Ning Zhou

To reveal the mechanisms by which obstacle distribution affects propane–air premixed deflagration under different blockage ratios, large eddy simulation (LES) was employed to investigate flame propagation and pressure rise in a confined duct with four obstacle distributions and four blockage ratios. The coupled effects of obstacle layout and blockage ratio on flame morphology, propagation velocity, vorticity evolution, and pressure rise rate were analyzed. The results show that obstacle distribution significantly changes flame front structures: One-side obstacles produce claw-like flames, center layout obstacles generate tongue-like flames with large vortex regions at low-to-moderate blockage ratios, and both-side or around layout obstacles form mushroom-like flames. At high blockage ratios, around layout obstacles redirect the flow into a high-speed axial jet, leading to the highest flame velocity and maximum pressure rise rate. These findings indicate that the dominant flame acceleration mechanism shifts from vortex-induced flame wrinkling at low-to-moderate blockage ratios to axial-jet-driven flame acceleration at high blockage ratios, providing guidance for obstacle layout optimization and explosion risk mitigation in confined propane–air systems.

During the operation of new energy vehicles (NEVs), such as starting, accelerating, climbing slopes, and high-speed driving, lithium-ion batteries (LIBs) generate a large amount of internal heat due to high-rate discharging. If the heat cannot be dissipated effectively or evenly, it will cause local overheating, which will adversely affect the safety, performance, and lifespan of the battery. To address this issue, this study adopts a Cell-to-Pack (CTP) battery system as the physical model and combines it with the NTGK electrochemical model to conduct numerical simulations of the thermal behavior under a 5C high-rate discharge condition. The following three aspects are focused on: (1) Four cooling fluid flow rates (i.e., 0.1 m/s, 0.5 m/s, 1 m/s, 2 m/s) were designed to investigate the influence of different cooling fluid flow rates on the heat dissipation performance of the liquid cooling system; (2) Three liquid cooling structures (i.e., straight flow structure, serpentine structure, and honeycomb structure) were designed to explore the influence of different flow channel designs on the efficiency of liquid cooling; (3) A comprehensive analysis was conducted to optimize the performance differences between the flow channels and the traditional flow channel liquid cooling systems. The results show that the increasing flow rate of the coolant improves the cooling efficiency of all three liquid cooling structures. When the flow rate is 2 m/s, the temperature distribution range of the system is significantly reduced, the thermal uniformity is optimal, and local hotspots can be effectively suppressed, demonstrating the system-level thermal safety regulation capability. The serpentine flow channel, due to its multiple bends, enhances the turbulence effect and performs well in terms of volume ratio (0.97), energy density (177.61 W/kg), and average flow rate (up to 1.91 m/s). The honeycomb structure has multi-point diversion and grid-shaped flow channels, achieving a good balance in structural compactness (volume ratio of 0.965) and cooling efficiency (an increase of 26.83%). The highest temperature in the lowest temperature module still remains at 41.34C. Compared with the traditional liquid cooling systems, these three new flow channel designs can meet the cooling requirements of high-rate discharge battery packs at flow rates lower than the current maximum standard rate (2.5 m/s). This study provides theoretical basis and engineering reference for the thermal design optimization of high-rate CTP battery systems.

1,1´‐Dihydroxam‐5,5´‐bisazide (1,1´‐BTO) is the precursor for the well‐known energetic material dihydroxylammonium 5,5´‐bistetrazole‐1,1´‐diolate. It always appears in an anionic state, BTO _anion , and a free radical state, BTO _biradical , depending on the chemical environments during its reactions. To demonstrate their molecular surface reactivity, starting from their electronic structure optimized on the theory level of ωB97XD/def2‐TZVP, theoretical calculations and results discussions were carried out on their atomic dipole moment corrected Hirshfeld population charges, molecular surface electrostatic potential, molecular surface average local ionization energy; to deeply understand thermodynamic stabilities characterized by their aromaticities, nuclear‐independent chemical shift (NICS) along with the direction perpendicular to their molecular plane, its variant free of in‐plane component NICS, magnetically induced current, were calculated from the wave function obtained on the theory level of PBE0/6‐311++G** and discussed. These discussions and conclusions are expected to enrich the deeper understanding of the states presented by 1,1´‐BTO during the reaction process.

