[Objective] Most historic buildings in China wood or brick-wood structures; consequently, they have low fire resistance ratings and large fire loading values. Furthermore, firefighting problems associated with these buildings include high building density, insufficient fire separation distances, and narrow fire passages; thus, historical buildings face a high risk of damage owing to fire. Therefore, to prevent and control fires in historical buildings, exploring efficient early fire detection and alarm systems for these buildings is necessary. Image fire detectors enable rapid identification of fires and provide the location of fires; thus, they are used in early fire detection and alarm systems for historic buildings. However, there is a lack of study that accurately calculate the coverage and evaluated the placement of image fire detectors. Currently, the placement of outdoor image fire detectors depends on semiquantitative approaches such as engineers’ experiences and existing regulations. [Methods] Consequently, this study proposes a placement optimization methodology for outdoor image fire detectors in historical buildings based on the set covering and maximum covering models. First, a three-dimensional model of the target area is constructed by integrating the structural information of historical buildings and mesh division. Second, the field of view of an image fire detector is determined based on its models and specifications to analyze its viewshed; subsequently, a set of candidate detector positions is constructed using the selection rule that selects more important areas and less occluded areas, thus providing support for the reasonable placement of image fire detectors. Furthermore, the extent of coverage of the target area grid by the candidate detectors is determined by constructing a binary observation matrix that maps the position and direction of image fire detectors in the target area. Third, considering the optimization of the cost and coverage of image fire detectors as the goal, a mathematical model for the placement optimization of image fire detectors is developed based on the set covering model and maximum covering model. Finally, based on the genetic algorithm, the optimal placement scheme of image fire detectors is obtained using the previously constructed placement optimization model. [Results] To demonstrate the feasibility and effectiveness of the proposed methodology, this study considers the joint area composed of the Hall of Supreme Harmony, Hall of Central Harmony, and Hall of Preserving Harmony of The Imperial Palace as a case study. Under the given target area coverage and predetermined cost, the optimal placement plan of image fire detectors is obtained, i.e., {11, 13, 16, 20, 23, 25, 33, 35, 48, 55, 58, 60, 62, 64, 67, 68} and {11, 22, 26, 29, 35, 44, 48, 59, 64, 67}. In addition, the joint area coverage reached 98.17% and 92.41%, respectively. [Conclusions] Compared with the existing semiquantitative approaches to placing image fire detectors, the proposed methodology simplifies several manual calculation processes and can meet different requirements for cost and coverage within the detection area, thus optimizing the placement of image fire detectors. In addition, this methodology can quickly and accurately obtain the position and direction of the detector, which can be used for installing and calibrating outdoor image fire detectors in historical buildings. Thus, the proposed methodology can be implemented in the construction of early fire detection and alarm system in historical buildings to prevent massive economic and cultural losses owing to fire damage.

[Objective] The unique design and complex morphological characteristics of roof structures in the “purlin-type” ancient buildings of the Ming and Qing dynasties significantly influenced the spread of smoke. However, the existing fire detection systems used in ancient structures lack sufficient consideration of the impact of roof architecture on the placement of smoke detectors, which makes it challenging to realize early fire detection effectively and accurately. Therefore, further research on the response time of fire detectors considering purlin height is necessary to strengthen fire prevention and control in ancient buildings. [Methods] Through the job-site survey of several gable-roofed buildings in the Forbidden City, the existing smoke detectors were found to be primarily installed on the purlins below the ridge or both sides of the ceiling. However, the scientific basis for these installation positions remains unclear. Thus, this study selected typical gable-roofed buildings with a slope of 27.41° constructed during the Ming and Qing Dynasties as the research object. Fire dynamics simulator was utilized for numerical fire simulation to analyze smoke movement in the different fire source positions (center, edge, and corner) and various purlin heights (low: 10-20 cm; middle: 30-40 cm; high: 50-60 cm). The research further explored the influence of smoke detector response performance on each fire position and purlin height by setting up a 9×11 smoke detector array and several slices of smoke mass fraction. In this model, as the purlin height increased, the detectors arranged under them maintained their x and y coordinates, while the z coordinate moved down the corresponding height. The position of the detectors on the ceiling remained unchanged. [Results] The findings showed the following: 1) The variation law of the smoke propagation path: In the scenarios of central and edge fires, smoke primarily spread along the horizontal direction of the main ridge. In those of corner fire, when the purlin height was low, the smoke tended to expand upward along the sloping roof; when the purlin height was high, the smoke propagated along the purlin in a “stepped” path. 2) The response time sequence of smoke detectors: For the center and edge fires, the differences in response time of smoke detectors affected by purlin height was approximately 30 s. For the corner fire, when the purlin height was below 20 cm, the detector at the main ridge responded within 60 s, and the variation in the response time of smoke detectors affected by the purlin height can reach up to 45 s. 3) The locations suggested for detector installation: When the purlin height is below 30 cm, the smoke detector should be installed at the center of the main ridge; when the purlin height is above 30 cm, the detectors should be placed on the ceiling near the center of the main ridge on both sides. [Conclusions] These findings provide technical support for the rational placement of fire detectors in similar “purlin style” buildings from the Ming and Qing dynasties to achieve comprehensive and timely early detection and warning of fire prevention in ancient buildings.

[Objective] Fire risk has consistently posed a significant challenge in traditional villages owing to their diverse layouts and architectural elements, complex geographical environments, and the inherent flammability of building materials. Most research on fire spread models predominantly focuses on individual buildings or forest fires, neglecting the unique conditions of traditional villages. Safety concerns also limit field studies. Therefore, it is crucial to study fire spread dynamics in Huizhou's traditional villages to develop a quantitative model that enhances both fire safety and emergency response capabilities in these communities. [Methods] This paper constructs a disaster field model using cellular automata to simulate fire spread in village conditions, considering factors like fire dynamics, environmental characteristics, building materials, and layout considerations. The model was validated using the Miaozhai fire incident in Wenquan village, Jianhe county, Guizhou province (February 2016), analyzing how ignition points, wind speeds, and wind directions influenced potential ignition sites, spread paths, and impact zones. Computer simulations were employed to mitigate the risks associated with live-fire experiments, enhancing simulation efficiency. By incorporating multiple influencing factors into the prediction framework, this approach models how fires spread through village environments, using initial conditions regarding village elements and fire states to create visual data of fire spread patterns. However, most studies utilizing cellular automata primarily address forest fires, making them unsuitable for traditional villages. This paper specifically investigates Hongcun, a dense village in Anhui province, and proposes a model tailored to its unique wildfire risks, applicable to similar settings in the region. [Results] Findings indicate that simulated outcomes align closely with real-world observations, confirming the method's feasibility. Fires follow distinct pathways depending on different ignition sources, and densely clustered buildings are more susceptible to extensive fire spread than less populated regions throughout the respective timelines analyzed herein. Layout configurations significantly influence both the paths flames take and the extent of their spread after ignition. This variability is influenced by how structures are distributed over time. Higher building densities are linked to faster fire spread, especially when wind plays a crucial role in determining how far the fire reaches. Wind direction significantly influences the path of the fire, largely pushing it downwind orientations and away from the point of ignition. However, with prevailing easterly or southerly winds, the fire range is considerably reduced, limiting the overall local impact despite ongoing combustion taking place nearby. Conversely, strategically placed roadways and water systems effectively slow down fire progression and limit its lateral spread. This helps mitigate threats beyond the immediate area affected by the fire, protecting surrounding locations from the phenomena previously discussed. [Conclusions] The developed modeling framework offers detailed analysis for assessing risks in clustered traditional settlements, facilitating informed decision-making. It supports the implementation of preventative measures, including hazard identification protocols and isolation strategies, specifically targeting identified vulnerabilities. This framework provides valuable insights for future planning, optimizing firefighting resources allocated and ensuring sustainable development practices are maintained.

