Search Results (75)

Concrete used in high-risk infrastructures must withstand elevated temperatures and thermal shocks. This study investigated the thermal transfer behavior of explosion-proof concrete exposed to 100–400 °C through a combined experimental and numerical approach. X-ray diffraction (XRD) revealed that the dominant crystalline phases remained identifiable across this range, but peak broadening and intensity reduction indicated partial decomposition of hydration products and microstructural disorder. Thermal conductivity reached its maximum of 1.48 W/(m·K) at 100 °C and decreased at higher temperatures due to porosity growth and microcracking, reflecting detrimental alterations in heat conduction pathways. In contrast, the specific heat capacity increased from 963.89 J/(kg·K) at 100 °C to 1122.22 J/(kg·K) at 400 °C, enhancing the material’s heat absorption. Density initially decreased with temperature but showed a temporary rebound at 300 °C due to secondary hydration, before dropping sharply to 1830 kg/m³ at 400 °C. Numerical simulations confirmed that high temperatures reduce surface–core temperature gradients, leading to more uniform but structurally weakened heat transfer. These findings highlight that explosion-proof concrete retains acceptable thermal stability below 200 °C, while significant degradation occurs beyond 300 °C. The novelty of this work lies in integrating experimental thermophysical tests with finite element simulations to link microstructural changes with macroscopic thermal behavior. Practically, the results provide guidance for optimizing concrete formulations and protective strategies in fire- and explosion-prone facilities such as LNG storage units and petrochemical infrastructures. Full article

To ensure the inherent safety of fine chemical nitration processes, the nitration reaction of benzotriazole ketone was selected as the research object. The thermal decomposition and reaction characteristics of the nitration system were studied using a combination of differential scanning calorimetry (DSC), reaction calorimetry (RC1), and accelerating rate calorimetry (ARC). The results showed that the nitration product released 455.77 kJ/kg of heat upon decomposition, significantly higher than the 306.86 kJ/kg of the original material, indicating increased thermal risk. Through process hazard analysis based on GB/T 42300-2022, key parameters such as the temperature at which the time to maximum rate is 24 h under adiabatic conditions (T_D24), maximum temperature of the synthesis reaction (MTSR), and maximum temperature for technical reason (MTT) were determined, and the reaction was classified as hazard level 5, suggesting a high risk of runaway and secondary explosion. Process intensification strategies were then proposed and verified by dynamic calorimetry: the adiabatic temperature increase (ΔT_ad) was reduced from 86.70 °C in the semi-batch reactor to 19.95 °C in the optimized continuous process, effectively improving thermal safety. These findings provide a reliable reference for the quantitative risk evaluation and safe design of nitration processes in fine chemical manufacturing. Full article

5-aminotetrazole (5AT)-based gas generators, particularly the 5AT/NaIO₄ system, have garnered interest for their high gas production and energy potential. This study investigates the impact of various metal oxides (MnO₂, Al₂O₃, TiO₂, CuO, Fe₂O₃, MgO, ZnO, and MoO₃) on the thermal decomposition and combustion performance of 5AT/NaIO₄. The REAL calculation program was used to infer reaction products, which indicated that the gas products are almost all harmless, with negligibly low percentages of NO and CO. Thermogravimetric analysis revealed that metal oxides, especially MoO₃, significantly advance the decomposition process above 400 °C, reducing the activation energy by 130 kJ/mol and lowering critical ignition and thermal explosion temperatures. Combustion performance tests and closed bomb tests confirmed MoO₃’s positive effect, accelerating reaction rates and enhancing decomposition efficiency. The system’s high Gibbs free energy indicates non-spontaneous reactions. These findings provide valuable insights for designing environmentally friendly gas generators, highlighting MoO₃’s potential as an effective catalyst. Full article

Structural research teams face significant challenges when conducting studies with explosives, including the costs and inherent risks associated with field detonation tests. This study presents a replicable method for loading spherical and bare TNT-based cast explosive charges, offering reduced costs and minimal risks. Over eighty TNT and Composition B charges (comprising 60% RDX, 39% TNT, and 1% wax) were prepared using spherical molds made of thin aluminum, which are low-cost, off-the-shelf solutions. The charges were bare, meaning they lacked any casing, as the molds were designed to be easily removed after casting. The resulting charges were safer due to their smaller dimensions and the absence of hazardous metallic debris. Composition B charges demonstrated promising results, with their performance characterized through blast and thermochemical experiments. Comprehensive data are provided for Composition B charges, including TNT equivalence, pressures, velocity of detonation, DSC/TGA curves at four different heating rates, activation energy, peak decomposition temperatures, X-ray analysis, and statistics on masses and densities. A comparison between detonation and deflagration processes, captured in high-speed footage, is also presented. This explosive characterization is crucial for structural teams to precisely understand the blast loads produced, ensuring a clear and accurate knowledge of the forces acting on structures. Full article

