Search Results (1,612)

A high-temperature gas plasma scheme using methane/air/K₂CO₃ mixed combustion is proposed for the application background of hypersonic aircraft. The actual combustion temperature was calculated by ANSYS Chemkin Pro software; the various components of the combustion reaction were determined; and the function between temperature and electrical conductivity was established, revealing the variation law of ionization decomposition of K₂CO₃ ionized seeds with gas temperature. At 1500 K, K₂CO₃ ionized seeds are close to complete ionization. Increasing the mass fraction of K₂CO₃ ionized seeds will enhance the endothermic effect of K₂CO₃ seed ionization decomposition. Under the same residual gas coefficient conditions, the combustion equilibrium temperature will correspondingly decrease. The increase in initial combustion temperature results in an approximately linear increase in equilibrium temperature and conductivity. With the increase in initial pressure, the equilibrium temperature of gas shows a logarithmic growth trend, while conductivity gradually decreases and the gradient of change gradually slows down. This study provides a new method for evaluating the ionization characteristics of high-temperature gas plasma formed by potassium carbonate (K₂CO₃) as ionization seed, and hydrocarbon fuel (CxHy) combined with air. Full article

This study aimed to develop the Five Cognitive Biases in Risk-Taking Scale (5 CBR-S) to measure five cognitive biases associated with risk-taking: overconfidence, illusion of control, belief in the law of small numbers, escalation of commitment, and optimism. Firefighters completed a series of five questionnaires: cognitive biases related to risk-taking, emotional intelligence, self-regulation behaviors, personality traits, and mental toughness. Data were collected from two distinct samples, each consisting of 202 firefighters. A series of exploratory and confirmatory factor analyses conducted on an initial version of the 5 CBR-S with 50 items provided structural evidence supporting a 5-factor, 19-item solution. Evidence of validity and reliability for the 5 CBR-S scores was provided by examining correlations with emotional intelligence, personality traits, and mental toughness. Overall, despite certain limitations, the 5 CBR-S constitutes a robust measure, offering the advantage of highlighting the five main cognitive biases related to risk-taking. It can be used both among firefighters and in other professional contexts involving high-intensity emergency decision-making. Full article

Numerical simulations reveal the combustion dynamics of hydrogen-blended natural gas (H-BNG) in semi-open spaces. In the typical semi-open space scenario, increasing the hydrogen blending ratio from 0% to 60% elevates peak internal pressure by 107% (259.3 kPa → 526.0 kPa) while reducing pressure rise time by 56.5% (95.8 ms → 41.7 ms). A vent size paradox emerges: 0.5 m openings generate 574.6 kPa internal overpressure, whereas 2 m openings produce 36.7 kPa external overpressure. Flame propagation exhibits stabilized velocity decay (836 m/s → 154 m/s, 81.6% reduction) at hydrogen concentrations ≥30% within 2–8 m distances. In street-front restaurant scenarios, 80% H-BNG leaks reach alarm concentration (0.8 m height) within 120 s, with sensor response times ranging from 21.6 s (proximal) to 40.2 s (distal). Forced ventilation reduces hazard duration by 8.6% (151 s → 138 s), while door status shows negligible impact on deflagration consequences (412 kPa closed vs. 409 kPa open), maintaining consistent 20.5 m hazard radius at 20 kPa overpressure threshold. These findings provide crucial theoretical insights and practical guidance for the prevention and management of H-BNG leakage and explosion incidents. Full article

The new eco-friendly flammable refrigerant in air conditioners has resulted in an annual increase in fire incidents associated with these units. Fire investigators face significant challenges in identifying the causes of these fires. In this study, copper tube samples were extracted from various locations of air conditioner condenser debris post fire. The morphology characteristics of the ruptured copper tubes formed by a high-temperature flame in fire and that formed by corrosion were analyzed, respectively. The findings indicate that the ruptures in the copper tubes of air conditioners may be classified into two types based on their origins: ruptures resulting from fire and ruptures resulting from corrosion. The ruptures in the copper tubes resulting from fire are associated with the presence of aluminum alloy fins. At elevated temperatures, the copper and aluminum atoms persist in diffusing and fracturing. A significant quantity of silver-white aluminum is present surrounding the ruptures, and distinct elemental layers may be seen in the cross-section. The corrosion-induced ruptures in the copper tubes are associated with ant nest corrosion. Despite the influence of high-temperature flame melting on surface corrosion pits, they will not entirely obscure the pits and the cross-section continues to exhibit the bifurcated structure characteristic of ant nest corrosion. This investigation demonstrates that corrosion of ant nests is the root cause of copper tube breakage obscured by flames. An investigation method for the refrigerant leakage air conditioning fire is proposed. The above findings can provide proof and method for air conditioning fire investigation. Full article