[Objective] Fires are disaster events with destructive power. In relation to fire-related accidents, fire monitoring is one of the effective measures to reduce the casualties and economic losses caused by such incidents. Compared to traditional methods in fire monitoring, target detection has shown its strengths in terms of cost and outcome. Many researchers have investigated various ways to improve the efficiency of target detection by proposing new algorithms. Thus, numerous algorithms suited for fire monitoring applications have been proposed. However, these typically lack the capacity to detect small targets, which is the main characteristic of flame targets in incipient fires. To enhance the capacity to detect small targets for fire target detection, this paper improved the YOLOv5 algorithm and trained a model based on it with corresponding datasets collected.[Methods] First, a fire image dataset with small target scene conditions is prepared for model training and performance testing. In the validation set, eight sets of mutually exclusive sub-datasets of environmental conditions are divided for the purpose of performance testing. Second, three improvements are introduced to improve the YOLOv5 algorithm: a) expansion of the multiscale detection layer to improve its receptive resolution; b) enhancement of the multiscale feature extraction capability by embedding the Swin transformer module, thus reducing the cost of calculation in algorithm deployment; and c) optimization of the postprocessing function by replacing the original algorithm with soft-NMS algorithm to maintain more potential adjacent targets. Next, an improved model YOLOv5s-SSS (swin transformer with soft-NMS for small target) is proposed. To verify the effect of every improvement and their contributions to the final model, the new model is evaluated using four sets of ablation experiments. After parameter optimization, a set of fire images is inputted into the models in the ablation experiment to compare and verify their outputs. [Results] The ablation experimental results indicate that, first, all the improvements introduced into the algorithm are valid. Furthermore, the average accuracy of the improved model is 16.3% higher than that of the original algorithm in flame image targets under challenging scene conditions and 5.9% higher in normal-sized image targets. The verification result shows, compared to the original model, the improved model has obvious improvements in terms of reducing the location range of fire targets, thus minimizing the missing detection of small-sized and densely-distributed fire targets and clearly dividing densely or overlapping distributed fire targets.[Conclusions] The dataset prepared in this paper can effectively support the training and testing of the improved fire detection algorithm model. Furthermore, the proposed model improvement has been shown to work effectively, along with the reliable performance test, thus providing a new improvement scheme for fire image detection technology. It can also serve as a reference in improving efficiency in various applications, such as accurate positioning of fire points in incipient forest fires and remote sensing monitoring of large-scale fires. However, the overall accuracy of the improved model is relatively low, possibly due to the images in the validation set being deliberately limited to small targets to assess the model's improvement. In the future, more improvements should be introduced to enhance the model's detection ability under various scenarios, such as low-light conditions, so that it can be adequate for industrial applications.

[Objective] The frequency and intensity of forest fires have increased considerably in recent years, driven largely by human activities and climate change. Vast quantities of carbon-containing substances released during forest fires exacerbate climate change through a positive feedback loop. Surface fires, the most common type of forest fire, account for more than half of the total emissions from forest fires. Accurate estimation of carbon emissions from surface fires is therefore essential for understanding their impact on carbon sinks and assessing the role of forest fires in driving climate change. [Methods] This study constructed indoor combustion beds to investigate the relationship between surface fire behavior and carbon consumption during the combustion of surface fuels in a planted Pinus koraiensis forest. Fuel beds were prepared with varying fuel loadings (0.4 kg·m^-2, 0.8 kg·m^-2, 1.2 kg·m^-2, and 1.6 kg·m^-2) and fuel moisture contents (5%, 10%, and 15%). Combustion experiments were conducted across 144 plots under varying slope conditions (0°, 10°, 20°, and 30°). The rate of spread, flame length, and fuel consumption during surface fire spread were measured, and fireline intensity was calculated using the Byram model. After the experiments, all combustion residues were collected, and their carbon content was determined using the dry burning method, enabling the calculation of carbon consumption from the fuel. A predictive model for fuel carbon consumption was developed by combining the fuel consumption parameters from the Byram fireline intensity equation with the observed fire behavior data. The model parameters were calibrated using the experimental results. [Results] All 144 combustion experiments produced low-intensity surface fires. Most variable interactions significantly influenced the four forest fire behavior characteristics, except for the interaction between fuel load and moisture content, which mainly affected fuel consumption. The four measured fire behavior characteristics increased with higher fuel loads. As fuel moisture content increased, spread rate, flame length, and fireline intensity decreased, although moisture content did not have a significant effect on fuel consumption. Under low slope conditions (0°~20°), the spread rate and fireline intensity increased gradually with the slope. However, when the slope exceeded 20°, these characteristics increased substantially. Flame length also increased with slope, while fuel consumption decreased. Initially, the model predicted surface fire fuel carbon consumption with limited accuracy, yielding R² =0.59, MAE=0.22 kg·m^-2, and MAPE=73.00%. After refitting the model parameters using data from the combustion tests, predictive accuracy improved considerably, with R² =0.60, MAE=0.10 kg·m^-2, and MAPE=30.99%. [Conclusions] The factors examined in this study generally align with the principles of forest burning, though under specific conditions, fire behavior characteristics may deviate from expected patterns. Refitting the model parameters using laboratory combustion data significantly enhanced the model's applicability and accuracy in predicting carbon consumption from surface fuel burning in Pinus koraiensis plantation forests.