Energetic co-particles have been proven effective in balancing high-energy and safety performance, which might be used as insensitive oxidizers in solid propellants. In this work, the high temperature interactions between several co-particles and aluminum (Al) powders in the presence of ammonium perchlorate (AP) have been studied. The co-particles are based on octogen (HMX) and hexanitrohexaazaisowurtzitane (CL-20), with balanced energy content and safety performance. They are used to combine with Al and AP to form either binary or ternary systems. Their energy release rate during decomposition and combustion have been fully evaluated. Due to the intimate contact between components in co-particles, the binary/ternary systems exhibit superior reaction efficiency compared to relevant mechanical mixtures with the same formulations. These novel energetic systems have maximum two times higher pressurization rate, 10% higher heat of explosion, 53.8% higher flame propagation rate, and much shorter ignition delay than the corresponding normal mixtures. For both HMX- and CL-20-based co-particle systems, the median size of condensed combustion products (CCPs) is smaller than those of the mechanical mixtures, with higher content of Al₂O₃. This indicates that co-particles have advantages in improving combustion efficiency of Al particles by eliminating their agglomeration. Full article

The intermediate joint of high-voltage cables, as a critical component in the power transmission system, plays a direct role in the stable operation of the entire electrical system. In recent years, frequent explosions of intermediate joints in high-voltage cables have led to significant economic losses and safety risks. Therefore, studying the explosion mechanisms and explosion prevention measures of high-voltage cable intermediate joints is particularly important. This article provides a systematic review of the explosion mechanisms and explosion prevention measures for high-voltage cable intermediate joints. It begins by introducing the composition of cable systems and the structural features of the 220 kV prefabricated cable joint. Next, the article elaborates on the spatiotemporal evolution process of cable joint explosions. Typically, a cable joint explosion undergoes several stages: partial discharge, arc breakdown, and insulation material decomposition, which ultimately leads to explosion and ignition. Subsequently, the article reviews each of these dynamic stages in detail. Finally, the article discusses the existing explosion prevention measures and their shortcomings, and proposes future directions for the development of explosion prevention measures. This article can provide a theoretical foundation and technical reference for the research on the explosion mechanisms of high-voltage cable joints, as well as for the development of explosion prevention measures. Full article

This study investigated the inhibitory effect and mechanism of modified ultrafine ABC powder on the explosion of a methane (CH₄)/coal dust mixed system. Through experiments, it was found that the addition of ABC powder significantly weakened the deflagration characteristics of the CH₄/coal dust mixture system. During decomposition, heat was absorbed to generate ammonia and phosphoric acid. Inert gases such as CO₂ and water vapor produced during decomposition could dilute the oxygen concentration. Phosphate ions produced during the decomposition of ammonium phosphate would bind with free radicals during combustion, reducing their reactivity. The explosion reaction was suppressed through a dual mechanism of physical cooling and chemical consumption of free radicals. The experimental results showed that the weight loss rate of modified ABC powder was 49% at 800 °C, while the weight loss rate of unmodified ABC powder was 78%. The modified ABC powder had better thermal stability and could absorb more heat at high temperatures, further suppressing explosive reactions. This study provides a new modification scheme for explosion suppressants for coal mine safety, which has important theoretical and practical application value. Full article

With the continuous deterioration of air quality and the increasingly serious environmental problem of air pollution, accurate air quality prediction is of great significance for environmental governance. Air quality index (AQI) prediction based on deep learning is currently a hot research topic. The neural network model method currently used for prediction has difficulty effectively coping with the high volatility of AQI data and capturing the complex nonlinear relationships and long-term dependencies in the data. To address these issues, this paper proposes multivariate air quality forecasting with a residual nested LSTM neural network based on the discrete stationary wavelet transform (DSWT) model. Firstly, the DSWT data-decomposition technique decomposes each AQI data point into multiple sub-signals. Then, each sub-signal is sent to the NLSTM layer for processing to capture the temporal relationships between different pollutants. The processed results are then combined, using residual connections to mitigate issues of gradient vanishing and explosion during the model training process. The inverse mean squared error method is combined with the simple weighted average method, to serve as the weight-update approach. Back propagation is then applied, to dynamically adjust the weights based on the prediction accuracy of each sample, further enhancing the model’s prediction accuracy. The experiment was conducted on the air quality index dataset of 12 observation stations in and around Beijing. The results show that the proposed model outperforms several existing models and data-processing methods in multi-task AQI prediction. There were significant improvements in mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R square (R²). Full article

