acceleration sensors. The blast signatures recorded by the airborne and ground‐based stations are compared to provide insight into the present capabilities, limitations, and possible improvements to future balloon‐borne collections of seismo‐acoustic signatures on Earth and beyond. Key Points Explosion signals were captured using acoustic sensors in an ascending balloon at a height of 35 km and a propagation distance of 127 km The signal was briefly trapped in the troposphere before escaping into the stratosphere Accelerometers can distinguish acoustic signals from wind noise on ascending balloons

The Micro Modular Reactor (MMR) represents a new paradigm of Ultra Safe Nuclear power with intrinsic safety. The safety starts from its revolutionary ceramic fuel, that cannot melt and makes it impossible to release radioactive elements in the environment. The elimination of water as a heat transport fluid eliminates the possibility of chemical explosions. Uranium 238, that absorbs more neutrons at higher temperatures shuts down the chain reaction without damage even in case of coolant loss at full power. These characteristics make MMR ideal to provide heat process directly inside factories, to replace natural gas, and to provide power to small communities.

The 2015 Tianjin Port chemical explosion highlighted the severe environmental and structural impacts of industrial disasters. This study presents an Adaptive Weighted Coherence Ratio technique, a novel approach for assessing such damage using synthetic aperture radar (SAR) data. Our method overcomes limitations in traditional techniques by incorporating temporal and spatial weighting factors—such as distance from the explosion epicenter, pre- and post-event intervals, and coherence quality—into a robust framework for precise damage classification. This approach effectively captures extreme damage scenarios, including crater formation in inner blast zones, which are challenging for conventional coherence scaling. Through a detailed analysis of the Tianjin explosion, we reveal asymmetric damage patterns influenced by high-rise buildings and demonstrate the method’s applicability to other industrial disasters, such as the 2020 Beirut explosion. Additionally, we introduce a technique for estimating crater dimensions from coherence profiles, enhancing assessment in severely damaged areas. To support structural analysis, we model air pollutant dispersal using HYSPLIT simulations. This integrated approach advances SAR-based damage assessment techniques, providing rapid reliable classifications applicable to various industrial explosions, aiding disaster response and recovery planning.

Rock damage from underground nuclear explosions (UNEs) has a strong influence on sub-surface gas movement and on seismic waveform characteristics, both of which are used to detect UNEs. Although advanced numerical simulation capabilities exist to predict rock damage patterns and corresponding detection signals, those predictions are dependent on (generally) unknown properties of the host rock. For example, the effects of in-situ mechanical heterogeneities on the explosively generated damage/fractures that provide gas flow pathways to the surface are not well understood, due largely to the difficulty in accessing and characterizing the near-source region. In this paper we demonstrate the emerging use of electrical resistivity tomography (ERT) for imaging rock damage and gas flow patterns resulting from two relatively small-scale underground chemical explosions. Pre-explosion ERT and crosshole seismic imaging revealed a natural fracture zone within the test bed. Post-explosion imaging revealed that the damage zone was non-symmetric and was focused primarily within the pre-existing fracture zone, located 10 m above the first explosion and 5 m above the second explosion. Time-lapse ERT imaging of heated air injected into the detonation borehole revealed the primary gas flow paths to be within the upper margin of the same primary damage zone. These results point to the utility of ERT imaging for understanding rock damage and gas flow patterns under experimental conditions, and to the importance of understanding the effects of geologic heterogeneity on UNE detection signals, particularly gas surface breakthrough times.

The article covers the problem of determination of loads of blast wave on aboveground and underground structures. In industrial production, the chemical sources of explosion occur, for which the environment is air. The most common and typical source of explosion is solid products of the explosion (PE) in the form of a packed charge. In a chemical explosion, highly compressed and heated gaseous products of the explosion (PE) are formed in the volume of the charge. The main parameters of the shock wave that determine its effect on the structure, i.e., excess pressure on the wave front, time of the wave's actionτ, and the impulse of the wave have been determined according to the empirical formulas obtained by experiment for each explosion source. Therefore, in this work, the formulas for determining the incident wave in large-scale experimental studies are presented. It has been found that loads on the structure of barriers of buildings are increase significantly as a result of secondary returning waves superimposing the first incident wave.