[Objective] The increasing frequency of forest and grassland fires poses significant threats to human safety, property, and ecological stability. Accurately predicting fire spread is crucial for effective fire prevention and control. Traditional methods, such as mechanical tillage, controlled burning, and biological firebreaks, have limitations in terms of efficiency and implementation speed. In contrast, firebreaks established using fire retardants offer numerous advantages, including high effectiveness, minimal disruption to vegetation, and rapid deployment, thus facilitating effective emergency responses. However, the nonlinear behavior of fire spread, influenced by various factors such as topography and fuel characteristics, complicates its analysis and prediction. This study focuses on integrating new fire retardants into efficient firebreaks to enable precise fire management and rapid emergency response. The study investigates the propagation characteristics of pine needle fires on varying slopes and evaluates the effectiveness of firebreaks, aiming to provide scientific insights for developing fire prevention strategies in complex terrains. [Methods] Herein, we utilized version 6.8 of the Wildland Fire Dynamics Simulator, developed by the National Institute of Standards and Technology. This simulation tool integrates extensive empirical physical parameters, enabling comprehensive analysis of the slope impact on fire spread. Practical tests were conducted to assess the combustion characteristics of pine needles, with key parameters such as flame propagation speed, heat release rate, and combustion temperature distribution being simulated under various slope conditions (0°, 30°, 45°, and 60°). Additionally, a 10-cm-wide firebreak was introduced to examine the influence of slope on fire behavior and evaluate the effectiveness of this fire control measure at different slopes. This methodology provides a solid scientific foundation for understanding fire spread mechanisms and developing effective countermeasures. [Results] The findings indicate a pronounced impact of slope on fire dynamics, with measurable variations in flame propagation velocity, heat release rate, and combustion duration. Specifically, as the slope was increased from 0° to 60°, the flame propagation velocity rose significantly, leading to a decrease in combustion duration and an increase in heat release rates. At a 30° slope, the 10-cm fire retardant barrier effectively curtailed flame spread, demonstrating a marked reduction in heat flux. However, at steeper slopes of 45° and 60°, the buoyancy effects intensified, resulting in a heightened risk of igniting vegetation located behind the involved barrier. This was evident as the maximum heat flux at a 60° slope reached 39.78 kW/m², surpassing the critical threshold necessary for igniting downwind vegetation. These results underscore the importance of considering slope in fire management strategies, particularly in areas where fire behavior can escalate rapidly. The predictive model developed also highlights the significant relationship between slope, vegetation properties, and fire spread rates, providing a scientific framework for improving fire prevention and control measures in diverse terrains. [Conclusions] The study concludes that slope, firebreak width, and fire retardant application conditions significantly affect fire behavior. Moreover, it highlights the importance of optimizing firebreak designs, particularly in areas with steep slopes. Future research should focus on widening firebreaks and improving the concentration and distribution of fire retardants to maximize fire prevention effectiveness. Overall, this study provides a robust foundation for developing effective fire management strategies that consider the complex interactions among topography, vegetation, and fire dynamics.

[Objective] With the rapid and continuous advancement of urbanization at an astonishing pace, fire accidents are happening with increasing frequency globally. A sudden fire outbreak holds a significantly high probability of causing extensive and severe harm to society. Research conducted on image-based fire detection algorithms is highly beneficial and valuable in terms of extracting the detailed morphological features of fires or smoke, aiding in effectively improving the efficiency of fire warnings. [Methods] This study presents and introduces an improved version of the YOLOv8 algorithm. Initially, the neck network of the algorithm is strengthened by integrating the SlimNeck lightweight module. Then, the inference framework of the YOLOv8 algorithm is substituted with slicing-aided hyper inference (SAHI) to further enhance the capability of the algorithm to detect small targets. Moreover, fire and smoke are two crucial target categories in fire scenarios. Given the inherent complexity of fire image backgrounds, which frequently contain numerous interferences from nonfire categories, fire dataset targets are classified as fire, smoke, and default. [Results] Experimental results clearly indicate that the SlimNeck-YOLOv8 algorithm showcases superior fire detection performance compared with other related advanced algorithms. In contrast to the YOLOv8 algorithm, the recall rate of this algorithm is elevated by 2.7%, mean average precision (mAP) is increased by 0.2%, and detection speed is accelerated by 35 frames/s. Simultaneously, with the developed algorithm, the computational burden is effectively reduced. [Conclusions] By integrating SlimNeck and SAHI, respectively, to optimize the network structure and inference framework of the YOLOv8 algorithm, the improved YOLOv8 algorithm is utilized for detecting fire and smoke, which has, to a certain extent, remedied the shortcomings of the YOLOv8 algorithm for this purpose. To effectively verify the performance and effectiveness of the proposed algorithm, the model is not merely trained on the fire dataset but is trained on coco128 dataset under precisely the same training epochs and parameters. This is done with the specific aim of conducting comprehensive tests to accurately evaluate model performance. The improved algorithm proposed in this study has successfully achieved the expected goals of significantly enhancing the mAP, recall, and speed of the YOLOv8 algorithm for detecting fire and smoke and concurrently reducing the rates of missed and false detections. This advancement holds great promise for enhancing the reliability and effectiveness of fire detection systems, providing prior and more accurate warnings to minimize potential losses and damages caused by fires. The combination of innovative techniques and targeted optimizations presented in this research offers valuable insights and practical solutions in the fire safety field and related applications.

[Objective] In recent years, forest fires have frequently occurred worldwide, leading to substantial casualties and property losses. Many fires are caused by hot metal particles igniting forest fuels. Therefore, understanding the ignition mechanisms of high-temperature metal particles is critical. Studying how these particles ignite combustible materials is a key focus in current forest fire research, with particular attention to factors such as temperature, size, and material of the metal particles. The nature of the fuel remarkably impacts ignition, with some fuel particles being large and others small. In this study, the particle size of combustibles is characterized by their mesh number. This research examines the influence of the mesh number on the ignition boundary and combustion behavior of hot metal particles. These findings provide a theoretical basis for assessing whether hot metal particles can ignite combustibles of different particle sizes and offer valuable insights for wildfire safety, prevention, and emergency response. [Methods] This study investigates the effect of mesh number on the ignition boundary and combustion behavior of hot metal particles. Pine powder with varying mesh numbers was selected as the research object, and steel balls were used as hot metal particles to ignite the pine powder. A serie of ignition experiments was conducted using experimental devices independently developed by the China People's Police University. Experimental phenomena were recorded and analyzed using ordinary and high-speed cameras. Key parameters, such as ignition probability, ignition boundary temperature, ignition delay time, flame height, and flame duration, were studied as functions of mesh numbers. In addition, the mechanism by which mesh number influences the ignition boundary and combustion behavior of hot metal particles was explored. [Results] The minimum ignition temperature of pine wood powder by metal hot particles decreases with increasing mesh number, decreasing from 965℃ at 50 mesh to 910℃ at 300 mesh. The ignition delay time of 1 000℃ high-temperature metal hot particles also decreases as the mesh number decreases, reducing from 49.8 ms at 50 mesh to 41.8 ms at 300 mesh. However, the flame height and duration increase with higher mesh numbers, increasing from 14.78 mm and 12.8 s at 50 mesh to 17.02 mm and 14 s at 300 mesh. [Conclusions] The ignition boundary temperature decreases with increasing mesh. When hot metal particles reach a high temperature of 1 000 ℃, combustibles with various mesh numbers can be ignited. The ignition delay time also decreases with increasing mesh number. However, as the temperature increases, the difference in ignition delay time between different mesh numbers decreases, indicating that the influence of temperature on ignition delay time gradually diminishes. The flame height and flame duration increase as the number of mesh increases, suggesting that an increase in mesh number elevates fire risk by producing taller flames and prolonged combustion.