Zenith Tropospheric Delay (ZTD) is a significant source of atmospheric error in the Global Navigation Satellite System (GNSS). Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and GNSS meteorology. To address the challenges of incomplete information extraction and gradient explosion present in current single and combined neural network models that utilize serial ensemble learning, this study proposes a VMD-Informer-BiLSTM-EAA hybrid model based on a parallel ensemble learning strategy. Additionally, it takes into account the non-stationarity of the ZTD sequence. The model employs the Variational Mode Decomposition (VMD) method to address the non-stationarity of ZTD. It utilizes both the informer and Bidirectional Long Short-Term Memory (BiLSTM) architectures to learn ZTD data in parallel, effectively capturing both long-term trends and short-term dynamic changes. The features are then fused using the Efficient Additive Attention (EAA) mechanism, which assigns weights to create a more comprehensive representation of the ZTD data. This enhanced representation ultimately leads to improved predictions of ZTD values. We fill in the missing parts of the GNSS-derived ZTD using the ZTD data from ERA5, sourced from the IGS stations in the Australian region, specifically at 12 IGS stations. These interpolated data are then used to develop a VMD-Informer-BiLSTM-EAA hybrid model for ZTD predictions with a one-year forecast horizon. We applied this model to predict the ZTD for each IGS station in our study area for the year 2021. The numerical results indicate that our model outperforms several comparative models, such as VMD–Informer, Transformer, BiLSTM and GPT3, based on the following key metrics: a Root Mean Square Error (RMSE) of 1.43 cm, a Mean Absolute Error (MAE) of 1.15 cm, a Standard Deviation (STD) of 1.33 cm and a correlation coefficient (R) of 0.96. Furthermore, our model reduces the training time by 8.2% compared to the Transformer model, demonstrating superior prediction performance and robustness in long-term ZTD forecasting. This study introduces a novel approach for high-accuracy ZTD modeling, which is significantly beneficial for precise GNSS positioning and the detection of water vapor content. Full article

The international challenges of water directed the scientists to face the environment-related problems because of the high concentrations of industrial pollutants. In this direction, the present study focuses on designing effective photocatalysts by explosive technique to use light as a driving force for removing industrial pollutants from water. These photocatalysts consist of gold, carbon species (nanotubes, nanofibers, and nanoparticles), and aluminum oxides. By controlling the explosive processes, two photocatalysts were prepared; one was based on carbon nanotubes and nanofibers combined with aluminum oxide, and the other contained the nanoparticles of both carbon and aluminum oxides. The Raman spectra, transmission electronic microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and mapping images confirmed the presence of these nanostructures in homogenous nanocomposites. The optical properties of the prepared nanocomposites were evaluated by UV–Vis absorbance, band gap energy, and photoluminescence (PL) measurements. The experimental results indicated that the presence of CNTs and CNFs led to a lowering of the band gap energy of the prepared nanocomposite to 2.3 eV. This band gap energy is suitable for obtaining an effective photocatalyst. This speculation was confirmed through photocatalytic degradation of the green dyes. The prepared photocatalyst caused a complete removal of the dyes from water after 21 min of light radiation. PL measurement indicated that the CNTs and CNFs have important roles in accelerating the photocatalytic degradation of the pollutants. A kinetic study confirmed that carbon nanotubes boosted the efficiency of the photocatalyst to accelerate the reaction rate of the photocatalytic decomposition of the green dyes more than four times faster than the photocatalyst based on the carbon nanoparticles. Finally, this study concluded that CNTs and CNFs are more favorable than carbon nanoparticles for designing effective photocatalysts to meet the special requirements of the markets of pollutant removal and water purification. Full article

In coal mines, the mixture of coal dust and gas is more ignitable than gas alone, posing a high explosion risk to workers. Using the explosion tube, this study examines the explosion propagation characteristics and flame temperature of low-concentration gas and coal dust mixtures with various particle sizes. The CPD model and Chemkin-Pro 19.2 simulate the reaction kinetics of these explosions. Findings show that when the gas concentration is below its explosive limit, coal dust addition lowers the gas’s explosive threshold, potentially causing an explosion. Coal particle size significantly affects explosion propagation dynamics, with smaller particles producing faster flame velocities and higher temperatures. Due to their larger surface area, smaller particles absorb heat faster and undergo thermal decomposition, releasing combustible gases that intensify the explosion flame. The predicted yield of light gases from both coal types exceeds 40 wt% daf, raising combustible gas concentrations in the system. When accumulated reaction heat elevates the gas concentration to its explosive limit, an explosion occurs. These results are crucial for preventing gas and coal dust explosion accidents in coal mines. Full article