In this study, we investigated the influence of pre-fire tree mortality on fire behavior. Although other studies have focused on the environmental factors affecting wildfire, the influence of pre-fire tree mortality has not been explored in detail. We used high-spatial-resolution (1.6 m) airborne multispectral orthoimages to detect and map pre-fire dead trees in a portion of the San Bernardino Mountains, where the ‘Old Fire’ burned in 2003, and assessed whether spatial patterns of fire intensity and burn severity coincide with patterns of tree mortality. Dead trees were mapped through a hybrid deep learning classification and manual editing approach and facilitated with Google Earth Pro historical images. Apparent thermal infrared (TIR) brightness temperature captured during the Old Fire was derived from maximum digital number values from FireMapper airborne thermal infrared imagery (7 m) as a measure of fire intensity. Burn severity was analyzed using normalized burn ratio maps derived from pre- and post-fire Landsat 5 satellite imagery (30 m). Pre-fire dead trees were prevalent with 192 dead trees and 108 live trees per ha, with most dead trees clustered near the northwestern part of the study area east of Lake Arrowhead. The degree of spatial correspondence among dead tree density, fire intensity, and burn severity was analyzed using graphical and statistical analyses. The results revealed a significant but weak spatial association of dead trees with fire intensity (R² = 0.31) and burn severity (R² = 0.14). The findings revealed that areas impacted by pre-fire tree mortality were subject to higher fire intensity, followed by severe burn effects, though other biophysical factors also influenced these fire behavior variables. These results contradict a previous study that found no effect of tree mortality on the behavior of the Old Fire. Full article

Mountain photovoltaic (PV) power stations cover vast areas and contain dense equipment. Once direct current arc faults occur in PV modules, they can pose a serious thermal threat to surrounding facilities and combustible materials, potentially resulting in a PV array fire accident. In this work, a series of PV module fire experiments were conducted to investigate the burning characteristics of PV modules exposed to the pool fire. The burning process, burning damage extent, and temperature distribution were measured and analyzed. The results showed that the surfaces of PV modules exhibited different burning characteristics due to the pool fire. Based on different characteristics, the front side was classified into four zones: intact zone, delamination zone, carbonization zone and burn-through zone. The back side was similarly divided into four zones: undamaged backsheet zone, burnt TPT zone, cell detachment zone and burn-through zone. Meanwhile, the burning process and surface temperature rise rate of intact PV modules were significantly lower than those of cracked modules at the same inclination angle. Cracked modules exhibited a heightened susceptibility to being rapidly burnt through by the pool fire. As the inclination angle increased from 0° to 60°, the burning damage extent and the expansion rate of high-temperature regions initially ascended and subsequently decreased, reaching their maximum at the inclination angle of 15°. These findings can offer valuable insights that can serve as a reference for the fire protection design and risk assessment of mountain PV power stations, ensuring their safe operation. Full article

The increasing frequency and severity of agricultural fires pose significant threats to food security, economic stability, and environmental sustainability. Traditional fire-detection methods, relying on satellite imagery and ground-based sensors, often suffer from delayed response times and high false-positive rates, limiting their effectiveness in mitigating fire-related damages. In this study, we propose an advanced deep learning-based fire-detection framework that integrates the Single-Shot MultiBox Detector (SSD) with the computationally efficient MobileNetV2 architecture. This integration enhances real-time fire- and smoke-detection capabilities while maintaining a lightweight and deployable model suitable for Unmanned Aerial Vehicle (UAV)-based agricultural monitoring. The proposed model was trained and evaluated on a custom dataset comprising diverse fire scenarios, including various environmental conditions and fire intensities. Comprehensive experiments and comparative analyses against state-of-the-art object-detection models, such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and SSD-based variants, demonstrated the superior performance of our model. The results indicate that our approach achieves a mean Average Precision (mAP) of 97.7%, significantly surpassing conventional models while maintaining a detection speed of 45 frames per second (fps) and requiring only 5.0 GFLOPs of computational power. These characteristics make it particularly suitable for deployment in edge-computing environments, such as UAVs and remote agricultural monitoring systems. Full article