[Objective] Given the confined and elongated configuration of ultrawide subsea immersed tube tunnels, fires can lead to a rapid spread of flames and smoke, posing a substantial threat to the structural integrity of the tunnel and the safety of personnel. Traditional fire protection designs are often not ideal for cooling and controlling fires in such environments. Therefore, developing effective fire prevention and control technologies specifically suited to subsea tube tunnels is of great practical importance for ensuring tunnel safety and facilitating personnel evacuation. The main objective of this study is to evaluate the fire suppression and cooling effects of high-pressure mist in subsea immersed tube tunnels. To simulate real-life conditions, a full-scale 1:1 experimental model of a subsea tunnel was constructed, with the width, height, and length being 18.0, 6.6, and 25.0 m, respectively. This study aims to evaluate the combustion behavior, temperature distribution, and cooling efficiency of high-pressure water mist under different heat release rates. [Methods] Gasoline was used as the fuel source in experiments conducted at three heat release rates: 2.5, 5.0, and 10.0 MW. Key parameters, including the flame height, temperature variation curves, and the cooling effects of high-pressure water mist, were measured throughout the experiments. The experimental setup was designed to closely replicate real fire scenarios in subsea tunnels, ensuring accurate and reproducible results. During the experiments, high-precision temperature sensors and imaging devices were used to continuously record and analyze the temperature distribution and flame characteristics. [Results] The experimental results showed significant differences in the flame characteristics and temperature distribution across different heat release rates. For example, the maximum flame heights under 2.5, 5.0, and 10.0 MW heat release rates were 4.5, 6.0, and 6.6 m, respectively. Without high-pressure water mist for fire suppression, the highest ceiling temperatures reached 180℃, 310℃, and 528℃, respectively, posing a certain threat to the structural integrity of the tunnel. Upon activation of the high-pressure water mist, ceiling temperatures in all fire scenarios dropped significantly, falling below 300℃, thereby effectively reducing the risk of structural damage. Furthermore, for the 2.5 MW heat release rate, the activation of the high-pressure water mist rapidly reduced the temperature in the burning area to below 150℃. While complete suppression of flames was more challenging in higher-power fire scenarios, the water mist significantly lowered the ceiling temperature and the temperature in the burning area. This slowed the fire progression, providing valuable time for personnel evacuation and firefighting efforts. [Conclusions] The experimental results confirm the effectiveness of high-pressure water mist in controlling fires within subsea immersed tube tunnels, particularly in reducing ceiling temperatures and minimizing structural damage risks. The use of high-pressure water mist in such tunnels can effectively reduce temperatures and mitigate the impact of fires on tunnel structures, providing technical support for improving fire safety. Overall, this research provides valuable insights for enhancing fire safety strategies in subsea immersed tube tunnels and provides practical recommendations for designing fire prevention and control systems.

[Objective] When a train catches fire in a tunnel, in order to facilitate the evacuation and rescue of passengers, the train should continue to operate until it exits the tunnel and arrives the next station or the emergency rescue station. To deeply understand the evolution process of moving train plumes in tunnels and provide theoretical guidance for the operation safety of railway tunnels, this study conducted moving model experiments to compare and analyze the differences of fire plumes between stationary and moving trains in a tunnel. [Methods] Based on the existing moving model test platform, a 1:10 train-tunnel model was designed by using the Froude similarity criterion. The head of the train model adopted a streamlined design to avoid the influence of the vortex caused by boundary layer separation on the experimental results. The front surface of the tunnel was designed with fire-resistant transparent tempered glass to facilitate the observation of the train plume behavior. Thermocouple array was used to measure the temperature distribution at the top of the train and under the tunnel ceiling, and the total and radiative heat flux at the top of the train were measured by heat flux gauges. Based on biharmonic spline interpolation algorithm, a Matlab script was written to reconstruct the discrete temperature values on the top of the train, and the two-dimensional temperature distribution contour on the top of the train was obtained. [Results] The results show that, unlike stationary train fires, the fire plume of the moving train moves forward by sweeping the roof of the tunnel. This paper defines this type of flow as “ceiling sweep” for the first time and divides its evolution into three stages: (1) Rise stage. The train fire plume rises and inclines to the upstream of the fire source under the coupling of inertia and viscous forces; (2) Contact stage. Depending on the heat release rate, the train speed and the tunnel height, this stage includes direct flame contact with the ceiling and smoke plume contact with the ceiling. (3) Sweep stage. After contacting the tunnel, the fire plume expands below the ceiling. At the same time, due to the relative motion between the train and the tunnel, the fire plume will sweep the tunnel ceiling and move forward. This aforementioned flow pattern is defined as “ceiling sweep” in this paper. Due to heat accumulation and thermal feedback of the tunnel, the maximum temperature, maximum total heat flux and maximum radiative heat flux at the top of the train under the action of the moving train fire plume in the tunnel are greater than that under the open line scenario. The maximum temperature under the roof of the tunnel is significantly decreases, and the longitudinal temperature presents an asymmetric distribution of higher upstream and lower downstream of the fire source. [Conclusions] The above results show that the threat of the fire plume of moving trains to the train body increases, but the threat to the tunnel decreases. This study can provide reference for the design of flame-retardant materials and emergency operation strategy of trains.

[Objective] Tunnel fires pose remarkable challenges for evacuation and fire rescue operations due to inadequate ventilation and associated hazards, such as smoke accumulation, elevated temperatures, rapid heat release rates (HRRs), and severely reduced visibility. While various monitoring techniques, such as thermocouples, fibers, and CCTV cameras, have been proposed to monitor fire development trends and assist in firefighting and evacuation efforts, obtaining critical tunnel fire information, specifically real-time fire HRR and fire source locations, remains challenging. These difficulties arise mainly because conventional detection methods are often disrupted by high temperatures or obstructed by dense smoke, hindering effective information transmission. Hence, an improved method to predict tunnel fires is urgently needed. [Methods] In this study, external smoke images, i.e., the smoke structure observed from outside the tunnel gate, and CNN-based deep-learning algorithms are used to predict real-time fire HRR and location within the tunnel. A 100-m full-scale tunnel is selected as the target, and its behavior is simulated using the Fire Dynamics Simulator to form an image database. During simulation, different fire parameters, such as maximum HRR, soot yield rate, and location, are varied based on typical vehicle types found in real tunnels, resulting in approximately 900 different tunnel models that generate diverse external smoke morphologies. The simulated smoke images are captured at 1 s intervals from four observation angles: front and side views from the left and right tunnel gates. As a result, approximately 388, 800 smoke images are collected in the database. For the deep-learning algorithm, the VGG16 model, proposed by the Oxford CNN team, is employed as the target AI model for tunnel prediction. During model training, the VGG16 model continuously refines its internal parameters to minimize the error between AI predictions and the FDS simulation.[Results] Results show that the proposed method can effectively predict real-time variations in fire HRR variation and location. The model trained using front-view images from both tunnel gates achieved the highest prediction accuracy, with an HRR error of less than 25% and a location error of less than 10 m. Additional tunnel simulations were conducted to further validate the robustness of the proposed method. In these simulations, the fire source is not stable but continuously moving within the tunnel at velocities ranging from 0 to 2 m/s, simulating a scenario where a vehicle catches fire but does not stop immediately. The results show that, although trained on stable fire cases, the AI model still maintains high accuracy in predicting the moving fire source, with small HRR and location errors, thus confirming the effectiveness of the smoke image-based detection method. [Conclusions] Notably, further efforts are still necessary for the application of this method in real tunnels because the current work does not consider the complex background interference in actual smoke images, nor does it consider the impacts of environmental factors such as wind, sprinklers, and exhaust systems on the external smoke structure. However, this study represents an important first step toward predicting tunnel fires based on external smoke, which could play a valuable role in future smart fire prediction and firefighting applications.