In order to overcome the static nature of traditional risk assessment and analyze the uncertainty propagation problem in risk assessment, this paper proposes a new dynamic risk assessment (DRA) methodology to quantify the input data uncertainty propagation in the tank risk analysis. In this paper, the bow-tie (BT) model is developed to identify the key factors and possible consequences of tank failure. The hierarchical Bayesian analysis (HBA) model is utilized to calculate the failure probabilities of the basic events and protective layers. The triangular fuzzy number in fuzzy set theory (FST) is utilized to preserve the uncertainty of the results, whereby fuzzy prior probabilities and fuzzy transfer probabilities are calculated, and then risk diagnosis and probability updating are performed by dynamic Bayesian network (DBN). Taking the floating-top storage tank leakage accident as an example, the uncertainty in the risk analysis is reduced by calculating the possibility range of the risk occurrence, and the possible accidents are accurately predicted. The results show that the method is able to objectively assess the risk level of most events and quantify the process of uncertainty propagation in the input data.

To explore the propagation laws of the domino effect in tank areas, this paper constructs a domino effect analysis model based on the unit accident chain and multifactor synergistic effect. The model not only evaluates the contribution of the unit accident chain to domino effect development but also analyzes the enhanced destructive effect when multiple factors interact. From the perspective of energy accumulation, the heat dose criterion is used to dynamically update the conditional probability tables in the dynamic Bayesian network (DBN), thus more realistically simulating the evolution of the domino effect. It overcomes the limitation of the traditional DBN that can only analyze the domino effect at specific time points and can identify critical vulnerabilities more accurately, providing timely decision support for reducing the impact of accidents. The effectiveness of this method is verified through case studies, confirming its reliability and practicality in analyzing the propagation patterns of the domino effect.

With the extensive use of liquefied petroleum gas (LPG) in the catering industry, leakage explosions have become frequent. This study employs numerical simulations to investigate the diffusion patterns of LPG leakage under various ventilation conditions. The results show that there is a logarithmic relationship between the wind speed and the volume of a propane gas cloud under natural ventilation. In the wind speed ranges of 1.5 to 3.3 m/s and 7.9 to 10.7 m/s, a small increase in wind speed leads to a significant reduction in gas cloud volume (97.2% and 95.05%, respectively). Under forced ventilation, the volume of the gas cloud decreases by 90.6%, from 6.67 m³ at higher air exchange rates (22.1 and 24.3 times/h), reducing explosion risks. When leakage occurs at the stove, the optimal combination for dispersing the propane combustible gas cloud is window opening at position 1 and fan at position a. The volume of the gas cloud at window position 1 increases exponentially with the distance between the fan and the leak source. The fan is installed within 2.786 m from the leak source to ensure that the gas cloud volume remains below 0.5 m³. These findings provide valuable insights for the design and the optimization of ventilation systems and layouts in commercial kitchens.

Liquefied petroleum gas (LPG) leaks and explosions have become increasingly frequent, posing serious threats. This study uses the “6.21” LPG explosion at the Fuyang Barbecue Restaurant in Yinchuan, Ningxia, as a case study. A computational fluid dynamics (CFD) model was developed to simulate the event. Findings indicate that propane created a nonuniform concentration field in the restaurant, with concentrations increasing from top to bottom. Pressure–time curves at three kitchen monitoring points (P3, window-left, and ignition point) showed an “M” shape due to structural and turbulence effects. The shock wave’s direct impact led to a “Λ” shape in overpressure time history curves at other first-floor points. Externally, the second-degree burn range for 1 s exposure reached 35.82 m. The hazardous overpressure zone measured 58.3 m long and 21.5 m wide, and the fatality zone extended to 25.1 m. This research offers crucial insights for preventing and investigating LPG cloud explosions in confined spaces.

Aiming at the problems of complex calculation processes, insufficient risk data, and reliance on experts’ subjective judgments that exist in traditional probability analysis methods, this paper proposes a probability analysis method for hazardous chemical storage tank leakage accidents based on neural networks and fuzzy dynamic fault trees (Fuzzy DFT). This method combines fuzzy set theory (FST) and Bootstrap technology to accurately quantify the failure probabilities of basic events (BEs) and reduce the dependence on experts’ subjective judgments. Furthermore, an artificial neural network (ANN) model for tank failures is constructed. This model can accurately calculate the probability of tank leakage accidents by taking into account the dependency relationships among basic events. Finally, a long short-term memory (LSTM) network is utilized to analyze the dynamic evolution trend of the probability of storage tank accidents over time. In this paper, this method is applied to the case of the “11.28” Shenghua vinyl chloride leakage accident. The results show that the calculation results of this method are highly consistent with the actual situation of the accident, indicating that it is a scientific and effective method for analyzing the probability of hazardous chemical storage tank leakage accidents.