Understanding environmental factors that control the ignition of fuel beds exposed to firebrands is necessary to help reduce the risk of losses of structures. Ignition by firebrands has been reported to be sensitive to wind, but identification and quantification of the physical cause(s) of such sensitivities are still limited. The objective of this study was to quantify the influence of wind speed and direction on the ignition of a fuel bed exposed to firebrands and to understand the causes of this sensitivity. Fuel beds of Douglas fir shavings were exposed to a firebrand surrogate (i.e., a resistive heater) to determine flaming ignition probability and time to ignition for three different wind speeds and three wind directions. Increases in wind speed above quiescent reduced the temperature required for flaming ignition. However, a wind speed threshold above which ignition probability decreased was observed for some wind directions. The temperatures required for flaming ignition to occur and the time to ignition were sensitive to the wind direction. High-speed images and corresponding CFD calculations indicated that ignition occurred in the regions with the most prominent recirculation zones. Thus, sensitivities to wind speed and direction are attributable to differences in the pyrolysate residence time as controlled by recirculation zones. The results indicate that the local flow characteristics can significantly influence ignition, and characterization of the freestream velocity alone may not be sufficient. Full article

The wind and slope are deemed to be the determinant factors driving the extreme or erratic spread behavior of wildfire, which, however, has not been fully investigated, especially to elaborate the mechanism of fire spread associated with heat transfer and fluid dynamics. A systematic study is therefore carried out based on a physical-based simulation and proper orthogonal decomposition (POD) analysis. Results show that compared to the wind, the slope plays a more profound effect on the fire structure; with the increase in slope, the fire line undergoes a transition from a W-shape to the U- and pointed V-shape, accompanied by stripe burning zones, indicating a faster spread but incomplete combustion. The wind effect is distinguished by mainly inducing a turbulent backflow ahead of the fire front, while the slope effect promotes convective heating via the enhanced slant fire plume. Different mechanisms are also identified for the heat transfer ahead of the fire line, i.e., the radiative heat is affected by the combined effects of the flame length and view angle, and in contrast, the convective part of the heating flux is dominated by the action of the flame attachment, which is demonstrated to play a crucial role for the fire spread acceleration at higher slopes (>20°). The POD analysis shows the distinct pattern of flame pulsating for the respective wind and slope effects, which sheds light on modeling the unsteady features of fire spreading and reconfirms the necessity of considering the different effects of these two environmental factors. Full article

This study investigates the behaviour of plumes from massive fires subjected to crosswinds, focusing on how varying wind profiles and terrain characteristics influence the formation of coherent vortical structures, particularly wake vortices, and the smoke distribution in the near-fire region. Large Eddy Simulations (LES) were employed to model the interaction between the plume, generated by a rectangular fireline with an intensity of $40\mathrm{MW}/\mathrm{m}$, and the crosswind. Upon the consideration of several wind intensities (from 5 to $10\mathrm{m}/\mathrm{s}$) and vertical distributions, it was verified that only for relatively low average wind velocities was there significant lateral entrainment to the flame, which promoted the formation of vertical vortical structures. Depending on the vertical distribution of the wind, different mechanisms can play a role in the formation of these structures, with a larger number of mechanisms involved for the cases where there is very low near-wall wind momentum, typical of large vegetation regions. The slope of the terrain (from $-{20}^{\circ }$ to $+{20}^{\circ }$) plays a role in these relations since it affects the fire plume inclination and, consequently, the entrainment process. These structures are more likely to appear in downslope terrains. Full article

Forest fires have a great destructive impact on the Earth’s ecosystem; therefore, the top priority of current research is how to accurately and quickly monitor forest fires. Taking into account efficiency and cost-effectiveness, deep-learning-driven UAV remote sensing fire detection algorithms have emerged as a favored research trend and have seen extensive application. However, in the process of drone monitoring, fires often appear very small and are easily obstructed by trees, which greatly limits the amount of effective information that algorithms can extract. Meanwhile, considering the limitations of unmanned aerial vehicles, the algorithm model also needs to have lightweight characteristics. To address challenges such as the small targets, occlusions, and image blurriness in UAV-captured wildfire images, this paper proposes an improved UAV forest fire detection model based on YOLOv8. Firstly, we incorporate SPDConv modules, enhancing the YOLOv8 architecture and boosting its efficacy in dealing with minor objects and images with low resolution. Secondly, we introduce the C2f-PConv module, which effectively improves computational efficiency by reducing redundant calculations and memory access. Thirdly, the model boosts classification precision through the integration of a Mixed Local Channel Attention (MLCA) strategy preceding the three detection outputs. Finally, the W-IoU loss function is utilized, which adaptively modifies the weights for different target boxes within the loss computation, to efficiently address the difficulties associated with detecting small targets. The experimental results showed that the accuracy of our model increased by 2.17%, the recall increased by 5.5%, and the mAP@0.5 increased by 1.9%. In addition, the number of parameters decreased by 43.8%, with only 5.96M parameters, while the model size and GFlops decreased by 43.3% and 36.7%, respectively. Our model not only reduces the number of parameters and computational complexity, but also exhibits superior accuracy and effectiveness in UAV fire image recognition tasks, thereby offering a robust and reliable solution for UAV fire monitoring. Full article