[Objective] Thermal effects are the primary means of damaging ammunition targets, and their impacts are particularly pronounced in enclosed environments. With the ongoing advancement of efficient damage technologies and the increasing complexity of future combat scenarios, it is crucial to evaluate weapon damage performance in high-altitude environments. Therefore, studying the propagation characteristics of explosion temperatures in high-altitude tunnels and developing a corresponding theoretical calculation model are of great significance for comprehensively assessing explosion damage under such conditions. [Methods] This study aimed to effectively characterize the propagation characteristics of the temperature at the blast wave front in long, straight tunnels with different types of condensed explosives at high altitudes. A multimaterial numerical calculation method was employed to investigate the propagation behavior of the blast wave front temperature in such tunnels. First, the two-dimensional axisymmetric numerical calculation method was validated by comparing the peak temperature data with the results of the existing explosion temperature field tests. Afterward, based on the above-described numerical calculation method, standard atmospheric parameters, and the existing explosive Jones-Wilkins-Lee(JWL) equation of state parameters, a numerical model is developed to simulate the explosion of different types of condensed explosives at high altitudes in a long straight tunnel. The model analyzes the explosion temperature field parameters, including the plane wave formation distance, peak temperature, shock wave front propagation velocity, and standard deviation of shock wave front arrival times. Finally, using the Hugoniot principle and Sachs dimensionless correction method, a mapping calculation model of the peak temperature and peak overpressure of the shock wave front in a typical high-altitude tunnel with condensed explosives is established, and the accuracy of the model is verified through numerical calculation results. [Results] The results indicate that the plane wave formation distance increases gradually with both the elevation and internal energy per unit volume of the explosive. At an altitude of 4 000 m, the plane wave formation distance for the different types of condensed explosives increases by an average of 24.8% compared with that in a flat environment. At the same altitude, the plane wave formation distance increases by an average of 0.89 m/GPa with a rise in internal energy per unit volume of the explosive. As a result, the propagation velocity and average deviation of the shock wave front arrival time increases with the elevation and internal energy. This result reflects the complexity of the interaction between the shock wave front and tunnel wall, leading to a decrease in the flatness of the shock wave front. At an altitude of 4 000 m, the peak temperature of the shock wave front for different condensed explosives increases by an average of 27%. At the same altitude, the peak temperature of the shock wave front increases by an average of 0.013 k℃/GPa with the rise in internal energy per unit volume of the explosive. The peak temperature for different altitudes and explosive types exhibits a decreasing trend with the increase in propagation distance, with the rate of decrease also reducing. Under various altitude and explosive-type conditions, the deviation between the theoretical analysis model and numerical calculation results is <10%, indicating good accuracy. [Conclusions] The results of this study provide a theoretical basis for understanding the temperature propagation of shock wave front explosions in condensed explosive tunnels under high-altitude conditions. They also offer guidance for weapon damage assessment and protection engineering design in high-altitude extreme combat environments.

[Objective] The operational safety resilience of urban lifeline systems refers to their ability to maintain critical functions and recover swiftly when faced with multiple disruptive events during their operational period. This resilience is crucial for ensuring the safe and uninterrupted operation of these critical systems. However, existing resilience evaluation methods often risk inaccuracies under multi-event scenarios, making it challenging to accurately assess the systems' overall resilience. To address this issue, this paper proposes a comprehensive resilience evaluation model and methodology tailored for urban lifeline systems during their operational phase. The approach considers multi-event contexts and is built on a detailed analysis of resilience mechanisms. It incorporates key factors such as resistance, recovery, and adaptability. [Methods] This study presents an evaluation method for assessing the operational safety resilience of urban lifeline systems under multi-event scenarios. The methodology is structured around three main steps: analyzing resilience mechanisms, constructing a resilience evaluation model for multi-event contexts, and applying the method in a case study. First, using resilience curve theory, scenario assumptions and theoretical deductions were used to analyze the specific roles and effects of resistance, recovery, and adaptability during operational performance loss and recovery under both single and multiple disruptive event scenarios. This analysis facilitated a systematic understanding of the resilience mechanisms governing urban lifeline systems. Second, building on existing resilience metrics, the study identified misjudgment risks and limitations in current resilience evaluation models. This led to the development of an enhanced model that integrates the advantages of existing methods. The improved model accounts for resilience mechanisms, computational efficiency, and reasonable distribution. An exponential function was utilized to establish an evaluation model for urban lifeline system resilience during the operational period. Key components, including system performance indicator settings, performance calculations, the resilience evaluation model, and a comprehensive resilience judgment matrix, were incorporated to form a “four-step” evaluation process tailored to multi-event scenarios. Finally, the methodology was validated through a case study on the Hong Kong (China) MTR East Rail Line from 2005 to 2009. Service performance indicators for the metro system were identified, and the causes of annual changes in comprehensive resilience levels were calculated and analyzed. The results demonstrated the feasibility and effectiveness of the proposed evaluation method. [Results] The research results revealed the following insights: (1) During the operational period, the resilience mechanisms of urban lifeline systems differ significantly between single and multiple disruptive events. In single-event scenarios, the system primarily relies on resistance to mitigate or eliminate disruptions and uses recovery to restore functionality. In contrast, in multiple-event scenarios, the system leverages adaptability, continuously optimizing resistance and post-event recovery. This enhances its capacity to respond to regular, extreme, or unknown events. (2) The proposed evaluation model, incorporating key resilience factors like resistance, recovery, and adaptability, assesses the comprehensive resilience level of the system throughout its operational period. It also measures the average resilience in single-event scenarios and cumulative resilience under multiple events. (3) Practical application demonstrates that a high average resilience in single-event scenarios does not necessarily correlate with high cumulative or comprehensive resilience. Enhancing comprehensive resilience is a long-term, dynamic process that relies on adaptability to repeatedly refine system resistance and recovery. This approach minimizes the impact of disruptive events and accelerates recovery. These findings validate the feasibility and effectiveness of the proposed evaluation method. [Conclusions] This method enhances the safe operation of urban lifeline systems and serves as a valuable methodological reference for future research on resilience enhancement strategies. It also holds significant potential for application in the resilience analysis of complex coupled systems.