We performed a theoretical investigation of the fluorinated compounds’ morphology and stability. The research was conducted using the widely adopted DFT approach, specifically the B3LYP method and the cc-pVTZ basis set, aiming to design high-energy materials that exhibit low sensitivity, toxicity, instability, and reduced proneness to decomposition or degradation over a short period. In the paper, we presented the investigation results for the compounds whose total energy is the lowest. Their thermal and chemical stability was evaluated based on stability indicators such as cohesion, chemical hardness, and softness. The oxygen–fluorine balance is assessed to determine the sensitivity of these advanced materials. The density, detonation pressure, and velocity of the selected conformers were theoretically obtained to reveal the influence of -CF₃, -OCF₃, and cyclic -O(CF₂)nO- fragments on the energetic properties of nitroaromatics as well as their stability and resistance to shock stimuli. The results enable the prediction of advanced energetic materials that achieve a favorable balance between power and stability. Based on the results achieved, we put forward CF3N2, OCF3N2, C2F6N2, 1CF2N2/O2CF2N2, and 2CF4N2/O2C2F4N2 for practical usage because these compounds possess greater stability compared to tetryl and better explosive properties than TNT. Full article

Named-Entity Recognition (NER) as a core task in Natural Language Processing (NLP) aims to automatically identify and classify specific types of entities from unstructured text. In recent years, the introduction of Transformer architecture and its derivative BERT model has pushed the performance of NER to unprecedented heights. However, these models often have high requirements for computational power and memory resources, making it difficult to train and deploy them on small computing platforms. Although ALBERT as a lightweight model uses parameter sharing and matrix decomposition strategies to reduce memory consumption to some extent consumption, it does not effectively reduce the computational load of the model. Additionally, due to its internal sharing mechanism, the model’s understanding ability of text is reduced leading to poor performance in named-entity recognition tasks. To address these challenges, this manuscript proposes an efficient lightweight model called GoalBERT. The model adopts multiple fusion technologies by integrating a lightweight and efficient BiGRU that excels at handling context into part of the Transformer’s self-attention layers. This reduces the high computational demand caused by stacking multiple self-attention layers while enhancing the model’s ability to process context information. To solve the problem of gradient disappearance and explosion during training, residual connections are added between core layers for more stable training and steady performance improvement. Experimental results show that GoalBERT demonstrates recognition accuracy comparable to standard models with accuracy surpassing ALBERT by 10% in multi-entity type scenarios. Furthermore, compared to standard models, GoalBERT reduces memory requirements by 200% and improves training speed by nearly 230%. Experimental results indicate that GoalBERT is a high-quality lightweight model. Full article

The reactive molecular dynamics method was employed to examine the thermal decomposition process of aluminized hydride (AlH₃) containing explosive nanoparticles with a core–shell structure under high temperature. The core was composed of the explosives RDX, HMX, and CL-20, while the shell was composed of AlH₃. It was demonstrated that the CL-20@AlH₃ NPs decomposed at a faster rate than the other NPs, and elevated temperatures could accelerate the initial decomposition of the explosive molecules. The incorporation of aluminized hydride shells did not change the initial decomposition mechanism of the three explosives. The yields of the main products (NO, NO₂, N₂, H₂O, H₂, and CO₂) were investigated. There was a large number of solid aluminized clusters produced during the decomposition, mainly Al_mO_n and Al_mC_n clusters, together with Al_mN_n clusters dispersed in the Al_mO_n clusters. Full article

Ventilation Air Methane (VAM) significantly contributes to global warming. Capturing and mitigating these emissions can help combat climate change. One effective method is the thermal decomposition of methane, but it requires careful control to prevent explosions from the high temperatures involved. This research investigates the influence of methane concentration and coal dust particle properties on the minimum ignition energy (MIE) required for fugitive methane thermal decomposition and flame propagation properties. This knowledge is crucial for the mining industry to effectively prevent and mitigate accidental fires and explosions in VAM abatement plants. Coal dust samples from three different sources were selected for this study. Experiments were conducted using a modified Hartmann glass tube and a Thermal Gravimetric Analyser (TGA). The chemical properties of coal dust were determined through ultimate and proximate analysis. The particle size distribution was determined using a Mastersizer 3000 apparatus (manufactured by Malvern Panalytical, Malvern, UK). The results showed that the MIE is significantly affected by coal dust particle size, with smaller particles (<74 µm) requiring less energy to ignite compared to coarser particles. Additionally, blending methane with coal dust further reduces the MIE. Introducing methane concentrations of 1% and 2.5% into the combustion space reduced the MIE by 25% and 74%, respectively, for the <74 µm coal dust size fraction. It was observed that coal dust concentration can either raise or lower the MIE. Larger coal dust concentrations, acting as a heat sink, reduce the likelihood of ignition and increase the MIE. This effect was noted at a methane concentration of 2.5% and coal dust levels above 3000 g/m³. In contrast, small amounts of coal dust had little impact on MIE variation. Moreover, the presence of methane during combustion increased the upward flame travel distance and propagation velocity. The flame’s vertical travel distance increased from 124 mm to 300 mm for a coal dust concentration of 300 g·m⁻³ blended with 1% and 2.5% methane, respectively. Full article