Current research on fires in high-speed train carriages mainly focuses on the temperature distribution of smoke, while the flow structure and dynamic evolution patterns of smoke have not been reviewed. This study employs numerical simulation to systematically investigate the fire ejection behavior from broken windows of a high-speed train carriage, and the influence of different broken window parameters was considered. The research results indicate that the asymmetric geometric configuration of seats on both sides of the fire source induces a lateral displacement of fire smoke toward the downstream direction due to the sidewall effect, resulting in an asymmetrical distribution of smoke flow velocity and temperature within the compartment. Compared to fire scenarios with one broken window, the presence of broken windows on both sides increases the number of smoke dispersion outlets, leading to a significant rise in smoke flow velocity around the fire source. In the aisle region far from the fire source, the smoke flow velocity decreases, consequently lowering the compartment temperature. Compared with the case of one broken window, the aisle temperature decreased by 83 K in the case of two broken windows. When the broken window is located farther from the fire source, smoke tends to accumulate extensively within the compartment before being discharged through the broken windows, which enhances the longitudinal smoke flow velocity and elevates the compartment temperature. When a window far from the fire source is breached, the temperature in the aisle increases by 270 K compared to when a window in the middle of the carriage is breached. Full article

Experience is crucial for enhancing task performance. However, rescue experience, as a key contributor to individual and team capabilities within fire brigades, has not received sufficient attention in fire safety management research. Based on the concept of shared team task-specific (STTS) experience, this study explored how it affects the rescue effectiveness of fire brigades. To test the proposed model, data were collected from a sample of 452 firefighters in China. The results indicated that STTS experience, including both task-specific experience and shared team experience, significantly improves the rescue effectiveness of fire brigades. Moreover, shared mental models, including both teammate mental models and task mental models, mediate the relationship between STTS experience and rescue effectiveness, while team tenure strengthens the effect of STTS experience on shared mental models and subsequent team performance. These findings enhance the understanding of fire brigade performance and contribute to improving emergency rescue effectiveness. Theoretical and practical implications, limitations, and future research directions are provided and discussed. Full article

Accurate wildfire spread modeling critically depends on the representation of wind dynamics, which vary with terrain, land cover characteristics, and height above ground. Many fire spread models are often coupled with coarse atmospheric grids that cannot explicitly resolve the vertical variation of wind near flame heights. Rothermel’s fire spread model, a widely used parameterization, relies on midflame wind speed to calculate the fire rate of spread. In coupled fire atmosphere models such as the Community Fire Behavior Model (CFBM), users are required to specify the midflame height before running a fire spread simulation. This study evaluates the use of logarithmic interpolation wind adjustment factors (WAF) for improving midflame wind speed estimates, which are critical for the Rothermel model. We compare the fixed wind height approach that is currently used in CFBM with WAF-derived winds for unsheltered and sheltered surface fire spread. For the first time in this context, these simulations are validated against satellite and ground-based observations of fire perimeters. The results show that WAF implementation improves fire perimeter predictions for both grass and canopy fires while reducing the overestimation of fire spread. Moreover, this approach solely depends on the fuel bed depth and estimation of canopy density, enhancing operational efficiency by eliminating the need for users to specify a wind height for simulations. Full article

Tunnel fires pose significant challenges to public safety due to their rapid development and the confined nature of tunnel environments. Traditional fire detection systems often struggle with delayed response times and high false alarm rates, particularly in complex scenarios. This study proposes a lightweight hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, offering an efficient and robust solution for real-time tunnel fire detection. Leveraging transfer learning, the model adapts to tunnel-specific fire scenarios with minimal training data, significantly improving its generalization capabilities. The lightweight architecture ensures computational efficiency, making it suitable for deployment in resource-constrained environments such as tunnels with limited processing capacity. The model was rigorously evaluated on datasets combining simulated and real-world fire scenarios. It achieved an accuracy of 92%, a precision of 89%, a recall of 90%, and an F1 score of 89.5%, outperforming state-of-the-art (SOTA) models in all key metrics. Furthermore, the model demonstrated resilience under varied environmental conditions, including high smoke density and sensor failures, maintaining reliable performance. This study highlights the potential of lightweight deep learning models in enhancing tunnel safety systems by providing accurate, fast, and dependable fire and smoke detection. Future work will extend the methodology to other critical infrastructures and optimize the model for broader applications. Full article