[Objective] This study aimed to enhance the efficiency of indoor evacuation procedures by developing an optimal path-planning model based on the Dijkstra algorithm. The primary goal was to provide a dynamic and intelligent solution for guiding individuals to exit safely and swiftly during emergencies. Considering the complexities of indoor environments and the unpredictability of crowd behavior, the study highlighted the urgent need for a path-planning decision model that can adapt to real-time changes in crowd density and pedestrian flow velocity. [Methods] This study employed the single shot detector (SSD) algorithm for person detection and the deep learning-based simple online and real-time tracking (Deep-SORT) algorithm for multi-pedestrian tracking. These approaches, integrated with monocular vision techniques, enabled the extraction of pedestrian counts and flow velocities from architectural surveillance video streams. The Industry Foundation Classes standard was utilized to extract detailed architectural spatial information required for constructing a path network tailored for evacuation purposes. A novel method for predictive edge setting within the existing camera surveillance network was then introduced, allowing for the calculation of residual predictive edge pedestrian flow velocities. This calculation informed the development of an edge weight calculation method for the navigation road network, which is crucial for optimizing evacuation routes. These calculated weights were input into the Dijkstra algorithm to identify the shortest-time evacuation routes from any given node to the nearest exit. [Results] The proposed model was validated through experiments conducted using a proprietary pedestrian database under simulated emergency conditions. The SSD algorithm achieved an average precision of 78.2% for pedestrian detection and counting, while the Deep-SORT algorithm achieved multiple object tracking precision and multiple object tracking accuracy scores of 71.2% and 78.5%, respectively. These metrics demonstrate the model's high accuracy in detecting and tracking pedestrians, a crucial aspect of effective evacuation planning. In addition, the model provided optimal path directions at each decision node, allowing for real-time adjustments as conditions change. Moreover, the system could adapt to fluctuating crowd dynamics, a critical feature given the unpredictable nature of human movement during emergencies. [Conclusions] This study demonstrated the feasibility of implementing a Dijkstra algorithm-based optimal path-planning model for indoor evacuations, achieving efficient and intelligent route optimization. The research provides a solid theoretical foundation and practical technical support for managing complex indoor evacuation scenarios, thereby contributing to the fields of emergency management and crowd control strategies. Further refinement and application of this model in real-world settings are expected to substantially enhance public safety measures. Additionally, the model's potential integration into existing building management systems presents a promising avenue for improving safety protocols and fostering a more systematic approach to handling indoor evacuations.

[Objective] With the increase in emergency incidents, research on pedestrian dynamics has received widespread attention. Bidirectional pedestrian flow has been identified as a major factor in numerous global disasters over the past few decades. Conflicts can arise between individuals moving in opposite directions. Additionally, under different emergency conditions, evacuation behavior can vary significantly. This study primarily examines the impact of proactive avoidance and urgency on bidirectional pedestrian flow. [Methods] Experiments were conducted by establishing an outdoor experimental setup and recruiting participants. A controlled variable method was used to design 17 experimental trials to investigate the movement of bidirectional pedestrian flow under different levels of urgency and the presence of proactive avoidance behavior. Variations in urgency and proactive avoidance were implemented through a monetary reward and penalty mechanism. Pixel coordinates were first recorded using the Tracker software and then converted into real-world coordinates using direct linear transformation. The analysis focused on the evacuation speed, evacuation time, movement trajectories of the evacuees, maximum offset during the evacuation process, and behavior exhibited throughout the evacuation. Moreover, variance analysis was conducted to assess the stability of the experimental conditions and the validity of the experiments. Finally, a questionnaire was administered to collect some basic parameters of the participants, such as height, weight, and age. Furthermore, information on whether the participants experienced feelings of anxiety during the experiment and their perspectives on whether the provision of additional rewards influenced their sense of urgency was obtained. [Results] Common phenomena observed during the evacuation process included overtaking, following, and side-by-side behavior. An innovative behavior termed boundary flow acceleration was identified, where individuals moving against the flow exhibited significantly higher walking speeds when adhering to the wall compared with those not using the wall for support. Notably, groups of individuals moving against the flow spontaneously formed lanes when faced with a larger number of pedestrians moving in the opposite direction, which enhanced their ability to navigate through the crowd. In emergencies, the evacuation speed of individuals with disabilities was slower than that of normal pedestrians. Therefore, individuals with disabilities need more attention during emergencies. In nonemergency situations, the average evacuation speed of individuals moving against the flow was 1.26 m/s, which increased to 1.49 m/s when proactive avoidance behavior was present. In emergencies, the average speed of individuals moving against the flow increased to 2.04 m/s. When no individuals were moving against the flow, the evacuation speed was 1.77 m/s in nonemergency situations and 3.50 m/s in emergencies. The questionnaire results indicated that approximately 82.9% of the participants experienced anxiety during the evacuation process, and 91.4% of the participants believed that providing additional rewards could enhance the urgency of the experiment. This finding validated the effectiveness of the monetary reward mechanism in simulating emergency conditions. [Conclusions] The results indicated that proactive avoidance behavior effectively reduced pedestrian conflicts. Moreover, the behavior improved the evacuation efficiency of individuals moving against the flow by 24.60% and increased the evacuation speed by 19.87%. Furthermore, the phenomenon of stratification became more pronounced with the introduction of proactive avoidance behavior. Compared with nonemergency situations, the evacuation speed of the larger crowd significantly increased compared with that of the smaller crowd during emergencies. These findings can provide valuable insights for managing bidirectional pedestrian flow in emergencies and offer relevant support for modeling research in this area.

[Objective] High-pressure hydrogen leakage is a common safety concern in hydrogen refueling stations, significantly affecting the safe operation of these facilities. Accurate and timely identification of the leak source location and continuous monitoring of hydrogen concentration are essential for preventing explosions and ensuring the safety of refueling operations. [Methods] In this study, we propose a novel deep learning-based hydrogen leak detection model that provides a smart, real-time detection solution. The model leverages computational fluid dynamics (CFD) simulations to construct a proprietary database of high-pressure hydrogen leaks under various conditions, including different leak locations, flow rates, and wind directions. [Results] Our analysis revealed that wind direction plays the most significant role in influencing hydrogen dispersion patterns, which is crucial for accurately identifying leak sources and predicting the affected area. We compared six deep learning models: a backpropagation neural network (BPNN) based on a multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) network, convolutional long short-term memory (CNN-LSTM), bidirectional long short-term memory (BiLSTM), and convolutional neural network bidirectional long short-term memory (CNN-BiLSTM). Among these models, the CNN-BiLSTM model exhibited the highest performance in leak detection tasks. By combining the strong local feature extraction capabilities of CNN with the long-term dependency capturing abilities of BiLSTM, this hybrid model significantly outperformed other models, achieving an accuracy and F1 score exceeding 98%. These results highlight the model's ability to handle complex temporal data efficiently, making it particularly effective for identifying hydrogen leaks in real-time industrial environments. The study also explores key factors influencing the performance of the detection models. We conducted sensitivity analyses on two critical hyperparameters: batch size and the number of training iterations. We found that a batch size of 16 and 400 iterations provided the optimal trade-off between convergence speed and detection accuracy. In addition, the robustness of the model has been demonstrated, maintaining high accuracy even in the face of complex conditions such as changes in wind direction and leak strength. The model has a high localization accuracy of more than 98.00% when detecting most leakage sources. The detection accuracy is slightly lower only in the hydrogen unloading region and the hydrogen storage region, mainly due to the limitation of the sensor layout. In addition, the research introduced data preprocessing techniques, including normalization, data dimension reduction, and feature selection, which significantly improved the efficiency of the detection process. By minimizing the dimensionality of input data, the computational load was reduced, enabling faster detection without sacrificing accuracy. Notably, the CNN-BiLSTM model also excelled in detecting rare but dangerous leak events, enhancing the overall safety monitoring capabilities of hydrogen refueling stations. [Conclusions] This study's findings not only provide a theoretical foundation for hydrogen leak detection in refueling stations but also present a practical solution that improves both detection accuracy and operational efficiency. The proposed CNN-BiLSTM model offers a robust and intelligent approach for monitoring hydrogen leaks, significantly enhancing real-time safety measures in complex industrial settings. Future work will focus on expanding the model's generalizability to broader industrial applications and exploring further optimization of the feature extraction and classification processes to support the development of intelligent safety monitoring systems.

[Objective] Monitoring and providing early warnings of spontaneous combustion fires in coal pose a significant challenge, hindering the safe advancement of the coal industry. Currently, the accuracy of single-indicator analysis in existing monitoring and early warning methods is insufficient. Multi-indicator judgment methods that rely on statistical analysis are limited by the number and types of indicators, making the judgment process complex and resulting in significant differences. Support vector machine algorithms that are capable of learning rules from limited samples, show potential for application in coal spontaneous combustion monitoring and early warning. [Methods] This study establishes a testing system for infrasound waves and acoustic emission signals during coal spontaneous combustion. It explores the relationship between the main frequency amplitude of these signals and temperature to determine whether they can serve as feature vectors for support vector machines. Based on this relationship, the coal spontaneous combustion process is divided into three stages: early, middle, and late, and a combustion support vector machine model is established. The models are trained with different kernel functions, and the one with the highest recognition accuracy for the three periods early, middle, and late stages of coal spontaneous combustion for further validation. Finally, untreated experimental data is used to validate the model's recognition performance. [Results] (1) There is a positive correlation between the amplitude of infrasound waves and the main frequency of acoustic emission with temperature, and the correlation coefficient R2 is high, all of which are above 0.90. This shows their effectiveness as indicators for monitoring coal spontaneous combustion. (2) The subsonic polynomial kernel support vector machine can accurately identify the three periods before, during, and after coal spontaneous combustion, outperforming linear and Gaussian kernel support vector machines. Meanwhile, the acoustic emission Gaussian kernel support vector machine surpasses the polynomial and linear kernel models in accuracy for the same phases. (3) The infrasonic support vector machine achieves classification accuracies of 97.75% for the early stage, 97.60% for the middle stage, and 100% for the late stage of coal spontaneous combustion. The acoustic emission model reaches accuracies of 95.65% for the early stage, 95.20% for the middle stage, and 90.20% for the late stage. [Conclusions] The multiclass support vector machine model for coal spontaneous combustion presented in this study can accurately identify and classify the state of coal spontaneous combustion. It holds practical significance in coal spontaneous combustion monitoring and early warning. This study introduces a novel method for efficient monitoring and early warning of coal spontaneous combustion.

[Objective] With the rapid advancement of the aviation industry, ensuring aircraft safety, particularly in sensitive areas like cargo holds, is of paramount importance. Fires in aircraft cargo can be triggered by various factors, such as electrical malfunctions, hazardous materials, or environmental conditions, and pose significant threats to passengers and crew. Given the growing complexity of fire detection in these confined spaces, more reliable and accurate fire detection methods are urgently needed. Traditional fire detection systems, which primarily depend on single-sensor technologies, like smoke or heat detectors, have long been criticized for their high false alarm rates and limited accuracy. These deficiencies often result in delayed responses or unnecessary interventions, which ultimately compromise operational safety and efficiency. Therefore, this study aims to develop an innovative fire detection system that can overcome the limitations of conventional methods while meeting the advanced safety standards of modern aviation. [Methods] To tackle these challenges, this research introduces an improved multiparameter fire detection method leveraging an advanced self-attention mechanism within the Transformer model architecture. The approach integrates data from multiple sensors, including carbon monoxide, smoke, humidity, and temperature sensors, to capture a wide range of environmental parameters in aircraft cargo holds. Data are gathered by simulating realistic fire scenarios within a laboratory setting, ensuring that the system is trained on diverse datasets that reflect the unpredictable nature of fire development in cargo spaces. The core of the proposed method is a Transformer-based model that incorporates two key innovations: local attention mechanism and multiscale feature extraction. The local attention mechanism addresses the computational complexity of processing long sequences of input data by dividing the data into smaller, manageable windows. This allows the model to focus on localized features without the burden of analyzing the entire sequence at once, making it more efficient and suitable for real-time applications. Furthermore, the multiscale feature extraction module processes data in parallel across different time windows, capturing short-term fluctuations and long-term trends, which is crucial for detecting gradual fires, such as slow-burning or smoldering fires, that traditional systems may miss. [Results] The proposed method was rigorously evaluated through a series of experiments on a fire detection dataset designed to mimic real-world conditions in aircraft cargo holds. A range of hyperparameters, including sequence lengths, activation functions, dropout rates, and optimizers, was tested to fine-tune model classification performance. Results revealed that the optimized model significantly outperformed traditional approaches, such as convolutional neural networks, recurrent neural networks, and long short-term memory networks, in terms of classification accuracy, particularly under challenging conditions involving noisy or incomplete sensor data. The model excelled at distinguishing between fire and non-fire events, showcasing its superior ability to handle real-world fire scenarios. Moreover, the Transformer's intrinsic parallel computing capability reduced training times, making it a practical solution for time-sensitive fire detection applications in aviation. [Conclusions] This study presents a novel multiparameter fire detection system that integrates an improved self-attention mechanism with local attention and multiscale feature extraction, offering several advantages over traditional models. The proposed method achieves higher accuracy, lower computational complexity, and faster training times, making it highly suitable for deployment in aircraft cargo hold fire detection systems. The promising results from the laboratory-based experiments suggest that this method can be readily adapted to real-world operational settings. Future research will focus on further validating the model's performance in live environments, aiming to extend its applicability to other safety-critical domains beyond aviation, such as industrial safety and transportation systems.

[Objective] In recent years, the development of the economy in plateau areas has resulted in the increase in the flights in plateau areas, resulting in a large demand for aviation kerosene. However, the occasional aviation kerosene pool fire that occurs in plateau areas poses a great threat to the safe storage of aviation kerosene. The burning behavior and the corresponding characteristics of liquid fuels, such as aviation kerosene, are different because of the influences of sub-atmospheric pressure and oxygen amount. Moreover, the available reports in the literature concerning sub-atmospheric pressure are mainly based on small-scale experiments, which are greatly affected by heat convection and heat conduction. Furthermore, the burning characteristics are far from the practical fire conditions in plateau areas, which are mainly controlled by heat radiation. Thus, the burning characteristics of liquid fuel pool fires at large scales remain unclear. This study aims to clarify the difference between the burning characteristics of aviation kerosene pool fire under sub-atmospheric pressure and that under atmospheric pressure as well as develop the corresponding prediction models under sub-atmospheric pressure. [Methods] A series of pool fire experiments using aviation kerosene with different pool diameters under sub-atmospheric pressure (69 kPa) were carried out. The burning behavior during the whole burning process was analyzed. Moreover, the evolution of some important parameters (including mass burning rate, flame height, and radiative fraction) with the pool diameter were measured and analyzed in detail, and the corresponding prediction models were proposed. [Results] The results showed that the burning rate of aviation kerosene under sub-atmospheric pressure was lower than that under atmospheric pressure for the same burning scale and that the ratio of the rate under sub-atmospheric pressure to that under atmospheric pressure was about 0.58. This result is primarily the result of the heat radiation and heat convection feedback between flame and fuel surface under sub-atmospheric pressure being lower than those under atmospheric pressure. Furthermore, a prediction model of the burning rate was proposed based on the heat feedback. The flame height under sub-atmospheric pressure was higher than that of the same burning scale under atmospheric pressure, primarily because the net oxygen content in the air under sub-atmospheric pressure was reduced and more air was required for fuel burning. In addition, based on the flame entrainment theory, a prediction model of the dimensionless flame height of aviation kerosene under sub-atmospheric pressure was obtained. The radiative fraction decreased slightly with the increase of pool diameter under sub-atmospheric pressure. And at the same burning scale, the radiative fraction under sub-atmospheric pressure was slightly lower than that under atmospheric pressure, primarily because of the reduction of soot particles generated during fuel burning under sub-atmospheric pressure. Subsequently, a prediction model of radiative fraction was developed by modifying the key parameters. [Conclusions] The evolution of the burning characteristics of radiation-dominated aviation kerosene pool fires under sub-atmospheric pressure with pool diameter was found to be consistent with those under atmospheric pressure; however, the values of different burning characteristics changed substantially. The results enrich the large-scale aviation kerosene pool fire data under sub-atmospheric pressure and have practical significance for ensuring the use and storage safety of aviation kerosene in plateau areas.

[Objective] In aviation transportation, which is characterized by low-pressure environments, aircraft fires pose an unpredictable threat. The interior wall materials of civil transport aircraft are predominantly composed of composite materials. The Federal Aviation Administration of the United States and the Civil Aviation Administration of China require experimental validation of their fire-resistant properties. This study aims to address the current research gap by investigating the development patterns of aircraft fires under low-pressure conditions. Specifically, this study examines the combustion rates of interior wall materials in civil aircraft in multiple-pressure environments. The goal is to detect and prevent aircraft fires under low-pressure conditions at the earliest opportunity. [Methods] This study investigates the sandwich structure panel material (Panel A) and the laminated panel material (Panel B) used in Airbus aircraft. Panel A comprises the upper and lower layers of resin-based substrates, an aramid honeycomb core intermediate layer, and an adhesive. Panel B is composed of resin-based glass fiber-reinforced laminate. The study is conducted using a low-pressure combustion chamber research facility in Guanghan, Sichuan (96 kPa), and Kangding, Sichuan (61 kPa). Combustion rates and flame phenomena of cabin wall panel materials are examined at 40, 50, 61, 70, 80, and 96 kPa pressure levels. A combustion rate model is applied to adjust the effect of pressure on the combustion rate of cabin wall panel materials. The heat release of the cabin wall panel materials is determined using a cone calorimeter, which enables the assessment of heat release in various pressure environments. Finally, following relevant regulations and standards, a fire penetration resistance test apparatus is constructed to investigate the fire penetration resistance characteristics of cabin wall panel materials under different pressure conditions. [Results] The relationship between the combustion rate and pressure for glass fiber/phenolic resin sandwich panels and glass fiber/phenolic resin laminated panels is approximately and, respectively. According to the fire-base dimensions, the combustion rate-pressure models are and, respectively. The peak heat release rates of aircraft interior wall panels in low-pressure environments are lower than those in atmospheric-pressure environments. When the air pressure drops from 96 kPa to 40 kPa, the peak heat release rate of glass fiber/phenolic resin sandwich and glass fiber/phenolic resin sandwich decreased by about 40.97% and 43.85%, Similarly, compared with atmospheric-pressure environments total heat release significantly decreases under low pressure by 14.20% and 24.71%, respectively. The flame color of aircraft interior wall panel materials shifts from bright yellow at atmospheric pressure to a lower degree of brightness under low pressure. Additionally, the flame height significantly decreases under low pressure compared with the height under atmospheric pressure, with glass fiber/phenolic resin sandwich panels and glass fiber/phenolic resin laminated panels experiencing a reduction of approximately 10.9% and 11.6%, respectively, compared with atmospheric pressure. Research on the fire penetration resistance of aircraft cabin interior wall panels reveals that the char layer of sandwich panel materials becomes more pronounced under low pressure than under atmospheric pressure, indicating increased fire resistance. Conversely, laminated panel materials are minimally affected by pressure. [Conclusions] According to the above research findings, pressure significantly affects the fire characteristics of cabin wall panel materials. This study provides direct relevance to the practical needs of aircraft fire prevention and control, offering data support for aircraft fire prevention efforts.

[Objective] Due to the height differences during the power transmission process, the bending installation of cables is a common method. The stress in the bending section of cables is usually relatively concentrated and more susceptible to damage, leading to a greater fire hazard. According to the different bending forms, the bending cables can be divided into upward-bending cables and downward-bending cables. [Methods] An experimental study was conducted to investigate the effect of the bending angle and number of cables on the flame spread behavior of downward-bending cables. [Results] Results show that: (1) the peak temperature on the cable surface of downward-bending cables gradually increases, as the number of cables increases. The temperature peak of 5 downward-bending with 60°bending angle was about 782.3℃, which was 1.8 times higher than that of a single cable at the same angle. This is mainly due to the fact that combustion of multiple cables laid side by side produces more combustible pyrolysis gases, while the flame has a stronger preheating effect on the cables. (2) As the bending angle increases the flame spread time of downward-bending cables is shortened and the average flame spread rate increases. Under the ignition condition of five 90°downward-bending cables, the average flame spread rate of the cables was 5.4 cm/min, which was 1.9 times of that of five 0° downward-bending cables; under the ignition condition of five 90°downward-bending cables, the temperature peak reached 868.3℃, which was about 1.4 times of that of that of five 0°downward-bending cables. This is mainly due to the larger bending angle, which was mainly due to the difference in the role of the flow of melt drippings on the cable under different bending angles, when the flame spread in the inclined section of the cables, the flow of high-temperature melt drippings was an important driving force to ignite the cables in the unburned section. As the bending angle increased, the force of gravity increased in the direction of the cables, and the drippings were more likely to flow downwards under the combined effect of the gravitational component force, surface tension, friction of the melt drippings. The melt drippings had a more significant effect on the preheating of the unburnt section of the cables, which in turn shortens the ignition time of the cable and increased the average flame spread rate. In addition, as the bending angle increased, the “flame attached" effect was more significant, which increased the thermal convection and thermal radiation in the unburned section of the cables. [Conclusions] The peak temperature on the cable surface of downward-bending cables are positively related to the number of cables and the bending angle. The average flame spread rate increases as the number of cables and the bending angle increase. Regressivity analyses between flame temperature and the number of cables were carried out to analyze the flow mechanism of melt drippings in inclined sections of cables in this work, these correlations are well described by physically based models for all the experimental results.